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The methanogenic degradation of oil hydrocarbons can proceed through syntrophic partnerships of hydrocarbon-degrading bacteria and methanogenic archaea1,2,3. However, recent culture-independent studies have suggested that the archaeon ‘Candidatus Methanoliparum’ alone can combine the degradation of long-chain alkanes with methanogenesis4,5. Here we cultured Ca. Methanoliparum from a subsurface oil reservoir. Molecular analyses revealed that Ca. Methanoliparum contains and overexpresses genes encoding alkyl-coenzyme M reductases and methyl-coenzyme M reductases, the marker genes for archaeal multicarbon alkane and methane metabolism. Incubation experiments with different substrates and mass spectrometric detection of coenzyme-M-bound intermediates confirm that Ca. Methanoliparum thrives not only on a variety of long-chain alkanes, but also on n-alkylcyclohexanes and n-alkylbenzenes with long n-alkyl (C≥13) moieties. By contrast, short-chain alkanes (such as ethane to octane) or aromatics with short alkyl chains (C≤12) were not consumed. The wide distribution of Ca. Methanoliparum4,5,6 in oil-rich environments indicates that this alkylotrophic methanogen may have a crucial role in the transformation of hydrocarbons into methane.
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The 16S rRNA gene amplicon sequences, metagenomic and metatranscriptomic data generated in current study are available in the NODE database (http://www.biosino.org/node/project/detail/OEP001282). The data of dereplicated MAGs analysed during the current study are available in the NODE database under the accession numbers OEZ006960 and OEZ007009–OEZ007026. Further details are provided in Supplementary Table 13. All other data are available in the main text or the Supplementary Information.
The sources of the code and programs used for analyses are mentioned in the Methods, and are also available at GitHub (https://github.com/liupfskygre/Methanoliparum_MS_code/tree/main).
Jones, D. M. et al. Crude-oil biodegradation via methanogenesis in subsurface petroleum reservoirs. Nature 451, 176–180 (2008).
ADS CAS PubMed Google Scholar
Zengler, K., Richnow, H. H., Rossello-Mora, R., Michaelis, W. & Widdel, F. Methane formation from long-chain alkanes by anaerobic microorganisms. Nature 401, 266–269 (1999).
ADS CAS PubMed Google Scholar
Dolfing, J., Larter, S. R. & Head, I. M. Thermodynamic constraints on methanogenic crude oil biodegradation. ISME J. 2, 442–452 (2008).
CAS PubMed Google Scholar
Laso Pérez, R. et al. Anaerobic degradation of non-methane alkanes by “Candidatus Methanoliparia” in hydrocarbon seeps of the Gulf of Mexico. mBio 10, e01814-19 (2019).
PubMed PubMed Central 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
Cheng, L. et al. Progressive degradation of crude oil n-alkanes coupled to methane production under mesophilic and thermophilic conditions. PLoS ONE 9, e113253 (2014).
ADS PubMed PubMed Central Google Scholar
Head, I. M., Jones, D. M. & Röling, W. F. M. Marine microorganisms make a meal of oil. Nat. Rev. Microbiol. 4, 173–182 (2006).
CAS PubMed Google Scholar
Van Hamme, J. D., Singh, A. & Ward, O. P. Recent advances in petroleum microbiology. Microbiol. Mol. Biol. Rev. 67, 503–549 (2003).
PubMed PubMed Central Google Scholar
Aitken, C. M., Jones, D. M. & Larter, S. R. Anaerobic hydrocarbon biodegradation in deep subsurface oil reservoirs. Nature 431, 291–294 (2004).
ADS CAS PubMed Google Scholar
Head, I. M., Jones, D. M. & Larter, S. R. Biological activity in the deep subsurface and the origin of heavy oil. Nature 426, 344–352 (2003).
ADS CAS PubMed Google Scholar
Gieg, L. M., Fowler, S. J. & Berdugo-Clavijo, C. Syntrophic biodegradation of hydrocarbon contaminants. Curr. Opin. Biotechnol. 27, 21–29 (2014).
