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Geomicrobiology | Spotlight

Genome-Resolved Metagenomics and Detailed Geochemical Speciation Analyses Yield New Insights into Microbial Mercury Cycling in Geothermal Springs

Caitlin M. Gionfriddo, Matthew B. Stott, Jean F. Power, Jacob M. Ogorek, David P. Krabbenhoft, Ryan Wick, Kathryn Holt, Lin-Xing Chen, Brian C. Thomas, Jillian F. Banfield, John W. Moreau
Isaac Cann, Editor
Caitlin M. Gionfriddo
aSchool of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia
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  • ORCID record for Caitlin M. Gionfriddo
Matthew B. Stott
bGNS Science, Wairakei Research Centre, Taupo, New Zealand
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Jean F. Power
bGNS Science, Wairakei Research Centre, Taupo, New Zealand
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Jacob M. Ogorek
cWisconsin Water Science Center, U.S. Geological Survey, Middleton, Wisconsin, USA
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David P. Krabbenhoft
cWisconsin Water Science Center, U.S. Geological Survey, Middleton, Wisconsin, USA
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Ryan Wick
dDepartment of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
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Kathryn Holt
dDepartment of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia
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Lin-Xing Chen
eDepartment of Earth and Planetary Science, UC Berkeley, Berkeley, California, USA
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Brian C. Thomas
eDepartment of Earth and Planetary Science, UC Berkeley, Berkeley, California, USA
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Jillian F. Banfield
aSchool of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia
eDepartment of Earth and Planetary Science, UC Berkeley, Berkeley, California, USA
fDepartment of Environmental Science, Policy, and Management, UC Berkeley, Berkeley, California, USA
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John W. Moreau
aSchool of Earth Sciences, The University of Melbourne, Parkville, Victoria, Australia
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Isaac Cann
University of Illinois at Urbana—Champaign
Roles: Editor
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DOI: 10.1128/AEM.00176-20
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  • FIG 1
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    FIG 1

    Rank abundance by scaffold coverage of ribosomal protein S3 within binned and unbinned genomes from Tiger (NW1) and Cub (NW2) Bath metagenomes. Analyses and annotations were performed in ggKbase (https://ggkbase.berkeley.edu/). Genomic bin phylogeny used ribosomal S3 proteins from genomic bins, while scaffold phylogeny to the lowest common ancestor is given for unbinned ribosomal proteins. When multiple ribosomal protein S3s had the same taxonomic classification, the average coverage is shown, with error bars representing standard deviations. Values are ranked by NW2 coverage.

  • FIG 2
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    FIG 2

    Heat map showing functional proteins from various biogeochemical pathways associated with Hg within genomic bins and unbinned (NGAWHA_1_UNK and NGAWHA_2_UNK) scaffolds from Tiger (NW1) and Cub (NW2) Bath metagenomes. Intensity of color refers to number of genes from each bin that encode the enzyme, operon, or pathway involved in mercury, sulfur, or methane cycling; values are provided in cells for reference. Genomic bins were ordered by the consensus coverage for all scaffolds within the bin (highest to lowest). Analyses and annotations were performed in ggKbase (https://ggkbase.berkeley.edu/).

  • FIG 3
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    FIG 3

    Maximum likelihood tree showing MerA phylogeny of 31 sequences pulled from assembled Tiger (NW1) and Cub (NW2) Bath metagenomes using ggKbase. Genomic bins or scaffold ID (when MerA was unbinned) are given in bold font. Included in analysis are 113 MerA homologues, including 46 sequences from Yellowstone National Park (YNP) metagenomes (82). Trees were constructed using Le Gascuel amino-acid substitution model with gamma distribution in MEGA6 (77). Data were bootstrapped with 100 replications. The initial neighbor-joining tree was constructed with pairwise distances estimated using a JTT model. Positions with less than 90% site coverage (e.g., alignment gaps, missing data, or ambiguous bases) were excluded. A total of 419 positions were used in the final data set.

  • FIG 4
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    FIG 4

    Maximum likelihood tree showing HgcA phylogeny of reads obtained from the Cub Bath (NW2) metagenome using HMM search. Included in analysis are 183 sequences, including 56 HgcA homologues from YNP metagenomes, pulled from JGI (see Table S3 in the supplemental material). Amino acid sequences are compared to HgcA homologues from known and predicted methylators. Also included are HgcAB fused proteins from hyperthermophilic bacteria and archaea and carbon monoxide dehydrogenase/acetyl coenzyme A (acetyl-CoA) synthase subunit gamma (HgcA paralogs) from nonmethylators. Trees were inferred from the Le Gascuel amino acid substitution model with gamma distribution in MEGA6 (77). Data were bootstrapped with 100 replications. The initial neighbor-joining tree was constructed with pairwise distances estimated using a JTT model. Positions with less than 93% site coverage (e.g., alignment gaps, missing data, or ambiguous bases) were excluded. A total of 55 positions were used in the final data set. Groups (I to IX) designate distinct subtrees that contain HgcA from hot spring metagenomes. Representative reference sequences are labeled within each subtree, while phylogeny of all branches is indicated by color.

