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:

Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota

Nature Microbiology volume 4, pages 614–622 (2019)Cite this article

Subjects

Abstract

Phylogenetic and geological evidence supports the hypothesis that life on Earth originated in thermal environments and conserved energy through methanogenesis or sulfur reduction. Here we describe two populations of the deeply rooted archaeal phylum Korarchaeota, which were retrieved from the metagenome of a circumneutral, suboxic hot spring that contains high levels of sulfate, sulfide, methane, hydrogen and carbon dioxide. One population is closely related to ‘Candidatus Korarchaeum cryptofilum OPF8’, while the more abundant korarchaeote, ‘Candidatus Methanodesulfokores washburnensis’, contains genes that are necessary for anaerobic methane and dissimilatory sulfur metabolisms. Phylogenetic and ancestral reconstruction analyses suggest that methane metabolism originated in the Korarchaeota, whereas genes for dissimilatory sulfite reduction were horizontally transferred to the Korarchaeota from the Firmicutes. Interactions among enzymes involved in both metabolisms could facilitate exergonic, sulfite-dependent, anaerobic oxidation of methane to methanol; alternatively, ‘Ca. M. washburnensis’ could conduct methanogenesis and sulfur reduction independently. Metabolic reconstruction suggests that ‘Ca. M. washburnensis’ is a mixotroph, capable of amino acid uptake, assimilation of methane-derived carbon and/or CO2 fixation by archaeal type III-b RuBisCO for scavenging ribose carbon. Our findings link anaerobic methane metabolism and dissimilatory sulfur reduction within a single deeply rooted archaeal population and have implications for the evolution of these traits throughout the Archaea.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Tetranucleotide frequency display and comparison of genome sequence for three populations of Korarchaeota.
Fig. 2: Phylogeny of the Korarchaeota.
Fig. 3: Possible energy metabolisms for ‘Ca. M. washburnensis’.
Fig. 4: Carbon and intermediate metabolism in the Korarchaeota.

Similar content being viewed by others

Data availability

Metagenome sequences used in this study are available on IMG/M (DOE-Joint Genome Institute) under genome identifier 3300005860. Metagenome-assembled genomes are available under NCBI BioProject accession number PRJNA492148. Access to the tSNE-based nucleotide frequency analysis algorithm can be obtained from the Center for Genomics and Bioinformatics at Indiana University. Newick files for three-domain and archaea-only phylogenomic trees are available as Supplementary Data 1–13.

References

  1. Woese, C. R. Bacterial evolution. Microbiol. Rev. 51, 221–271 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Ueno, Y., Yamada, K., Yoshida, N., Maruyama, S. & Isozaki, Y. Evidence from fluid inclusions for microbial methanogenesis in the early Archaean era. Nature 440, 516–519 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Shen, Y., Buick, R. & Canfield, D. E. Isotopic evidence for microbial sulphate reduction in the early Archaean era. Nature 410, 77–81 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Dhillon, A., Goswami, S., Riley, M., Teske, A. & Sogin, M. Domain evolution and functional diversification of sulfite reductases. Astrobiology 5, 18–29 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 16170 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Laso-Perez, R. et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature 539, 396–401 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Spang, A., Caceres, E. F. & Ettema, T. J. G. Genomic exploration of the diversity, ecology, and evolution of the archaeal domain of life. Science 357, eaaf3883 (2017).

  9. Anantharaman, K. et al. Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle. ISME J. 12, 1715–1728 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Scheller, S., Goenrich, M., Boecher, R., Thauer, R. K. & Jaun, B. The key nickel enzyme of methanogenesis catalyses the anaerobic oxidation of methane. Nature 465, 606–608 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Loy, A. et al. Reverse dissimilatory sulfite reductase as phylogenetic marker for a subgroup of sulfur-oxidizing prokaryotes. Environ. Microbiol. 11, 289–299 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hinrichs, K. U., Hayes, J. M., Sylva, S. P., Brewer, P. G. & DeLong, E. F. Methane-consuming archaebacteria in marine sediments. Nature 398, 802–805 (1999).

