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:

Coupled anaerobic methane oxidation and reductive arsenic mobilization in wetland soils

Nature Geoscience volume 13, pages 799–805 (2020)Cite this article

Subjects

Abstract

Anaerobic methane oxidation is coupled to the reduction of electron acceptors, such as sulfate, and contributes to their biogeochemical cycling in the environment. However, whether arsenate acts as an alternative electron acceptor of anaerobic methane oxidation and how this influences global arsenic transformations remains elusive. Here, we present incubations of arsenate-contaminated wetland soils from seven provinces in China. Using isotopically labelled methane, we find that anaerobic methane oxidation was linked to arsenate reduction at a rate approaching the theoretical arsenic/methane stoichiometric ratio of 4. In microcosm incubations with natural wetland soils, we find that the coupled pathway of anaerobic methane oxidation and arsenate reduction contributed 26 to 49% of total arsenic release from soils, with arsenic in the more soluble and toxic form arsenite. Comparative gene quantification and metagenomic sequencing suggest that the coupled pathway was facilitated by anaerobic methanotrophs, either independently or synergistically with arsenate-reducing bacteria through reverse methanogenesis and respiratory arsenate reduction. Further bioinformatic analyses show that genes coding for reverse methanogenesis and respiratory arsenate reduction are universally co-distributed in nature. This suggests that coupling of anaerobic methane oxidation and arsenate reduction is a potentially global but previously overlooked process, with implications for arsenic mobilization and environmental contamination.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Geographic map of field-sampling locations.
Fig. 2: Stoichiometry and correlation of As(V) and 13C-DIC concentrations in experiments inoculated by soil samples.
Fig. 3: Phylogeny of mcrA based on amino acid sequences.
Fig. 4: Phylogeny of DMSOR superfamily based on amino acid sequences.
Fig. 5: Increased dissolved arsenic and gaseous 13CO2 of microcosm incubations using representative soils.
Fig. 6: Global map with environmental samples containing both ANME-mcrA and arrA on the basis of bioinformatic prediction.

Similar content being viewed by others

Data availability

All the metagenomic sequencing data have been submitted to the Sequence Read Archive, with BioProject accession number of PRJNA612595. The source data have been deposited into the Open Science Framework (OSF) at https://doi.org/10.17605/OSF.IO/N4SEF. Source data are provided with this paper.

References

  1. Zhu, Y. G., Yoshinaga, M., Zhao, F. J. & Rosen, B. P. Earth abides arsenic biotransformations. Annu. Rev. Earth Planet. Sci. 42, 443–467 (2014).

    Google Scholar 

  2. Singh, R., Singh, S., Parihar, P., Singh, V. P. & Prasad, S. M. Arsenic contamination, consequences and remediation techniques: a review. Ecotoxicol. Environ. Saf. 112, 247–270 (2015).

    Google Scholar 

  3. Cullen, W. R. & Reimer, K. J. Arsenic speciation in the environment. Chem. Rev. 89, 713–764 (1989).

    Google Scholar 

  4. Masscheleyn, P. H., Delaune, R. D. & Patrick, W. H. Effect of redox potential and pH on arsenic speciation and solubility in a contaminated soil. Environ. Sci. Technol. 25, 1414–1419 (1991).

    Google Scholar 

  5. Abernathy, C. O., Thomas, D. J. & Calderon, R. L. Health effects and risk assessment of arsenic. J. Nutr. 133, 1536S–1538S (2003).

    Google Scholar 

  6. Jia, Y. et al. Microbial arsenic methylation in soil and rice rhizosphere. Environ. Sci. Technol. 47, 3141–3148 (2013).

    Google Scholar 

  7. Chen, C. et al. Sulfate-reducing bacteria and methanogens are involved in arsenic methylation and demethylation in paddy soils. ISME J. 13, 2523–2535 (2019).

