Abstract
Anoxic subsurface sediments contain communities of heterotrophic microorganisms that oxidize organic carbon at extraordinarily slow rates. In order to assess the mechanisms by which subsurface microorganisms access detrital sedimentary organic matter, we measured kinetics of a range of extracellular peptidases in anoxic sediments of the White Oak River estuary, NC. Nine distinct peptidase substrates were enzymatically hydrolyzed at all depths. Potential peptidase activities (Vmax) decreased with increasing sediment depth, although Vmax expressed on a per cell basis was approximately the same at all depths. Half-saturation constants (Km) decreased with depth, indicating that subsurface enzymes are adapted to low substrate concentrations. Potential activities of extracellular peptidases acting on molecules that are enriched in degraded organic matter (D-phenylalanine and L-ornithine) increased relative to enzymes that act on L-phenylalanine, further suggesting microbial community adaptation to access degraded organic matter. Nineteen classes of exported peptidases were identified in genomic data from the same site, of which genes for class C25 (gingipain-like) peptidases represented more than 40% at each depth. Methionine aminopeptidases, zinc carboxypeptidases, and class S24-like peptidases, which are involved in single-stranded DNA repair, were also abundant. These results suggest a subsurface heterotrophic microbial community that primarily accesses low-quality detrital organic matter via a diverse suite of well-adapted extracellular enzymes.
Importance Burial of organic carbon in marine and estuarine sediments represents a long-term sink for atmospheric carbon dioxide. Globally, ~40% of organic carbon burial occurs in anoxic estuaries and deltaic systems. However, the ultimate controls on the amount of organic matter that is buried in sediments, versus oxidized into CO2, are poorly constrained. Here we used a combination of enzyme assays and metagenomic analysis to identify how subsurface microbial communities catalyze the first step of proteinaceous organic carbon degradation. Our results show that microbial communities in deeper sediments are adapted to access molecules characteristic of degraded organic matter, suggesting that those heterotrophs are adapted to life in the subsurface.
Introduction
A large fraction of the microorganisms in subsurface sediments are heterotrophs that metabolize aged, microbially altered organic matter (Lomstein et al., 2009; Jørgensen and Marshall, 2016; Biddle et al., 2006). These communities’ metabolisms can be more than a million-fold more slower than cells in culture (Hoehler and Jørgensen, 2013; Lomstein et al., 2009). A recent meta-analysis showed that only about 12% of cells in marine sediments belonged to cultured species, while 27% belong to phyla that contain no cultured representatives (Lloyd et al., 2018). Consequently, the mechanisms by which these microorganisms access detrital organic matter are poorly understood.
In surface environments, where carbon transformations are relatively rapid, heterotrophic microorganisms gain energy by metabolizing a combination of small molecules (<600-1000 Da), which can be taken up directly via general uptake porins (Benz and Bauer, 1988) and macromolecules, which must be broken down outside of the cell by extracellular enzymes. Most freshly-produced organic matter is macromolecular, and large molecules tend to be more bioavailable than small ones (Benner and Amon, 2015), so the nature and activity of extracellular enzymes present in surface environments is a major control on the rate of microbial carbon oxidation in such environments.
It is not clear whether microbial extracellular enzymes play the same role in subsurface sediments. It is conceivable that macromolecules are broken down primarily by non-enzymatic mechanisms in sediments. For instance, in soils, MnO4 catalyzes the depolymerization of proteins without requiring enzymes. Certain bacterial species can use TonB-dependant transporters to transport polysaccharides that are substantially larger than 600 Da into the periplasm (although enzymatic hydrolysis is still required prior to uptake into the cytoplasm; Reintjes et al., 2017). Furthermore, some of the unique aspects of subsurface sediments suggest that extracellular enzymes might not be an effective strategy to obtain carbon or energy. In order for the production of extracellular enzymes to be part of a viable metabolic strategy, each enzyme must, over its lifetime, provide the cell with at least as much carbon or energy as was required to synthesize the enzyme (Vetter et al., 1998; Allison, 2005; Schimel and Weintraub, 2003). In subsurface sediments, where cell division times may be on the order of decades to millenia, enzyme lifetimes would need to be correspondingly longer to remain ‘profitable’. Since enzyme lifetimes are finite, there must exist a community metabolic rate below which extracellular enzyme lifetimes are too short to become profitable. That limit is difficult to quantify because enzyme lifetimes in any environment are poorly constrained (e.g., Steen and Arnosti, 2011). Thus, it is plausible that extracellular enzyme-mediated carbon acquisition is impractical in sediments in which metabolic rates are particularly slow.
