Cultivation and visualization of a methanogen of the phylum Thermoproteota (original) (raw)

• 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
• 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
• 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
• 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
• Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science 350, 434–438 (2015).
  ADS CAS PubMed Google Scholar
• Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 1–9 (2016).
  Google Scholar
• Saunois, M. et al. The Global Methane Budget 2000-2017. Earth Syst. Sci. Data 12, 1561–1623 (2020).
  ADS Google Scholar
• 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
• 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
• Evans, P. N. et al. An evolving view of methane metabolism in the Archaea. Nat. Rev. Microbiol. 17, 219–232 (2019).
  CAS PubMed Google Scholar
• Stephenson, M. & Stickland, L. H. Hydrogenase: the bacterial formation of methane by the reduction of one-carbon compounds by molecular hydrogen. Biochem. J. 27, 1517–1527 (1933).
  CAS PubMed PubMed Central Google Scholar
• Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).
  CAS PubMed Google Scholar
• Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).
  ADS PubMed PubMed Central Google Scholar
• McKay, L. J. et al. Co-occurring genomic capacity for anaerobic methane metabolism and dissimilatory sulfite reduction discovered in the Korarchaeota. Nat. Microbiol. 4, 614–622 (2019).
  CAS PubMed Google Scholar
• 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
• 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
• 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 Commun. 3, 22 (2023).
  ADS PubMed PubMed Central Google Scholar
• Buessecker, S. et al. Mcr-dependent methanogenesis in Archaeoglobaceae enriched from a terrestrial hot spring. ISME J. 17, 1649–1659 (2023).
  CAS PubMed Google Scholar
• Wang, J. et al. Evidence for nontraditional mcr-containing archaea contributing to biological methanogenesis in geothermal springs. Sci. Adv. 9, eadg6004 (2023).
  CAS PubMed PubMed Central Google Scholar
• Lynes, M. M., Jay, Z. J., Kohtz, A. J. & Hatzenpichler, R. Methylotrophic methanogenesis in the Archaeoglobi revealed by cultivation of Ca. Methanoglobus hypatiae from a Yellowstone hot spring. ISME J. 18, wrae026 (2024).
• Liu, Y.-F. et al. Long-term cultivation and meta-omics reveal methylotrophic methanogenesis in hydrocarbon-impacted habitats. Engineering 24, 264–275 (2023).
• 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).
  PubMed Google Scholar
• Zeikus, J., Ben-Bassat, A. & Hegge, P. Microbiology of methanogenesis in thermal, volcanic environments. J. Bacteriol. 143, 432–440 (1980).
  CAS PubMed PubMed Central Google Scholar
• 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, e01157–01120 (2021).
  CAS PubMed PubMed Central Google Scholar
• 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).
  ADS CAS PubMed PubMed Central Google Scholar
• Balk, M., Weijma, J. & Stams, A. J. Thermotoga lettingae sp. nov., a novel thermophilic, methanol-degrading bacterium isolated from a thermophilic anaerobic reactor. Int. J. Syst. Evol. Microbiol. 52, 1361–1368 (2002).
  CAS PubMed Google Scholar
• Paulo, P. et al. Pathways of methanol conversion in a thermophilic anaerobic (55 C) sludge consortium. Appl. Microbiol. Biotechnol. 63, 307–314 (2003).
  CAS PubMed 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).
• Hatzenpichler, R. et al. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal− bacterial consortia. Proc. Natl Acad. Sci. USA 113, E4069–E4078 (2016).
  CAS PubMed PubMed Central Google Scholar
• Wu, K. et al. Isolation of a methyl-reducing methanogen outside the Euryarchaeota. Nature https://doi.org/10.1038/s41586-024-07728-y (2024).
• Kohtz, A. J., Jay, Z. J., Lynes, M. M., Krukenberg, V. & Hatzenpichler, R. Culexarchaeia, a novel archaeal class of anaerobic generalists inhabiting geothermal environments. ISME Commun. 2, 1–13 (2022).
  Google Scholar
• Major, T. A., Liu, Y. & Whitman, W. B. Characterization of energy-conserving hydrogenase B in Methanococcus maripaludis. J. Bacteriol. 192, 4022–4030 (2010).
