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.

  • Article
  • Published:

A novel bacterial protein family that catalyses nitrous oxide reduction

Nature volume 646, pages 152–160 (2025) Cite this article

Subjects

Abstract

Nitrous oxide (N2O), a driver of global warming and climate change, has reached unprecedented concentrations in Earth’s atmosphere1. Current N2O sources outpace N2O sinks, emphasizing the need for comprehensive understanding of processes that consume N2O. Microbes that express the enzyme N2O reductase (N2OR) convert N2O to climate change-neutral dinitrogen (N2). Known N2ORs belong to the canonical clade I and clade II NosZ reductases and are considered key enzymes for N2O reduction2,3,4. Here we report a previously unrecognized protein family with a role in N2O reduction, clade III lactonase-type N2OR (L-N2OR), which diverges in sequence from canonical NosZ but conserves three-dimensional protein structural features. Integrated physiological, metagenomic, proteomic and structural modelling studies demonstrate that L-N2ORs catalyse N2O reduction. L-N2OR genes occur in several phyla, predominantly in uncultured taxa with broad geographic distribution. Our findings expand the known diversity of N2ORs and implicate previously unrecognized taxa (for example, Nitrospinota) in N2O consumption. The expansion of N2OR diversity and the identification of a novel type of catalyst for N2O reduction advances the understanding of N2O sinks, has implications for greenhouse gas emission and climate change modelling, and expands opportunities for innovative biotechnologies aimed at curbing N2O emissions5,6.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Identification of L-NosZ in Sab enrichment cultures.
The alternative text for this image may have been generated using AI.
Fig. 2: S. acidovorans strain Mol expresses L-NosZ to reduce N2O.
The alternative text for this image may have been generated using AI.
Fig. 3: The L-nosZ locus of D. nosdiversum strain Sab5 differs from C-nos gene clusters of other Desulfitobacterium spp.
The alternative text for this image may have been generated using AI.
Fig. 4: Structure- and gene cluster-based analyses separate clade I and clade II C-NosZ from clade III L-NosZ.
The alternative text for this image may have been generated using AI.
Fig. 5: Geographic distribution of C-nosZ and L-nosZ across soil microbiomes.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

The Sab enrichment culture metagenomic datasets (both Illumina and PacBio data) and the resulting closed genome of D. nosdiversum strain Sab5 are deposited under NCBI BioProject ID PRJNA1176277. The Sabana microcosm metagenomes are publicly available in NCBI Bioproject ID PRJNA901179. Illumina and PacBio datasets used for construction of a circular genome of S. acidovorans strain Mol are publicly available under NCBI BioProject ID PRJNA310710. Genome bins analysed in this study are from the Genome Taxonomy Database (https://gtdb.ecogenomic.org), a global soil genome bins database (https://microbma.github.io/project/SMAG.html) and an ocean genome bins database (https://doi.org/10.6084/m9.figshare.c.5564844.v1). Other genomes and raw metagenomic sequence reads analysed in this study are publicly available in the NCBI database. Lists of the datasets are summarized in Supplementary Tables 7, 8, 10–14, 16 and 17. The mass spectrometry proteomics raw data and sequence files are available at the ProteomeXchange Consortium via the MassIVE database with the unique dataset identifier MSV000096274. The metaproteomic search results are publicly available through the MassIVE database (ftp://massive-ftp.ucsd.edu/v07/MSV000096274/).

References

  1. Tian, H. et al. Global nitrous oxide budget (1980–2020). Earth Syst. Sci. Data 16, 2543–2604 (2024).

    Article  ADS  CAS  Google Scholar 

  2. Sanford, R. A. et al. Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils. Proc. Natl Acad. Sci. USA 109, 19709 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Jones, C. M., Graf, D. R. H., Bru, D., Philippot, L. & Hallin, S. The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink. ISME J. 7, 417–426 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Hallin, S., Philippot, L., Löffler, F. E., Sanford, R. A. & Jones, C. M. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol. 26, 43 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Hiis, E. G. et al. Unlocking bacterial potential to reduce farmland N2O emissions. Nature 630, 421–428 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. He, G. & Löffler, F. E. Nitrogen-hungry bacteria added to farm soil curb greenhouse-gas emissions. Nature 630, 310–311 (2024).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Ravishankara, A. R., Daniel, J. S. & Portmann, R. W. Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science 326, 123–125 (2009).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Core Writing Team. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Lee, H. & Romero, J.) (IPCC, 2023).

  9. Smith, C., Hill, A. K. & Torrente-Murciano, L. Current and future role of Haber–Bosch ammonia in a carbon-free energy landscape. Energy Environ. Sci. 13, 331–344 (2020).

