Adam PS, Borrel G, Gribaldo S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. Proc Natl Acad Sci USA. 2018;115:E1166–73.
Google Scholar
Borrel G, Adam PS, McKay LJ, Chen LX, Sierra-García IN, Sieber CM, et al. Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea. Nat Microbiol. 2019;4:603–13.
Google Scholar
Ragsdale SW, Pierce E. Acetogenesis and the Wood–Ljungdahl pathway of CO2 fixation. BBA-Proteins Proteom. 2008;1784:1873–98.
Google Scholar
Müller B, Sun L, Schnürer A. First insights into the syntrophic acetate‐oxidizing bacteria–a genetic study. MicrobiologyOpen. 2013;2:35–53.
Google Scholar
Schuchmann K, Müller V. Energetics and application of heterotrophy in acetogenic bacteria. Appl Environ Microbiol. 2016;82:4056–69.
Google Scholar
Schuchmann K, Müller V. Autotrophy at the thermodynamic limit of life: a model for energy conservation in acetogenic bacteria. Nat Rev Microbiol. 2014;12:809–21.
Google Scholar
Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32.
Google Scholar
Vavourakis CD, Andrei AS, Mehrshad M, Ghai R, Sorokin DY, Muyzer G. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome. 2018;6:1–18.
Google Scholar
Liu YF, Chen J, Liu ZL, Shou LB, Lin DD, Zhou L, et al. Anaerobic degradation of paraffins by thermophilic Actinobacteria under methanogenic conditions. Environ Sci Technol. 2020;54:10610–20.
Google Scholar
Zhang Y, Wu G, Jiang H, Yang J, She W, Khan I, et al. Abundant and rare microbial biospheres respond differently to environmental and spatial factors in tibetan hot springs. Front Microbiol. 2018;9:2096.
Google Scholar
Hua ZS, Han YJ, Chen LX, Liu J, Hu M, Li SJ, et al. Ecological roles of dominant and rare prokaryotes in acid mine drainage revealed by metagenomics and metatranscriptomics. ISME J. 2015;9:1280–94.
Google Scholar
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
Google Scholar
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165
Google Scholar
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
Google Scholar
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.
Google Scholar
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Google Scholar
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59.
Google Scholar
Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182–5.
Google Scholar
Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.
Google Scholar
Lagesen K, Hallin P, Rødland EA, Stærfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35:3100–8.
Google Scholar
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
Google Scholar
Kalyaanamoorthy S, Minh BQ, Wong TK, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587.
Google Scholar
Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.
Google Scholar
Carnevali PBM, Schulz F, Castelle CJ, Kantor RS, Shih PM, Sharon I, et al. Hydrogen-based metabolism as an ancestral trait in lineages sibling to the Cyanobacteria. Nat Commun. 2019;10:1–15.
Google Scholar
Jaffe AL, Castelle CJ, Dupont CL, Banfield JF. Lateral gene transfer shapes the distribution of RuBisCO among candidate phyla radiation bacteria and DPANN archaea. Mol Biol Evol. 2019;36:435–46.
Google Scholar
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
Google Scholar
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.
Google Scholar
Søndergaard D, Pedersen CNS, Greening C. HydDB: a web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:1–8.
Google Scholar
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32.
Google Scholar
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. 2016;44:W242–5.
Google Scholar
Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16:157.
Google Scholar
Ronquist F, Teslenko M, Van Der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61:539–42.
Google Scholar
Csűös M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics. 2010;26:1910–2.
Google Scholar
Hua ZS, Wang YL, Evans PN, Qu YN, Goh KM, Rao YZ, et al. Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea. Nat Commun. 2019;10:1–11.
Google Scholar
Jiao JY, Liu L, Hua ZS, Fang BZ, Zhou EM, Salam N, et al. Microbial dark matter coming to light: challenges and opportunities. Natl Sci Rev. 2021;8:nwaa280.
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.
Google Scholar
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–42.
Google Scholar
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
Google Scholar
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
Google Scholar
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:1–9.
Google Scholar
Brown CT, Olm MR, Thomas BC, Banfield JF. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol. 2016;34:1256–63.
Google Scholar
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:1–8.
Google Scholar
Langmead B, Wilks C, Antonescu V, Charles R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics. 2019;35:421–32.
Google Scholar
Salam N, Jiao JY, Zhang XT, Li WJ. Update on the classification of higher ranks in the phylum Actinobacteria. Int J Syst Evol Microbiol. 2020;70:1331–55.
Google Scholar
Richter M, Rosselló-Móra R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA. 2009;106:19126–31.
Google Scholar
Chun J, Oren A, Ventosa A, Christensen H, Arahal DR, da Costa MS, et al. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Micr. 2018;68:461–6.
Google Scholar
Merino N, Kawai M, Boyd ES, Colman DR, McGlynn SE, Nealson KH, et al. Single-cell genomics of novel actinobacteria with the Wood–Ljungdahl pathway discovered in a serpentinizing system. Front Microbiol. 2020;11:1031.
Google Scholar
Coleman GA, Davín AA, Mahendrarajah T, Spang AA, Hugenholtz P, Szöllősi GJ, et al. A rooted phylogeny resolves early bacterial evolution. 2020. https://doi.org/10.1101/2020.07.15.205187.
Spear JR, Walker JJ, McCollom TM, Pace NR. Hydrogen and bioenergetics in the Yellowstone geothermal ecosystem. Proc Natl Acad Sci USA. 2005;102:2555–60.
Google Scholar
Vignais PM, Billoud B. Occurrence, classification, and biological function of hydrogenases: an overview. Chem Rev. 2007;107:4206–72.
Google Scholar
Marreiros BC, Batista AP, Duarte AM, Pereira MM. A missing link between complex I and group 4 membrane-bound [NiFe] hydrogenases. BBA-Bioenerg. 2013;1827:198–209.
Google Scholar
Martin WF. Older than genes: the acetyl CoA pathway and origins. Front Microbiol. 2020;11:817.
Google Scholar
Daniel SL, Hsu T, Dean SI, Drake HL. Characterization of the H2-and CO-dependent chemolithotrophic potentials of the acetogens Clostridium thermoaceticum and Acetogenium kivui. J Bacteriol. 1990;172:4464–71.
Google Scholar
Fontaine FE, Peterson WH, McCoy E, Johnson MJ, Ritter GJ. A new type of glucose fermentation by Clostridium thermoaceticum. J Bacteriol. 1942;43:701.
Google Scholar
Drake HL, Daniel SL. Physiology of the thermophilic acetogen Moorella thermoacetica. Microbiol Res. 2004;155:869–83.
Google Scholar
Hügler M, Hube H, Molyneaux SJ, Vetriani C, Sievert SM. Autotrophic CO2 fixation via the reductive tricarboxylic acid cycle in different lineages within the phylum Aquificae: evidence for two ways of citrate cleavage. Environ Microbiol. 2007;9:81–92.
Google Scholar
Hou J, Sievert SM, Wang Y, Seewald JS, Natarajan VP, Xiao X, et al. Microbial succession during the transition from active to inactive stages of deep-sea hydrothermal vent sulfide chimneys. Microbiome. 2020;8:102.
Google Scholar
Müller AL, Kjeldsen KU, Rattei T, Pester M, Loy A. Phylogenetic and environmental diversity of DsrAB-type dissimilatory (bi) sulfite reductases. ISME J. 2015;9:1152–65.
Google Scholar
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, 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. 2019;4:1138–48.
Google Scholar
Seitz KW, Lazar CS, Hinrichs KU, Teske AP, Baker BJ. Genomic reconstruction of a novel, deeply branched sediment archaeal phylum with pathways for acetogenesis and sulfur reduction. ISME J. 2016;10:1696–705.
Google Scholar
Orsi WD, Vuillemin A, Rodriguez P, Coskun ÖK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2020;5:248–55.
Google Scholar
Raymann K, Brochier-Armanet C, Gribaldo S. The two-domain tree of life is linked to a new root for the Archaea. Proc Natl Acad Sci USA. 2015;112:6670–5.
Google Scholar
Source: Ecology - nature.com
