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Applied and Environmental Microbiology, August 2008, p. 4910-4922, Vol. 74, No. 15
0099-2240/08/$08.00+0     doi:10.1128/AEM.00233-08
Copyright © 2008, American Society for Microbiology. All Rights Reserved.

Molecular Characterization of the Diversity and Distribution of a Thermal Spring Microbial Community by Using rRNA and Metabolic Genes ^,

Justine R. Hall,¹ Kendra R. Mitchell,¹ Olan Jackson-Weaver,^1, Ara S. Kooser,² Brandi R. Cron,¹ Laura J. Crossey,² and Cristina D. Takacs-Vesbach^1*

Department of Biology, University of New Mexico, 167 Castetter Hall, MSC03-2020 1, University of New Mexico, Albuquerque, New Mexico 87131,¹ Department of Earth and Planetary Sciences, University of New Mexico, Northrop Hall, MSC03-2040 1, University of New Mexico, Albuquerque, New Mexico 87131²

Received 25 January 2008/ Accepted 21 May 2008

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The diversity and distribution of a bacterial community from Coffee Pots Hot Spring, a thermal spring in Yellowstone National Park with a temperature range of 39.3 to 74.1°C and pH range of 5.75 to 6.91, were investigated by sequencing cloned PCR products and quantitative PCR (qPCR) of 16S rRNA and metabolic genes. The spring was inhabited by three Aquificae genera—Thermocrinis, Hydrogenobaculum, and Sulfurihydrogenibium—and members of the Alpha-, Beta-, and Gammaproteobacteria, Firmicutes, Acidobacteria, Deinococcus-Thermus, and candidate division OP5. The in situ chemical affinities were calculated for 41 potential metabolic reactions using measured environmental parameters and a range of hydrogen and oxygen concentrations. Reactions that use oxygen, ferric iron, sulfur, and nitrate as electron acceptors were predicted to be the most energetically favorable, while reactions using sulfate were expected to be less favorable. Samples were screened for genes used in ammonia oxidation (amoA, bacterial gene only), the reductive tricarboxylic acid (rTCA) cycle (aclB), the Calvin cycle (cbbM), sulfate reduction (dsrAB), nitrogen fixation (nifH), nitrite reduction (nirK), and sulfide oxidation (soxEF1) by PCR. Genes for carbon fixation by the rTCA cycle and nitrogen fixation were detected. All aclB sequences were phylogenetically related and spatially correlated to Sulfurihydrogenibium 16S rRNA gene sequences using qPCR (R² = 0.99). This result supports the recent finding of citrate cleavage by enzymes other than ATP citrate lyase in the rTCA cycle of the Aquificaceae family. We briefly consider potential biochemical mechanisms that may allow Sulfurihydrogenibium and Thermocrinis to codominate some hydrothermal environments.

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The Aquificales are a strictly thermophilic bacterial lineage that has been recovered from hydrothermal systems worldwide (9, 19, 26, 42, 52, 53, 59). This lineage has attracted much interest in the last 20 years because of its proposed deep phylogenetic position (10, 16, 29, 49). Culturing of the Aquificales has demonstrated that they are largely chemolithoautotrophs that utilize H₂ to reduce O₂ (Knallgas reaction) (30). However, culturing data are often inadequate to understand natural communities as the availability of metabolic substrates and energy yield depend on factors such as geochemistry and microbial community composition, which vary within and between hydrothermal environments.

Although Aquificales are chemolithoautotrophs, culture studies and environmental sampling suggest that they use a diversity of metabolic reactions. Hydrogen oxidation is one of the most exergonic reactions in Aquificales-dominated hot springs in Yellowstone National Park (42, 59), but most species are able to oxidize elemental sulfur, thiosulfate, or ferrous iron and reduce nitrate, ferric iron, arsenate, selenate, selenite, or elemental sulfur in addition to or instead of the Knallgas reaction to yield energy (1, 2, 19, 24, 27, 28, 36, 37, 44-46, 64-66, 69). Furthermore, there is a pattern between the general metabolic strategy and habitat of each species. Species isolated from terrestrial hot springs and compost are the only Aquificales capable of using organic compounds as carbon and energy sources. For example, Thermocrinis ruber and Sulfurihydrogenibium species, which are often the dominant species in high-temperature, near-neutral hot springs (9, 26, 42, 58), are facultative heterotrophs (28, 45). In contrast, all marine Aquificales, including Hydrogenothermus marinus, all Aquifex species, all Persephonella species, and all species in the Desulfurobacteriaceae family (incertae sedis), are obligate autotrophs (2, 24, 29, 36, 46, 61, 66, 69). Only two species fall outside these categories: the chemolithoautotroph Hydrogenivirga caldilitoris isolated from a coastal hot spring, and the facultative chemolithoautotroph Hydrogenobacter subterraneus, isolated from the deep subsurface (22, 44, 65).