    Article  Google Scholar 

  10. Fowler, D. et al. The global nitrogen cycle in the twenty-first century. Phil. Trans. R. Soc. B 368, 20130164 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ritter, S. K. The Haber–Bosch reaction: an early chemical impact on sustainability. Chem. Eng. News 86, 53 (2008).

    Article  Google Scholar 

  12. Gong, C. et al. Global net climate effects of anthropogenic reactive nitrogen. Nature 632, 557–563 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Harris, E. et al. Denitrifying pathways dominate nitrous oxide emissions from managed grassland during drought and rewetting. Sci. Adv. 7, eabb7118 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang, S. et al. Ammonium-derived nitrous oxide is a global source in streams. Nat. Commun. 15, 4085 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Buessecker, S. et al. Coupled abiotic-biotic cycling of nitrous oxide in tropical peatlands. Nat. Ecol. Evol. 6, 1881–1890 (2022).

    Article  PubMed  Google Scholar 

  16. Si, Y. et al. Direct biological fixation provides a freshwater sink for N2O. Nat. Commun. 14, 6775 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Li, G., Hong, H., Lin, W. & Ji, Q. Substrate competition of diazotrophic nitrous oxide assimilation over dinitrogen fixation. J. Geophys. Res. Biogeosci. 129, e2024JG008187 (2024).

    Article  ADS  CAS  Google Scholar 

  18. Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Cai, S. et al. Optimal nitrogen rate strategy for sustainable rice production in China. Nature 615, 73–79 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Sun, Y. et al. pH selects for distinct N2O-reducing microbiomes in tropical soil microcosms. ISME Commun. 4, ycae070 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Tian, W. & Skolnick, J. How well is enzyme function conserved as a function of pairwise sequence identity. J. Mol. Biol. 333, 863–882 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Rost, B. Twilight zone of protein sequence alignments. Protein Eng. 12, 85–94 (1999).

    Article  CAS  PubMed  Google Scholar 

  23. Song, Y.-J. et al. Structural and functional insights into PpgL, a metal-independent β-propeller gluconolactonase that contributes to Pseudomonas aeruginosa virulence. Infect. Immun. 87, e00847-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Pomowski, A., Zumft, W. G., Kroneck, P. M. H. & Einsle, O. N2O binding at a [4Cu:2S] copper–sulphur cluster in nitrous oxide reductase. Nature 477, 234–237 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. He, G. et al. Sustained bacterial N2O reduction at acidic pH. Nat. Commun. 15, 4092 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yoshinari, T. & Knowles, R. Acetylene inhibition of nitrous oxide reduction by denitrifying bacteria. Biochem. Biophys. Res. Commun. 69, 705–710 (1976).

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Gao, Y. et al. Competition for electrons favours N2O reduction in denitrifying Bradyrhizobium isolates. Environ. Microbiol. 23, 2244–2259 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Ollivier, B., Cord-Ruwisch, R., Lombardo, A. & Garcia, J.-L. Isolation and characterization of Sporomusa acidovorans sp. nov., a methylotrophic homoacetogenic bacterium. Arch. Microbiol. 142, 307–310 (1985).

    Article  CAS  Google Scholar 

  29. Pomowski, A. et al. Revisiting the metal sites of nitrous oxide reductase in a low-dose structure from Marinobacter nauticus. J. Biol. Inorg. Chem. 29, 279–290 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Müller, C. et al. Molecular interplay of an assembly machinery for nitrous oxide reductase. Nature 608, 626–631 (2022).

    Article  ADS  PubMed  Google Scholar 

  31. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Thomas, C. & Tampé, R. Structural and mechanistic principles of ABC transporters. Annu. Rev. Biochem. 89, 605–636 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Orellana, L. H., Rodriguez-R, L. M. & Konstantinidis, K. T. ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 45, e14 (2016).

    PubMed Central  Google Scholar 

  35. Schneider, L. K. & Einsle, O. Role of calcium in secondary structure stabilization during maturation of nitrous oxide reductase. Biochemistry 55, 1433–1440 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Ma, B. et al. A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources. Nat. Commun. 14, 7318 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nishimura, Y. & Yoshizawa, S. The OceanDNA MAG catalog contains over 50,000 prokaryotic genomes originated from various marine environments. Sci. Data 9, 305 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Graf, D. R. H., Jones, C. M. & Hallin, S. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS ONE 9, e114118 (2014).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  40. Saghaï, A., Pold, G., Jones, C. M. & Hallin, S. Phyloecology of nitrate ammonifiers and their importance relative to denitrifiers in global terrestrial biomes. Nat. Commun. 14, 8249 (2023).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. Pavlopoulos, G. A. et al. Unraveling the functional dark matter through global metagenomics. Nature 622, 594–602 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wu, D., Seshadri, R., Kyrpides, N. C. & Ivanova, N. N. A metagenomic perspective on the microbial prokaryotic genome census. Sci. Adv. 11, eadq2166 (2025).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Shimoshige, H., Kobayashi, H., Shimamura, S., Miyazaki, M. & Maekawa, T. Fundidesulfovibrio magnetotacticus sp. nov., a sulphate-reducing magnetotactic bacterium, isolated from sediments and freshwater of a pond. Int. J. Syst. Evol. Microbiol. https://doi.org/10.1099/ijsem.0.005516 (2022).

