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A ubiquitous gammaproteobacterial clade dominates expression of sulfur oxidation genes across the mesopelagic ocean

Nature Microbiology volume 8, pages 1137–1148 (2023)Cite this article

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Abstract

The deep ocean (>200 m depth) is the largest habitat on Earth. Recent evidence suggests sulfur oxidation could be a major energy source for deep ocean microbes. However, the global relevance and the identity of the major players in sulfur oxidation in the oxygenated deep-water column remain elusive. Here we combined single-cell genomics, community metagenomics, metatranscriptomics and single-cell activity measurements on samples collected beneath the Ross Ice Shelf in Antarctica to characterize a ubiquitous mixotrophic bacterial group (UBA868) that dominates expression of RuBisCO genes and of key sulfur oxidation genes. Further analyses of the gene libraries from the ‘Tara Oceans’ and ‘Malaspina’ expeditions confirmed the ubiquitous distribution and global relevance of this enigmatic group in the expression of sulfur oxidation and dissolved inorganic carbon fixation genes across the global mesopelagic ocean. Our study also underscores the unrecognized importance of mixotrophic microbes in the biogeochemical cycles of the deep ocean.

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Fig. 1: Key role of UBA868 in the ocean under the RIS.
Fig. 2: Phylogenomics of UBA868 and expression profiles.
Fig. 3: Simplified overview of major components constituting sulfur oxidation and carbon processing pathways in UBA868 family.
Fig. 4: Distribution of UBA868 in the global ocean.
Fig. 5: Single-cell heterotrophic and chemoautotrophic activity of UBA868.
Fig. 6: Distribution of UBA868 sulfur oxidation genes in the global ocean.

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Data availability

The sequence data generated in this study have been deposited in the EMBL Nucleotide Sequence Database (ENA) database under Bioproject PRJEB35712 (metagenomic and metatranscriptomic raw reads, metagenomic and metatranscriptomic assemblies, metagenomic assembled genomes and single-cell amplified genomes) and in the NCBI Sequence Read Archive (SRA) under Bioproject PRJNA593264 (16S rRNA amplicon reads).

Code availability

Scripts available at Zenodo (https://doi.org/10.5281/zenodo.7721930.2023).

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Acknowledgements

We thank the Victoria University of Wellington Hot Water Drilling Team led by A. Pyne and D. Mendeno. We also thank L. Montiel and V. Balagué from the Institut de Ciències del Mar (ICM), CSIC, Spain, for extracting RNA, and the CNAG staff for RNA sequencing library preparation and sequencing. Bioinformatics analyses were performed at the LiSC Cluster (University of Vienna), and the MARBITS platform (ICM; http://marbits.icm.csic.es). This research was facilitated by the New Zealand Antarctic Research Institute (NZARI)-funded Aotearoa New Zealand Ross Ice Shelf Program. Samples for MICRO-CARD-FISH were collected on several research cruises led by M. Simon (Sonne 248 cruise), B. Quéguiner and I. Obernosterer (MobyDick) and L. J. A. Gerringa (Geotraces-1). F.B. was supported by the Austrian Science Fund (FWF) projects OCEANIDES (P34304-B), ENIGMA (TAI534), EXEBIO (P35248) and OCEANBIOPLAST (P35619-B). G.J.H. was supported by the FWF project ARTEMIS (P28781-B21) and I486-B09 and by the ERC under the European Community’s 7th Framework Programme (FP7/2007-2013)/ERC grant agreement no. 268595 (MEDEA project). R.L. was supported by INTERACTOMICS, CTM2015-69936-P, and J.M.G. by project PID2019-110011RB-C32 (Spanish Ministry of Science and Innovation, Spanish State Research Agency, doi: 10.13039/501100011033).

