Microorganisms are well recognized as key drivers of biogeochemical cycles in marine environments (Webster et al., 2003; Santos et al., 2011). Measuring the changes in microbial communities is of particular interest to understanding how environmental factors modulate their structure and how that, in turn, is related to the function and stability of the ecosystem (Huber et al., 2007; Jones et al., 2012; Fuhrman, Cram & Needham, 2015). In marine sediments, the type of substrate, energy and carbon sources, and the variables that are correlated with water depth (e.g., temperature, oxygen concentration, light penetration and hydrostatic pressure), generally describe the most important aspects of the variability amongst habitats (Miller & Wheeler, 2012).
In the Gulf of Mexico (GoM), changes in bacterial community structure have been mainly related with depth in the water column, and likely result from differences in temperature, dissolved oxygen and suspended particles (King et al., 2013) which occur across these depth differences. In marine sediments from the GoM, bacterial community composition has been determined at different depths below the seafloor (from sediment cores). It has been proposed that the bacterial community composition of these sediments likely results from the interaction between the water column and a benthic microbial population limited to the upper layer of the sediments (Reese et al., 2013). In contrast, the microbial diversity present in different sediment depths from seep systems in the GoM have been directly related to the composition and magnitude of hydrocarbon seepage (e.g., natural oils, methane, and non-methane hydrocarbons) (Orcutt et al., 2010), as well as to the presence of overlying microbial mats (Mills et al., 2004).
Knowledge about microbial communities in the GoM increased notably with the Deepwater Horizon (DWH) massive oil spill in 2010, which occurred in the north zone of the GoM across an enormous area with different environmental conditions (Kostka et al., 2011; Mason et al., 2012; Liu & Liu, 2013; Joye, Teske & Kostka, 2014). During the spill, it was observed that different bacterial phyla in the deep-water plume (e.g., Oceanospirillales, Cycloclasticus and Colwellia) rapidly responded and were enriched within hours - weeks following the DWH well blow out (Redmond & Valentine, 2011; Mason et al., 2012). In natural hydrocarbon seep sites sampled at the same time, several rare taxa increased rapidly in abundance after the spill, emphasizing the importance of specialized sub-populations and potential ecotypes during massive deep-sea oil discharges (Kleindienst et al., 2015). The oil transported to the shoreline after the discharge also had a profound impact on the abundance and community composition of indigenous bacteria in beach sands, where members of the Gammaproteobacteria and Alphaproteobacteria participated as key players in oil degradation (Kostka et al., 2011).
The precise volume of oil spilled and the trajectory of the oil slicks from the DHW are still controversial (Salcedo et al., 2017). Based on the surface circulation models of the waters of the GoM, it was inferred that the Mexican region of the Perdido Fold Belt (PFB), was also susceptible to this environmental disturbance (Soto & Vázquez-Botello, 2013). Knowledge of environmental and biotic data of this region is, however, scarce despite being a geologic province with oil reservoirs. The present study assessed changes in the bacterial community structure of surficial sediments with respect to a depth gradient and specific environmental variables in the PFB region, where oil exploration and extraction activities are predicted to impact environmental conditions, and consequently the structure of bacterial communities. Additionally, bacterial phylotypes putatively involved in hydrocarbon degradation were highlighted in this work.
Material and Methods
Sample collection and physicochemical variables from marine sediments
The samples used in this study were collected in the Northwestern Gulf of Mexico in April 2014. Sediment samples from a depth gradient (20–3,700 m), were collected perpendicular to the coastline at 11 sites on the parallels 25 and 25.15°N (Fig. 1, Table 1). Samples were collected with a Hessler-Sandia MK-II boxcore (40 × 40 cm) from which three different surficial (0–5 cm) subsamples were taken: (1) sterile 100 mL plastic containers immediately frozen at −20 °C on board for further molecular analysis; (2) a 2 inch core extracted to determine total sulfur concentration (TS) and redox potential on board, with a sulfide ion selective electrode and potentiometer (Bricker, 1982; Brassard, 1997); and (3) approximately 400 g of sediment stored in high density polyethylene bags at 4 °C until the determination of total organic matter (TOM), total organic carbon (TOC), and grain size in the laboratory. TOM was determined by the wet oxidation technique with an excess of dichromate and back titration with iron (II) (Buchanan, 1984), TOC content was quantified using the oxidation with potassium dichromate in acid medium, and titration of excess oxidant with ferrous sulfate and diphenylamine as an indicator (Buchanan, 1984). The amounts of sand, lime and clay were estimated using a hydrometer (Buchanan, 1984). These physicochemical variables were compared among shallow and deep sites using a one-way analysis of variance (ANOVA p < 0.05 and F-values) and were correlated with the depth variable.
