An analysis of thaumarchaeota populations from the northern gulf of Mexico.

We sampled Thaumarchaeota populations in the northern Gulf of Mexico, including shelf waters under the Mississippi River outflow plume that are subject to recurrent hypoxia. Data from this study allowed us to: (1) test the hypothesis that Thaumarchaeota would be abundant in this region; (2) assess phylogenetic composition of these populations for comparison with other regions; (3) compare the efficacy of quantitative PCR (qPCR) based on primers for 16S rRNA genes (rrs) with primers for genes in the ammonia oxidation (amoA) and carbon fixation (accA, hcd) pathways; (4) compare distributions obtained by qPCR with the relative abundance of Thaumarchaeota rrs in pyrosequenced libraries; (5) compare Thaumarchaeota distributions with environmental variables to help us elucidate the factors responsible for the distributions; (6) compare the distribution of Thaumarchaeota with Nitrite-Oxidizing Bacteria (NOB) to gain insight into the coupling between ammonia and nitrite oxidation. We found up to 10(8) copies L(-1) of Thaumarchaeota rrs in our samples (up to 40% of prokaryotes) by qPCR, with maximum abundance in slope waters at 200-800 m. Thaumarchaeota rrs were also abundant in pyrosequenced libraries and their relative abundance correlated well with values determined by qPCR (r (2) = 0.82). Thaumarchaeota populations were strongly stratified by depth. Canonical correspondence analysis using a suite of environmental variables explained 92% of the variance in qPCR-estimated gene abundances. Thaumarchaeota rrs abundance was correlated with salinity and depth, while accA abundance correlated with fluorescence and pH. Correlations of Archaeal amoA abundance with environmental variables were primer-dependent, suggesting differential responses of sub-populations to environmental variables. Bacterial amoA was at the limit of qPCR detection in most samples. NOB and Euryarchaeota rrs were found in the pyrosequenced libraries; NOB distribution was correlated with that of Thaumarchaeota (r (2) = 0.49).

Introduction

The Mississippi River outflow forms a surface plume up to 10 m thick upon entering the northern Gulf of Mexico. Stratification and nutrient (especially nitrogen) enrichment of river water (Turner et al., 2006) lead to elevated primary production in the plume and thus to increased organic matter deposition 10 to 100 km away from river discharge sites (Rabalais et al., 2002; Green et al., 2008). Decomposition of this organic matter is thought to contribute to the formation of a recurrent hypoxic zone in the northern Gulf of Mexico that profoundly affects the ecology, fisheries biology, and geochemistry of the region (Rabalais et al., 2002; Dagg et al., 2007; Cai et al., 2011). Intermittent hypoxia ([O2] ≤ 2 mL/L or ∼90 μM; Diaz and Rosenberg, 2008) begins to develop in February and typically is most pronounced from mid-May to mid-September (Rabalais et al., 2010).

Processes such as coupled nitrification/denitrification that remove excess fixed nitrogen affect primary production and thus may be important determinants of the extent and duration of hypoxia. Ammonia oxidation is the first step in the biogeochemical pathway leading to denitrification. Members of the β- and γ-subdivisions of the Proteobacteria (Ammonia-Oxidizing Bacteria, AOB) and Marine Group 1 Archaea (Ammonia-Oxidizing Archaea, AOA) can grow chemoautotrophically by oxidizing ammonia to nitrite (Ward, 2011). The nitrite produced can be oxidized further to nitrate by Nitrite-Oxidizing Bacteria (NOB) and then denitrified (Jetten, 2001; Francis et al., 2007; Ward et al., 2009).

Ammonia monooxygenase genes (amoA) from AOA have been observed in marine environments at 10–1,000 times greater abundance than the amoA homolog from AOB, suggesting that the AOA play a key role in the marine nitrogen cycle (Francis et al., 2005, 2007; Wuchter et al., 2006; Mincer et al., 2007; Prosser and Nicol, 2008; Santoro et al., 2010; Ward, 2011). Currently, the functional guild of marine AOA includes members of the Marine Group 1 Archaea (DeLong, 1992; Fuhrman et al., 1992) and organisms related to a deeply branching clade (pSL12) of hot-spring crenarchaeotes (Barns et al., 1996) that are predicted to possess the amoA gene (Mincer et al., 2007). Genomic evidence suggests that Marine Group 1 Archaea and related organisms from benthic, terrestrial, and hot-spring habitats, as well as a sponge symbiont, should be assigned to a new phylum, the Thaumarchaeota, within the kingdom Archaea (Brochier-Armanet et al., 2008; Spang et al., 2010; Kelly et al., 2011). We use this term hereinafter in place of “Marine Group 1 Archaea.”

Pelagic marine Thaumarchaeota are typically most abundant below ∼100 m depth in the water column (DeLong, 1992; Fuhrman et al., 1992; Massana et al., 1997; Karner et al., 2001; Mincer et al., 2007; Church et al., 2010; Santoro et al., 2010), in surface waters at higher latitudes and polar oceans (Massana et al., 1998; Murray et al., 1998, 1999b; Church et al., 2003; Alonso-Sáez et al., 2008; Kalanetra et al., 2009), and in hypoxic regions and oxygen minimum zones (OMZs; [O2] ≤ 0.5 mL/L or ≤22 μM; Levin, 2003) such as the Black Sea, Baltic Sea, Gulf of California, Arabian Sea, and the eastern tropical Pacific Ocean (Coolen et al., 2007; Lam et al., 2007, 2009; Beman et al., 2008; Labrenz et al., 2010; Molina et al., 2010). Previous studies are contradictory but have pointed to environmental factors such as salinity, light, temperature, ammonium, oxygen, and sulfide as major determinants of this distribution (e.g., Murray et al., 1999a; Caffrey et al., 2007; Santoro et al., 2008; Bernhard et al., 2010; Gubry-Rangin et al., 2010; reviewed in Prosser and Nicol, 2008; Erguder et al., 2009; Nicol et al., 2011; Ward, 2011). Bacterial or phytoplankton biomass has also been thought to influence Thaumarchaeota distributions (Murray et al., 1999a,b; Church et al., 2003), perhaps through competition for resources.

One of the goals of the present study was to quantify the distribution of AOA in the northern Gulf of Mexico in the area influenced by the Mississippi River plume and recurrent hypoxia. We hypothesized that ammonia oxidizers would be abundant there because of the high riverine nitrogen loading to the region and the importance of respiration (Cai et al., 2011), and thus presumably nitrogen regeneration, in the region experiencing hypoxia. We also hypothesized that AOA would dominate ammonia oxidizer populations at pelagic stations, although AOB were found to be more abundant than AOA in sediments from Weeks Bay, Alabama (Caffrey et al., 2007). To test these hypotheses, we determined AOA and AOB distributions by quantitative PCR (qPCR) measurements of the abundance of rrs and amoA genes. We also pyrosequenced rrs genes from our samples as an independent check on distributions based on qPCR data. A second goal was to analyze variation in sequences of rrs and compare this to genes from two metabolic pathways that are important to AOA, ammonia oxidation and carbon fixation, to provide a more highly resolved description of the composition of Thaumarchaeota populations than can be obtained from analyses of single genes. AOA can grow autotrophically (Könneke et al., 2005) using the 3-hydroxypropionate/4-hydroxybutyrate pathway (Berg et al., 2007). The potential for AOA autotrophy can be detected in the environment using primers targeting the genes in this pathway, notably acetyl-CoA/propionyl-CoA carboxylase (accA; Yakimov et al., 2009) and 4-hydroxybutyryl-CoA dehydratase (hcd; Offre et al., 2011). We tested both of these primer sets with our samples. We compared the phylogenetic diversity present in their amplicons with diversity represented in amplicons from more widely used primer sets for amoA and rrs. We then used rrs sequences from the pyrosequencing effort to extend phylogenetic inferences based on analyses from samples taken at one station more broadly across the study area. A third goal was to investigate the relationship between Thaumarchaeota distributions and environmental variables to provide insight into the factors controlling their distribution. Pyrosequencing data were also used to compare the distribution of NOB with AOA to gain insights into the coupling between these two steps of nitrification.

Materials and Methods

Sample collection and DNA extraction

Samples were collected during the R/V Cape Hatteras GulfCarbon 5 cruise in the northern Gulf of Mexico (30°07′N, 088°02′W to 27°39′N, 093°39′W; Figure 1) from March 10–21, 2010. Samples were collected using Niskin bottles and a General Oceanics rosette sampling system equipped with an SBE25 CTD and sensors for [O2], beam attenuation (turbidity), and relative fluorescence (calibrated to chlorophyll a equivalents). The [O2] sensor was cross-calibrated against Winkler titrations of [O2] in samples collected at fixed depths. pH data were collected using a glass electrode by W.-J. Huang of Dr. W.-J. Cai’s group. Euphotic depth (defined as 1% PAR, 400–700 nm) was calculated for each station from Aqua MODIS satellite data using an average of the Lee and Morel models by H. Reader and C. Fichot. Nutrient data were collected at some of the station/depths we sampled by Dr. S. Lohrenz’s group. Since nutrient sample collections were biased in favor of near-surface samples on the continental shelf, these data were used only in BEST analysis (see Appendix). Approximately 1 L of water from each Niskin bottle was pressure filtered (at ∼60 kPa) through 0.22 μm Durapore filters (Millipore); filters were frozen in 2 mL of lysis buffer (0.75 M sucrose, 40 mM EDTA, 50 mM Tris; pH 8.3). DNA was extracted by enzymatic hydrolysis with lysozyme (50 mg mL−1), proteinase K (20 mg mL−1), and sodium dodecyl sulfate (100 μL of a 10% solution), and then purified by phenol-chloroform extraction as described previously (Bano and Hollibaugh, 2000).

