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Environmental Microbiology (2015) 17(10), 3847–3856
doi:10.1111/1462-2920.12853
Archaeal enrichment in the hypoxic zone in the northern Gulf of Mexico Lauren E. Gillies,1 J. Cameron Thrash,2 Sergio deRada,3 Nancy N. Rabalais4 and Olivia U. Mason1* 1 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA. 2 Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA. 3 Ocean Sciences Branch, Naval Research Laboratory, Stennis Space Center, MS 39529, USA. 4 Louisiana Universities Marine Consortium, Cocodrie, LA 70344, USA. Summary Areas of low oxygen have spread exponentially over the past 40 years, and are cited as a key stressor on coastal ecosystems. The world’s second largest coastal hypoxic (≤2 mg of O2 l−1) zone occurs annually in the northern Gulf of Mexico. The net effect of hypoxia is the diversion of energy flow away from higher trophic levels to microorganisms. This energy shunt is consequential to the overall productivity of hypoxic water masses and the ecosystem as a whole. In this study, water column samples were collected at 39 sites in the nGOM, 21 of which were hypoxic. Analysis of the microbial community along a hypoxic to oxic dissolved oxygen gradient revealed that the relative abundance (iTag) of Thaumarchaeota species 16S rRNA genes (> 40% of the microbial community in some hypoxic samples), the absolute abundance (quantitative polymerase chain reaction; qPCR) of Thaumarchaeota 16S rRNA genes and archaeal ammonia-monooxygenase gene copy number (qPCR) were significantly higher in hypoxic samples. Spatial interpolation of the microbial and chemical data revealed a continuous, shelfwide band of low dissolved oxygen waters that were dominated by Thaumarchaeota (and Euryarchaeota), amoA genes and high concentrations of phosphate in the nGOM, thus implicating physicochemical forcing on microbial abundance.
Received 10 January, 2015; revised 18 March, 2015; accepted 18 March 2015. *For correspondence. E-mail [email protected]; Tel. 850 645 1725; Fax 850 644 2581.
Introduction Marine hypoxic zones are regions with low dissolved oxygen (DO) concentrations often below 2 mg l−1 or 62.5 μmol kg−1 (hypoxic). At this threshold, prolonged exposure can lead to mortality or migration of marine fauna (Rabalais and Turner, 2001). Eutrophicationassociated areas of hypoxia have been reported in over 400 systems that span the globe (Diaz and Rosenberg, 2008), with the total number increasing more recently (Conley et al., 2011). Typically, coastal hypoxia occurs near major population centres and is associated with high nutrient input from rivers and stratified conditions caused by freshwater input and thermal warming (Diaz and Rosenberg, 2008). The number of hypoxic zones have increased significantly (doubling each decade since the 1960s) concomitant with the increased use of industrially produced nitrogen fertilizer (Diaz and Rosenberg, 2008). In particular, the northern Gulf of Mexico (nGOM) is the site of the second largest zone of human-caused coastal hypoxia in the world, influenced by the freshwater input and nutrient load from the Mississippi River (Rabalais and Turner, 2006). Hypoxia in the nGOM has increased in severity during the summer months in direct response to additional nutrient loading in the Mississippi watershed, beginning around the 1950s, after which the nitrate flux to the nGOM continental shelf tripled (Rabalais et al., 2007). The general coastal hypoxia scheme is an increase in phytoplankton biomass due to nutrient input, followed by microbial respiration of this decaying biomass (Rabalais et al., 2007). The nutrient loading has the dual effect of increasing algal blooms and altering the type and characteristics of the phytoplankton community. The flux of fixed carbon in the form of senescent phytoplankton, zooplankton faecal pellets or aggregates to the lower water column provides a large carbon source for decomposition by aerobic bacteria. In a stratified water column, aerobic decomposition of these fixed carbon sources consumes DO at a higher rate than is resupplied from the upper water column, leading to large areas of hypoxia, for months at a time, from the spring to the fall (Rabalais and Turner, 2001; Rabalais et al., 2002; 2007). During microbial respiration, oxygen is consumed and carbon dioxide is produced resulting in acidification (Cai et al., 2011). Further, energy flow is shunted from the benthos to
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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microorganisms (Diaz and Rosenberg, 2008), which decouples predator–prey interactions (Wright et al., 2012). Thus, microbial respiration is implicated in causing low oxygen conditions (Rabalais et al., 2002) and acidification (Cai et al., 2011) in the nGOM, and overall, in a diversion of energy from benthic organisms to microorganisms (Diaz and Rosenberg, 2008). Microbial community structure in the nGOM hypoxic zone has not yet been characterized. In contrast, the microbiology of other oxygen minimum zones (OMZs) have received significant attention. In these well-studied OMZs, microbial and metabolic diversity is spread across a variety of energetic niches, all dependent upon the concentrations of oxygen, nitrogen and sulfur species, and the overall redox state (Ulloa et al., 2012; Wright et al., 2012). With declining oxygen concentrations, a metabolic shift from aerobic to anaerobic processes is initiated with nitrate, for example, serving as an alternative electron acceptor to oxygen (Wright et al., 2012). The dominant microorganisms in OMZs are Proteobacteria, Bacteroidetes, marine group A, Actinobacteria and Planctomycetes (Wright et al., 2012). In the archaea, the relative abundance of Thaumarchaeota have been reported to increase in abundance at the oxycline (Zaikova et al., 2010; Belmar et al., 2011; Tolar et al., 2013). It has also been reported that archaeal ammoniamonooxygenase (amoA) transcript abundances increase in OMZs (Lam et al., 2007; Beman et al., 2008; Stewart et al., 2012). Specifically, Stewart and colleagues (2012) identified nitrification, carried out by relatives of the ammonia-oxidizing Thaumarchaeota, Nitrosopumilus maritimus (Könneke et al., 2005), as a dominant energy source in the Chilean OMZ. To our knowledge, however, the enrichment in both Thaumarchaeota 16S rRNA genes and archaeal amoA genes has not yet been reported in the expansive nGOM hypoxic zone, nor, as mentioned above, has the overall microbial community been described. The goal of our research was to evaluate linkages among the (micro)biological, chemical and physical variables in the nGOM hypoxic area to better understand the microbial response to low oxygen conditions in a large coastal hypoxic zone. We describe herein the results of our July 2013 nGOM sampling effort within and outside of the continuous 5840 mi2 (15126 km2) hypoxic zone. A total of 39 water column samples were collected for oxygen, nutrients, chlorophyll a (chla) measurements and for analyses of the in situ microbial community. We used iTag sequencing and quantitative polymerase chain reaction (qPCR) analyses of 16S rRNA genes to characterize the microbial community structure. Quantitative polymerase chain reaction was also used to determine archaeal ammonia-oxidizing gene (amoA) copy numbers. Statistical analyses were used to explore linkages among microbiological, chemical and physical variables.
Results and discussion Water column properties and chemistry For each of the 39 samples collected at the oxygen minimum zone [avg. collection depth = 18 meters below sea level (mbsl)] during the 2013 shelfwide cruise (Fig. 1), temperature, depth, salinity and in situ chemistry were determined (Fig. 1 and Supporting Information Table S1). Specifically, concentrations of DO, ammonium (NH4), nitrite (NO2), nitrate (NO3), phosphate (PO4), silicic acid (Si(OH)4) and chla were determined (Fig. 1 and Supporting Information Table S1). To evaluate the relationships among these variables, correlation coefficients (Spearman) and significance (P-value ≤ 0.05) were determined (Fig. 2, Supporting Information Tables S2 and S3). Depth, salinity and temperature were highly correlated, as was temperature and DO. Depth and salinity were inversely correlated with NO2 (Fig. 2, Supporting Information Tables S2 and S3). Dissolved oxygen and PO4 had a strong inverse relationship (Fig. 2 and Supporting Information Table S2). The inverse relationship between DO and PO4 has been reported before and may be due to several factors, for example, PO4 flux from anoxic to near anoxic sediments (Rabalais and Turner, 2006), reduced mixing of the water column (Rabalais and Turner, 2006; Baustian et al., 2011; 2013) and microbial remineralization. Overall, the nutrient profiles in relationship to DO are consistent with previous reports of bottom water in the nGOM hypoxic zone (Rabalais and Turner, 2006). nGOM bacterioplankton structure and diversity We used iTag sequencing of 16S rRNA genes to characterize the microbial communities in the nGOM across the shelf during the annual hypoxic event. This analysis revealed that the microbial communities were dominated by Proteobacteria, Thaumarchaeota and to a lesser extent Bacteroidetes, Actinobacteria, MGII Euryarchaeota and Cyanobacteria (Fig. 3 and Supporting Information Fig. S1). The dominant phyla were similar regardless of oxygen status (Fig. 3 and Supporting Information Fig. S1). The dominance of these bacterial clades is consistent with the only study that has previously characterized the microbial community in the nGOM water column (King et al., 2013), with the exception of Thaumarchaeota abundance, for which their March 2010 samples were collected when oxic conditions prevailed. In the King et al. (2013) analysis, the primary driver affecting the abundance of Thaumarchaeota was depth, with an increase observed at ≥ 100 mbsl. In their study, oxygen and nutrients were not significantly correlated with Thaumarchaeota abundance. Our analyses of microbial community compositions were similar at the phylum level regardless of DO
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 1. Sampling map showing the 39 stations sampled during the July 2013 nGOM shelfwide cruise. Interpolation plots of microbiological and chemical data are shown. The iTag species abundances are relative abundances (RA).
