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:

Methyl-reducing methanogenesis by a thermophilic culture of Korarchaeia

Nature volume 632, pages 1131–1136 (2024)Cite this article

Subjects

Abstract

Methanogenesis mediated by archaea is the main source of methane, a strong greenhouse gas, and thus is critical for understanding Earth’s climate dynamics. Recently, genes encoding diverse methanogenesis pathways have been discovered in metagenome-assembled genomes affiliated with several archaeal phyla1,2,3,4,5,6,7. However, all experimental studies on methanogens are at present restricted to cultured representatives of the Euryarchaeota. Here we show methanogenic growth by a member of the lineage Korarchaeia within the phylum Thermoproteota (TACK superphylum)5,6,7. Following enrichment cultivation of ‘Candidatus Methanodesulfokora washburnenis’ strain LCB3, we used measurements of metabolic activity and isotope tracer conversion to demonstrate methanol reduction to methane using hydrogen as an electron donor. Analysis of the archaeon’s circular genome and transcriptome revealed unique modifications in the energy conservation pathways linked to methanogenesis, including enzyme complexes involved in hydrogen and sulfur metabolism. The cultivation and characterization of this new group of archaea is critical for a deeper evaluation of the diversity, physiology and biochemistry of methanogens.

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: Methane-producing enrichment cultures from hot spring sediments.
Fig. 2: Growth and translational activity of culture LCB3.
Fig. 3: Phylogenetic affiliation of ‘Ca. M. washburnensis’ strain LCB3.
Fig. 4: Metabolic reconstruction and gene expression of ‘Ca. M. washburnensis’ strain LCB3.

Similar content being viewed by others

Data availability

The 16S rRNA gene and mcrA gene amplicon data, metagenomic reads and metatranscriptomic reads are deposited at NCBI under BioProject PRJNA913929. Metagenomes and genomes are available on IMG/M (JGI) under IMG Genome IDs 3300005860 (WHS), 3300043541 (LCB058), 3300028675 (LCB003), 8012931703 (‘Ca. M. washburnensis’ strain LCB3), 8015587805 (Archaeoglobaceae archaeon LCB3), 8015589684 (Thermofilum sp. LCB3-A), 8015591669 (Thermofilum sp. LCB3-B), 8015593670 (Fervidicoccaceae archaeon LCB3), 8015596852 (Ignisphaera sp. LCB3) and 8015595177 (Desulfurococcaceae archaeon LCB3). Source data are provided with this paper.

References

  1. Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

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

    ADS  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  4. Hua, Z. S. et al. Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea. Nat. Commun. 10, 4574 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  5. Borrel, G. et al. Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea. Nat. Microbiol. 4, 603–613 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, Y., Wegener, G., Hou, J., Wang, F. & Xiao, X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat. Microbiol. 4, 595–602 (2019).

    CAS  PubMed  Google Scholar 

  7. McKay, L. J. et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat. Microbiol. 4, 614–622 (2019).

    CAS  PubMed  Google Scholar 

  8. Saunois, M. et al. The global methane budget 2000-2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).

    ADS  Google Scholar 

  9. Conrad, R. The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep 1, 285–292 (2009).

    CAS  PubMed  Google Scholar 

  10. Garcia, P. S., Gribaldo, S. & Borrel, G. Diversity and evolution of methane-related pathways in Archaea. Annu. Rev. Microbiol. 76, 727–755 (2022).

    CAS  PubMed  Google Scholar 

  11. Enzmann, F., Mayer, F., Rother, M. & Holtmann, D. Methanogens: biochemical background and biotechnological applications. AMB Express 8, 2–22 (2018).

    Google Scholar 

  12. Kurth, J. M., Huub, Op Den Camp, J. M. & Welte, C. U. Several ways one goal-methanogenesis from unconventional substrates. Appl. Microbiol. Biotechnol. 104, 6839–6854 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Thauer, R. K. Methyl (alkyl)-coenzyme M reductases: nickel F-430-containing enzymes Involved in anaerobic methane formation and in anaerobic oxidation of methane or of short chain alkanes. Biochemistry 58, 5198–5220 (2019).

    CAS  PubMed  Google Scholar 

  14. 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).

    ADS  CAS  PubMed  Google Scholar 

  15. 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).