While extracellular enzyme activity in surface sediments has frequently been reported, few reports exist of extracellular enzyme activity from deeper than 20 cm below the seafloor (cmbsf) (Arnosti et al., 2014). Enzyme activity has been reported in sapropels up to 389 cmbsf in sapropels in the Eastern Mediterranean Sea (Coolen and Overmann, 2000; Coolen et al., 2002) and in sediment from 600-630 cmbsf in Aarhus Bay sediments (Lloyd et al., 2013a), as well as in a few other subsurface environments, such as the interior of seafloor basalts at the Loihi seamount (Jacobson Meyers et al., 2014). Furthermore, an analysis of transcriptomes from subsurface sediments of the Peru Margin revealed diverse exported peptidases and carbohydrate-active enzymes, which decreased in relative abundance with increasing depth (Orsi et al., 2018).
In order to better understand how heterotrophic microorganisms in subsurface sediments access organic matter, we assayed a diverse set of peptidases (protein-degrading enzymes) in sediment cores from the White Oak River estuary, NC. We paired these assays with analysis of the potential for extracellular peptidase production from existing metagenomic data sets. We chose this site because the porewater geochemistry and microbiology of these sediments has been well-characterized (Martens and Goldhaber, 1978; Kelley et al., 1990; Baker et al., 2015; Lazar et al., 2016; Lloyd et al., 2011) and because they contain abundant Bathyarchaeota and Thermoprofundales archaea, which appear to be capable of metabolizing detrital organic matter (Kubo et al., 2012; Lloyd et al., 2013a; Meng et al., 2014; Zhou et al., 2018b). We focused on peptidases because protein degradation appears to be an important metabolism for some subsurface archaea (Lloyd et al., 2013a) and because peptidases were more active than other enzymes in similar environments (Coolen and Overmann, 2000; Jacobson Meyers et al., 2014). Because environmental samples contain a wide range of distinct peptidases at variable activities (Obayashi and Suzuki, 2005; Steen and Arnosti, 2013) we measured the hydrolysis of eleven different substrates, which may be hydrolyzed by structurally and genetically diverse extracellular peptidases.
Results
Peptidase kinetics
Combining data from all three sampling dates, unambiguous hydrolysis of eleven different peptidase substrates was observed. All peptidase substrates assayed with the more-sensitive single-cuvette methodology were hydrolyzed much faster in untreated sediments than in autoclaved controls. Kinetics of substrate hydrolysis were qualitatively consistent with the Michaelis-Menten rate law, (Fig 1). Estimated Vmax values ranged from 40 to 3400 nmol g-1 sediment hr-1 (median 310, interquartile range 190 to 560 nmol g-1 sediment hr-1). Throughout the core, AAF-AMC, GGR-AMC, and Gly-AMC were hydrolyzed the fastest, and Arg-AMC was hydrolyzed slowest (Fig S1). Summed Vmax values decreased with depth from 9.09 μmol AMC g-1 sed hr-1 at the surface to 1.24 μmol AMC g-1 sed hr-1, or 13% of the surface value, at 82.5 cmbsf. Estimated Km values ranged from 36.1 μM to 1310 μM (median 138 μM, interquartile range 102 to 326 μM), and trended downward (i.e., to greater substrate affinity) with increasing depth (Fig 2). Km values for hydrolysis of Leu-AMC were the greatest (i.e., lowest substrate affinity) while Km values for hydrolysis of BocVPR-AMC, GGR-AMC, and Arg-AMC were the least.
