Microbial community assembly and evolution in subseafloor sediment Piotr Starnawskia, Thomas Bataillonb, Thijs J. G. Ettemac, Lara M. Jochuma, Lars Schreibera, Xihan Chena, Mark A. Levera,1, Martin F. Polzd, Bo B. Jørgensena, Andreas Schramma,d,2, and Kasper U. Kjeldsena,2 a Center for Geomicrobiology, Section for Microbiology, Department of Bioscience, Aarhus University, 8000 Aarhus C, Denmark; bBioinformatics Research Centre, Aarhus University, 8000 Aarhus C, Denmark; cDepartment of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, 75123 Uppsala, Sweden; and dParsons Laboratory for Environmental Science and Engineering, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
Edited by Edward F. DeLong, University of Hawaii at Manoa, Honolulu, HI, and approved February 1, 2017 (received for review August 24, 2016)
Bacterial and archaeal communities inhabiting the subsurface seabed live under strong energy limitation and have growth rates that are orders of magnitude slower than laboratory-grown cultures. It is not understood how subsurface microbial communities are assembled and whether populations undergo adaptive evolution or accumulate mutations as a result of impaired DNA repair under such energy-limited conditions. Here we use amplicon sequencing to explore changes of microbial communities during burial and isolation from the surface to the >5,000-y-old subsurface of marine sediment and identify a small core set of mostly uncultured bacteria and archaea that is present throughout the sediment column. These persisting populations constitute a small fraction of the entire community at the surface but become predominant in the subsurface. We followed patterns of genome diversity with depth in four dominant lineages of the persisting populations by mapping metagenomic sequence reads onto single-cell genomes. Nucleotide sequence diversity was uniformly low and did not change with age and depth of the sediment. Likewise, there was no detectable change in mutation rates and efficacy of selection. Our results indicate that subsurface microbial communities predominantly assemble by selective survival of taxa able to persist under extreme energy limitation.
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marine sediment bacteria single-cell genomics
According to “Drake’s rule” (17), DNA-based organisms only experience one fitness-affecting mutation per 300 genomes replicated. Therefore, in the highly structured subsurface sediment environment, for which generation times dramatically increase as burial proceeds (8), we predict that only a handful of adaptive mutations will arise during burial and that these will not be able to sweep through populations. Alternatively, depending on the strength of environmental selection, cells present in the subsurface seabed may accumulate mutations as a result of reduced potential for DNA repair due to extreme energy limitation. Here we test these hypotheses by exploring the microbial community assembly and evolutionary processes in the transition from shallow to deep marine sediment. Our findings have implications for understanding microbial life in one of the largest, yet most inaccessible, ecosystems on Earth: the marine deep biosphere. Significance Our study shows that deep subseafloor sediments are populated by descendants of rare members of surface sediment microbial communities that become predominant during burial over thousands of years. We provide estimates of mutation rates and strength of purifying selection in a set of taxonomically diverse microbial populations in marine sediments and show that their genetic diversification is minimal during burial. Our data suggest that the ability of subseafloor microbes to subsist in the energy-deprived deep biosphere is not acquired during burial but that these microbes were already capable of living in this unique environment. These findings represent a significant step toward understanding the bounds for life in the deep biosphere and its connection to life in the surface world.
| metagenomics | evolution |
B
acteria and archaea inhabiting the subsurface seabed account for more than half of all microbial cells in the oceans (1, 2). The subsurface communities are cut off from fresh detrital organic matter deposited on the sea floor. The energy available for cellular maintenance and growth decreases rapidly with depth and age of the sediment (3–5). As a consequence, the microbial abundance and cell-specific metabolic rates decrease by orders of magnitude already within the top few meters of sediment (3, 6–8). However, even hundreds of meters below the seafloor sediments are populated by microbes that actively turn over their biomass (3, 8). The growth characteristics of these microbial populations are unlike anything studied in pure culture as estimates suggest that the generation times of subsurface cells are tens to hundreds of years (3, 7, 8). As a model for how these deep biosphere communities are assembled, it was suggested that the microbes found in the subsurface seabed represent descendants of surface communities that were buried in the past (9–12). So far this model has not been tested systematically. Furthermore, it is not known if subsurface microorganisms during the burial evolve unique genetic traits conferring adaptation to this environment (13). Studies of adaptation and evolution in freeliving microorganisms reported high genetic heterogeneity and rapid evolutionary change within natural populations. However, these studies have focused on dynamic or mixed environments with large population sizes and rapid growth, e.g., biofilms or planktonic communities (14–16) that are very different from the subsurface seabed environment. 2940–2945 | PNAS | March 14, 2017 | vol. 114 | no. 11
Author contributions: P.S., T.B., M.F.P., A.S., and K.U.K. designed research; P.S., T.J.G.E., L.M.J., L.S., X.C., M.A.L., and K.U.K. performed research; T.J.G.E. contributed new reagents/analytic tools; P.S., T.B., L.M.J., B.B.J., and K.U.K. analyzed data; and P.S., T.B., A.S., and K.U.K. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. Data deposition: PCR amplicon and metagenome sequence data have been deposited at the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under the BioProject accessions PRJNA308429 and PRJNA305566 and BioSample accessions: SAMN04395423, and SAMN04328853–SAMN04328860, respectively. Single-cell amplified genome assemblies were uploaded to the Integrated Microbial Genomes portal (https://img.jgi.doe.gov) with the following genome IDs: 2606217225, 2626541591, 2606217224, 2609459604, 2606217227, 2606217228, 2609459605, 2626541542, 2626541544, 2626541633, 2626541550, 2626541552, 2626541551, 2609459610, 2609459611, 2609459606, 2609459607, 2626541553, 2615840654 and 2615840655. 1
Present address: Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Sciences, Eidgenö ssische Technische Hochschule Zurich, 8092 Zurich, Switzerland.
2
To whom correspondence may be addressed. Email:
[email protected] or
[email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1614190114/-/DCSupplemental.
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low relative abundance in the Top layer (Fig. 2B) but collectively made up a large proportion of the total microbial community in the deeper layers (up to 40–50%, Fig. 2B). We confirmed the trend of persisting OTUs at two stations by PCR amplifying and sequencing the dsrB gene, which encodes the dissimilatory sulfite reductase in sulfate-reducing microorganisms (SI Appendix, Fig. S2). The dsrB gene evolves faster than the 16S rRNA gene (20). However, even when using a 98% similarity cutoff to cluster all dsrB sequence reads into OTUs, we observed OTUs persisting across depths and making up a large proportion of the reads in the subsurface dsrB sequence libraries (SI Appendix, Fig. S2). We consistently observed the same persisting OTUs at different sampling stations (SI Appendix, Fig. S3 A–C), indicating that the community change was due to environmental selection and not random sampling. These results offer strong empirical evidence for an assembly of subsurface communities by selection of those populations from the surface community that can effectively conserve energy for cellular maintenance and growth in deep sediments (9, 11, 12). Approximately half of the microbial communities in the deeper sediment layers represents OTUs that did not persist across sediment depth (Fig. 2B). To assess whether these OTUs represent a unique subsurface community, we identified 855 OTUs that were exclusively present in the MG and Bottom zones compared with the Top and SR zones of each of the four stations (SI Appendix, Fig. S3D). Many of these OTUs were of low relative abundance and specific to individual stations, yet 380 OTUs were shared between the MG and Bottom zones of at least three of the four stations (SI Appendix, Fig. S3 E and F). This indicates the presence of a core subsurface community, which accounted for 8–14% of the reads in the subsurface sequence libraries. The dominant persisting 16S rRNA gene OTUs belong to bacterial and archaeal lineages usually considered key components of the deep marine biosphere, such as Atribacteria, Dehalococcoidia, Bathyarchaeota, and Marine Benthic Group-B archaea (SI Appendix, Fig. S3). However, we find that the absolute abundance of these lineages peaks in the Top or SR zone (SI Appendix, Fig. S5). These lineages lack cultured representatives; however, our limited knowledge of their physiology points to a range of organotrophic metabolic strategies for generating ATP (SI Appendix, Table S2). Although the overall microbial abundance decreased strongly from the surface to the sulfate reduction zone and below (Fig. 1B), the persisting OTUs did not
Fig. 1. Vertical biogeochemical zonation and distribution of microbial biomass and turnover in Aarhus Bay sediments. (A) Biogeochemical zonation. Top, surface sediment; SR, upper sulfate-rich sediment; SMT, sulfate–methane transition zone; MG, methanogenic sediment; Bottom, deep methanogenic zone. Pore water concentrations of sulfate and methane and sediment depth and age relate to Station M29A (SI Appendix, Table S1). Sediment depth and age axes are not drawn to scale. (B) Microbial cell abundances determined by qPCR quantification of 16S rRNA genes in extracted DNA assuming that bacterial and archaeal cells on average harbor 4.1 and 1.6 gene copies, respectively. (C) Average generation time for cells as estimated from cell-specific rates of carbon oxidation, cellular carbon content, and growth yield (Materials and Methods). In the box-and-whisker plots, the middle line represents the median value for four different sampling stations and horizontal lines represent minimum and maximum values. The box stretches from the lower to the upper quartile and dots show outliers (>1.5 times the corresponding quartile).
