Baker, B. J. et al. Diversity, ecology and evolution of Archaea. Nat. Microbiol. 5, 887–900 (2020).
Google Scholar
Baker, B. J., Appler, K. E. & Gong, X. New microbial biodiversity in marine sediments. Ann. Rev. Mar. Sci. 13, 161–175 (2020).
Google Scholar
Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
Google Scholar
Kozubal, M. A. et al. Geoarchaeota: a new candidate phylum in the Archaea from high-temperature acidic iron mats in Yellowstone National Park. ISME J. 7, 622–634 (2013).
Google Scholar
Jay, Z. J. et al. Marsarchaeota are an aerobic archaeal lineage abundant in geothermal iron oxide microbial mats. Nat. Microbiol. 3, 732–740 (2018).
Google Scholar
Hua, Z. S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).
Google Scholar
Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).
Google Scholar
Spang, A. et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat. Microbiol. 4, 1138–1148 (2019).
Google Scholar
Orsi, W. D. et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat. Microbiol. 5, 248–255 (2020).
Google Scholar
Eme, L., Spang, A., Lombard, J., Stairs, C. W. & Ettema, T. J. G. Archaea and the origin of eukaryotes. Nat. Rev. Microbiol. 15, 711–723 (2017).
Google Scholar
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).
Google Scholar
Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl Acad. Sci. U.S.A. 114, E4602 –E4611 (2017).
Google Scholar
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).
Trembath-Reichert, E. et al. Methyl-compound use and slow growth characterize microbial life in 2-km-deep subseafloor coal and shale beds. Proc. Natl Acad. Sci. USA 114, E9206–E9215 (2017).
Google Scholar
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).
Google Scholar
Chistoserdova, L. Modularity of methylotrophy, revisited. Environ. Microbiol. 13, 2603–2622 (2011).
Google Scholar
Chistoserdova, L. & Kalyuzhnaya, M. G. Current trends in methylotrophy. Trends Microbiol. 26, 703–714 (2018).
Google Scholar
Sun, J., Mausz, M. A., Chen, Y. & Giovannoni, S. J. Microbial trimethylamine metabolism in marine environments. Environ. Microbiol. 21, 513–520 (2018).
Google Scholar
Zhuang, G.-C., Montgomery, A. & Joye, S. B. Heterotrophic metabolism of C1 and C2 low molecular weight compounds in northern Gulf of Mexico sediments: controlling factors and implications for organic carbon degradation. Geochim. Cosmochim. Acta 247, 243–260 (2019).
Google Scholar
Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 16170 (2016).
Google Scholar
Lazar, C. S. et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ. Microbiol. 18, 1200–1211 (2016).
Google Scholar
Zhuang, G. Methylotrophic methanogenesis and potential methylated substrates in marine sediment. (University of Bremen, 2014).
Richards, M. A. et al. Exploring hydrogenotrophic methanogenesis: a genome scale metabolic reconstruction of Methanococcus maripaludis. J. Bacteriol. 198, 3379–3390 (2016).
Google Scholar
Sousa, D. Z. et al. The deep-subsurface sulfate reducer Desulfotomaculum kuznetsovii employs two methanol-degrading pathways. Nat. Commun. 9, 239 (2018).
Google Scholar
Dombrowski, N., Teske, A. P. & Baker, B. J. Extensive metabolic versatility and redundancy in microbially diverse, dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).
Fricke, W. F. et al. The genome sequence of Methanosphaera stadtmanae reveals why this human intestinal archaeon is restricted to methanol and H2 for methane formation and ATP synthesis †. J. Bacteriol. 188, 642–658 (2006).
McKay L., et al. Co-occurring genomic capacity for anaerobic methane and dissimilatory sulfur metabolisms discovered in the Korarchaeota. Nat. Microbiol.4, 614–622 (2019).
Muñoz-Velasco, I. et al. Methanogenesis on early stages of life: ancient but not primordial. Orig. Life Evol. Biosph. 48, 407–420 (2019).
Adam, P. S., Borrel, G. & Gribaldo, S. An archaeal origin of the Wood–Ljungdahl H4MPT branch and the emergence of bacterial methylotrophy. Nat. Microbiol. 4, 2155–2163 (2019).