In a separate core, hydrolysis rates of D-Phe-AMC, L-Phe-AMC, and L-Orn-AMC were assessed. These were measured using a plate-reader technique that proved insufficiently precise to accurately measure Vmax or Km, so we have only reported v0, which was measured at a high substrate concentration (400 μM) and therefore approximates Vmax. Ratios of v0 for D-Phe-AMC : L-Phe-AMC hydrolysis and L-Orn-AMC : L-Phe-AMC hydrolysis rates increased approximately linearly downcore (Fig 3).
Microbial abundance, cell-specific peptidase activity, and organic carbon oxidation rates
Concordantly with potential activities, cell counts decreased more-or-less steadily downcore from 4.5 × 108 cells ml-1 wet sediment at 1.5 cmbsf to 7.4 × 107 cells ml-1 wet sediment at 82.5 cmbsf. Consequently, cell-specific total potential peptidase activity was roughly constant at 32 +/- 14 amol AMC cell-1 hr-1 (Fig 2b) with no significant trend as a function of depth. Most of the error in cell-specific peptidase activities results from variance in cell counts rather than in Vmax estimations.
Organic carbon oxidation rates were estimated using a 2-G model driven by porewater methane and sulfate concentrations. The total organic carbon oxidation rate at 82.5 cmbsf was approximately 0.17% relative to that at 4.5 cmbsf (the top of the model domain), a decrease of almost 3 orders of magnitude (Fig 2c). Thus, summed Vmax relative to G increased more than 200-fold in the deepest sediments relative to surface sediments.
Peptidase genes
Samples for genomic analysis were taken from three broad sedimentary zones: the sulfate reduction zone (SRZ, 8-12 cmbsf), sulfate-methane transition zone (SMTZ, two distinct samples from nearby locations, 24-32 cmbsf and 26-30 cmbsf), and the methane-rich zone (52-54 cmbsf; data originally published in Baker et al., 2015). A total of 3739 genes encoding extracellular peptidases were identified among metagenomes from the three depth zones examined, including 685 from SRZ, 1994 from SMTZ, and 1060 from MRZ. Of the genes encoding for peptidases, 0-71% (depending on class of peptidase, algorithm and sediment depth) contained a signal peptide and are likely secreted by the SEC-dependent transport system (supplementary data). Among the genes associated with signal peptides, members of peptidase family C25, belonging to the gingipain family, were by far the most abundant at all depths, accounting for 41-45% of all SP-associated peptidases (Fig. 4). Genes annotated as encoding extracellular methionine aminopeptidases and zinc carboxypeptidases were also abundant (13%-19%). Together, these peptidase classes accounted for 73%, 76%, and 73% of exported peptidases in the SRZ, SMTZ, and MRZ respectively. The composition of protein families was generally consistent with depth, particularly among the more-abundant peptidases. Five peptidase annotations were an exception to this trend: peptidase family M1, peptidase family M20/M25/M40, peptidase family M3, M61 glycyl aminopeptidase, and thermophilic metalloprotease (M29) were found in much lower abundance at the SMTZ than the MRZ or SRZ. Given that those correspond to differences of one or a few total reads, these are well within the range of noise.
Discussion
Identities of extracellular peptidases present in White Oak River Estuary sediments
Kinetics of fluorogenic substrate hydrolysis was consistent with the Michaelis-Menten rate law and hydrolysis rates were dramatically slower in autoclaved controls than in “live” treatments, indicating that the substrates were hydrolyzed by enzymes rather than by abiotic factors. The enzyme substrates used here encompassed a diverse range of amino acid and peptide chemistries, including polar and non-polar R groups at the P1 site (i.e., the amino acid N-terminal to the scissile bond) and substrates with and without steric protecting groups, which must have been hydrolyzed by endopeptidases (which cleave proteins from within) and aminopeptidases (which cleave proteins from the N terminus) respectively. Peptide bonds adjacent to a diverse set of amino acid residues were cleaved, including glycine (the smallest amino acid), phenylalanine (among the largest amino acids), arginine (positively charged at porewater pH) and leucine (uncharged, hydrophobic), consistent with the presence of a diverse range of extracellular peptidases throughout the core.