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Results and Discussion We combined PCR amplicon sequencing with single-cell and metagenomic sequencing to investigate how microbial communities assemble and evolve across sediment depths at four stations in Aarhus Bay, Denmark (Materials and Methods). The bay has a well-described sedimentary history with up to 10-m thick organic-rich deposits that represent the past 8,700 y of stable marine sedimentation (18). The sampled depths varied between stations, but were selected to sample the five same biogeochemical zones at each station, namely: Top, the bioturbated surface sediment; SR, the underlying zone of anaerobic sulfate reduction; SMT, the sulfate–methane transition where sulfatedependent anaerobic methane oxidation takes place; MG, the methanogenic zone where biogenic methane is accumulating; and Bottom, the deepest sampled part of the sediments (methanogenic zone) (Fig. 1A and SI Appendix, Table S1). Although the depth distribution of sediment age and microbial carbon mineralization rates show little variation across stations in Aarhus Bay (19), the depth of the SMT zone varies between stations due to differences in the thickness of the Holocene mud layer forming the marine sediment (19). Across the sampled sediment depths (0–7 m) the microbial cell density decreased from 109 to 107 cells·g−1 wet weight (Fig. 1B) and the estimated mean generation time of cells increased from months to tens of years (Fig. 1C). The generation times were estimated as the turnover time of cellular carbon by applying a model combining cell-specific carbon oxidation rates (inferred from experimentally measured sulfate reduction rates and cell abundances), estimated growth yield, and the carbon content of cells (Materials and Methods). We used high-throughput 16S rRNA gene PCR amplicon sequencing to compare microbial community compositions in samples from the different geochemical zones. The community composition [expressed in operational taxonomic units (OTUs) defined by a 97% sequence similarity cutoff] changed strongly with depth, with the surface and sulfate-rich zones each supporting distinct communities whereas the deeper zones had more similar composition and lower richness (SI Appendix, Fig. S1). Within this dataset we successfully traced OTUs across the geochemical zones in the sediment and discovered that a small number of OTUs (79 OTUs) were present throughout all sampled depths of the sediment column (Fig. 2A). This core set of persisting OTUs represented less than 10% of the total OTU richness (SI Appendix, Table S1). The persisting OTUs showed
Fig. 2. Persisting OTUs and potential for population growth. (A) Abundance of 16S rRNA gene sequence OTUs persisting across different biogeochemical sediment zones. Top, total number of OTUs in the top zone. All other zones: number of OTUs from the Top zone that persist at each of these zones. Bottom: Number of OTUs persisting throughout all zones. (B) Relative abundance in each zone of the OTUs persisting throughout all zones. (C) Cumulative number of cell generations during burial, estimated from cell-generation times (Fig. 1C) and sediment age (Materials and Methods). Bar plots in A and B show mean ± SD for four sampling stations. Top, surface sediment; SR, upper sulfate-rich sediment; SMT, sulfate–methane transition zone; MG, methanogenic zone; Bottom, deep methanogenic zone.
follow this pattern. They either showed little change with depth or increased in absolute abundance in the sulfate-reduction zone (SI Appendix, Figs. S3C and S5B), suggesting that this zone is where the assembly of the subsurface microbial community mostly takes place. In agreement, our estimates of generation times suggest that the upper zones of the sediment sustain much faster microbial growth rates than the deeper zones and thus offer a potential for more dynamic changes in microbial community composition (Figs. 1C and 2C). Do the persisting populations undergo adaptive evolution in response to the decreasing energy availability with sediment depth, or are they already adapted to live under extreme energy limitation as previously hypothesized (9)? To answer this central question, we used metagenomics and single-cell genomics to assess the extent and nature of genomic change in four dominant persisting lineages with sediment depth and age. We extracted cells from the upper part of the sulfate reduction zone (0.25 m depth) and from the methanogenic zone (1.75 m) at station M5 (SI Appendix, Table S1), sorted individual cells randomly by flow cytometry, and amplified and sequenced each of their genomes. We obtained 17 single-cell amplified genomes (SAGs) representing four different taxonomic lineages, which all harbor persisting populations (SI Appendix, Table S3 and Fig. S6). We also constructed metagenomic DNA sequence libraries from 0.25, 0.75, 1.25, and 1.75 m sediment depth of station M5 (SI Appendix, Table S1). We then mapped the reads of each library onto predicted genes in the assembled SAGs with a conservative nucleotide identity cutoff of 95%, which represents the recognized species boundary in prokaryotic systematics (21). From these data we could measure the genetic diversity in the persisting lineages represented by the SAGs and evaluate their mode of evolution. We first calculated genomic nucleotide diversity (π) as the average number of single-nucleotide polymorphism positions (SNPs) per base pair for each lineage and depth. The observed nucleotide diversity was low [≤0.0075 SNPs bp−1; notably, the sequencing error rate was >10-fold lower (Materials and Methods)] and mostly varied within less than twofold over depth depending on the population (Fig. 3A). These results indicate that absolute levels of diversity in persisting populations are similar across sediment depth. We also found that, within lineages, individuals from different sediment depths shared a high proportion of identical SNPs within their genomes (40–60%; SI Appendix, Fig. S7A). It is highly unlikely that populations in different subsurface zones have exchanged cells or genes because the clay sediments show no advective fluid 2942 | www.pnas.org/cgi/doi/10.1073/pnas.1614190114
or gas flow, and the energy limitation prevents dispersal of cells by motility (7). We thus conclude that populations separated in time (thousands of years) and space (meters) throughout the sediment column are genetically very similar because they hardly accumulated any mutations during burial. With our data it was possible to infer mutation rates (μg) in natural sediment populations assuming μg = π/2Ne (22, 23), where π is the observed nucleotide diversity and Ne the effective population size. For the calculations, we assumed that Ne equals the total population size, which we determined for each SAG from the relative abundance of mapped reads in the metagenomes and microscopic cell counts (Fig. 3B); see refs. 24 and 25 for a discussion of using the relative abundance of mapped reads in metagenomic libraries for estimating population sizes. We furthermore normalized for genome size (SI Appendix, Table S3) and the mean number of generations that separate the populations across sediment depth (Materials and Methods). For one atribacterial population, the inferred mutation rate decreased 100-fold with depth from 3·10−3 to 10−5 per genome per generation (Fig. 3C). For the other populations mutation rates were rather constant at 10−4–10−3 per genome per generation (Fig. 3C). Overall our data suggest that mutation rates and repair in the slow-growing and energy-limited subsurface microbes are not inherently different from those of fast-growing species because they are remarkably similar to rates reported by Drake for DNA-based genomes (17), for Escherichia coli cultures (26), and for natural biofilm communities (15). The low mutation rates of 1. We used the populations represented by the SAGs to compare the strength of purifying Starnawski et al.