Figure 1

Stations occupied during the GulfCarbon 5 cruise, March 10–21, 2010. Inshore stations represented with a filled star; offshore stations have an open star.

Quantitative PCR

Quantitative PCR was performed using an iCycler iQ™ Real-Time qPCR detection system (Bio-Rad) and the primers listed in Table A1 in Appendix. qPCR reactions were run in triplicate with standards made from environmental amplicons as described in the “Methods” in Appendix. TaqMan® (Applied Biosystems) chemistry was used to detect amplification of Bacteria and Thaumarchaeota 16S rRNA genes (rrs) following Kalanetra et al. (2009); all other amplifications were detected using SYBR® Green Supermix (Bio-Rad). We compared two primer sets for detecting Archaeal amoA: Arch-amoA-for and Arch-amoA-rev (“Wuchter primers”; Wuchter et al., 2006) and ArchamoAF and ArchamoAR (“Francis primers”; Francis et al., 2005). Reactions using the Wuchter primers were set up as described in Kalanetra et al. (2009), while PCR conditions for the Francis primers followed Santoro et al. (2010), except that SYBR® Green Supermix (Bio-Rad) was used with no additional MgCl2. Amplification of pSL12 rrs followed Mincer et al. (2007), with the number of amplification cycles reduced to 40 to prevent quenching of the fluorescence signal. Archaeal accA genes were amplified following Yakimov et al. (2009) with shorter cycle lengths (Hu et al., 2011). Specificity of SYBR® Green reactions was confirmed by melting curve analysis; accA amplicons were also checked by sequencing clones created with qPCR primers Crena_529F and Crena_981R (Yakimov et al., 2009). We also tested published primers for hcd genes (Offre et al., 2011), but found that non-specific amplification rendered them unsuitable for qPCR with our samples (see Appendix). Inhibition of qPCR reactions was tested using dilutions of DNA 10–1,000× with the Bacterial rrs qPCR assay; samples that showed higher copy number than expected from typical dilution were determined to have PCR inhibitors present and run at the dilution which gave the highest copy number for all other gene assays. Calculations of gene abundance and ratios are discussed in the “Methods” in Appendix, and qPCR efficiencies for reactions are reported in Table A1 in Appendix.

Table A1

Primers used in this study.

Target gene*	Primer/probe**	Sequence (5′→3′)	Application	Detection limit	qPCR efficiency	Reference
Archaeal rrs	21F 958R	TTCCGGTTGATCCYGCCGGA YCCGGCGTTGAMTCCAATT	PCR and sequencing	N/A	N/A	DeLong (1992)
Thaumarchaeal rrs	G1_334F G1_554R	AGATGGGTACTGAGACACGGAC CTGTAGGCCCAATAATCATCCT	qPCR	4.08 × 103 copies L−1	96.5−112.7%	Suzuki et al. (2000)
	TM519AR	TTACCGCGGCGGCTGGCAC				Suzuki et al. (2000)
pSL12 rrs	pSL12_750F pSL12_876R	GGTCCRCCAGAACGCGC GTACTCCCCAGGCGGCAA	qPCR	1.07 × 104 copies L−1	96.9−103.1%	Mincer et al. (2007)
Bacterial rrs	BACT1369F PROK1492R	CGGTGAATACGTTCYCGG GGWTACCTTGTTACGACTT	qPCR	1.14 × 104 copies L−1	91.6−113.2%	Suzuki et al. (2000)
	TM1389F	CTTGTACACACCGCCCGTC
Universal rrs	515F	GCCTTGCCAGCCCGCTCAG GTGTGCCAGCMGCCGCGGTAA	pyrosequencing	N/A	N/A	King et al. (2013)
	806R#	GCCTCCCTCGCGCCATCAGNN NNNNNNNNNNGGGGACTACV SGGGTATCTAAT
Archaeal amoA (W+)	Arch-amoA-for Arch-amoA-rev	CTGAYTGGGCYTGGACATC TTCTTCTTTGTTGCCCAGTA	qPCR	1.44 × 104 copies L−1	95.1−103.6%	Wuchter et al. (2006)
Archaeal amoA (F+)	ArchamoAF ArchamoAR	STAATGGTCTGGCTTAGACG GCGGCCATCCATCTGTATGT	qPCR and sequencing	1.79 × 104 copies L−1	88.4−96.0%	Francis et al. (2005)
Bacterial amoA	amoA-1F amoA-r New	GGGGTTTCTACTGGTGGT CCCCTCBGSAAAVCCTTCTTC	qPCR	1.63 × 104 copies L−1	88.4−95.6%	Rotthauwe et al. (1997)
						Hornek et al. (2006)
Archaeal accA	Crena_529F Crena_981R	GCWATGACWGAYTTTGTYRTAATG TGGWTKRYTTGCAAYTATWCC	qPCR and sequencing	1.25 × 104 copies L−1	80.5−91.1%	Yakimov et al. (2009)
Archaeal hcd	hcd-465F (S) hcd-911F (Q)	GGHGGTGCWATGACTGAT AGCTATGTBTGCAARACAGG	PCR, qPCR, sequencing	N/A	N/A	Offre et al. (2011)
	hcd-1267R (S,Q)	CTCATTCTGTTTTCHACATC

*rrs, 16S rRNA gene; amoA, ammonia monooxygenase gene, Bacteria amoA primers only amplify amoA genes from β-Proteobacteria; accA, biotin-dependent acetyl-CoA/propionyl-CoA carboxylase gene; hcd, 4-hydroxybutyryl-CoA dehydratase.

**(S), Sequencing, (Q), qPCR, TM, TaqMan Probe.

+Archaeal amoA (W) and (F) refer to Wuchter et al. (.

.

Phylogenetic analysis

We sequenced cloned rrs, amoA, and accA amplicons to obtain phylogenetic descriptions of the Thaumarchaeota populations in the study area and to verify specificity of qPCR reactions. Libraries were generated from samples collected at Station D5, located on the southern edge of the area influenced by the Mississippi River plume and over the continental slope (Figure 1) using methods described previously (Kalanetra et al., 2009) and summarized below. This station was chosen for its depth and as representative of slope stations influenced by hypoxia. We compared samples from different depths at this station as others (e.g., Lam et al., 2007; Beman et al., 2008; Kalanetra et al., 2009; Church et al., 2010; Santoro et al., 2010) have shown segregation of Thaumarchaeota populations by depth. rrs and amoA were amplified from DNA collected at 100 and 200 m, while accA amplicons were generated from samples collected at 2, 50, 100, 200, and 450 m to test the accA primer set across a wider depth range. PCR amplifications of Archaeal rrs, amoA, and accA used the primers listed in Table A1 in Appendix. Three separate amplifications were pooled to minimize potential PCR bias and electrophoresed on a 1% agarose gel. The band of the expected DNA product size was excised, extracted and purified using the QIAquick® Gel Extraction Kit (QIAGEN), and incorporated into a TOPO 4 vector (Invitrogen) prior to cloning using chemically competent TOP10 E. coli cells with the TOPO TA cloning kit (Invitrogen) following the manufacturer’s instructions. Clones from each library were selected randomly and sequenced (Genewiz, Inc.) using the plasmid primer M13F(−21). Euryarchaeota rrs sequences were identified by BLAST (Zhang et al., 2000) and not analyzed further.

Sequences were inspected manually and checked for vector contamination using Geneious v. 5.41. Thaumarchaeota rrs sequences were checked for chimeras using Bellerophon (Huber et al., 2004); three chimeric sequences were identified and discarded. Nucleotide and inferred amino acid sequences for amoA and accA were aligned in Geneious, while rrs nucleotide sequences were first aligned using the Silva aligner (v.1.2.5; Pruesse et al., 2007) and then imported into ARB (v. 5.2; Ludwig et al., 2004), manually trimmed, and inspected for alignment errors. Sequences obtained from these libraries have been deposited in GenBank (NCBI) under accession numbers KC330756 to KC330822 (rrs – Thaumarchaeota, n = 67), KC330823 to KC330871 (rrs – Euryarchaeota, n = 49), KC349137 to KC349317 (amoA, n = 181), and KC349318 to KC349551 (accA, n = 234).

Operational taxonomic units (OTUs) were determined from sequence alignments using mothur (v. 1.21.1; Schloss et al., 2009) with cutoffs of 0.02 (≥98% similarity) for Thaumarchaeota rrs and 0.03 (≥97% similarity) for Archaeal amoA and accA. Diversity indices and richness estimates (Shannon, Simpson, Chao, and ACE) were calculated in mothur. Neighbor-joining trees were constructed using ARB (Ludwig et al., 2004) with the Jukes–Cantor correction and 1,000 bootstrap resamplings for nucleotide trees; protein trees were constructed without the Kimura correction and re-sampled 100 times. Trees were edited using FigTree (v. 1.3.1).