concentrations, with the one exception that Thaumarchaeota abundance increased with decreasing DO (Fig. 3 and Supporting Information Fig. S1); therefore, we next endeavoured to determine if the relative abundance of specific members of these phyla were similar across the DO gradients sampled. Alpha diversity metrics of iTag sequence data for hypoxic and oxic samples were compared. Microbial diversity was significantly lower in
hypoxic samples than it was in oxic samples. Specifically, the Shannon diversity indices averaged 6.0 in hypoxic samples compared with 6.8 in oxic samples (Student’s t-test, P-value = 0.00004; Supporting Information Table S4). In assessing differences in diversity in oxic versus hypoxic samples, both richness and evenness were evaluated. The rarefaction curve did not reveal a trend in the species richness (Student’s t-test) in comparing oxic to
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 2. Correlation matrix showing the sign and magnitude of the correlation (Spearman) coefficients between biological, chemical and physical data. Blue indicates a positive value and red a negative value (see scale bar). The diagonal lines also indicate the direction of the correlation. The intensity of each of these colors increases as the correlation value moves away from zero (see scale bar). Correlations that were statistically significant (P-value ≤ 0.05) are shown by a black square around the borders (e.g. , has a black border indicating a significant correlation versus , indicating a correlation that was not significant).
hypoxic samples (Supporting Information Fig. S2), which is consistent with the similarities in microbial phyla across sites. Further, richness was not significantly different (Student’s t-test) in either sample type. However, a test of equitability (evenness) between the two sample types showed a significant difference with the oxic samples averaging 0.65 compared with the hypoxic samples, which averaged 0.58 (the scale is 0–1.0; with 1.0 indicating that all species are equally abundant). This suggested similar microbial species were present in oxic verses hypoxic
water masses, but that their abundances oscillated. These oscillations were, as will be discussed below, directly related to oxygen and nutrient concentrations. Non-parametric statistics were used to determine which species were responsible for the significant difference in evenness in comparing hypoxic with oxic samples. Specifically, samples were categorized as hypoxic or oxic and the abundances of the 7027 Operational Taxonomic Units (OTU)s were tested for significance using the MannWhitney test. Of these, only 15 OTUs showed significant
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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0.6
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Other Chloroflexi Verrucomicrobia SAR406 Planctomycetes Cyanobacteria Euryarchaeota Actinobacteria Bacteroidetes Thaumarchaeota Proteobacteria
H1_G3 H2_I5 H3_E2A H4_I3 H5_J5-2 H6_F2A H7_I6 H8_E4 H9_D2 H10_D3 H11_A3 H12_H3 H13_H4 H14_F4 H15_C6C-2 H16_H5 H17_F6 H18_C3-2 H19_G6 H20_C7-2 H21_A'2 O1_A5 O2_H7 O3_J7 O4_E2 O5_C3 O6_A'4 O7_D5 O8_B2 O9_K2 O10_C9-2 O11_K6 O12_D'1 O13_D'5 O14_C9 O15_B8 O16_C6C O17_G2 O18_I2
0.0
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Relative abundance (%)
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0.05 0.06 0.07 0.08 0.09 0.14 0.15 0.31 0.33 0.40 0.48 0.48 0.54 0.78 0.86 1.17 1.23 1.27 1.32 1.76 1.82 2.22 2.32 2.41 2.64 2.96 3.04 3.20 3.35 3.75 3.84 4.06 4.12 4.26 4.37 4.43 4.78 4.84 5.43
Hypoxic Oxic
DO (mg l–1)
Fig. 3. Bar graph of relativized 16S rRNA gene iTag sequence data. Only the more abundant bacterial and archaeal groups are shown. Less abundant groups were summed under ‘Other’. As shown by the DO scale, samples are sorted from lowest to highest DO concentrations.
differences in their relative abundance between hypoxic and oxic samples, with seven being more abundant in hypoxic samples. We found that these same species were significantly correlated with DO at ≤ 1 mg of O2 l−1 compared with samples at > 1 mg of O2 l−1, further validating the statistical relationship between species abundance and DO concentrations. We recognize however, that other environmental variables beyond DO concentrations are drivers in structuring the microbial communities described here. The relative abundance of the Thaumarchaeota was comprised almost exclusively of a single species (OTU4369009), which was > 40% in some hypoxic samples (Fig. 1; avg. abundance was 26% hypoxic/14% oxic samples). In contrast, as discussed above, previous analysis of shallow nGOM samples using pyrotag sequencing revealed a low abundance of Thaumarchaeota (King et al., 2013). Specifically, in their shallow and comparable water column samples, the abundance of Thaumarchaeota was low at ∼3% avg. relative abundance (King et al., 2013). Thaumarchaeota were reported to dominate the archaeal community in Arctic Ocean surface waters, particularly coastal samples (Galand et al., 2009), thus our findings of high abundances in a coastal, shallow water environment are not unprecedented. An MGII Euryarchaeota species (OTU3134565) was > 5% in several hypoxic samples (avg. 3% hypoxic/0.8% oxic;
Fig. 1). The other species that increased in abundance in hypoxic samples comprised ≤ 1.2% of the microbial community in both hypoxic and oxic samples. These were, in order of decreasing abundance: Thiohalorhabdaceae (OTU266046; avg. 1.2% hypoxic/0.7% oxic), SAR406 (Marine Group A; OTU118018; avg. 1.1% hypoxic/0.3% oxic), Nitrospina (OTU571236; avg. 0.9% hypoxic/0.7% oxic), Synechococcus (OTU533688; avg. 0.8% hypoxic/ 0.7% oxic) and Halomonadaceae (Candidatus Portiera; OTU275493; avg. 0.8% hypoxic/0.5% oxic). Beta diversity To examine the primary drivers in structuring the microbial communities, including the species discussed above, iTag sequence data and chemical and physical variables were examined using non-metric multidimensional scaling (Fig. 4). The results revealed disparate microbial communities in hypoxic samples as compared with non-hypoxic samples (Fig. 4). The primary physicochemical drivers in influencing the microbial community structure were oxygen and phosphate and physical properties (Fig. 4). Of the seven OTUs that were significantly higher in hypoxic samples, five were significantly correlated with an ordination (non-metric multidimensional) axis (P-value ≤ 0.02; Fig. 4). These five species are discussed in more detail below.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 4. NMDS ordination of relativized 16S rRNA gene iTag sequence data. A. Dissolved oxygen is shown by bubble size, with larger bubbles indicating higher DO concentrations. B. The same ordination as in A. In this figure, vectors are shown for significant variables, including those microbial species that were significantly higher in hypoxic samples, and for chemical and physical data.
Biological, chemical and physical variable correlation analyses Correlations of these five species and DO, nutrient concentrations and physical properties are displayed in Fig. 2. Only those variables with P-values ≤ 0.05 are discussed here (see Supporting Information Table S3 for full correlation matrix). There was an inverse relationship between the relative abundance of the Thaumarchaeota species and DO (r = −0.61) and a strong positive correlation with PO4 (r = 0.75; Fig. 2 and Supporting Information Table S2). The significantly higher abundance of a Thaumarchaeota, closely related to N. maritimus, in our hypoxic samples and the strong relationship between DO and its abundance suggested that Thaumarchaeota may carry out core metabolic reactions at low oxygen concentrations. Several studies have reported an increase in abundance of Thaumarchaeota in low-oxygen marine environments (Lam et al., 2007; Beman et al., 2008; Molina et al., 2010; Belmar et al., 2011; Tolar et al., 2013). However, this is the first study carried out in the month of July during the peak of the annual hypoxic event in the nGOM. As discussed above, the relative abundance of the Thaumarchaeota species was > 40% in some hypoxic samples. Additionally, it was recently reported, that N. maritimus has an extremely low ammonium threshold (Martens-Habbena et al., 2009). In several hypoxic samples, submicromolar ammonium concentrations were observed, conditions in which the growth of Nitrosopumilus-like ammonia-oxidizing archaea (AOA) is supported, but not ammonia-oxidizing bacteria (MartensHabbena et al., 2009). A low-substrate threshold could allow AOA to successfully compete with heterotrophic bacteria and phytoplankton for ammonium (MartensHabbena et al., 2009). Further, N. maritimus ammonium
and oxygen uptake kinetics were indistinguishable (mean half-saturation constants were 0.132 and 0.133 μM NH3+NH4+ respectively; Martens-Habbena et al., 2009). In our Thaumarchaeota-dominated hypoxic samples, DO was ≤ 2 mg l−1, or ≤ 62.5 μM (Fig. 1 and Supporting Information Table S1). Although low, these DO concentrations could support maximum rates of ammonia oxidization by AOA (Martens-Habbena et al., 2009) when hypoxic conditions prevail. The high relative abundance of a Thaumarchaeota species, which may have low ammonium requirements and, perhaps, oxygen requirements, has implications for the nGOM hypoxic zone. A plausible scenario for the observed increase in abundance of these microorganisms could be nitrate input leading to a phytoplankton bloom, followed by microbial decomposition of phytoplankton biomass and an increase in ammonium concentrations near the seabed. As ammonium is consumed by the microbial community to build cellular constituents, reduced ammonium concentrations would result in conditions where Thaumarchaeota species may be able to sustain high growth rates and standing stocks, as suggested by Martens-Habbena and colleagues (2009), while concomitantly drawing down oxygen. Two obligate mixotrophic AOA were recently isolated from a coastal environment (Qin et al., 2014). The dominant Thaumarchaeota species (OTU4369009) in our study was 99–100% similar to these coastal strains. This suggests a broader environmental niche for ammoniaoxidizing Thaumarchaeota that includes utilization of organic carbon. This is significant in the nGOM hypoxic zone in that Thaumarchaeota may be able to compete with heterotrophic bacteria in consuming remnant phytoplankton biomass, while subsequently outcompeting heterotrophs when low oxygen, low ammonium and low
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