Environmental surveys of the Aquificales suggest that their metabolic capabilities play important roles in biogeochemical cycles. Culture studies indicate that Aquificales can oxidize sulfur to sulfuric acid or reduce it to hydrogen sulfide, and molecular analyses indicate that Aquificae are a dominant phylum of high-sulfide hot springs (28, 29, 58). Genes for thiosulfate oxidation have been identified in the Aquifex aeolicus genome (16) and amplified from a Sulfurihydrogenibium species (GenBank accession number AB254380). Nitrate reduction is present in at least one species each in the Aquifex, Hydrogenobacter, Persephonella, Hydrogenivirga, and Sulfurihydrogenibium genera, while nitrite reduction has been demonstrated in Hydrogenobacter thermophilus TK-6 through the characterization of the nirS gene and by weak growth of Aquifex pyrophilus with nitrite as the only electron acceptor (24, 29, 35, 44, 46, 62, 64). Additionally, Aquificales have been postulated to be primary producers in environments where photosynthesis is temperature limited. The reductive tricarboxylic acid (rTCA) cycle has recently been identified as the carbon fixation mechanism used by chemolithoautotrophs in all three Aquificales families (32).

We investigated the diversity and distribution of thermophilic bacteria and the potential metabolic processes of Coffee Pots Hot Spring, a remote spring located on Yellowstone's Mirror Plateau. We detected Sulfurihydrogenibium and Thermocrinis in a single sample using 16S rRNA gene libraries and determined that both species were abundant throughout Coffee Pots using quantitative PCR (qPCR) assays specific for these genera. We investigated the spatial patterns of these two species in relation to genes used in ammonia oxidation (amoA, bacterial gene only), the rTCA cycle (aclB), the Calvin cycle (cbbM), sulfate reduction (dsrAB), nitrogen fixation (nifH), nitrite reduction (nirK), and sulfide oxidation (soxEF1) using PCR and qPCR (12, 13, 20, 41, 55, 68, 70). Overall, we found a diversity of sequences indicative of carbon fixation by the rTCA cycle and linked their distribution to the abundance of Sulfurihydrogenibium 16S rRNA gene sequences.

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Sample site and collection.
Samples were collected in July 2003 from Coffee Pots Hot Spring (UTM Easting: 554771.2; UTM Northing: 4955983.7) at seven points along a thermal gradient with a temperature range of 39.3 to 74.1°C and pH range of 5.75 to 6.91 (Fig. 1). Sample names represent the temperature at which they were collected. Coffee Pots is within the northwestern quadrant of Yellowstone National Park on the northwestern edge of the Mirror Plateau and consists of two main areas located in a north-south orientation. We sampled the lower area, a long stream that flows east to west. Filamentous biomass was collected in triplicate for all seven points using sterile syringes. Two replicates (R1 and R2) were stored in an equal volume of sucrose lysis buffer (SLB; 20 mM EDTA, 200 mM NaCl, 0.75 M sucrose, 50 mM Tris-HCl, pH 9.0), and the third was preserved in an equal volume of guanidine isothiocyanate medium (5 M guanidine isothiocyanate, 50 mM Tris, pH 7.4, 25 mM EDTA, pH 8, 0.8% 2-mercaptoethanol). Replicates at all points were taken within 2 cm of each other. Because of the remote location, samples were held at ambient air temperature (10 to 26°C) for up to 10 days before they were stored at –80°C. Independent experiments have indicated that storage of samples in SLB at ambient air temperatures does not result in a loss of DNA or diversity in the samples relative to samples immediately frozen in liquid nitrogen (K. Mitchell and C. Takacs-Vesbach, unpublished data). However, we cannot rule out any long-term storage effect on gene abundances. Presumably, any decreases would be uniform across all members of the community.

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FIG. 1. Panoramic view of Coffee Pots Hot Spring (Mirror Plateau, northeastern quadrant of Yellowstone National Park). Image is a compilation of overlapping individual digital photographs with brightness uniformly increased in Adobe Photoshop 10. Sample sites are indicated by triangles with pH in parentheses. The arrow indicates the source of the spring.

Geochemistry measurements and metabolic energetic calculations.
Temperature and pH were measured using a Thermo Orion 290A+ meter, and conductivity was measured using a WTW 340i meter. Spring water was collected for geochemical analysis at sample COF_65.7 but not at the other sites along the stream. The water directly overlying the biomass was sampled by syringe and filtered (0.2-µm pore size) into appropriately washed polyethylene or glass bottles. Water samples were preserved when necessary as appropriate for the analysis to be performed. Briefly, water for cation determination was preserved in 1% concentrated redisitilled HNO₃, the iron and arsenic species and dissolved organic carbon samples were stored in 1% 6N HCl, and the ammonium sample was preserved in 0.1% H₂SO₄. Geochemical analyses, including anions, cations, trace metals, and nutrients were conducted using standard U.S. Geological Survey (USGS) methods (39). Sulfide concentration was measured spectrophotometrically in the field on triplicate water samples using a Hach kit. Sample was diluted with deionized water when necessary, and a temperature correction was applied to the measurement following the method of McCleskey et al. (39). Anions and cations were determined using ion chromatography and inductively coupled plasma optical emission spectrometry; alkalinity was measured by titration, total and ferrous iron was determined colorimetrically by the ferrozine method, arsenic species were measured by atomic absorption spectroscopy, and dissolved organic carbon was determined by the wet oxidation method using a total organic carbon analyzer. Details of the analyses are given in McCleskey et al. (39).

