Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Methanogenesis in the oily sludge and visualization of microorganisms.
Fig. 2: Methanogenic hexadecane degradation by Ca. Methanoliparum.
Fig. 3: Hexadecane degradation pathway of Ca. Methanoliparum.
Fig. 4: Identification of the intermediate hexadecyl-CoM.

Similar content being viewed by others

Data availability

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.

Code availability

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).

References

  1. Jones, D. M. et al. Crude-oil biodegradation via methanogenesis in subsurface petroleum reservoirs. Nature 451, 176–180 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. 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).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Dolfing, J., Larter, S. R. & Head, I. M. Thermodynamic constraints on methanogenic crude oil biodegradation. ISME J. 2, 442–452 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Borrel, G. et al. Wide diversity of methane and short-chain alkane metabolisms in uncultured archaea. Nat. Microbiol. 4, 603–613 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  7. Head, I. M., Jones, D. M. & Röling, W. F. M. Marine microorganisms make a meal of oil. Nat. Rev. Microbiol. 4, 173–182 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Van Hamme, J. D., Singh, A. & Ward, O. P. Recent advances in petroleum microbiology. Microbiol. Mol. Biol. Rev. 67, 503–549 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Aitken, C. M., Jones, D. M. & Larter, S. R. Anaerobic hydrocarbon biodegradation in deep subsurface oil reservoirs. Nature 431, 291–294 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. 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).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Gieg, L. M., Fowler, S. J. & Berdugo-Clavijo, C. Syntrophic biodegradation of hydrocarbon contaminants. Curr. Opin. Biotechnol. 27, 21–29 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. 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 

  13. 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).

    Article  CAS  PubMed  Google Scholar 

  14. 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).

    Article  CAS  PubMed  Google Scholar 

  15. Hahn, C. J. et al. “Candidatus Ethanoperedens”, a thermophilic genus of Archaea mediating the anaerobic oxidation of ethane. mBio 11, e00600-20 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Laso-Pérez, R. et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature 539, 396–401 (2016).

    Article  ADS  PubMed  Google Scholar 

  17. Chen, S.-C. et al. Anaerobic oxidation of ethane by archaea from a marine hydrocarbon seep. Nature 568, 108–111 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  18. 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).

    Article  CAS  PubMed  Google Scholar 

  19. 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).

    Article  Google Scholar 

  20. Boyd, J. A. et al. Divergent methyl-coenzyme M reductase genes in a deep-subseafloor Archaeoglobi. ISME J. 13, 1269–1279 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Baker, B. J. et al. Diversity, ecology and evolution of Archaea. Nat. Microbiol. 5, 887–900 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat. Commun. 10, 1822 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  23. Cheng, L. et al. Preferential degradation of long-chain alkyl substituted hydrocarbons in heavy oil under methanogenic conditions. Org. Geochem. 138, 103927 (2019).

    Article  CAS  Google Scholar 

  24. 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).

    Article  CAS  Google Scholar 

  25. Cheng, L. et al. DNA-SIP reveals that Syntrophaceae play an important role in methanogenic hexadecane degradation. PLoS ONE 8, e66784 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu, Y.-F. et al. Anaerobic hydrocarbon degradation in candidate phylum ‘Atribacteria’ (JS1) inferred from genomics. ISME J. 13, 2377–2390 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu, Y.-F. et al. Anaerobic degradation of paraffins by thermophilic Actinobacteria under methanogenic conditions. Environ. Sci. Technol. 54, 10610–10620 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  28. 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).

    Article  CAS  PubMed  Google Scholar 

  29. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. 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).

    Article  Google Scholar 

  31. Lyu, Z., Shao, N., Akinyemi, T. & Whitman, W. B. Methanogenesis. Curr. Biol. 28, R727–R732 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Ferry, J. G. & Lessner, D. J. Methanogenesis in marine sediments. Ann. N. Y. Acad. Sci. 1125, 147–157 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  33. 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).

    Article  CAS  PubMed  Google Scholar 

  34. Mayumi, D. et al. Methane production from coal by a single methanogen. Science 354, 222–225 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  35. 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).