CAS PubMed Google Scholar
Rabus, R. et al. Anaerobic microbial degradation of hydrocarbons: from enzymatic reactions to the environment. J. Mol. Microbiol. Biotechnol. 26, 5–28 (2016).
CAS PubMed Google Scholar
Fowler, S. J., Dong, X., Sensen, C. W., Suflita, J. M. & Gieg, L. M. Methanogenic toluene metabolism: community structure and intermediates. Environ. Microbiol. 14, 754–764 (2012).
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
Hahn, C. J. et al. “Candidatus Ethanoperedens”, a thermophilic genus of Archaea mediating the anaerobic oxidation of ethane. mBio 11, e00600-20 (2020).
CAS PubMed PubMed Central Google Scholar
Laso-Pérez, R. et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature 539, 396–401 (2016).
ADS PubMed Google Scholar
Chen, S.-C. et al. Anaerobic oxidation of ethane by archaea from a marine hydrocarbon seep. Nature 568, 108–111 (2019).
ADS CAS PubMed 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
Wang, Y., Wegener, G., Ruff, S. E. & Wang, F. Methyl/alkyl-coenzyme M reductase-based anaerobic alkane oxidation in Archaea. Environ. Microbiol. 23, 530–541 (2020).
Google Scholar
Boyd, J. A. et al. Divergent methyl-coenzyme M reductase genes in a deep-subseafloor Archaeoglobi. ISME J. 13, 1269–1279 (2019).
CAS PubMed PubMed Central Google Scholar
Baker, B. J. et al. Diversity, ecology and evolution of Archaea. Nat. Microbiol. 5, 887–900 (2020).
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
Cheng, L. et al. Preferential degradation of long-chain alkyl substituted hydrocarbons in heavy oil under methanogenic conditions. Org. Geochem. 138, 103927 (2019).
CAS Google Scholar
Oldenburg, T. B. P. et al. The controls on the composition of biodegraded oils in the deep subsurface—part 4. Destruction and production of high molecular weight non-hydrocarbon species and destruction of aromatic hydrocarbons during progressive in-reservoir biodegradation. Org. Geochem. 114, 57–80 (2017).
CAS Google Scholar
Cheng, L. et al. DNA-SIP reveals that Syntrophaceae play an important role in methanogenic hexadecane degradation. PLoS ONE 8, e66784 (2013).
ADS CAS PubMed PubMed Central Google Scholar
Liu, Y.-F. et al. Anaerobic hydrocarbon degradation in candidate phylum ‘Atribacteria’ (JS1) inferred from genomics. ISME J. 13, 2377–2390 (2019).
CAS PubMed PubMed Central Google Scholar
Liu, Y.-F. et al. Anaerobic degradation of paraffins by thermophilic Actinobacteria under methanogenic conditions. Environ. Sci. Technol. 54, 10610–10620 (2020).
ADS CAS PubMed Google Scholar
Breese, K., Boll, M., Alt‐Mörbe, J., Schägger, H. & Fuchs, G. Genes coding for the benzoyl‐CoA pathway of anaerobic aromatic metabolism in the bacterium Thauera aromatica. Eur. J. Biochem. 256, 148–154 (1998).
CAS PubMed Google Scholar
Egland, P. G., Pelletier, D. A., Dispensa, M., Gibson, J. & Harwood, C. S. A cluster of bacterial genes for anaerobic benzene ring biodegradation. Proc. Natl Acad. Sci. USA 94, 6484–6489 (1997).
ADS CAS PubMed PubMed Central Google Scholar
Borrel, G. et al. Comparative genomics highlights the unique biology of Methanomassiliicoccales, a Thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genom. 15, 679 (2014).
Google Scholar
Lyu, Z., Shao, N., Akinyemi, T. & Whitman, W. B. Methanogenesis. Curr. Biol. 28, R727–R732 (2018).
CAS PubMed Google Scholar
Ferry, J. G. & Lessner, D. J. Methanogenesis in marine sediments. Ann. N. Y. Acad. Sci. 1125, 147–157 (2008).
ADS CAS PubMed Google Scholar
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
Mayumi, D. et al. Methane production from coal by a single methanogen. Science 354, 222–225 (2016).