The metagenomic results also indicated the potential for a diverse range of secreted peptidases, produced by a broad range of taxa, throughout the sediment column. The metagenomic results represent a minimum estimate for the genomic potential for extracellular peptidase production, because they rely on the assumption that only those peptidases associated with signal peptides (SPs) are secreted. Non-SP-based enzyme secretion pathways may also contribute to the pool of extracellular enzymes, including Sec-independent secretion systems (Bendtsen et al., 2005) and release of internal enzymes into the extracellular medium by viral lysis (Danovaro et al., 2008; Breitbart et al., 2004).
The dominance of genes for exported gingipain-like endopeptidases (class C25) at all depths is consistent with rapid hydrolysis rates of fluorogenic substrates for endopeptidases. Gingipains are endopeptidases with preference for arginine at the P1 position (i.e., the N-terminal side of the hydrolyzed bond), which would be active towards the substrates GGR-AMC and Boc-VPR-AMC. Those were among the fastest-hydrolyzed substrates at each depth (Fig. 1), indicating that genes for C25 peptidases were likely expressed. Previously, gingipains have been identified in Thermoprofundales (formerly Marine Benthic Group D) and in Bathyarchaeota, and appear to be widespread in marine sediments (Zhou et al., 2018a; Lloyd et al., 2013b; Orsi et al., 2018). The M28 family, also among the most abundant annotations, contains a diverse range of aminopeptidases and carboxypeptidases including leucine aminopeptidase, consistent with the observed hydrolysis of Leu-AMC (Rawlings et al., 2018). Genes for D-aminopeptidases genes were observed, consistent with hydrolysis of D-Phe-AMC.
Other abundant genes were annotated as methionine aminopeptidase, zinc carboxypeptidase, a C-terminal domain from bacterial pre-peptidases, and peptidases from the MEROPS families M24, S24, M50, and M28. Potential activities of these peptidases were not assayed. Zinc carboxypeptidases (M20) cleave enzymes from the carboxy-terminus and have strong specificity for Gly at the P1 position (i.e., the position C-terminal to the scissile bond), but little preference for the residue at the P1’ position (the position C-terminal to the scissile bond; in a carboxypeptidase this would be the C-terminus of the protein). Methioine aminopeptidases (M24) are metallopeptidases with preference for glycine at the P1 position.
S24 and M50 peptidases are less likely to be directly relevant to organic matter processing. S24 peptidases are involved in the SOS response for single-stranded DNA repair (Rawlings et al., 2018). M50 peptidases are membrane-bound enzymes that act as sporulation factors in Bacillus subtilis, and possibly other Bacteria (Green and Cutting, 2000; Feng et al., 2007), and which are not secreted. However, DNA repair (Orsi et al., 2013; Bird et al.) and spore formation (O’Sullivan et al., 2015; Lomstein et al., 2012) both appear to be important survival mechanisms for microorganisms in subsurface sediments. The bacterial C-terminal pre-peptidase domain is often found in secreted peptidases, but is removed prior the peptidase becoming active, and could be associated with a wide range of peptidases (Yeats et al., 2003).
Each of these annotations is plausible in terms of what is known about peptidase activities in sediments, and the annotations and observed activities generally agreed with each other. We did not assay for carboxypeptidases (e.g. MEROPS family M20) or methionine aminopeptidase, but carboxypeptidases have previously been observed to be active in estuarine sediments (Pantoja et al., 1997) and the generally broad substrate specificities of extracellular aminopeptidases suggests that methionine aminopeptidases could have contributed to the hydrolysis of the other aminopeptidase substrates (Steen et al., 2015). However, a note of caution is also warranted when interpreting peptidase annotations from deeply branching microorganisms: the high diversity of hydrolases makes precise annotations difficult, and the exact substrate specificities of the peptidases in these samples may differ somewhat from those inferred from the annotations (Michalska et al., 2015). Thus, while these annotations are environmentally plausible and generally consistent with the fluorogenic enzyme assays, they should nevertheless be viewed with some skepticism.