    CAS  PubMed  Google Scholar 

  16. Oren, A. & Garrity, G. M. Candidatus list no. 2. lists of names of prokaryotic candidatus taxa. Int. J. Syst. Evol. Microbiol. https://doi.org/10.1099/ijsem.0.004671 (2021).

  17. Lynes, M. M. et al. Diversity and function of methyl-coenzyme M reductase-encoding archaea in Yellowstone hot springs revealed by metagenomics and mesocosm experiments. ISME Comm. 3, 22 (2023).

    ADS  Google Scholar 

  18. 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).

    ADS  PubMed  PubMed Central  Google Scholar 

  19. Zeikus, J. G., Ben-Bassat, A. & Hegge, P. W. Microbiology of methanogenesis in thermal, volcanic envirorunments. J. Bacteriol. 143, 432–440 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. McKay, L. J., Klingelsmith, K. B., Deutschbauer, A. M., Inskeep, W. P. & Fields, M. W. Draft genome sequence of Methanothermobacter thermautotrophicus WHS, a thermophilic hydrogenotrophic methanogen from Washburn Hot Springs in Yellowstone National Park, USA. Microbiol. Resour. Announc. 10, e01157-20 (2021).

    PubMed  PubMed Central  Google Scholar 

  21. Cheng, L. et al. Methermicoccus shengliensis gen. nov., sp. nov., a thermophilic, methylotrophc methanogen isolated from oil-production water, and proposal of Methermicoccaceae fam. nov. Int. J. Syst. Evol. Microbiol. 57, 2964–2969 (2007).

    CAS  PubMed  Google Scholar 

  22. Sorokin, Di. Y. et al. Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis. Nat. Microbiol. 2, 17081 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 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).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep. 6, 34212 (2016).

    ADS  PubMed  PubMed Central  Google Scholar 

  25. Calisto, F. & Pereira, M. M. The ion-translocating NrfD-like subunit of energy-transducing membrane complexes. Front. Chem. 9, 663706 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Ogawa, T., Yoshimura, T. & Hemmi, H. Geranylfarnesyl diphosphate synthase from Methanosarcina mazei: different role, different evolution. Biochem. Biophys. Res. Commun. 393, 16–20 (2010).

    CAS  PubMed  Google Scholar 

  27. Ellenbogen, J. B., Jiang, R., Kountz, D. J., Zhang, L. & Krzycki, J. A. The MttB superfamily member MtyB from the human gut symbiont Eubacterium limosum is a cobalamin-dependent γ-butyrobetaine methyltransferase. J. Biol. Chem. 297, 101327 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Ticak, T., Kountz, D. J., Girosky, K. E., Krzycki, J. A. & Ferguson, D. J. Jr A nonpyrrolysine member of the widely distributed trimethylamine methyltransferase family is a glycine betaine methyltransferase. Proc. Natl Acad. Sci. USA 111, E4668–E4676 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kountz, D. J., Behrman, E. J., Zhang, L. & Krzycki, J. A. MtcB, a member of the MttB superfamily from the human gut acetogen Eubacterium limosum, is a cobalamin-dependent carnitine demethylase. J. Biol. Chem. 295, 11971–11981 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Kulkarni, G., Mand, T. D. & Metcalf, W. W. Energy conservation via hydrogen cycling in the methanogenic archaeon Methanosarcina barkeri. mBio. 9, e01256-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Schut, G. J., Lipscomb, G. L., Nguyen, D. M. N., Kelly, R. M. & Adams, M. W. W. Heterologous production of an energy-conserving carbon monoxide dehydrogenase complex in the hyperthermophile Pyrococcus furiosus. Front. Microbiol. 7, 29 (2016).

    PubMed  PubMed Central  Google Scholar 

  32. Kim, M. S. et al. Co-dependent H2 production by genetically engineered Thermococcus onnurineus NA1. Appl. Environ. Microbiol. 79, 2048–2053 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kim, M. S. et al. A novel co-responsive transcriptional regulator and enhanced H2 production by an engineered Thermococcus onnurineus NA1 strain. Appl. Environ. Microbiol. 81, 1708–1714 (2015).

    ADS  PubMed  PubMed Central  Google Scholar 

  34. Laso-Pérez, R., Krukenberg, V., Musat, F. & Wegener, G. Establishing anaerobic hydrocarbon-degrading enrichment cultures of microorganisms under strictly anoxic conditions. Nat. Protoc. 13, 1310–1330 (2018).