ADS CAS PubMed Google Scholar
Suflita, J. M., Davidova, I. A., Gieg, L. M., Nanny, M. & Prince, R. C. in Studies in Surface Science and Catalysis Vol. 151 (eds Vazquez-Duhalt, R. & Quintero-Ramirez, R.) 283–305 (Elsevier, 2004).
Bryant, M. Commentary on the Hungate technique for culture of anaerobic bacteria. Am. J. Clin. Nutr. 25, 1324–1328 (1972).
CAS PubMed Google Scholar
Friedrich, W., Antje, B. & Ralf, R. in The Prokaryotes: Ecophysiology and Biochemistry Vol. 2 (eds Martin Dworkin et al.) 1028–1049 (Springer, 2006).
Aydin, O. & Yassikaya, M. Y. Validity and reliability analysis of the plotdigitizer software program for data extraction from single-case graphs. Perspect. Behav. Sci. (2021).
Dolfing, J. & Mulder, J.-W. Comparison of methane production rate and coenzyme F420 content of methanogenic consortia in anaerobic granular sludge. Appl. Environ. Microbiol. 49, 1142–1145 (1985).
ADS 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
Ma, T.-T. et al. Coexistence and competition of sulfate-reducing and methanogenic populations in an anaerobic hexadecane-degrading culture. Biotechnol. Biofuels 10, 207 (2017).
PubMed PubMed Central Google Scholar
Stumm, W. & Morgan, J. J. Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters (Wiley, 1996).
Deines, P., Langmuir, D. & Harmon, R. S. Stable carbon isotope ratios and the existence of a gas phase in the evolution of carbonate ground waters. Geochim. Cosmochim. Acta 38, 1147–1164 (1974).
ADS CAS Google Scholar
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
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).
ADS CAS PubMed PubMed Central Google Scholar
Daims, H., Brühl, A., Amann, R., Schleifer, K.-H. & 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
Stahl, D. A. in Nucleic Acid Techniques in Bacterial Systematics 205–248 (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).
ADS CAS PubMed PubMed Central Google Scholar
Sofie, T. et al. Comparative evaluation of four bacteria-specific primer pairs for 16S rRNA gene surveys. Front. Microbiol. 8, 494 (2017).
Google Scholar
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).
CAS PubMed Google Scholar
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
PubMed PubMed Central Google Scholar
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
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
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
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS PubMed PubMed Central Google Scholar
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
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
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
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).
PubMed PubMed Central Google Scholar
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).
CAS PubMed Google Scholar
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).
CAS PubMed PubMed Central Google Scholar
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).
CAS Google Scholar
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).
CAS PubMed PubMed Central Google Scholar
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).
CAS PubMed Google Scholar
Qin, Q.-L. et al. A proposed genus boundary for the prokaryotes based on genomic insights. J. Bacteriol. 196, 2210–2215 (2014).
PubMed PubMed Central Google Scholar
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Google Scholar
Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4, e1000069 (2008).
ADS MathSciNet PubMed PubMed Central Google Scholar
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).
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
Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
CAS PubMed 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).
CAS PubMed PubMed Central Google Scholar
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).
CAS 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
Mendler, K. et al. AnnoTree: visualization and exploration of a functionally annotated microbial tree of life. Nucleic Acids Res. 47, 4442–4448 (2019).
CAS PubMed PubMed Central Google Scholar
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).
CAS PubMed PubMed Central Google Scholar
Lane, D. J. 16S/23S rRNA Sequencing 205–248 (John Wiley & Sons, 1991).
Selvaraj, V. A.-O. et al. Development of a duplex droplet digital PCR assay for absolute quantitative detection of “Candidatus Liberibacter asiaticus”. PLoS ONE 13, e0197184 (2018).
PubMed PubMed Central Google Scholar
Peng, J., Lü, Z., Rui, J. & Lu, Y. Dynamics of the methanogenic archaeal community during plant residue decomposition in an anoxic rice field soil. Appl. Environ. Microbiol. 74, 2894–2901 (2008).
