Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science 350, 434–438 (2015).
Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 16170 (2016).
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).
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).
Martin, W. & Russell, M. J. On the origin of biochemistry at an alkaline hydrothermal vent. Philos. Trans. R. Soc. Lond. B 362, 1887–1925 (2007).
Saunois, M. et al. The global methane budget 2000-2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).
Ferry, J. G. Methanogenesis: Ecology, Physiology, Biochemistry & Genetics (Springer, 2012).
Woese, C. R., Kandler, O. & Wheelis, M. L. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc. Natl Acad. Sci. USA 87, 4576–4579 (1990).
Ermler, U., Grabarse, W., Shima, S., Goubeaud, M. & Thauer, R. K. Crystal structure of methyl-coenzyme M reductase: the key enzyme of biological methane formation. Science 278, 1457–1462 (1997).
Wang, J. et al. Evidence for nontraditional mcr-containing archaea contributing to biological methanogenesis in geothermal springs. Sci. Adv. 9, eadg6004 (2023).
Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl Acad. Sci. USA 116, 5037–5044 (2019).
Borrel, G. et al. Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea. Nat. Microbiol. 4, 603–613 (2019).
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).
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).
Mand, T. D. & Metcalf, W. W. Energy conservation and hydrogenase function in methanogenic archaea, in particular the genus Methanosarcina. Microbiol. Mol. Biol. Rev. 83, e00020-19 (2019).
Zhou, Z. et al. Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species. Nature 601, 257–262 (2022).
Mayumi, D. et al. Methane production from coal by a single methanogen. Science 354, 222–225 (2016).
Garcia, P. S., Gribaldo, S. & Borrel, G. Diversity and evolution of methane-related pathways in archaea. Annu. Rev. Microbiol. 76, 727–755 (2022).
Liu, Y.-F. et al. Long-term cultivation and meta-omics reveal methylotrophic methanogenesis in hydrocarbon-impacted habitats. Engineering 24, 265–274 (2023).
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).
Evans, P. N. et al. An evolving view of methane metabolism in the Archaea. Nat. Rev. Microbiol. 17, 219–232 (2019).
Cheng, L. et al. Isolation and characterization of Methanoculleus receptaculi sp. nov. from Shengli oil field, China. FEMS Microbiol. Lett. 285, 65–71 (2008).
Oren, A., Garrity, G. M., Parker, C. T., Chuvochina, M. & Trujillo, M. E. Lists of names of prokaryotic Candidatus taxa. Int. J. Syst. Evol. Microbiol. 70, 3956–4042 (2020).
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).
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).
Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).
Konstantinidis, K. T., Rosselló-Móra, R. & Amann, R. Uncultivated microbes in need of their own taxonomy. ISME J. 11, 2399–2406 (2017).
Speth, D. R. et al. Microbial communities of Auka hydrothermal sediments shed light on vent biogeography and the evolutionary history of thermophily. ISME J. 16, 1750–1764 (2022).
Wang, Y. et al. A methylotrophic origin of methanogenesis and early divergence of anaerobic multicarbon alkane metabolism. Sci. Adv. 7, eabj1453 (2021).
Liu, Y.-F. et al. Anaerobic degradation of paraffins by thermophilic Actinobacteria under methanogenic conditions. Environ. Sci. Technol. 54, 10610–10620 (2020).
Feldewert, C., Lang, K. & Brune, A. The hydrogen threshold of obligately methyl-reducing methanogens. FEMS Microbiol. Lett. 367, fnaa137 (2020).
Zhuang, G.-C., Lin, Y.-S., Elvert, M., Heuer, V. B. & Hinrichs, K.-U. Gas chromatographic analysis of methanol and ethanol in marine sediment pore waters: validation and implementation of three pretreatment techniques. Mar. Chem. 160, 82–90 (2014).
Zhuang, G.-C., Peña-Montenegro, T. D., Montgomery, A., Hunter, K. S. & Joye, S. B. Microbial metabolism of methanol and methylamine in the Gulf of Mexico: insight into marine carbon and nitrogen cycling. Environ. Microbiol. 20, 4543–4554 (2018).
Miller, T. L. & Wolin, M. J. Methanosphaera stadtmaniae gen. nov., sp. nov.: a species that forms methane by reducing methanol with hydrogen. Arch. Microbiol. 141, 116–122 (1985).
Sprenger, W. W., van Belzen, M. C., Rosenberg, J., Hackstein, J. H. & Keltjens, J. T. Methanomicrococcus blatticola gen. nov., sp. nov., a methanol- and methylamine-reducing methanogen from the hindgut of the cockroach Periplaneta americana. Int. J. Syst. Evol. Microbiol. 50, 1989–1999 (2000).
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).
Sorokin, D. Y. et al. Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis. Nat. Microbiol. 2, 17081 (2017).
Verhees, C. H. et al. The unique features of glycolytic pathways in Archaea. Biochem. J 375, 231–246 (2003).
Tersteegen, A., Linder, D., Thauer, R. K. & Hedderich, R. Structures and functions of four anabolic 2‐oxoacid oxidoreductases in Methanobacterium thermoautotrophicum. Eur. J. Biochem. 244, 862–868 (1997).
Glasemacher, J., Bock, A. K., Schmid, R. & Schönheit, P. Purification and properties of acetyl‐CoA synthetase (ADP‐forming), an archaeal enzyme of acetate formation and ATP synthesis, from the hyperthermophile Pyrococcus furiosus. Eur. J. Biochem. 244, 561–567 (1997).
Adam, P. S., Kolyfetis, G. E., Bornemann, T. L., Vorgias, C. E. & Probst, A. J. Genomic remnants of ancestral methanogenesis and hydrogenotrophy in Archaea drive anaerobic carbon cycling. Sci. Adv. 8, eabm9651 (2022).