Activities and speciation of metabolically important chemical compounds were calculated with both PHREEQC Interactive (version 2; USGS, www.brr.cr.usgs.gov/projects/GWC_coupled/phreeqc/) and Geochemist's Workbench (version 7.0; Rockware), which yielded virtually identical results. The activities were calculated for the redox reactions by decoupling the reactions to better approximate the disequilibrium found in natural environments. These activities were used in conjunction with thermodynamic data for high temperatures (3) to calculate the chemical affinity as previously described (57). Our calculation method was compared with previous results from Obsidian Pool: for all 41 reactions considered here, the chemical affinities were well within the ranges previously reported (see supplemental material) (3, 57). The chemical affinity is a measure of the disequilibrium state of many oxidation-reduction reactions that potentially serve as energy pathways for microbial metabolism under the specific sample location conditions. As gas chemistry was not available for Coffee Pots, we ran a number of models with various combinations of H₂ and O₂ (see Fig. 2 for accompanying model conditions and reaction list). Models 1 to 4 examine low (4.1 x10^–6 ppm), medium (2.05 x 10^–5 ppm), medium-high (2.05 x 10^–4 ppm), and high (6.67 x 10^–4 ppm) concentrations of hydrogen encompassing the ranges reported for Yellowstone hot springs (59) for an oxygen concentration of 0.1 ppm. We evaluate the effect of varying O₂ in models 5, 3, and 6 (0.01, 0.1, and 0.5 ppm, respectively) at a fixed H₂ of 2.05 x 10^–4 ppm. Because nitrate concentrations were below the analytical detection limit, we used the detection limit value (0.1 ppm) in all model runs.

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FIG. 2. Energy yield of metabolic reactions common to hydrothermal systems as a function of H2 and O2 concentrations reported for other Yellowstone springs (3, 57, 59). Forty-one reactions were evaluated using the analytical results for Coffee Pots (Table 3). Reactions were evaluated for a suite of six models using a range of H2 and O2 concentrations reported for Yellowstone hot springs: models 1 to 4 examine low (4.1 x 10–6 ppm), medium (2.05 x 10–5 ppm), medium-high (2.05 x 10–4 ppm), and high (6.67 x 10–4 ppm) H2 concentrations, respectively, with an O2 concentration of 0.1 ppm; and models 5, 3, and 6 examine a range of O2 concentrations (0.01, 0.1, and 0.5 ppm, respectively) at a fixed H2 concentration of 2.05 x 10–4 ppm. The chemical reactions are displayed and ranked in the order obtained from Obsidian Pool in a previous study (57) of decreasing energy released (per mole of electrons transferred in the reaction).

DNA extraction.
Nucleic acids were extracted using a variation of the cetyltrimethylammonium bromide (CTAB) method (73). CTAB buffer (400 µl of 1% CTAB, 0.75 M NaCl, 50 mM Tris, pH 8, 10 mM EDTA) and proteinase K (final concentration, 100 µg/ml) were added to 200 µl of SLB-preserved sample and incubated for 1.25 h at 60°C. Sodium dodecyl sulfate was then added to a final concentration of 2%, and samples were incubated for 1 h. DNA was extracted once with an equal amount of phenol-chloroform-isoamyl alcohol (50:49:1) and then extracted twice with an equal volume of chloroform. DNA was precipitated with 0.1 volume of 3 M sodium acetate and 2 volumes of 95% ethanol, followed by incubation at –20°C for 1 to 24 h. The samples were then centrifuged for 45 min (21,000 x g), washed in 70% ethanol, and resuspended in 10 mM filter-sterilized Tris buffer, pH 8.0.

Gene amplification and sequencing.
The 16S rRNA gene was amplified from one extraction of each SLB replicate of sample COF_65.7 by 50 µl of PCR mixture containing 5 µl of 10x buffer (Promega buffer B with 1.5 mM MgCl₂), a 12.5 mM concentration of each deoxynucleoside triphosphate (BioLine USA, Inc.), 20 pmol each of the 8F and 1492R primers, 2.5 U of Taq polymerase (Promega), and approximately 50 ng of DNA. The PCR was incubated for 5 min at 94°C, followed by 30 cycles of 30 s at 94°C, 30 s at 50°C, and 90 s at 72°C, with a final extension of 72°C for 7 min. PCR was used to detect amoA, aclB, cbbM, dsrAB, nifH, nirK, and soxEF1 genes using the primers and amplification conditions listed in Table 1. Temperature gradient and touchdown thermocycling programs were used to optimize the annealing temperature for each primer set. Appropriate positive controls were identified for each primer set and used in every PCR. The 50-µl PCR mixtures contained 5 µl of 10x buffer, a 12.5 mM concentration of each deoxynucleoside triphosphate, 3 µl of 2% bovine serum albumin, 2.5 U of DNA Taq polymerase, and 10 to 50 ng of DNA with the following amounts of primers: 40 pmol for amoA, 80 pmol for aclB, 100 pmol for cbbM, 40 pmol for dsrAB, 140 pmol for nifH, 80 pmol for nirK, and 100 pmol for soxEF1.

