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Activity-targeted metaproteomics uncovers rare syntrophic bacteria central to anaerobic community metabolism

Nature Microbiology volume 10, pages 2749–2767 (2025)Cite this article

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Abstract

Syntrophic microbial consortia can contribute substantially to the activity of anoxic ecosystems but are often too rare to allow the study of their in situ physiologies using traditional molecular methods. Here we combined bioorthogonal non-canonical amino acid tagging (BONCAT), stable isotope probing and metaproteomics to improve the recovery of proteins from active members and track isotope incorporation in an anaerobic digestion community. Click-chemistry-enabled cell sorting and direct protein pull-down coupled to metaproteomics improved recovery of isotopically labelled proteins during anaerobic acetate oxidation. BONCAT-enabled protein profiles revealed elevated activity and labelling of a rare and so-far uncharacterized syntrophic bacterium belonging to the family Natronincolaceae that expressed a previously hypothesized oxidative glycine pathway for syntrophic acetate oxidation. Stable-isotope-probing-informed metabolic modelling predicted that this organism accounted for a majority of acetate flux, suggesting that the oxidative glycine pathway is an important route for anaerobic carbon transformation and is probably central to community metabolism in natural and engineered ecosystems.

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Fig. 1: Conceptual overview of the experimental design.
Fig. 2: Comprehensive genomic database of the study anaerobic digester.
Fig. 3: Enrichment of an active subpopulation with BONCAT-targeted metaproteomics and mini-metagenomics.
Fig. 4: BONCAT-targeted methods enriched active populations in mini-metagenomes and MPs.
Fig. 5: Tracking 13C incorporation in the MP.
Fig. 6: Metabolism of acetate in Natronincolaceae_JAAZJW01_1, named Syntrophacetatiphaga salishiae.

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Data availability

All sequencing reads from the time-series metagenomes, PacBio metagenomes, and the 466 high-quality MAGs are available through the NCBI BioProject identifier PRJNA1180090. Sequencing reads from the BONCAT mini-metagenomes are available through the NCBI BioProject identifier PRJNA1177472. Details of all metagenome samples, including individual NCBI accession numbers, can be found in Supplementary Table 1. NCBI accessions for individual MAGs from this study are provided in Supplementary Table 2. All proteomic data are available through the ProteomeXchange accession PXD057167. Additional data and metadata used in the analysis, including metabolomics, flow cytometry, metabolic modelling, metagenomics and genomic analyses can be found in the OSF repository at https://doi.org/10.17605/OSF.IO/U5VYC.

Code availability

Computational code used to analyse the metagenomic and metaproteomic data, conduct metabolic modelling and generate the figures shown in this paper are included in the Zenodo repository at https://doi.org/10.5281/zenodo.16903890 (ref. 108).

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Acknowledgements

This work was performed under the auspices of the Natural Sciences and Engineering Research Council of Canada (grant number CRDPJ 543962-19 and RGPIN-2018-04585, both to R.M.Z.), Genome British Columbia (grant number SIP027 to R.M.Z. and S.J.H.), the Canada Foundation for Innovation (grant number 37513 to R.M.Z.), and the US Department of Energy (DOE) Joint Genome Institute (JGI) and EMSL Facilities Integrating Collaborations for User Science (JGI-EMSL FICUS) programme (10.46936/fics.proj.2020.51515/60000205 to R.M.Z.) and used resources at the JGI (https://ror.org/04xm1d337) and EMSL (https://ror.org/04rc0xn13) under contract numbers DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). A portion of this research was supported under the EMSL user project award https://doi.org/10.46936/intm.proj.2022.60473/60008546 to V.S.L., H.M.O. and R.M.Z. for developing technologies for and using capabilities operating at the EMSL, a DOE Office of Science User Facility, sponsored by the Biological and Environmental Research programme under contract number DE-AC05-76RL01830. The LABGeM (CEA/Genoscope and CNRS UMR8030), the France Génomique and French Bioinformatics Institute national infrastructures (funded as part of Investissement d’Avenir programme managed by Agence Nationale pour la Recherche, contracts ANR-10-INBS-09 and ANR-11-INBS-0013) are acknowledged for support within the MicroScope annotation platform. We thank C. Nicora for her advice and guidance with sample milling. We thank R. Moore and P. Lalli for performing proteomics runs. We thank M. Schmid for help with confocal microscopy. We thank M. Kazak and F. Crozier for assisting with sample collection from the full-scale bioenergy facility.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada

    Skyler Friedline, Elizabeth A. McDaniel, Maxwell Madill, Kate Waring & Ryan M. Ziels

  2. Genome Science and Technology Program, University of British Columbia, Vancouver, British Columbia, Canada

    Skyler Friedline, Steven J. Hallam & Ryan M. Ziels

  3. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Elizabeth A. McDaniel & Steven J. Hallam

  4. Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT, USA

    Matthew Scarborough

  5. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    Vivian S. Lin, Leo J. Gorham, Chathuri J. Kombala, Lydia H. Griggs, Heather M. Olson & Jesse Trejo

  6. DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Rex R. Malmstrom & Danielle Goudeau

  7. Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA

    William Chrisler, Sophie B. Lehmann, Nathalie Munoz, Nikola Tolic, Ljiljana Pasa-Tolic, Sarah M. Williams & Mary Lipton

  8. Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark

    Morten K. D. Dueholm

  9. Nuclear Material Science Division, Pacific Northwest National Laboratory, National Security Directorate, Richland, WA, USA

    Sophie B. Lehmann

  10. Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, Canada

    Steven J. Hallam

  11. Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada

    Steven J. Hallam

  12. Bradshaw Research Institute for Minerals and Mining, Vancouver, British Columbia, Canada

    Steven J. Hallam

Authors
  1. Skyler Friedline
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  2. Elizabeth A. McDaniel
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  3. Matthew Scarborough
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  4. Maxwell Madill
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  5. Kate Waring
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  6. Vivian S. Lin
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  7. Rex R. Malmstrom
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  8. Danielle Goudeau
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  9. William Chrisler
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  10. Morten K. D. Dueholm
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  11. Leo J. Gorham
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  12. Chathuri J. Kombala
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  13. Lydia H. Griggs
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  14. Heather M. Olson
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  15. Sophie B. Lehmann
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  16. Nathalie Munoz
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  17. Jesse Trejo
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  18. Nikola Tolic
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  19. Ljiljana Pasa-Tolic
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  20. Sarah M. Williams
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  21. Mary Lipton
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  22. Steven J. Hallam
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  23. Ryan M. Ziels
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Contributions

R.M.Z. and S.J.H. conceived the research. S.F. and R.M.Z. processed the metagenomic and metaproteomic data and analysed the results. S.F. and E.A.M. performed the microcosm incubation experiment. M.M. and K.W. performed the time-series sample collection and advised on the microcosm incubation. M.K.D.D. performed the global distribution analysis based on 16S rRNA gene amplicon sequencing data from the MiDAS project. R.R.M. and D.G. designed and performed the mini-metagenome FACS protocol. W.C. performed the FACS MP protocol. L.J.G., C.J.K., L.H.G., H.M.O. and V.S.L. designed and performed the BONCAT protein pull-down assay and MS. H.M.O performed protein extraction for the bulk MP. J.T. and N.M. performed the bulk metabolomics extraction and untargeted metabolomics analysis by GC–MS. S.M.W. performed MS for the bulk and BONCAT–FACS samples. S.B.L. performed IRMS analysis. N.T. and L.P.-T. performed the µPOTS method. M.L. advised on the project formation. R.M.Z. and N.T. advised on the proteomic data analysis. S.F. and R.M.Z. drafted the paper. All authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Ryan M. Ziels.

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Competing interests

S.J.H. is a co-founder of Koonkie Inc., a bioinformatics consulting company that designs and provides scalable algorithmic and data analytics solutions in the cloud. Koonkie Inc. was not involved in any aspect of this research. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Metabolic fluxes predicted for the five most 13C labeled members at 24 h.