selection acting on these populations at 0.25 and 1.75 m sediment depth and asked the question whether the selection regime shifts with sediment depth. Pn/Ps ratios were generally well below 1 and thus indicative of stringent selection. In three of four taxonomic lineages, we could not detect a significant shift in genome-wide Pn/Ps ratios with depth (SI Appendix, Fig. S7B). In the Atribacteria, we observed a small, yet statistically significant, shift toward higher Pn/Ps ratios at 1.75 m depth, compatible with modest relaxation of purifying selection in deeper sediments. We expected that genes encoding key functions in subsurface sediment still undergo purifying or adaptive selection, whereas genes obsolete in this environment may experience relaxed purifying selection. We compared Pn/Ps across broad functional gene categories, but found no trend with depth (SI Appendix, Fig. S7B). Our data thus indicate that the selection regime remains mostly unchanged across depth in the subsurface sediments. Although we explored evolutionary changes occurring within the upper 2 m of sediment, covering a time span of 2,000 y (or 500 generations), we expect that our findings can be generalized PNAS | March 14, 2017 | vol. 114 | no. 11 | 2943
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Fig. 3. Genetic diversity and rates of evolution within subsurface microbial populations. (A) Genome nucleotide diversity (π) as measured by mapping metagenome reads onto genomes of single cells derived from 0.25 m or 1.75 m sediment depth and quantifying the number of SNPs. Taxonomic names indicate the identities of the single-cell genomes (Atribacteria, n = 7; Dehalococcoidia, n = 1; Desulfatiglans, n = 2; MBG-D, n = 2; SI Appendix, Table S3). (B) Abundance of the populations represented by the single cells as inferred by multiplying the fraction of reads within a metagenomic library that mapped to the SAGs by microscopic total cell counts from the respective sediment depths. (C) Mutation rates (μg) calculated according to μg = π/2Ne, assuming that effective population size (Ne) equals the total population size shown in B and using π and the estimated genome sizes to infer SNPs genome−1 (Materials and Methods). The dashed lines show, for comparison, the μg of growing E. coli cultures. Box-and-whisker plots are as defined in Fig. 1.
to deep, million-year-old subsurface sediments: because generation times increase successively upon burial, even in 1 millionyear-old sediment layers [spanning over 6,000 generations (8)], most generations, and thus the greatest scope for adaptive evolution, is still within the top few meters, i.e., in the zone that we analyzed. In conclusion, our study shows that subsurface microbial communities in nonadvective marine sediments predominantly assemble by selective survival of surface populations without detectable further diversification or adaptation in deeper sediments. Our results also indicate that mutation repairs, and therefore gene functions, are maintained in the subsurface sediment despite the extreme energy limitation, which is consistent with existing biogeochemical evidence for microbial metabolic activity throughout the marine deep biosphere. Materials and Methods
its established protocols as previously described (31). SAGs were sequenced on a MiSeq instrument with paired-end 2× 300-bp chemistry. Estimation of Genomic Mutation Rates. Per generation mutation rates (μg) were calculated according to π = 2Ne × μg (22, 23) assuming that effective population size (Ne) equals the total observed population size. The π values (SNPs base pair−1) were calculated by mapping metagenomic reads onto predicted genes in SAG assemblies following a stringent procedure for selecting genes and SNP positions for the analyses to minimize bias from differences in mapping coverage between sediment depths and between lineages (SI Appendix). Genomic mutation rates were then calculated from estimated genome sizes of SAGs and the estimated average number of microbial cell generations separating a given sediment depth from the surface sediment derived as described in Estimation of Rates of Microbial Biomass Turnover. Pn/Ps Ratios. The ratio of nonsynonymous to synonymous number of SNPs (Pn/Ps) was calculated by mapping metagenomic sequence reads from 25-cm and 175-cm sediment depths onto genes predicted in the SAG assemblies.
Please see SI Appendix for a detailed description of the materials and methods. Sediment Sampling. Sediment cores were collected from five stations in Aarhus Bay (SI Appendix, Table S1). The stations differed by the sediment depth of the sulfate–methane transition zone (the depth at which sulfate becomes depleted and methane begins to accumulate). The cores were subsampled for DNA extraction by a protocol, which removes extracellular DNA (30), and for whole-cell extraction for single-cell sorting. The biogeochemistry of the studied cores is reported elsewhere (19). PCR Amplicon Sequence Libraries. The 16S rRNA and dsrB gene fragments were PCR-amplified using universal primer pairs. The dsrB gene encodes the β-subunit of the dissimilatory sulfite reductase and represents a phylogenetic and functional marker gene for dissimilatory sulfate-reducing microorganisms (20). Barcoded PCR products were sequenced on an Ion Torrent PGM using 300-bp chemistry (Life Technologies). A mock 16S rRNA gene community was sequenced to test for contamination during sequencing procedures and to test the fidelity of our OTU clustering procedure. Quantitative PCR. SYBR-Green–based quantitative PCR (qPCR) was used to quantify total bacterial and archaeal 16S rRNA gene copies in extracted DNA using published general primer pairs. Furthermore, 16S rRNA gene copies of selected bacterial and archaeal lineages were quantified using customdesigned primer pairs.
Estimation of Rates of Microbial Biomass Turnover. The average rate by which cells turn over their biomass in subsurface sediments was estimated from measured rates of sulfate-dependent carbon oxidation, total cell abundance, and sediment age (8). Sulfate reduction is the main terminal electronaccepting process in coastal marine sediments and accounts for the entire net oxidation of organic matter to CO2 in the SR zone. Sulfate reduction rates therefore represent the metabolic activity of the entire anaerobic microbial food chain in this zone (6, 8). In marine sediments, including Aarhus Bay, sulfate reduction rates decrease with sediment depth according to a power law function that can be extrapolated below the SMT zone to reflect carbon mineralization rates in the methanogenic zone (i.e., by methanogenesis) (19, 32). We therefore used such a power law function determined for Aarhus Bay sediments (19) to calculate organic carbon oxidation rates. Cell-specific carbon oxidation rates were calculated from total cell abundances and were used to estimate biomass turnover of cells assuming a growth yield of 8% (8) and a cellular carbon content of 21.5 fg (27). The biomass turnover serves as a proxy for the generation times of cells assuming that the cells indeed are growing by cell division in the energydepleted subseafloor as opposed to being in a nongrowth state where cells merely repair and sustain their biomolecules (9).
Generation of Single-Cell Amplified Genomes. Single-cell sorting, whole-genome amplification, and 16S rRNA gene PCR screening of single cells were performed at the Bigelow Laboratory Single Cell Genomics Center (scgc.bigelow.org) using
ACKNOWLEDGMENTS. We thank Britta Poulsen, Susanne Nielsen, Trine Bech Søgaard, and Lina Juzokaite for laboratory technical assistance; the crew of the R/V Aurora for help during sediment sampling; Sabine Flury and Hans Røy for sharing unpublished data on sulfate reduction rates; and Eva Fernández Cáceres for carrying out initial quality-filtering of metagenome sequence data. All metagenome sequencing was performed at the National Genomics Infrastructure sequencing platforms at the Science for Life Laboratory at Uppsala University, a national infrastructure supported by the Swedish Research Council (VR-RFI) and the Knut and Alice Wallenberg Foundation. This work was funded by Danish National Research Foundation Grant DNRF104; FP7 European Research Council (ERC) Advanced Grant 294200 (to B.B.J.); ERC Starting Grant PUZZLE_CELL (to T.J.G.E.); Swedish Foundation for Strategic Research Grant SSF-FFL5 (to T.J.G.E.); and grants from the Carlsberg Foundation, Julie-von-Moellen Foundation, and the Danish Agency for Science, Technology and Innovation (to A.S.).
