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Adaptations of Atribacteria to life in methane hydrates: hot traits for cold life
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Jennifer B. Glass1*, Piyush Ranjan2#, Cecilia B. Kretz1§, Brook L. Nunn3, Abigail M. Johnson1,
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James McManus4, Frank J. Stewart2
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1School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA;
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2School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; 3Department
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of Genome Sciences, University of Washington, Seattle, WA; 4Bigelow Laboratory for Ocean
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Sciences, East Boothbay, ME, USA
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*Corresponding Author: jennifer.glass@eas.gatech.edu
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#Now at: Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA;
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§Now at: Division of Scientific Education and Professional Development, Epidemiology
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Workforce Branch, Laboratory Leadership Service, Field Assignee New York City Public Health
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Laboratory, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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Running Title: Atribacteria adaptions in methane hydrate ecosystem
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Dedication: To Katrina Edwards
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Originality-Significance Statement: This work provides insights into the metabolism and
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adaptations of elusive Atribacteria (JS-1 clade) that are ubiquitous and abundant in methane-rich
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ecosystems. We show that JS-1 (Genus 1) from methane hydrate stability zones contain
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metabolisms and stress survival strategies similar to hyperthermophilic archaea.
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Summary: Gas hydrates harbor gigatons of natural gas, yet their microbiomes remain
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mysterious. We bioprospected methane hydrate-bearing sediments from under Hydrate Ridge
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(offshore Oregon, USA, ODP Site 1244) using 16S rRNA gene amplicon, metagenomic, and
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metaproteomic analysis. Atribacteria (JS-1 Genus 1) sequences rose in abundance with increasing
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sediment depth. We characterized the most complete JS-1 Genus 1 metagenome-assembled
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genomic bin (B2) from the deepest sample, 69 meters below the seafloor (E10-H5), within the
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gas hydrate stability zone. B2 harbors functions not previously reported for Atribacteria,
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including a primitive respiratory complex and myriad capabilities to survive extreme conditions
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(e.g. high salt brines, high pressure, and cold temperatures). Several Atribacteria traits, such as a
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hydrogenase-Na+/H+ antiporter supercomplex (Hun) and di-myo-inositol-phosphate (DIP)
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synthesis, were similar to those from hyperthermophilic archaea. Expressed Atribacteria proteins
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were involved in transport of branched chain amino acids and carboxylic acids. Transporter genes
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were downstream from a novel helix-turn-helix transcriptional regulator, AtiR, which was not
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present in Atribacteria from other sites. Overall, Atribacteria appear to be endowed with unique
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strategies that may contribute to its dominance in methane-hydrate bearing sediments. Active
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microbial transport of amino and carboxylic acids in the gas hydrate stability zone may influence
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gas hydrate stability.
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Introduction
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Gas hydrates, also known as clathrates, are cages of ice-like water crystals encasing gas
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molecules such as methane (CH4). Because hydrates form under high pressure and low
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temperature, their distribution on Earth is limited to permafrost and continental margins (Hester
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and Brewer, 2009). These hydrates harbor gigatons of natural gas, which may serve as a potential
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energy source for the future (Chong et al., 2016). They are also susceptible to dissociation due to
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rising ocean temperatures, which could release massive methane reservoirs to the atmosphere and
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exacerbate global warming (Archer et al., 2009; Ruppel and Kessler, 2017).
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Despite the global importance of gas hydrates, their microbiomes remain mysterious.
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Microbial cells are physically associated with hydrates (Lanoil et al., 2001), and the taxonomy of
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these hydrate-associated microbiomes is distinct from non-hydrate-bearing sites (Inagaki et al.,
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2006). Because salt ions are excluded during hydrate formation (Ussler III and Paull, 2001;
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Bohrmann and Torres, 2006), hydrate-associated microbes likely possess adaptations to survive
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high salinity and low water activity, as well as low temperatures and high pressures (Honkalas et
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al., 2016). However, knowledge of the genetic basis of such adaptations is incomplete, as
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genomic data for hydrate communities are sparse and most hydrate microbiomes have been
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characterized primarily through single-gene taxonomic surveys.
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Global 16S rRNA gene surveys show that the JS-1 sub-clade of the uncultivated bacterial
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candidate phylum Atribacteria is the dominant taxon in gas hydrates (Reed et al., 2002; Inagaki et
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al., 2003; Kormas et al., 2003; Newberry et al., 2004; Webster et al., 2004; Inagaki et al., 2006;
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Webster et al., 2007; Fry et al., 2008; Kadnikov et al., 2012; Parkes et al., 2014; Chernitsyna et
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al., 2016) and in other deep sediment ecosystems with abundant methane (Gies et al., 2014; Carr
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et al., 2015; Hu et al., 2016). The other major Atribacteria lineage, OP-9, has only been found in
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hot springs (Dodsworth et al., 2013; Rinke et al., 2013) and thermal bioreactors (Nobu et al.,
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2015). Marine Atribacteria are dispersed through ejection from submarine mud volcanoes
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(Hoshino et al., 2017; Ruff et al., 2019), and environmental heterogeneity may select for locally
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adapted genotypes. Indeed, Atribacteria phylogeny is highly diverse, suggesting the potential for
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wide functional variation and niche specialization.
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Genomic evidence for such Atribacteria specialization remains limited. To date, near-
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complete single-cell and metagenomic sequences from hot springs, wastewater, lake sediments,
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and non-hydrate bearing marine sediments have shown that Atribacteria lack respiratory
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pathways. The high-temperature OP-9 lineage likely ferments sugars (Dodsworth et al., 2013)
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whereas the low-temperature JS-1 lineage ferments propionate to hydrogen, acetate, and ethanol
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(Nobu et al., 2016). Both JS-1 and OP-9 lineages possess genes encoding bacterial
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microcompartment shell proteins that may sequester toxic aldehydes, enabling their condensation
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to carbohydrates (Nobu et al., 2016). The available data on Atribacteria genomes suggest
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diversification linked to organic substrate utilization, although a range of other factors, including
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physical environmental conditions (e.g., temperature and pressure) undoubtedly also play a role.