Pyrosequencing analyses

We also analyzed the distribution of ribotypes in 41 of our 52 samples by massively parallel sequencing (pyrosequencing) using a Roche 454/FLX instrument running Titanium chemistry. rrs in DNA extracted from our samples were amplified by PCR using universal rrs primers 515F and 806R (Table A1 in Appendix), modified for bar-coded pyrosequencing. PCR protocols and primer sequences, including barcodes, adaptors, and linkers, followed Bates et al. (2011). Purified DNA from three reactions for each sample was pooled to produce a mixture in which amplicons from each sample were represented equally. The final mixture was sequenced using standard protocols by Engencore (University of South Carolina, Columbia, SC, USA). Sequence data have been deposited with MG-RAST at accession numbers 4509220.3–4509263.3. Metadata are available via the project page: “Analysis of composition and structure of coastal to mesopelagic bacterioplankton communities in the nGoM.”

A total of 435,290 sequences were filtered and trimmed (minimum length 200 bp, minimum quality score 20; 221,410 sequences passed) and then sorted into OTUs using the PANGEA pipeline (Giongo et al., 2010). Phylogenetic affiliations of these sequences were determined by a megablast analysis using a reference set of more than 170,000 rrs sequences from described isolates obtained from the RDP II database (Giongo et al., 2010). Amplicon sequences were binned into OTUs at domain, phylum, class, order, family, genus, and species levels based on megablast results, and then grouped into phylogenetic clusters and sorted by station and depth (average number of sequences per sample: 5,400; range 764–9,176). The PANGEA pipeline assigns all Archaea sequences to one group that also includes divergent Bacteria sequences. In order to more accurately assess the proportion of Thaumarchaeota in our samples, we manually enumerated hits to Thaumarchaeota in the megablast output for each sample. We also counted hits to known AOB, NOB, and Euryarchaeota.

Thaumarchaeota rrs sequences obtained from pyrosequencing were included for phylogenetic analysis using mothur (v. 1.21.1; Schloss et al., 2009). Unique sequences were grouped together and aligned against the Silva Archaea reference database. The resulting alignment, including rrs sequences from Station D5 clone libraries and outgroups, was trimmed to a set length and eight chimeric sequences were removed with Uchime (Edgar et al., 2011); additional potential chimeras and erroneous sequences were checked manually using BLAST and removed if necessary. The remaining 23,677 Thaumarchaeota sequences were clustered and representatives from each OTU obtained. A maximum likelihood tree was constructing using representative sequences grouped at 98% similarity (2,772 sequences total) with the RAxML program (Stamatakis et al., 2005) within ARB (Ludwig et al., 2004); 100 trees were generated using rapid bootstrap analysis, and the consensus tree was constructed from these iterations. Rarefaction analysis was completed using mothur as described for clone library samples above. The Bacteria populations of these samples are analyzed in King et al. (2013).

Statistical analyses

Model II ordinary least squares pairwise regressions were calculated following Legendre and Legendre (1998) using software available at the R-Project web site. Coefficients of determination and confidence limits of regression equations were calculated from 999 bootstrap permutations. PRIMER (v.6; Clarke and Gorley, 2006) was used to compare environmental and biological data from each station. We normalized environmental data in PRIMER to reduce the influence of variable unit scales before principal components analysis (PCA). The software package CANOCO (v. 4.5; ter Braak and Šmilauer, 2002) was used for canonical correspondence analysis (CCA; ter Braak, 1986) using PCA values and log-transformed qPCR gene abundances. Significance of CCA was determined using 499 Monte-Carlo permutations (reduced model) as recommended in the program documentation. The RAxML tree constructed from 454-generated Thaumarchaeota rrs sequences was used in Fast UniFrac (Hamady et al., 2009) to investigate phylogenetic patterns by sample location and depth. Weighted abundances of sequences within samples were used in both Principal Coordinates Analysis (PCoA) and sample clustering, as well as to calculate pairwise Unifrac distances. Counts were normalized to reduce the influence of larger sample sizes (greater number of sequences) at certain stations. The significance of sample clusters was tested using 100 jackknife permutations and resampling of the minimum (2), first quartile (100), or median (520) number of sequences across all samples; any sample containing less than the number of re-sampled sequences was eliminated from the analysis.

Results

Gene abundance and distribution

The abundance of Bacterial rrs in these samples ranged from 105 to 1010 copies L−1(Table 1; Table A2 in Appendix). Thaumarchaeota rrs genes were present in the same samples at up to 108 copies L−1 (Table A2 in Appendix) with population maxima occurring typically between 100 and 200 m depth and at lower [O2] and temperature (Figure 2). The abundance of rrs genes attributable to the pSL12-like clade was much lower, near the limit of detection (see Table A1 in Appendix) in most samples with a maximum abundance of 105 copies L−1(Table A2 in Appendix). Similar trends with depth for pSL12 rrs were observed as Thaumarchaeota rrs, though pSL12 rrs abundance was generally 100- to 10,000-fold lower (Figure 2), except in one sample (Station H1–7 m), where pSL12 rrs was 10% of Thaumarchaeota rrs. No Thaumarchaeota rrs were detected at the freshwater Mississippi River station (MR1-2 m) where pSL12 rrs was present at 105 copies L−1 (Table A2 in Appendix).

Table 1

Summary of qPCR-estimated gene abundances (copies L.

	Thaum. W amoA	Thaum. F amoA	Thaum. accA	Thaum. rrs	pSL12 rrs	AOB amoA	Bacteria rrs
Near-surface inshore	3.86 × 107 (9.74 × 104 −1.74 × 108)	5.82 × 106 (9.28 × 102 −2.97 × 107)	1.29 × 106 (9.09 × 102 −1.00 × 107)	1.85 × 107 (1.37 × 105 −1.10 × 108)	4.77 × 104 (1.12 × 102 −3.30 × 105)	3.67 × 105 (6.09 × 103 −2.10 × 106)	3.20 × 109 (3.11 × 105 −1.26 × 1010)
Near-surface offshore	1.16 × 107 (3.91 × 104 −3.29 × 107)	4.19 × 106 (1.24 × 105 −1.33 × 107)	5.66 × 105 (3.16 × 102 −2.79 × 106)	6.95 × 106 (4.79 × 104 −2.14 × 107)	1.30 × 103 (1.89 × 101 −3.98 × 103)	2.81 × 103 (1.67 × 102 −7.07 × 103)	7.16 × 108 (3.48 × 108 −1.34 × 109)
Deep offshore	3.68 × 106 (4.65 × 103 −2.12 × 107)	1.11 × 107 (5.11 × 105 −5.86 × 107)	8.72 × 106 (1.48 × 105 −1.80 × 107)	1.79 × 107 (3.23 × 106 −5.45 × 107)	1.00 × 104 (3.52 × 103 −2.92 × 104)	2.93 × 103 (1.34 × 102 −8.80 × 103)	2.14 × 108 (2.49 × 107 −1.83 × 109)

Means for each reaction are listed in bold; ranges follow the mean in parentheses. .

Table A2

qPCR estimates of the abundance of .