|Geographical location||Physicochemical variables|
|Site||Sample||Longitude||Latitude||Depth (m)||Redox ↑ (mV)||TS ↑ (µM)||TOM (%)||TOC (µM)||Sand (%)||Lime ↑ (%)||Clay ↓ (%)|
TS, total sulfur; TOM, total organic matter and TOC, total organic carbon. Physicochemical variables measured with no replicates. Pearson coefficient showed significant correlation at a p-value <0.01 among TS (r2 = 0.98), redox potential (r2 = 0.98), and the percentages of lime (r2 = 0.78) and clay (r2 = −0.78) particles with depth.
↑ Variables with positive correlation with depth.
↓ Variables with negative correlation with depth.
Sediment samples were stored during five months at −20 °C. Then, in October 2014 sediments were thawed at 4 °C, homogenized mechanically under sterile conditions, and centrifuged 1 min/10,000 ×g in order to separate the sediment from remaining water, which was discarded. Total DNA was extracted from 0.25 g of sediment using the PowerSoil®DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA) following the manufacturer’s protocol. The quality of the DNA extractions was verified by agarose gel electrophoresis and the concentration was determined through UV absorption analysis with a NanoDrop 2000 Spectrometer (ThermoFisher Scientific Inc., Wilmington, DE, USA). Extracted DNA was stored at −20 °C for further 16S rRNA Illumina sequencing and DGGE analysis (see a detailed description in Supplemental Information 1).
16S rRNA gene sequencing
Amplicons from environmental DNA were prepared for sequencing the 16S rRNA V3 and V4 variable regions by using a two PCR steps approach following the “16S Metagenomic Sequencing Library Preparation” protocol (Illumina). Briefly, the first PCR step amplified the template out of the DNA samples using the forward primer 5′-CCTACGGGNGGCWGCAG-3′ and the reverse primer 5′-GACTACHVGGGTATCTAA TCC-3′ with Illumina overhang adapters attached, to obtain ∼550 bp fragments (Klindworth et al., 2013). The PCR program was performed in a thermal cycler (Applied Biosystems Veriti ABI Inc., Foster City, CA, USA) with an initial denaturation at 95 °C–3 min, 25 cycles of 95 °C–30 s, 55 °C–30 s, 72 °C–30 s and a final extension at 72 °C–5 min. Each PCR reaction (20 µl) included 2 µl of environmental DNA (5 ng/µl), 0.5 µl of each primer (10 µM) and 10 µl of 2× Phusion High-Fidelity MasterMix (Thermo Scientific, Waltham, MA, USA). The correct size of the amplicons was verified on an QIAxcel Advanced system (QIAGEN, Hilden, Germany), DNA and the PCR clean-up were carried out using AMPure XP beads to discard free primers and primer dimer species. In the second PCR, eight cycles attached dual indices and the Illumina sequencing adapters using the Nextera XT Index Kit. PCR barcoded amplicons were verified and purified as previously described and quantified using a Qubit 3.0 fluorometer (Life Technology, Shah Alam, Selangor, Malaysia). The individual barcoded amplicons were diluted on 10 mM Tris (pH 8.5) and pooled in equimolar concentrations (9 pM). Paired-end sequencing (2 × 300 bp) was carried out using the MiSeq platform (Illumina, San Diego, CA, USA) with a MiSeq Reagent Kit V3 (600 cycles). Sequencing was performed in the Aquatic Pathology laboratory at CINVESTAV-Mérida.
Demultiplexing of the pooled amplicons and trimming the barcode and adapter sequences was performed using the MiSeq Reporter Metagenomics Workflow (Illumina, 2014). Reads were overlapped with the fast length adjustment of short reads to improve genome assemblies (FLASH) software (Magoč & Salzberg, 2011), and processed using the QIIME (version 1.9) (Caporaso et al., 2010) pipeline with the parameters q (phred_quality_treshold) = 20, r (max_bad_run_length,) = 3, p (min_per_read_length_fraction) = 0.75, for quality filtering. The demultiplexed sequences were clustered in operational taxonomic units (OTUs) with the ‘pick_open_reference_otus.py’ script at 97% of similarity using the usearch61 method (Edgar, 2010) with a minimum OTU cluster size of 5. Chimeric sequences were removed with the uchime2 algorithm in the reference mode (v 9.1.13) (Edgar, 2016) and, the taxonomic assignment was performed by the SortMeRna (Kopylova, Noé & Touzet, 2012) with an e-value of 3 e−6 and default parameters from QIIME, using the GreenGenes database (v13.8). An OTUs alignment was performed with the Mafft algorithm (Katoh & Standley, 2013) to build a phylogenetic tree using the Fasttree software (Price, Dehal & Arkin, 2009) for its subsequent use in the UniFrac (Lozupone, Hamady & Knight, 2006) distance analysis.