Swan, B., Reifel, K. & Valentine, D. Periodic sulfide irruptions impact microbial community structure and diversity in the water column of a hypersaline lake. Aquat. Microb. Ecol. 60, 97–108 (2010).
Google Scholar
Adam, P. S., Borrel, G. & Gribaldo, S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. PNAS 115, E5837 (2018).
Google Scholar
Orita, I. et al. The ribulose monophosphate pathway substitutes for the missing pentose phosphate pathway in the archaeon Thermococcus kodakaraensis. J. Bacteriol. 188, 4698–4704 (2006).
Google Scholar
Urschel, M. R., Kubo, M. D., Hoehler, T. M., Peters, J. W. & Boyd, E. S. Carbon source preference in chemosynthetic hot spring communities. Appl. Environ. Microbiol. 81, 3834–3847 (2015).
Google Scholar
Yokohama, H., Wagner, I. D. & Wiegel, J. Caldicoprobacter oshimai gen. nov., sp. nov., an anaerobic, xylanolytic, extremely thermophilic bacterium isolated from sheep faeces, and proposal of Caldicoprobacteraceae fam. nov. Int. J. Syst. Evol. Microbiol. 60, 67–71 (2010).
Google Scholar
Zhang, X. et al. Petroclostridium xylanilyticum gen. Nov., sp. nov., a xylan-degrading bacterium isolated from an oilfield, and reclassification of clostridial cluster iii members into four novel genera in a new hungateiclostridiaceae fam. nov.Int. J. Syst. Evol. Microbiol. 68, 3197–3211 (2018).
Google Scholar
Girbal, L., Croux, C., Vasconcelos, I. & Soucaille, P. Regulation of metabolic shifts in Clostridium acetobutylicum ATCC 824. FEMS Microbiol. Rev. 17, 287–297 (1995).
Google Scholar
Qi, F. et al. Improvement of butanol production in Clostridium acetobutylicum through enhancement of NAD(P)H availability. J. Ind. Microbiol. Biotechnol. 45, 993–1002 (2018).
Google Scholar
Branduardi, P., Longo, V., Berterame, N. M., Rossi, G. & Porro, D. A novel pathway to produce butanol and isobutanol in Saccharomyces cerevisiae. Biotechnol. Biofuels 6, 68 (2013).
Google Scholar
Johnsen, U. & Schönheit, P. Novel xylose dehydrogenase in the halophilic archaeon Haloarcula marismortui. J. Bacteriol. 186, 6198–6207 (2004).
Google Scholar
Ravachol, J. et al. Mechanisms involved in xyloglucan catabolism by the cellulosome-producing bacterium Ruminiclostridium cellulolyticum. Sci. Rep. 6, 22770 (2016).
Google Scholar
Macdonald, S. S., Blaukopf, M. & Withers, S. G. N-acetylglucosaminidases from CAZy family GH3 are really glycoside phosphorylases, thereby explaining their use of histidine as an acid/Base catalyst in place of glutamic acid. J. Biol. Chem. 290, 4887–4895 (2015).
Google Scholar
Wang, Y. et al. Environmental conditions constrain the distribution and diversity of Archaeal merA in Yellowstone National Park, Wyoming, U.S.A. Microb. Ecol. 62, 739–752 (2011).
Google Scholar
Nunes, C. I. P. et al. ArsC3 from Desulfovibrio alaskensis G20, a cation and sulfate-independent highly efficient arsenate reductase. J. Biol. Inorg. Chem. 19, 1277–1285 (2014).
Google Scholar
Silver, S. & Phung, L. T. Genes and enzymes involved in bacterial oxidation and reduction of inorganic arsenic. Appl. Environ. Microbiol. 71, 599–608 (2005).
Google Scholar
Colman, D. R., Lindsay, M. R. & Boyd, E. S. Mixing of meteoric and geothermal fluids supports hyperdiverse chemosynthetic hydrothermal communities. Nat. Commun. 10, 681 (2019).
Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems 5, e00795-19 (2020).
Google Scholar
Rabus, R., Venceslau, S. S., Lars, W., Wall, J. D. & Pereira, I. A. C. A post-genomic view of the ecophysiology, catabolism and biotechnological relevance of sulphate-reducing prokaryotes. Adv. Micro. Physiol. 66, 55–321 (2015).
Google Scholar
Tóth, A., Takács, M., Groma, G., Rákhely, G. & Kovács, K. L. A novel NADPH-dependent oxidoreductase with a unique domain structure in the hyperthermophilic Archaeon, Thermococcus litoralis. FEMS Microbiol. Lett. 282, 8–14 (2008).
Google Scholar
Ma, K., Weiss, R. & Adams, M. W. W. Characterization of hydrogenase II from the hyperthermophilic archaeon Pyrococcus furiosus and assessment of its role in sulfur reduction. J. Bacteriol. 182, 1864–1871 (2000).
Google Scholar
Jenney, F. E. & Adams, M. W. W. Hydrogenases of the model hyperthermophiles. Ann. N. Y. Acad. Sci. 1125, 252–266 (2008).
Google Scholar
Van Haaster, D. J., Silva, P. J., Hagedoorn, P. L., Jongejan, J. A. & Hagen, W. R. Reinvestigation of the steady-state kinetics and physiological function of the soluble NiFe-hydrogenase I of Pyrococcus furiosus. J. Bacteriol. 190, 1584–1587 (2008).
Google Scholar
Greening, C. et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 10, 761–777 (2016).
Google Scholar
Stetter, K. O. Hyperthermophiles in the history of life. Philos. Trans. R. Soc. B Biol. Sci. 361, 1837–1842 (2006).
Google Scholar
Collins, T., Gerday, C. & Feller, G. Xylanases, xylanase families and extremophilic xylanases. FEMS Microbiol. Rev. 29, 3–23 (2005).
Google Scholar
Schädel, C., Richter, A., Blöchl, A. & Hoch, G. Hemicellulose concentration and composition in plant cell walls under extreme carbon source-sink imbalances. Physiol. Plant. 139, 241–255 (2010).
Google Scholar
Chen, S. et al. The Great Oxidation Event expanded the genetic repertoire of arsenic metabolism and cycling. 117, 10414–10421 (2020).
Rogers, K. L. & Schulte, M. D. Organic sulfur metabolisms in hydrothermal environments. Geobiology 10, 320–332 (2012).
Google Scholar
Baker, B. J. et al. Genomic inference of the metabolism of cosmopolitan subsurface Archaea, Hadesarchaea. Nat. Microbiol. 1, 16002 (2016).
Google Scholar
Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).
Google Scholar
Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).
Google Scholar
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).
Google Scholar
Dick, G. J. et al. Community-wide analysis of microbial genome sequence signatures. Genome Biol. 10, R85 (2009).
Darling, A. E. et al. PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2, e243 (2014).
Google Scholar
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Google Scholar
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).
Google Scholar
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Google Scholar
Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
Google Scholar
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
Google Scholar
Søndergaard, D., Pedersen, C. N. S. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 34212 (2016).
Google Scholar
Zhang, H. et al. DbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101 (2018).
Google Scholar
De Anda, V. et al. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. Gigascience 6, 1–17 (2017).
Google Scholar
Zhichao, Z. et al METABOLIC: High-throughput. profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks. Preprint at bioRxiv 761643 (2019).
Rawlings, N. D., Morton, F. R., Kok, C. Y., Kong, J. & Barrett, A. J. MEROPS: the peptidase database. Nucleic Acids Res. 38, D227–D233 (2010).
Google Scholar
Taboada, B., Estrada, K., Ciria, R. & Merino, E. Operon-mapper: a web server for precise operon identification in bacterial and archaeal genomes. Bioinformatics 34, 4118–4120 (2018).
Google Scholar
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).
Google Scholar
Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).
Google Scholar
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).
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).
Google Scholar
Chaumeil, P., Mussig, A. J., Parks, D. H. & Hugenholtz, P. Genome analysis GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).
Google Scholar
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