Station ID	Depth (m)	Amount filtered (L)	Wuchter Arch amoA copies/L	Francis Arch amoA copies/L	Arch accA copies/L	Thaum rrs copies/L	pSL12 rrs copies/L	AOB amoA copies/L	Bacteria rrs copies/L	Thaum% of total prokarya
A2	18	1.0	ND	ND	ND	5.85E + 05	1.12E + 02	ND	4.60E + 08	0.23
A4	17	1.2	ND	ND	ND	2.08E + 06	ND	ND	8.70E + 08	0.43
A4	43	1.2	ND	ND	ND	2.20E + 06	ND	ND	4.32E + 08	0.91
A6	2	1.1	2.32E + 06	8.82E + 05	6.48E + 02	2.62E + 06	4.50E + 01	2.79E + 02	5.27E + 08	0.89
A6	20	1.1	1.15E + 07	3.24E + 06	1.33E + 05	9.52E + 06	1.72E + 03	5.23E + 03	1.02E + 09	1.66
A6	80	1.1	3.29E + 07	7.61E + 06	3.30E + 05	1.81E + 07	1.52E + 03	7.07E + 03	3.48E + 08	8.54
A6	160	1.1	1.46E + 07	5.86E + 07	1.80E + 07	5.45E + 07	1.32E + 04	8.80E + 03	2.42E + 08	28.88
A6	200	1.1	4.72E + 06	1.25E + 07	5.83E + 06	2.00E + 07	6.57E + 03	1.43E + 03	1.77E + 08	16.94
A6	250	1.1	1.74E + 06	9.41E + 06	8.56E + 06	2.34E + 07	5.98E + 03	1.03E + 03	1.28E + 08	24.80
A6	350	1.1	5.86E + 05	4.64E + 06	3.95E + 06	8.86E + 06	4.50E + 03	5.50E + 02	4.46E + 07	26.36
A6	700	1.1	2.42E + 05	8.36E + 06	3.47E + 06	9.28E + 06	3.61E + 03	LD	2.49E + 07	40.17
A6	1700	1.2	1.53E + 05	5.11E + 05	1.74E + 06	3.23E + 06	1.05E + 04	ND	4.31E + 07	11.92
B4	200	1.1	6.20E + 06	9.20E + 06	1.56E + 07	2.28E + 07	1.30E + 04	3.52E + 03	3.30E + 08	11.06
B4	530	1.2	6.45E + 05	6.05E + 06	5.46E + 06	1.42E + 07	5.78E + 03	5.24E + 03	1.08E + 08	19.11
B5	2	1.1	9.62E + 06	8.21E + 06	5.54E + 05	8.50E + 06	2.71E + 03	4.71E + 03	6.07E + 08	2.46
B5	200	1.1	6.05E + 06	3.43E + 07	1.21E + 07	3.25E + 07	3.52E + 04	1.92E + 03	3.68E + 08	13.72
B5	450	1.1	9.08E + 05	1.60E + 07	1.32E + 07	3.38E + 07	1.54E + 04	2.27E + 03	2.04E + 08	23.01
B5	760	1.1	4.65E + 03	1.36E + 06	ND	3.48E + 06	4.17E + 03	1.34E + 02	4.40E + 07	12.49
C1	12	1.1	1.74E + 08	1.50E + 07	9.53E + 05	1.10E + 08	LD	2.10E + 06	1.08E + 10	1.79
C4	2	1.0	ND	ND	ND	1.76E + 06	LD	ND	6.03E + 08	0.52
C4	200	1.0	7.21E + 06	1.66E + 07	1.00E + 07	4.93E + 07	2.39E + 04	3.35E + 03	2.04E + 08	30.29
C4	700	1.0	3.84E + 05	4.62E + 06	7.17E + 06	1.26E + 07	1.21E + 04	2.35E + 03	4.33E + 07	34.30
D3	25	1.1	7.66E + 07	1.01E + 07	5.10E + 05	1.47E + 07	7.17E + 03	2.54E + 05	6.60E + 08	3.85
D3	68	1.1	8.13E + 06	7.53E + 05	1.02E + 03	ND	5.27E + 02	1.53E + 04	3.11E + 05	0.30
D5	2	1.1	7.31E + 06	1.91E + 06	1.46E + 05	4.71E + 06	6.21E + 02	4.55E + 02	7.34E + 08	1.14
D5	50	1.1	5.42E + 06	2.40E + 06	3.14E + 05	5.37E + 06	1.64E + 03	1.29E + 03	8.93E + 08	1.07
D5	100	1.1	2.85E + 07	7.08E + 06	1.36E + 06	2.14E + 07	3.61E + 03	2.28E + 03	3.89E + 08	9.00
D5	200	1.2	1.94E + 06	1.08E + 07	1.08E + 07	1.69E + 07	8.86E + 03	2.77E + 03	1.23E + 08	19.84
D5	350	1.1	9.70E + 05	8.69E + 06	1.39E + 07	2.45E + 07	6.54E + 03	1.49E + 03	8.53E + 07	34.08
D5	450	1.0	4.88E + 05	3.76E + 06	7.18E + 06	1.08E + 07	4.47E + 03	5.24E + 03	3.56E + 07	35.34
D5	900	1.1	4.43E + 05	2.72E + 06	6.84E + 06	7.50E + 06	6.27E + 03	2.34E + 03	5.50E + 07	19.72
E2	6	1.0	ND	LD	ND	ND	ND	ND	4.50E + 09	0.00
E6	200	1.1	3.63E + 06	1.25E + 07	1.55E + 07	1.57E + 07	1.63E + 04	4.83E + 03	1.80E + 08	13.56
E6	800	1.1	5.40E + 05	3.49E + 06	6.05E + 06	4.73E + 06	5.30E + 03	1.14E + 03	1.49E + 08	5.41
F4	50	1.1	1.12E + 07	7.60E + 05	7.42E + 04	5.92E + 06	9.58E + 02	4.23E + 04	5.17E + 08	2.02
F6	2	1.1	ND	ND	ND	4.79E + 04	LD	ND	4.08E + 08	0.02
F6	200	1.2	1.03E + 07	1.17E + 07	1.75E + 07	1.56E + 07	1.23E + 04	3.18E + 03	2.59E + 08	9.80
F6	950	1.1	4.52E + 05	1.79E + 06	5.42E + 06	5.19E + 06	5.43E + 03	2.78E + 03	3.52E + 07	20.99
G1	15	1.1	6.46E + 06	2.50E + 05	9.09E + 02	9.80E + 05	6.41E + 02	2.37E + 04	9.27E + 08	0.19
G5	80	1.1	2.39E + 07	2.69E + 06	5.93E + 05	9.77E + 06	2.56E + 03	6.09E + 03	6.20E + 08	2.76
H1	7	1.0	3.79E + 05	9.28E + 02	7.73E + 04	5.17E + 05	5.93E + 04	5.14E + 04	5.68E + 09	0.02
H3	20	1.2	9.74E + 04	6.01E + 04	ND	1.37E + 05	1.75E + 02	ND	6.74E + 08	0.04
H6	2	1.1	7.64E + 04	1.24E + 05	3.16E + 02	4.45E + 05	4.14E + 01	ND	1.04E + 09	0.08
H6	25	1.1	3.91E + 04	3.95E + 05	ND	2.05E + 05	3.41E + 02	1.67E + 02	1.34E + 09	0.03
H6	45	1.1	3.18E + 06	1.00E + 06	2.67E + 04	1.86E + 06	6.21E + 02	1.02E + 03	9.42E + 08	0.35
H6	80	1.1	2.64E + 07	1.33E + 07	2.79E + 06	1.59E + 07	3.98E + 03	5.64E + 03	4.72E + 08	5.74
H6	110	1.1	2.12E + 07	1.39E + 07	1.48E + 06	2.03E + 07	8.10E + 03	8.18E + 03	3.17E + 08	10.33
H6	200	1.2	2.16E + 06	ND	ND	1.66E + 07	ND	1.23E + 03	1.13E + 08	20.98
H6	280	1.1	2.71E + 06	4.57E + 06	2.16E + 06	4.80E + 06	2.92E + 03	2.29E + 03	1.83E + 09	0.47
MR1	2	0.6	2.02E + 05	3.79E + 05	2.02E + 05	ND	3.30E + 05	7.44E + 05	1.26E + 10	0.00
MR2	8	1.1	6.52E + 07	1.02E + 07	4.24E + 05	2.96E + 07	7.68E + 04	2.83E + 05	7.43E + 09	0.71
MR3	110	1.0	5.82E + 07	2.97E + 07	1.00E + 07	4.60E + 07	2.93E + 04	1.50E + 05	1.78E + 09	4.46

ND, Not determined; qPCR abundance undetectable for this specific gene in this sample.

LD, Limit of detection; sample ran below limit of detection with high variability in assay.

*Note that some values shown below the limit of detection (italicized) by our assay are included here because these values had low standard deviation in replicates.

Figure 2

Depth profiles of the abundance of selected genes and of environmental variables at Stations (A) A6, (B) D5, and (C) H6. Gene abundances are given as copies L−1 of sample filtered as determined from triplicate qPCR amplifications of Archaeal and β-Proteobacterial amoA and Archaeal accA (left) and Thaumarchaeota, pSL12, and Bacterial rrs (center); note that scales for β-Proteobacterial amoA and pSL12 rrs are reduced by 10–100 to allow for visualization of variation with depth. Environmental data were taken from a CTD attached to the frame of the rosette sampler (right). Sampling depths are shown as X’s on the depth axis; missing points indicate that the measurement was below the limit of detection (see Table A1 in Appendix for detection limits).

Thaumarchaeota accounted for a high proportion (up to 40% by qPCR, up to 54% of pyrosequenced rrs) of the total prokaryotic community in our samples. This percentage varied with depth (Figure 3), with deeper (>100 m) samples containing an average of 21% Thaumarchaeota (range 0.5–40%) while samples from near-surface water (≤100 m) contained only 1.8% Thaumarchaeota (range 0–9%). Differences were also observed with distance from shore, with shallower (<100 m) samples from inshore stations having fewer Thaumarchaeota than those from offshore stations (1.1 versus 2.8% of prokaryotes, respectively). Pyrosequencing also showed that Thaumarchaeota rrs genes were most abundant in samples from depths of 100–200 m, though they were present at low abundances in all samples with the exception of MR1-2 m (Table A3 in Appendix), in agreement with qPCR analyses. Thaumarchaeota accounted for 0.1–54% of the prokaryotes in pyrosequencing libraries and their distributions based on qPCR estimates of gene abundance compared favorably with the contribution of Thaumarchaeota ribotypes to pyrosequenced rrs libraries from these samples (Figure 4; model II regression, n = 41, r2 = 0.82, 95% CL of slope = 0.54–0.73).

Figure 3

Abundance of Thaumarchaeota as a percentage of total bacterioplankton plotted against sample depth.

Table A3

Abundance of Thaumarchaeota, Euryarchaeota, Nitrite-Oxidizing Bacteria (NOB), and Ammonia-Oxidizing Bacteria (AOB) .