Community structure and composition analyses were performed by processing the OTU table in the R environment (R Core Team, 2014) with the Phyloseq (McMurdie & Holmes, 2013), vegan (Oksanen, 2013) and ggplot2 (Wickham, 2010) packages. The data set was rarefied at the depth from the smallest library (20,400). We reported the observed OTUs, the Shannon and Simpson diversity indexes (H′ and D, respectively) and the nonparametric richness from Chao1. The Good’s coverage was calculated to corroborate the adequate sampling depth.
In order to compare the microbial community composition among shallow and deep sampling sites, a non-metric multidimensional scaling (NMDS) was plotted (Lozupone, Hamady & Knight, 2006; Giloteaux, Goñi Urriza & Duran, 2010) with the weighted UniFrac distance metric (Lozupone, Hamady & Knight, 2006) and a test of beta significance among groups of sample was performed using a two-sided Student’s two-sample t-test with the ‘make_distance_boxplots.py’ script of QIIME, p-values were calculated using 1,000 Monte Carlo permutations. The physicochemical variables were fitted with the envfit function to the NMDS ordination to correlate them with the community composition (p-value <0.05 and 10,000 permutations). Physicochemical variables were also tested with a PERMANOVA analysis at a p-value <0.05 (Table S1). A differential abundance analysis was performed with the DESeq2 (Love, Huber & Anders, 2014) R library to identify the OTUs that have significant fold changes among the shallow and deep zones. The p-value was corrected with the false discovery rate (FDR) method (Benjamini & Hochberg, 1995) and a Log2 fold change plot was made with the significant OTUs at a p-value <0.01.
All the sequences are available at SRA site from NCBI database in the BioProject PRJNA429278 and biosample accession ID’s SRR6457706 to SRR6457716 and the raw processed data in the supplementary material.
Physicochemical properties from marine sediments
The physicochemical variables and textures of the sediment samples are shown in Table 1. Redox potential ranged from −204 to 236 mV. The electronegative values were found in samples from 20 to 600 m depth, while the electropositive values corresponded to the deep sediments (2,800–3,700 m). TS was constant at depths from 20 to 600 m at a concentration of 0.05 µM, while the maximum values were detected for deep sites (0.10–0.12 µM). The percentage of TOM and the concentration of TOC varied for all sites with averages of 0.872 ± 0.22% and 0.484 ± 0.123 µM, respectively. The percentages of sand, lime and clay in the sediment samples ranged from 20.50–34.5, 51.2–70.30 and 5.9–28.4%, respectively. Values of TS, redox potential, and the percentages of lime and clay showed significant correlations with depth (p-value <0.01) (Table 1).
Bacterial community structure in a depth gradient
The NMDS ordination method showed that microbial communities from shallow sites grouped separately from those from the deep sites (Fig. 2), and it was according with the test of significance were the mean of the distances of Shallow and Deep samples were different with a p-value <0.05. Samples taken from shallow sites displayed greater dissimilarity distance from each other, while those derived from the deep sites displayed a closer proximity to each other (Fig. 2). The environmental variables fitted on the NMDS ordination were: depth (r 2 = 0.79), redox potential (r2 = 0.77), the concentration of total sulfur (r2 = 0.73) and percentages of clay (r2 = 0.71) and lime (r2 = 0.7), at a p-value <0.05.