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Supporting Information Appendix Piotr Starnawski, Thomas Bataillon, Thijs J. G. Ettema, Lara M. Jochum, Lars Schreiber, Xihan Chen, Mark A. Lever, Martin F. Polz, Bo B. Jørgensen, Andreas Schramm, and Kasper U. Kjeldsen (2017) Microbial Community Assembly and Evolution in Subseafloor Sediment. Detailed Materials and Methods Sampling. Sediment was sampled from 5 stations (M5, M24, M26, M27A and M29A) in Aarhus Bay by gravity coring (SI Appendix Table S1). Cores ranged from 5.5 to 10.8 m in length and were collected at water depths between 19 and 28 m (SI Appendix Table S1). The stations differed by the sediment depth of the sulfate-methane transition-zone (SMTZ: the depth at which sulfate becomes depleted and methane begins to accumulate). Subsamples for molecular biology analyses were collected with sterile, cut-off 5 mL syringes on ship or from intact cores in the laboratory less than 6 h upon their retrieval. The subsamples were immediately stored at -80 °C until processed for DNA extraction. Four to five subsamples from cores were used for molecular biology analyses, each subsample representing different geochemical zones of a given station (SI Appendix Table S1). Sediment subsamples for cell extraction were taken from intact core material stored at 4 oC for less than 24 h since coring. Sulfate and methane profiles and 35S-SO42- tracer-based sulfate reduction rates (1) were determined for all cores to map the geochemical zonation, these data are reported elsewhere (2). DNA and cell extractions. DNA extractions for generating PCR amplicon sequence libraries were done according to a protocol, which included an initial washing step to remove extracellular DNA (3). For the metagenomic sequencing DNA was extracted from 5 g of sediment by the latter method, and in parallel by the PowerMax Soil DNA Isolation Kit (MoBio Laboratories) following the manufacturer´s instructions. Cells for single cell sorting were extracted from the sediment by density gradient centrifugation as previously described (4). Extracted cells were stored at -80 oC in 1x TE-buffer containing 5% (w/v) glycerol (final concentrations) until shipped on dry ice to the Bigelow Laboratory Single Cell Genomics Center (SCGC, https://scgc.bigelow.org). 16S rRNA gene sequence libraries. A ~283 bp- long fragment was PCR amplified from bacterial and archaeal 16S rRNA genes using the primers Univ519F/Univ802R (5, 6). This primer pair has excellent predicted coverage of archaeal and bacterial taxonomic diversity (7). PCR was performed in 25 µl-reactions using the KAPA HiFi PCR Kit (Kapa Biosystems) with 1-2 ng template DNA and 10 µM of each primer. Thermal cycling was as follows: initial denaturation at 95 °C for 5 min; 20 cycles of denaturing at 95 °C for 20 sec, annealing at 49 °C for 20 sec, and elongation at 72 °C for 25 sec; final elongation at 72 °C for 5 min. In a second round of PCR, each sample was supplied with unique Ion Xpress (Life Technologies) barcodes by using barcoded versions of the forward primer. PCR reaction conditions were modified as follows relative to the first round of PCR: 1 µl of the first round PCR product served as template in a total reaction volume of 50 µl, only 10 PCR cycles were run, and the annealing temperature was increased to 61 °C. PCR amplicons were purified using the Agencourt AMPure XP system (Beckman Coulter) with the recommended 1.8:1 bead-to-product ratio. For a few samples, additional E-Gel purification was performed (E-Gel iBase and E-Gel Safe Imager 1
Transilluminator system; Life Technologies) to remove short, unspecific PCR amplicons. Purified samples were quantified with the Qubit dsDNA BR Assay Kit and a Qubit 2.0 Fluorometer (Life Technologies). Amplicons were pooled in equimolar amounts and the size distribution and DNA concentration of the pooled amplicons were determined on a Bioanalyzer 2100 using the High Sensitivity DNA Analysis Kit (Agilent Technologies). Sequencing was done on an Ion Torrent PGM using 300 bp chemistry and two Ion 316 Chips (Life Technologies), with 15 samples per chip, according to the manufacturer’s instructions. The sequence data was initially analyzed within the Ion Torrent PGM software to remove reads of low quality and trim adaptor sequences. Subsequent sequence data analyses were performed using the usearch v7.0.959_i86osx32 (8, 9) and mothur v.1.36.1 (10) software. A high-quality (fastq_maxee 1.0 and -fastq_trunclen 200) dataset for OTU clustering was prepared first by the fastq_filter command in uparse removing reads shorter than 200 bp. All reads were then called based on a perfect match with a barcode sequence using a custom shell script. Reads were clustered into operational taxonomic units (OTUs) with a 97% similarity cutoff. OTUs were assigned a taxonomy using mothur with the Silva SSU Ref NR release 123 database serving as a reference (11). All calculations and plots were done in the R statistical environment v3.2.0 (R Core Team, http://R-project.org) using RStudio v0.99.466 (RStudio Team, http://rstudio.com). Mock community 16S rRNA gene sequence libraries. To test for contamination during our PCR and amplicon sequence library preparation procedures and to validate our OTU clustering and sequence analysis approach, we sequenced and analyzed a 3-species mock community in parallel with our environmental samples. The mock community consisted of 3 pGEM-T plasmids (Promega) mixed in equimolar amounts each to a final concentration of 106 plasmid copies µL-1. Each plasmid carried a near full length 16S rRNA gene insert respectively of the archaeal species Methanoccocus maripaludis S2, an environmental alphaproteobacterium affiliated with the genus Altererythrobacter (for sequence, see SI Appendix Table S4) and an environmental Flavobacteriaceae bacterium (HM238120). The two environmental sequences were retrieved by PCR and cloning from a biofilter treating pig manure. A PCR product of this mock community was generated, sequenced and analyzed along with the PCR products from sediment samples using the procedures described in the section above. Our sequence analysis pipeline recovered 5 OTUs from the sequence library of the mock community. Four of the OTUs contained sequences 98-100% identical to the mock community input sequences. The Altererythrobacter affiliated input sequence gave rise to two OTUs. The 5th OTU was affiliated with Escherichia coli and thus represented a laboratory contaminant. E. coli affiliated sequence reads were not present in significant amounts in sequence libraries from sediment samples. The mock community sequence library consisted of 9,249 reads with the following distribution among detected OTUs: M. maripaludis-OTU: 3%; Altererythrobacter-OTUs 42% and 16%; Flavobacteriaceae-OTU: 38%; E. coli-OTU: 0.8%. The M. maripaludis input sequence was underrepresented in the sequence library likely as a result of PCR bias although the primers have perfect match to the sequence. To counteract this bias against Archaea, we treated bacterial and archaeal sequence reads separately in the analysis of our 16S rRNA gene sequence libraries and normalized their relative abundance by separate bacterial and archaeal qPCR assays. However the mock community sequence library showed that: (i) our amplicon sequence
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libraries are minimally affected by laboratory contamination; (ii) the IonTorrent sequencing and our sequence analysis pipeline artificially inflates OTU richness only to a minor extent. dsrB sequence libraries. Approximately 350 bp long fragments of the dsrB gene, which encodes the beta subunit of the dissimilatory sulfite reductase and which represents a phylogenetic and functional marker gene for dissimilatory sulfate-reducing microorganisms (12), were PCR amplified from the same DNA extracts as used for generating the 16S rRNA gene PCR amplicon libraries. The PCR reactions were made with the primer variant mixtures dsrB-F1a-h and dsrB-4RSI1a-f (13), which targets most known dsrB sequence diversity (12). PCR reaction mixtures included 12.5 µL Hot Star Taq Master Mix (Qiagen), 1 µL of each primer variant mixture (containing 2 pmol µL-1 of each variant), 2 µL BSA (10 mg µL-1) and 1 µL DNA template. Thermal cycling included 95 °C for 15 min; 40 cycles of 93 °C for 30 s, 56 °C for 30 s, 72 °C for 20 s and a final extension step of 72 oC for 10 min. PCR products were supplied with Ion Xpress barcodes (Life Technologies) and sequenced on an Ion Torrent PGM as described for the 16S rRNA gene sequence libraries. Resultant dsrB sequence libraries were initially