ADS CAS PubMed PubMed Central Google Scholar
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
CAS PubMed Google Scholar
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
CAS PubMed PubMed Central Google Scholar
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).
CAS PubMed PubMed Central Google Scholar
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS PubMed PubMed Central Google Scholar
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
PubMed PubMed Central Google Scholar
Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11.12.1–11.12.34 (2014).
Google Scholar
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).
Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).
Google Scholar
RCore Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.R-project.org/
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We thank A. Oren (The Hebrew University of Jerusalem) for discussing the naming of the different Ca. Methanoliparum species; R. Conrad and W. B. Whitman for discussing the manuscript; K. Wrighton for providing access to the server Zenith; Q. Yuan, Y. Liu, J. Pan, M.-w. Cai and Y.-n. Tang for assisting in data analysis; L.-r. Dai, D. Zhang and L. Li for assisting in cultivation and experiments; and Z. Zhou for technical support. This study was supported by National Natural Science Foundation of China (nos 92051108, 91851105, 41802179, 31970066, 31570009 and 31970105), Agricultural Science and Technology Innovation Project of the Chinese Academy of Agriculture Science (no. CAAS-ASTIP-2016-BIOMA), the Innovation Team Project of Universities in Guangdong Province (no. 2020KCXTD023) and the Shenzhen Science and Technology Program (no. JCYJ20200109105010363), the Fundamental Research Funds for the Central Universities (LZUJBKY-2021-KB16), the Central Public-interest Scientific Institution Basal Research Fund (Y2021PT02, Y2021XK06). R.L.-P. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (EXC-2077-390741603) via Excellence Chair Victoria Orphan. G.W. was funded by DFG under Germany’s Excellence Strategy-EXC-2077-390741603 and the Max Planck Society.
Rafael Laso-Pérez
Present address: Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
These authors contributed equally: Zhuo Zhou, Cui-jing Zhang, Peng-fei Liu, Lin Fu
Key Laboratory of Development and Application of Rural Renewable Energy, Biogas Institute of Ministry of Agriculture and Rural Affairs, Chengdu, China
Zhuo Zhou, Peng-fei Liu, Lin Fu, Lu Yang, Li-ping Bai, Jiang Li, Min Yang & Lei Cheng
Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, China
Cui-jing Zhang & Meng Li
Center for The Pan-Third Pole Environment, Lanzhou University, Lanzhou, China
Peng-fei Liu
MARUM, Center for Marine Environmental Sciences, University Bremen, Bremen, Germany
Rafael Laso-Pérez & Gunter Wegener
Max Planck Institute for Marine Microbiology, Bremen, Germany
Rafael Laso-Pérez & Gunter Wegener
Key Laboratory of Microbial Enhanced Oil Recovery, SINOPEC, Dongying, China
Jun-zhang Lin & Wei-dong Wang
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L.C. and M.L. initiated the study. L.C., M.L., G.W. and P.-f.L. designed research. J.-z.L., W.-d.W. and Z.Z. collected the oily sludge samples. Z.Z., J.L., M.Y. and L.C. conducted cultivation experiments. Z.Z. and L.Y. performed oil analysis. C.-j.Z., P.-f.L., Z.Z., R.L.-P. and M.L. performed all bioinformatics analyses. R.L.-P. and L.C. designed CARD-FISH probes, and R.L.-P. performed CARD-FISH and cell visualization. L.F., L.C. and L.-p.B. performed metabolite analyses. P.-f.L., R.L.-P., G.W., M.L. and L.C. analysed data and wrote the manuscript with contributions from all of the co-authors.
Correspondence to Gunter Wegener, Meng Li or Lei Cheng.
The authors declare no competing interests.