    PubMed  Google Scholar 

  35. Friedrich Widdel, F. B. Gram-negative mesophilic sulfate-reducing bacteria. Prokaryotes 4, 3352–3378 (1992).

    Google Scholar 

  36. Brandis, A. & Thauer, R. K. Growth of Desulfurovibrio species on hydrogen and sulphate as sole energy source. J. Gen. Microbiol. 126, 249–252 (1981).

    CAS  Google Scholar 

  37. Ai, G., Zhu, J., Dong, X. & Sun, T. Simultaneous characterization of methane and carbon dioxide produced by cultured methanogens using gas chromatography/isotope ratio mass spectrometry and gas chromatography/mass spectrometry. Rapid Commun. Mass Spectrom. 27, 1935–1944 (2013).

    ADS  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Pernthaler, A., Pernthaler, J. & Amann, R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl. Environ. Microbiol. 68, 3094–3101 (2002).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Daims, H., Bruhl, A., Amann, R., Schleifer, K. & Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 22, 434–444 (1999).

    CAS  PubMed  Google Scholar 

  41. Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).

  42. Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA‐targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).

    CAS  PubMed  Google Scholar 

  43. Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).

    ADS  CAS  PubMed  Google Scholar 

  44. Hatzenpichler, R. et al. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal–bacterial consortia. Proc. Natl Acad. Sci. USA 113, 4069–4078 (2016).

    Google Scholar 

  45. Steinberg, L. M. & Regan, J. M. mcrA-targeted real-time quantitative PCR method to examine methanogen communities. Appl. Environ. Microbiol. 75, 4435–4442 (2009).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Angel, R., Claus, P. & Conrad, R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 6, 847–862 (2012).

    CAS  PubMed  Google Scholar 

  47. Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).

    CAS  PubMed  Google Scholar 

  48. Apprill, A., Mcnally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).

    Google Scholar 

  49. Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. Krukenberg, V., Reichart, N. J., Spietz, R. L. & Hatzenpichler, R. Microbial community response to polysaccharide amendment in anoxic hydrothermal sediments of the Guaymas Basin. Front. Microbiol. 12, 763971 (2021).

    PubMed  PubMed Central  Google Scholar 

  51. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

  52. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  53. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    CAS  PubMed  Google Scholar 

  55. Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).

    PubMed  PubMed Central  Google Scholar 

  56. Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Feng, X., Cheng, H., Portik, D. & Li, H. Metagenome assembly of high-fidelity long reads with hifiasm-meta. Nat. Methods 19, 671–674 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  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).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Chklovki, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 20, 1203–1212 (2023).

    Google Scholar 

  60. Prichard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. & Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8, 12–24 (2016).

    Google Scholar 

  61. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Chen, I. M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).

    ADS  CAS  PubMed  Google Scholar 

  63. Kletzin, A. et al. Cytochromes c in Archaea: distribution, maturation, cell architecture, and the special case of Ignicoccus hospitalis. Front. Microbiol. 6, 439 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Hallgren, J. et al. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. Preprint at bioRxiv https://doi.org/10.1101/2022.04.08.487609 (2022).

  66. Li, G., Rabe, K. S., Nielsen, J. & Engqvist, M. K. M. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8, 1411–1420 (2019).

    CAS  PubMed  Google Scholar 

  67. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 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).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    CAS  PubMed  Google Scholar 

  71. Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Ou, Y. F. et al. Expanding the phylogenetic distribution of cytochrome b-containing methanogenic archaea sheds light on the evolution of methanogenesis. ISME J. 16, 2373–2387 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Lagkouvardos, I. et al. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci. Rep. 6, 33721 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was funded through a NASA Exobiology programme award (grant no. 80NSSC19K1633). V.K. was supported in part by a grant from the NSF (grant no. MCB-1817428 to R.H.). A.K. was supported in part by a fellowship by the Montana Space Grant Consortium. A portion of this research was performed under the Facilities Integrating Collaborations for User Science programme (proposal no. 10.46936/fics.proj.2017.49972/6000002) and the Community Science Program (proposal no. 10.46936/10.25585/60008108), and used resources at the Department of the Environment (DOE) Joint Genome Institute (https://ror.org/04xm1d337), which is a DOE Office of Science User Facility operated under contract no. DE-AC02-05CH11231. We thank the US National Park Service for permitting work in YNP under permit number YELL-SCI-8010. We thank L. McKay and M. Lynes (both Montana State University) for discussions that informed field sampling, cultivation, taxonomy and manuscript preparation, S. Scheller (Aalto University) for discussing methanogen biochemistry and P. Schlegel (Montana State University) for assistance in cultivation.