Station ID	Depth	Thaumarchaeota as a % of prokaryotes by qPCR of rrs (%)	Total number of rrs sequences in pyrosequenced library	Bacteria rrs hits in pyrosequenced libraries	Thaumarchaeota rrs hits in pyrosequenced libraries	Euryarchaeota rrs hits in pyrosequenced libraries	Nitrite-oxidizing bacteria rrs hits in pyrosequenced libraries	Ammonia-oxidizing bacteria rrs hits in pyrosequenced libraries	Thaumarchaeota as a % of prokaryotes by pyrosequencing rrs amplicons	Euryarchaeota as a % of prokaryotes by pyrosequencing rrs amplicons	Nitrite-oxidizing bacteria as a % of prokaryotes by pyrosequencing rrs amplicons	Ammonia-oxidizing bacteria as a % of prokaryotes by pyrosequencing rrs amplicons
A2	18	0.23	2297	2283	11	3	0	0	0.86	0.23	0.00	0.00
A4	17	0.43	9146	8947	71	128	3	1	1.41	2.54	0.03	0.02
A4	43	0.91	6185	5131	727	327	63	0	20.32	9.14	0.88	0.00
A6	2	0.89	6288	5888	111	289	2	0	3.28	8.54	0.03	0.00
A6	20	1.66	8487	7626	164	697	5	0	3.73	15.84	0.06	0.00
A6	80	8.53	8021	7132	583	306	36	1	12.83	6.73	0.40	0.02
A6	160	28.86	5787	3578	1779	430	121	0	47.23	11.42	1.61	0.00
A6	350	26.38	3552	2415	992	145	24	0	42.51	6.21	0.51	0.00
A6	700	40.18	3428	1993	1287	148	20	0	53.75	6.18	0.42	0.00
A6	1700	11.91	879	626	232	21	2	0	40.02	3.62	0.17	0.00
B4	200	11.05	8564	6925	1383	256	88	0	26.44	4.89	0.84	0.00
B4	530	19.06	5539	4243	1154	142	17	0	32.87	4.04	0.24	0.00
B5	2	2.46	764	683	12	69	0	0	3.07	17.63	0.00	0.00
B5	200	13.72	4053	3191	698	164	51	0	28.25	6.64	1.03	0.00
B5	450	23.07	4787	3304	1301	182	30	0	41.48	5.80	0.48	0.00
B5	760	12.51	5041	3891	1061	89	15	0	32.92	2.76	0.23	0.00
C4	2	0.52	7792	7654	127	11	0	0	2.90	0.25	0.00	0.00
C4	200	30.33	5898	4277	1359	262	62	1	36.38	7.01	0.83	0.03
C4	700	34.40	3587	2388	1083	116	13	0	44.94	4.81	0.27	0.00
D3	25	3.84	7369	6157	751	461	53	1	18.00	11.05	0.64	0.02
D3	68	0.30	7208	6231	720	257	2	0	17.22	6.15	0.02	0.00
D5	2	1.14	6892	6659	137	96	2	0	3.57	2.50	0.03	0.00
D5	50	1.07	7442	7033	134	275	1	1	3.32	6.80	0.01	0.02
D5	100	9.01	6019	5161	538	320	24	0	15.80	9.40	0.35	0.00
D5	350	34.12	2960	1835	979	146	23	0	48.99	7.31	0.58	0.00
D5	450	35.39	1572	945	573	54	10	0	52.19	4.92	0.46	0.00
D5	900	19.72	3574	2596	857	121	9	0	37.27	5.26	0.20	0.00
E2	6	0.00	8486	7953	444	89	16	0	9.13	1.83	0.16	0.00
E6	200	13.60	4454	3334	924	196	67	1	33.28	7.06	1.21	0.04
E6	800	5.41	6293	5659	570	64	5	0	15.35	1.72	0.07	0.00
F6	2	0.02	6839	6651	56	132	2	0	1.49	3.52	0.03	0.00
F6	200	9.79	6863	4953	1445	465	115	0	34.43	11.08	1.37	0.00
F6	950	21.04	4945	3800	1036	109	12	0	32.92	3.46	0.19	0.00
H1	7	0.02	4997	4942	2	53	0	0	0.07	1.93	0.00	0.00
H3	20	0.04	1179	1131	8	40	0	0	1.26	6.29	0.00	0.00
H6	2	0.08	7112	7030	6	76	0	0	0.15	1.94	0.00	0.00
H6	110	10.30	5364	4195	872	297	115	0	27.23	9.27	1.80	0.00
H6	280	0.47	7367	6022	1051	294	75	0	23.91	6.69	0.85	0.00
MR1	2	0.00	5265	5259	4	2	4	7	0.14	0.07	0.07	0.24
MR2	8	0.71	5759	5607	90	62	4	1	2.81	1.93	0.06	0.03
MR3	110	4.46	3356	2849	417	90	36	0	20.85	4.50	0.90	0.00
Total			221410	188177	25749	7484	1127	14
Average			5400	4590	628	183	27	0	21.33	5.83	0.41	0.01
Max			9146	8947	1779	697	121	7	53.75	17.63	1.80	0.24
Min			764	626	2	2	0	0	0.07	0.07	0.00	0.00
Count	41	41	41	41	41	41	41	41

Figure 4

Fraction of Thaumarchaeota . Line represents a model II pairwise regression.

Archaeal amoA was present at up to 108 copies L−1 (Table 1; Figure 2; Table A2 in Appendix). Bacterial amoA was at the limit of detection (Table A1 in Appendix) in most samples, with a maximum of 106 copies L−1. The ratio of AOA:AOB amoA was found on average to be 2100:1 (Wuchter primers) to 3300:1 (Francis primers). The ratio of Bacterial amoA:Bacterial rrs averaged 0.001 across all samples, with a maximum of 0.05 at Station D3–68 m (Figure A5A in Appendix). Abundances of accA genes ranged from the limit of detection (104 copies L−1) to 107 copies L−1(Table 1; Figure 2; Table A2 in Appendix). Archaeal amoA (quantified using Wuchter primers) showed similar distribution by depth as Thaumarchaeota rrs (Figure 2). However, accA abundances showed opposite trends with depth, leading to higher ratios of amoA:accA or rrs:accA in near-surface (≤100 m) water (Figure 2; Table 2).

Figure A5

Scatter plot of Archaeal (A) . Solid lines indicate the 1:1 ratios expected from the “Ca. N. maritimus” strain SCM1 genome (Walker et al., 2010); dashed lines indicate ratios of 0.1 or 10. “Inshore,” over the continental shelf; “offshore,” shelf break and beyond.

Table 2

Mean and ranges of the ratios of Thaumarchaeota gene abundances.

	amoA W:rrs	amoA F:rrs	accA:rrs
Near-surface inshore	2.5 (0.71−6.6)	0.32 (0.002−0.69)	0.06 (0.001−0.22)
Near-surface offshore	1.2 (0.17−1.8)	0.62 (0.28−1.9)	0.04 (0.0002−0.17)
Deep offshore	0.19 (0.001−1.0)	0.57 (0.16−1.1)	0.58 (0.07−1.3)

Gene ratios were calculated by dividing the abundance of each of the genes tested by the abundance of .

We used PCA (Figure A3 in Appendix) to identify samples from similar environments and group them into a few categories to simplify comparisons. The first two PCA axes explained 63.2% of the variation between samples (Figure A3; Table A5 in Appendix), which supported placing stations into three groups: near-surface inshore, near-surface offshore, and deep offshore sets. CCA was included (Figure 8) to investigate relationships between gene abundances and environmental conditions (similar to BEST analysis, see Appendix). The primary CCA axis (CCA1) explained 47.9% of the gene abundance-environment relationship; adding the second axis (CCA2) increased the variance explained by 44% (91.7% total; Figure 8; Table A6 in Appendix). A global permutation test gave a statistical significance of p < 0.05 for station groupings based on both canonical axes considered together (F = 2.26, p = 0.014), while CCA1 considered alone did not explain the gene abundance-environment relationship (F = 8.43, p = 0.086). Thaumarchaeota rrs abundance was negatively correlated with most environmental variables, except for salinity and depth (Figure 8). Bacterial rrs abundance correlated positively with euphotic zone depth and had a strong negative correlation with pH, with little influence from any variable primarily contributing to CCA2 (beam attenuation, oxygen; Figure 8). The distribution of Archaeal amoA genes as assessed with the Wuchter primers, in contrast, was not strongly influenced by variables contributing to CCA1 (fluorescence, pH, latitude, longitude; Figure 8) but showed a weak positive correlation with temperature and beam attenuation (turbidity). Archaeal amoA gene abundance assessed by the Francis primers showed the opposite trend, with strongest positive correlations to latitude (which covaries with distance offshore and depth in this region) and oxygen concentrations (Figure 8). Bacterial amoA gene abundance correlated with beam attenuation (turbidity) and temperature (positive correlation), as well as depth (negative correlation). accA gene abundance had strong positive correlations with relative fluorescence (chlorophyll a equivalents) and pH (Figure 8).

Figure A3

Principal components analysis (PCA) of samples using environmental variables. Fluorescence, relative fluorescence, chlorophylla equivalents; beam attenuation, turbidity. Samples are shown as symbols representing three groupings based on depth and location: orange circles = near-surface inshore (≤100 m depth, over the continental shelf), green diamonds = near-surface offshore (≤100 m depth, shelf break and beyond), and blue squares = deep offshore (>100 m, shelf break and beyond).

Table A5

Variables contributing to principal components axes.

Variable	PC1	PC2
Latitude (°N)	−0.26	+0.30
Longitude (°W)	+0.066	+0.24
Depth (m)	+0.39	+0.21
Temperature (°C)	−0.36	−0.38
Salinity (PSU)	+0.19	−0.49
Dissolved oxygen (mg/L)	−0.36	−0.12
Rel. fluorescence (μg/L)	−0.39	+0.15
Beam attenuation (1/m)	−0.21	+0.44
pH (NBS)	−0.42	−0.32
Euphotic depth (m)	+0.31	−0.30

Coefficients (values) are a measure of contribution of each variable to each of the principal component axes (PC1 and PC2) such that the higher the value, the greater the influence of the variable. A positive or negative sign represents the type of correlation each variable has on each axis. The total amount of variance explained by PC1 was 38.9% and 24.3% for PC2. Depth, water column depth; Rel. Fluorescence, Relative fluorescence, chlorophyll a equivalents; beam attenuation, turbidity; euphotic depth, photic zone depth.