  • FIG 5
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    FIG 5

    Phylogenetic analysis of DsrAB by maximum likelihood method. The 15 DsrAB homologues in this study were pulled from IDBA-UD assembled Tiger and Cub metagenomes using ggKbase. They were compared to 218 reference DsrAB sequences pulled from the Dome database, including 15 representative DsrAB sequences from thermophilic environments (39). There were six distinct groups of DsrAB homologues found at Ngawha. The tree was constructed using MEGA6 (77) with the Le Gascuel 2008 model with gamma distribution; pairwise distances were estimated using a JTT model. All positions with less than 95% site coverage were eliminated, including alignment gaps, missing data, and ambiguous bases. There was a total of 492 positions in the final data set, with 100 bootstrap replicates.

  • FIG 6
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    FIG 6

    Conceptual model of biogeochemical cycling of mercury (Hg), sulfur (S), and iron (Fe) in Hg-enriched, sulfidic, low pH mesothermal springs. Gaseous elemental mercury [Hg(0)] [as well as Hg(II)] from deep geological sources enters the surface waters of the springs where it becomes oxidized to Hg(II) (enhanced by chloride [Cl−]) (32) and then complexes with sulfides (S2-) to produce cinnabar (red rhombohedral symbols; HgS(s)). Here, S2− is indicative of all reduced sulfide species. Round icons represent microbially mediated reactions, white are primarily aerobic-associated mechanisms, and black are primarily anaerobic. Sulfur-oxidizing bacteria (SOB) equipped with the Sox pathway along with Fe-oxidizing bacteria and archaea (FeOBA) are able to enhance dissolution of metal sulfides, such as pyrite (silver rhombohedral symbols; FeS2) and HgS(s). Sulfate-reducing bacteria (SRB) and Fe-reducing bacteria (FeRB) further mediate the redox chemistry of S, Fe, and Hg. As Hg(II) becomes bioavailable to microbes, it can be reduced to Hg(0) by microbes equipped with mercuric reductase (MerA) or methylated to MeHg by HgcAB-equipped microbes. MeHg can be demethylated by MerB-equipped microbes to Hg(II) and CH, and then reduced to Hg(0) by MerA. At the surface of the springs, photoreduction can also contribute to Hg(II) reduction to Hg(0), as well as the degradation of MeHg. Photolytic oxidation may also counter Hg(0) volatilization from surface waters, keeping Hg(II) in spring water to be transformed by microbes or partitioned to sulfide minerals. Advective mixing of spring waters ensures that Hg species travel across the redox boundaries that likely partition the Mer-equipped microbes to oxic surface waters from the HgcAB-equipped microbes that likely occupy the anaerobic sediment/water boundary.

Tables

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  • TABLE 1

    Anion and cation measurements in filtered water from hot springs in the Ngawha Geothermal Field

    TABLE 1
    • ↵a NM, not measured; BDL, below detection limit (0.0005 mg liter−1).

    • ↵b S2− is indicative of all reduced sulfide species.

  • TABLE 2

    Mercury analyses of NGF hot springs

    TABLE 2
    • ↵a NGF, Ngawha Geothermal Field.

    • ↵b Measurements taken during each sampling campaign are provided as a range.

    • ↵c MeHg, methylmercury.

    • ↵d HgT, total mercury.

    • ↵e The fraction of total Hg as MeHg (as %) in filtered water samples.

    • ↵f THg(s), solid total mercury in hot spring sediments.

    • ↵g Hg(0), gaseous elemental mercury above the springs.

    • ↵h Published reference and regional background values are included for comparison.

    • ↵i YNP, Yellowstone National Park.

Additional Files

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    • Supplemental file 1 -

      Tables S1 to S8, Fig. S1 to S11, sequences

      PDF, 8.0M

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Genome-Resolved Metagenomics and Detailed Geochemical Speciation Analyses Yield New Insights into Microbial Mercury Cycling in Geothermal Springs
Caitlin M. Gionfriddo, Matthew B. Stott, Jean F. Power, Jacob M. Ogorek, David P. Krabbenhoft, Ryan Wick, Kathryn Holt, Lin-Xing Chen, Brian C. Thomas, Jillian F. Banfield, John W. Moreau
Applied and Environmental Microbiology Jul 2020, 86 (15) e00176-20; DOI: 10.1128/AEM.00176-20

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Genome-Resolved Metagenomics and Detailed Geochemical Speciation Analyses Yield New Insights into Microbial Mercury Cycling in Geothermal Springs
Caitlin M. Gionfriddo, Matthew B. Stott, Jean F. Power, Jacob M. Ogorek, David P. Krabbenhoft, Ryan Wick, Kathryn Holt, Lin-Xing Chen, Brian C. Thomas, Jillian F. Banfield, John W. Moreau
Applied and Environmental Microbiology Jul 2020, 86 (15) e00176-20; DOI: 10.1128/AEM.00176-20
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    • ABSTRACT
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KEYWORDS

biogeochemistry
geothermal
hgcAB
MerA
mercuric ion detoxification
mercury
metagenomics
methylmercury

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