Author information

Author notes

  1. These authors contributed equally: Viola Krukenberg, Anthony J. Kohtz

Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA

    Viola Krukenberg, Anthony J. Kohtz, Zackary J. Jay & Roland Hatzenpichler

  2. Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA

    Roland Hatzenpichler

Authors
  1. Viola Krukenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony J. Kohtz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zackary J. Jay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roland Hatzenpichler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.K., A.J.K. and R.H. designed experiments. V.K., A.J.K. and Z.J.J. conducted field work, reconstructed the metabolic potential of strain LCB3 and performed phylogenetic analysis. V.K. and A.J.K. performed cultivation and analysed gene expression data. Z.J.J. and A.J.K. processed metagenomic data and performed phylogenomic analysis. V.K. performed amplicon sequencing, BONCAT and CARD-FISH experiments. A.J.K. extracted DNA for metagenome sequencing, performed FISH, stable isotope tracing, substrate testing and transcriptomics experiments, and developed gas chromatography and mass spectrometry protocols. Z.J.J. performed whole-genome comparisons and determined environmental distributions. R.H. was responsible for funding and supervision of the project. All authors contributed to writing of the paper.

Corresponding authors

Correspondence to Viola Krukenberg or Roland Hatzenpichler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Cornelia Welte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Images of hot springs used as source material for cultivation.

A. Washburn Hot Springs. B. Hot spring LCB003. C. Hot spring LCB058. All three hot springs are located in Yellowstone National Park (WY, USA). All work in Yellowstone National Park was performed under research permit YELL-SCI-8010 to R.H.

Extended Data Fig. 2 Comparison of Korarchaeia genomes and MAGs.

A. Pairwise comparisons of average nucleotide (below dotted line) and amino acid (above dotted line) identities (%) of ‘Ca. Methanodesulfokora’ and ‘Ca. Korarchaeum’ genomes and Washburn Hot Spring MAGs. B. ‘Ca. M. washburnensis’ strain LCB3 genome and 16S rRNA gene read abundances in environmental metagenomes LCB003, LCB058, and WHS and in culture LCB3 at day 180 and 352.

Source Data

Extended Data Fig. 3 Phylogenetic affiliation of ‘Ca. M. washburnensis’ strain LCB3.

A. Maximum likelihood phylogenomic tree of archaea based on 33 single copy marker genes. The tree was constructed with IQtree and the best-fit model LG+F+R10, from the concatenated alignment of conserved arCOGs. The ‘Ca. M. washburnensis’ strain LCB3 genome is highlighted in red. Circles indicate ultrafast bootstrap values >95. B. Maximum likelihood phylogenetic tree of 16S rRNA genes from culture LCB3. The tree was constructed with fasttree including the 16S rRNA gene from the LCB3 genome (red), 16S rRNA genes in MAGs from culture LCB3 and reference sequences. Circles indicate bootstrap support values >95.

Extended Data Fig. 4 Representative fluorescence micrographs of culture LCB3.

A. Cells visualized with DAPI (blue). B. Cells visualized by DOPE-FISH using a ‘Ca. M. washburnensis’ specific 16S rRNA-targeted oligonucleotide probe (KRmw515, red). C. Overlay of A and B. D. Cells visualized with DAPI (blue). E. Cells visualized with DAPI (blue, same field of view as in D) combined with dual CARD-FISH using the ‘Ca. M. washburnensis’ specific 16S rRNA-targeted oligonucleotide probe (KRmw515, red) and a general archaea probe (Arch915, green). Note that because of two mismatches of the Arch915 probe to the 16S rRNA of ‘Ca. M. washburnensis’ there is no overlap of signal from probes KRmw515 and Arch915. Representative micrographs from n = 3 independent samples from culture LCB3. Scale bars 5 µm.