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TABLE 1. Primers used for the detection of metabolic genes in this study

PCR products were spin purified using a DNA Purification Kit (Mo Bio Laboratories, Carlsbad, CA) and cloned using a TOPO-TA cloning kit (Invitrogen Corp., Carlsbad, CA). Clones were grown on kanamycin-selective plates, and plasmids from 92 to 103 colonies (per PCR) were harvested and isolated using an Eppendorf Perfectprep Plasmid 96 Vac Direct kit. Restriction fragment length polymorphism analysis was performed for each gene by reamplifying the inserted gene using vector-specific primers (M13F and M13R) in a 50-µl PCR mixture. The amplified genes were digested with restriction enzymes (New England Biolabs) for 6 h in 25-µl reaction mixtures containing 2.5 µl of 10x NEB-2 buffer, 2.5 µl of 1% Triton X-100, and 12 µl of PCR product with one unit of HinP1I and 0.5 units of MspI for the 16S rRNA gene, one unit each of DdeI and MseI and 0.25 µl of 100 µg/ml bovine serum albumin for the aclB gene, and 10 units each of MspI, EcoRI, and HindIII for the nifH gene. The digested genes were separated on agarose gels consisting of 3% agarose in 1x Tris-acetate-EDTA buffer for 2 h at 2.5 V cm^–1 of electrode length. Representative clones of each unique banding pattern were fully sequenced in both directions using primers M13F and M13R and internal primers using a BigDye terminator cycle sequencing kit (PE Applied Biosystems). The COF_65.7 R2 and six aclB libraries were screened for unique clones by sequencing the entire library with primer 8F (for the 16S rRNA gene) or M13F (for the aclB gene). Clones that were at least 2% dissimilar from other clones in the library were fully sequenced and included in phylogenetic analysis.

qPCR.
qPCR assays were designed to quantify the number of gene copies of aclB and 16S rRNA gene sequences specific for Thermocrinis, Sulfurihydrogenibium, and a divergent sequence ("Toll" clone) detected in the R1 COF_65.7 clone library. New primers and fluorogenic TaqMan probes with minor groove binders on the 3' end (Applied Biosystems) were designed for each assay (Table 2). The aclB, Thermocrinis, and Toll primers and probes were designed from an alignment of sequences amplified from Coffee Pots sample BLAST searches, and alignment with other aclB sequences using NCBI's bl2seq tool indicated that this primer and probe matched other Sulfurihydrogenibium sequences from Yellowstone but not other Aquificales species. The ThermoR primer matches mostly Thermocrinis sequences from Yellowstone springs but also matched sequences from a spring in the Alvord Desert Basin of Oregon (GenBank accession number DQ645256) and a hot spring from Nevada (GenBank accession number DQ490016.1). Sulfurihydrogenibium-specific primers appropriate for qPCR under our conditions could not be designed, so bacterial-specific primers were used in conjunction with a Sulfurihydrogenibium-specific probe already described (52). Although the primers could amplify any bacterial or archaeal 16S rRNA gene, fluorescence would be detected only from sequences to which the Sulfurihydrogenibium-specific probe had also bound.

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TABLE 2. Primers and probes used for qPCR detection in this study

Primers and probes were used in 25-µl qPCR mixtures containing 12.5 µl of TaqMan Universal PCR Master Mix without AmpErase uracyl N-glycosylase, 10 pmol each of the forward and reverse primers, 0.625 pmol of TaqMan probe, 0.5 U of Taq DNA polymerase, and 10 to 50 ng of DNA. Each reaction mixture was spiked with Promega Taq to increase the assay's resistance to inhibitors, a common problem in environmental samples. The 16S rRNA gene assays were incubated at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 60 s, and the aclB assay was incubated for 35 cycles of 94°C for 30 s, 51°C for 30 s, and 72°C for 1 min using an ABI Prism Sequence Detection System. To ensure specificity of the primers and probes, the Sulfurihydrogenibium assay was tested on Thermocrinis-containing environmental samples, and the Thermocrinis assay was tested on Sulfurihydrogenibium-containing environmental samples; both results were negative. Three replicate reactions were performed and averaged for each assay.

Standards for the Thermocrinis, Toll, and aclB assays were generated by amplifying environmental DNA in 50-µl PCR mixtures with assay-specific primers, spin-purifying the PCR products, and quantifying DNA with an ND-1000 Spectrophotometer (Nanodrop Technologies). For the Sulfurihydrogenibium 16S rRNA gene, bacteria-specific primers 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 907R (5'-CCGTCAATTCCTTTRAGTTT-3') were used to amplify a 412-bp region of the 16S rRNA gene extracted from Sulfurihydrogenibium azorense genomic DNA (from A.-L. Reysenbach). Standard curves were created by diluting each standard over seven orders of magnitude and obtaining three replicate threshold cycle values for each dilution. DNA concentration was converted to number of gene copies using a conversion factor of 600 g of double-stranded DNA mol^–1 nucleotide^–1. A single gene copy was assumed for all assays. Slope of the standard curves for the assays averaged –3.6, and R² values ranged from 0.98 to 1.00. The sensitivity of each assay was calculated from the standard curve equation for each reaction and ranged from 1 log(gene copies) to 4 log(gene copies).