quality-filtered using mothur (10) removing sequences: (i) not carrying a perfect match to the barcode and one of the forward primer variants; (ii) sequences with ambiguous base calls or homopolymeric stretches exceeding 6 nt; and (iii) sequences shorter than 240 nt. The nucleotide sequences were corrected for homopolymer sequencing errors causing frameshifts in their inferred amino acid sequence using the FrameBot tool (14) of the Functional Gene Pipeline & Repository website (15). The inferred amino acid sequences were aligned against a custom-made reference set of aligned DsrAB sequences (12) using hmmalign of the HMMER 3.0 package (http://hmmer.org). The coding nucleotide sequences representing inferred amino acid sequences not aligning to the reference set were removed from further analysis. The dsrB nucleotide sequences were aligned using their respective aligned amino acid sequence as reference with a custom made Python script (http://python.org) kindly provided by Dr. Ian Marshall (Center for Geomicrobiology, Aarhus University, Denmark). The aligned nucleotide sequences were further denoised with the precluster algorithm implemented in mothur and then chimeric sequences were removed with the Chimera.uchime algorithm also implemented in mothur. The resultant final nucleotide sequence dataset were clustered into OTUs using the cluster_otus command in USEARCH v7.0.959_i86osx32 (8). Quantitative PCR (qPCR). Total bacterial and archaeal 16S rRNA gene copies in the extracted DNA were quantified by SYBR-Green based qPCR. The primer pair Bac908F (5´AACTCAAAKGAATTGACGGG-3´) (16) / Bac1075R (5´-CACGAGCTGACGACARCC-3´) (16), was used for the bacterial assay, and the primer pair Arch915F (5´AATTGGCGGGGGAGCAC-3´) (17) / Arch1059R (5´-GCCATGCACCWCCTCT-3´) (18), for the archaeal assay. Reaction and thermal cycling conditions as well as qPCR standards were described previously (19). 16S rRNA gene copies of specific bacterial and archaeal taxonomic groups were quantified by SYBR-Green-based qPCR by designing primer pairs that specifically targeted a narrow range of predominant OTUs within these groups. The groups and respective primer sequences (5´-3´) were as follows: Atribacteria (F- GGTCTTAAAAGTCAGGTGTGA,
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RTCAGCGTCAGAGATAGACC); Marine Benthic Group-D archaea (FGGTAATACCTGCAGCTCAG, R- CGTCAGACCCGTTCTAGC); Dehalococcoidea (FGCGTAAAGAGGGCGTAGG, R- TCAGAAAYAGCCCAGGAGG). Thermal cycling, with an annealing temperature of 57 oC, and reaction conditions were similar to the total 16S rRNA gene quantification qPCR assays. Standards were prepared from pGEM-T (Promega) plasmids carrying a 16S rRNA gene insert of a representative member of the respective target groups by PCR amplifying the insert and flanking regions with a common M13 primer pair. The PCR products were purified (GenElute PCR Clean-up Kit, Sigma), and the concentrations of the purified products were determined by the Qubit dsDNA BR Assay Kit and a Qubit 2.0 Fluorometer (Life Technologies). The purified products were used for preparing 10-fold serial dilutions for standard curves. All standards were Sanger sequenced to ensure the integrity of the PCR priming sites using a commercial sequencing service (GATC Biotech). The standard curves were linear within a range of 102-108 target gene copies µL-1 template and PCR efficiencies as determined from the slope of standard curves ranged from 2.02-2.09. The specificity of the designed qPCR primer pairs was evaluated by cloning and sequencing endpoint PCR products generated from DNA extracted from 25 cm sediment depth from station M5 in Aarhus Bay (SI Appendix Table S1). The insert of 31-36 clones from each clone library was Sanger sequenced on one strand (GATC Biotech). All sequenced clones represented the intended target group of a given assay. Metagenome library construction and generation of single cell amplified genomes. For metagenomic sequence libraries DNA extracts were purified and concentrated with the DNA Clean and Concentrator kit (Zymo Research). Barcoded DNA sequence libraries were prepared using the Nextera DNA Library Preparation Kit (Illumina) according to the manufacturer´s protocol, with 50 ng of input DNA. Paired end sequencing was carried out on a HiSeq2500 instrument (Illumina) working in rapid mode using 2x 150 bp chemistry for libraries generated from 25 and 75 cm sediment depth and 2x 250 bp chemistry for libraries generated from 125 and 175 cm depth. We quantified sequencing error rates by analyzing the sequencing reads from the phiX174 phage genomic DNA added to all sequencing runs as an internal control as part of the standard Illumina sequencing protocol. These reads were mapped onto the reference phiX174 phage genome sequence (NCBI GI:764044028) using BBMap v34.x (Bushnell B. – http://sourceforge.net/projects/bbmap) and single nucleotide polymorphisms (SNPs) were identified with the SAMtools software v1.2 (20). Per site error rate were calculated by counting the number of SNPs per nucleotide position in the genome and dividing this with the total read coverage of that position. Mean sequencing error rates (±SD) ranged from 0.00022 (± 0.00015) to 0.00022 (±0.00010), which is much lower than the observed levels of genome nucleotide diversity (0.002-0.008; Fig. 3A). In addition, we observed a high proportion of SNP positions, which are conserved across sediment depth (SI Appendix Fig. S7A). These results prove that the influence of sequencing errors is negligible for the interpretation of our data. Single-cell sorting, whole-genome amplification, and 16S rRNA gene PCR screening of single cells were performed at the SCGC using their established protocols as previously described (4). Single cells were sorted on two 384-well microplates and produced a total of 242 successful SAGs. Barcoded sequence library preparation from SAGs was done by the Nextera XT DNA
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Library Preparation Kit (Illumina), with 1 ng of input DNA. The sequencing was performed on a MiSeq instrument with paired-end 2x 300 bp chemistry. SAG and metagenome assembly and analysis. Metagenomic libraries were prepared and sequenced from DNA extracted by two independent methods from the same homogenized sediment samples. Reads from the two libraries from a given sample were pooled and analyzed. Sequence reads from metagenomes and SAGs were processed with the trimmomatic software v0.32 (21) for adapter trimming and quality filtering (flags used: Paired End [PE], ILLUMINACLIP:nextera_adaptors.fasta:2:30:10 for the adaptor trimming and SLIDINGWINDOW:5:30 for the quality filtering). SAGs were assembled using SPAdes v3.5.0 (22) with the --careful flag. Annotation was performed using the IMG annotation service (23). If 16S or 23S rRNA sequences were not present in a SAG assembly its taxonomy was inferred from its predicted set of conserved single copy genes identified with Phylosift v1.0.1 (24). Mapping of metagenomic reads on to SAG assemblies was done in BBMap v34.x with a lower identity cutoff of 0.95. Mapped reads in .sam format were converted to .bam format. SNPs were called using the SAMtools software v1.2 (20). All calculations were done in RStudio. Estimation of genomic mutation rates. Per generation mutation rates (µg) were calculated according to π = 2Ne × µg (25, 26) assuming that effective population size (Ne) equals the total observed population size. π values (SNPs base pair-1) were calculated by mapping metagenomic reads onto predicted genes in SAG assemblies. Predicted genes included in the analyses were carefully selected by a stringent procedure. Only genes mapped by metagenomes from all four sediment depths and which had more than 5x metagenomic coverage across minimum 60 bp long gene fragments were considered in the analyses. Furthermore, only genes from SAGs with more than 10 genes that met the former criteria were included in the final reported results. The final dataset included 2,264 genes from 12 SAGs each contributing between 48 and 388 genes which were mapped with 4·103 to 106 metagenomic reads (SI Appendix Table S3). The π values were scaled to yield a total number of genome pairwise nucleotide differences (SNPs genome-1) by multiplying them with the estimated size of the genomes represented by the individual SAGs. Size estimates were obtained by dividing the size of the SAG assemblies with their estimated respective completeness taking the average values for all SAGs of a given taxonomic lineage (SI Appendix Table S3). Finally, a total mutation rate per genome per generation (SNPs generation1 ) was calculated by dividing scaled π values with the estimated average number of microbial cell generations separating a given sediment depth from the surface sediment derived as described in the “Estimation of rates of microbial biomass turnover” section below. The total population size was calculated as the product of the relative abundance of metagenomic reads mapping to a given SAG and the total cell density as obtained from fluorescence microscopy counts of DNA stained cells (27). Pn/Ps ratios. The ratio of nonsynonymous to synonymous number of SNPs (Pn/Ps) was calculated by mapping metagenomic sequence reads from 25 and 175 cm sediment depth onto genes predicted in the SAG assemblies. The genes included in the analyses were the same as those selected for estimating mutation rates. Given that genes had typically low counts of SNPs we standardized counts by adding 1 to all counts of SNPs in each gene. This reduces the 5