The observed OTUs, Shannon (H′) and Simpson (D) diversity indexes, and the nonparametric richness estimation from Chao1, were different among shallow and deep-sea sites (p-value <0.05). These ecological estimators showed higher values in the sediment samples from shallow sites (observed OTUs = 2,888.2 ± 567.88, H′ = 9.6 ± 0.85, D = 0.99 ± 0.005 and Chao1 = 3,791.2 ± 737.81) than those obtained from the deep ocean floor (observed OTUs = 1,884.7 ± 464.2, H′ = 7.74 ± 1.02, D = 0.97 ± 0.02, and Chao1 = 2,806.18 ± 589.39) (Table 2).
|Site||Sample||Depth (m)||Total counts||Observed OTUs||H′||D||Chao1||Good’s coverage (%)|
Alfa diversity data from observed OTUs, H′ Shannon diversity index, D Simpson diversity index, and Chao1for nonparametric richness estimation. Statistical differences were observed among sampling sites Shallow and Deep-sea (p-value <0.05). Percentages of Good’s coverage shows the fraction of the OTUs subsampled more than once.
Bacterial community composition
Microbial community analysis resulted in the detection of 25 bacterial phyla, however only 14 were ≥1% in relative abundance (Table S2). Proteobacteria, represented by Gamma-, Alpha- and Deltaproteobacteria classes, were dominant in all the analyzed samples (Fig. 3). Nevertheless, Bacteroidetes, Acidobacteria, Chloroflexi, Nitrospirae, Planctomycetes, Gemmatimonadetes and the NC10 phyla were also well represented in the samples. Two archaeal phyla, corresponding to the Euryarchaeota (Thermoplasmata class) and CreNarchaeota including Miscellaneous CreNarchaeota Group (MCG) and ThauMarchaeota classes (based on the GreenGene database), were also detected in low abundances (0.3 to 1.2%) (Fig. 3). At lower taxonomic levels, 51 families were detected, from which Piscirickettsiaceae, Rhodobacteraceae, Flavobacteriaceae, Syntrophobacteraceae, Thermovibrionaceae, Desulfobacteraceae, Colwelliaceae, Marinicellaceae, Alcanivoracaceae, Colwelliaceae, and CeNarchaeaceae were among the families with higher relative abundances in the samples (Fig. S1A). Moreover, 23 genera were identified at >1% of relative abundances (Fig. S4). Desulfococcus, Alcanivorax, Fulvivirga, Amphritea, BD2-6, Shewanella, ZD0117 and Nitrospina were the better represented in the samples (Fig. S4).
Differential abundance analysis identified 147 OTUs with significant fold changes between the deep and shallow zones, 107 from shallow and 40 from deep sites (Fig. 4A). From the shallow samples, most of these OTUs belonged to Delta-, Alpha- and Gammaproteobacteria (38, 24 and 18, respectively), while the rest of the OTUs were distributed in 13 classes of 10 phyla (Fig. 4A). In deep samples, all of the 40 OTUs belonged to Gamma-, Alphaproteobacteria, Bacteroidetes and Nitrospirae (27, six, six and one, respectively) (Fig. 4A). The relative abundances of the 147 OTUs with significant fold changes constituted approximately 10 to 40% of the total microbial communities (Fig. 4B). Kordiimonadales, Rhodospirillales, Desulfobacterales, Syntrophobacterales and Nitrospirales, were the orders with highest relative abundances for shallow samples whilst the orders Chromatiales, Oceanospirillales, Methylococcales, Flavobacteriales and Rhodobacterales were the best represented orders for deep samples (Fig. 4B). At the genus taxonomic level, differential abundance analysis allowed to identify Marinicella, Alcanivorax, Shewanella and ZD0117 with significant fold changes for deep samples; and Nitrosopumilus, GOUTA19, LCP-6, BD2-6, Amphritea, Desulfococcus and Mariprofundus for shallow sites (Fig. S5).
Physicochemical properties of sediment samples
Physicochemical properties (e.g., temperature, pH, redox) in marine sediments commonly change according to water depth (Miller & Wheeler, 2012). In this study, lime percentage, redox potential and total sulfur were positively correlated to depth, while the clay particles percentages were negatively correlated with depth (Table 1). The electronegative redox values measured in the first 5 cm of the sediments in all shallow sites (Table 1), suggested that oxygen was depleted by microbial respiration as common reported for this layer in shallow marine sediments (Aranda et al., 2015). Electronegative redox values in the shallow samples were likely enhanced at higher fine-grained clay content, since this diminishes the sediment permeability as reported elsewhere for sediments in the continental platform (Probandt et al., 2017). In contrast, the electropositive redox values detected in the first 5 cm of the sediments in all the deep sites (Table 1) suggested oxidizing conditions. This condition has also been observed at different latitudes in the GoM (Deming & Carpenter, 2008) due to both the circulation of deep water masses carrying oxygen (Jochens & DiMarco, 2008), and low rates of oxygen consumption (Rowe et al., 2008).