Author information

Authors and Affiliations

  1. Department of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria

    Federico Baltar, Clara Martínez-Pérez, Chie Amano, Marion Vial, Thomas Reinthaler, Gerhard J. Herndl & Zihao Zhao

  2. Institute for Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, Eidgenossische Technische Hochschule (ETH) Zurich, Zurich, Switzerland

    Clara Martínez-Pérez

  3. Department of Microbiology, University of La Laguna, La Laguna, Spain

    Semidán Robaina-Estévez & José M. González

  4. Vienna Metabolomics Center, University of Vienna, Vienna, Austria

    Gerhard J. Herndl

  5. NIOZ, Department of Marine Microbiology and Biogeochemistry, Royal Netherlands Institute for Sea Research, Utrecht University, AB Den Burg, The Netherlands

    Gerhard J. Herndl

  6. Institute of Marine Sciences (ICM), CSIC, Barcelona, Spain

    Ramiro Logares

  7. Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand

    Sergio E. Morales

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  1. Federico Baltar
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Contributions

F.B. and S.E.M. performed sampling and experiments at RIS. S.E.M. and R.L. performed nucleic acid extraction and library preparation for metagenomics and metatranscriptomics, respectively. T.R. collected samples and provided materials. C.M.-P., C.A., Z.Z., R.L., S.R.-E., J.M.G. and F.B. analysed the data. F.B. and J.M.G. wrote the paper with contributions from all co-authors.

Corresponding authors

Correspondence to Federico Baltar or José M. González.

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Nature Microbiology thanks Maliheh Mehrshad and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Phylogenetic reconstruction of RbcL peptides from UBA868 and representatives from other groups.

Out of 683 Arenicellales genomes 528 were predicted to harbor recA, a single copy gene conserved in bacteria. Form IA and form II rbcL were predicted in 23 and 89 genomes, respectively, which encodes for the large subunit of the RuBisCO enzyme. Peptide sequences were aligned with MUSCLE and poorly aligned positions in the alignment were removed with TrimAl. The tree was built with RaxML-NG with the model LG + I + G4 as estimated with ModelTest-NG. Numbers at nodes are bootstrap values ≥ 70% (100 replicates). Scale bar indicates the number of substitutions per site.

Extended Data Fig. 2 Depth distribution for UBA868 and non-UBA868 genomes across longitude.

Depths are represented in two scales: 0 and 1000 m (A) and 6000 m (B). Genomes classified as UBA868 (depicted in orange) are mainly retrieved from samples at greater depths than other genomes (blue) across longitudes (Mann-Whitney-Wilcoxon test, p-value = 2.5 × 10−36). Specifically, UBA868 shows a median depth value of 284 m, while non-UBA868 samples display a median depth of 34 m. Genomes are those from Paoli et al., 2022 (doi: 10.1038/s41586-022-04862-3).

Extended Data Fig. 3 Relative abundance of UBA868 based on MICRO-CARD-FISH and cell volume.

(A) Percent contribution of UBA868 to total DAPI stained cells. (B) cell volume (log) of UBA868 positive cells in samples collected from the North Atlantic (175 data points), Tropical Atlantic (163 data points), Tropical Pacific (38 data points) and Southern Ocean (off Kerguelen islands; 88 data points). The upper and lower whiskers extend from the hinge to the largest and lowest value no further than 1.5 × interquartile range from the hinge. To the right, the microscopic image shows the appearance of UBA868 positive cocci. The sample was collected from 1750 m during Archimedes 3 cruise in the tropical North Atlantic Ocean. CF: a colored image of CARD-FISH with DAPI (blue) and FISH (green) channels. DAPI: an image of DAPI channel at the same field of view as CF. Scale bar: 1 μm.

Extended Data Fig. 4 Contribution of UBA868 sulfur oxidizing genes to the overall expression and in the metagenomes, and phylogeny of SoxB peptides.