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Here we examined the distribution, phylogeny, and metabolic potential of uncultivated
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JS-1 Atribacteria in cold, salty, and high-pressure sediments beneath Hydrate Ridge, off the coast
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of Oregon, USA, using a combination of 16S rRNA gene amplicon, metagenomic, and
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metaproteomic analysis. We found that JS-1 Genus-1 are abundant in the gas hydrate stability
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zone (GHSZ) and that they harbor numerous strategies for tolerance of osmotic stress, including
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many biosynthesis pathways for unusual osmolytes similar to those of thermophiles.
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Results and Discussion
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Geochemical gradients. Sediment core samples spanned four geochemical zones from 0-69
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meters below seafloor (mbsf) at the ODP Site 1244C,D,E at Hydrate Ridge, off the coast of
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Oregon, USA (Fig. S1; Tréhu et al., 2003): near surface (0-2 mbsf), sulfate-methane transition
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zone (SMTZ; 2-9 mbsf), metal reduction zone (18-36 mbsf), and GHSZ (45-124 mbsf; Fig. 1).
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Figure 1. Porewater geochemistry (methane, sulfate, manganese, iron, and iodide) and 16S rRNA
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gene composition from sediment depth profiles at ODP 204 Site 1244, Hydrate Ridge, offshore
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Oregon, USA. Hatched and solid bars are archaeal and bacterial 16S rRNA genes, respectively.
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“Others” category represents bacterial and archaeal phyla with <2% of total sequences.
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Sediment porewater methane concentrations rose from negligible at the seafloor to 8% by volume
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at 3-5 mbsf, and remained <5% below 5 mbsf, with the exception of one sample at 21 mbsf.
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Sulfate rapidly dropped from 28 to <1 mM from 0-9 mbsf and remained <1 mM below 9 mbsf,
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with the exception of one sample at 50.7 mbsf (2.3 mM sulfate). Outside of the metal reduction
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zone, dissolved Mn was ~1 µM and dissolved Fe was 3-10 µM. Dissolved Mn and Fe peaked at 6
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and 27 µM, respectively, coincident with a single layer of disseminated gas hydrate in the metal
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reduction zone. Dithionite-extractable Fe and Mn increased slightly from 2 to 21 mbsf (0.4 to
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1.1% and 0.002 to 0.005%, respectively; Table S1). Iodide concentrations were highest in the
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GHSZ (1.4 mM), where liquid brines form as a result of methane hydrate formation. Estimated in
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situ salinity ranged from seawater salinity (35 g kg-1) to >100 g kg-1 (Milkov et al., 2004). Total
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organic carbon concentrations in sediment varied between 1-2%. In situ temperature ranged from
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~4°C at the seafloor to ~6-11°C in the GHSZ.
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Phylogenetic diversity. Phylogenetic diversity and species richness in 16S rRNA gene amplicons
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were highest in the SMTZ and decreased with depth except in the metal reduction zone (Fig. S2).
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The relative abundance of Atribacteria (JS-1)-affiliated amplicons increased with depth, from
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15% in the near surface to 86% in the GHSZ (Table S1). GHSZ sediment (sample E10-H5 from
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69 mbsf) contained 230 Atribacteria OTUs (89-92% ANI) that spanned a wide diversity of clades
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within JS-1 Genus 1 (Yarza et al. 2014) (Fig. 2). A single OTU matching GenBank AB804573.1,
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from an ocean drilling core from offshore Shimokita Peninsula, Japan, comprised 69% of
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Atribacteria 16S rRNA sequences in the GHSZ (Table S2). Other Atribacteria 16S rRNA
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sequences also matched marine samples from shallower Hydrate Ridge sediments (Marlow et al.,
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2014) and methane hydrate sediment off Taiwan (Lin et al., 2014) (Table S2). 16S rRNA
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sequences from amplicons and metagenomes generally showed consistent trends (Fig. S3).
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Atribacteria OTU abundance and composition varied significantly with sediment depth (Fig. S4).
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Figure 2: Phylogenetic reconstruction of Atribacteria 16S rRNA gene sequences from sample E10-
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H5 (69 mbsf). The tree includes the 230 Atribacteria OTUs with two or more sequences as well as
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reference sequences from environmental clones, SAGs, and MAGs, with Firmicutes as the
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outgroup. Reconstruction was performed in RAxML with 275 positions spanning the V3-V4 region
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of the 16S rRNA gene using a GAMMA model of rate heterogeneity, a GTR model of substitution,
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and 500 bootstraps followed by a thorough Maximum Likelihood search. The relative abundances
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of recovered amplicons from diverse lineages/OTUs is shown in the outermost circle. Additional
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information on the most abundant JS-1 OTUs from E10-H5 is provided in Table S2.
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JS-1 Genus-1 partial genome. To gain insight into the function of JS-1 Atribacteria in the
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GHSZ, we analyzed a 4-Mbp metagenome-assembled genome (MAG) from sample E10-H5
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(Table S3). This MAG, hereafter designated “B2”, was chosen for its relatively high
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completeness (69%) and low contamination (2%). B2 lacked a 16S rRNA gene, but contained a
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rpoB gene with 94% similarity to Atribacteria bacterium 34_128 from an oil reservoir (Hu et al.,
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2016). B2 had 35% GC content, similar to other Atribacteria (Carr et al., 2015). Phylogenetic
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placement based on 69 concatenated single-copy genes confirmed that B2 belonged to JS1-Genus
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1 and was most closely related to JS1-Genus 1 genomes from a sediment-hosted aquifer at Rifle,
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Colorado (RBG_COMBO_35; Anantharaman et al., 2016), cold CO2-rich fluids at Crystal
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Geyser, Utah (CG2_30_33_13; Probst et al., 2017), and hydrothermal vent sediments at Guaymas
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Basin, Gulf of California (4572_76; Dombrowski et al., 2017) (Fig. 3).
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Figure 3: Maximum likelihood phylogeny for B2 with 220 representative and 20 previously
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found Atribacteria SAGs and population genomes using multiple (minimum 6, maximum
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69) core single copy genes. Tree made in RAxML with GAMMA model, 1000 rapid bootstraps,
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MRE convergence bootstop (50 replicates) followed by a thorough ML search.