    Google Scholar 

  8. Burton, E. D., Johnston, S. G. & Kocar, B. D. Arsenic mobility during flooding of contaminated soil: the effect of microbial sulfate reduction. Environ. Sci. Technol. 48, 13660–13667 (2014).

    Google Scholar 

  9. Bao, P. et al. The role of sulfate-reducing prokaryotes in the coupling of element biogeochemical cycling. Sci. Total Environ. 613, 398–408 (2018).

    Google Scholar 

  10. Wolfe, A. L. & Wilkin, R. T. Evidence of sulfate-dependent anaerobic methane oxidation within an area impacted by coalbed methane-related gas migration. Environ. Sci. Technol. 51, 1901–1909 (2017).

    Google Scholar 

  11. Amstaetter, K., Borch, T., Larese-Casanova, P. & Kappler, A. Redox transformation of arsenic by Fe(II)-activated goethite (alpha-FeOOH). Environ. Sci. Technol. 44, 102–108 (2010).

    Google Scholar 

  12. Ma, J. F. et al. Transporters of arsenite in rice and their role in arsenic accumulation in rice grain. Proc. Natl Acad. Sci. USA 105, 9931–9935 (2008).

    Google Scholar 

  13. Zobrist, J., Dowdle, P. R., Davis, J. A. & Oremland, R. S. Mobilization of arsenite by dissimilatory reduction of adsorbed arsenate. Environ. Sci. Technol. 34, 4747–4753 (2000).

    Google Scholar 

  14. Macur, R. E., Wheeler, J. T., McDermott, T. R. & Inskeep, W. P. Microbial populations associated with the reduction and enhanced mobilization of arsenic in mine tailings. Environ. Sci. Technol. 35, 3676–3682 (2001).

    Google Scholar 

  15. Bruschi, M., Barton, L. L., Goulhen, F. & Plunkett, R. M. in Sulphate-Reducing Bacteria: Environmental and Engineered Systems (eds Barton, L. L. & Hamilton, W. A.) 435–458 (Cambridge Univ. Press, 2007).

  16. Qiao, J. T. et al. Transcriptional activity of arsenic-reducing bacteria and genes regulated by lactate and biochar during arsenic transformation in flooded paddy soil. Environ. Sci. Technol. 52, 61–70 (2018).

    Google Scholar 

  17. Kudo, K. et al. Release of arsenic from soil by a novel dissimilatory arsenate-reducing bacterium, Anaeromyxobacter sp. strain PSR-1. Appl. Environ. Microbiol. 79, 4635–4642 (2013).

    Google Scholar 

  18. Qiao, J. et al. Humic substances facilitate arsenic reduction and release in flooded paddy soil. Environ. Sci. Technol. 53, 5034–5042 (2019).

    Google Scholar 

  19. Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).

    Google Scholar 

  20. Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).

    Google Scholar 

  21. Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009).

    Google Scholar 

  22. Devol, A. H. & Ahmed, S. I. Are high rates of sulfate reduction associated with anaerobic oxidation of methane. Nature 291, 407–408 (1981).

    Google Scholar 

  23. Niemann, H. et al. Novel microbial communities of the Haakon Mosby mud volcano and their role as a methane sink. Nature 443, 854–858 (2006).

    Google Scholar 

  24. Boetius, A. et al. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature 407, 623–626 (2000).

    Google Scholar 

  25. Orphan, V. J., House, C. H., Hinrichs, K. U., McKeegan, K. D. & DeLong, E. F. Methane-consuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science 293, 484–487 (2001).

    Google Scholar 

  26. Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).

    Google Scholar 

  27. Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 12, 1929–1939 (2018).

    Google Scholar 

  28. Leu, A. O. et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 14, 1030–1041 (2020).

    Google Scholar 

  29. Caldwell, S. L. et al. Anaerobic oxidation of methane: mechanisms, bioenergetics, and the ecology of associated microorganisms. Environ. Sci. Technol. 42, 6791–6799 (2008).

    Google Scholar 

  30. Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325-20 (2020).