    Article  CAS  PubMed  Google Scholar 

  13. Boetius, A. et al. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature 407, 623–626 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).

    Article  CAS  PubMed  Google Scholar 

  17. Elkins, J. G. et al. A korarchaeal genome reveals insights into the evolution of the Archaea. Proc. Natl Acad. Sci. USA 105, 8102–8107 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Inskeep, W. P. et al. Phylogenetic and functional analysis of metagenome sequence from high-temperature archaeal habitats demonstrate linkages between metabolic potential and geochemistry. Front. Microbiol. 4, 95 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. McKay, L. J., Hatzenpichler, R., Inskeep, W. P. & Fields, M. W. Occurrence and expression of novel methyl-coenzyme M reductase gene (mcrA) variants in hot spring sediments. Sci. Rep. 7, 7252 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Barns, S. M., Delwiche, C. F., Palmer, J. D. & Pace, N. R. Perspectives on archaeal diversity, thermophily and monophyly from environmental rRNA sequences. Proc. Natl Acad. Sci. USA 93, 9188–9193 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  23. Dick, G. J. et al. Community-wide analysis of microbial genome sequence signatures. Genome. Biol. 10, R85 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Slesarev, A. I. et al. The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens. Proc. Natl Acad. Sci. USA 99, 4644–4649 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Huber, G., Spinnler, C., Gambacorta, A. & Stetter, K. O. Metallosphaera sedula gen, and sp. nov. represents a new genus of aerobic, metal-mobilizing, thermoacidophilic Archaebacteria. Syst. Appl. Microbiol. 12, 38–47 (1989).

    Article  Google Scholar 

  26. Medini, D., Donati, C., Tettelin, H., Masignani, V. & Rappuoli, R. The microbial pan-genome. Curr. Opin. Genet. Dev. 15, 589–594 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Jay, Z. J. et al. Marsarchaeota are an aerobic archaeal lineage abundant in geothermal iron oxide microbial mats. Nat. Microbiol. 3, 732–740 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Da Cunha, V., Gaia, M., Gadelle, D., Nasir, A. & Forterre, P. Lokiarchaea are close relatives of Euryarchaeota, not bridging the gap between prokaryotes and eukaryotes. PLoS Genet. 13, e1006810 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dridi, B., Fardeau, M. L., Ollivier, B., Raoult, D. & Drancourt, M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int. J. Syst. Evol. Microbiol. 62, 1902–1907 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Lever, M. A. & Teske, A. P. Diversity of methane-cycling archaea in hydrothermal sediment investigated by general and group-specific PCR primers. Appl. Environ. Microbiol. 81, 1426–1441 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Hua, Z. S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Müller, A. L., Kjeldsen, K. U., Rattei, T., Pester, M. & Loy, A. Phylogenetic and environmental diversity of DsrAB-type dissimilatory (bi)sulfite reductases. ISME J. 9, 1152–1165 (2015).

    Article  PubMed  Google Scholar 

  36. Rabus, R. et al. A post-genomic view of the ecophysiology, catabolism and biotechnological relevance of sulphate-reducing prokaryotes. Adv. Microb. Physiol. 66, 55–321 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Borrel, G. et al. Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea. Nat. Microbiol. https://doi.org/10.1038/s41564-019-0363-3 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu, Y. & Whitman, W. B. Metabolic, phylogenetic, and ecological diversity of the methanogenic archaea. Ann. NY Acad. Sci. 1125, 171–189 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Thauer, R. K., Kaster, A. K., Seedorf, H., Buckel, W. & Hedderich, R. Methanogenic archaea: ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6, 579–591 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Wagner, T., Koch, J., Ermler, U. & Shima, S. Methanogenic heterodisulfide reductase (HdrABC-MvhAGD) uses two noncubane [4Fe-4S] clusters for reduction. Science 357, 699–703 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Lang, K. et al. New mode of energy metabolism in the seventh order of methanogens as revealed by comparative genome analysis of “Candidatus methanoplasma termitum”. Appl. Environ. Microbiol. 81, 1338–1352 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Amend, J. P. & Shock, E. L. Energetics of overall metabolic reactions of thermophilic and hyperthermophilic Archaea and Bacteria. FEMS Microbiol. Rev. 25, 175–243 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Hedderich, R. et al. Anaerobic respiration with elemental sulfur and with disulfides. FEMS Microbiol. Rev. 22, 353–381 (1998).