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Despite the relatively cool in situ temperature of the E10-H5 sediment (7-8°C
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(ShipboardScientificParty, 2003)), the most closely related genomes from cultured isolates were
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thermophilic gram-positive Firmicutes: halophilic Halothermothrix orenii spp. (Mavromatis et
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al., 2009) and metal-respiring Therminocola potens strain JR (Byrne and Nicholas, 1986). Below
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we highlight features of the B2 genome and proteome potentially relevant to life in the unique
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environment of methane clathrates, with particular focus on a putative respiratory complex and
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genes involved in stress response and environmental homeostasis.
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Predicted respiratory function of the Hun supercomplex. B2 contained genes for a putative
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operon encoding a 16-subunit respiratory complex, hereafter designated Hun. The hun operon
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was also present in two other MAGs from ODP Site 1244 (Planctomycetes C1H3-B36 and
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Firmicutes E5H5-B3) and in Atribacteria, Actinobacteria, and Omnitrophica MAGs from other
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deep subsurface ecosystems (Rinke et al., 2013; Baker et al., 2015; Anantharaman et al., 2016;
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Probst et al., 2017) (Table S4). The gene arrangement and predicted function of the putative Hun
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complex are similar to those of an ancient Mrp-Mbh-type membrane-bound [NiFe] hydrogenase-
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Na+/H+ antiporter respiratory complex in hyperthermophilic archaea (Yu et al., 2018), which is
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thought to be the ancestor of Complex I, also known as NADH:ubiquinone oxidoreductase (Nuo)
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(Friedrich and Scheide, 2000; Moparthi and Hägerhäll, 2011; Schut et al., 2013). Complex I’s
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modules likely had separate origins: the ubiquinone-reducing subunits NuoBCD (“Q-module”)
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evolved from an ancient membrane-bound [NiFe] hydrogenase, while its proton-pumping
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subunits NuoLMN (“P module”) evolved from an ancient Na+/H+ antiporter (Mathiesen and
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Hägerhäll, 2002; Moparthi et al., 2014; Spero et al., 2015).
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Atribacteria hun genes likely encode a complex of four protein modules that couple H+
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and Na+ translocation to H2 production, similar to Mrp-Mbh-type complexes in hyperthermophilic
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archaea (Fig. 4). Based on the similarity of HunAB to anaerobic sulfite reductase (Asr) subunits
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A and B, which transfer electrons from ferredoxin to the active site in AsrC (missing in the hun
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operon), we inferred that N module-like subunits HunABC likely accept electrons from
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ferredoxin and pass them through iron-sulfur clusters to Q-module-like subunit HunEFGP.
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Instead of accepting electrons from NADPH and passing them to ubiquinone as in Complex I,
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HunABC likely accepts electrons from ferredoxin and passes them to 2H+ for reduction to H2 at
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HunEFGP’s Ni-Fe active site (Table 1; Fig. 4).
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Figure 4: Predicted structure and function of a multi-subunit respiratory complex,
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hereafter “Hun”, found in B2 and other deep subsurface genomes. Top: conserved gene
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cluster arrangement, with each color representing a different predicted protein. Below: predicted
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cellular locations and functions based on homologs of the genes of the same colors encoded by
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the putative hun operon, and predicted regeneration of substrates by the heterodisulfide reductase
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(HdrA)-methyl viologen hydrogenase (MvhAGD) complex. Predicted functions of hun genes are
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based on Mrp-Mbh complexes in thermophilic archaea (Schut et al., 2013; Yu et al., 2018). See
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Table S4 for accession numbers.
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P-module-like subunits HunDHILK are predicted to be proton-pumping transmembrane proteins
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and Na-module-like subunits HunIJKLMNO are homologs of the Na+/H+ antiporter
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MnhABCDEFGH in Mrp-Mbh-type complexes. The presence of F0F1-type and V-type ATPases
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suggest that H+ and Na+ ions pumped outward by HunIJKLMNO are pumped back in to make
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ATP. Electrons from H2 could be transferred back to ferredoxin by the activity of the
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heterodisulfide reductase (HdrA)-methyl viologen hydrogenase (MvhAGD) complex (Fig. 4). A
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redox-sensing transcriptional repressor gene (hunR) immediately upstream of the hun operon
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suggests that the hydrogenase may not be used strictly for energy conservation, but could also be
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for balancing reducing equivalent by disposing of extra electrons (McLaughlin et al., 2010).
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Osmotic stress survival. Any life that can persist in brine pockets within methane hydrate must
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contend with high salinity (up to ~3x that of seawater) and low water potential. B2 contained
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numerous genes for the “salt out” survival strategy, in which osmotic pressure is maintained by
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exporting cations (Wood, 2015). B2’s cation export systems included efflux systems,
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mechanosensitive ion channels, and Na+-H+ antiporters (Table 1).
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A second salt survival strategy is import and/or biosynthesis of osmolytes, most often
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polar, water-soluble, and uncharged organic compounds and/or extracellular polymers. For
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example, glycine betaine is abundant in saline fluids from deep sediment basins (Daly et al.,
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2016). B2 contained genes for transport of trehalose and biosynthesis of the common osmolytes
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glutamine, glutamate, and poly-gamma-glutamate, all of which had homologs in other
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Atribacteria MAGs (Table 1). B2 also encoded genes for glycine betaine and dihydroxyacetone
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biosynthesis without homologs in other Atribacteria. Surprisingly, B2 also encoded biosynthetic
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genes (myo-inositol-1 phosphate synthase (MIPS)/bifunctional IPC transferase and DIPP
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synthase (IPCT-DIPPS)) for the unusual solute di-myo-inositol-phosphate (DIP) made by
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hyperthermophiles (Santos and Da Costa, 2002). The MIPS gene had closest similarity to
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halophilic and psychrophilic Euryarchaeota, without homologs in other Atribacteria. The IPCT-
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DIPPS gene was also present in Atribacteria HGW-1 from subsurface Japan (Hernsdorf et al.,
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2017) and Atribacteria 4572_76 from Guaymas Basin (Dombrowski et al., 2017).