Extended Data Fig. 5 Methane production in culture LCB3 under different experimental conditions.

A. Methane production curves for SIP incubations (shown in Fig. 1b) amended with 12C-methanol (open white circles), 13C-methanol (gray filled circles), or 13C-methanol with BES (black filled circles). Data are presented as mean values +/− standard deviation (n = 3 biological replicates). B. Methane production curves for BONCAT-FISH incubations (shown in Fig. 3c) amended with methanol and hydrogen, spiked with BES at day 15 (filled symbols, dashed line) and spiked with HPG at day 18 (filled symbols). Controls were not spiked (open symbols), representing growth under standard cultivation conditions, or were spiked with BES at day 0 (open symbols, dashed line), inhibiting methane production. Incubations were sampled for cell visualization via BONCAT-FISH after 24 h of incubation with HPG (day 19). Data are presented as mean values +/− standard deviation (n = 3 biological replicates). C. Methane produced with different substrates after 85 days of incubation. Concentrations were 10 mM for methanol (MeOH), monomethylamine (MMA), dimethylamine (DMA), trimethylamine (TMA), ethanol (EtOH), and isopropanol (Prop); 2mM for lactate (Lac); and 50% for hydrogen (H2).

Source Data

Extended Data Fig. 6 Gene expression of ‘Ca. M. washburnensis’ strain LCB3 during methanogenic growth on hydrogen and methanol.

Dashed line represents the average transcripts per kilobase million (TPM) of all genes (n = 1 experiment). Each dot represents the expression of a subunit in a multi-subunit enzyme complex. For abbreviations see Fig. 4. See SI Tables 10, 11 for details on gene annotation and gene expression.

Source Data

Extended Data Fig. 7 Phylogenetic classification of [NiFe]-hydrogenases in ‘Ca. M. washburnensis’ strain LCB3.

A. Maximum likelihood phylogenetic tree of group 1 [NiFe]-hydrogenases. The tree was constructed using IQtree2 with the best fit model LG+R10 and 1000 ultrafast bootstraps. Hydrogenase classes were assigned according to the HydDB. The sequence from the LCB3 genome is highlighted in red. Black circles indicate ultrafast bootstrap values >95. B. Maximum likelihood phylogenetic tree of group 4 [NiFe]-hydrogenases. The tree was constructed using IQtree2 with the best fit model LG+R10 and 1000 ultrafast bootstraps. Clades containing sequences from the LCB3 genome are highlighted in red. Black circles indicate ultrafast bootstrap values >95.

Extended Data Fig. 8 Annotation of enzyme complexes in ‘Ca. M. washburnensis’ strain LCB3.

A. Comparison of group 1 [NiFe]-hydrogenase organization and structure between ‘Ca. M. washburnensis’ strain LCB3 and other MCR-encoding archaeal lineages. Models of the enzyme arrangement in the membrane with matching colors between the models indicating conserved function. Transmembrane helix (TMH) probabilities for intermembrane subunits. Groups 1j/1k have b-type cytochrome containing subunits with five helices, while the Korarchaeia group 1g [NiFe]-hydrogenase has an NrfD-like subunit (HcaC) with ten helices and lacks b-type cytochromes. B. Annotation of the ‘Ca. M. washburnensis’ strain LCB3 HdrDE complex. Model representing the arrangement of genes in the membrane. All HdrE subunits analyzed have 5 TMHs. The HdrD subunit in the LCB3 genome has cysteine residues that are conserved in HdrD subunits from other lineages. Transmembrane helices were predicted using the TMHMM 2.0 server.

Extended Data Fig. 9 Phylogenetic tree of geranylfarnesyl diphosphate synthase homologs in archaea and bacteria.

Red clades and text correspond to homologs encoded in ‘Ca. M. washburnensis’ strain LCB3. Red star indicates the clade containing the Methanosarcina mazei homolog involved in methanophenazine biosynthesis. Circles represent ultrafast bootstrap values >95. Short-chain and long-chain designations according to Ou et al.73.

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1–11.

Reporting Summary

Source data

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

Krukenberg, V., Kohtz, A.J., Jay, Z.J. et al. Methyl-reducing methanogenesis by a thermophilic culture of Korarchaeia. Nature 632, 1131–1136 (2024). https://doi.org/10.1038/s41586-024-07829-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-024-07829-8

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