Figure 8

Canonical correspondence analysis (CCA) ordination plot of qPCR-estimated abundances for . The length and angle of arrows shows the contribution of a particular environmental variable to the CCA axes. Fluorescence, relative fluorescence, chlorophyll a equivalents; beam attenuation, turbidity. Eigenvalues, correlation values, and percentage variance for CCA are given in Table A6 in Appendix.

Table A6

Results of CCA analysis of relationship between qPCR-estimated gene abundances and environmental data in the northern Gulf of Mexico.

Axes	CCA1	CCA2	CCA3	CCA4
Eigenvalues	0.148	0.135	0.012	0.008
Gene-environment correlations	0.655	0.765	0.352	0.230
CUMULATIVE PERCENTAGE VARIANCE
Of gene abundance data	17.0	32.6	34.0	34.9
Of gene-environment relation	47.9	91.7	95.7	98.3

Values for all four canonical axes are shown, but only CCA1 and CCA2 were used to construct a biplot of the data (Figure .

Thaumarchaeota community composition at station D5

Phylogenetic analysis of 67 Sanger-sequenced Thaumarchaeota rrs sequences obtained from 100 and 200 m depth at Station D5 revealed 10 different OTUs (Figure 5; Table A4 in Appendix; 98% similarity cutoff). All but one of the sequences retrieved from the 100 m sample clustered into a single OTU (the “Near-Surface Group,” Figure 5), that also contained one sequence retrieved from the 200 m sample and the reference sequence from Nitrosopumilus sp. NM25 (AB546961; Matsutani et al., 2011). We did not retrieve any sequences related to the marine pSL12-like clade. Sequences retrieved from the 200 m sample displayed greater richness and evenness (Table A4 in Appendix; 9 OTUs) and included some OTUs that appear unique to the northern Gulf of Mexico.

Figure 5

Phylogenetic analysis of Thaumarchaeota . Clone libraries were generated from DNA in samples collected at depths of 100 m (green) and 200 m (blue). The Neighbor-Joining tree was constructed using ARB (Ludwig et al., 2004). Reference sequences in bold are from isolates or enrichment cultures of AOA. Bootstrap values obtained from resampling 1000 times; only values above 75% bootstrap support are shown.

Table A4

Diversity indices for sequenced clones obtained from Station D5 calculated using mothur (v. 1.21.1; Schloss et al., .

	Observed	Chao	ACE	Shannon	Simpson
accA	51	74.0	109	3.02	0.0862
amoA	47	86.4	107	3.19	0.0615
amoA 100m	22	35.2	55.7	2.39	0.139
amoA 200m	30	39.4	41.8	2.89	0.0801
rrs	10	11.0	12.8	1.30	0.451
rrs 100m	2	2.00	0.000	0.103	0.957
rrs 200m	9	10.0	11.8	1.96	0.141
rrs 454	2768	18700	57100	4.15	0.0654

OTU similarity cutoffs were 2% (.

We retrieved 184 amoA sequences from Station D5. Phylogenetic analysis of the translated and aligned amino acid sequences revealed two OTUs (similarity cutoff of 97%) of AmoA (Figure 6A): one containing primarily near-surface (100 m) sequences (“Group A” following Beman et al., 2008) and the other dominated by sequences from 200 m (“Group B”). amoA nucleotide sequences also grouped primarily by depth, but with greater richness and diversity (Table A4 in Appendix) at a given depth than we observed for Thaumarchaeota rrs genes. Clusters of sequences that appear to be unique to the Gulf of Mexico were observed in both 100 and 200 m samples (Figure A1A in Appendix).

Figure 6

Phylogenetic analysis of inferred amino acid sequences from (A) . Numbers beside groups (in triangles) indicate the number of sequences from each depth sampled according to color: clades in green are from 2, 50, or 100 m; clades in blue are from 200 or 450 m. Neighbor-Joining Trees were constructed with ARB (Ludwig et al., 2004) from sequences 199 aa (AmoA) or 137 aa (AccA) in length. Sequences in bold were obtained from isolates or enrichment cultures of AOA. Bootstrap values were obtained from 100 resamplings; only values above 75% bootstrap support are shown.

Figure A1

Phylogenetic analysis of (A) . Neighbor-Joining Trees built with ARB (Ludwig et al., 2004) from nucleotide sequences 595 bp (amoA) or 411 bp (accA) in length. Sequences in bold obtained from isolates or enrichment cultures. Bootstrap values obtained from resampling tree 1,000 times; only values above 75% bootstrap support shown on tree.

The top BLASTx hits for all but 30 of 257 sequences obtained from accA amplicons were to carboxylase or carboxyltransferase genes from Archaea. The remaining 30 amplicons were most similar to non-Thaumarchaeota reference sequences with low (≤65%) sequence identities. Because they did not return hits to Thaumarchaeota reference sequences, we did not consider them further. Phylogenetic analysis of the inferred amino acid sequences for AccA (Figure 6B) revealed three major OTUs: OTU 1 contained a majority of near-surface sequences (2, 50, and 100 m), while OTUs 2 and 3 contained mostly sequences from deep water (200 and 450 m). Analysis of accA nucleotide sequences revealed similar clusters with depth as inferred amino acid sequences for AccA and Thaumarchaeota rrs gene sequences (Figure A1B in Appendix) with a total of 51 OTUs observed at a 97% similarity cutoff (Table A4 in Appendix). Some of these seem unique to the Gulf of Mexico (Figure A1B in Appendix), but this may be an artifact of the limited representation of accA sequences in reference databases.

Pyrosequencing: Phylogenetic patterns and sample groupings

Microbial community composition varied dramatically with depth as shown by comparisons of libraries from surface (≤25 m depth) versus subsurface (≥100 m depth) samples (Figure A2 in Appendix, Table A3 in Appendix; these data are discussed fully in King et al., 2013). Proteobacteria, especially α- and γ-Proteobacteria, dominated the microbial community of near-surface waters at most stations. Consistent with distributions of rrs and amoA indicated by qPCR analyses, Thaumarchaeota were greatly enriched in deeper waters. Only 14 (out of a total of 221,410) rrs sequences binned to AOB, confirming the much lower abundance of AOB relative to AOA found by qPCR quantification of amoA. Half of the AOB sequences were retrieved from one sample: MR1–2 m, taken upstream of the mouth of the Mississippi River with a salinity of 0. Only four Thaumarchaeota sequences were retrieved from this sample (Table A3 in Appendix), two of which were most similar to the terrestrial thaumarchaeota, “Candidatus Nitrososphaera gargensis” strain EN76, at 15% similarity.

Figure A2

Distribution of various taxa in pyrosequenced libraries of .

Sequences most closely related to NOB were retrieved from most samples (mean = 0.4%, range 0–1.8% of prokaryotes as calculated in “Methods” in Appendix, but assuming 2 rrs per NOB genome from Mincer et al., 2007). These sequences were primarily identified as Nitrospina sp. 3005 (AM110965), though Nitrospira ribotypes were also detected. The abundance of NOB rrs was greatest at depth (∼200 m, Table A3 in Appendix, Figure 7A) and was significantly correlated with the abundance of Thaumarchaeota in the same samples (Figure 7A; model II regression, n = 41, r2 = 0.49, 95% CL of slope = 0.032–0.064). Euryarchaeota only accounted for a few percent of the microbial community (mean 5.8%, range 0.1–17.6%). Euryarchaeota were most abundant in near-surface samples (<100 m; Table A3 in Appendix) and their abundance was poorly correlated with the abundance of Thaumarchaeota (Figure 7B; model II regression, n = 41, r2 = 0.14, 95% CL of slope = 0.021–0.20).

Figure 7

Comparison of the abundance of . Triangles, near-surface (≤100 m) samples; squares, Deep (>100 m) samples. Lines are model II regressions (Legendre and Legendre, 1998) of all data.

UniFrac distances calculated between samples indicate significant (p ≤ 0.05) similarities in Thaumarchaeota rrs assemblages among offshore, near-surface samples and inshore, near-surface samples from Stations A2, A4, D3, E2, and MR2 (data not shown). The Station D5–100 m sample was assigned to the near-surface group (p ≤ 0.05) regardless of the method used to obtain rrs sequences (pyrosequencing versus Sanger sequencing from clone libraries). Among deep offshore samples, those from 160–950 m were similar to each other (p ≤ 0.05); sequences from clone libraries generated from Station D5–200 m were also included in this group. The phylogenetic composition of Thaumarchaeota rrs in the deepest sample, Station A6–1700 m, was only similar to samples from D5–900 m and F6–950 m (p ≤ 0.05).

Analysis of phylogenetic patterns across samples using PCoA in Fast UniFrac (Figure 9) revealed two major groups of pyrosequenced Thaumarchaeota rrs – one of deep (>100 m) samples and another including the near-surface samples (both inshore and offshore), which agrees with PCA groupings (Figure A3 in Appendix). The primary PCoA axis explained 70% of the variation in phylogenetic composition of the samples, with the secondary axis explaining an additional 11% (total 81%) of the variation. The sample from Mississippi River Station MR1 was an outlier; however, PCoA analysis with this sample included revealed the same general pattern (Figure A7 in Appendix). Samples clustered using the minimum resampling of 2 sequences (Figure A4A in Appendix) only showed significant separation of Station MR1 sample from the rest of the samples (>99.9% jackknife support). For 100 re-sampled sequences (32 of 43 samples; Figure A4B in Appendix), a clear separation was observed between surface and deep samples (60% support) and between near-surface inshore samples (excluding Station A4) and near-surface offshore samples (>99.9% support). When the median number of sequences was applied to cluster analysis (520 sequences, 22 of 43 samples; Figure A4C in Appendix), the separation of deep and near-surface samples was statistically significant (>99.9% support). Station D3 (inshore, <100 m depth) samples clustered most closely (>99.9% support), followed by inshore Station A4–43 m and offshore Station A6–80 m (95% support). Amongst deep samples, a further separation was observed within the deep offshore samples, with the deepest samples (Stations D5–900 m and F6–950 m) and those from 350–760 m forming distinct clusters 50 and 61% of the time, respectively (Figure A4C in Appendix).