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Immediately upstream from B2’s MIPS/IPCT-DIPPS genes was an acyl carrier protein
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(acpP) gene, commonly involved in fatty acid and polyketide biosynthesis. Sixteen additional
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acpP copies were present in B2, often flanked by transposon scars, suggestive of recent
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horizontal gene transfer (Table S5). Other Atribacteria MAGs had only 1-2 copies of acpP,
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usually near fatty acid biosynthesis genes.
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Table 1. Putative osmotic stress-related genes in B2. Atribacteria homologs all had >80%
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AAI. AAI to other taxa (56-76%) are provided. *indicates multiple copies.
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Like other Atribacteria, B2 contained genes encoding a sugar phosphate-utilizing class of
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proteinaceous bacterial microcompartments that neighbored sugar isomerases, RnfC NADH
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dehydrogenase and an oxidoreductase (Axen et al., 2014; Nobu et al., 2016) (Table S6). Further
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exploration of sugar-related genes revealed that B2 and other Atribacteria encode the non-
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mevalonate pathway for isoprenoid biosynthesis (ispDEFGH), exopolysaccharide synthesis
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proteins, numerous glycosyltransferases for transferring UDP- and GDP-linked sugars to a variety
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of substrates, and several proteins related to N-linked glycosylation (Table S7). The capacity for
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glycosylation may be another adaptation for survival of salt stress (Kho and Meredith, 2018).
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Expression of lipopolysaccharide and transport-related proteins. Metaproteomic analysis
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identified six expressed peptides affiliated with B2, all associated with assembly or transport
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(Table 2). One was an outer member lipopolysaccharide assembly protein (LptD), also known as
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Annotation
Gene
Accession
Top hit
Top hit
Na+/H+ antiporter
mrpEFGB
RXG65834.1-
RXG65838.1
OQY40657.1-
OQY40661.1
Atribacteria 4572_76
Na+ efflux
natB
RXG65900.1
OGD31203.1
Atribacteria RBG....
Threonine efflux
rhtB
RXG66248.1
OGD15641.1
Atribacteria RBG....
Na+ channel
DUF554
RXG63559.1
KUK55705.1
Atribacteria 34_128
Mechanosensitive
ion channel
mscS
RXG63036.1
KUK56353.1
Trehalose
transporter
sugAB
RXG66833.1-
RXG66834.1
KUK55397.1
KUK55398.1
Glutamine synthetase
glnA
RXG65164.1
KUK55578.1
K+ transport
trkAH*
RXG63511.1
RXG63512.1
PKP56013.1
PKP56012.1
Atribacteria HGW-1
Aromatic aa exporter
yddG*
RXG63201.1
PKP55084.1
Glutamate synthase
gltD
RXG66270.1
PKP56573.1
Proline racemase
prdF
RXG63210.1
PKP58887.1
Poly-gamma
glutamate synthase
pgsCBW
RXG66317.1-
RXG66319.1
PKP60458.1-
PKP60460.1
Glycerol uptake
glpF
RXG65629.1
OHV10031.1 (61%)
Kushneria YCWA18
Betaine-aldehyde
dehydrogenase
betB
RXG62957.1
KUJ28189.1 (56%)
Catabacter
hongkongensis
Dihydroxy-
acetone kinase
dhaKLM
RXG65626.1-
RXG65628.1
RLC64130.1- (67%)
RLC64131.1 (61%)
Chloroflexi
bacterium
DIPP synthesis
pathway
MIPS/IPCT-
DIPPS*
RXG66889.1
RXG66888.1
AAU82306.1 (76%)
PKP58414.1
Archaeon GZfos13E1
Atribacteria HGW-1
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Imp/OstA (increased membrane permeability/organic solvent tolerance (Braun and Silhavy,
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2002). Another was a capsular polysaccharide biosynthesis protein (YveK). The other expressed
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peptides were predicted to be transporters of purines (BmpA), branched chain amino acids (LivH,
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LivM), and C4-dicarboxylates (DctQ). All liv genes on the operon with the expressed livH had
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homologs in other Atribacteria genomes (Table S8) with the exception of livG, which encodes a
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protein related to the lipopolysaccharide export system ATP-binding protein LptB that may serve
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a specific purpose in methane-hydrate Atribacteria. Upstream of liv genes we found a ykkC-yxkD
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riboswitch implicated in detoxification and efflux control (Barrick et al., 2004), suggesting that
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branched chain amino acids may be involved in environmental stress response, as seen in other
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microbes (Liu et al., 2005).
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Table 2. Metaproteomic peptide hits for B2.
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In addition to numerous transporters for branched chain amino acids, B2 encoded abundant
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TRAP (tripartite ATP-independent periplasmic) transporters of dicarboxylic (DctPQM) and
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tricarboxylic (TctCBA) acids (Table S7; Fig. 5). TRAP transporters use an electrochemical
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gradient (H+ or Na+) and a substrate-binding protein to transport solutes across the membrane
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(Fischer et al., 2010). A conserved arginine residue in the DctP substrate-binding protein confers
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specificity for carboxylate groups (Lecher et al., 2009; Fischer et al., 2015).
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Peptide
Protein
Contig
Gene
Top hit (% identity)
Top hit
EYKPKEDWKMNFSS
SYNLNTK
LptD
C10125
33494
OQY39007.1 (90%)
Atribacteria 4572_76
GIIILIFLIAVITAVLV
SYFVLSPTP
YveK
C456
RXG64813.1
PKP59499.1 (74%)
Atribacteria HGW-1
CSNLIIKALLVVLVL
SLGITLGIAKAP
BmpA
C473
RXG64193.1
PKP58720.1 (94%)
Atribacteria HGW-1
KPFRKSPGLIILLSTV
AVGFIIR
LivH
C8009
30420
OQY40503.1 (94%)
Atribacteria 4572_76
LIFLLLLAVAVVVPF
LLGLLILRF
LivM
C2171
15004
RKY02958.1 (46%)
Spirochaetes bacterium
NKINLIFSILIIIFLIVL
TYEGIILVKVGLNA
DctQ
C95
RXG62936.1
AEG13811.1 (34%)
Desulfofundulus kuznetsovii
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Figure 5: Phylogeny of HTH-XRE regulators/antitoxins (yellow), hereafter “AtiR”, from B2
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and synteny of downstream genes. Genes highlighted in thick red lines were expressed in the
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metaproteome. A) AtiR maximum likelihood phylogeny based on contigs (labeled on the right)
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from E10-H5 B2, with Anaerococcus prevotii as the outgroup. B) Additional putative operons from
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B2 likely regulated by atiR, which is truncated partially or completely on these contigs. C) Legend
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for panels A and B; D) AtiR amino acid alignment for the 13 AtiR sequences from Atribacteria
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E10-H5-B2 shown in panel A. Abbreviations: bmpA: basic membrane protein A; dctPQM: C4-
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dicarboxylate transporter; gabT: 4-aminobutyrate aminotransferase; livHMGF: branched chain
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amino acid transporter; rbs: ribose transporter; sat: sulfate adenylyltransferase; tctCBA:
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tricarboxylate transporter; ugpBAE: sn-glycerol-3-phosphate transporter. See Table S7 for
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accession numbers and % identity to closest gene hits in other genomes.