statistical noise of these ratios but for a given SAG-defined lineage still allows for proper comparisons of Pn/Ps values obtained from different sediment depths (see also reference [28] for further discussion). The Pn/Ps ratios were further standardized to account for the fact that, given the structure of the genetic code, a non-synonymous mutation event is approximately 3-fold more likely than a synonymous mutation event. Thus Pn/Ps was calculated by the following formula: Pn/Ps = [(1+Pn)/3]/(1+Ps). To test the hypothesis of relaxation of purifying selection across depths, we counted the number of genes exhibiting a higher Pn/Ps ratio at 175 relative to 25 cm depth. A P-value for the hypothesis of “no change in purifying selection between depths” was obtained (binomial exact test). The predicted genes included in the analyses were grouped according to their inferred functional role by classifying them against the eggNOG database, release May 2015 (29). The sequence searches against the eggNOG database were performed with the hmmsearch program of the HMMER software package (30), recording only the best match based on the expected value. Estimation of rates of microbial biomass turnover. The average rate by which cells turn over their biomass in subsurface sediments can be estimated from the rate of carbon oxidation in the sediment and the total cell densities (31). The rate of biomass turnover serves as a proxy for the generation times of cells assuming that the cells indeed are growing by cell division in the energy-depleted subseafloor as opposed to being in a non-growth state where cells merely repair and sustain their biomolecules (32). Depth profiles of organic carbon oxidation rates were calculated from a power law-based model relating sediment depth and sulfate reduction rates. The model is based on measurements of sulfate reduction rates by 35S-tracer techniques at 12 different sampling Stations in Aarhus Bay (2). The rates of carbon oxidation were determined from the sulfate reduction rates assuming 2 moles of carbon oxidized per mole of sulfate reduced. Cell densities were derived from qPCR quantification of bacterial and archaeal 16S rRNA genes for stations M24, M26, M27A and M29A (33) assuming that bacterial and archaeal cells on average harbor 4.1 and 1.6 copies of the 16S rRNA gene, respectively (34). For station M5, cell densities were determined by epifluorescence microscopy cell counts (27). Average cell specific rates of carbon oxidation were converted to rates of cellular biomass turnover assuming a growth yield of 8% (2) and a cellular carbon content of 21.5 fg (35). The relationship between sediment depth and age was determined from 14C dating of bivalve shells for station M5 (27). For station M24, M26, M27A and M29A the relationship was determined from average rates of sedimentation at the individual stations (2). The latter assumes that sedimentation was constant across time in Aarhus Bay, and is supported by the 14C-based age model for station M5. By relating sediment age and the rate of biomass turnover we then calculated the cumulative number of cell generations across depths of the various sampling stations. Extensive sediment mixing by bioturbation extends down to around 7 cm below sea floor in Aarhus Bay (36). Therefore the residence time of a cell in the upper mixed layer of the sediment is unknown, and our calculation of cell generation times thus begins at 7 cm depth. For the same reason, the evolutionary analysis of the persister lineages does first start at 25 cm depth, well below the mixed layer.
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SI Figures SI Figure S1. Changes in species richness and composition across geochemical zones. (A) Chao1 estimates for 16S rRNA gene sequence OTU (97% cut-off) richness in different geochemical zones. In the Box-and-Whisker plots the middle line represents the median value for four different sampling stations and vertical lines minimum and maximum values. The box stretches from the lower to the upper quartile and dots show outliers (>1.5 times the corresponding quartile). The low Chao1 estimate for the Top zone is likely a sampling artifact caused by the uneven species distribution characteristic for this zone with a few highly predominant OTUs dominating the sequence libraries (SI Appendix Table S1). (B) Comparison of microbial community compositions from different geochemical zones and sampling stations by non-metric multidimensional scaling (NMDS) ordination of 16S rRNA gene sequence OTU frequencies. The NMDS ordination was made with a Wisconsin normalized Bray-Curtis dissimilatory matrix calculated from a subsampled data set (n=5,814 sequences per sample). Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone.
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SI Figure S2. Persisting dsrB OTUs. Number of dsrB OTUs persisting across depth in sediment cores from two sampling stations (M24 and M29A). Bars show the number of OTUs shared with the Top zone (i.e. persisting OTUs). Filled circles connected by lines show the frequency of sequences assigned to persisting OTUs present in all zones of a given station. Sequences were clustered into OTUs based on either (A) a 90% sequence identity cut-off (corresponding to a 97% 16S rRNA gene sequence cut off (12) or (B) a 98% identity cutoff. Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone.
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SI Figure S3. Depth distribution of persisting 16S rRNA gene sequence OTUs (A-C) and identity of OTUs uniquely present in subsurface part of sediments (D-F). Sequences were clustered into OTUs with a 97% cut-off and originate from PCR amplicon sequence libraries from different geochemical zones of sediment cores from four different sampling stations (M24, M26, M27A, M29A) in Aarhus Bay (SI Table S1). (A) The top ten most abundant OTUs persisting across all zones, relative abundances and (B) Top ten most abundant OTUs present in the deepest zone, relative abundances. In addition (C) absolute abundances of OTUs shown in A were estimated by multiplying the library-based relative abundance of individual OTUs with qPCR-derived total bacterial or archaeal 16S rRNA gene abundances. The validity of this approach was confirmed by independent qPCR quantification of selected groups (SI Appendix Fig. S4). White cells represent the absence of a given OTU at a given sample and relative/absolute abundance of "0" means that this value was below 0.1 but still detectable. (D) Relative abundance of 16S rRNA gene sequence OTUs in sequence libraries from the surficial sediment (Top and SR zones) and subsurface sediment (MG and Bottom zones). Shown values represent the average abundance of a given OTU in the Top and SR or the MG and Bottom zones across all 4 stations. Data points shown in blue color represent OTUs present in the surficial sediments but absent in the subsurface sediments. Conversely data points shown in black color represent OTUs only detected in the subsurface sediments. The axes show log10 scales, except for the origo, which show a value of 0 (i.e. the absence of OTUs). (E) Distribution of the 855 OTUs unique to the subsurface sediment across the zones of the 4 different stations. A set of 380 OTUs is present in the subsurface part of ≥3 of the four stations. These 380 OTUs make up 8-14% of the subsurface communities at the 4 individual stations; their taxonomic distribution is shown in panel (F), where shown values represent averages for the 4 stations. Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone. Taxonomic classification according to Silva taxonomy (11).
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SI Figure S3.