Peptidase kinetics suggest adaptaion of subsurface peptidases to degraded organic matter
Heterotrophic microorganisms in subsurface sediments have little access to fresh organic matter. In the cores described here, which represented ~275 years of sediment deposition, organic matter reactivity decreased by at least three orders of magnitude between the surface and 82.5 cmbsf (Fig 2c). It is challenging to determine what fraction of high-vs low-molecular weight organic matter subsurface microorganisms metabolize. However, the fact that cell-specific Vmax was more-or-less constant downcore (Fig 2b) suggests that the heterotrophic community relied on complex organic matter to a similar degree at all depths. The cell-specific Vmax values for Leu-AMC hydrolysis, 21-51 amol cell-1 hr-1, are comparable to previous measurements in active environments such as surface sediments (2-100 amol cell-1 hr-1) and seawater (mostly less than 100 amol cell-1 hr-1, but with some measurements up to 10 nmol cell-1 hr-1; Vetter and Deming, 1994 and references therein), consistent with communities that relied primarily on organic carbon derived from macromolecules.
The ratio of SVmax : OC oxidation rate is sensitive to the mix of enzymes included in the sum, and to the substrate specificity of enzymes assayed (some enzymes will be capable of hydrolyzing multiple substrates). The absolute value of that sum, therefore, is not particularly meaningful. The trend, however, is informative: as sediment depth increased, the potential activity of extracellular peptidases decreased much more slowly than the actual rate of organic carbon oxidation, so the ratio of SVmax : OC oxidation rate increased dramatically (Fig 2d). Vmax is a proxy for enzyme concentration, so the observed increase in SVmax : OC oxidation rate combined with the trend in cell-specific SVmax suggests that deeper heterotrophic communities exhibited similar demand for detrital OM, but that those enzymes returned bioavailable hydrolysate at a much slower rate because substrate concentrations were lower.
The White Oak River subsurface communities were similar to their surface counterparts in terms of reliance on extracellular enzymes for bioavailable organic carbon, although subsurface metabolisms were considerably slower. However, enzyme kinetics and potential activities of D-phenylalanine aminopeptidase, L-phenylalanine aminopeptidase, and L-ornithine aminopeptidase all suggested microbial community adaptation to old, degraded organic matter in deeper sediments.
Most amino acids are biosynthesized as L-stereoisomers. As organic matter ages, the ratio of D-amino acids to L-amino acids (D:L ratio) increases with depth, due to abiotic racemization and increased abundance of D-amino acids derived from bacterial cell walls (Bada et al., 1970; Lomstein et al., 2009). Accordingly, the potential activity of D-phenylalanyl aminopeptidase increased relative to that of L-phenylalanyl aminopeptidase, indicating an increased capacity to access degraded organic matter. Ornithine, which is a product of the release of urea from arginine, is another marker for degraded organic matter, while phenylalanine is more characteristic of fresher organic matter (Dauwe et al., 1999), and the relative potential activity of L-phenylalanine aminopeptidase : ornithine aminopeptidase followed the same increasing trend with depth. Finally, the decrease of Km values with increasing depth indicates peptidases that function more efficiently at lower substrate concentrations. It is intuitive that the concentration of enzyme-labile organic matter concentrations would decrease downcore, and the observed increase in SVmax : OC oxidation rate provides direct evidence of that. Taken together, these three observations provide strong evidence for a subsurface heterotrophic microbial community that is increasingly adapted to persist using degraded organic matter at increasing depth.
This evidence raises the ecological question of how selective pressure produces a heterotrophic community adapted to degraded organic matter. Modeling and genomic observations in other sedimentary environments suggest that microbial growth rates are too slow for community adaptation by enhanced growth rates of more successful taxa; rather, communities in deeper sediments consist of taxa that were deposited at the sediment-water interface and died at the slowest rates (Starnawski et al., 2017; Bradley et al., 2018). If those findings can be generalized to environments characterized by recalcitrant organic matter, that poses a question: in which aquatic environments are microorganisms capable gaining reproductive advantage by growing on recalcitrant organic carbon? The studies cited above addressed sites at which sedimentation rates, microbial respiration, and likely cell doubling times were considerably slower than in the sediments described here, so even if growth (as opposed to persistence) on recalcitrant organic carbon is not possible in those environments, it may have been in the White Oak River sediments. Alternately, microbial taxa may gain adaptations to metabolize recalcitrant organic matter in environments where labile organic matter is more abundant and growth rates are higher. This scenario would imply that organisms which primarily metabolize more labile organic matter would gain some selective advantage by also metabolizing recalcitrant organic matter. Further analysis of the mechanisms by which subsurface heterotrophs access degraded sedimentary organic matter may yield insights into how microorganisms survive in low-energy environments, and into the controls on organic carbon preservation in sediments over geological timescales.