Phylogenetic analysis.
The 16S rRNA, aclB, and nifH electropherograms were base called using the PHRED program and assembled using PHRAP in CodonCode Aligner. All sequences of >98% similarity were clustered together in the same phylotype. The Greengenes program (18) was used to align the 16S rRNA gene sequences and find the most closely related 16S rRNA gene from cultured and uncultured bacteria. Aligned sequences were imported into the ARB program (38) and manually adjusted according to conserved regions of the gene and the established secondary structure to ensure that only homologous regions were compared. Nucleotide positions that were not conserved in more than 50% of the aligned sequences or were ambiguously aligned were masked out of the alignment so the final phylogenetic analysis was based on 1,293 nucleotides. Phylogenetic analysis was performed in PAUP* (version 40.b10; Sinauer Associates, Sunderland, MA) using parsimony, neighbor-joining, and maximum likelihood analyses. Potential long-branch attractions were investigated by adding and removing sequences across the phylogeny, especially in the Aquificales clades. The final 16S rRNA gene tree was created by neighbor-joining analysis with a maximum-likelihood correction using heuristic tree search with tree bisection-reconnection (TBR) branch swapping in PAUP*. The transition/transversion ratio and nucleotide frequencies were estimated according to the F84 model (21). The final 16S rRNA gene tree for the Toll sequence was created using maximum-likelihood analysis corresponding to the general time-reversible model ( = 0.63). The starting trees were obtained by stepwise addition for both 16S rRNA gene trees. Bootstrap proportions were determined from 1,000 and 100 resamplings for the inclusive and Toll subset 16S rRNA gene trees, respectively.

All aclB sequences were subjected to a BLAST search to identify sequences to include in the alignment. The aclB alignment was based on amino acid residues and was compared to a published aclB alignment to ensure the correct reading frame was employed (12). Topology of the tree was explored using Mus musculus and Chlorobium limicola outgroups because the evolution of this gene is currently unclear (12, 32). The final aclB tree was based on 107 amino acid residues and was constructed with neighbor-joining analysis using a heuristic tree search with TBR branch swapping in PAUP*. The phylogeny of the nifH gene was explored through neighbor-joining analysis of amino acid and nucleotide alignments that included sequences representative of the four known nifH clusters (72). Trees were rooted with the Chlorobium tepidum bchL gene (encodes light-independent protochlorophyllide reductase), which is phylogenetically related to nifH sequences and has previously been used as an outgroup for nifH (41). The final nifH tree was based on 113 amino acid residues and was constructed with neighbor-joining analysis using a heuristic tree search with TBR branch-swapping in PAUP*.

16S rRNA secondary structure determination.
The 16S rRNA secondary structure was determined for the Toll sequence detected in this study to ensure that its novelty was not artifactual. The 16S rRNA gene sequence was overlain onto the established structures of A. pyrophilus, Thermus aquaticus, Deinococcus thermophilus, and Thermotoga maritima obtained from the Comparative RNA Website (http://www.rna.ccbb.utexas.edu/). The structure of the hypervariable regions was determined by hand, and the molecule was checked for commonly conserved structures, compensatory changes, and long-range interactions.

Statistical analysis.
A pairwise dissimilarity index F statistic (F_ST) was used to analyze the distribution of genetic diversity of the aclB sequences found in each sample using Arlequin (version 2.00; Genetics and Biometry Lab, Department of Anthropology, University of Geneva, Switzerland [http://lgb.unige.ch/arlequin/]). The forward sequences of the 333-bp aclB fragments were assembled into phylotypes (based on 98% similarity) by sample and aligned using Clustal W (14) in CodonCode. F_ST values were estimated in Arlequin for each site and were tested for significance against 1,000 randomized bootstrap resamplings.

Nucleotide sequence accession numbers.
The full-length sequences determined in this study were deposited in the GenBank database (http://www.ncbi.nlm.nih.gov/GenBank/index.html) under the following accession numbers: EU156124 to EU156131 for nifH sequences, EU156131 to EU156141 for aclB sequences, and EU156142 to EU156157 for 16S rRNA gene sequences. The alignments used in this study are available from http://pearl3/unm.edu/site/ynp_inv_data_products.html.

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Chemical properties and energy yield potential of sample COF_65.7.
Coffee Pots is a circum-neutral to slightly acidic system that contains low concentrations of dissolved cations and anions relative to other Yellowstone thermal springs (Table 3). The low chloride and high ammonium conditions found in Coffee Pots are typical of the steam-heated, meteoric water systems found on the Mirror Plateau (23). Chemical affinities (normalized per mole of electrons transferred in the reaction) were modeled using the chemical measurements made at COF_65.7 for chemolithotrophic reactions that are frequently important in hydrothermal systems (Fig. 2). Results are presented in the same order as in previous investigations of chemical affinity (57) in Yellowstone systems. Generally, the energy available for the sequence of potential metabolic reactions is similar to that noted in Obsidian Pool, with H₂S-, H₂-, Fe²⁺- and S⁰-utilizing reactions yielding the most energy.