Archaea in the hypoxic northern GOM water column carbon conditions are established. Thaumarchaeota are thus implicated in sustained oxygen drawdown using, potentially, a myriad of growth strategies. Further, as was suggested by Stewart and colleagues (2012), nitrification by N. maritimus relatives, may be a dominant energy source in environments where oxygen concentrations are low. There was a similar inverse relationship between DO and the MGII Euryarchaeota species (r = −0.54; Fig. 2 and Supporting Information Table S2). This species was also correlated with NO3 (r = 0.60; Fig. 2 and Supporting Information Table S2). It has been reported that MGII bloom in summer North Sea surface water (Pernthaler et al., 2002), but their potential role in biogeochemical cycling in hypoxic water masses is largely uncharacterized given that there are currently no cultured representatives described in the literature. To date, one MGII has been assembled from metagenomic data (Iverson et al., 2012). The genome encoded a photo-heterotrophic metabolism (Iverson et al., 2012). A pathway for nitrate reduction is not encoded in the MGII genome; thus, it appears to require a reduced nitrogen source (Iverson et al., 2012). The MGII genome encoded proteorhodopsin (Iverson et al., 2012), which has been shown in a Vibrio sp. to enhance survival in a resource limited environment (Gómez-Consarnau et al., 2010). A Marine Group A (MGA) species (OTU118018), closely related to subgroup SAR406, had a significantly higher abundance in hypoxic samples and was strongly correlated with DO (r = −0.71), Si(OH)4 (r = 0.75), temperature (r = −0.81) and salinity (r = 0.68) (Fig. 2 and Supporting Information Table S2). MGAs have been found in low-oxygen zones of other environments, such as in the Northeast subarctic Pacific Ocean (NESAP) OMZ (Wright et al., 2012; Allers et al., 2013). Consistent with our findings, Allers et al. (2013) used fluorescent in situ hybridization and found that the MGA in the NESAP OMZ had a strong inverse correlation with DO concentrations with similar correlation coefficients to ours (r = −0.3 to −0.824 vs. −0.71 respectively). A Nitrospina species (OTU571236) had a statistically significantly higher relative abundance in hypoxic samples and was positively correlated with NO3 (r = 0.79) and negatively with NO2 (r = −0.44). This is in agreement with the metabolism of, for example, N. gracilis, during which NO2 is oxidized to NO3 (Lücker et al., 2013). It has been reported that N. gracilis has microaerophilic ancestry, which is a plausible explanation for why it is often reported in marine oxygen minimum zones (Lücker et al., 2013). The Nitrospina species (OTU571236) was also correlated with salinity (r = 0.70), depth (r = 0.55), chla (r value = −0.71) and temperature (r = −0.60). The Synechococcus species (OTU533688) was not strongly correlated with any environmental variables measured.
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Bacteria and Thaumarchaeota 16S rRNA gene and amoA gene copy number The microbial data presented herein has, thus far, been an analysis of relative species abundances from iTag sequencing. To evaluate changes in the microbial community structure using absolute abundances, we used qPCR analysis of 16S rRNA genes. Total bacteria ranged from 3.42 × 102 to 9.13 × 107 (avg. 2.45 × 107) copies L−1 (Supporting Information Table S5). The absolute abundance of bacteria was not significantly different when comparing hypoxic with oxic samples. In contrast, the absolute abundance of Thaumarchaeota 16S rRNA gene copy number was significantly higher in hypoxic samples [ranged from undetectable to 2.02 × 107 (avg. 5.53 × 106) copies L−1; Supplemental Table 5]. Tolar and colleagues (2013) reported that in more shallow (< 100 m) nGOM samples, Thaumarchaeota 16S rRNA gene copy number (qPCR) comprised only 1.8% of the microbial community. Given the high absolute (qPCR) abundance of Thaumarchaeota and the high relative (iTag) abundance of a Thaumarchaeota closely related to the known AOA, N. maritimus, we next determined the copy number of archaeal amoA genes l−1 using qPCR (Supporting Information Table S5; no known ammonia-oxidizing bacteria were observed in the iTag data and this paper focused on archaea; therefore, bacterial amoA genes l−1 were not quantified). Archaeal amoA copy number was significantly higher in hypoxic samples compared with oxic samples [ranged from undetectable to 1.83 × 107 (avg. 5.61 × 106) copies l−1; Supporting Information Table S5). This average amoA gene copy number is lower than the near-surface inshore nGOM copy number (avg. 3.86 × 107) in the nGOM as reported by Tolar and colleagues (2013). The Thaumarchaeota 16S rRNA:amoA gene copy number l−1 was highly correlated (r = 0.93; Supporting Information Table S5). The ratio of Thaumarchaeota 16S rRNA:amoA gene copy number l−1 ranged from one to three, with an average of one. This ratio suggested that on average, the Thaumarchaeota observed here had a single copy of amoA, which is consistent with other reports showing a 1:1 ratio of the copy number of Thaumarchaeota 16S rRNA genes:amoA genes (Sintes et al., 2013). These data support a link between the dominant microbial player in the hypoxic zone and the nitrogen cycle. Conclusion We examined the linkages among the microbiological, chemical and physical properties in the water column during the month of July on a 2013 survey over a broad area of the continental shelf in the nGOM that experiences extensive hypoxia on an annual basis. We found that while hypoxia did not change the microbial community composition, with regard to richness, it had a
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
3854 L. E. Gillies et al. significant impact on microbial species abundances. In particular, the abundance of ammonia-oxidizing Thaumarchaeota increased significantly in correlation with decreasing dissolved oxygen concentrations. These ammonia-oxidizing Thaumarchaeota appeared to be most abundant when oxygen concentrations were less than 0.5 mg l−1 and especially at 0.1 mg l−1 in the core of a continuous low oxygen area between 15 and 25 m water depth on the western part of the study area. These areas are consistent with low dissolved oxygen concentrations, higher dissolved phosphate, silicate and ammonium consistent with features of waters above the sea bed where hypoxia forms (Rabalais and Turner, 2006). Hypoxic archaeal hotspots might persist over time in the hypoxic area and may continue to serve as a site where energy flow is diverted away from higher trophic levels to, in this case, ammonia-oxidizing Thaumarchaeota. This population of Thaumarchaeota could carry out sustained draw down of oxygen in these water masses, as well as potentially influence the nitrogen cycle in the nGOM. The trajectory of change in the microbial community composition over a period of decreasing dissolved oxygen concentrations remains to be elucidated, but our findings identify the dominant microbial players and their potential metabolism over a broad area of perennial low oxygen on a continental shelf. Experimental procedures Sample collection Samples were collected 21–28 July 2013 on the R/V Pelican in the nGOM. The 39 stations sampled in this study ranged from just south of the mouth of the Mississippi River to south of the Louisiana Texas border. Samples were collected using niskin bottles on a rosette with a CTD sensor at the oxygen minimum zone for each station, usually near the sea bed. Collection depths ranged from 6 meters to 35 mbsl (avg. collection depth was 18 mbsl). The sample location map and subsequent plots of chemical and biological data (Fig. 1) were made with Ocean Data View (Schlitzer, 2013).
Oxygen, chla and nutrients Oxygen concentrations were determined in situ with the CTD sensor. Oxygen concentrations were verified using the Winkler method shipboard. Chlorophyll a samples were concentrated on 25 mm of Whatman GF/F filters from 500 ml to 1 litre of seawater and stored at −20°C. Chlorophyll a was extracted using the methods described in the Environmental Protection Agency Method 445.0, ‘In Vivo Determination of Chlorophyll a in Marine and Freshwater Algae by Fluorescence’; however, no mechanical tissue grinder or HCl were used. Chlorophyll a concentrations were determined using a fluorometer with a chla standard (Anacystis nidulans chla). For nutrients, 60 ml of seawater was filtered through a Whatman GF/D filter (EMD Millipore, Billerica, MA, USA) into two 30 ml Nalgene bottles using a swinnex filter holder and syringe and stored at −20°C. Nutrient concentrations were
determined by the marine chemistry lab at the University of Washington following the WOCE Hydrographic Program using a Technicon AAII system (http://www.ocean .washington.edu/story/Marine+Chemistry+Laboratory).
Microbial sampling and DNA extractions From each station, up to 10 litre of seawater were collected and filtered with a peristaltic pump. A 2.7 μM Whatman GF/D pre-filter was used and samples were concentrated on 0.22 μM Sterivex filters (EMD Millipore; Billerica, Massachusetts). Sterivex filters were sparged and filled with RNAlater. DNA was extracted directly off of the filter by placing half of the Sterivex filter in a Lysing matrix E (LME) glass/zirconia/silica beads Tube (MP Biomedicals, Santa Ana, CA, USA). To each tube, 50 μl of 0.1 M ammonium aluminum sulfate, 500 μl of CTAB and 500 μl of phenol:chloroform:isoamyalcohol (25:24:1) was added. Samples were subjected to bead beating using a MP Biomedicals (Santa Ana, CA, USA) FastPrep Instrument at 5.5 m s−1 for 45 s, followed by centrifugation at 16 000 × g for 5 min at 4°C. The supernatant (∼500 μl) was transferred to a new tube. A second extraction was carried out using 500 μl of CTAB in the original LME tube, and the above steps were repeated. Samples were centrifuged at 16 000 × g for 5 min at 4°C. Next, 0.5 ml of chloroform was added to supernatant. The sample was vortexed and centrifuged at 16 000 × g for 5 min at 4°C. The supernatant (∼200 μl) was transferred to a new tube, 1000 μl of PEG 6000 precipitate solution was added and the samples were incubated at room temperature for 2 h. Samples were centrifuged at 16 000 × g for 10 min at 4°C. The aqueous top layer was removed and 500 μl of 70% ethanol was added. Samples were centrifuged at 16 000 × g for 5 min at 4°C. Ethanol was removed, leaving the pellet to air dry for 5 min. Samples were then stored at −80°C until purified. DNA was purified using Qiagen (Valencia, CA, USA) AllPrep DNA/RNA Kit. DNA quantity was determined using a Qubit2.0 Fluorometer (Life Technologies, Grand Island, NY, USA).