(A) Average contribution of UBA868 for other genes in the pathways to oxidize sulfur oxidation is highest in the mesopelagic (MES) than in the deep chlorophyll maximum (DCM) or in the surface (SRF). soxB expression and gene abundance were quantified with the KO specific for it and HMM profiles specific for peptides annotated as “thiosulfohydrolase SoxB” in RefSeq and the peptides retrieved with the TIGRFAM specific for SoxB. The contribution of the two subunits for the RuBisCO genes, large and small subunits, rbcL and rbcS is also represented. HMM profiles were also designed to tell apart the contribution of rbcL type I and II. (B) SAR324 SoxB peptides fall in two subclusters, most of which are outside the groups with RefSeq and UBA868 peptides. Only those SAR324 genomes that cluster with RefSeq are predicted to have soxAXYZ (predicted with their KOs). For the rest of taxa, the pathway seems incomplete. The phylogenetic reconstruction suggests that the KO specific for it retrieves SoxB peptides in the SAR324, Flavobacteriales, Pirellulales and part of the Alphaproteobacteria, that might instead have a related but different function from SoxB. Phylogenetic trees were obtained with IQ-TREE as described in the main text.

Extended Data Fig. 5 Phylogenetic reconstruction of representative SoxB peptides from marine bacteria.

The maximum-likelihood tree was built with IQ-TREE. A total of 402 Arenicellales genomes were predicted to contain the soxB gene involved in thiosulphate oxidation, while 528 were predicted to harbor recA. The gene was only found in the families UBA868 (399 genomes), BMS3Bbin11 (one genome), LS-SOB (one in four genomes) and in one unclassified Arenicellales. Only UBA868 SoxB are included in the tree since none of the other Arenicellales families were represented in the metatranscriptomes. This reference tree was used to predict the function and taxonomy of metatranscriptome reads by placing the corresponding peptides on the tree. The smaller phylogenetic tree at the upper left shows the position of some of the Ocean Microbial Reference Gene Catalog (OM-RGC) peptides in part of the UBA868 subcluster. The placement of these OM-RGC peptides was used to assign the label UBA868 to them. The names in bigger fonts belong to the subclusters in Figs. 1 and 6, which include the taxa with smaller fonts. Circles at nodes are proportional to bootstrap values ≥ 70% based on 100 replicates). Scale bar indicates number of substitutions per site.

Extended Data Fig. 6 Phylogenetic reconstruction of rDsrA peptides from marine bacteria.

The maximum-likelihood tree was built with IQ-TREE. Sequences represent the reverse dissimilatory sulfite reductase subunits A (rDsrA), for sulfite oxidation. A total of 458 Arenicellales genomes were predicted to have the rdsrA gene, while 528 Arenicellales were predicted to contain recA. The gene was found in the families UBA868 (433 genomes), UBA5680 (present in all but one of the 23 genomes), BMS3Bbin11 (represented by one genome) and LS-SOB (present in two of the four genomes). UBA5680 peptides are within the Gammaproteobacteria 2 and LS-SOB in the Gammaproteobacteria 3 subclusters in the tree. This reference tree was used to predict the function and taxonomy of metatranscriptome reads by placing the corresponding peptides on the tree. The metatranscriptome peptides were taxonomically classified after placement in one of the subclusters as shown in the smaller phylogenetic tree to the left that represents part of the UBA868 subcluster and some of the Ocean Microbial Reference Gene Catalog (OM-RGC) rDsrA peptides. The names in bigger fonts belong to the subclusters in Figs. 1 and 6, which include the taxa with smaller fonts. Circles at nodes are proportional to the bootstrap values ≥70% based on 100 replicates. Scale bar indicates number of substitutions per site.

Extended Data Fig. 7 Phylogenetic reconstruction of AprA peptides.

The maximum-likelihood tree was built with IQ-TREE. A total of 421 Arenicellales genomes were predicted to encode AprA, while 528 were predicted to contain the single copy gene recA. The aprA gene was found in the families UBA868 (398 genomes), UBA5680 (17 genomes), BMS3Bbin11 (represented by one genome), LS-SOB (present in all four genomes) and one unclassified Arenicellales. UBA868 peptides fall in three clusters. UBA5680, BMS3Bbin11 and LS-SOB AprA peptides are within the Gammaproteobacteria in the figure. This reference tree was used to predict the function and taxonomy of the metatranscriptome reads by placing the corresponding peptides on the tree. The smaller tree at the upper right shows part of the UBA868 sequences and some of the Ocean Microbial Reference Gene Catalog (OM-RGC) peptides that were placed within this subcluster. For the sake of simplicity in Figs. 1 and 6, subclusters with the bigger font in their names include the taxa with smaller font. Circles at nodes are proportional to the bootstrap values ≥70% based on 100 replicates. Scale bar indicates number of substitutions per site.