  45. Liu, Y., Balkwill, D. L., Aldrich, H. C., Drake, G. R. & Boone, D. R. Characterization of the anaerobic propionate-degrading syntrophs Smithella propionica gen. nov., sp. nov. and Syntrophobacter wolinii. Int. J. Syst. Evol. Microbiol. 49, 545–556 (1999).

    Article  CAS  Google Scholar 

  46. Harmsen, H. J. M. et al. Syntrophobacter fumaroxidans sp. nov., a syntrophic propionate-degrading sulfate-reducing bacterium. Int. J. Syst. Evol. Microbiol. 48, 1383–1387 (1998).

    CAS  Google Scholar 

  47. Jiang, Q. et al. Cold seeps are potential hotspots of deep-sea nitrogen loss driven by microorganisms across 21 phyla. Nat. Commun. 16, 1646 (2025).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Spieck, E., Keuter, S., Wenzel, T., Bock, E. & Ludwig, W. Characterization of a new marine nitrite oxidizing bacterium, Nitrospina watsonii sp. nov., a member of the newly proposed phylum “Nitrospinae”. Syst. Appl. Microbiol. 37, 170–176 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Kop, L. F. M., Koch, H., Jetten, M. S. M., Daims, H. & Lücker, S. Metabolic and phylogenetic diversity in the phylum Nitrospinota revealed by comparative genome analyses. ISME Commun. 4, ycad017 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mosley, O. E. et al. Nitrogen cycling and microbial cooperation in the terrestrial subsurface. ISME J. 16, 2561–2573 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Mosley, O. E., Gios, E. & Handley, K. M. Implications for nitrogen and sulfur cycles: phylogeny and niche-range of Nitrospirota in terrestrial aquifers. ISME Commun. 4, ycae047 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Fortin, S. G., Sun, X., Jayakumar, A. & Ward, B. B. Nitrite-oxidizing bacteria adapted to low-oxygen conditions dominate nitrite oxidation in marine oxygen minimum zones. ISME J. 18, wrae160 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e1021 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kruse, T. et al. Comparative genomics of the genus Desulfitobacterium. FEMS Microbiol. Ecol. 93, fix135 (2017).

    Article  Google Scholar 

  55. Jones, C. M. et al. Phenotypic and genotypic heterogeneity among closely related soil-borne N2- and N2O-producing Bacillus isolates harboring the nosZ gene. FEMS Microbiol. Ecol. 76, 541–552 (2011).

    Article  CAS  PubMed  Google Scholar 

  56. Jones, C. M., Stres, B., Rosenquist, M. & Hallin, S. Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory enzymes reveal a complex evolutionary history for denitrification. Mol. Biol. Evol. 25, 1955–1966 (2008).

    Article  CAS  PubMed  Google Scholar 

  57. Neukirchen, S., Pereira, I. A. C. & Sousa, F. L. Stepwise pathway for early evolutionary assembly of dissimilatory sulfite and sulfate reduction. ISME J. 17, 1680–1692 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Morris, R. L. & Schmidt, T. M. Shallow breathing: bacterial life at low O2. Nat. Rev. Microbiol. 11, 205–212 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Barnum, T. P. et al. Predicting microbial growth conditions from amino acid composition. Preprint at bioRxiv https://doi.org/10.1101/2024.03.22.586313 (2024).

  60. Das, A., Silaghi-Dumitrescu, R., Ljungdahl, L. G. & Kurtz, D. M. Cytochrome bd oxidase, oxidative stress, and dioxygen tolerance of the strictly anaerobic bacterium Moorella thermoacetica. J. Bacteriol. 187, 2020–2029 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Zhang, L. et al. Anammox coupled with photocatalyst for enhanced nitrogen removal and the activated aerobic respiration of anammox bacteria based on cbb3-type cytochrome c oxidase. Environ. Sci. Tech. 57, 17910–17919 (2023).

    Article  CAS  Google Scholar 

  62. Heidelberg, J. F. et al. The genome sequence of the anaerobic, sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough. Nat. Biotechnol. 22, 554–559 (2004).