    Article  CAS  Google Scholar 

  44. Pereira, I. A. et al. A comparative genomic analysis of energy metabolism in sulfate reducing bacteria and archaea. Front. Microbiol. 2, 69 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Dong, M. et al. In vitro methanol production from methyl coenzyme M using the Methanosarcina barkeri MtaABC protein complex. Biotechnol. Prog. 33, 1243–1249 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).

    Article  CAS  PubMed  Google Scholar 

  47. Welander, P. V. & Metcalf, W. W. Mutagenesis of the C1 oxidation pathway in Methanosarcina barkeri: new insights into the Mtr/Mer bypass pathway. J. Bacteriol. 190, 1928–1936 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Meyerdierks, A. et al. Metagenome and mRNA expression analyses of anaerobic methanotrophic archaea of the ANME-1 group. Environ. Microbiol. 12, 422–439 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Kono, T. et al. A RuBisCO-mediated carbon metabolic pathway in methanogenic archaea. Nat. Commun. 8, 14007 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sato, T., Atomi, H. & Imanaka, T. Archaeal type III RuBisCOs function in a pathway for AMP metabolism. Science 315, 1003–1006 (2007).

    Article  CAS  PubMed  Google Scholar 

  51. 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 

  52. 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 

  53. Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  57. Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Delmont, T. O. & Eren, A. M. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ 6, e4320 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. van Dongen, S. & Abreu-Goodger, C. Using MCL to extract clusters from networks. Methods Mol. Biol. 804, 281–295 (2012).

    Article  PubMed  Google Scholar 

  60. 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).

    Article  CAS  PubMed  Google Scholar 

  61. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Richter, M. & Rosselló-Móra, R. Shifting the genomic gold standard for the prokaryotic species definition. Proc. Natl Acad. Sci. USA 106, 19126–19131 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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 

  65. Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Abascal, F., Zardoya, R. & Posada, D. ProtTest: selection of best-fit models of protein evolution. Bioinformatics 21, 2104–2105 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Article  CAS  PubMed  Google Scholar 

  69. Pruesse, E., Peplies, J. & Glockner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors appreciate support from the NASA Postdoctoral Program through the NASA Astrobiology Institute (L.J.M.), the Montana Agricultural Experiment Station (project 911300; W.P.I.), the National Institutes of Health IDeA Program (COBRE grant GM110732; M.D.), the NSF Integrative Graduate Education and Research Traineeship Program (NSF DGE 0654336; Z.J.J.), the W. M. Keck Foundation (L.J.M., M.W.F., K.B.K. and W.P.I.), NSF 1736255 (L.J.M. and M.W.F.) and by the US Department of Energy—Ecosystems and Networks Integrated with Genes and Molecular Assemblies (contract number DE-AC02–05CH11231; M.W.F.). Metagenome sequencing of DNA from Washburn Hot Springs was conducted at the DOE-Joint Genome Institute under the Community Sequencing Program (CSP 701; W.P.I.). Computations were performed on the Hyalite High-Performance Computing System, operated and supported by MSU’s Information Technology Center. We thank the NSF EarthCube ECOGEO RCN (1440066) for metagenomics analysis support and individuals involved in the 2016 ‘Omics Workshop’ at the University of Hawaii; V. Krukenberg, G. Borrel, S. Gribaldo, R. Hatzenpichler, M. Lever and A. Teske for helpful discussions and J. Beam for assistance with sample collection and processing.