    Google Scholar 

  31. Ruff, S. E. et al. Methane seep in shallow-water permeable sediment harbors high diversity of anaerobic methanotrophic communities, Elba, Italy. Front. Microbiol. 7, 374 (2016).

    Google Scholar 

  32. Hu, B. L. et al. Evidence for nitrite-dependent anaerobic methane oxidation as a previously overlooked microbial methane sink in wetlands. Proc. Natl Acad. Sci. USA 111, 4495–4500 (2014).

    Google Scholar 

  33. Muehe, E. M., Wang, T. M., Kerl, C. F., Planer-Friedrich, B. & Fendorf, S. Rice production threatened by coupled stresses of climate and soil arsenic. Nat. Commun. 10, 4985 (2019).

    Google Scholar 

  34. Seyfferth, A. L., McCurdy, S., Schaefer, M. V. & Fendorf, S. Arsenic concentrations in paddy soil and rice and health implications for major rice-growing regions of Cambodia. Environ. Sci. Technol. 48, 4699–4706 (2014).

    Google Scholar 

  35. Sohn, E. The toxic side of rice. Nature 514, S62–S63 (2014).

    Google Scholar 

  36. Das, S., Liu, C. C., Jean, J. S. & Liu, T. L. Dissimilatory arsenate reduction and in situ microbial activities and diversity in arsenic-rich groundwater of Chianan Plain, Southwestern Taiwan. Microbiol. Ecol. 71, 365–374 (2016).

    Google Scholar 

  37. Jiang, J., Bauer, I., Paul, A. & Kappler, A. Arsenic redox changes by microbially and chemically formed semiquinone radicals and hydroquinones in a humic substance model quinone. Environ. Sci. Technol. 43, 3639–3645 (2009).

    Google Scholar 

  38. Shen, L. D., Ouyang, L., Zhu, Y. & Trimmer, M. Active pathways of anaerobic methane oxidation across contrasting riverbeds. ISME J. 13, 752–766 (2019).

    Google Scholar 

  39. Aromokeye, D. A. et al. Rates and microbial players of iron-driven anaerobic oxidation of methane in methanic marine sediments. Front. Microbiol. 10, 3041 (2020).

    Google Scholar 

  40. Wang, F. P. et al. Methanotrophic archaea possessing diverging methane-oxidizing and electron-transporting pathways. ISME J. 8, 1069–1078 (2014).

    Google Scholar 

  41. Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).

    Google Scholar 

  42. Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).

    Google Scholar 

  43. McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).

    Google Scholar 

  44. Scheller, S., Yu, H., Chadwick, G. L., McGlynn, S. E. & Orphan, V. J. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science 351, 703–707 (2016).

    Google Scholar 

  45. Kletzin, A. et al. Cytochromes c in Archaea: distribution, maturation, cell architecture, and the special case of Ignicoccus hospitalis. Front. Microbiol. 6, 439 (2015).

    Google Scholar 

  46. Zhu, Y. G., Xue, X. M., Kappler, A., Rosen, B. P. & Meharg, A. A. Linking genes to microbial biogeochemical cycling: lessons from arsenic. Environ. Sci. Technol. 51, 7326–7339 (2017).

    Google Scholar 

  47. Shi, L. D. et al. Multi-omics reveal various potential antimonate reductases from phylogenetically diverse microorganisms. Appl. Microbiol. Biotechnol. 103, 9119–9129 (2019).

    Google Scholar 

  48. Ahmann, D., Roberts, A. L., Krumholz, L. R. & Morel, F. M. M. Microbe grows by reducing arsenic. Nature 371, 750 (1994).

    Google Scholar 

  49. Tian, H. X., Shi, Q. T. & Jing, C. Y. Arsenic biotransformation in solid waste residue: comparison of contributions from bacteria with arsenate and iron reducing pathways. Environ. Sci. Technol. 49, 2140–2146 (2015).