Figure 9

Principal Coordinates Analysis (PCoA) of Thaumarchaeota . Shapes indicate sample groupings: dark gray squares, deep, offshore; open triangles, near-surface, offshore; light gray circles, near-surface, inshore. The percentage of the variance explained by an axis is given in parentheses next to the axis title.

Figure A7

Principal coordinates analysis (PCoA) of Thaumarchaeota . Shapes indicate sample groupings: dark gray squares = deep, offshore; open triangles = near-surface, offshore; light gray circles = near-surface, inshore. The percentage of the variance explained by an axis is given in parentheses next to the axis title.

Figure A4

Jackknife clustering analysis of pyrosequenced Thaumarchaeota rrs genes using Fast UniFrac (Hamady et al., . Resampling of (A) 2 (minimum; n = 43 samples), (B) 100 (first quartile; n = 32 samples), or (C) 520 (median; n = 22 samples) sequences were performed for each of 100 iterations of the jackknife analysis. Colors indicate the percentage of iterations supporting a given node – red (>99.9%), yellow (90–99.9%), green (70–90%), blue (50–70%), or gray (<50%). Sample groups are indicated as: IS# = inshore, near-surface; OS# = offshore, near-surface; OD# = offshore, deep; with # indicating the sample as Station.Depth.

Discussion

Community comparisons

We found a strong correlation between qPCR and pyrosequencing estimates of AOA relative abundance indicating that, despite potential biases associated with individual qPCR primers, qPCR estimates of Thaumarchaeota distributions at this coastal site are robust. Thaumarchaeota were abundant in deeper waters of the northern Gulf of Mexico, increasing in abundance with depth to a broad maximum between ∼200 and 800 m (Figures 2 and 3), coinciding with the oxygen minimum (Figure 2). Two shallow water stations (C1, 12 m; MR2, 8 m) contained up to 108 copies L−1 of Thaumarchaeota rrs; both of these stations are near the Mississippi River Plume, which may indicate an influence of riverine nutrients on AOA. It is important to note, however, that these are marine ribotypes and not terrestrial or freshwater ribotypes carried into the Gulf by the Mississippi River, since we did not retrieve similar ribotypes from Mississippi River sample MR1. In contrast, AOB amoA genes were below the limit of detection except in a few near-surface samples from inshore stations (Stations C1, D3, D5, G1, and H1) and in river stations MR1 and MR2. Consistent with many other studies of amoA in coastal water columns (Wuchter et al., 2006; Herfort et al., 2007; Beman et al., 2010), AOA amoA was always >10- to 100-fold more abundant than AOB amoA. The relative abundance of Thaumarchaeota and AOB rrs in pyrosequenced libraries (Table A3 in Appendix) is consistent with the distribution of amoA genes determined by qPCR, suggesting that the observed ratio of AOA:AOB amoA is not an artifact of primer bias. Although we do not have ammonia oxidation rate measurements for these samples, the greater abundance of AOA than AOB amoA suggests that Thaumarchaeota are likely to dominate nitrification in this region (Beman et al., 2008).

We did not quantify the distribution of NOB by qPCR (cf. Santoro et al., 2010, which is limited to Nitrospina); however, we were able to determine the distribution of all known NOB relative to Thaumarchaeota from pyrosequenced rrs libraries. We found that NOB abundance correlated well with that of Thaumarchaeota (r2 = 0.49), as reported by others (Mincer et al., 2007; Santoro et al., 2010). The correlation between the distributions of these two groups suggests relatively tight coupling between them, presumably leading to efficient conversion of ammonia to nitrate in the northern Gulf of Mexico. However, NOB rrs abundance was only ∼5% of that of Thaumarchaeota (slope of model II regression; Figure 7A), in contrast to estimates of 20–100% reported by Mincer et al. (2007) or ∼25% reported by Santoro et al. (2010). This ratio would change if the rrs gene dosages we used in our calculations changed; however, the discrepancy suggests that alternative pathways, e.g., anammox, might be more significant for nitrite removal in the northern Gulf of Mexico than in the temperate Pacific upwelling zone sampled by Mincer et al. (2007) and Santoro et al. (2010).

Environmental factors

The connection between pH and AOA abundance has been examined closely in soils, where Archaeal amoA typically dominates in more acidic samples (reviewed in Prosser and Nicol, 2008; Erguder et al., 2009). The Mississippi River plume is a site of respiration-induced acidification (Cai et al., 2011), and we observed a negative correlation between the abundance of Thaumarchaeota rrs and pH in our samples. In contrast, the abundance of Archaeal accA genes and of AOA amoA genes detected by the Francis primers was positively correlated with pH values (Figure 8). AOB amoA abundance was positively correlated with temperature and negatively correlated with depth, while AOA amoA abundance showed the opposite trends (Figure 8). These correlations correspond to AOB abundance being greatest in surface samples, versus AOA abundance being greater in samples from deeper, colder water, as observed in other studies (e.g., Santoro et al., 2010). We also observed a strong negative correlation between AOB amoA gene abundances and salinity, but we did not find a statistically significant (p > 0.05) correlation between AOA amoA genes and salinity. This contrasts with AOA distributions reported for sediments from an aquifer at Huntington Beach, CA, USA (Santoro et al., 2008) or from the San Francisco Bay Estuary (Mosier and Francis, 2008), where AOB were more abundant in high salinity sediments, while AOA were more prominent in low salinity environments.

Fluorescence (chlorophyll a) contributed significantly to PC1 (Figure A3 in Appendix) and accA, pSL12 rrs, and Archaeal amoA gene abundance (Francis primers) were all positively correlated with fluorescence in CCA analysis (Figure 8). Most other studies have reported inverse correlations between Thaumarchaeota abundance and chlorophyll a (Murray et al., 1999a,b; Wells and Deming, 2003; Kirchman et al., 2007). A study of AOA and AOB dynamics in estuarine sediments, though, showed that potential nitrification rates and the abundance of Archaeal amoA genes (Wuchter primers) correlated positively with sediment chlorophyll a concentrations (Caffrey et al., 2007). Archaeal abundance in the Arctic Ocean near the Mackenzie River mouth correlated positively with chlorophyll a (Wells et al., 2006), although a previous study at similar sites showed the opposite trend (Wells and Deming, 2003). We observed a strong positive correlation between Bacterial amoA abundance and turbidity in the Gulf of Mexico while Archaeal amoA genes were inversely correlated with turbidity (Figure 8). We detected greatest abundances of AOB amoA genes in shallow, near-shore waters (especially at Station C1 and all three Mississippi River stations), which may indicate a salinity effect or an association of AOB with particles originating from estuaries, coastal embayments, or the river. Since we did not sequence the AOB amplicons we obtained, we cannot use the phylogenetic position of the AOB to differentiate between these hypotheses (e.g., Phillips et al., 1999; O’Mullan and Ward, 2005). Caffrey et al. (2007) reported that AOB were more abundant than AOA in sediments from Weeks Bay, Alabama, a subembayment of Mobile Bay. Our near-shore waters also had higher ammonia concentrations (up to 3 μM; data not shown) than at other stations, which is consistent with the conceptual model that AOB are more competitive in environments with elevated ammonia concentrations (Martens-Habbena et al., 2009).

Oxygen concentrations are typically higher in surface than deep water, especially in this region of the Gulf of Mexico where bottom waters become seasonally hypoxic (Rabalais et al., 2002, 2010). Although samples for this study were collected before hypoxia had fully developed ([O2] ranged from 3.5 to 8.4 mg L−1; 150–375 μM), we found clades of AOA similar to those observed in other hypoxic waters (Beman et al., 2008; Labrenz et al., 2010; Molina et al., 2010). Additionally, we determined that the distribution of amoA phylotypes detected by the Francis primers correlated positively with [O2] (as did Archaeal accA genes), while those detected by the Wuchter primers were not correlated with [O2] (Figure 8). Our data suggests that these primer sets have different PCR biases such that certain AOA ecotypes are amplified more efficiently by one set than the other. As we observed correlations between different environmental variables and amoA phylotypes amplified by each primer, we believe these differences may reflect ecotype-specific sequence variation, as proposed for the two primer sets given in Beman et al. (2008).

amoA and accA abundance

The abundance of Archaeal amoA genes reported in this study (up to 108 copies L−1) is comparable to abundances reported for other continental shelf regions (Galand et al., 2006; Mincer et al., 2007; Kalanetra et al., 2009; Santoro et al., 2010), in the mesopelagic Pacific Ocean (Church et al., 2010), and in hypoxic zones (Beman et al., 2008; Molina et al., 2010). Differences in estimates of amoA abundance depended on the primer set used. Previous studies using the Wuchter primers reported low abundance of amoA relative to rrs in deep waters (Agogué et al., 2008; De Corte et al., 2009) compared to studies that used the Francis primers (Beman et al., 2010; Church et al., 2010; Santoro et al., 2010), suggesting that the Wuchter primers are biased against deep water clades of AOA. Our study supports these conclusions, but we also found that the Francis primers underestimated amoA abundance relative to rrs in surface water samples (Figure A6 in Appendix). Comparisons of primer sequences to alignments of amoA sequences from this study show single base-pair differences within Wuchter primer binding sites that could affect primer annealing and thus amplification (Figure A8 in Appendix). Our findings support the use of two different primer sets for the quantification of Archaeal amoA in near-surface versus deep water samples, as recommended by Beman et al. (2008). Alternatively, Thaumarchaeota abundance in DNA extracted from our samples estimated by qPCR of rrs agreed well with an independent assessment based on pyrosequencing. This suggests that the 334F/534R rrs primer set originally proposed by Suzuki et al. (2000) for quantifying Marine Group 1 Archaea may be more robust than amoA primer sets for quantifying Thaumarchaeota.