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SI Figure S4. Depth distribution of taxonomic groups in sediment cores from Aarhus Bay. Comparison of group-specific 16S rRNA gene qPCR-derived abundances (qPCR) and abundances estimated by multiplying the 16S rRNA PCR amplicon library-based relative abundance of individual groups with qPCR-derived total bacterial (Atribacteria [A] and Dehalococcoidia [C]) or archaeal (MBG-D [B]) 16S rRNA gene abundances (seq+qPCR). Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone. Error bars show SD (n=3).
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SI Figure S5. Depth distribution of major taxonomic groups across geochemical zones. (A) 16S rRNA PCR amplicon library-based relative abundances. (B) Absolute abundances estimated by multiplying the relative abundances shown in A by qPCR-derived total bacterial or archaeal 16S rRNA gene abundances. The Box-and-Whisker plot shows values for four different sampling stations as described for SI Appendix Fig. S1. The validity of this approach was confirmed by independent qPCR quantification of selected groups (SI Appendix Fig. S4). Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone. Names of archaeal groups are show in white on a darkgrey background. *Formerly named: Candidate phylum JS1, †Formerly named: Candidate phylum OP8, ‡Marine Benthic Group-B, §Marine Benthic Group-D, ¶Marine Hydrothermal Vent Group, # Formerly named: Miscellaneous Crenarchaeotal Group, ||Formerly named: Candidate phylum TM6.
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SI Figure S6. Phylogenetic affiliation of 16S rRNA gene sequences from single cell amplified genomes (SAGs, highlighted in bold). SAGs originate from 0.25 or 1.75 m sediment depth of Aarhus Bay station M5. The tree was inferred by FastDNA maximum likelihood analysis, as implemented in the ARB program package (37) considering 1,277 conserved alignment positions between E. coli 16S rRNA gene sequence positions 63-1,373. The bar shows 5% estimated sequence divergence. Bootstrap analysis was performed by Neighbor Joining analysis with 1000 resamplings, nodes receiving >50% and >80% support are indicated by grey and white circles, respectively. The insert shows a scale-up of the lineage formed by atribacterial SAGs. The phylogenetic affiliation of predominant persisting 16S rRNA gene sequence OTUs (SI Appendix Table S3) is indicated as follows: *SAG 190_F8 shares 97% sequence identity with OTU3. †SAG 190_A10 and G5 share 98% sequence identity with OTU1. ‡SAG 190_F3 shares 96% sequence identity with OTU16. §SAG 190_A8 shares 99.4% identity with OTU 31. ¶ MBG-D = Marine Benthic Group-D. #Short sequence added to the tree without changing the overall tree topology with the Parsimony tool of the ARB program package The pairwise identities were based on a comparison between the 16S rRNA gene sequence of the SAGs and a representative sequence of a given OTU. Reference sequences are identified by GenBank accession numbers or locus tags.
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SI Fig. S7. Unique genetic diversity within persisting populations and strength of purifying selection. (A) Proportion of single nucleotide polymorphism (SNPs) only observed at a single sediment depth. SNPs were recorded by mapping metagenome reads onto predicted genes from SAGs. Only genes mapped by the metagenomes of all 4 sediment depths were considered. SAGs from 4 different taxonomic lineages were included in the analyses. The Box-and-Whisker plot shows values for the different SAGs of a given lineages as described for SI Appendix Fig. S1. (B) Strength of purifying selection expressed as the ratio of non-synonymous to synonymous single nucleotide polymorphisms (Pn/Ps). Pn/Ps ratios were inferred for proteincoding genes from SAGs derived from 0.25 and 1.75 m sediment depth by mapping metagenomic reads onto the SAG-genes (see Materials and Methods). Only genes mapped by metagenomes at all sediment depths were considered in the analyses. Genes are colored according to their inferred functional role. SAGs from four different taxonomic lineages were included in the analyses. P-values indicate the likelihood that Pn/Ps ratios have a similar numerical distribution at the two sediment depths (binomial exact test). *Marine Benthic groupD.
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SI Table S1. Sampling depths, geochemical zonation and summary of sequencing data Station* (water depth) M5 (28 m)
M24 (29 m)
M26 (19 m)
M27A (19 m)
M29A (19 m)
Geochemical zone† SR Methane Methane Methane Top SR SMT Methane Bottom Top SR SMT Methane Bottom Top SR SMT Methane Bottom Top SR SMT Methane Bottom
Sampling depth (mbsf) 0.25 0.75 1.25 1.75 0.09 0.44 4.15 5.05 5.60 0.09 0.44 3.64 4.14 5.21 0.12 0.37 2.87 3.25 7.12 0.12 0.36 2.45 2.70 7.20
Age (years bp) 250 750 1,250 1,750 88 438 3,282 3,785 4,091 89 441 3,002 3,280 3,874 122 365 2,574 2,782 4,935 119 360 2,341 2,480 4,980
Analysis‡
Sequence reads§
ML & SCG ML ML ML & SCG AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL AL
298,041,740 257,680,782 198,932,824 223,003,452 163 rRNA/3,000 dsrB 5,463 rRNA/3,000 dsrB 9,857 rRNA/3,000 dsrB 7,322 rRNA73,000 dsrB 9,868 rRNA/3,000 dsrB 11,330 rRNA 34,267 rRNA 69,966 rRNA 14,877 rRNA 20,212 rRNA 18,083 rRNA 26,416 rRNA 37,951 rRNA 34,255 rRNA 23,166 rRNA 7,935 rRNA/3,000 dsrB 9,885 rRNA/3,000 dsrB 3,2196 rRNA/3,000 dsrB 23,604 rRNA/3,000 dsrB 24,977 rRNA/3,000 dsrB
Number of OTUs observed¶ – – – – 84 rRNA/1,313 dsrB 913 rRNA/755 dsrB 883 rRNA/631 dsrB 980 rRNA/583 dsrB 1,016 rRNA/859 dsrB 891 rRNA 1,727 rRNA 1,705 rRNA 1,270 rRNA 1,384 rRNA 1,232 rRNA 1,904 rRNA 1,501 rRNA 1,467 rRNA 1,069 rRNA 947 rRNA/1,156 dsrB 1,299 rRNA/883 dsrB 1,346 rRNA/622 dsrB 1,281 rRNA/786 dsrB 937 rRNA/658 dsrB
Evenness (16S rRNA¶) – – – – – 0.81 0.79 0.83 0.79 0.72 0.74 0.64 0.81 0.78 0.72 0.78 0.67 0.74 0.55 0.74 0.80 0.73 0.75 0.58
*Latitude-longitude. M5: 56.103333-10.457833. M24: 56.111883-10.41635. M26: 56.11155-10.41715. M27A: 56.111317-10.41805. M29A: 56.11075-10.419583. † Top = surface sediment; SR = upper sulfate-rich sediment; SMT = sulfate-methane transition zone; MG = methanogenic zone; Bottom = deep methanogenic zone. ‡ AL: PCR amplicon sequence library. ML: Metagenome libraries. SCG: Single cell genomes. § Number of sequence reads after quality-filtering. Total number of forward + reverse sequence reads (Illumina NextSeq 150 bp chemistry) are shown for metagenomes. All dsrB sequence libraries were subsampled to a size of 3,000 reads. ¶ PCR amplicon sequence libraries. rRNA: 16S rRNA gene. 16S rRNA and dsrB gene amplicon sequences were clustered into OTUs using a distance cutoff of 3.0 and 2.0%, respectively. 16
SI Table S2. Identity and function of predominant persisting 16S rRNA gene sequence OTUs OTU OTU1
Domain Bacteria
Taxonomic classification (alternative name)* Atribacteria (Candidate phylum JS1)
OTU3
Bacteria
Deltaproteobacteria/Desulfarculaceae/Desulfatiglans
OTU7
Bacteria
Deltaproteobacteria/Syntrophaceae/Desulfobacca
OTU11
Bacteria
Aerophobetes (BHI80-139)/unclassified
OTU16
Bacteria
Chloroflexi/Dehalococcoidia/GIF9
OTU18
Bacteria
Aminicenantes (Candidate phylum OP8)
OTU78
Archaea
Marine Benthic Group-B (MBG-B)
OTU105
Bacteria
Candidate phylum LCP-89
OTU490
Archaea
Bathyarchaeota
OTU31
Archaea
Thermoplasmatales/Marine Benthic Group-D (MBG-D)
Inferred energy metabolism† Organotrophic, fermentative Organotrophic, dissimilatory sulfate reducer Organotrophic, dissimilatory sulfate reducer Uncertain Organotrophic, fermentative, sulfite-reducing Organotrophic, fermentative Putative organotrophic, dissimilatory Fe- or Mn-reducer Uncertain Organotrophic, fermentative, acetogen, methanogen Organotrophic, fermentative
Reference 38, 39 40 41 42 43, 44, 45, 46 42 47 – 4, 48, 49 4
*Silva-based taxonomy (11). † Based on the cited reference(s).