16S rRNA gene sequencing and analysis 16S rRNA genes were amplified from 10 ng of purified DNA in duplicate using archaeal and bacterial primers 515F and 806R, which targets the V4 region of Escherichia coli in accordance with the protocol described by Caporaso et al. (2011; 2012) and used by the Earth Microbiome Project (http://www.earthmicrobiome.org/emp-standard-protocols/ 16s/), with a slight modification: the annealing temperature was modified to 60C. Polymerase chain reaction amplicons were purified using Agencourt AMPure XP PCR Purification beads (Beckman Coulter, Indianapolis, IN, USA). Sequencing was carried out using the MiSeq (Illumina, San Diego, CA, USA) platform. Samples were analysed using the QIIME version 1.7.0 (Caporaso et al., 2010a) pipeline. Raw sequences were demultiplexed and then quality filtered using the default parameters in QIIME. These sequences are available at http://mason.eoas.fsu.edu and from NCBI’s sequence read archive (accession SRP056891). Sequences were then clustered into OTUs, which was defined as ≥ 97% 16S rRNA gene sequence similarity, using UCLUST (Edgar, 2010) using the open reference clustering protocol (http://www.qiime.org/
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
Archaea in the hypoxic northern GOM water column tutorials/open_reference_illumina_processing.html). The resulting representative sequences were aligned using PyNAST (Caporaso et al., 2010b) and given a taxonomic classification using RDP (Wang et al., 2007), retrained with the Greengenes version 13.5 (McDonald et al., 2012). The resulting OTU table was filtered to keep only OTUs that had at least 10 sequences, and then converted to relative abundance, by summing the total sequence count in a sample and then dividing each sample OTU count by this total.
Statistics The differences in relativized OTU abundances in communities from hypoxic or oxic conditions were analysed using non-parametric statistics (Mann–Whitney) to test for statistically significant differences using METAGENassist (Arndt et al., 2012) and the application of a false discovery rate to account for multiple comparisons. Further statistical analyses were carried out in R, including determination of Spearman correlation coefficients among all variables. The NMDS ordination was done using the Vegan package. Alpha diversity metrics were determined after multiple rarefactions using QIIME. Statistical differences in these metrics, as well as in biological, chemical and physical variables were assessed by Student’s t-test. The QIIME-generated normalized (by conversion to relative abundance) OTU abundances in the 39 different samples were then analysed using NMDS ordination in R using metaMDS in Vegan package. P-values were derived from 999 permutations of the data.
qPCR Thaumarchaeota and bacterial 16S rRNA and archaeal amoA genes were quantified in duplicate using qPCR. For each qPCR reaction, 10 ng of genomic DNA was used. Thaumarchaeota 16S rRNA genes were amplified using 334F and 554R with an annealing temperature of 59°C (Suzuki et al., 2000). Bacterial 16S rRNA genes were amplified using 1369F and 1492R with 56°C as the annealing temperature (Suzuki et al., 2000). Archaeal amoA genes were amplified using Arch-amoA-for and Arch-amoA-rev with 58.5°C as the annealing temperature (Wuchter et al., 2006). Standards (DNA cloned from our samples for Thaumarchaeota 16S rRNA and archaeal amoA and E. coli for bacterial 16S rRNA) were linearized, purified and quantified by fluorometry. The reaction efficiencies for all qPCR assays were 91.1% for Thaumarchaeota 16S rRNA genes, 88.5% for bacterial 16S rRNA genes and 85.3% for archaeal amoA genes.
Acknowledgements Vessel and logistical support was provided by the National Oceanic and Atmospheric Administration, Center for Sponsored Coastal Ocean Research, award numbers NA09NOS4780204 to Louisiana Universities Marine Consortium, N. N. Rabalais, PI, and NA09NOS4780230 to Louisiana State University, R. E. Turner, PI. We thank the science and vessel crews of the R/V Pelican for their valuable shipboard and onshore support.
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References Allers, E., Wright, J.J., Konwar, K.M., Howes, C.G., Beneze, E., Hallam, S.J., et al. (2013) Diversity and population structure of Marine Group A bacteria in the Northeast subarctic Pacific Ocean. ISME J 7: 256–268. Arndt, D., Xia, J., Liu, Y., Zhou, Y., Guo, A.C., Cruz, J.A., et al. (2012) METAGENassist: a comprehensive web server for comparative metagenomics. Nucleic Acids Res 40: W88– W95. Baustian, M.M., Rabalais, N.N., Morrison, W.L., and Turner, R.E. (2011) Seasonal microphytobenthos on the hypoxic northern Gulf of Mexico continental shelf. Mar Ecol Prog Ser 436: 51–66. Baustian, M.M., Rabalais, N.N., Morrison, W.L., and Turner, R.E. (2013) Microphytobenthos along the Louisiana continental shelf during mid-summer hypoxia. Cont Shelf Res 52: 1–136. Belmar, L., Molina, V., and Ulloa, O. (2011) Abundance and phylogenetic identity of archaeoplankton in the permanent oxygen minimum zone of the eastern tropical South Pacific. FEMS Microbiol Ecol 78: 314–326. Beman, J.M., Popp, B.N., and Francis, C.A. (2008) Molecular and biogeochemical evidence for ammonia oxidation by marine Crenarchaeota in the Gulf of California. ISME J 2: 429–441. Cai, W.-J., Hu, X., Huang, W.-J., Murrell, M.C., Lehrter, J.C., Lohrenz, S.E., et al. (2011) Acidification of subsurface coastal waters enhanced by eutrophication. Nat Geosci 4: 766–770. Caporaso, J.G., Bittinger, K., Bushman, F.D., DeSantis, T.Z., Andersen, G.L., and Knight, R. (2010a) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26: 266–267. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F., Costello, E.K., et al. (2010b) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7: 335–336. Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., Turnbaugh, P.J., et al. (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA 108 (Suppl.): 4516–4522. Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Huntley, J., Fierer, N., et al. (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6: 1621–1624. Conley, D.J., Carstensen, J., Aigars, J., Axe, P., Bonsdor, E., Eremina, T., et al. (2011) Hypoxia Is Increasing in the Coastal Zone of the Baltic Sea. Environ Sci Technol 45: 6777–6783. Diaz, R.J., and Rosenberg, R. (2008) Spreading dead zones and consequences for marine ecosystems. Science 321: 926–929. Edgar, R.C. (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460– 2461. Galand, P.E., Casamayor, E.O., Kirchman, D.L., Potvin, M., and Lovejoy, C. (2009) Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing. ISME J 3: 860–869.
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3856 L. E. Gillies et al. Gómez-Consarnau, L., Akram, N., Lindell, K., Pedersen, A., Neutze, R., Milton, D.L., et al. (2010) Proteorhodopsin phototrophy promotes survival of marine bacteria during starvation. PLoS Biol 8: e1000358. Iverson, V., Morris, R.M., Frazar, C.D., Berthiaume, C.T., Morales, R.L., and Armbrust, E.V. (2012) Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 335: 587– 590. King, G.M., Smith, C.B., Tolar, B., and Hollibaugh, J.T. (2013) Analysis of composition and structure of coastal to mesopelagic bacterioplankton communities in the northern gulf of Mexico. Front Microbiol 3: 438. Könneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B., Waterbury, J.B., and Stahl, D.A. (2005) Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437: 543–546. Lam, P., Jensen, M.M., Lavik, G., McGinnis, D.F., Müller, B., Schubert, C.J., et al. (2007) Linking crenarchaeal and bacterial nitrification to anammox in the Black Sea. Proc Natl Acad Sci USA 104: 7104–7109. Lücker, S., Nowka, B., Rattei, T., Spieck, E., and Daims, H. (2013) The genome of Nitrospina gracilis illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front Microbiol 4: 27. McDonald, D., Price, M.N., Goodrich, J., Nawrocki, E.P., DeSantis, T.Z., Probst, A., et al. (2012) An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6: 610–618. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., and Stahl, D.A. (2009) Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461: 976–979. Molina, V., Belmar, L., and Ulloa, O. (2010) High diversity of ammonia-oxidizing archaea in permanent and seasonal oxygen-deficient waters of the eastern South Pacific. Environ Microbiol 12: 2450–2465. Pernthaler, A., Preston, C.M., Pernthaler, J., Delong, E.F., and Amann, R. (2002) Comparison of fluorescently labeled oligonucleotide and polynucleotide probes for the detection of pelagic marine bacteria and archaea. Appl Environ Microbiol 68: 661–667. Qin, W., Amin, S.A., Martens-Habbena, W., Walker, C.B., Urakawa, H., Devol, A.H., et al. (2014) Marine ammoniaoxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci 111: 12504– 12509. Rabalais, N., and Turner, R. (2001) Coastal hypoxia: consequences for living resources and ecosystems. In Coastal and Estuarine Studies 58. Bowman, M.J., Barber, R.T., Mooers, C.N.K., and Raven, J.A. (eds). Washington, DC, USA: American Geophysical Union. Rabalais, N., Turner, R., and Wiseman, W.J. (2007) Characterization and long-term trends of hypoxia in the northern Gulf of Mexico: does the science support the Action Plan? Estuaries Coasts 30: 753–772. Rabalais, N.N., and Turner, R.E. (2006) Oxygen depletion in the Gulf of Mexico adjacent to the Mississippi River. In Past and Present Water Column Anoxia. Neretin, L.N. (ed.). Netherlands: Springer, pp. 225–245.