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TABLE 3. Geochemical and physical measurements for sample COF_65.7

Phylogenetic diversity of bacteria in sample COF_65.7.
The two clone libraries constructed for sample COF_65.7 (R1 and R2) contained different community compositions (Fig. 3). The R1 library was dominated by members of the Aquificales and Deinococcus-Thermus, with 58 Thermocrinis and 30 Thermus clones out of 92 total. This replicate also contained three clones of a sequence (Toll) that was approximately 99% similar to BH60, a novel lineage found in Black Pool, another Yellowstone hot spring (9). All other sequences that matched Toll in a BLAST search were 85% similar or less. Aquificales dominated the R2 library, but Sulfurihydrogenibium sequences accounted for most of the clones (88 of 103). Hydrogenobaculum sequences (three clones) were also present. We attempted to detect Thermocrinis sequences from the R2 DNA sample using the Thermocrinis-specific primers listed in Table 2, but no amplification was detected. The remaining nine phylotypes (represented by one to three clones each) grouped within the Alpha-, Beta-, and Gammaproteobacteria; Firmicutes; Acidobacteria; Deinococcus-Thermus; and candidate division OP5. Both replicate libraries had significant Aquificales populations, but R1 contained only the Aquificaceae family (Thermocrinis sp.) while R2 contained representatives from the Aquificaceae family (Hydrogenobaculum sp.) and the Hydrogenothermaceae family (Sulfurihydrogenibium sp.).

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FIG. 3. (A) Phylogenetic analysis of 16S rRNA gene sequences obtained from both clone libraries for sample COF_65.7. (B) Phylogenetic tree of the Toll clone and a subset of sequences from the tree in panel A. Both trees are based on 1,293 nucleotides of the 16S rRNA gene and are rooted with Methanocaldococcus jannaschii. Bold names indicate sequences obtained in this study. Single-letter designations and the Toll clone represent sequences from the first clone library, and alphanumeric names represent sequences from the second clone library. The number of clones found for each phylotype is given in parentheses.

16S rRNA secondary structure.
The 16S rRNA secondary structure was determined for the Toll sequence by comparing it to the established structure of A. pyrophilus, Thermotoga maritima, Thermus aquaticus, and Deinococcus thermophilus to confirm that Toll's novelty was not a result of PCR artifact or sequencing error (Fig. 4). Conserved regions of the gene corresponded well to those found in A. pyrophilus in both sequence and structure. Most base differences throughout the structure had compensatory base differences across a stem-loop that resulted in canonical base pairing. All tertiary interactions either contained the same bases as in A. pyrophilus (T1, T4, T5, T6, T7, and T8) or had corresponding base changes that conserved canonical pairing (T2 and T3). Although the sequences in the V1, V3, and V4 variable regions had significantly different sequences between Toll and A. pyrophilus, the structures in these regions were not significantly different. Stem-loops in V2, V7, and V9 are shorter than in A. pyrophilus, resulting in different loop structures and a lack of bulged, nonpaired bases in the middle of stems. V2 (bases 221 to 241) is similar to the other thermophilic bacteria used for comparison, but V7 (bases 1151 to 1173) contains a much larger loop than any of the four compared structures. V9 (bases 1450 to 1476) lacks 10 paired bases in addition to a bulge of nonpaired bases, which resulted in a significantly shorter stem. However, three of the four loop bases are conserved between A. pyrophilus and Toll. Regions in V5 and V6 contained sequences longer than those in A. pyrophilus, resulting in a longer stem and larger loop in V5 and an internal loop in V6. Overall, the Toll sequence detected appears to be a nonchimeric, nonartifactual sequence with structural elements found in both the Aquificales and the Thermotogales.

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FIG. 4. 16S rRNA secondary structure for the Toll sequence. Capital letters are bases conserved between A. pyrophilus and Toll. Regions V1 to V9 are hypervariable regions as determined by Ashelford et al. (5); approximate nucleotide positions are given in parentheses. Regions T1 to T9 are established tertiary interactions. Numbering is unique to this structure.

Amplification of metabolic genes along the temperature gradient.
Two of the seven genes that were tested, nifH and aclB, were detected by PCR amplification. The remaining five genes, amoA, cbbM, dsrAB, nirK, and soxEF1, failed to amplify from our samples (validated by the use of positive controls). The nitrogen fixation gene, nifH, was detected in samples COF_39.3 and COF_65.7, collected more than 54 m apart. Eight phylotypes were recovered with seven in COF_65.7 and one in COF_39.3. Interestingly, the most similarity (98%) was seen between phylotype F6, represented by one clone in COF_65.7, and the single phylotype found in COF_39.3. These two phylotypes plus another one grouped with the Alphaproteobacteria, two grouped with the Betaproteobacteria, and three grouped together in a clade of anaerobes (Fig. 5).

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FIG. 5. Phylogenetic analysis of the nifH gene sequences obtained from COF_39.3 and COF_65.7. Bold text indicates sequences obtained in this study. The tree was based on 113 amino acid residues and was constructed with neighbor-joining analysis using a heuristic tree search with TBR branch swapping. The tree was rooted with the C. tepidum bchL gene. Bootstrap values are based on 100 resamplings.

The gene for ATP citrate lyase (aclB) was detected in all seven samples by PCR amplification using the primers listed in Table 1. The aclB primers also amplified catalase genes that were similar to those found in a variety of aerobic bacteria. These sequences dominated sample COF_39.3 (65 of 77 clones) but were not amplified in any other samples. The specificity of the aclB forward primer and probe designed for qPCR was confirmed by comparing them with the amplified catalase genes using the NCBI bl2seq alignment tool. Ten phylotypes of the aclB gene were detected in the seven samples. The phylotypes were 88.3 to 100% similar at the amino acid level. The distribution of phylotypes among samples was widespread, except for phylotype G6, which was found only in sample COF_51.9. All 10 phylotypes grouped with Sulfurihydrogenibium species, represented by Sulfurihydrogenibium subterraneum in the tree (Fig. 6). Sulfurihydrogenibium sp. strain 153IV-9, S. azorense, and Sulfurihydrogenibium yellowstonense aclB sequences also grouped in this clade but were removed from the final phylogenetic analysis because they were significantly shorter (258 bp) than the aclB sequences obtained in this study.