Materials and Methods
Study site
Samples were collected from Station H in the White Oak River Estuary, 34° 44.490’ N, 77° 07.44’ W, first described by Gruebel and Martens (1984). The White Oak River Estuary occupies a drowned river valley in the coastal plain of North Carolina. Station H is characterized by salinity in the range of 10 to 28 and water depth on the order of 2 m. The flux of ΣCO2 across the sediment-water interface was 0.46 ± 0.02 mmol m-2 hr-1 (measured in May of 1987), primarily due to organic carbon oxidation via sulfate reduction. The sediment accumulation rate averages 0.3 cm yr-1 (Kelley et al., 1990). Total organic carbon content is approximately 5%. For this study, push cores of 40-85 cm were collected from Station H by swimmers on May 28, 2013, and October 22, 2014. In 2013, cores were transported to the nearby Institute of Marine Sciences (University of North Carolina) at Morehead City, where they were sectioned and processed for enzyme activities, porewater geochemistry, and cell counts within 6 hours of sample collection. Porewater sulfate in 2013 was depleted by 43.5 cm, and methane peaked at 79.5 cm (Fig. S1). In 2014, cores were transported on the day of sampling to the University of Tennessee, Knoxville, stored at 4 °C, and processed for enzyme activities the following day. Samples for metagenomic analysis were collected separately in October 2010 from three sites (sites 1, 2, and 3, as previously described by Baker et al., 2015), all of which are within 550 m of Station H.
Enzyme assays
Enzyme assays were performed using different protocols in 2013 (data presented in Figs 1–3) versus 2014 (data presented in Fig 4). In 2013, enzyme assays were performed according to a protocol similar to the one described in Lloyd et al (2013b). Cores were sectioned into 3 cm intervals. The following intervals were selected for enzyme assays: 0-3 cm, 3-6 cm, 27-30 cm, 57-60 cm, and 81-83 cm. Each section was homogenized, and approximately 0.5 ml wet sediment was transferred into separate 5 ml amber glass serum vials, which had been pre-weighed and preloaded with 4 ml anoxic artificial seawater (Sigma Sea Salts, salinity = 15, pH=7.5) Samples were weighed again to determine the precise mass of wet sediment added, and then an appropriate quantity of 20 mM peptidase substrate stock dissolved in DMSO was added, up to 90 μL, for final substrate concentrations of 0, 25, 50, 75, 100, 200, or 300 μM. Substrates are listed in Table 1. Triplicate incubations with 400 μM Arg-AMC, Gly-AMC, Leu-AMC and Gly-Gly-Arg-AMC were also created, but these were omitted for Ala-Ala-Phe-AMC and Boc-Phe-Val-Arg-AMC because the latter two substrates are considerably more expensive. Each serum vial was vortexed, briefly gassed with N2 to remove oxygen introduced with the sample, and approximately 1.3 ml slurry was immediately removed, transferred to a microcentrifuge tube, and placed on ice to quench the reaction. The precise time of quenching was recorded. This was centrifuged at 10,000 × g within approximately 15 minutes. The supernatant was transferred to a methacrylate cuvette and fluorescence was measured with a Turner Biosystems TBS-380 fluorescence detector set to UV mode (λex=365-395 nm, λem=465-485 nm). Samples were then incubated at 16 °C, approximately the in situ temperature, and the sampling procedure was repeated after approximately 3 hours. The rate of fluorescence production was calculated as the increase in fluorescence for each sample divided by the elapsed time between sample quenching. Killed controls were made using homogenized, autoclaved sediments from 35-45 cmbsf. However, we note that autoclaving does not completely destroy sediment enzymes because sorption to mineral surfaces stabilizes enzyme structure, vastly increasing their ability to maintain a functional conformation at high temperatures (Stursova and Sinsabaugh, 2008; Carter et al., 2007; Schmidt, 2016). We therefore used the autoclaved samples as a qualitative control for the null hypothesis that enzymes were responsible for none of the observed substrate hydrolysis, rather than as a quantitative method to distinguish enzymatic substrate hydrolysis from potential abiotic effects. In some sediments, a large fraction of fluorophore can sorb to particles, requiring a correction to observed fluorescence (Coolen et al., 2002; Coolen and Overmann, 2000), but we observed negligible sorption of fluorophore to the White Oak River sediments.