Rabalais, N.N., Turner, R.E., and Wiseman, W.J. (2002) Gulf of Mexico Hypoxia, a.k.a. ‘the Dead Zone. Annu Rev Ecol Syst 33: 235–263. Schlitzer, R. (2013) Ocean Data View [WWW document]. URL http://www.odv.awi.de. Sintes, E., Bergauer, K., De Corte, D., Yokokawa, T., and Herndl, G.J. (2013) Archaeal amoA gene diversity points to distinct biogeography of ammonia-oxidizing Crenarchaeota in the ocean. Environ Microbiol 15: 1647–1658. Stewart, F.J., Ulloa, O., and DeLong, E.F. (2012) Microbial metatranscriptomics in a permanent marine oxygen minimum zone. Environ Microbiol 14: 23–40. Suzuki, M.T., Taylor, L.T., and DeLong, E.F. (2000) Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl Environ Microbiol 66: 4605–4614. Tolar, B.B., King, G.M., and Hollibaugh, J.T. (2013) An analysis of thaumarchaeota populations from the northern gulf of Mexico. Front Microbiol 4: 72. Ulloa, O., Canfield, D.E., DeLong, E.F., Letelier, R.M., and Stewart, F.J. (2012) Microbial oceanography of anoxic oxygen minimum zones. Proc Natl Acad Sci 109: 15996– 16003. Wang, Q., Garrity, G.M., Tiedje, J.M., and Cole, J.R. (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 5261–5267. Wright, J.J., Konwar, K.M., and Hallam, S.J. (2012) Microbial ecology of expanding oxygen minimum zones. Nat Rev Microbiol 10: 381–394. Wuchter, C., Abbas, B., Coolen, M.J.L., Herfort, L., van Bleijswijk, J., Timmers, P., et al. (2006) Archaeal nitrification in the ocean. Proc Natl Acad Sci USA 103: 12317–12322. Zaikova, E., Walsh, D.A., Stilwell, C.P., Mohn, W.W., Tortell, P.D., and Hallam, S.J. (2010) Microbial community dynamics in a seasonally anoxic fjord: Saanich Inlet, British Columbia. Environ Microbiol 12: 172–191.
Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Scatter plots of relativized 16S rRNA gene iTag sequence data in relationship to dissolved oxygen (DO) concentrations. Only the more abundant bacterial and archaeal groups are shown. Less abundant groups were summed under ‘Other’. Hypoxic samples are shown in blue, while oxic samples are shown in red. Fig. S2. Rarefaction curve of 16S rRNA gene iTag sequence data. Sequence data was rarified to 117 000 sequences. Blue indicates hypoxic (O2 < 2 mg l−1) samples, red indicates oxic samples. Table S1. July 2013 shelfwide nGOM Dead Zone cruise metadata, depth, temperature, salinity, DO, nutrients and chlorophyll a. Table S2. Correlation (Spearman) coefficients. Table S3. P-values for correlation (Spearman) coefficients. Table S4. Alpha diversity analysis of iTag 16S rRNA gene sequences. Table S5. qPCR data for archaeal amoA,Thaumarchaeota and Bacterial 16S rRNA genes.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
Environmental Microbiology (2015) 17(10), 3847–3856
doi:10.1111/1462-2920.12853
Archaeal enrichment in the hypoxic zone in the northern Gulf of Mexico Lauren E. Gillies,1 J. Cameron Thrash,2 Sergio deRada,3 Nancy N. Rabalais4 and Olivia U. Mason1* 1 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA. 2 Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA. 3 Ocean Sciences Branch, Naval Research Laboratory, Stennis Space Center, MS 39529, USA. 4 Louisiana Universities Marine Consortium, Cocodrie, LA 70344, USA. Summary Areas of low oxygen have spread exponentially over the past 40 years, and are cited as a key stressor on coastal ecosystems. The world’s second largest coastal hypoxic (≤2 mg of O2 l−1) zone occurs annually in the northern Gulf of Mexico. The net effect of hypoxia is the diversion of energy flow away from higher trophic levels to microorganisms. This energy shunt is consequential to the overall productivity of hypoxic water masses and the ecosystem as a whole. In this study, water column samples were collected at 39 sites in the nGOM, 21 of which were hypoxic. Analysis of the microbial community along a hypoxic to oxic dissolved oxygen gradient revealed that the relative abundance (iTag) of Thaumarchaeota species 16S rRNA genes (> 40% of the microbial community in some hypoxic samples), the absolute abundance (quantitative polymerase chain reaction; qPCR) of Thaumarchaeota 16S rRNA genes and archaeal ammonia-monooxygenase gene copy number (qPCR) were significantly higher in hypoxic samples. Spatial interpolation of the microbial and chemical data revealed a continuous, shelfwide band of low dissolved oxygen waters that were dominated by Thaumarchaeota (and Euryarchaeota), amoA genes and high concentrations of phosphate in the nGOM, thus implicating physicochemical forcing on microbial abundance.
Received 10 January, 2015; revised 18 March, 2015; accepted 18 March 2015. *For correspondence. E-mail [email protected]; Tel. 850 645 1725; Fax 850 644 2581.
Introduction Marine hypoxic zones are regions with low dissolved oxygen (DO) concentrations often below 2 mg l−1 or 62.5 μmol kg−1 (hypoxic). At this threshold, prolonged exposure can lead to mortality or migration of marine fauna (Rabalais and Turner, 2001). Eutrophicationassociated areas of hypoxia have been reported in over 400 systems that span the globe (Diaz and Rosenberg, 2008), with the total number increasing more recently (Conley et al., 2011). Typically, coastal hypoxia occurs near major population centres and is associated with high nutrient input from rivers and stratified conditions caused by freshwater input and thermal warming (Diaz and Rosenberg, 2008). The number of hypoxic zones have increased significantly (doubling each decade since the 1960s) concomitant with the increased use of industrially produced nitrogen fertilizer (Diaz and Rosenberg, 2008). In particular, the northern Gulf of Mexico (nGOM) is the site of the second largest zone of human-caused coastal hypoxia in the world, influenced by the freshwater input and nutrient load from the Mississippi River (Rabalais and Turner, 2006). Hypoxia in the nGOM has increased in severity during the summer months in direct response to additional nutrient loading in the Mississippi watershed, beginning around the 1950s, after which the nitrate flux to the nGOM continental shelf tripled (Rabalais et al., 2007). The general coastal hypoxia scheme is an increase in phytoplankton biomass due to nutrient input, followed by microbial respiration of this decaying biomass (Rabalais et al., 2007). The nutrient loading has the dual effect of increasing algal blooms and altering the type and characteristics of the phytoplankton community. The flux of fixed carbon in the form of senescent phytoplankton, zooplankton faecal pellets or aggregates to the lower water column provides a large carbon source for decomposition by aerobic bacteria. In a stratified water column, aerobic decomposition of these fixed carbon sources consumes DO at a higher rate than is resupplied from the upper water column, leading to large areas of hypoxia, for months at a time, from the spring to the fall (Rabalais and Turner, 2001; Rabalais et al., 2002; 2007). During microbial respiration, oxygen is consumed and carbon dioxide is produced resulting in acidification (Cai et al., 2011). Further, energy flow is shunted from the benthos to
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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microorganisms (Diaz and Rosenberg, 2008), which decouples predator–prey interactions (Wright et al., 2012). Thus, microbial respiration is implicated in causing low oxygen conditions (Rabalais et al., 2002) and acidification (Cai et al., 2011) in the nGOM, and overall, in a diversion of energy from benthic organisms to microorganisms (Diaz and Rosenberg, 2008). Microbial community structure in the nGOM hypoxic zone has not yet been characterized. In contrast, the microbiology of other oxygen minimum zones (OMZs) have received significant attention. In these well-studied OMZs, microbial and metabolic diversity is spread across a variety of energetic niches, all dependent upon the concentrations of oxygen, nitrogen and sulfur species, and the overall redox state (Ulloa et al., 2012; Wright et al., 2012). With declining oxygen concentrations, a metabolic shift from aerobic to anaerobic processes is initiated with nitrate, for example, serving as an alternative electron acceptor to oxygen (Wright et al., 2012). The dominant microorganisms in OMZs are Proteobacteria, Bacteroidetes, marine group A, Actinobacteria and Planctomycetes (Wright et al., 2012). In the archaea, the relative abundance of Thaumarchaeota have been reported to increase in abundance at the oxycline (Zaikova et al., 2010; Belmar et al., 2011; Tolar et al., 2013). It has also been reported that archaeal ammoniamonooxygenase (amoA) transcript abundances increase in OMZs (Lam et al., 2007; Beman et al., 2008; Stewart et al., 2012). Specifically, Stewart and colleagues (2012) identified nitrification, carried out by relatives of the ammonia-oxidizing Thaumarchaeota, Nitrosopumilus maritimus (Könneke et al., 2005), as a dominant energy source in the Chilean OMZ. To our knowledge, however, the enrichment in both Thaumarchaeota 16S rRNA genes and archaeal amoA genes has not yet been reported in the expansive nGOM hypoxic zone, nor, as mentioned above, has the overall microbial community been described. The goal of our research was to evaluate linkages among the (micro)biological, chemical and physical variables in the nGOM hypoxic area to better understand the microbial response to low oxygen conditions in a large coastal hypoxic zone. We describe herein the results of our July 2013 nGOM sampling effort within and outside of the continuous 5840 mi2 (15126 km2) hypoxic zone. A total of 39 water column samples were collected for oxygen, nutrients, chlorophyll a (chla) measurements and for analyses of the in situ microbial community. We used iTag sequencing and quantitative polymerase chain reaction (qPCR) analyses of 16S rRNA genes to characterize the microbial community structure. Quantitative polymerase chain reaction was also used to determine archaeal ammonia-oxidizing gene (amoA) copy numbers. Statistical analyses were used to explore linkages among microbiological, chemical and physical variables.