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A novel regulator. Three out of six of the expressed transporter proteins were encoded by genes
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located downstream from a novel gene predicted to encode a helix-turn-helix xenobiotic response
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element transcriptional regulator, which we named “AtiR” (Table S8; Fig. 5). AtiR was not
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found in Atribacteria MAGs (the top BLAST hit was the skin firmicute Anaerococcus prevotii
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(41-49% AAI)), suggesting that it may serve a specific purpose in methane-hydrate Atribacteria.
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Genes downstream of atiR were dominated by transporters for organic solutes (tct, dct, ugp),
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branched chain amino acids (liv), hydrolases (choline sulfatase, sialidase, tryptophanase, cysteine
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desulfurase), peptidases, and racemases (Table S8; Fig. 5). In two instances, genes encoding
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RTX-toxin repeats were located on atiR contigs (Table S8). B2 also contained numerous MazEF
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toxin-antitoxin systems (Table S9), which trigger programmed cell death in response to stress
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(Engelberg-Kulka et al., 2005). Atribacteria may use AtiR to regulate cellular degradation of
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G
G
G
sat sat
M20 amidohydrolase
c95
c473
c2922
c2
c107
c194
c687
c4712
c486
c1742
c238
gabT
sialidase P MQ
H
HK
K
M
M
M
ABC
permease ABC
permease
H
choline sulfatase choline sulfatase
DUF5058 hyp
F
F
F
rbsA
C
C A
B
B
A
putative cysteine
desulfurase
Nha antiporter
M
c664
tryptophanase
P Q
bmpA
c238
c1316
c103
c85
hyp
hyp
dct genes
XRE regulator = atiR
P
P MQ
tct genes
liv genes
hydrolase or racemase
K
MQ
other genes
expressed genes
rbs genes
HGT genes
c8009
HK
RTX-like toxin
c5280
ridA racemase
DNA binding site
3
4
12
11
10
5
6
7
13
1
2
1
2
5
6
7
8
9
11
12
13
10
4
3
HTH XRE regulator domain
9
0.1
8
Anaerococcu s prevotii
A.prevotii
D-Galacturonate and D-Glucuronate metabolism threonine
synthase asparate
aminotransfase
metal dependent
hydrolase
B
B A
A
E
E
ugp genes
Inositol
catabolism creatin-
inase
A
D
C
B
racemase
mycobacterial-like (64 aa)
aminotransferase
G c2171
HMG
peptidase G
!
14!
peptides and proteins to amino acids, either for nutrients acquisition or for survival under
281
environmental stress (Bergkessel et al., 2016).
282
283
Adaptations to life in methane hydrates. The GHSZ in deep subsurface sediments is dominated
284
by Atribacteria that appear to contain unique adaptations for survival in an extreme system with
285
high salinity, high pressure, low water activity, and low temperatures. Our analysis of the B2
286
Atribacteria MAG from the GHSZ (69 mbsf at Hydrate Ridge, offshore Oregon, in situ sediment
287
temperature ~6-11°C) revealed multiple survival strategies with similarity to hyperthermophiles.
288
In B2, these “hot traits in cold life” included genes for an ancient respiratory system (Hun) and an
289
unusual osmolyte (DIP). Other probable environmental stress adaptations include glycosylation,
290
membrane modifications, and a novel regulatory mechanism (AtiR) for transport of carboxylic
291
acids and branched chain amino acids.
292
Our findings suggest that Atribacteria may actively modulate the composition and
293
concentration of organic compounds in methane hydrate sediments. Active cellular transport of
294
organics would change environmental concentrations, which in turn could influence hydrate
295
stability. The hydrophobicity of branched chain amino acids has been shown to influence hydrate
296
stability; less hydrophobic amino acids like glycine and alanine inhibit hydrate formation by
297
disrupting the hydrogen bond network, while more hydrophobic amino acids, such as leucine,
298
valine and isoleucine, promote hydrate growth by strengthening the local water structure (Sa et
299
al., 2013; Liu et al., 2015; Veluswamy et al., 2017). Gas hydrate growth is also promoted by
300
anionic surfactants (Kumar et al., 2015), which include carboxylic acids. Thus, we surmise that
301
bacterial transport of organic compounds may influence hydrate stability. Our results motivate
302
future studies of methane stability that account for the influence of microbial processes, in
303
particular those of abundant Atribacteria.
304
305
!
15!
Acknowledgments. We thank Vinayak Agarwal, Jennifer Biddle, Jordan Bird, Frederick
306
Colwell, Sheng Dai, Konstantinos Konstantinidis, Peter Girguis, Julie Huber, Raquel Lieberman,
307
Karen Lloyd, Katie Marshall, Alejandra Prieto Davo, Brandi Reese, Claudia Remes, Despina
308
Tsementzi, Paula Welander, Loren Williams, Jieying Wu, and Jenny Yang for helpful
309
discussions; Phil Rumford and curatorial staff at the ODP Gulf Coast Repository for providing
310
samples; and Shweta Biliya, Annie Hartwell, and Janet Hatt for technical assistance. This
311
research was funded by Center for Dark Energy Biosphere Investigations (C-DEBI) Small
312
Research Grant to J.B.G. and C.B.K. (NSF OCE-0939564), NASA Exobiology grant to J.B.G.