Total organic matter percentage (TOM%) was determined to be below 2% for all the sediments analyzed (Table 1). These low organic contents are similar to those reported in studies of two onshore-offshore transects in the Northwestern GoM (Goñi, Ruttenberg & Eglinton, 1998). Shallow sites likely receive inputs from terrestrial organic matter (Balsam & Beeson, 2003), and yet these sediments presented similar values of TOM than those detected for deep sites (Table 1). Previous studies have reported high production and consumption rates of oxygen measured for shallow sediments in Northeastern GoM (Mills et al., 2008). TOM values determined in this study for shallow sites are in concordance with previous reports for the GoM (Balsam & Beeson, 2003). We hypothesize that these values are related to the consequence of high rates of microbial metabolism in those same zones.
Microbial ecology assessment
The effect of storage temperature on microbial community structure has been previously studied, and it is well recognized that microbial metabolism could keep on going at −20 °C; however, the survival at these subzero temperatures requires several genetic and physiological strategies that exclusively cold-adapted microorganisms (permafrost and seasonally frozen soil microbial communities) have developed (Jansson & Tas, 2014; Boetius et al., 2015). In the present study, microbial communities from marine sediments living at temperatures ranging from 4 to 27 °C were analyzed. Thus, we considered that changes in the microbial community composition due to the storage temperature (−20 °C) were unlikely, because the sediments were immediately frozen at −20 °C on board and they were never thawed during the transport neither in the laboratory before DNA extraction, ashas been recommended in previous studies (Tedjo et al., 2015). The storage of the samples at −20 °C is a standard procedure commonly used for microbial community analyses from marine sediment (e.g., Bienhold et al., 2016; Probandt et al., 2017).
Diversity indices are commonly used in microbial ecology studies to understand the links between the environmental conditions and the community (Kimes et al., 2013; Bargiela et al., 2015). It has been suggested that microbial richness and diversity could be an expression of environmental variation correlated to energy sources and temperature (Santelli et al., 2008; Rosano-Hernández, Ramírez-Saad & Fernández-Linares, 2012; Bargiela et al., 2015). Our results revealed higher richness and diversity in shallow sites compared to deep sites (Table 2). This can be a result of more dynamic shallow environments, characterized by strong physical mixing and seasonal variation. In shallow environments the interaction among atmosphere, land and ocean increase environmental variation, and therefore higher microbial diversity, as reported for coastal environments elsewhere (Gobet et al., 2012). In contrast, lower richness and diversity in the deep sites could be the result of relatively constant environmental conditions. Microbes from the abyssal plains environment are able to overcome the extreme conditions of temperature, pressure, oligotrophy and darkness (Jochens & DiMarco, 2008) but are subjected to far less environmental variability.
It is well recognized that marine sediments are dynamic environments shaped by interactions among biotic and abiotic processes, including the redox reactions (Wasmund, Mußmann & Loy, 2017). In the present study, the electronegative redox values detected in the first 5 cm of the sediments for shallow sites (Table 1), suggested high microbial respiration or anoxic/reducing conditions (Valdes & Real, 2004; Aranda et al., 2015). However, genera known to be both aerobic (e.g., Marinicella and Nitrosopumilus) and anaerobic (e.g., Amphritea, Desulfococcus, GOUTA-19, LCP-6, and Pseudidiomarina) were found in shallow samples (Fig. S4). This suggested the co-occurrence of different metabolisms in these sites, in accordance with previous reports for other shallow marine sediments (Jørgensen, 1977; Mills et al., 2008; Acosta-González, Rosselló-Móra & Marqués, 2013; Tolar, King & Hollibaugh, 2013; Cerqueira et al., 2015).
Electropositive redox values detected in the deep sites (Table 1), suggested oxidizing conditions at those water depths, likely because the upper sediment layers remain oxygenated due to relatively high oxygen concentrations in deep waters of the GoM (Jochens & DiMarco, 2008) as well as low rates of sediment community oxygen consumption previously reported in the deep GoM (Rowe et al., 2008). In these oxidizing deep sediments, genera known to be aerobic and facultative anaerobic bacteria, such as Shewanella, Alcanivorax, Marinicella, Nitrospina and ZD0117 (Alteromonadacea e) were well represented (Fig. S4). Thus, these results suggest that aerobic (and facultatively anaerobic) lifestyles seem to be favored in abyssal sediments of the studied region.