Extended Data Fig. 8 Flowchart to construct the reference phylogenetic tree and assign a function and taxonomic label to environmental peptides.

SoxB is shown as an example case. In panel A, the set of SoxB peptides were first predicted in the genome database and then a reference phylogenetic tree was constructed and analyzed. Panel B depicts how SoxB is predicted in the environmental peptide sequences and a taxonomic label is assigned.

Extended Data Fig. 9 Phylogenetic reconstruction of form I and II RbcL peptides.

Among the Arenicellales, form I gene was found in 21 of the UBA868 family genomes, in one LS-SOB family genome and an unclassified Arenicellales (A). The form II gene was found in 88 UBA868 family and one BMS3Bbin11 family genomes and was not detected in the rest of the families in the order (B). The BMS3Bbin11 RbcL peptide fell within the unclassified Bacteria in (B). This maximum-likelihood tree was built with IQ-TREE. This reference phylogenetic tree was used to identify the metatranscriptome sequence reads from samples under the Ross Sea ice by placing the corresponding peptides on the tree. RbcL peptides from the Ocean Microbial Reference Gene Catalog (OM-RGC) were also taxonomically classified according to their placement within the subclusters. Circles at nodes are proportional to the bootstrap values ≥70% based on 100 replicates. Scale bar indicates number of substitutions per site.

Supplementary information

Supplementary Information

Supplementary Materials and Methods

Reporting Summary

Supplementary Data

SoxB alignment file in phylip format.

Supplementary Data

Profile HMM for Rbcl type.

Supplementary Data

Profile HMM for Rbcl type II.

Supplementary Data

Profile HMM for reverse DsrA.

Supplementary Data

Fasta file to construct the alignment file.

Supplementary Data

Fasta file to construct the alignment file.

Supplementary Data

Fasta file to construct the alignment file.

Supplementary Data

Fasta file to construct the alignment file.

Supplementary Data

Fasta file to construct the alignment file.

Supplementary Data

List of TIGRFAMs and their description to build the concatenated phylogenetic tree (Fig. 2).

Supplementary Data

AprA alignment file in phylip format.

Supplementary Data

RbcL type I alignment file in phylip format.

Supplementary Data

RbcL type II alignment file in phylip format.

Supplementary Data

Reverse DsrA alignment file in phylip format.

Supplementary Tables 1–5

Supplementary Table 1. Genomes analysed, taxonomic classification and key genes involved in the carbon, sulfur and nitrogen cycles. Identification of genes is based on hits to Pfam, TIGRFAM or KOfam databases. Those labelled with ‘hmm’ between parenthesis are based on HMM designed in this study and described in Methods, except for DmdA, which is from González et al. (2019). Supplementary Table 2. Relative abundance to the etotal prokaryotic community in the global ocean of UBA868. Data derived from Tara Oceans and Malaspina expeditions. For details, see Methods. Supplementary Table 3. Contribution of UBA868 to total DIC fixation based on MICRO-CARD-FISH (for details, see Methods). Supplementary Table 4. Contribution of UBA868 to total leucine uptake based on MICRO-CARD-FISH (for details, see Methods). Supplementary Table 5. Normalized expression and abundances of genes in the sulfur and carbon cycle. Normalized per-cell abundance was taken directly from the functional profiles, based on KO, at http://ocean-microbiome.embl.de/companion.html. The methods to quantify the average abundances for each gene are described in Supplementary Materials and Methods.

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Baltar, F., Martínez-Pérez, C., Amano, C. et al. A ubiquitous gammaproteobacterial clade dominates expression of sulfur oxidation genes across the mesopelagic ocean. Nat Microbiol 8, 1137–1148 (2023). https://doi.org/10.1038/s41564-023-01374-2

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