313
and F.J.S. (NNX14AJ87G), NSF Biology Oceanography grant to F.J.S and J.B.G. (NSF OCE-
314
1558916), and a Georgia Tech Earth and Atmospheric Sciences Frontiers Postdoctoral Fellowship
315
to C.B.K. Metaproteomic analysis by B.L.N. was partially supported by the University of
316
Washington’s Proteomic Resource (UWPR95794). This is C-DEBI contribution [provided upon
317
paper acceptance].
318
!
16!
Experimental Procedures
319
Sample collection. Sediments were cored at ODP site 1244 (44°35.1784´N; 125°7.1902´W; 895
320
m water depth; Fig. S1) on the eastern flank of Hydrate Ridge ~3 km northeast of the southern
321
summit on ODP Leg 204 in 2002 (Tréhu et al., 2003) and stored at -80°C at the ODP Gulf Coast
322
Repository.
323
324
Geochemistry. Data for dissolved methane, sulfate, manganese, iron, and iodide in sediment
325
porewaters were obtained from (Tréhu et al., 2003). Reactive iron and manganese were extracted
326
from frozen sediments using the citrate-dithionite method (Roy et al., 2013) and measured by
327
inductively coupled plasma optical emission spectrometer (Agilent Technologies 700 Series).
328
Total carbon, total nitrogen and total sulfur were determined by CNS analyzer (Perkin Elmer
329
2400). Total inorganic carbon was measured by CO2 coulometer (CM5130) with a CM5130
330
acidification module. Geochemical metadata are given in Table S1 and archived in BCO-DMO
331
project 626690.
332
333
DNA extraction. DNA was extracted, in duplicate, from 8-20 g of sediment from the following
334
depths in meters below seafloor (mbsf): 1.95-2.25 (C1-H2); 3.45-3.75 (C1-H3); 8.60 (F2-H4);
335
18.10 (F3-H4); 20.69 (C3-H4); 35.65 (E5-H5); 68.55 (E10-H5); 138.89 (core E19-H5) using a
336
MO-BIO PowerSoil total RNA Isolation Kit with the DNA Elution Accessory Kit, following the
337
manufacturer protocol without beads. Approximately 2 grams of sediments were used per
338
extraction, and DNA pellets from the two replicates from each depth were pooled together. DNA
339
concentrations were measured using a Qubit 2.0 fluorometer with dsDNA High Sensitivity
340
reagents (Invitrogen, Grand Island, NY, USA). DNA yields ranged from 4-15 ng per gram of
341
sediments. Core E19-H5 (139 mbsf) yielded only 2 ng DNA per gram of sediment and yielded
342
unreliable data due to contamination with sequences from the enzymes used in the library
343
preparations. Therefore, this core segment was excluded from further analysis.
344
345
16S rRNA gene amplicon sequencing. Microbial community composition was assessed by
346
Illumina sequencing of the V3-V4 region of the 16S rRNA gene. The V3-V4 region was PCR-
347
amplified using primers F515 and R806 (Caporaso et al., 2011), each appended with barcodes
348
and Illumina-specific adapters according to (Kozich et al., 2013). Reactions consisted of 1-2 µL
349
DNA template (2 ng), 5 µL of 10x Taq Mutant reaction buffer, 0.4 µL of Klentaq LA Taq
350
Polymerase (DNA Polymerase Technology, St. Louis, MO, USA), 2 µL of 10 mM dNTP mix
351
(Sigma Aldrich, St. Louis, MO, USA), 2 µL of reverse and forward primers (total concentration
352
0.4 µM), and the remainder DNA-free water to 50 µL (Ambion, Grand Island, NY, USA). PCR
353
conditions were an initial 5-min denaturation at 94oC, followed by 35 cycles of denaturation at
354
94oC (40 sec), primer annealing at 55oC (40 sec), and primer extension at 68oC (30 sec).
355
Amplicon libraries were purified using a QIAquick PCR Purification Kit (Qiagen, Germantown,
356
MD, USA), quantified by Qubit (Life Technologies), and pooled in equimolar concentration.
357
Amplicons were sequenced on an Illumina MiSeq across two different runs using the V2 500-
358
cycle kit with 5% PhiX to increase read diversity. 16S rRNA sequences were deposited into
359
NCBI SAMN04214977-04214990 (PRJNA295201).
360
361
16S rRNA gene amplicon analysis. Sequences were trimmed using Trim Galore (criteria: length
362
>100 bp length, Phred score >25), and paired reads were merged using FLASH (Magoč &
363
Salzberg, 2011) with the criteria of a minimum length of 250 bp per input read, minimum length
364
of 300 bp for merged fragments, and maximum fragment standard deviation of 30 bp. Merged
365
reads were imported into QIIME1 (Caporaso et al., 2010) and chimeric sequences were detected
366
by searches using ‘identity_chimeric_seqs’ and then removed. Sequences sharing 97% nucleotide
367
similarity were clustered into operational taxonomic units (OTUs) using
368
‘pick_open_reference_otus’ with taxonomy assigned to OTUs by comparison to the greengenes
369
!
17!
database (DeSantis et al., 2006). The datasets were rarefied to a uniform depth of 14,391
370
sequences, and the rarefied OTU table was used for all downstream analyses. A core set of
371
QIIME diversity analyses was performed using ‘core_diversity_analyses’. The phylogenetic
372
diversity (PD) metric (Faith, 1992) was used to quantify alpha diversity across samples.
373
374
Atribacteria OTU phylogenetic analysis. We generated a reference alignment of Atribacteria
375
full length 16S rRNA sequences to use as a scaffold for mapping OTU sequences generated in
376
this study. The reference alignment included Atribacteria 16S rRNA gene sequences from
377
environmental clones (from Nobu et al. (2016), Carr et al. (2015) and Yarza et al. (2014)) and
378
published SAGs and MAGs available in Prokka at the time of analysis (spring 2018), as well as 8
379
sequences from Firmicutes bacteria for use as an outgroup. The sequences were aligned in
380
MAFFT with the linsi option, alignment reordering, and reverse complement matching enabled.