Results and discussion Water column properties and chemistry For each of the 39 samples collected at the oxygen minimum zone [avg. collection depth = 18 meters below sea level (mbsl)] during the 2013 shelfwide cruise (Fig. 1), temperature, depth, salinity and in situ chemistry were determined (Fig. 1 and Supporting Information Table S1). Specifically, concentrations of DO, ammonium (NH4), nitrite (NO2), nitrate (NO3), phosphate (PO4), silicic acid (Si(OH)4) and chla were determined (Fig. 1 and Supporting Information Table S1). To evaluate the relationships among these variables, correlation coefficients (Spearman) and significance (P-value ≤ 0.05) were determined (Fig. 2, Supporting Information Tables S2 and S3). Depth, salinity and temperature were highly correlated, as was temperature and DO. Depth and salinity were inversely correlated with NO2 (Fig. 2, Supporting Information Tables S2 and S3). Dissolved oxygen and PO4 had a strong inverse relationship (Fig. 2 and Supporting Information Table S2). The inverse relationship between DO and PO4 has been reported before and may be due to several factors, for example, PO4 flux from anoxic to near anoxic sediments (Rabalais and Turner, 2006), reduced mixing of the water column (Rabalais and Turner, 2006; Baustian et al., 2011; 2013) and microbial remineralization. Overall, the nutrient profiles in relationship to DO are consistent with previous reports of bottom water in the nGOM hypoxic zone (Rabalais and Turner, 2006). nGOM bacterioplankton structure and diversity We used iTag sequencing of 16S rRNA genes to characterize the microbial communities in the nGOM across the shelf during the annual hypoxic event. This analysis revealed that the microbial communities were dominated by Proteobacteria, Thaumarchaeota and to a lesser extent Bacteroidetes, Actinobacteria, MGII Euryarchaeota and Cyanobacteria (Fig. 3 and Supporting Information Fig. S1). The dominant phyla were similar regardless of oxygen status (Fig. 3 and Supporting Information Fig. S1). The dominance of these bacterial clades is consistent with the only study that has previously characterized the microbial community in the nGOM water column (King et al., 2013), with the exception of Thaumarchaeota abundance, for which their March 2010 samples were collected when oxic conditions prevailed. In the King et al. (2013) analysis, the primary driver affecting the abundance of Thaumarchaeota was depth, with an increase observed at ≥ 100 mbsl. In their study, oxygen and nutrients were not significantly correlated with Thaumarchaeota abundance. Our analyses of microbial community compositions were similar at the phylum level regardless of DO
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 1. Sampling map showing the 39 stations sampled during the July 2013 nGOM shelfwide cruise. Interpolation plots of microbiological and chemical data are shown. The iTag species abundances are relative abundances (RA).
concentrations, with the one exception that Thaumarchaeota abundance increased with decreasing DO (Fig. 3 and Supporting Information Fig. S1); therefore, we next endeavoured to determine if the relative abundance of specific members of these phyla were similar across the DO gradients sampled. Alpha diversity metrics of iTag sequence data for hypoxic and oxic samples were compared. Microbial diversity was significantly lower in
hypoxic samples than it was in oxic samples. Specifically, the Shannon diversity indices averaged 6.0 in hypoxic samples compared with 6.8 in oxic samples (Student’s t-test, P-value = 0.00004; Supporting Information Table S4). In assessing differences in diversity in oxic versus hypoxic samples, both richness and evenness were evaluated. The rarefaction curve did not reveal a trend in the species richness (Student’s t-test) in comparing oxic to
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 2. Correlation matrix showing the sign and magnitude of the correlation (Spearman) coefficients between biological, chemical and physical data. Blue indicates a positive value and red a negative value (see scale bar). The diagonal lines also indicate the direction of the correlation. The intensity of each of these colors increases as the correlation value moves away from zero (see scale bar). Correlations that were statistically significant (P-value ≤ 0.05) are shown by a black square around the borders (e.g. , has a black border indicating a significant correlation versus , indicating a correlation that was not significant).
hypoxic samples (Supporting Information Fig. S2), which is consistent with the similarities in microbial phyla across sites. Further, richness was not significantly different (Student’s t-test) in either sample type. However, a test of equitability (evenness) between the two sample types showed a significant difference with the oxic samples averaging 0.65 compared with the hypoxic samples, which averaged 0.58 (the scale is 0–1.0; with 1.0 indicating that all species are equally abundant). This suggested similar microbial species were present in oxic verses hypoxic
water masses, but that their abundances oscillated. These oscillations were, as will be discussed below, directly related to oxygen and nutrient concentrations. Non-parametric statistics were used to determine which species were responsible for the significant difference in evenness in comparing hypoxic with oxic samples. Specifically, samples were categorized as hypoxic or oxic and the abundances of the 7027 Operational Taxonomic Units (OTU)s were tested for significance using the MannWhitney test. Of these, only 15 OTUs showed significant
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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0.6
0.8
Other Chloroflexi Verrucomicrobia SAR406 Planctomycetes Cyanobacteria Euryarchaeota Actinobacteria Bacteroidetes Thaumarchaeota Proteobacteria
H1_G3 H2_I5 H3_E2A H4_I3 H5_J5-2 H6_F2A H7_I6 H8_E4 H9_D2 H10_D3 H11_A3 H12_H3 H13_H4 H14_F4 H15_C6C-2 H16_H5 H17_F6 H18_C3-2 H19_G6 H20_C7-2 H21_A'2 O1_A5 O2_H7 O3_J7 O4_E2 O5_C3 O6_A'4 O7_D5 O8_B2 O9_K2 O10_C9-2 O11_K6 O12_D'1 O13_D'5 O14_C9 O15_B8 O16_C6C O17_G2 O18_I2
0.0
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Relative abundance (%)
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0.05 0.06 0.07 0.08 0.09 0.14 0.15 0.31 0.33 0.40 0.48 0.48 0.54 0.78 0.86 1.17 1.23 1.27 1.32 1.76 1.82 2.22 2.32 2.41 2.64 2.96 3.04 3.20 3.35 3.75 3.84 4.06 4.12 4.26 4.37 4.43 4.78 4.84 5.43
Hypoxic Oxic
DO (mg l–1)
Fig. 3. Bar graph of relativized 16S rRNA gene iTag sequence data. Only the more abundant bacterial and archaeal groups are shown. Less abundant groups were summed under ‘Other’. As shown by the DO scale, samples are sorted from lowest to highest DO concentrations.
differences in their relative abundance between hypoxic and oxic samples, with seven being more abundant in hypoxic samples. We found that these same species were significantly correlated with DO at ≤ 1 mg of O2 l−1 compared with samples at > 1 mg of O2 l−1, further validating the statistical relationship between species abundance and DO concentrations. We recognize however, that other environmental variables beyond DO concentrations are drivers in structuring the microbial communities described here. The relative abundance of the Thaumarchaeota was comprised almost exclusively of a single species (OTU4369009), which was > 40% in some hypoxic samples (Fig. 1; avg. abundance was 26% hypoxic/14% oxic samples). In contrast, as discussed above, previous analysis of shallow nGOM samples using pyrotag sequencing revealed a low abundance of Thaumarchaeota (King et al., 2013). Specifically, in their shallow and comparable water column samples, the abundance of Thaumarchaeota was low at ∼3% avg. relative abundance (King et al., 2013). Thaumarchaeota were reported to dominate the archaeal community in Arctic Ocean surface waters, particularly coastal samples (Galand et al., 2009), thus our findings of high abundances in a coastal, shallow water environment are not unprecedented. An MGII Euryarchaeota species (OTU3134565) was > 5% in several hypoxic samples (avg. 3% hypoxic/0.8% oxic;
Fig. 1). The other species that increased in abundance in hypoxic samples comprised ≤ 1.2% of the microbial community in both hypoxic and oxic samples. These were, in order of decreasing abundance: Thiohalorhabdaceae (OTU266046; avg. 1.2% hypoxic/0.7% oxic), SAR406 (Marine Group A; OTU118018; avg. 1.1% hypoxic/0.3% oxic), Nitrospina (OTU571236; avg. 0.9% hypoxic/0.7% oxic), Synechococcus (OTU533688; avg. 0.8% hypoxic/ 0.7% oxic) and Halomonadaceae (Candidatus Portiera; OTU275493; avg. 0.8% hypoxic/0.5% oxic). Beta diversity To examine the primary drivers in structuring the microbial communities, including the species discussed above, iTag sequence data and chemical and physical variables were examined using non-metric multidimensional scaling (Fig. 4). The results revealed disparate microbial communities in hypoxic samples as compared with non-hypoxic samples (Fig. 4). The primary physicochemical drivers in influencing the microbial community structure were oxygen and phosphate and physical properties (Fig. 4). Of the seven OTUs that were significantly higher in hypoxic samples, five were significantly correlated with an ordination (non-metric multidimensional) axis (P-value ≤ 0.02; Fig. 4). These five species are discussed in more detail below.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
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Fig. 4. NMDS ordination of relativized 16S rRNA gene iTag sequence data. A. Dissolved oxygen is shown by bubble size, with larger bubbles indicating higher DO concentrations. B. The same ordination as in A. In this figure, vectors are shown for significant variables, including those microbial species that were significantly higher in hypoxic samples, and for chemical and physical data.