    Google Scholar 

  50. Shi, L. et al. Extracellular electron transfer mechanisms between microorganisms and minerals. Nat. Rev. Microbiol. 14, 651–662 (2016).

    Google Scholar 

  51. Lovley, D. R. & Malvankar, N. S. Seeing is believing: novel imaging techniques help clarify microbial nanowire structure and function. Environ. Microbiol. 17, 2209–2215 (2015).

    Google Scholar 

  52. Zhao, F. J., Ma, J. F., Meharg, A. A. & McGrath, S. P. Arsenic uptake and metabolism in plants. New Phytol. 181, 777–794 (2009).

    Google Scholar 

  53. Bao, S. D. Soil Agrochemical Analysis 3rd edn (China Agricultural Press, 2011).

  54. Teske, A. et al. Microbial diversity of hydrothermal sediments in the Guaymas Basin: evidence for anaerobic methanotrophic communities. Appl. Environ. Microbiol. 68, 1994–2007 (2002).

    Google Scholar 

  55. Biddle, J. F. et al. Anaerobic oxidation of methane at different temperature regimes in Guaymas Basin hydrothermal sediments. ISME J. 6, 1018–1031 (2012).

    Google Scholar 

  56. Oswald, K. et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol. Oceanogr. 61, S101–S118 (2016).

    Google Scholar 

  57. Nunoura, T. et al. Quantification of mcrA by quantitative fluorescent PCR in sediments from methane seep of the Nankai Trough. FEMS Microbiol. Ecol. 57, 149–157 (2006).

    Google Scholar 

  58. Vaksmaa, A., Jetten, M. S. M., Ettwig, K. F. & Lüke, C. McrA primers for the detection and quantification of the anaerobic archaeal methanotroph ‘Candidatus Methanoperedens nitroreducens’. Appl. Microbiol. Biotechnol. 101, 1631–1641 (2017).

    Google Scholar 

  59. Song, B., Chyun, E., Jaffe, P. R. & Ward, B. B. Molecular methods to detect and monitor dissimilatory arsenate-respiring bacteria (DARB) in sediments. FEMS Microbiol. Ecol. 68, 108–117 (2009).

    Google Scholar 

  60. Paszczynski, A. J. et al. Proteomic and targeted qPCR analyses of subsurface microbial communities for presence of methane monooxygenase. Biodegradation 22, 1045–1059 (2011).

    Google Scholar 

  61. Zhao, H. P. et al. Interactions between perchlorate and nitrate reductions in the biofilm of a hydrogen-based membrane biofilm reactor. Environ. Sci. Technol. 45, 10155–10162 (2011).

    Google Scholar 

  62. Freitag, T. E. & Prosser, J. I. Correlation of methane production and functional gene transcriptional activity in a peat soil. Appl. Environ. Microbiol. 75, 6679–6687 (2009).

    Google Scholar 

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

    Google Scholar 

  64. Li, D. et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).

    Google Scholar 

  65. Dong, X. L. & Strous, M. An integrated pipeline for annotation and visualization of metagenomic contigs. Front. Genet. 10, 999 (2019).

    Google Scholar 

  66. Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    Google Scholar 

  67. Saunders, J. K., Fuchsman, C. A., McKay, C. & Rocap, G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc. Natl Acad. Sci. USA 116, 9925–9930 (2019).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  70. Ma, L. P. et al. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces. Environ. Sci. Technol. 50, 420–427 (2016).

    Google Scholar 

  71. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).

    Google Scholar 

  72. Water Quality – Determination of Iron-phenanthroline Spectrophotometry HJ/T 345-2007 (in Chinese) (Ministry of Ecology and Environment of the People’s Republic of China, 2007).

  73. China Climate Bulletin in 2019 (in Chinese) (China Meteorological Administration, 2020).

  74. Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2019).