381
We then extracted representative sequences from 230 OTU clusters identified as Atribacteria OP-
382
9 and JS-1 in the E10-H5 amplicon dataset; OTUs represented by only a single sequence were
383
excluded. These sequences were recruited to the reference alignment via MAFFT using
384
previously described parameters, without modifying base pair positions in the reference
385
alignment. The alignment was manually inspected and trimmed to include only the V3-V4 region
386
spanned by the Atribacteria OTU sequences, resulting in a final alignment with 275 bases.
387
This alignment was used for phylogeny reconstruction in RAxML with a GTR model of
388
base substitution and GAMMA model of rate heterogeneity, and 500 rapid bootstraps followed by
389
a thorough ML search. The resulting phylogenetic tree was edited for viewing in iTOL. The
390
relative abundance of each OTU (from which a representative sequence was extracted) was
391
mapped onto the resulting phylogeny and shown as a proportion of total sequences in the
392
amplicon dataset.
393
Pairwise distances between all Atribacteria sequences in the alignment were calculated
394
using the p-distance method in MEGA7 and summarized in R as: min 0.0, 1st quartile 0.5, median
395
0.09, mean 0.11, 3rd quartile 0.18 and max 0.27. Pairwise distances between only the OTUs
396
generated in this study were summarized in R as: min 0.004, 1st quartile 0.056, median 0.075,
397
mean 0.076, 3rd quartile 0.095 and max 0.194.
398
399
Atribacteria community structure. OTU abundance from the rarified Atribacteria OTU table
400
(previously generated during diversity analysis) was used for NMDS analysis after square root
401
transformation and calculation of Bray-Curtis dissimilarity metrics, all processed via the
402
metaMDS function from Vegan package in R. After examination of the Shepard plot for scatter
403
around the regression line, the NMDS plot was created showing individual OTUs and the
404
midpoint for whole communities. A hierarchical clustering dendrogram was generated using
405
Bray-Curtis dissimilarities.
406
407
Multiple displacement amplification, library preparation, and sequencing. Genomic DNA
408
was amplified using a REPLI-g Single Cell Kit (Qiagen, Germantown, MD, USA) using UV-
409
treated sterile plasticware and reverse transcription-PCR grade water (Ambion, Grand Island, NY,
410
USA). Quantitative PCR showed that the negative control began amplifying after 5 hr of
411
incubation at 30°C, and therefore, the 30°C incubation step was shortened to 5 hr using a Bio-Rad
412
C1000 Touch thermal cycler (Bio-Rad, Hercules, CA, USA). DNA concentrations were measured
413
by Qubit. Two micrograms of MDA-amplified DNA were used to generate genome libraries
414
using a TruSeq DNA PCR-Free Kit following the manufacturer’s protocol (Illumina, San Diego,
415
CA, USA). The resulting libraries were sequenced using a Rapid-Run on an Illumina HiSeq 2500
416
to obtain 100 bp paired-end reads. Sequencing statistics are provided in Table S3. Metagenomic
417
sequences were deposited into NCBI SAMN07256342-07256348 (PRJNA390944).
418
419
Metagenome assembly, binning, and annotation. Demultiplexed Illumina reads were mapped
420
to known adapters using Bowtie2 in local mode to remove any reads with adapter contamination.
421
!
18!
Demultiplexed Illumina read pairs were quality trimmed with Trim Galore (Babraham
422
Bioinformatics) using a base Phred33 score threshold of Q25 and a minimum length cutoff of 80
423
bp. Paired-end reads were then assembled into contigs using SPAdes assembler with --meta
424
option for assembling metagenomes, iterating over a range of k-mer values
425
(21,27,33,37,43,47,51,55,61,65,71,75,81,85,91,95). Assemblies were assessed with reports
426
generated with QUAST. Features on contigs were predicted through the Prokka pipeline with
427
RNAmmer for rRNA, Aragorn for tRNA, Infernal and Rfam for other non-coding RNA and
428
Prodigal for protein coding genes. Metagenomic 16S rRNA sequences were analyzed by
429
BLASTN analysis against the Greengenes reference database. Matches with a bit score above 50
430
and reads matching multiple reference genes with the highest bit score were retained for
431
comparison with 16S rRNA amplicons (Fig. S3). Annotation of protein-coding genes was
432
performed as follows: 1) BLASTP search against the default set of core genomes, followed by
433
HMM search against a set of default core HMM profiles available in Prokka, 2) use of the
434
BLAST Descriptor Annotator algorithm in BLAST2GO, which conducts BLAST against the
435
NCBI nr database, 3) KEGG orthology assignment using GhostKoala and 4) InterProScan
436
analysis, which involves cross-reference HMM searches across multiple databases to find Pfam
437
families with close homology.
438
Metagenome contigs were partitioned through MetaBAT (Kang et al., 2015) into
439
metagenome-assembled genomes (MAGs) using tetranucleotide frequency and sequencing depth.
440
Sequencing depth was estimated by mapping reads on to assembled contigs using Bowtie2 and
441
Samtools. Completeness, contamination and strain level heterogeneity were assessed using single
442
copy marker genes in CheckM (Parks et al., 2015). Gene features and their functional annotations
443
for genome bins were extracted from the metagenome for the contigs that belong to the bins.
444
Initial taxonomic affiliation for bins was inferred via the least common ancestor (LCA) algorithm
445
in MEGAN6 and by the top BLAST matches to the marker gene rpoB. The B2 MAG was
446
deposited into Genbank as “Candidatus Atribacteria bacterium 1244-E10-H5-B2”
447
(SAMN07342547; NMQN00000000.1).
448
449
Phylogeny reconstruction for MAGS. Coding sequences from whole genomes were
450
downloaded from the NCBI representative genomes collection using NCBI e-utilities, comprising
451
405 genomes in total, spanning all bacterial lineages. Only one candidate per genus with more
452
than 1000 genes and maximum isolate information available was selected for this purpose.