Nature thanks Guillaume Borrel, Rudolph Thauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Accumulation of methane in the headspace of treatments at different temperatures over an incubation time of 301 days. The estimates of reported methane production rates base on the time interval for the formation of 5% and 90% of the maximum methane formation. b, Mass spectrometric analysis of extracted residual oil for n-alkanes m/z = 85, n-alkylcyclohexanes m/z = 82, n-alkylbenzenes m/z = 92. Exemplary data of the 55 °C culture is presented in Figs. 1b–1d. Data shown are mean ± standard deviation (n = 3 biologically independent replicates). c and d, Archaeal and bacterial community structure revealed by amplicon sequencing in the different temperature treatments after 204 days of incubation, respectively. Only families with relative abundances ≥ 1% are shown. “Other” indicates the sum of groups with relative abundance < 1%. Data shown are mean – standard deviation (n = 3 biologically independent replicates).
a-c, Visualization of archaea (green) and bacteria (red). d-f, Visualization of ‘Ca. Methanoliparum’ (green) and archaea (red). Hybridization of ‘Ca. Methanoliparum’ with the general archaeal probe and the specific DC06-660Mlp probe. The vast majority of archaea hybridized also with the probe for ‘Ca. Methanoliparum’. g-i, Visualization of ‘Ca. Methanoliparum’ (green) and bacteria (red). Oligonucleotide probes were ARCH-915 for archaea, EUB388 I-III for bacteria and DC06-660Mlp for ‘Ca. Methanoliparum’. Three representative recorded images from n = 3 independent samples (a-i, 9 rows of images in total) of one culture are shown. Scale bars in all images are 10 µm.
a, Phylogenomic analyses of ‘Ca. Methanoliparia’ MAGs based on the concatenated alignments of 16 ribosomal proteins67. Bootstrap values > 0.95 are marked with grey dots, ‘Ca. Bathyarchaeota’ set as outgroup. The maximum-likelihood tree was constructed by using the IQ-TREE software with the parameters ‘-m WAG -bb 1000’. b, Phylogenetic analysis of 16S rRNA gene sequences retrieved from all ‘Ca. Methanoliparia’ MAGs. For MAG- derived sequences source information is given: i.e., T55 indicates temperature of the culture (55 °C) and after the MAGs (bin) number the substrate used is indicated (e.g., n-hexadecane). The asterisk (*) marking ‘Ca. M. whitmanii’ sequence identifiers indicates 16S rRNA genes that were truncated during assembly. In these cases, the longest partial sequence was used for the phylogenetic analyses. The 16S rRNA gene sequences were added to the consensus tree with ‘quick add’ option, thus no bootstrap values are available.
a, Identities of the 16S rRNA gene. b, Genome based average Amino Acid Identity (AAI). c, Genome based Average Nucleotide Identity (ANI). d, Identity based on the percentage of conserved proteins (POCP). All matrices consistently showed that all ‘Ca. Methanoliparia’ MAGs from this study grouped into four species-level clusters within the genus ‘Ca. Methanoliparum’. In the box plots the central line represents the median; the lower and upper box limits correspond to the 25th and 75th percentiles, respectively; Numbers represent the times of pairwise comparisons of MAGs between two groups. Cluster 1 (C1): ‘Ca. M. thermophilum’; Clusters 2 (C2): ‘Ca. M. widdelii’; Cluster 3 (C3): ‘Ca. M. whitmanii’; Cluster 4 (C4): ‘Ca. M. zhangii’. Mv indicates the genomes of the sister marine clade ‘Ca. Methanolliviera’. e, Maximum-likelihood tree of the protein sequences of AcrA and McrA present in ‘Ca. Methanoliparum’ MAGs retrieved in the present studied. Different colours indicate the different ‘Ca. Methanoliparum’ species. Numbers in parenthesis indicate the number of acrA/mcrA sequences detected in the different metagenomes. In each MAG, maximum one acr and one mcr were detected. Trees were constructed by using IQ-TREE with the parameters ‘-m WAG, -bb 1000’, with bootstrap values >0.95 shown in grey dots.
Several copies of fadD and ACADM were detected and only copies with the highest transcript abundances are shown. In orange, alkane activation and conversion to a fatty acid; in blue, beta oxidation pathway and in red, the ACS/CODH complex and the methanogenesis pathway. Details of all copies are included in Supplementary Table 6.