    Article  CAS  PubMed  Google Scholar 

  63. Yin, Y. et al. Nitrous oxide inhibition of methanogenesis represents an underappreciated greenhouse gas emission feedback. ISME J. 18, wrae027 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Yin, Y. et al. Nitrous oxide is a potent inhibitor of bacterial reductive dechlorination. Environ. Sci. Technol. 53, 692 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. Park, D., Kim, H. & Yoon, S. Nitrous oxide reduction by an obligate aerobic bacterium, Gemmatimonas aurantiaca strain T-27. Appl. Environ. Microbiol. 83, e00502-17 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  66. Truchon Alicia, N. et al. Plant-pathogenic Ralstonia phylotypes evolved divergent respiratory strategies and behaviors to thrive in xylem. mBio 14, e03188-22 (2023).

  67. Karthikeyan, S. et al. Metagenomic characterization of soil microbial communities in the Luquillo experimental forest (Puerto Rico) and implications for nitrogen cycling. Appl. Environ. Microbiol. 87, e00546–00521 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Löffler, F. E., Sanford, R. A. & Ritalahti, K. M. Enrichment, cultivation, and detection of reductively dechlorinating bacteria. Methods Enzymol. 397, 77–111 (2005).

    Article  PubMed  Google Scholar 

  69. Yan, J. et al. Purinyl-cobamide is a native prosthetic group of reductive dehalogenases. Nat. Chem. Biol. 14, 8–14 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  70. Krakau, S., Straub, D., Gourlé, H., Gabernet, G. & Nahnsen, S. nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genom. Bioinformatics 4, lqac007 (2022).

    Article  Google Scholar 

  71. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Institute https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  72. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. 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 

  74. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. 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 

  76. Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2015).

    Article  PubMed  Google Scholar 

  77. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. 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).

    Article  CAS  PubMed  Google Scholar 

  79. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Darriba, D. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol. Biol. Evol. 37, 291–294 (2019).

    Article  PubMed Central  Google Scholar 

  82. Konstantinidis, K. et al. FastAAI: efficient estimation of genome average amino acid identity and phylum-level relationships using tetramers of universal proteins. Nucleic Acids Res. 53, gkaf348 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).

    Article  Google Scholar 

  84. Humphreys, J. R., Daniel, R. & Poehlein, A. Genome sequence of the homoacetogenic, Gram-negative, endospore-forming bacterium Sporomusa acidovorans DSM 3132. Genome Announc. 5, 00981-17.

  85. Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. Hunt, M. et al. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biol. 16, 294 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  88. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  89. Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  93. Hackl, T., Ankenbrand, M., van Adrichem, B., Wilkins, D. & Haslinger, K. gggenomes: effective and versatile visualizations for comparative genomics. Preprint at https://doi.org/10.48550/arXiv.2411.13556 (2024).

  94. Aroney, S. T. et al. CoverM: Read alignment statistics for metagenomics. Bioinformatics 41, btaf147 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Wilkinson, L. ggplot2: Elegant Graphics for Data Analysis by WICKHAM, H. Biometrics 67, 678–679 (2011).

    Article  Google Scholar 

  96. Gerhardt, K., Ruiz-Perez, C. A., Rodriguez-R, L. M., Conrad, R. E. & Konstantinidis, K. T. RecruitPlotEasy: an advanced read recruitment plot tool for assessing metagenomic population abundance and genetic diversity. Front. Bioinformatics 1, 826701 (2022).

    Article  Google Scholar 

  97. Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).

  100. Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article  CAS  PubMed  Google Scholar 

  101. Batth, T. S. et al. Protein aggregation capture on microparticles enables multipurpose proteomics sample preparation. Mol. Cell. Proteom. 18, 1027–1035 (2019).

    Article  CAS  Google Scholar 

  102. Dorfer, V. et al. MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J. Proteome Res. 13, 3679–3684 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Zhou, L. et al. ggmsa: a visual exploration tool for multiple sequence alignment and associated data. Brief. Bioinform. 23, bbac222 (2022).

    Article  PubMed  Google Scholar 

  105. Moi, D. et al. Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses. Preprint at bioRxiv https://doi.org/10.1101/2023.09.19.558401 (2023).

  106. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    Article  PubMed  Google Scholar 

  107. Gilchrist, C. L. M. et al. cblaster: a remote search tool for rapid identification and visualization of homologous gene clusters. Bioinform. Adv. 1, vbab016 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Gilchrist, C. L. M. & Chooi, Y.-H. clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics 37, 2473–2475 (2021).

    Article  CAS  PubMed  Google Scholar 

  109. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  111. Zhou, T. et al. itol.toolkit accelerates working with iTOL (Interactive Tree of Life) by an automated generation of annotation files. Bioinformatics 39, btad339 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 52, W78–W82 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    Article  CAS  PubMed  Google Scholar 

  114. Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59–e59 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformormatics 11, 538 (2010).

    Article  Google Scholar 

  117. Saghaï, A. Phyloecology of nitrate ammonifiers and their relative importance with denitrifiers in global terrestrial biomes. Zenodo https://doi.org/10.5281/zenodo.8026657 (2023).