Figure A6

Comparison of Archaeal . (A) Profiles of amoA abundance at Stations A6 and B5 obtained using each primer set.  = Wuchter,  = Francis. (B) Abundance of amoA genes estimated using Francis primers versus abundance estimated using the Wuchter primers. “Deep,” >100 m sample depth; “near-surface,” = ≤ 100 m sample depth; “inshore,” = above continental shelf; “offshore,” = shelf break and beyond. (A) Station profiles of amoA quantified with different primer sets (B) amoA quantified by Wuchter et al. (2006) primers versus Francis et al. (2005) primers.

Figure A8

Mismatches between . Sequences shown were collected from Station D5, 200 m depth, and were trimmed to regions complementary to the Wuchter et al. (2006) and Francis et al. (2005) primer sets. The top line represents the consensus sequence, while arrows indicate key differences between environmental and primer sequences.

The accA gene, a proposed marker for archaeal autotrophy, was found at abundances almost equal to Thaumarchaeota rrs and amoA (amplified by the Francis primers) below 100 m depth (Table 2), in agreement with findings from the original accA survey of the Tyrrhenian Sea (Yakimov et al., 2009). accA was least abundant in surface water samples (2–70 m depth; e.g., Figure 2), especially at inshore stations and in the Mississippi River. A similar trend has been reported for South China Sea samples, where accA approached the limit of detection in samples <100 m (Hu et al., 2011). Since the accA primers were designed using a very small database, the apparent discrepancy between accA and Thaumarchaeota rrs abundance in near-surface samples may be due to the presence of populations in surface waters with divergent accA that are not detected by this primer set.

Community composition

We identified a number of clades that appear to be unique to the northern Gulf of Mexico. These were seen in rrs genes from both clone libraries and pyrosequencing reads (e.g., D5–200 m-66 [KC330801], -71 [KC330804], -85 [KC330810]; D5–100 m-15 [KC330788]; Figure 5), in amoA gene sequences (e.g., D5–100 m-amoA-21 [KC349156], -35 [KC349170], -41 [KC349176], -51 [KC349185]; D5–200 m-amoA-30 [KC349251], -44 [KC349264]; Figure A1A in Appendix), and in accA gene sequences (e.g., D5–2 m-accA-05 [KC349402], -44 [KC349436]; D5–50 m-accA-53 [KC349545]; D5–100 m-accA-21 [KC349333], -29 [KC349340], -47 [KC349355]; D5–200 m-accA-11 [KC349365], -27 [KC349380], -36 [KC349389], -41 [KC349393]; D5–450 m-accA-20 [KC349475], -26 [KC349480]; Figure A1B in Appendix). Since the global distribution of accA genes has not been thoroughly surveyed, it is difficult to determine whether these clades are indeed unique to the Gulf of Mexico. Generally, the sub-populations of Thaumarchaeota represented by distinct OTUs of each gene grouped according to sample depth, with the most stringent segregation by depth observed for rrs and accA, which segregated as deep (200 and 450 m) and near-surface (2, 50, and 100 m) OTUs, as has been observed elsewhere for amoA (Francis et al., 2005; Beman et al., 2008, 2010; Kalanetra et al., 2009; Church et al., 2010; Santoro et al., 2010). Archaeal amoA phylotypes retrieved from Station D5 were also distributed according to sample depth (Figure 6A), with a near-surface “Group A” and deep “Group B” (Francis et al., 2005). Since these distributions of each of these genes were determined by independent PCR amplifications, it is not possible to directly associate rrs, amoA, and accA genotypes in our samples; however, the coincident groupings of these three markers of completely different physiological functions suggest differentiation of these Thaumarchaeota populations at a genomic level. Unifrac analysis suggests that Thaumarchaeota populations at these stations resolve into three sub-populations, segregated by depth and by factors covarying with depth, with strongest separation between surface (depth < 100 m) and deep water populations (Figure 9; Figures A4 and A7 in Appendix).

A few of the accA gene sequences retrieved from Station D5 clustered with previously defined ecotypes of the “Deep Water accA Clade” (Yakimov et al., 2009, 2011), referred to here as Deep Ecotypes 1a, 1b, and 2 (Figure 6B). Inferred amino acid sequences of all but 8 of the 87 accA amplicons we retrieved from 200 and 450 m grouped into Deep Ecotype 2. No representatives of Deep Ecotypes 1a or 1b were identified, although a group of more divergent sequences similar to these ecotypes was evident (Figure 6B). Since previous studies concentrated on samples from deeper waters, we have added Near-Surface Ecotypes 1a and 1b to the “Shallow Water accA Clade” (Yakimov et al., 2011). Both of the Sargasso Sea reference sequences from this clade fit into Ecotype 1a, which contained only sequences from near-surface waters (≤100 m) of the northern Gulf of Mexico. The accA sequence from “Ca. Nitrosopumilus maritimus” SCM1 (Walker et al., 2010) grouped with marine sediment clones and with “Ca. Nitrosoarchaeum limnia” SFB1 (Blainey et al., 2011); we have thus allocated these sequences to a “Nitrosopumilus-like group.” We also note a distinct lineage of accA (OTU 2, “Near-Surface Ecotype 1b”; Figure 6B) containing sequences from the northern Gulf of Mexico and the South China Sea (“Shallow group II” in Hu et al., 2011).The sequences we retrieved extend coverage of the diversity of accA environmental sequences to near-surface sites and provide additional references for refining ecotype characterizations as more sequences are added to the databases.

Conclusion

AOA and Thaumarchaeota were abundant in the northern Gulf of Mexico coastal waters we sampled, accounting for up to 40% (qPCR) or 54% (pyrosequencing) of the total bacterioplankton population and outnumbering AOB by 10- to 100-fold. The ratio of AOA to NOB in our samples was lower than reported in other studies, suggesting that other pathways for nitrite oxidation may be more important in the northern Gulf of Mexico than elsewhere. A diverse community of Thaumarchaeota was observed at Station D5 near the Mississippi River plume in clone libraries constructed from archaeal genes of interest (rrs, amoA, and accA), with clades that seem to be unique to waters of the northern Gulf of Mexico. Consistent with this observation, and in contrast to studies of many other coastal waters, the amoA sequence most similar to Nmar_1500, the amoA gene from “Ca. N. maritimus” strain SCM1, was only 91% similar. Through analysis of rrs sequences generated using 454 pyrosequencing, we observed distinct clades of Thaumarchaeota that were distributed primarily by depth, with clear differences between near-surface (≤100 m) and deep (>100 m) populations. The distribution of rrs sequences in clone libraries generated from samples collected at Station D5 was consistent with this pattern, suggesting that parallel differences in the composition of Thaumarchaeota populations defined by other genes observed at this station were applicable to the rest of the northern Gulf of Mexico. Finally we found correlations between abundances of Thaumarchaeota genes in this region and environmental variables depth, temperature, turbidity, pH, and oxygen; however, the manner in which these variables influence Thaumarchaeota metabolism and thus distribution remains unclear.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Table A7

Results of BEST analysis comparing gene abundance to .

Gene	Number of variables	Correlation (ρ*)	Contributing environmental variables
(A) BEST ANALYSIS WITH ENVIRONMENTAL VARIABLES
Thaumarchaeota rrs	3	0.507	Salinity, RF, beam attenuation
pSL12 rrs	1	0.457	RF
Bacterial rrs	3	0.613	Temperature, salinity, beam attenuation
Archaeal amoA W	4	0.442	Latitude, temperature, salinity, RF
Archaeal amoA F	4	0.474	Latitude, salinity, oxygen, RF
Bacterial amoA	1	0.462	RF
Archaeal accA	1	0.460	RF
Gene	Number of variables	Correlation (ρ*)	p-Value+	Contributing environmental variables and nutrients
(B) BEST ANALYSIS WITH ENVIRONMENTAL VARIABLES AND NUTRIENTS
Thaumarchaeota rrs#	1	0.429	0.018	Beam attenuation
pSL12 rrs	5	0.337	0.102	Latitude, salinity, RF, nitrate, silicate
Bacterial rrs#	2	0.587	0.001	Beam attenuation, silicate
Archaeal amoA W	3	0.374	0.067	Latitude, RF, silicate
Archaeal amoA F#	2	0.374	0.010	Latitude, RF
Bacterial amoA	2	0.264	0.269	Latitude, RF
Archaeal accA	2	0.269	0.246	Latitude, RF

BEST analysis (Clarke, .

*ρ in section A is the Spearman rank correlation coefficient where ρ > 0 rejects the null hypothesis; all results had a significance .

*ρ in section B is the Spearman rank correlation coefficient where ρ > 0 rejects the null hypothesis; p-values are given.

.

.