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Dissimilatory sulfate reduction in the archaeon ‘Candidatus Vulcanisaeta moutnovskia’ sheds light on the evolution of sulfur metabolism

Nature Microbiology volume 5, pages 1428–1438 (2020)Cite this article

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Abstract

Dissimilatory sulfate reduction (DSR)—an important reaction in the biogeochemical sulfur cycle—has been dated to the Palaeoarchaean using geological evidence, but its evolutionary history is poorly understood. Several lineages of bacteria carry out DSR, but in archaea only Archaeoglobus, which acquired DSR genes from bacteria, has been proven to catalyse this reaction. We investigated substantial rates of sulfate reduction in acidic hyperthermal terrestrial springs of the Kamchatka Peninsula and attributed DSR in this environment to Crenarchaeota in the Vulcanisaeta genus. Community profiling, coupled with radioisotope and growth experiments and proteomics, confirmed DSR by ‘Candidatus Vulcanisaeta moutnovskia’, which has all of the required genes. Other cultivated Thermoproteaceae were briefly reported to use sulfate for respiration but we were unable to detect DSR in these isolates. Phylogenetic studies suggest that DSR is rare in archaea and that it originated in Vulcanisaeta, independent of Archaeoglobus, by separate acquisition of qmoABC genes phylogenetically related to bacterial hdrA genes.

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Fig. 1: Growth of the binary culture on sulfate.
Fig. 2: Distribution of sulfate reduction and sulfur oxidation genes in archaea versus established bacterial groups.
Fig. 3: Phylogenetic analysis of AprA proteins.
Fig. 4: Gene synteny and cofactor composition of the Qmo/Hdr family.
Fig. 5: Phylogenetic analysis of HdrA-like proteins, including QmoA and QmoB.

Data availability

Candidatus Vulcanisaeta mountnovskia 768-28 has been deposited into the All-Russian Collection of Microorganisms (VKM) under accession number VKM B-3479. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE71 partner repository with the dataset identifier PXD012750. Pyrosequencing read data obtained for the 16S rRNA gene fragments were deposited in the Sequence Read Archive under the accession number PRJNA642261. To reproduce the results reported, the raw data are to be downloaded from the Sequence Read Archive and submitted to the SILVAngs analysis platform (https://ngs.arb-silva.de/silvangs/). We did not obtain any new genome assemblies, as the genomes used for computational analyses have already been reconstructed in previous and published work and are publicly available. The accession codes and taxonomic information of the genomes from the dataset are given in Supplementary Table 4. The binary culture genomes used here have been previously sequenced and published31,48 and have the GenBank accession codes CP002529 and CP002590. The alignments and phylogenies that form the basis of Figs. 3 and 5 and Extended Data Figs. 8–10 are available in Figshare (https://figshare.com/s/fb5a1541be68639f4026). Source data are provided with this paper.

Code availability

The small scripts used in this paper are available at https://figshare.com/s/fb5a1541be68639f4026.

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Acknowledgements

We thank G. Landan for sharing with us the updated version of the minimal ancestor deviation method. This work was supported by the Russian Science Foundation (grant number 17-74-30025) and in part by the grant from the Russian Ministry of Science and Higher Education (to N.A.C., A.V.L., E.N.F., M.L.M., A.Y.M., N.V.P. and E.A.B.-O.). Sequencing of PCR amplicons was performed using the scientific equipment of the core research facility ‘Bioengineering’ by T. Kolganova. The proteomics analysis was performed at the Proteomics Facility of the Spanish National Center for Biotechnology (CNB-CSIC), which belongs to ProteoRed, PRB2-ISCIII, supported by grant PT13/0001 (to S.C., M.C.M. and M.F.). P.N.G. acknowledges funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) within the ERA NET-IB2 programme, grant number ERA-IB-14-030 and the European Union Horizon 2020 Research and Innovation programme (Blue Growth: Unlocking the Potential of Seas and Oceans) under grant agreement number 634486, as well as support from the Centre for Environmental Biotechnology project, part funded by the European Regional Development Fund (ERDF) through the Welsh Government, and support from the Centre of Environmental Biotechnology. D.Y.S. was supported by the SIAM/Gravitation Program (Dutch Ministry of Education, Culture and Science; grant 24002002) and RFBR grant 19-04-00401. F.L.S. and S.N. acknowledge support from the Wiener Wissenschafts, Forschungs- und Technologiefonds (Austria) through the grant VRG15-007. F.L.S. gratefully acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation programme (grant agreement 803768). I.A.C.P. acknowledges support from the Fundação para a Ciência e Tecnologia (Portugal) through grant PTDC/BIA-BQM/29118/2017 and R&D unit MOSTMICRO-ITQB (UIDB/04612/2020 and UIDP/04612/2020).