  118. Yoon, S., Nissen, S., Park, D., Sanford, R. A. & Löffler, F. E. Nitrous oxide reduction kinetics distinguish bacteria harboring clade I NosZ from those harboring clade II NosZ. Appl. Environ. Microbiol. 82, 3793 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  119. Sander, R. Compilation of Henry’s law constants (version 4.0) for water as solvent. Atmos. Chem. Phys. 15, 4399–4981 (2015).

    Article  ADS  CAS  Google Scholar 

  120. Ludbrook, J. Multiple comparison procedures updated. Clin. Exp. Pharmacol. Physiol. 25, 1032–1037 (1998).

    Article  CAS  PubMed  Google Scholar 

  121. Zhang, L., Wüst, A., Prasser, B., Müller, C. & Einsle, O. Functional assembly of nitrous oxide reductase provides insights into copper site maturation. Proc. Natl Acad. Sci. USA 116, 12822–12827 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  122. 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).

    Article  CAS  PubMed  Google Scholar 

  123. Murali, R. et al. Diversity and evolution of nitric oxide reduction in bacteria and archaea. Proc. Natl Acad. Sci. USA 121, e2316422121 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge funding through the Dimensions of Biodiversity programme of the US National Science Foundation (awards 1831599 to F.E.L. and 1831582 to K.T.K.). G.H., W.W. and Y.X. received financial support from the China Scholarship Council. M.E.D. is recipient of a US National Science Foundation Graduate Research Fellowship. R.L.H. acknowledges support of the metaproteomics infrastructure from the Oak Ridge National Laboratory Plant-Microbe Interfaces Science Focus Area funded by the US Department of Energy Biological and Environmental Research Genome Sciences Program. J.M.P. was supported by the Laboratory Directed Research and Development programme at Oak Ridge National Laboratory, which is managed by UT-Battelle under contract DE-AC05-00OR22725 for the US Department of Energy.

Author information

Author notes

  1. Yongchao Xie  (谢永超)

    Present address: State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing, China

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA

    Guang He  (何广), Gao Chen  (陈高), Yongchao Xie  (谢永超) & Frank E. Löffler

  2. Department of Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, TN, USA

    Guang He  (何广) & Frank E. Löffler

  3. Department of Plant Sciences, University of Tennessee, Knoxville, TN, USA

    Weijiao Wang  (王威姣)

  4. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Jerry M. Parks, Megan E. Davin & Robert L. Hettich

  5. Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA

    Megan E. Davin

  6. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Konstantinos T. Konstantinidis

  7. Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, USA

    Frank E. Löffler

Authors
  1. Guang He  (何广)
    View author publications

    Search author on:PubMed Google Scholar

  2. Weijiao Wang  (王威姣)
    View author publications

    Search author on:PubMed Google Scholar

  3. Gao Chen  (陈高)
    View author publications

    Search author on:PubMed Google Scholar

  4. Yongchao Xie  (谢永超)
    View author publications

    Search author on:PubMed Google Scholar

  5. Jerry M. Parks
    View author publications

    Search author on:PubMed Google Scholar

  6. Megan E. Davin
    View author publications

    Search author on:PubMed Google Scholar

  7. Robert L. Hettich
    View author publications

    Search author on:PubMed Google Scholar

  8. Konstantinos T. Konstantinidis
    View author publications

    Search author on:PubMed Google Scholar

  9. Frank E. Löffler
    View author publications

    Search author on:PubMed Google Scholar

Contributions

G.H. and F.E.L. conceptualized the research. F.E.L. supervised the project. F.E.L. and K.T.K. acquired funding. G.H., G.C., Y.X. and F.E.L. contributed to cultivation and analytical efforts. G.H. performed phenotypic characterizations and molecular analyses. G.H. and W.W. performed bioinformatic analyses with guidance from K.T.K., and G.H. and J.M.P. performed protein structural modelling. G.H., M.E.D. and R.L.H. performed proteomics analyses. All authors contributed to data analysis and interpretation. G.H. and F.E.L. wrote the manuscript with inputs from all authors. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Frank E. Löffler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Åsa Frostegård and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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 Schematic of the workflow leading from Sabana forest soil to soil microcosms to solid-free Sab enrichment cultures.

Metagenome analysis of the original soil suggested tropical soils harbor diverse C-nosZ genes67. Efforts aimed at unraveling the microbiology of N2O consumption in Sabana soil microcosms experiencing different N2O feeding regimes revealed an incongruity between N2O consumption and nosZ abundance20. Twenty-five consecutive transfers starting with the Sabana soil microcosm experiencing a high N2O feeding regime yielded a Sab enrichment culture with robust N2O consumption activity. The Sab enrichment culture was incubated in 160 mL glass serum bottles containing 100 mL of basal salt medium amended with 0.02 g L−1 TSB, 5 mM fumarate, 10 mL (4.16 mM nominal; 416 µmol) H2, and 10 mL (4.16 mM nominal; 416 µmol) N2O. While the Sab enrichment culture exhibited robust N2O reduction activity, metagenomic investigations of Sab subcultures did not generate evidence for the presence of clade I or clade II C-nosZ.