In 2014, enzymes were assayed using a protocol based on the approach of Bell et al (2013), which was designed for soil enzyme assays. In this approach, peptidase substrates were mixed with sediment-buffer slurries in 2-mL wells of a deep-well plate. These plates were periodically centrifuged and 250 μL aliquots of supernatant were transferred into a black 96-well microplate. Fluorescence was read using a BioTek Cytation 3 microplate reader (λex = 360 nm, λem = 440 nm). Results from this method proved considerably noisier than the single-cuvette method used in 2013, so kinetic parameters (Vmax and Km) were not calculated for these data. Nevertheless, results were qualitatively similar to those from 2013, and we have reported Vmax from 2014 as v0 measured at 400 μM substrate concentration, which was saturating. In June 2014, the following substrates were assayed: AAF-AMC, Arg-AMC, Boc-VPR-AMC, D-Phe-AMC, Gly-AMC, Leu-AMC, L-Phe-AMC, Orn-AMC, Z-Phe-Arg-AMC, and Z-Phe-Val-Arg-AMC. In October 2014, L-Phe-AMC, D-Phe-AMC, and Orn-AMC were assayed according to the same protocol in 3-cm core sections at 1.5, 4.5, 7.5, 10.5, 19.5, 22.5, 25.5, 28.5, 34.5, 37.5, 40.5, 43.5, 49.5, 52.5, 58.5, and 61.5 cmbsf.
Peptidase kinetic data were analyzed using R. All raw data and scripts related to enzyme analysis are posted at http://github.com/adsteen/WOR_enz_2013_2014. For samples taken using the more sensitive single-cuvette method, Michaelis-Menten parameters were estimated from nonlinear least squares fits to kinetic data. In the case of Leu-AMC at 4.5 centimeters below seafloor, kinetic data could not successfully be fit to a Michaelis-Menten function, so no Km was reported and the value of v0 at the highest substrate concentration was substituted for Vmax. For analysis of correlations, data sets were qualitatively evaluated for homoskedasticity and normality of residuals using q-q plots and plots of residuals vs fitted values. When untransformed data met those criteria, the null hypothesis of no correlation was tested using linear least-squares regressions. When untransformed data failed to meet those criteria, data were first log-transformed. In cases in which log-transformed data were wither heteroskedastic or residuals were non-normally distributed, data were rank-transformed and correlations were tested using Spearman’s ρ.
Geochemical and microbiological measurements
Sediment porosity was measured by mass after drying at 80 °C, according to the equation
Here, mw represents mass lost after drying, ρ w represents the density of pure water, md represents the mass of the dry sediment, S represents salinity in g kg-1, and ρds represents the density of dry sediment (assumed to be 2.5 g cm-3). Porewater sulfate concentrations were measured using a Dionex Ion Chromatograph (Sunnyvale, CA) in porewater that was separated by centrifugation in 15 ml centrifuge tubes at 5000 × g for 5 minutes, filtered at 0.2 μm, and acidified with 10% HCl. Methane was measured using 3 ml sediment subsamples that were collected from a cutoff syringe, entering through the side of a core section, immediately after core extrusion. Subsamples were deposited immediately in a 20 ml serum vial containing 1 ml, 0.1 M KOH. These were immediately stoppered and shaken to mix sediment with KOH. Methane was later measured by injecting 500 μl of bottle headspace into a GC-FID (Agilent, Santa Clara, CA) using a headspace equilibrium method (Lapham et al., 2008).