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FIG. 6. Phylogenetic analysis of the aclB gene sequences obtained from the seven sample sites. Bold text indicates sequences obtained in this study. The tree was based on 107 amino acid residues and was constructed with neighbor-joining analysis using a heuristic tree search with TBR branch swapping. The tree was rooted with C. limicola. Bootstrap values are based on 1,000 resamplings.

qPCR quantification of 16S rRNA and aclB genes.
Thermocrinis and Sulfurihydrogenibium 16S rRNA genes were detected by qPCR in all seven samples (Fig. 7). The abundance of Thermocrinis 16S rRNA genes was higher than Sulfurihydrogenibium 16S rRNA genes in every sample and ranged over an order of magnitude (10⁷ to 10⁸ gene copies per µl of DNA). Sulfurihydrogenibium had a wider range of gene copies, 10^4.5 to 10^6.5 gene copies per µl of DNA. The aclB gene was detected in every sample except COF_39.3. Although fluorescence was detected in this sample, exponential amplification was not observed, and the threshold cycle values were high and inconsistent, indicating that the aclB gene may be present in this sample at a level close to the limit of detection. The aclB gene copy numbers were lower than both 16S rRNA genes and ranged over an order of magnitude (10⁵ to 10^6.3 gene copies per µl of DNA). The distribution of aclB gene copies was closely correlated with the distribution of Sulfurihydrogenibium 16S rRNA gene copies along the transect (R² = 0.99), suggesting that Sulfurihydrogenibium species may be the source of the aclB genes detected along the transect.

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FIG. 7. Gene copy numbers for 16S rRNA and aclB gene assays for each sampling location. Increasing distance from the source waters of the spring correlates to decreasing temperature. Gene copy numbers were determined using assay-specific standard curves and normalized by calculating the number of gene copies per microliter of total extracted DNA in the reaction. Error bars represent the standard deviation of three replicates.

Statistical analysis of the aclB sequences.
Dissimilarity indices were calculated for pairs of samples, and each sample contained a significantly different (P < 0.05) population of aclB sequences (Table 4). Potential F_ST values ranged from 0 (variation between and within samples is equal) to 1 (all variation is between samples). The highest F_ST values resulted from the pairwise comparisons with COF_65.7 and COF_61.7. These samples were taken from neighboring sites (Fig. 1) and were the population maxima of Thermocrinis and Sulfurihydrogenibium, respectively, as determined by qPCR. The pairwise comparisons of the remaining five samples had similar, relatively low F_ST values (0.18 to 0.23).

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TABLE 4. FST values for significant pairwise comparisons of the 333-bp aclB sequences found in each samplea

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Potential electron and energy sources in sample COF_65.7.
It is important to note that our thermodynamic calculations are largely a theoretical exercise because we did not actually measure or detect oxygen and nitrate, respectively. Assuming low (detection limit) concentrations for nitrate, the modeled energetic profile of Coffee Pots showed that the processes that use oxygen, nitrate, and elemental sulfur as electron acceptors are the most energetically favorable (Fig. 2) among the 41 reactions that were run. Our results follow the general trend observed in the thermodynamic calculations for Obsidian Pool (57). It is not surprising that both systems harbor large communities of Aquificales, which are mostly microaerophilic or facultatively anaerobic, as hydrogen sulfide, hydrogen, and elemental sulfur oxidation were most favorable at the oxygen concentrations we used. It is interesting that within our modeled ranges, varying H₂ has the most significant effect on the ranking of the most energetically favorable reactions: at the highest values, reactions coupling H₂ oxidation to iron and sulfate reduction are favored over sulfur-utilizing reactions. Although we did not detect any bacterial amoA genes, the high energy yield for ammonium oxidation (Fig. 2, reactions 19 and 21) in Coffee Pots makes it possible that archaea are using this pathway (17, 25).

Diversity and phylogeny of bacteria in sample COF_65.7.
In this study, we amplified a significant number of Sulfurihydrogenibium and Thermocrinis sequences from two clone libraries constructed from replicates of a single sample. However, neither clone library contained sequences from both of these genera, which we suspect is due to the bias of molecular techniques. The lower G+C content of the 16S rRNA gene in Sulfurihydrogenibium (56% for G9; 57% for A12) compared to Thermocrinis (61%) would favor the amplification of Sulfurihydrogenibium because its template would melt more efficiently (50). The predicted melting temperature of the 16S rRNA genes from the two organisms differed by 5°C. However, this does not explain why Thermocrinis would be detected at all, much less exclusively, when Sulfurihydrogenibium sequences are present in situ as well. We suspect that stochastic overamplification of one template, a nonreplicable bias that occurs in the early cycles of amplification, is responsible for this result as qPCR showed both species present at high levels in R1.