Extended Data Fig. 2 Fumarate, H2, and N2O utilization in Sab enrichment cultures following 25 sequential transfers.

Panels a-e show fumarate consumption and the formation of acetate, propionate and succinate in medium amended with (a) fumarate, (b) fumarate + H2 + N2O, (c) fumarate + H2 + N2O + TSB, (d) fumarate + H2 + TSB, and (e) fumarate + N2O + TSB. Panels f-i depict H2 and N2O consumption in medium that received (f) fumarate + H2 + N2O, (g) fumarate + H2 + N2O + TSB, (h) fumarate + H2 + TSB, (i) fumarate + N2O + TSB. Data shown represent averages and standard deviations of triplicate cultures. Error bars are not visible when smaller than the symbols.

Extended Data Fig. 3 Phylogenomic analysis revealing the taxonomic affiliation of Desulfitobacterium nosdiversum strain Sab5.

(a) The phylogenomic analysis used 120 conserved bacterial marker genes and included Desulfitobacteriaceae genomes available in the NCBI database. The scale bar indicates 0.05 nucleotide substitutions per site. Bootstrap (BP) values are divided into the categories BP < 70 (solid black circles) and BP ≥ 90 (solid grey circles). (b) Line graph showing Single Copy Protein- (SCP-) based average amino acid identity (AAI) between the strain Sab5 genome and individual genomes included in the phylogenomic analysis, with the scale displayed on the x-axis. Values for each comparison between the strain Sab5 genome and related genomes are represented by black dots, with the comparison to itself being 100%.

Extended Data Fig. 4 Growth yield of Desulfitobacterium nosdiversum strain Sab5 and protein expression during growth with N2O.

(a) Growth yields were estimated by correlating L-nosZ gene copy numbers with amounts of N2O consumed. Cell suspension samples were collected following complete consumption of N2O from triplicate vessels. Strain Sab5 cell numbers were determined with a qPCR assay targeting the L-nosZ gene. The genome of strain Sab5 contains a single L-nosZ gene, and L-nosZ gene counts equal the cell numbers. Sab enrichment cultures produced (6.8 ± 0.77) × 109 Desulfitobacterium nosdiversum strain Sab5 cells mmol N2O−1, equivalent to a growth yield (dry weight) of 4.21 ± 0.48 mg mmol N2O−1 (assuming a dry weight of 6.13 × 10−10 mg per cell27). The gray shading indicates the 95% confidence intervals around the mean values. (b) Metaproteomic analysis assigned 1,720 proteins to Desulfitobacterium nosdiversum strain Sab5 cells collected from Sab enrichment cultures grown with 5 mM fumarate, 0.02 g L−1 TSB, 4.16 mM (nominal) H2, and 4.16 mM (nominal) N2O for 3 days when >50% of the initial N2O dose had been consumed. L-NosZ, MacB type ATP-binding cassette (ABC) transporter, c-type cytochrome (K03888), b-type cytochrome (K00412), AtoC (K07714) and SasA (COG4191) were all detected (red arrows), in addition to a protein of unknown function encoded by a gene neighboring L-nosZ. The curved line shows the Log2 transformed protein abundances, and the vertical lines represent the standard deviation of triplicate cultures.

Extended Data Fig. 5 Growth and N2O reduction in cultures of Sporomusa acidovorans strain Mol.

Panels a-d show growth of Sporomusa acidovorans strain Mol and N2O consumption at different O2 partial pressures and pH conditions. (a) Growth of strain Mol at pH 7 with aqueous phase O2 concentrations of 0, 4.55 µM (355 Pa), 9.1 µM (709 Pa), 22.75 µM (1,780 Pa), and 45.5 µM (3,550 Pa). (b) N2O consumption in bottles with different O2 partial pressures at pH 7. The growth medium was prepared anoxically, and O2 was introduced by injecting 0, 1, 2, 5, 10 ml of air (i.e., 21% O2, v/v). (c) Growth of strain Mol in medium with the pH adjusted to 3.5, 4.5, 5.5, 6.5, 7.5, and 8.5. (d) N2O consumption observed at the different medium pH values. Panels e-h depict strain Mol cultures grown in basal salt medium amended with 1 g l−1 yeast extract in the presence of different amounts of N2O. (e) N2O consumption, (f) growth, and (g) acetate production in Sporomusa acidovorans cultures, with each set of triplicate cultures receiving different amounts of N2O. Acetate production was measured in samples withdrawn from cultures on day 14. (h) Correlation between N2O consumption and acetate yields. A linear regression was performed (blue line with 95% confidence interval), yielding a significant positive correlation (R² = 0.90, p = 2.07 × 10−6). No N2O consumption was observed in control vessels that received autoclaved inocula or in live cultures amended with 6 mL (v/v, 10%) acetylene. Data shown represent averages and standard deviations of triplicate cultures. Error bars are not shown when smaller than the symbols. Pairwise comparisons were performed using two-sided Student’s t-tests with p values adjusted for multiple testing using the Holm-Bonferroni method120.