Author information

Authors and Affiliations

  1. Winogradsky Institute of Microbiology, Federal Research Center of Biotechnology, Russian Academy of Sciences, Moscow, Russia

    Nikolay A. Chernyh, Evgenii N. Frolov, Margarita L. Miroshnichenko, Alexander Y. Merkel, Nikolay V. Pimenov, Dimitry Y. Sorokin, Alexander V. Lebedinsky & Elizaveta A. Bonch-Osmolovskaya

  2. Archaea Biology and Ecogenomics Unit, Department of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria

    Sinje Neukirchen & Filipa L. Sousa

  3. Proteomics Facility, Centro Nacional de Biotecnología, CSIC, Madrid, Spain

    Sergio Ciordia & María Carmen Mena

  4. Institute of Catalysis, CSIC, Madrid, Spain

    Manuel Ferrer

  5. School of Biological Sciences, Bangor University, Bangor, UK

    Peter N. Golyshin

  6. Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal

    Inês A. Cardoso Pereira

  7. Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia

    Elizaveta A. Bonch-Osmolovskaya

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  1. Nikolay A. Chernyh
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Contributions

N.A.C. designed the research. N.A.C. and E.N.F. performed the fieldwork and sediment activity determinations. E.N.F. and M.L.M. enriched and maintained archaeal cultures and performed microbiological studies on them. S.N., F.L.S., N.A.C. and A.V.L. performed genomic analysis and evolutionary reconstructions. A.Y.M. analysed 16S rRNA genes in sediments and carried out qPCR. N.V.P. performed radioisotope research. S.C., M.C.M., M.F. and P.N.G. performed the proteomic analysis. N.A.C., A.V.L., I.A.C.P., F.L.S., S.N., E.N.F., D.Y.S. and E.A.B.-O. analysed the data and wrote the paper. All authors have seen and approved the final version submitted.

Corresponding authors

Correspondence to Nikolay A. Chernyh, Filipa L. Sousa or Inês A. Cardoso Pereira.

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Extended data

Extended Data Fig. 1 The microbial community composition of Oreshek Spring.

Analysis by high throughput sequencing of 16S rRNA gene fragments.

Extended Data Fig. 2 Growth of Thermoproteus tenax.

The error bars are SD from independent growth replicates (n = 3).

Source data

Extended Data Fig. 3 Growth of Caldivirga maquilingensis.

Error bars represent standard deviations from independent growth replicates (n = 3).

Source data

Extended Data Fig. 4 H35S−formation from 35SO4-2 the binary culture 768-28 and the cultures of hyperthermophilic Crenarchaeota strains earlier claimed to be capable of sulphate reduction, but without experimental evidence.

Error bars represent standard deviations of independent radioisotopic measurement experiments (n = 3).

Source data

Extended Data Fig. 5 Genes related to sulphate reduction.

The key sulphate reduction genes from genomes of selected sulphate reducing archaea (‘Candidatus V. moutnovskia’, Caldivirga sp. Obs2 and A. fulgidus) and bacteria (D. vulgaris Hildenborough and A. degensii) are shown, as well as from genomes of archaea not capable of sulphate reduction (V. distributa, V. souniana, Caldivirga sp. MU80, Ca. Methanodesulfokores washburnensis MDKW and unclassified Aigarchaeota JZ bin 15).

Extended Data Fig. 6 Proteomic analysis of the soluble fraction of cells in the binary culture.

Protein extraction was performed with a non-denaturing detergent. Only the 150 most abundant proteins of ‘Candidatus V. moutnovskia’ are shown. a. Binary culture grown on medium with yeast extract and sulphate as the electron acceptor. b. Binary culture grown on medium with yeast extract and elemental sulphur as the electron acceptor. Protein abundances in mol% were estimated from nemPAI values58. Data are shown as the mean of n = 2 independent experiments fitted into a model.

Source data

Extended Data Fig. 7 Proteomic analysis of the binary culture after protein extraction with denaturing detergent.

Only proteins of ‘Candidatus V. moutnovskia’ are shown. The binary culture was grown on medium with yeast extract and sulphate as the electron acceptor. Protein abundances in mol% were estimated from nemPAI values58. Data are shown as the mean of n = 3 independent experiments fitted into a model.

Source data

Extended Data Fig. 8 Phylogenetic analysis of AprAB.

Maximum likelihood phylogenetic reconstruction (LG+I+G4) of a. AprA and b. AprB proteins present in the selected dataset. Only support values above 50 are shown. The scale bar indicates number of substitutions per site.

Extended Data Fig. 9 Phylogenetic analysis of Sat.

Maximum likelihood phylogenetic reconstruction (LG+I+G4) of Sat proteins within the selected dataset. Only non-parametric support values above 70 are shown. The scale bar indicates number of substitutions per site.

Extended Data Fig. 10 Phylogenetic analysis of HdrA-like proteins.

Maximum-likelihood reconstruction (LG+I+G4) of the HdrA-domain present in 1,391 non-redundant HdrA, QmoA and QmoB proteins. The ‘Candidatus V. moutnovskia’ sequences are marked in red. Expanded insets of the ‘Candidatus V. moutnovskia’ QmoA (a) and QmoB (b) clades (marked in blue) are shown in Fig. 5. The scale bar indicates number of substitutions per site.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Tables 1–3.

Reporting Summary

Supplementary Table 4

Genomic dataset used in the analysis, including accession codes, taxonomic information and file location in the original database.

Source data

Source Data Fig. 1

Binary culture growth source data.

Source Data Fig. 2

Protein accessions of the hits underlying each symbol in Fig. 2.

Source Data Extended Data Fig. 2

Raw data and calculations supporting Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Raw data and calculations supporting Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Raw data and calculations supporting Extended Data Fig. 4.

Source Data Extended Data Fig. 6

Raw data and calculations supporting Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Raw data and calculations supporting Extended Data Fig. 7.

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Chernyh, N.A., Neukirchen, S., Frolov, E.N. et al. Dissimilatory sulfate reduction in the archaeon ‘Candidatus Vulcanisaeta moutnovskia’ sheds light on the evolution of sulfur metabolism. Nat Microbiol 5, 1428–1438 (2020). https://doi.org/10.1038/s41564-020-0776-z

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