    Google Scholar 

  75. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Google Scholar 

Download references

Acknowledgements

The authors greatly thank the National Natural Science Foundation of China (grants 51878596, 41991332 and 21577123), the Natural Science Funds for Distinguished Young Scholar of Zhejiang Province (LR17B070001) and the National Key Technology R&D Program (2018YFC1802203) for their financial support.

Author information

Authors and Affiliations

  1. MOE Key Lab of Environmental Remediation and Ecosystem Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Ling-Dong Shi, Ting Guo, Pan-Long Lv, Zi-Fan Niu, Yu-Jie Zhou, Xian-Jin Tang, Ping Zheng, Li-Zhong Zhu & He-Ping Zhao

  2. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China

    Yong-Guan Zhu

  3. Center for Applied Geosciences, University of Tübingen, Tübingen, Germany

    Andreas Kappler

Authors
  1. Ling-Dong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pan-Long Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zi-Fan Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-Jie Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xian-Jin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ping Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li-Zhong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yong-Guan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Andreas Kappler
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. He-Ping Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.-D.S. conceived and conducted the incubations with soil inocula, performed and analysed metagenomics and other bioinformatics, evaluated and arranged the results, and drafted the manuscript; T.G. took natural samples, measured physicochemical characteristics and quantified the functional genes/transcripts; P.-L.L. and Z.-F.N. helped perform the experiments inoculated by soil samples and microcosm incubations; Y.-J.Z. performed the microcosm incubations; X.-J.T. supervised the project, conceived the experiments and wrote the manuscript; P.Z., L.-Z.Z., Y.-G.Z. and A.K. assisted in design and set-up of the project and contributed to the manuscript preparation; H.-P.Z. initiated and supervised the project, conceived the experiments and wrote the manuscript; all authors contributed to revising the manuscript.

Corresponding authors

Correspondence to Xian-Jin Tang or He-Ping Zhao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editors: Clare Davis; Xujia Jiang.

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

Extended data

Extended Data Fig. 1 Reduced As(V) and produced 13DIC of experiments inoculated by soil samples during the whole incubation (7 days).

The main differences between these two groups are 13CH4 (top comparison) and As(V) (bottom comparison), respectively. Error bars indicate standard deviations of triplicate setups. The value of ΔAs(III)/ΔAs(V) represents the ratio of increased dissolved As(III) to decreased dissolved As(V). Asterisks indicate the statistical significance of the difference between experiment setups and corresponding control setups calculated by Kruskal-Wallis test.

Extended Data Fig. 2 Stoichiometry and kinetics of AOM-AsR for the AH-MAS-1 soil sample.

Mass balance was tested for 13CH4 (a) and As(V) (b). Electron balance between 13CH4 oxidation and As(V) reduction was studied in terms of rate (c) and ratio (d). Error bars indicate standard deviations of triplicate setups. The confidence levels for fitting curves are 95%. Please note the different y-axis scales for different items.

Extended Data Fig. 3 Gene abundance and correlation with methane oxidation and arsenate reduction rates.

Absolute abundances of mcrA and arrA (copies/g dry soil) were normalized by logarithm with a base of ten (a). Gene abundances and reaction rates were correlated using Spearman rank correlation (b and c), where ρ and p indicate the correlation efficiency and statistic probability, respectively. Error bars indicate standard deviations of triplicates. The points (SC-MY-1, YN-KM, and AH-MAS-1) in the grey circle (c) of which standard residual is 2-fold more than the standard deviation are removed based on Casewise Diagnostics. Their relatively high ATL values might explain why As(V) could be reduced rapidly at low arrA abundances (Supplementary Fig. 4).

Extended Data Fig. 4 Detailed phylogeny of respiratory arsenate reductase.

The proteins recovered in this study are highlighted in purple. Bootstrap values are generated from 100 replicates. The scale bar represents amino acid changes.

Extended Data Fig. 5 Phylogeny of respiratory arsenate reductase beta subunit (ArrB) and closely related anaerobic arsenite oxidase beta subunit (ArxB).