Extended Data Fig. 6 AlphaFold models of L-NosZ and the MacB-type ABC transporter.

Panels a-f show Cu-binding active sites in C-NosZ derived from a 1.7 Å X-ray crystal structure of Pseudomonas stutzeri (PsNosZ, PDB entry 3SBQ) and representative AlphaFold3 models of apo L-NosZ of Desulfitobacterium nosdiversum strain Sab5 and Sporomusa acidovorans strain Mol. (a) CuA site in Pseudomonas stutzeri NosZ; (b) putative CuA site in Desulfitobacterium nosdiversum strain Sab5 NosZ; (c) putative CuA site in Sporomusa acidovorans strain Mol NosZ; (d) the CuZ site in Pseudomonas stutzeri NosZ; (e) putative CuZ site in Desulfitobacterium nosdiversum strain Sab5 NosZ; (f) putative CuZ site in Sporomusa acidovorans strain Mol NosZ. The root-mean-square deviations (RMSDs) between paired H130 in 3SBQ and either H117 in Desulfitobacterium nosdiversum strain Sab5 L-NosZ or H126 in Sporomusa acidovorans strain Mol NosZ (indicated by red arrows) are higher than other paired histidine residues because H117 and H126 originate from different ß-strands in the C-NosZ and L-NosZ enzymes, respectively. Although the position and relative orientation of this histidine residue differ, it is expected to coordinate Cu3 (see panel d) in a similar manner. Specifically, H129 and H130 in the PsNosZ X-ray structure are located on the same ß-strand. In comparison, H116 and H117 in the model of NosZ from Desulfitobacterium nosdiversum strain Sab5 are located on the same ß-strand. Similarly, H125 and H126 in Sporomusa acidovorans strain Mol NosZ model come from the same ß-strand. Panels g-h show a crystal structure and AlphaFold model of MacB-type ABC transporter. (g) Homodimeric crystal structure of the ABC transporter MacB from Acinetobacter baumannii (PDB entry 5WS4). (h) AlphaFold2 model of the putative MacB transporter encoded on the Desulfitobacterium nosdiversum strain Sab5 genome generated using ColabFold with 5WS4 as a template. The model is a dimer of homodimers comprising four monomers, i.e., two copies of MacB and two copies of the ABC-type transporter. (i) Predicted aligned error (PAE) for the MacB transporter protein chains A, B, C, D, with lower PAE values (blue) indicating higher model confidence. The predicted local distance difference test (pLDDT) scores, a per-residue measure of local confidence, are indicated by colors.

Extended Data Fig. 7 Features differentiating L-NosZ from C-NosZ sequences.

(a) Maximum likelihood phylogenetic tree based on protein sequences of clade I C-NosZ (n = 105), clade II C-NosZ (n = 68) and clade III L-NosZ (n = 95). The phylogenetic analysis included 1,451 sites. The colored dots at the nodes indicate the RaxML-ng bootstrap values; bootstrap values > 90% are not shown. The tree scale bar represents the mean number of amino acid substitution per site. (b) The CuA motifs involved in electron transfer to the CuZ site are 100% conserved (CXXFCXXXHXEM) in C-NosZ and L-NosZ. (c) Independent alignments of C-NosZ and L-NosZ sequences showing differences in conserved histidine residues associated with the CuZ site. The copper binding sites, CuA and CuZ, were inferred based on analysis of X-ray crystal structure data121. The height of the symbols (i.e., the size of letter abbreviations for amino acids) reflects the relative frequency of the amino acid at that position. The multiple sequence alignment plots were created with ggmsa104.

Extended Data Fig. 8 Population genome-resolved and metagenomic read-resolved surveys of the environmental distribution of L-nosZ.