  36. Bryant, M. Commentary on the Hungate technique for culture of anaerobic bacteria. Am. J. Clin. Nutr. 25, 1324–1328 (1972).

    Article  CAS  PubMed  Google Scholar 

  37. Friedrich, W., Antje, B. & Ralf, R. in The Prokaryotes: Ecophysiology and Biochemistry Vol. 2 (eds Martin Dworkin et al.) 1028–1049 (Springer, 2006).

  38. 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).

  39. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Stumm, W. & Morgan, J. J. Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters (Wiley, 1996).

  43. 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).

    Article  ADS  CAS  Google Scholar 

  44. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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).

    Article  CAS  PubMed  Google Scholar 

  47. Stahl, D. A. in Nucleic Acid Techniques in Bacterial Systematics 205–248 (1991).

  48. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sofie, T. et al. Comparative evaluation of four bacteria-specific primer pairs for 16S rRNA gene surveys. Front. Microbiol. 8, 494 (2017).

    Google Scholar 

  50. 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).

    Article  CAS  PubMed  Google Scholar 

  51. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 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 Central  Google Scholar 

  60. Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    Article  CAS  PubMed  Google Scholar 

  63. Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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).

    Article  CAS  PubMed  Google Scholar 

  65. Qin, Q.-L. et al. A proposed genus boundary for the prokaryotes based on genomic insights. J. Bacteriol. 196, 2210–2215 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Article  Google Scholar 

  67. 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 

  68. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

    Article  CAS  PubMed  Google Scholar 

  71. 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).

    Article  CAS  PubMed  Google Scholar 

  72. Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Mendler, K. et al. AnnoTree: visualization and exploration of a functionally annotated microbial tree of life. Nucleic Acids Res. 47, 4442–4448 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lane, D. J. 16S/23S rRNA Sequencing 205–248 (John Wiley & Sons, 1991).

  77. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  78. 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).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    Article  CAS  PubMed  Google Scholar 

  80. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinform. 47, 11.12.1–11.12.34 (2014).

    Article  Google Scholar 

  85. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  86. Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).

    Article  Google Scholar 

  87. RCore Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.R-project.org/

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Contributions

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.

Corresponding authors

Correspondence to Gunter Wegener, Meng Li or Lei Cheng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Guillaume Borrel, Rudolph Thauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Molecular characterization of the methanogenic oily sludge incubated at different temperatures.

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).

Extended Data Fig. 2 Epifluorescence micrographs of different community members of the oily sludge.

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.

Extended Data Fig. 3 Phylogenetic analyses of MAGs and 16S rRNA gene sequences of ‘Ca. Methanoliparia’.

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.

Extended Data Fig. 4 Identities between ‘Ca. Methanoliparum’ clusters and phylogenetic analysis of their AcrA and McrA protein sequences.

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.

Extended Data Fig. 5 Gene clusters associated with the alkane degradation and methanogenesis pathways detected in the representative MAGs of the four ‘Ca. Methanoliparum’ species.

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.

Extended Data Fig. 6 Relative transcript abundances of alkane-degrading and methane-producing pathways coding genes.

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.

Extended Data Fig. 7 Identification of coenzyme M derivatives in cultures by HPLC-MS/MS based on the corresponding retention times.

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.

Extended Data Fig. 8 Identification of coenzyme M derivatives in cultures incubated with specific hydrocarbons.

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.

Extended Data Fig. 9 Semi-continuous cultivation of the ‘Ca. Methanoliparum’ cultures at 55 °C.

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.

Extended Data Fig. 10 Proposed metabolic pathway and related gene clusters for benzene-CoA degradation in ‘Ca. Methanoliparum’.

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 information

Supplementary Information

Supplementary Figs. 1–6 and the legends for Supplementary Tables 1–13.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–13.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Zhang, Cj., Liu, Pf. et al. Non-syntrophic methanogenic hydrocarbon degradation by an archaeal species. Nature 601, 257–262 (2022). https://doi.org/10.1038/s41586-021-04235-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-04235-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing