Cited by 21Gut microbiota and healthAnaerobic Digestion and Biogas ProductionMicrobial Fuel Cells and Bioremediation

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Targeted isolation of Methanobrevibacter strains from fecal samples expands the cultivated human archaeome

Open access
Duller, Stefanie, Vrbancic, Simone, Szydłowski, Łukasz, Mahnert, Alexander, Blohs, Marcus, Predl, Michael, Kumpitsch, Christina, Zrim, Verena, Högenauer, Christoph, Kosciolek, Tomasz, Schmitz, Ruth A., Eberhard, Anna, Dragovan, Melanie, Schmidberger, Laura, Zurabischvili, Tamara, Weinberger, Viktoria, Moser, Adrian Mathias, Kolb, Dagmar, Pernitsch, Dominique, Mohammadzadeh, Rokhsareh, Kühnast, Torben, Rattei, Thomas, Moissl-Eichinger, Christine·2024·Nature Communications

Published version of a preprint

Abstract

Archaea are vital components of the human microbiome, yet their study within the gastrointestinal tract (GIT) is limited by the scarcity of cultured representatives. Our study presents a method for the targeted enrichment and isolation of methanogenic archaea from human fecal samples. The procedure combines methane breath testing, in silico metabolic modeling, media optimization, FACS, dilution series, and genomic sequencing through Nanopore technology. Additional analyzes include the co-cultured bacteriome, comparative genomics of archaeal genomes, functional comparisons, and structure-based protein function prediction of unknown differential traits. Successful establishment of stable archaeal cultures from 14 out of 16 fecal samples yielded nine previously uncultivated strains, eight of which are absent from a recent archaeome genome catalog. Comparative genomic and functional assessments of Methanobrevibacter smithii and Candidatus Methanobrevibacter intestini strains from individual donors revealed features potentially associated with gastrointestinal diseases. Our work broadens available archaeal representatives for GIT studies, and offers insights into Candidatus Methanobrevibacter intestini genomes’ adaptability in critical microbiome contexts.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-024-52037-7

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 72

  1. Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015). · DOI
  2. Chibani, C. M. et al. A catalogue of 1167 genomes from the human gut archaeome. Nat. Microbiol. 7, 48–61 (2022). · DOI
  3. Mohammadzadeh, R., Mahnert, A., Duller, S. & Moissl-Eichinger, C. Archaeal key-residents within the human microbiome: characteristics, interactions and involvement in health and disease. Curr. Opin. Microbiol. 67, 102146 (2022). · DOI
  4. Borrel, G., Brugère, J. F., Gribaldo, S., Schmitz, R. A. & Moissl-Eichinger, C. The host-associated archaeome. Nat. Rev. Microbiol. 18, 622–636 (2020). · DOI
  5. Bang, C. & Schmitz, R. A. Archaea associated with human surfaces: not to be underestimated. FEMS Microbiol. Rev. 39, 631–648 (2015). · DOI
  6. Fricke, W. F. et al. The genome sequence of Methanosphaera stadtmanae reveals why this human intestinal archaeon is restricted to methanol and H2 for methane formation and ATP synthesis. J. Bacteriol. 188, 642–658 (2006). · DOI
  7. Thauer, R. K. & Shima, S. Methane and microbes. Nature 440, 878–879 (2006). · DOI
  8. Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol 1, 16170 (2016). · DOI
  9. Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl Acad. Sci. 116, 5037–5044 (2019). · DOI
  10. Polag, D. & Keppler, F. Global methane emissions from the human body: Past, present and future. Atmos. Environ. 214, 116823 (2019). · DOI
  11. Hudson, M. J., Tomkins, A. M., Wiggins, H. S. & Drasar, B. S. Breath methane excretion and intestinal methanogenesis in children and adults in rural Nigeria. Scand. J. Gastroenterol. 28, 993–998 (1993). · DOI
  12. Gottlieb, K., Wacher, V., Sliman, J. & Pimentel, M. inhibition of methanogenic archaea by statins as a targeted management strategy for constipation and related disorders. Aliment Pharm. Ther. 43, 197–212 (2016). · DOI
  13. Polag, D. & Keppler, F. Effect of immune responses on breath methane dynamics. J. Breath. Res 17, 046005 (2023). · DOI
  14. Ernst, L. et al. Methane formation driven by reactive oxygen species across all living organisms. Nature 603, 482–487 (2022). · DOI
  15. Ghyczy, M. et al. Hypoxia-induced generation of methane in mitochondria and eukaryotic cells - an alternative approach to methanogenesis. Cell. Physiol. Biochem. 21, 251–258 (2008). · DOI
  16. Kuehnast, T. et al. Exploring the human archaeome: its relevance for health, disease, and its complex interplay with the human immune system. FEBS J. https://doi.org/10.1111/febs.17123 (2024). Online ahead of print. · DOI
  17. Dridi, B., Henry, M., El Khechine, A., Raoult, D. & Drancourt, M. High prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol. PLoS One 4, e7063–e7063 (2009). · DOI
  18. Levitt, M. D., Furne, J. K., Kuskowski, M. & Ruddy, J. Stability of human methanogenic flora over 35 years and a review of insights obtained from breath methane measurements. Clin. Gastroenterol. Hepatol. 4, 123–129 (2006). · DOI
  19. Kumpitsch, C. et al. Reduced B12 uptake and increased gastrointestinal formate are associated with archaeome-mediated breath methane emission in humans. Microbiome 9, 1–18 (2021). · DOI
  20. Catlett, J. L. et al. Metabolic synergy between human symbionts Bacteroides and Methanobrevibacter. Microbiol Spectr. 10, e0106722 (2022). · DOI
  21. Miller, T. L., Wolin, M. J., de, Macario, E. C. & Macario, A. J. Isolation of Methanobrevibacter smithii from human feces. Appl Environ. Microbiol. 43, 227–232 (1982). · DOI
  22. Mahnert, A., Blohs, M., Pausan, M. R. & Moissl-Eichinger, C. The human archaeome: methodological pitfalls and knowledge gaps. Emerg. Top. Life Sci. 2.4, 469–482 (2018).
  23. Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Micro. Cell Fact. 16, 180 (2017). · DOI
  24. Mohammadzadeh, R. et al. Age-related dynamics of methanogenic archaea in the human gut microbiome: implications for longevity and health. Preprint at bioRxiv https://doi.org/10.1101/2024.02.09.579604 (2024). · DOI
  25. Scanlan, P. D., Shanahan, F. & Marchesi, J. R. Human methanogen diversity and incidence in healthy and diseased colonic groups using mcrA gene analysis. BMC Microbiol. 8, 1–8 (2008). · DOI
  26. Gligorijević, V. et al. Structure-based protein function prediction using graph convolutional networks. Nat. Commun. 12, 3168 (2021). · DOI
  27. May, H. D., Patel, P. S. & Ferry, J. G. Effect of molybdenum and tungsten on synthesis and composition of formate dehydrogenase in Methanobacterium formicicum. J. Bacteriol. 170, 3384–3389 (1988). · DOI
  28. Weinberger, V. et al. Expanding the cultivable human archaeome: methanobrevibacter intestini sp. nov. and strain Methanobrevibacter smithii“GRAZ-2” from human feces. BioRxiv (in revision: IJSEM) https://doi.org/10.1101/2024.05.15.594450. (2024) · DOI
  29. Gasińska, A. & Gajewska, D. Tea and coffee as the main sources of oxalate in diets of patients with kidney oxalate stones. Rocz. Panstw. Zakl. Hig. 58, 61–67 (2007).
  30. Sun, Y., Liu, Y., Pan, J., Wang, F. & Li, M. Perspectives on cultivation strategies of archaea. Microb Ecol. 79, 770–784 (2019) · DOI
  31. Morris, B. E. L., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013). · DOI
  32. Lee, S. J. et al. Metabolic engineering of escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ. Microbiol. 71, 7880–7887 (2005). · DOI
  33. Neal, M., Thiruppathy, D. & Zengler, K. Genome-scale metabolic modeling of the human gut bacterium Bacteroides fragilis strain 638R. PLoS Comput Biol. 19, e1011594 (2023). · DOI
  34. Kaeberlein, T., Lewis, K. & Epstein, S. S. Isolating ‘uncultivable’ microorganisms in pure culture in a simulated natural environment. Science (1979) 296, 1127–1129 (2002).
  35. Zinder, S. H. & Salyers, A. A. Microbial Ecology—New Directions, New Importance. in Bergey’s Manual® of Systematic Bacteriology 101–109 (Springer New York, 2001). · DOI
  36. Koehler Leman, J. et al. Sequence-structure-function relationships in the microbial protein universe. Nat. Commun. 14, 2351 (2023). · DOI
  37. Maranga, M. et al. Comprehensive functional annotation of metagenomes and microbial genomes using a deep learning-based method. mSystems 8, e0117822 (2023). · DOI
  38. Wang, Z. et al. Comparison of fecal collection methods for microbiome and metabolomics studies. Front Cell Infect. Microbiol. 8, 301 (2018). · DOI
  39. Kieser, S., Brown, J., Zdobnov, E. M., Trajkovski, M. & McCue, L. A. ATLAS: a snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinforma. 21, 1–8 (2020). · DOI
  40. Lu, J., Breitwieser, F. P., Thielen, P., Salzberg, S. L. & Bracken estimating species abundance in metagenomics data. PeerJ Comput Sci. 3, e104 (2017). · DOI
  41. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 1–13 (2019). · DOI
  42. Balch, W. E., Fox, G. E., Magrum, L. J., Woese, C. R. & Wolfe, R. S. Methanogens: reevaluation of a unique biological group. Microbiol. Rev. 43, 260 (1979). · DOI
  43. Mauerhofer, L.-M. et al. Hyperthermophilic methanogenic archaea act as high-pressure CH 4 cell factories. Commun. Biol. 4, 1–12 (2021). · DOI
  44. Paul, K., Nonoh, J. O., Mikulski, L. & Brune, A. Methanoplasmatales,” Thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Appl Environ. Microbiol. 78, 8245–8253 (2012). · DOI · Paper page
  45. Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 2021 22:1 22, 1–35 (2021).
  46. Seaver, S. M. D. et al. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res. 49, D575–D588 (2021). · DOI
  47. Marinos, G., Kaleta, C. & Waschina, S. Defining the nutritional input for genome-scale metabolic models: A roadmap. PLoS One 15, e0236890 (2020). · DOI
  48. Predl, M., Mießkes, M., Rattei, T. & Zanghellini, J. PyCoMo: a python package for community metabolic model creation and analysis. Bioinformatics 40, btae153 (2024). · DOI
  49. Plastira, I. et al. MAPK signaling determines lysophosphatidic acid (LPA)-induced inflammation in microglia. J. Neuroinflammation 17, 127 (2020). · DOI
  50. Steinberg, L. M. & Regan, J. M. Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge. Appl Environ. Microbiol. 74, 6663–6671 (2008). · DOI
  51. Burggraf, S., Olsen, G. J., Stetter, K. O. & Woese, C. R. A phylogenetic analysis of Aquifex pyrophilus. Syst. Appl Microbiol. 15, 352–356 (1992). · DOI
  52. Lane, D. 16S/23S rRNA sequencing. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (John Wiley & Sons, Chichester, 1991).
  53. Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004). · DOI
  54. Tamura, K., Stecher, G. & Kumar, S. MEGA11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021). · DOI
  55. De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669 (2018). · DOI · Paper page
  56. Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020). · DOI
  57. Li, H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572–4574 (2021). · DOI
  58. Gremme, G., Steinbiss, S. & Kurtz, S. GenomeTools: a comprehensive software library for efficient processing of structured genome annotations. IEEE/ACM Trans. Comput Biol. Bioinform 10, 645–656 (2013). · DOI
  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 (2020). · DOI
  60. Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 20, 1203–1212 (2023). · DOI · Paper page
  61. Bernecker, C. et al. Biomechanical properties of native and cultured red blood cells–interplay of shape, structure and biomechanics. Front Physiol. 13, 979298 (2022). · DOI
  62. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021). · DOI
  63. Arkin, A. P. et al. KBase: The United States Department of Energy Systems biology knowledgebase. Nat. Biotechnol. 36, 566–569 (2018). · DOI
  64. 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). · DOI · Paper page
  65. Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021). · DOI
  66. Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019). · DOI
  67. Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput Biol. 7, e1002195 (2011). · DOI · Paper page
  68. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021). · DOI · Paper page
  69. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017). · DOI · Paper page
  70. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010). · DOI
  71. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016). · DOI
  72. Varadi, M. et al. Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022). · DOI · Paper page

Metadata

Authors
Duller, S., Vrbancic, S., Szydłowski, Ł., Mahnert, A., Blohs, M., Predl, M., Kumpitsch, C., Zrim, V., Högenauer, C., Kosciolek, T., Schmitz, R. A., Eberhard, A., Dragovan, M., Schmidberger, L., Zurabischvili, T., Weinberger, V., Moser, A. M., Kolb, D., Pernitsch, D., Mohammadzadeh, R., Kühnast, T., Rattei, T., & Moissl-Eichinger, C.
Affiliations
  • Medical University of Graz (AT)
  • Jagiellonian University (PL)
  • University of Vienna (AT)
  • Christian-Albrechts-Universität zu Kiel (DE)
Venue
Nature Communications
Published
2024-08-31
Volume
15
Issue
1
Pages
7593-7593
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4402087069
Cited by
21
References
5
Citations by year
2026: 12 · 2025: 9
FWCI
2.83
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Austrian Science Fund (P 30796, SFB F-83, COE 7)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Anaerobic Digestion and Biogas Production
  • Physical Sciences › Environmental Science › Environmental Engineering › Microbial Fuel Cells and Bioremediation
Keywords
Methanobrevibacter, gut archaea, fecal samples, target enrichment, Methanobrevibacter smithii, methane breath test, in silico metabolic modeling, co-culture, comparative genomics, protein function prediction, gastrointestinal diseases, human gut microbiome
MeSH terms
Gastrointestinal Microbiome, Adult, Feces, Female, Humans, Male, Methane, Phylogeny, Genome, Archaeal, Methanobrevibacter, Gastrointestinal Tract

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review