  CAS PubMed PubMed Central Google Scholar
• Ma, K., Schicho, R. N., Kelly, R. M. & Adams, M. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase: evidence for a sulfur-reducing hydrogenase ancestor. Proc. Natl Acad. Sci. USA 90, 5341–5344 (1993).
  ADS CAS PubMed PubMed Central Google Scholar
• 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).
  ADS PubMed PubMed Central Google Scholar
• Loh, H. Q., Hervé, V. & Brune, A. Metabolic potential for reductive acetogenesis and a novel energy-converting [NiFe] hydrogenase in Bathyarchaeia from termite guts–A genome-centric analysis. Front. Microbiol. 11, 635786 (2021).
  PubMed PubMed Central Google Scholar
• Kröninger, L., Berger, S., Welte, C. & Deppenmeier, U. Evidence for the involvement of two heterodisulfide reductases in the energy‐conserving system of Methanomassiliicoccus luminyensis. FEBS J. 283, 472–483 (2016).
  PubMed Google Scholar
• Kröninger, L. et al. Energy conservation in the gut microbe Methanomassiliicoccus luminyensis is based on membrane‐bound ferredoxin oxidation coupled to heterodisulfide reduction. FEBS J. 286, 3831–3843 (2019).
  PubMed Google Scholar
• Bryant, M., Campbell, L. L., Reddy, C. & Crabill, M. Growth of Desulfovibrio in lactate or ethanol media low in sulfate in association with H2-utilizing methanogenic bacteria. Appl. Environ. Microbiol. 33, 1162–1169 (1977).
  ADS CAS PubMed PubMed Central Google Scholar
• McInerney, M. J. & Bryant, M. P. Anaerobic degradation of lactate by syntrophic associations of Methanosarcina barkeri and Desulfovibrio species and effect of H2 on acetate degradation. Appl. Environ. Microbiol. 41, 346–354 (1981).
  ADS CAS PubMed PubMed Central Google Scholar
• Hwang, W. C. et al. LUD, a new protein domain associated with lactate utilization. BMC Bioinf. 14, 1–9 (2013).
  Google Scholar
• Young, L. N. & Villa, E. Bringing Structure to Cell Biology with Cryo-Electron Tomography. Annu. Rev. Biophys. 52, 573–595 (2023).
  CAS PubMed PubMed Central Google Scholar
• Briegel, A. et al. Structural conservation of chemotaxis machinery across A rchaea and B acteria. Environ. Microbiol. Rep. 7, 414–419 (2015).
  CAS PubMed Google Scholar
• Albers, S.-V. & Jarrell, K. F. The archaellum: an update on the unique archaeal motility structure. Trends Microbiol. 26, 351–362 (2018).
  CAS PubMed Google Scholar
• Quax, T. E., Albers, S.-V. & Pfeiffer, F. Taxis in archaea. Emerg. Top. Life Sci. 2, 535–546 (2018).
  CAS PubMed PubMed Central Google Scholar
• Baidya, A. K., Bhattacharya, S., Dubey, G. P., Mamou, G. & Ben-Yehuda, S. Bacterial nanotubes: a conduit for intercellular molecular trade. Curr. Opin. Microbiol. 42, 1–6 (2018).
  CAS PubMed Google Scholar
• Liu, J. et al. Extracellular membrane vesicles and nanotubes in Archaea. microLife 2, uqab007 (2021).
  PubMed PubMed Central Google Scholar
• Sivabalasarma, S. et al. Analysis of cell–cell bridges in Haloferax volcanii using electron cryo-tomography reveal a continuous cytoplasm and S-layer. Front. Microbiol. 11, 612239 (2021).
  PubMed PubMed Central Google Scholar
• Brandis, A. & Thauer, R. K. Growth of Desulfovibrio species on hydrogen and sulphate as sole energy source. Microbiology 126, 249–252 (1981).
  CAS Google Scholar
• 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).
  PubMed PubMed Central Google Scholar
• Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
  CAS PubMed PubMed Central Google Scholar
• 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
• Stahl, D. A. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).
• Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA‐targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytom.: J. Int. Soc. Anal. Cytol. 14, 136–143 (1993).