Biological, chemical and physical variable correlation analyses Correlations of these five species and DO, nutrient concentrations and physical properties are displayed in Fig. 2. Only those variables with P-values ≤ 0.05 are discussed here (see Supporting Information Table S3 for full correlation matrix). There was an inverse relationship between the relative abundance of the Thaumarchaeota species and DO (r = −0.61) and a strong positive correlation with PO4 (r = 0.75; Fig. 2 and Supporting Information Table S2). The significantly higher abundance of a Thaumarchaeota, closely related to N. maritimus, in our hypoxic samples and the strong relationship between DO and its abundance suggested that Thaumarchaeota may carry out core metabolic reactions at low oxygen concentrations. Several studies have reported an increase in abundance of Thaumarchaeota in low-oxygen marine environments (Lam et al., 2007; Beman et al., 2008; Molina et al., 2010; Belmar et al., 2011; Tolar et al., 2013). However, this is the first study carried out in the month of July during the peak of the annual hypoxic event in the nGOM. As discussed above, the relative abundance of the Thaumarchaeota species was > 40% in some hypoxic samples. Additionally, it was recently reported, that N. maritimus has an extremely low ammonium threshold (Martens-Habbena et al., 2009). In several hypoxic samples, submicromolar ammonium concentrations were observed, conditions in which the growth of Nitrosopumilus-like ammonia-oxidizing archaea (AOA) is supported, but not ammonia-oxidizing bacteria (MartensHabbena et al., 2009). A low-substrate threshold could allow AOA to successfully compete with heterotrophic bacteria and phytoplankton for ammonium (MartensHabbena et al., 2009). Further, N. maritimus ammonium
and oxygen uptake kinetics were indistinguishable (mean half-saturation constants were 0.132 and 0.133 μM NH3+NH4+ respectively; Martens-Habbena et al., 2009). In our Thaumarchaeota-dominated hypoxic samples, DO was ≤ 2 mg l−1, or ≤ 62.5 μM (Fig. 1 and Supporting Information Table S1). Although low, these DO concentrations could support maximum rates of ammonia oxidization by AOA (Martens-Habbena et al., 2009) when hypoxic conditions prevail. The high relative abundance of a Thaumarchaeota species, which may have low ammonium requirements and, perhaps, oxygen requirements, has implications for the nGOM hypoxic zone. A plausible scenario for the observed increase in abundance of these microorganisms could be nitrate input leading to a phytoplankton bloom, followed by microbial decomposition of phytoplankton biomass and an increase in ammonium concentrations near the seabed. As ammonium is consumed by the microbial community to build cellular constituents, reduced ammonium concentrations would result in conditions where Thaumarchaeota species may be able to sustain high growth rates and standing stocks, as suggested by Martens-Habbena and colleagues (2009), while concomitantly drawing down oxygen. Two obligate mixotrophic AOA were recently isolated from a coastal environment (Qin et al., 2014). The dominant Thaumarchaeota species (OTU4369009) in our study was 99–100% similar to these coastal strains. This suggests a broader environmental niche for ammoniaoxidizing Thaumarchaeota that includes utilization of organic carbon. This is significant in the nGOM hypoxic zone in that Thaumarchaeota may be able to compete with heterotrophic bacteria in consuming remnant phytoplankton biomass, while subsequently outcompeting heterotrophs when low oxygen, low ammonium and low
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
Archaea in the hypoxic northern GOM water column carbon conditions are established. Thaumarchaeota are thus implicated in sustained oxygen drawdown using, potentially, a myriad of growth strategies. Further, as was suggested by Stewart and colleagues (2012), nitrification by N. maritimus relatives, may be a dominant energy source in environments where oxygen concentrations are low. There was a similar inverse relationship between DO and the MGII Euryarchaeota species (r = −0.54; Fig. 2 and Supporting Information Table S2). This species was also correlated with NO3 (r = 0.60; Fig. 2 and Supporting Information Table S2). It has been reported that MGII bloom in summer North Sea surface water (Pernthaler et al., 2002), but their potential role in biogeochemical cycling in hypoxic water masses is largely uncharacterized given that there are currently no cultured representatives described in the literature. To date, one MGII has been assembled from metagenomic data (Iverson et al., 2012). The genome encoded a photo-heterotrophic metabolism (Iverson et al., 2012). A pathway for nitrate reduction is not encoded in the MGII genome; thus, it appears to require a reduced nitrogen source (Iverson et al., 2012). The MGII genome encoded proteorhodopsin (Iverson et al., 2012), which has been shown in a Vibrio sp. to enhance survival in a resource limited environment (Gómez-Consarnau et al., 2010). A Marine Group A (MGA) species (OTU118018), closely related to subgroup SAR406, had a significantly higher abundance in hypoxic samples and was strongly correlated with DO (r = −0.71), Si(OH)4 (r = 0.75), temperature (r = −0.81) and salinity (r = 0.68) (Fig. 2 and Supporting Information Table S2). MGAs have been found in low-oxygen zones of other environments, such as in the Northeast subarctic Pacific Ocean (NESAP) OMZ (Wright et al., 2012; Allers et al., 2013). Consistent with our findings, Allers et al. (2013) used fluorescent in situ hybridization and found that the MGA in the NESAP OMZ had a strong inverse correlation with DO concentrations with similar correlation coefficients to ours (r = −0.3 to −0.824 vs. −0.71 respectively). A Nitrospina species (OTU571236) had a statistically significantly higher relative abundance in hypoxic samples and was positively correlated with NO3 (r = 0.79) and negatively with NO2 (r = −0.44). This is in agreement with the metabolism of, for example, N. gracilis, during which NO2 is oxidized to NO3 (Lücker et al., 2013). It has been reported that N. gracilis has microaerophilic ancestry, which is a plausible explanation for why it is often reported in marine oxygen minimum zones (Lücker et al., 2013). The Nitrospina species (OTU571236) was also correlated with salinity (r = 0.70), depth (r = 0.55), chla (r value = −0.71) and temperature (r = −0.60). The Synechococcus species (OTU533688) was not strongly correlated with any environmental variables measured.
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Bacteria and Thaumarchaeota 16S rRNA gene and amoA gene copy number The microbial data presented herein has, thus far, been an analysis of relative species abundances from iTag sequencing. To evaluate changes in the microbial community structure using absolute abundances, we used qPCR analysis of 16S rRNA genes. Total bacteria ranged from 3.42 × 102 to 9.13 × 107 (avg. 2.45 × 107) copies L−1 (Supporting Information Table S5). The absolute abundance of bacteria was not significantly different when comparing hypoxic with oxic samples. In contrast, the absolute abundance of Thaumarchaeota 16S rRNA gene copy number was significantly higher in hypoxic samples [ranged from undetectable to 2.02 × 107 (avg. 5.53 × 106) copies L−1; Supplemental Table 5]. Tolar and colleagues (2013) reported that in more shallow (< 100 m) nGOM samples, Thaumarchaeota 16S rRNA gene copy number (qPCR) comprised only 1.8% of the microbial community. Given the high absolute (qPCR) abundance of Thaumarchaeota and the high relative (iTag) abundance of a Thaumarchaeota closely related to the known AOA, N. maritimus, we next determined the copy number of archaeal amoA genes l−1 using qPCR (Supporting Information Table S5; no known ammonia-oxidizing bacteria were observed in the iTag data and this paper focused on archaea; therefore, bacterial amoA genes l−1 were not quantified). Archaeal amoA copy number was significantly higher in hypoxic samples compared with oxic samples [ranged from undetectable to 1.83 × 107 (avg. 5.61 × 106) copies l−1; Supporting Information Table S5). This average amoA gene copy number is lower than the near-surface inshore nGOM copy number (avg. 3.86 × 107) in the nGOM as reported by Tolar and colleagues (2013). The Thaumarchaeota 16S rRNA:amoA gene copy number l−1 was highly correlated (r = 0.93; Supporting Information Table S5). The ratio of Thaumarchaeota 16S rRNA:amoA gene copy number l−1 ranged from one to three, with an average of one. This ratio suggested that on average, the Thaumarchaeota observed here had a single copy of amoA, which is consistent with other reports showing a 1:1 ratio of the copy number of Thaumarchaeota 16S rRNA genes:amoA genes (Sintes et al., 2013). These data support a link between the dominant microbial player in the hypoxic zone and the nitrogen cycle. Conclusion We examined the linkages among the microbiological, chemical and physical properties in the water column during the month of July on a 2013 survey over a broad area of the continental shelf in the nGOM that experiences extensive hypoxia on an annual basis. We found that while hypoxia did not change the microbial community composition, with regard to richness, it had a
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
3854 L. E. Gillies et al. significant impact on microbial species abundances. In particular, the abundance of ammonia-oxidizing Thaumarchaeota increased significantly in correlation with decreasing dissolved oxygen concentrations. These ammonia-oxidizing Thaumarchaeota appeared to be most abundant when oxygen concentrations were less than 0.5 mg l−1 and especially at 0.1 mg l−1 in the core of a continuous low oxygen area between 15 and 25 m water depth on the western part of the study area. These areas are consistent with low dissolved oxygen concentrations, higher dissolved phosphate, silicate and ammonium consistent with features of waters above the sea bed where hypoxia forms (Rabalais and Turner, 2006). Hypoxic archaeal hotspots might persist over time in the hypoxic area and may continue to serve as a site where energy flow is diverted away from higher trophic levels to, in this case, ammonia-oxidizing Thaumarchaeota. This population of Thaumarchaeota could carry out sustained draw down of oxygen in these water masses, as well as potentially influence the nitrogen cycle in the nGOM. The trajectory of change in the microbial community composition over a period of decreasing dissolved oxygen concentrations remains to be elucidated, but our findings identify the dominant microbial players and their potential metabolism over a broad area of perennial low oxygen on a continental shelf. Experimental procedures Sample collection Samples were collected 21–28 July 2013 on the R/V Pelican in the nGOM. The 39 stations sampled in this study ranged from just south of the mouth of the Mississippi River to south of the Louisiana Texas border. Samples were collected using niskin bottles on a rosette with a CTD sensor at the oxygen minimum zone for each station, usually near the sea bed. Collection depths ranged from 6 meters to 35 mbsl (avg. collection depth was 18 mbsl). The sample location map and subsequent plots of chemical and biological data (Fig. 1) were made with Ocean Data View (Schlitzer, 2013).