Author information

Author notes

  1. Tom O. Delmont

    Present address: Genoscope, Évry, France

Authors and Affiliations

  1. Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA

    Luke J. McKay & William P. Inskeep

  2. Center for Biofilm Engineering, Montana State University, Bozeman, MT, USA

    Luke J. McKay, Matthew W. Fields, Zackary J. Jay & Korinne B. Klingelsmith

  3. Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA

    Mensur Dlakić & Matthew W. Fields

  4. Department of Medicine, University of Chicago, Chicago, IL, USA

    Tom O. Delmont & A. Murat Eren

  5. Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, USA

    A. Murat Eren

  6. Informatics Group, Indiana University, Bloomington, IN, USA

    Douglas B. Rusch

  7. Thermal Biology Institute, Montana State University, Bozeman, MT, USA

    William P. Inskeep

Authors
  1. Luke J. McKay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mensur Dlakić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew W. Fields
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tom O. Delmont
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Murat Eren
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zackary J. Jay
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Korinne B. Klingelsmith
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Douglas B. Rusch
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. William P. Inskeep
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.P.I. and L.J.M. designed the investigation. W.P.I. and Z.J.J. collected samples and performed initial metagenome processing. L.J.M. and M.D. performed clustering, coverage analyses and phylogenetic analyses. L.J.M., M.W.F. and W.P.I. built metabolic reconstructions. M.D. performed additional metagenome assemblies, phylogenomic, emergent self-organizing maps and sequence reconstruction analyses. T.O.D. and A.M.E. performed pangenomics and microdiversity analyses. M.D., K.B.K. and L.J.M. performed phylogenetic analyses of McrA and DsrAB protein sequences. K.B.K. and L.J.M. performed phylogenetic analysis of mcrA genes. D.B.R. and M.D. assisted with nucleotide frequency analyses. L.J.M., W.P.I. and M.D. wrote the manuscript and responses to reviewer comments. All authors contributed to manuscript editing.

Corresponding authors

Correspondence to Luke J. McKay or William P. Inskeep.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Supplementary Table 5, Supplementary Table 7, Supplementary Figures 1–12, Supplementary Discussion, Supplementary Data Legends and Supplementary References.

Reporting Summary

Supplementary Table 3

Gene clusters, clusters of orthologous genes identification numbers and functions corresponding to each of the Korarchaeota displayed in Fig. 1.

Supplementary Table 4

List of archaeal clusters of orthologous genes used in phylogenomic analysis.

Supplementary Table 6

Protein BLAST comparisons of ancestral dissimilatory sulfur reductase sequences to Candidatus Methanodesulfokores washburnensis and the NCBI protein database.

Supplementary Table 8

List of abbreviations used in metabolic reconstruction (Fig. 4).

Dataset 1

Newick tree files corresponding to Fig. 2a.

Dataset 2

Newick tree files corresponding to Supplementary Figure 5a.

Dataset 3

Newick tree files corresponding to Supplementary Figure 5b.

Dataset 4

Newick tree files corresponding to Supplementary Figure 5c.

Dataset 5

Newick tree files corresponding to Supplementary Figure 5d.

Dataset 6

Newick tree files corresponding to Supplementary Figure 5e.

Dataset 7

Newick tree files corresponding to Supplementary Figure 5f.

Dataset 8

Newick tree files corresponding to Supplementary Figure 5g.

Dataset 9

Newick tree files corresponding to Supplementary Figure 5h.

Dataset 10

Newick tree files corresponding to Supplementary Figure 5i.

Dataset 11

Newick tree files corresponding to Supplementary Figure 5j.

Dataset 12

Newick tree files corresponding to Supplementary Figure 5k.

Dataset 13

Concatenated alignment file (.afa) of 56 conserved proteins from three domains. File size = 2 MB.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McKay, L.J., Dlakić, M., Fields, M.W. et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat Microbiol 4, 614–622 (2019). https://doi.org/10.1038/s41564-019-0362-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-019-0362-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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