Geochemical modeling
Organic carbon remineralization rates as a function of depth were estimated by applying a multi-component reaction-transport model to depth distributions of sulfate and methane concentration. The model is based on equations described in Boudreau (Boudreau, 1996) and includes only sulfate reduction and methane production due to lack of data regarding oxic and suboxic processes. Thus, the model is limited to depths greater than 4.5 cm where sulfate reduction and methane production are the dominant processes, and bioirrigation and bioturbation may be assumed to be negligible. The organic matter remineralization rate is parameterized using the multi-G model first proposed by Jørgensen (1978); a two-component model was sufficient to accurately simulate the sulfate and methane data. For solutes, the upper boundary conditions were measured values at 4.5 cm while the lower boundary conditions (200 cm) were set to zero-gradient. The flux of reactive organic carbon to 4.5 cm was calculated from the sulfate flux across the 4.5 cm horizon and an estimate of methane burial below the lower boundary (the methane flux at the upper boundary was observed to be zero), with an assumed oxidation state of reactive carbon of -0.7. The model contains four adjustable parameters that are set to capture the major details of measured sulfate and methane data: first-order rate constants for both fractions of the reactive carbon pool; the partitioning factor for both fractions, and the rate constant for methane oxidation.
Cell Enumeration
Cells were enumerated by direct microscopic counts. One mL of sediment was placed in a 2-mL screw-cap tube with 500 μl of 3% paraformaldehyde in phosphate buffered saline (PBS), in which it was incubated overnight before being centrifuged for 5 minutes at 3000 × g. The supernatant was removed and replaced with 500 μl of PBS, vortexed briefly and centrifuged again at 3000 × g. The supernatant was subsequently removed and replaced with a 1:1 PBS:ethanol solution. Sediments were then sonicated at 20% power for 40 seconds to disaggregates cells from sediments and diluted 40-fold into PBS prior to filtration onto a 0.2 μm polycarbonate filter (Fisher Scientific, Waltham, MA) and mounted onto a slide. Cells were stained with 4′,6-diamidino-2-phenylindole (DAPI) and enumerated by direct counts using a Leica Epifluorescence Microscope.
Metagenomic Analysis
To resolve the taxonomic distribution of extracellular peptidases we searched a pre-existing White Oak River de novo assembled and binned metagenomic dataset (Table S2; Baker et al., 2015) for genes that were assigned extracellular peptidase functions. These assignments were based on best matches to extracellular peptidases in KEGG, pfam, and NCBI-nr (non-redundant) databases using the IMG annotation pipeline (Markowitz et al., 2014). Genes were additionally screened for signal peptidase motifs using the following programs: PrediSI setting the organism group to gram-negative bacteria (Hiller et al., 2004), PRED-Signal trained on archaea (Bagos et al., 2009), the standalone version of PSORT v.3.0 trained against archaea (Yu et al., 2010), and SignalP 4.1 using gram-negative bacteria as the organism group (Petersen et al., 2011). All programs were used with default settings if not stated otherwise. Binned genomes from three different depth zones of White Oak River sediments were examined. The sulfate-rich zone (SRZ) genomes were obtained from sites 2 and 3 core sections 8-12 and 8-10 cm, respectively. The sulfate-methane transitions zone (SMTZ) genomes were recovered from site 2 and 3 and depths of 30-32 cm and 24-28 cm. The methane-rich zone (MRZ) was from site 1 and 52-54 cm.
Conflict of interest
The authors declare no competing financial interests in relation to the work described.
Acknowledgements
We thank Michael Piehler for access to his laboratory facilities at the University of North Carolina Institute of Marine Sciences, the captain of the R/V Capricorn for sampling assistance, and Terry Hazen for use of lab equipment at the University of Tennessee. We thank Oliver Jeffers for reminding ADS that science is cool. Funding for KHM was provided by NSF grant DBI-1156644 to Steven W. Wilhelm. Funding for ADS was provided by NSF grant OCE-1431598 and a C-DEBI subaward. This work is C-DEBI Contribution number <<to be determined.>>
Footnotes
The text and analysis has been completely rewritten after journal rejection.