Bars show metagenomic reads (%) recruited to the clade III L-NosZ-encoding genomes. The x-axis displays the metagenome accession IDs. Relevant information on metagenomes and genome bins is summarized in Supplementary Tables 13 and 14, respectively. The numbers above each bar indicate the MAGs used as reference genomes in metagenome recruitment analysis. The 16 MAGs detected along with the highest taxonomic classification are represented by numbers. (1) GCA009782775 (Desulfovibrionaceae); (2) GCA015231995 (Nitrospinota); (3) GCA016223025 (Gallionellaceae); (4) GCA015232395 (Magnetococcales); (5) GCA008933335 (Casimicrobiaceae); (6) GCA001804905 (Nitrospinota); (7) GCA011052055 (Nitrospinota); (8) GCA016214995 (Desulfobacterota); (9) GCA016200645 (Desulfobacterota); (10) GCA024244705 (Alphaproteobacteria); (11) GCA011051655 (Chromatiales); (12) GCA016218475 (Gallionella); (13) GCA031269345 (Frigididesulfovibrio); (14) GCA030739775 (Alphaproteobacteria); (15) GCA003450795 (Desulfobacterota); (16) GCA016191995 (Rubrivivax). Individual genome bins are distinguished by colors. The world map inset shows the geographic distribution of 1,053 metagenomes representing 12 distinct biomes from six continents. The pie charts indicate the normalized clade I (red), clade II (green), and clade III (blue) nosZ counts relative to the total nosZ counts in each metagenome, with the radii indicating the total nosZ counts per metagenome. A nosZ phylotype was inferred based on the phylogenetic placement on the GraftM NosZ reference tree (Extended Data Fig. 7).

Extended Data Fig. 9 nosZ transcripts detected in the Tara Oceans metatranscriptome (Ocean metaT) dataset53.

To explore nosZ expression, an HMM was built from 173 C-NosZ and 95 L-NosZ sequences to search the protein sequences provided in the Ocean metaT dataset. Transcripts with expression levels higher than 1 RPKM were considered expressed and are represented in the graphs. The search and filtering criteria resulted in a total of four clade I-type sequences (NosZ transcripts 1-4), three clade II-type sequences (NosZ transcripts 5-7), and one clade III-type sequence (NosZ transcript 8). Relevant metadata (e.g., dissolved O2 concentrations) corresponding to the metatranscriptomes were obtained from the Ocean metaT dataset. The taxonomic assignments of each NosZ transcript are: NosZ_1 (Gammaproteobacteria); NosZ_2 (Gammaproteobacteria); NosZ_3 (Unclassified bacteria); NosZ_4 (Unclassified bacteria); NosZ_5 (Flavobacteriaceae); NosZ_6 (Bacteroides); NosZ_7 (Unclassified bacteria); NosZ_8 (Nitrospinota). The dissolved O2 concentrations varied in the Tara Oceans sampling locations: Arctic Ocean (200 µM <c[O2] <400 µM); Indian Ocean (c[O2] <4 µM); North Pacific Ocean (c[O2] <3 µM); South Pacific Ocean (3 µM <c[O2] <250 µM). The transcriptome accession IDs correspond to the Ocean metaT dataset.

Extended Data Fig. 10 Potential utilization of electron acceptors by bacteria represented by genome bins harboring clade III L-nosZ and their predicted O2 preference.

The tree was constructed using the GTDB-TK workflow79. The scale bar represents the mean number of amino acid substitution per site. Terminal reductases investigated include nitrate reductase (encoded by napAB or narGHIJ), NO-forming nitrite reductase (nirK or nirS), ammonium-forming nitrite reductase (nrfAH), sulfate adenylytransferase (sat), and dissimilatory sulfite reductase (dsrAB) based on the annotations derived from eggNOG database v5122. Seven curated HMMs, comprising genes encoding nitric oxide reductases (NOR, NO → N2O)123, were queried against the protein sequences. The resulting NOR sequences were then filtered by conserved motif sequences in the different NOR families (i.e., cNOR, qNOR, gNOR, nNOR, sNOR, bNOR and eNOR). Genes encoding bacterial high affinity (ccoNOP and cydAB) and low affinity (ctaABCDE) oxidases catalyzing the reduction of O2 reduction were also investigated58. Bacteria represented by genome bins were classified as facultative and strict anaerobes with genome-based computational models using GenomeSpot v1.0.159, and the red squares indicate strict anaerobes. About two-thirds (n = 70) of the genome bins harbor NOR genes. Seventeen genome bins contain all genes required for complete denitrification (\({{\rm{NO}}}_{3}^{-}\) → \({{\rm{NO}}}_{2}^{-}\) → NO → N2O → N2).

Supplementary information

Supplementary Information (download PDF )

This file contains Supplementary Notes 1–4 and Supplementary Figs. 1–12.

Reporting Summary (download PDF )

Supplementary Tables (download XLSX )

This file contains Supplementary Tables 1–17.

Peer Review file (download PDF )

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, G., Wang, W., Chen, G. et al. A novel bacterial protein family that catalyses nitrous oxide reduction. Nature 646, 152–160 (2025). https://doi.org/10.1038/s41586-025-09401-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-025-09401-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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