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SI Table S3. Single cell amplified genome (SAG) assembly statistics and taxonomic identity SAG name* 190_A5
§
Depth (mbsf)
Sequence reads†
Assembly size (bp)
Contigs
G+C (%)
Completeness (%)‡
ORFs/ mapped
Taxonomy¶
0.25
1,706,811
593,086
195
35.3
14
686/388
Atribacteria
0.25
5,476,605
247,485
92
34.9
4
267/220
Atribacteria
1.75
4,190,296
413,447
167
35
15
488/76
Atribacteria
1.75
1,086,654
865,462
320
35.2
16
992/172
Atribacteria
1.75
2,592,013
1,146,637
440
35.2
50
1,352/256
Atribacteria
190_G5
1.75
2,807,664
624,023
183
35.4
–
691/188
Atribacteria
190_H6
1.75
1,450,546
621,098
245
35.7
11
767/124
Atribacteria
190_B1 190_D3 191_B3c
0.25 0.25 0.25
1,809,626 1,678,001 4,458,811
854,207 1,146,176 1,556,758
152 293 338
47.5 48.6 48.5
26 37 51
1,007 1,353 1,835
Dehalococcoidia Dehalococcoidia Dehalococcoidia
190_F3
0.25
1,698,338
353,271
64
47
31
399/152
Dehalococcoidia
190_H5 190_H1 190_D9 190_F2
1.75 0.25 1.75 0.25
1,461,517 2,405,842 1,703,977 1,613,790
848,699 793,811 1,068,743 712,387
287 308 227 123
47.1 47.4 49.7 48.3
34 40 36 28
1,061 1,055 1,276 791
190_A9
1.75
1,754,307
897,160
334
42.2
43
1,104
190_F8
1.75
1,708,599
487,358
181
45.9
14
618/380
191_B12
1.75
1,779,247
1,199,698
327
48.4
19
1,430/48
190_A2
0.25
1,592,081
2,173,979
711
36.6
56
2,347/80
190_A8
1.75
1,593,526
1,064,389
290
35.5
35
1,220/180
Dehalococcoidia Dehalococcoidia Dehalococcoidia Dehalococcoidia ∂-Proteobacteria /Desulfatiglans ∂-Proteobacteria /Desulfatiglans ∂-Proteobacteria /Desulfatiglans Thermoplasmatales/ MBG-D# Thermoplasmatales/ MBG-D#
190_C5c 190_A10 190_D7 190_F6
Metagenome reads mapped|| 23,050;39,969; 67,098;72,040 10,204;16,892; 30,353;30,575 4,545;53,897; 378,210;673,177 8,043;72,225; 585,044;697,454 12,929;113,391; 976,491;1,078,552 8,046;79,878; 525,659;965,389 6,766;50,549; 373,985;463,871
13,275;36,206; 43,256;27,752
17,225;20,380; 29,682;50,730 24,412;21,973; 14,432;13,148 9,581;9,263; 34,052;27,509 25,851;28,132; 41,684;54,437
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*SAGs were obtained from the uppermost part of the sulfate reduction zone (0.25 m depth) or the methanogenic zone (1.75 m depth) of sediment from station M5. SAGs highlighted in bold were used for estimating genetic diversity within persisting populations by mapping metagenomic reads onto the SAG assemblies and counting single nucleotide polymorphisms within predicted genes. † Number of quality-filtered forward and reverse reads (Illumina MiSeq 300 bp chemistry) used for assembly. ‡ Genome completeness as estimated from the presence of conserved single-copy genes by CheckM (50). –, not possible to calculate completeness. § Predicted number of protein-coding genes (open reading frames, ORFs)/number of ORFs mapped by metagenomic reads for counting single nucleotide polymorphisms. ¶ Silva-based taxonomy (11) as inferred from assembled 16S rRNA gene sequences. # MBG-D, Marine Benthic Group-D. || Number of metagenome sequence reads mapped to SAGs for mapping SNPs. The four numbers separated by semicolons represent mapped reads from metagenomes from 25, 27, 125 and 175 cm sediment depth, respectively.
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SI Table S4. 16S rRNA gene sequence of the plasmid used for the mock community test and as bacterial qPCR standard > clone Grp2_Bac1: Altererythrobacter/Erythrobacter AGAGTTTGATCATGGCTCAGAACGAACGCTGGCGGCATGCCTAACACATG CAAGTCGAACGAACCCTTCGGGGTGAGTGGCGCACGGGTGCGTAACGCGT GGGAACCTGCCCTTAGGTTCGGAATAACAGTGAGAAATCGCTGCTAATAC CGGATAATGTCTTCGGACCAAAGATTTATCGCCAAAGGATGGGCCCGCGT TGGATTAGCTAGTTGGTGAGGTAAAGGCTCACCAAGGCGACGATCCATAG CTGGTCTTAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGAC TCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGAAAGCCTGA TCCAGCAATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTT TACTAGGGATGATAATGACAGTACCTAGAGAATAAGCTCCGGCTAACTCC GTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAAATAC TGGGCGTAAAGCGCACGTAGGCGGCGTCGTAAGTCAGGGGTGAAATCCCA GAGCTCAACTCTGGAACTGCCCTTGAAACTGCAATGCTAGAATATTGGAG AGGTCAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAG AACACCAGTGGCGAAGGCGACTGACTGGACAATTATTGACGCTGAGGTGC GAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTA AACGATGATAACTAGCTGTTCGGGCTCATAGAGCTTGAGTGGCGCAGCTA ACGCATTAAGTTATCCGCCTGGGGAGTACGGTCGCAAGATTAAAACTCAA AGGAATTGACGGGGGCCTGCACAAGCGGTGGAGCATGTGGTTTAATTCGA AGCAACGCGCAGAACCTTACCAGCCTTTGACATCCTTCGACGGTTTCTAG AGATAGATTCCTTCCTTCGGGACGAAGTGACAGGTGCTGCATGGCTGTCG TCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCC TCATCCTTAGTTGCCATCATTCAGTTGGGCACTTTAAGGAAACTGCCGGT GATAAGCCGGAGGAAGGTGGGGATGACGTCAAGTCCTCATGGCCCTTACA GGCTGGGCTACACACGTGCTACAATGGCATCTACAGTGAGTAGCGATCCC GCGAGGGCTAGCTAATCTCCAAAAGATGTCTCAGTTCGGATTGTTCTCTG CAACTCGAGAGCATGAAGGCGGAATCGCTAGTAATCGCGGATCAGCATGC CGCGGTGAATACGTTCCCAGGCCTTGTACACACCGCCCGTCACGCCATGG GAGTTGGTTTCACCCGAAGGTGGTGCGCTAACCGGTTTACCGGAGGCAGC CAACCACGGTGGGATCAGCGACTGGGGTGAAGTCG
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