The proteins recovered in this study are highlighted in purple. A 4Fe-4S ferredoxin is used as the outgroup. Bootstrap values are generated from 100 replicates. The scale bar represents amino acid changes.

Extended Data Fig. 6 Putative metabolic pathways of AOM-AsR.

Methanoperedenaceae members perform AOM-AsR independently using arsenate reductase acquired through lateral gene transfer (a). Electrons are generated from methane oxidation via the reverse methanogenesis pathway in ANME groups, and then transferred to arsenate reducers possibly through multi-haem cytochromes (b). Abbreviations: Mcr, methyl-coenzyme M reductase; MHC, multi-haem cytochrome; Arr, respiratory arsenate reductase.

Extended Data Fig. 7 Contents of 13CO2 in the headspace in the microcosm incubations.

(a), (c), and (e) are incubated with YN-MG-2, SC-CD-1, and SC-MY-2, respectively, while (b), (d), and (f) use the corresponding autoclaved samples. The dark and light grey in each figure indicate the experimental setups amended with environmentally relevant concentrations of 13CH4, and the control setups without methane addition, respectively. The probability (i.e. p-values) of the differences between two setups was calculated using Kruskal-Wallis test. Error bars indicate standard deviations of triplicate setups.

Extended Data Fig. 8 Concentrations of As(V) and As(III) in the porewaters in the microcosm incubations.

(a), (c), and (e) were incubated with YN-MG-2, SC-CD-1, and SC-MY-2, respectively, while (b), (d), and (f) show the corresponding autoclaved samples. The dark and light red in each figure indicate the experimental setups amended with environmentally relevant concentrations of 13CH4, and the control setups without methane addition, respectively. The upper parts show As(III) while the bottom ones show As(V). Please note the different y-axis scales in the different panels. The probability (i.e. p-values) of differences between two setups was calculated using Kruskal-Wallis test. Error bars indicate standard deviations of triplicate setups.

Extended Data Fig. 9 Concentrations of total Fe and Fe(II) in the porewaters in the microcosm incubations.

(a), (c), and (e) were incubated with YN-MG-2, SC-CD-1, and SC-MY-2, respectively, while (b), (d), and (f) used the corresponding autoclaved samples. The dark and light pink in each figure indicate the experimental setups amended with environmentally relevant concentrations of 13CH4, and the control setups without methane addition, respectively. The upper parts show total Fe while the bottom ones show Fe(II). Please note the different y-axis scales in the different panels. The probability (i.e. p-values) of differences between two setups was calculated using Kruskal-Wallis test. Error bars indicate standard deviations of triplicate setups.

Extended Data Fig. 10 Concentrations of total Mn in the porewaters during the microcosm incubations.

(a), (c), and (e) were incubated with YN-MG-2, SC-CD-1, and SC-MY-2, respectively, while (b), (d), and (f) used the corresponding autoclaved samples. The dark and light green in each figure indicate the experimental setups amended with environmentally relevant concentrations of 13CH4, and the control setups without methane addition, respectively. The probability (i.e. p-values) of differences between two setups was calculated using Kruskal-Wallis test. Error bars indicate standard deviations of triplicate setups.

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–11, Tables 1–4, captions for Supplementary Datasets 1–3 and references for Supplementary Information citations.

Supplementary Dataset 1

Geographical information of collected natural soil samples.

Supplementary Dataset 2

Physicochemical characteristics of collected natural soil samples.

Supplementary Dataset 3

Geographical information of bioinformatic predicted samples.

Source data

Source Data Fig. 2

Numerical data for Fig. 2.

Source Data Fig. 5

Numerical data for Fig. 5.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, LD., Guo, T., Lv, PL. et al. Coupled anaerobic methane oxidation and reductive arsenic mobilization in wetland soils. Nat. Geosci. 13, 799–805 (2020). https://doi.org/10.1038/s41561-020-00659-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-020-00659-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

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

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