Model fluxes are based on consumption of one mol of acetate, and thus represent the stoichiometry across the metabolic networks at the discrete time point of 24 h of the SIP incubation. Values were predicted with parsimonious flux balance analysis while constraining each organism’s ATP production to be proportional to the mass of 13C labeled proteins at 24 h. Ack = Acetate kinase, Acs = Acetyl-CoA synthetase, CODH = carbon monoxide dehydrogenase, Dld = Dihydrolipoyl dehydrogenase, Ech = Energy-converting hydrogenase, Fdh = Formate dehydrogenase, Fhs = Formate-tetrahydrofolate ligase, FolD = Methylenetetrahydrofolate cyclohydrolase, Fpo = F420H2 dehydrogenase, Frh = F420-reducing hydrogenase, Ftr = Formylmethanofuran:tetrahyromethanopterin formyltransferase, Fwd = Formylmethanofuran dehydrogenase, GcvH = Glycine cleavage system H protein, GcvP = Glycine cleavage system P protein, GcvT = Glycine cleavage system T protein, GlyA = Glycine hydroxymethyltransferase, Grd = Glycine reductase complex, Hdr = heterodisulfide reductase, Hyd = Bifurcating [FeFe] hydrogenase, Mch = Methenyltetrahydromethanopterin cyclohydrolase, Mcr = Methyl-CoM reductase, Mer = 5,10-Methylenetetrahydromethanopterin reductase, Met = 5,10-Methylenetetrahydrofolate reductase, Mtd = F420-dependent methylenetetrahydromethanopterin dehydrogenase, Mtr = 5-Tetrahydromethanopterin:CoM-S-methyltransferase, Mvh = F420-nonreducing hydrogenase, Por = Pyruvate:ferredoxin oxidoreductase, Pta = Phosphate acetyltransferase, Sda = Serine deaminase, Rnf = Na+-translocating ferredoxin:NAD+ oxidoreductase complex, Vho = F420-nonreducing hydrogenase.

Extended Data Fig. 2 Metabolic fluxes predicted for the five most 13C labeled members at 72 h.

Model fluxes are based on consumption of one mol of acetate, and thus represent the stoichiometry across the metabolic networks at the discrete time point of 72 h of the SIP incubation. Values were predicted with parsimonious flux balance analysis while constraining each organism’s ATP production to be proportional to the mass of 13C labeled proteins at 72 h. Ack = Acetate kinase, Acs = Acetyl-CoA synthetase, CODH = carbon monoxide dehydrogenase, Dld = Dihydrolipoyl dehydrogenase, Ech = Energy-converting hydrogenase, Fdh = Formate dehydrogenase, Fhs = Formate-tetrahydrofolate ligase, FolD = Methylenetetrahydrofolate cyclohydrolase, Fpo = F420H2 dehydrogenase, Frh = F420-reducing hydrogenase, Ftr = Formylmethanofuran:tetrahyromethanopterin formyltransferase, Fwd = Formylmethanofuran dehydrogenase, GcvH = Glycine cleavage system H protein, GcvP = Glycine cleavage system P protein, GcvT = Glycine cleavage system T protein, GlyA = Glycine hydroxymethyltransferase, Grd = Glycine reductase complex, Hdr = heterodisulfide reductase, Hyd = Bifurcating [FeFe] hydrogenase, Mch = Methenyltetrahydromethanopterin cyclohydrolase, Mcr = Methyl-CoM reductase, Mer = 5,10-Methylenetetrahydromethanopterin reductase, Met = 5,10-Methylenetetrahydrofolate reductase, Mtd = F420-dependent methylenetetrahydromethanopterin dehydrogenase, Mtr = 5-Tetrahydromethanopterin:CoM-S-methyltransferase, Mvh = F420-nonreducing hydrogenase, Por = Pyruvate:ferredoxin oxidoreductase, Pta = Phosphate acetyltransferase, Sda = Serine deaminase, Rnf = Na+-translocating ferredoxin:NAD+ oxidoreductase complex, Vho = F420-nonreducing hydrogenase.

Extended Data Fig. 3 Global distribution of Natronincolaceae_JAAZJW01_1 (that is, Syntrophacetatiphaga salishiae).

(A) Relative abundance in publicly available metagenomes on NCBI (database generated on 20 Feb, 2025) based on Sandpiper 1.0.1.109 metagenomic profiling, in which Syntrophacetatiphaga salishiae was detected at over 0.05% relative abundance (n = 72 anaerobic digester metagenomes, n = 9 landfill metagenomes). (B, C) Maximum observed relative abundance across anaerobic digesters organized by (B) country and (C) substrate type and temperature based on 16S rRNA gene amplicon sequencing data from the MiDAS global survey of anaerobic digesters38. V4 region amplicon sequence variants (ASVs) representing Natronincolaceae_JAAZJW01_1 (>97% sequence identity) were identified by mapping the ASVs against the 16S rRNA genes from the MAG using usearch110 with the command -usearch_global -maxrejects 0 -maxaccepts 0 -id 0 -top_hit_only.

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Friedline, S., McDaniel, E.A., Scarborough, M. et al. Activity-targeted metaproteomics uncovers rare syntrophic bacteria central to anaerobic community metabolism. Nat Microbiol 10, 2749–2767 (2025). https://doi.org/10.1038/s41564-025-02146-w

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