In this study, water depth was highly correlated with microbial community structure (Fig. 2). It is known that water depth and its dependent variables, such as temperature and pressure, are among the most important factors explaining variation in the bacterial community composition of seafloor sediments (Orcutt et al., 2010; Bienhold et al., 2016). As an example of this, our results showed a relatively high abundance of psychropiezophilicorganisms of the genera Colwellia and Shewanella for the deep sites (Fig. S2 and Fig. S4), where conditions are favorable for the development of these microbial metabolisms (Nogi et al., 2004).
Detection of phylotypes putatively related to hydrocarbons degradation
Due to the presence of natural hydrocarbon effluents and the oil spills which have occurred in the GoM (e.g., DWH spill), the potential effects of hydrocarbons on microbial community structure has been extensively studied there (Rosano-Hernández, Ramírez-Saad & Fernández-Linares, 2012; Orcutt et al., 2010; Kleindienst et al., 2015). Recent investigations have provided insights about microbial populations responding to the presence of hydrocarbons, highlighting microorganisms capable of using these organic compounds as carbon source (Kostka et al., 2011; Redmond & Valentine, 2011; Mason et al., 2012). In this work, we report the occurrence of several genera, such as Shewanella, Alcanivorax, Pseudoalteromonas and Phaeobacter (Fig. S4), which have been previously associated with hydrocarbon consumption (Beazley et al., 2012; Mason et al., 2014; Barbato et al., 2016; Liu, Bacosa & Liu, 2017).
In addition, this work reports for the first time NC10 and Kordiimonadales phylotypes for sediments of the GoM (Fig. 3 and Fig. S1), that based on the available information, are capable of anaerobic methane oxidation with nitrite denitrification, and aerobic degradation of alkanes and polycyclic aromatic hydrocarbons, respectively (Kwon et al., 2005; Xu et al., 2011; Math et al., 2012; Zhou & Xing, 2015; Padilla et al., 2016). The Kordiimonadales phylotype was even detected by DGGE band sequencing approach (Fig. S2B), supporting their relatively high abundances in the analyzed samples.
The potential capability to degrade aromatic compounds, such as nitrotoluene, ethylbenzene, chlorocyclohexane and fluorobenzoate were predicted based on trait modeling software (PICRUSt) (Fig. S3). This also suggests that microbial metabolisms related to the hydrocarbon degradation might be well distributed in the Mexican region of the PFB and could contribute with the bioremediation in case of hydrocarbon contamination due to forthcoming oil exploration and extraction activities in this area.
To our knowledge, this is the first study to report differences in abundances and composition of the microbial communities inhabiting surficial sediments in a water depth gradient in an important province for oil extraction in the Mexican region of the Gulf of Mexico. In this study, the combination of nucleic acid–based molecular methods and physicochemical measurements allowed the detection of changes in the structures of microbial communities which were mainly related to the redox potential, total sulfur concentration, grain size, as well as depth and the variables that change with it, such as temperature and pressure. Our findings indicate that shallow microbial communities are taxonomically richer than communities in the deep sediments. Bacterial phylotypes putatively related to hydrocarbon degradation appear to be well represented for all the analyzed samples and could ameliorate future anthropogenic oil spills in this region of the GoM. These results contribute to the current knowledge of the environmental and biological dataset of the Perdido Fold Belt, which will be useful for further monitoring of this area in the Gulf of Mexico.
Relative abundances of microbial communities at taxonomic levels of Family (A) and Order (B)
DGGE fingerprinting and phylogenetic analysis of sequenced bands
(A) DGGE band pattern from bacterial communities through the depth gradient based on the V3 hypervariable region of the 16S rRNA gene. Triangles indicate sequenced bands. The names of the stations are indicated in the bottom. (B) Phylogenetic tree of sequenced DGGE bands (∼200 pb, in bold) and GenBank sequences (accession numbers in parenthesis). Unrooted phylogenetic tree showing representative taxa inferred by Neighbor-Joining method. The percentage of replicates tree in which the associated taxa cluster together in the bootstrap test (1,000 replicates) are shown next to the branches ( >75). The evolutionary distances were computed using the Jukes-Cantor method, and the bar indicates an evolutionary distance of 0.05 substitutions for each nucleotide compared.
Metabolic activities predicted from shallow and deep microbial communities
Differences in the proportions of metabolic pathways among shallow and deep-sea sediment samples were observed based on the metagenomic simulation using PICRUST software and the KEGG database. 95% confidence intervals are shown.