  CAS Google Scholar
• Daims, H. Use of fluorescence in situ hybridization and the daime image analysis program for the cultivation-independent quantification of microorganisms in environmental and medical samples. Cold Spring Harb. Protoc. 2009, pdb. prot5253 (2009).
  PubMed Google Scholar
• Daims, H., Lücker, S. & Wagner, M. Daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8, 200–213 (2006).
  CAS PubMed Google Scholar
• 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
• Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome research 27, 824–834 (2017).
  CAS PubMed PubMed Central Google Scholar
• Bushnell, B. BBMap: a fast, accurate, splice-aware aligner (Lawrence Berkeley National Lab., 2014).
• Wu, Y.-W., Tang, Y.-H., Tringe, S. G., Simmons, B. A. & Singer, S. W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2, 1–18 (2014).
  Google Scholar
• Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
  CAS PubMed Google Scholar
• Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
  PubMed PubMed Central Google Scholar
• Miller, I. J. et al. Autometa: automated extraction of microbial genomes from individual shotgun metagenomes. Nucleic Acids Res. 47, e57–e57 (2019).
  CAS PubMed PubMed Central Google Scholar
• Sieber, C. M. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).
  CAS PubMed PubMed Central Google Scholar
• 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
• Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).
  CAS PubMed PubMed Central Google Scholar
• Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).
  ADS CAS PubMed PubMed Central Google Scholar
• Zimin, A. V. & Salzberg, S. L. The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies. PLoS Comput. Biol. 16, e1007981 (2020).
  ADS CAS PubMed PubMed Central Google Scholar
• Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806 R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
  Google Scholar
• 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
• Reichart, N. J. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).
  CAS PubMed PubMed Central Google Scholar
• Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
  CAS PubMed PubMed Central Google Scholar
• 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
• Nawrocki, E. P., Kolbe, D. L. & Eddy, S. R. Infernal 1.0: inference of RNA alignments. Bioinformatics 25, 1335–1337 (2009).
  CAS PubMed PubMed Central Google Scholar
• 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
• 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
• Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
  CAS PubMed PubMed Central Google Scholar
• 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
• 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
• Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
  CAS PubMed Google Scholar
• Lu, S. et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48, D265–D268 (2020).
  CAS PubMed Google Scholar
• Zimmermann, L. et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).
  CAS PubMed Google Scholar
• 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
• Søndergaard, D., Pedersen, C. N. & Greening, C. HydDB: a web tool for hydrogenase classification and analysis. Sci. Rep. 6, 1–8 (2016).
  Google Scholar
• Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
  CAS PubMed PubMed Central Google Scholar
• 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
• Lagkouvardos, I. et al. IMNGS: a comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci. Rep. 6, 1–9 (2016).
  Google Scholar
• Iancu, C. V. et al. Electron cryotomography sample preparation using the Vitrobot. Nat. Protoc. 1, 2813–2819 (2006).
  CAS PubMed Google Scholar
• Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).
  PubMed Google Scholar
• Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).
  CAS PubMed Google Scholar
• Mastronarde, D. Correction for non‐perpendicularity of beam and tilt axis in tomographic reconstructions with the IMOD package. J. Microsc. 230, 212–217 (2008).
  MathSciNet CAS PubMed Google Scholar
• Tegunov, D. & Cramer, P. Real-time cryo-electron microscopy data preprocessing with Warp. Nat. Methods 16, 1146–1152 (2019).
  CAS PubMed PubMed Central Google Scholar
• Pettersen, E. F. et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
  CAS PubMed Google Scholar
• Schaible, G. A., Kohtz, A. J., Cliff, J. & Hatzenpichler, R. Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes. ISME Commun. 2, 52 (2022).
  PubMed PubMed Central Google Scholar
• Fernando, E. Y. et al. Resolving the individual contribution of key microbial populations to enhanced biological phosphorus removal with Raman–FISH. ISME J. 13, 1933–1946 (2019).
  CAS PubMed PubMed Central Google Scholar
• Majed, N. & Gu, A. Z. Application of Raman microscopy for simultaneous and quantitative evaluation of multiple intracellular polymers dynamics functionally relevant to enhanced biological phosphorus removal processes. Environ. Sci. Technol. 44, 8601–8608 (2010).
  ADS CAS PubMed Google Scholar