Oxygen, chla and nutrients Oxygen concentrations were determined in situ with the CTD sensor. Oxygen concentrations were verified using the Winkler method shipboard. Chlorophyll a samples were concentrated on 25 mm of Whatman GF/F filters from 500 ml to 1 litre of seawater and stored at −20°C. Chlorophyll a was extracted using the methods described in the Environmental Protection Agency Method 445.0, ‘In Vivo Determination of Chlorophyll a in Marine and Freshwater Algae by Fluorescence’; however, no mechanical tissue grinder or HCl were used. Chlorophyll a concentrations were determined using a fluorometer with a chla standard (Anacystis nidulans chla). For nutrients, 60 ml of seawater was filtered through a Whatman GF/D filter (EMD Millipore, Billerica, MA, USA) into two 30 ml Nalgene bottles using a swinnex filter holder and syringe and stored at −20°C. Nutrient concentrations were
determined by the marine chemistry lab at the University of Washington following the WOCE Hydrographic Program using a Technicon AAII system (http://www.ocean .washington.edu/story/Marine+Chemistry+Laboratory).
Microbial sampling and DNA extractions From each station, up to 10 litre of seawater were collected and filtered with a peristaltic pump. A 2.7 μM Whatman GF/D pre-filter was used and samples were concentrated on 0.22 μM Sterivex filters (EMD Millipore; Billerica, Massachusetts). Sterivex filters were sparged and filled with RNAlater. DNA was extracted directly off of the filter by placing half of the Sterivex filter in a Lysing matrix E (LME) glass/zirconia/silica beads Tube (MP Biomedicals, Santa Ana, CA, USA). To each tube, 50 μl of 0.1 M ammonium aluminum sulfate, 500 μl of CTAB and 500 μl of phenol:chloroform:isoamyalcohol (25:24:1) was added. Samples were subjected to bead beating using a MP Biomedicals (Santa Ana, CA, USA) FastPrep Instrument at 5.5 m s−1 for 45 s, followed by centrifugation at 16 000 × g for 5 min at 4°C. The supernatant (∼500 μl) was transferred to a new tube. A second extraction was carried out using 500 μl of CTAB in the original LME tube, and the above steps were repeated. Samples were centrifuged at 16 000 × g for 5 min at 4°C. Next, 0.5 ml of chloroform was added to supernatant. The sample was vortexed and centrifuged at 16 000 × g for 5 min at 4°C. The supernatant (∼200 μl) was transferred to a new tube, 1000 μl of PEG 6000 precipitate solution was added and the samples were incubated at room temperature for 2 h. Samples were centrifuged at 16 000 × g for 10 min at 4°C. The aqueous top layer was removed and 500 μl of 70% ethanol was added. Samples were centrifuged at 16 000 × g for 5 min at 4°C. Ethanol was removed, leaving the pellet to air dry for 5 min. Samples were then stored at −80°C until purified. DNA was purified using Qiagen (Valencia, CA, USA) AllPrep DNA/RNA Kit. DNA quantity was determined using a Qubit2.0 Fluorometer (Life Technologies, Grand Island, NY, USA).
16S rRNA gene sequencing and analysis 16S rRNA genes were amplified from 10 ng of purified DNA in duplicate using archaeal and bacterial primers 515F and 806R, which targets the V4 region of Escherichia coli in accordance with the protocol described by Caporaso et al. (2011; 2012) and used by the Earth Microbiome Project (http://www.earthmicrobiome.org/emp-standard-protocols/ 16s/), with a slight modification: the annealing temperature was modified to 60C. Polymerase chain reaction amplicons were purified using Agencourt AMPure XP PCR Purification beads (Beckman Coulter, Indianapolis, IN, USA). Sequencing was carried out using the MiSeq (Illumina, San Diego, CA, USA) platform. Samples were analysed using the QIIME version 1.7.0 (Caporaso et al., 2010a) pipeline. Raw sequences were demultiplexed and then quality filtered using the default parameters in QIIME. These sequences are available at http://mason.eoas.fsu.edu and from NCBI’s sequence read archive (accession SRP056891). Sequences were then clustered into OTUs, which was defined as ≥ 97% 16S rRNA gene sequence similarity, using UCLUST (Edgar, 2010) using the open reference clustering protocol (http://www.qiime.org/
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
Archaea in the hypoxic northern GOM water column tutorials/open_reference_illumina_processing.html). The resulting representative sequences were aligned using PyNAST (Caporaso et al., 2010b) and given a taxonomic classification using RDP (Wang et al., 2007), retrained with the Greengenes version 13.5 (McDonald et al., 2012). The resulting OTU table was filtered to keep only OTUs that had at least 10 sequences, and then converted to relative abundance, by summing the total sequence count in a sample and then dividing each sample OTU count by this total.
Statistics The differences in relativized OTU abundances in communities from hypoxic or oxic conditions were analysed using non-parametric statistics (Mann–Whitney) to test for statistically significant differences using METAGENassist (Arndt et al., 2012) and the application of a false discovery rate to account for multiple comparisons. Further statistical analyses were carried out in R, including determination of Spearman correlation coefficients among all variables. The NMDS ordination was done using the Vegan package. Alpha diversity metrics were determined after multiple rarefactions using QIIME. Statistical differences in these metrics, as well as in biological, chemical and physical variables were assessed by Student’s t-test. The QIIME-generated normalized (by conversion to relative abundance) OTU abundances in the 39 different samples were then analysed using NMDS ordination in R using metaMDS in Vegan package. P-values were derived from 999 permutations of the data.
qPCR Thaumarchaeota and bacterial 16S rRNA and archaeal amoA genes were quantified in duplicate using qPCR. For each qPCR reaction, 10 ng of genomic DNA was used. Thaumarchaeota 16S rRNA genes were amplified using 334F and 554R with an annealing temperature of 59°C (Suzuki et al., 2000). Bacterial 16S rRNA genes were amplified using 1369F and 1492R with 56°C as the annealing temperature (Suzuki et al., 2000). Archaeal amoA genes were amplified using Arch-amoA-for and Arch-amoA-rev with 58.5°C as the annealing temperature (Wuchter et al., 2006). Standards (DNA cloned from our samples for Thaumarchaeota 16S rRNA and archaeal amoA and E. coli for bacterial 16S rRNA) were linearized, purified and quantified by fluorometry. The reaction efficiencies for all qPCR assays were 91.1% for Thaumarchaeota 16S rRNA genes, 88.5% for bacterial 16S rRNA genes and 85.3% for archaeal amoA genes.
Acknowledgements Vessel and logistical support was provided by the National Oceanic and Atmospheric Administration, Center for Sponsored Coastal Ocean Research, award numbers NA09NOS4780204 to Louisiana Universities Marine Consortium, N. N. Rabalais, PI, and NA09NOS4780230 to Louisiana State University, R. E. Turner, PI. We thank the science and vessel crews of the R/V Pelican for their valuable shipboard and onshore support.
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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Scatter plots of relativized 16S rRNA gene iTag sequence data in relationship to dissolved oxygen (DO) concentrations. Only the more abundant bacterial and archaeal groups are shown. Less abundant groups were summed under ‘Other’. Hypoxic samples are shown in blue, while oxic samples are shown in red. Fig. S2. Rarefaction curve of 16S rRNA gene iTag sequence data. Sequence data was rarified to 117 000 sequences. Blue indicates hypoxic (O2 < 2 mg l−1) samples, red indicates oxic samples. Table S1. July 2013 shelfwide nGOM Dead Zone cruise metadata, depth, temperature, salinity, DO, nutrients and chlorophyll a. Table S2. Correlation (Spearman) coefficients. Table S3. P-values for correlation (Spearman) coefficients. Table S4. Alpha diversity analysis of iTag 16S rRNA gene sequences. Table S5. qPCR data for archaeal amoA,Thaumarchaeota and Bacterial 16S rRNA genes.
© 2015 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3847–3856