453
Sequence duplication (100% identity, unlikely to be biological duplication) within genomes was
454
removed using CD-HIT. Available reference Atribacteria genomes, 24 in total, as either single-
455
cell amplified genomes (SAGs) or MAGs, were downloaded and annotated using the Prokka
456
pipeline. A list of 139 core single copy genes (CSCG) as HMM profiles was obtained from Rinke
457
et al. (2013). B2 and representative reference Atribacteria genomes were then scanned for the
458
presence of these HMM profiles using HMMer with the recommended score threshold for each
459
profile as provided in Rinke et al. (2013). In a series of manual subsampling steps, 69 CSCG
460
clusters were selected in 220 representative genomes and 20 Atribacteria genomes where 1) 69
461
clusters were present in only a single copy, 2) all 69 clusters were present in 220 representative
462
genomes and 3) the minimum number of clusters present in any Atribacteria genome was 6. All
463
69 CSCG clusters were aligned individually using the L-INS-i mode in MAFFT. Alignments
464
were then concatenated using a custom script Aln.cat.rb from the Enveomics collection (link)
465
with invariable sites removed. Phylogeny reconstruction was performed in RAxML using a
466
GAMMA model of rate heterogeneity, iterating over all models of protein substitution to choose
467
the one with best log likelihood. The analysis was performed with 1000 rapid bootstraps with the
468
MRE convergence bootstrap criterion (50 bootstrap replicates performed), followed by a
469
thorough ML search. The resulting phylogenetic tree was modified for optimal viewing in iTOL
470
with a full view including all lineages and a pruned view confirming placement of MAG B2 in
471
the Atribacteria phylogeny. Atribacteria taxonomic classifications were based on Yarza et al.
472
(2014). To examine gene orthology between B2 and other reference Atribacteria, 23 reference
473
!
19!
Atribacteria (MAGs and SAGs) genomes were annotated using Prokka. The predicted genes were
474
analyzed by BLAST best hit (BBH) clustering for orthologous group identification through
475
Proteinortho5. In B2, 55% of genes (2333/4254) lacked orthologs in other Atribacteria genomes.
476
477
Metaproteomic sample preparation, mass spectrometry, and data analyses. Proteins from
478
E10-H5 were extracted from a 10 g of frozen sediment using a protocol adapted from Nicora et
479
al. (2013). Briefly, 2.5 mL of desorption buffer (0.5 M NaCl, 0.1 M glycerol, 0.2% SDS, 6 M
480
urea, 1 mM EDTA, 100 mM ammonium bicarbonate) and 2 mL of a pH-buffered amino acid
481
solution (containing equimolar histidine, lysine, and arginine, all 83 g 1 L-1 in ultra-pure water,
482
pH 7.0) was added to the sample on ice. The goal of the pH-buffered amino acid solution is to fill
483
the electronegative mineral sites in the sample with positively charged amino acids to reduce
484
absorption of proteins to the particles. Samples were vortexed 4x, alternating 5 minutes vortexing
485
and 5 min ice. The sediment slurry was then sonicated with Bronson probe sonicator (4 x 30 s) to
486
lyse cells and heated at 95oC for 5 min. The sediment was pelleted by centrifugation (10,000 x g,
487
30 min, 4oC), and the supernatant was collected and stored on ice. The sediment pellet was
488
washed 2 more times with 3 mL desorption buffer and supernatants were combined. In order to
489
remove the SDS prior to protein digestion and mass spectrometry analysis, the filter aided sample
490
preparation (FASP) method was used (Ostasiewicz et al., 2010). Millipore Amicon 10 kDa filter
491
units were used and cleaned following manufacturer’s directions. Samples were loaded on top of
492
filters (~9 mL) and centrifuged (3000 rpm, 90 min, 4oC). In order to remove all SDS, proteins
493
retained on the filter were rinsed 3 times by adding 5 mL of 8 M urea in 50 mM ammonium
494
bicarbonate and repeating the prior centrifugation step. Iodoacetamide (3 mL, 15 mM) was added
495
to samples, incubated in the dark at room temperature for 30 minutes, and then centrifuged (3000
496
rpm, 90 min, 4oC). Proteins were then rinsed two times with 10 mL of 100 mM ammonium
497
bicarbonate and centrifuged to remove liquid (3000 rpm, 90 min, 4oC). To digest protein on the
498
filter, 0.5 µg of trypsin (modified, sequencing grade, Promega) was added to the filter, topped
499
with 2.5 mL of 25 mM ammonium bicarbonate, vortexed, and incubated 12 hr at room
500
temperature. Filtrate was collected by centrifugation (3000 rpm, 90 min, 4oC), and SpeedVaced to
501
near dryness at 4oC. Peptides were then resuspended in 50 µL of 2% acetonitrile and 0.1% formic
502
acid and desalted using Nest Group C18 Proto centrifugal macro columns following
503
manufacturer’s instructions. Each 10 µL sample was separated on a NanoAquity UPLC with a 60
504
min gradient (2-35% acetonitrile) and analyzed on a Thermo Scientific Orbitrap Fusion Tribrid
505
Mass Spectrometer operated in top20 data dependent acquisition mode.
506
A protein database for identifying the collected fragmentation spectra was generated from
507
Atribacteria MAGs (C1H2_C3H4ab_E10H5_contam.fasta).These databases were concatenated
508
with 50 common contaminants, yielding a protein database of 10,325 proteins. To assign spectra
509
to peptide sequences, correlative database searches were completed using Comet v. 2015.01 rev.
510
2 (Eng et al., 2013; Eng et al., 2015). Comet parameters included: trypsin enzyme specificity,
511
semi-digested, allowance of 1 missed cleavage, 10 ppm mass tolerance, cysteine modification of
512
57 Da (resulting from the iodoacetamide) and modifications on methionine of 15.999 Da
513
(oxidation). Minimum protein and peptide thresholds were set at P > 0.95 on Protein and Peptide
514
Prophet (Nesvizhskii et al., 2003). Protein inferences from the whole-cell lysates were accepted
515
by ProteinProphet if the thresholds noted above were passed, two or more peptides were
516
identified, and at least one terminus was tryptic (Keller et al., 2002; Nesvizhskii et al., 2003;
517
Pedrioli, 2010). For each peptide discussed in the manuscript, manual inspection of the spectral
518
identification was completed. The mass spectrometry proteomics data have been deposited to the
519
ProteomeXchange Consortium via the PRIDE partner repository (Vizcaíno et al., 2015) with the
520
dataset identifier PXD01247 (https://www.ebi.ac.uk/pride/archive/ Login:
521
reviewer08969@ebi.ac.uk Password: BP2V3yGA).
522
523
524
!
20!
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525
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