The colour code shows the log2(FPKM) values of each gene. For enzymes or subunits with several putative coding genes, only the ones with the highest level of log2(FPKM) are shown here. Two samples were taken for cultures with n-hexadecane addition (Hex.) at day 31 and 55, while sampling at one time point (day 55) with 3 replicates (designated as r1-r3) was performed for control cultures without n-hexadecane amendment (Con.). Grey cells indicate that the corresponding genes were not found in the MAGs. Details of all copies are included in Supplementary Table 7.
a and b, hexadecyl-CoM and the corresponding 3 characterized fragments (in blue) in cell extracts from cultures with hexadecane (C16H34) addition. c and d, eicosyl-CoM and 3 characterized fragments (in blue) in cell extracts from cultures with eicosane (C20H42) additions. Standard appears in black primary anions and second anions (produced by fragmentation) detected in hexadecane and eicosane cultures showed the same retention time as the synthetic standards of hexadecyl-CoM and eicosyl-CoM, respectively.
a, Scheme for the activation of long-chain alkanes and alkyl-substituted compounds as CoM thioethers in ACR, and their expected fragmentation patterns. The residual ‘R-’ describes a methyl-, cyclohexane- or aromatic unit with an alkyl chain CnH2n+1 for n ≥ 13. Dash arrows and numbers above indicate the fragmentation positions. b and c, QE Plus-Orbitrap MS analyses of cultures supplemented with eicosane resulted in a mass peak of eicosyl-CoM (C20H41-SC2H4SO3− at m/z = 421.28162 and the fragments eicosyl-thiol (C20H33S–, m/z = 313.29373), ethenesulfonate (C2H3SO3−, m/z = 106.98092) and bisulfite (HSO3−, m/z = 80.96519). All peaks match those of an eicoysl-CoM standard. d-i, QE Plus-Orbitrap MS analyses of cultures supplemented with a mixture of n-docosane (C22H46), n-hexadecyl benzene (C22H38) and n-hexadecyl cyclohexane (C22H44) as substrates, and detection of d and e docosyl-CoM (C24H49S2O3−, m/z = 449.31134) with the fragment C22H45S− (m/z = 341.32495); of f and g n-hexadecyl benzene coenzyme M (C24H41S2O3−, m/z = 441.25064) with the predicted fragment C22H37S− (m/z = 333.26212) and of h and i n-hexadecyl cyclohexane CoM (C24H47S2O3−, m/z = 447.29730) with the fragment C22H43S− (m/z = 339.30939). The mass error for all mass peaks shown here are < 5 p.p.m.
Microorganisms were cultured using a mixture of n-docosane, n-hexadecyl benzene, n-hexadecyl cyclohexane as substrate. The culture was transferred when 15 to 20 mmol of methane were formed, and 30% to 50% of the culture were transferred. Displayed are transfers 3 to 6. a, Methane formation in the headspace. Grey arrows indicate transfer events. b and c, Abundance of 16S rRNA gene of ‘Ca. Methanoliparum’ and bacteria as determined by qPCR, respectively. d, Relative abundance of main archaeal groups determined by 16S rRNA gene sequencing with archaeal primer set Arch519F/Arch915R.
a, Gene clusters found in the four representative MAGs with potential for benzoyl-CoA degradation. Numbers in the gene clusters indicate kilobases. b, Annotations and Locus tag for the corresponding genes shown in panel a that are found in the representative MAG of ‘Ca. M. thermophilum’ (XY_C20_T55_P2_bin.5 of Cluster 1). c, Proposed pathway for the degradation of benzoyl-CoA based on the pairwise comparison of the candidate genes of ‘Ca. Methanoliparum’ (red) with the genes involved in benzoyl-CoA degradation in the model organisms Thauera aromatica (green) and Rhodopseudomonas palustris (blue). The letters for candidate genes of ‘Ca. Methanoliparum’ refer to the letters indicate in the panel a (see Supplementary Table 10 for more details).
Supplementary Figs. 1–6 and the legends for Supplementary Tables 1–13.
Supplementary Tables 1–13.
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Zhou, Z., Zhang, Cj., Liu, Pf. et al. Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species. Nature (2021). https://doi.org/10.1038/s41586-021-04235-2
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DOI: https://doi.org/10.1038/s41586-021-04235-2
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