Beulig, F., Røy, H., McGlynn, S. E. & Jørgensen, B. B. Cryptic CH4 cycling in the sulfate-methane transition of marine sediments apparently mediated by ANME-1 archaea. ISME J. 13, 250–262 (2019).
Kohtz, A. J. et al. Cultivation and visualization of a methanogen of the phylum Thermoproteota. Nature https://doi.org/10.1038/s41586-024-07631-6 (2024).
Rabus, R., Hansen, T., Widdel, F. & Dworkin, M. The Prokaryotes: Ecophysiology and Biochemistry (Springer, 2006).
Wolin, E. A., Wolin, M. J. & Wolfe, R. S. Formation of methane by bacterial extracts. J. Biol. Chem. 238, 2882–2886 (1963).
Plugge, C. M. Anoxic media design, preparation, and considerations. Methods Enzymol. 397, 3–16 (2005).
Wu, K. et al. Gudongella oleilytica gen. nov., sp. nov., an aerotorelant bacterium isolated from Shengli oilfield and validation of family Tissierellaceae. Int. J. Syst. Evol. Microbiol. 70, 951–957 (2020).
Neubauer, S. et al. U13C cell extract of Pichia pastoris—a powerful tool for evaluation of sample preparation in metabolomics. J. Sep. Sci. 35, 3091–3105 (2012).
Ver Eecke, H. C., Akerman, N. H., Huber, J. A., Butterfield, D. A. & Holden, J. F. Growth kinetics and energetics of a deep-sea hyperthermophilic methanogen under varying environmental conditions. Environ. Micobiol. Rep. 5, 665–671 (2013).
Cheng, L., Dai, L., Li, X., Zhang, H. & Lu, Y. Isolation and characterization of Methanothermobacter crinale sp. nov., a novel hydrogenotrophic methanogen from the Shengli oil field. Appl. Environ. Microbiol. 77, 5212–5219 (2011).
Cheng, L., Rui, J., Li, Q., Zhang, H. & Lu, Y. Enrichment and dynamics of novel syntrophs in a methanogenic hexadecane-degrading culture from a Chinese oilfield. FEMS. Microbiol. Ecol. 83, 757–766 (2013).
Cheng, L. et al. DNA-SIP reveals that Syntrophaceae play an important role in methanogenic hexadecane degradation. PLoS ONE 8, e66784 (2013).
Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) (Wiley, 1991).
Pernthaler, A., Preston, C. M., Pernthaler, J., DeLong, E. F. & Amann, R. Comparison of fluorescently labeled oligonucleotide and polynucleotide probes for the detection of pelagic marine bacteria and archaea. Appl. Environ. Microbiol. 68, 661–667 (2002).
Chi, Z.-L., Yu, G.-H., Kappler, A., Liu, C.-Q. & Gadd, G. M. Fungal–mineral interactions modulating intrinsic peroxidase-like activity of iron nanoparticles: implications for the biogeochemical cycles of nutrient elements and attenuation of contaminants. Environ. Sci. Technol. 56, 672–680 (2021).
Yu, G.-H. et al. Fungal nanophase particles catalyze iron transformation for oxidative stress removal and iron acquisition. Curr. Biol. 30, 2943–2950 (2020).
Zeng, Z. et al. GDGT cyclization proteins identify the dominant archaeal sources of tetraether lipids in the ocean. Proc. Natl Acad. Sci. USA 116, 22505–22511 (2019).
Chen, Y. et al. The production of diverse brGDGTs by an Acidobacterium providing a physiological basis for paleoclimate proxies. Geochim. Cosmochim. Acta 337, 155–165 (2022).
Thiele, B. & Matsubara, S. in Plant and Food Carotenoids. Methods in Molecular Biology (eds Rodríguez-Concepción, M. & Welsch, R.) (263–277 (Humana, 2020).
Knappy, C. et al. Mono‐, di‐ and trimethylated homologues of isoprenoid tetraether lipid cores in archaea and environmental samples: mass spectrometric identification and significance. J. Mass Spectrom. 50, 1420–1432 (2015).
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, 1991).
Grosskopf, R., Janssen, P. H. & Liesack, W. Diversity and structure of the methanogenic community in anoxic rice paddy soil microcosms as examined by cultivation and direct 16S rRNA gene sequence retrieval. Appl. Environ. Microbiol. 64, 960–969 (1998).
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2012).
Hiergeist, A., Reischl, U. & Gessner, A. Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability. Int. J. Med. Microbiol. 306, 334–342 (2016).
Wei, S. et al. Comparative evaluation of three archaeal primer pairs for exploring archaeal communities in deep-sea sediments and permafrost soils. Extremophiles 23, 747–757 (2019).
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2013).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).
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).
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
Yoon, S. H., Ha, S. M., Lim, J., Kwon, S. & Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie Van Leeuwenhoek 110, 1281–1286 (2017).
Qin, Q. L. et al. A proposed genus boundary for the prokaryotes based on genomic insights. J. Bacteriol. 196, 2210–2215 (2014).
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).
The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2020).
Marchler-Bauer, A. & Bryant, S. H. CD-Search: protein domain annotations on the fly. Nucleic Acids Res. 32, W327–W331 (2004).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).
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).
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).
Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
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).
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arxiv.org/abs/1303.3997 (2013).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11.12.11–11.12.34 (2014).
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Brownrigg., R., Becker, R. A. & Wilks, A. R. maps: draw geographical maps. R package version 3.4.2 (2023); cran.r-project.org/web/packages/maps/maps.pdf
Wilkinson, L. ggplot2: elegant graphics for data analysis. Biometrics 67, 678–679 (2011).