We also detected a highly divergent, phylogenetically basal sequence that we have called Toll. Comparison of this sequence to the NCBI database revealed that its closest relative (99% similar) was a partial sequence (BH60; 986 bp) from Black Pool, another Yellowstone spring. All other matches, including another novel sequence from Black Pool (BH1), were 85% similar or less. Phylogenetic analysis grouped Toll with other highly divergent sequences in the most basal clade. This clade is well supported with bootstrap values of 100 at each of the nodes (Fig. 3B). However, the outer nodes of the basal lineages had lower bootstrap values as EM19 and OPB92, sequences from two candidate divisions, were frequently drawn into the Toll clade, together and separately. Additionally, Toll consistently grouped between the Thermotogae and Thermodesulfobacteria clades when 20 to 40% filters were used on all of the sequences and when EM19, BH60, BP-B68, and OPB92 were removed from the analysis, regardless of the filter used. We suspect the observed phylogenetic relatedness of these sequences is due to long-branch attractions and that their exact phylogenetic placement will remain uncertain until more sequences from these divisions are obtained.

The sequences in the Toll clade belong to organisms that seem to be minor, but persistent, constituents in Yellowstone hot springs. Although we detected only three Toll clones (3.3% of library; below detection by the 16S rRNA qPCR assay), the closest relatives of Toll also have low frequencies and accounted for only 0.8 to 8.2% of the sequences detected in their respective springs (9, 31, 42, 53). Additionally, we have detected 6 clones (of 96 total) of a sequence that is <1% different from Toll in Bechler Hot Springs, a spring more than 60 km from Coffee Pots. It is unlikely that these sequences represent transitory populations as they were collected from six springs over a decade. In addition, Toll is likely not a chimera as it formed a realistic secondary structure that was similar in structure to cultured organisms phylogenetically related to Toll. These results suggest that Toll and related sequences belong to actual thermophilic organisms with unknown in situ functions.

We also detected 11 phylotypes that grouped within eight phyla. B2, B6, F11, and E5 grouped with mesophilic genera (Aquaspirillum, Desulfosporosinus, Acetivibrio, and Geothrix, respectively) and were likely washed in from soil and groundwater, habitats from which they are commonly isolated (15, 34, 48, 51). The remaining seven phylotypes grouped with genera that grow above 40°C and are likely functional in Coffee Pots. Phylotypes D1, N, and H12 grouped with thermophilic heterotrophs found in a variety of thermal areas. The latter two grouped with Thermus, the only genus represented in both clone libraries, but they were more similar to other Thermus isolates than to each other. Phylotype H grouped with Azospirillum, a nitrogen-fixing heterotroph (67). It is possible that three of the nifH phylotypes originate from this organism as they grouped in the Azospirillum clade within the Alphaproteobacteria. Phylotypes H11 and D12 grouped with members of the Gammaproteobacteria that are capable of reducing elemental sulfur or sulfate and sulfite, respectively (6, 43). The latter organism may not be a numerically significant member of the Coffee Pots community as we did not detect the gene for dissimilatory sulfite reductase (dsrAB), an enzyme involved in both sulfate and sulfite reduction. Phylotype B9 grouped with sequences from candidate division OP5 and likely represents a novel organism.

Metabolic genes.
The nifH gene was detected at 39.3 and 65.7°C. This gene has previously been shown to group into four clusters loosely based on 16S rRNA gene phylogeny (72). The single phylotype detected at 39.3°C grouped with the Alphaproteobacteria sequences of cluster I. The functionality of nitrogenases from this cluster has been well documented in a wide range of environments, and it is likely that the nifH sequence we detected here also represents a functional enzyme. The seven nifH phylotypes detected at 65.7°C are above the temperature limit (64°C) of known bacterial diazotrophs (8, 60) but lower than the recent report of archaeal diazotrophy at 92°C (40). These sequences are likely bacterial as phylogenetic analysis placed them within the Alpha- and Betaproteobacteria of cluster I and the anaerobic bacteria of cluster III, which also contains functional enzymes from known bacterial diazotrophs. Although nifH sequences detected in the environment are not always expressed in situ (72), the presence of these sequences suggests high-temperature bacterial diazotrophy is worthy of further investigation.

The aclB gene was detected in every sample by PCR, and all 10 phylotypes recovered grouped with Sulfurihydrogenibium. The phylogenetic reconstruction of the aclB sequences is consistent with the recently proposed evolution of the rTCA cycle. Initially, the rTCA cycle was thought to be operational in all species of the Aquificales as acl genes had been amplified from the Hydrogenothermaceae family (22) and activity had been demonstrated in the Aquificaceae family (7, 56). However, the Aquificaceae have recently been shown to utilize two enzymes, citryl coenzyme A synthetase and citryl coenzyme A lyase, to cleave citrate to oxaloacetate in place of ATP citrate lyase (32). Therefore, it is not surprising that the phylogeny and quantification of the aclB gene point to Sulfurihydrogenibium as the source of these sequences. The derived positions of these sequences in relation to Epsilonproteobacteria sequences is consistent with another phylogenetic reconstruction (11) and the proposed acquisition of the acl gene by lateral gene transfer (32). The overall topology of the aclB tree did not change regardless of whether C. limicola or M. musculus was used as the outgroup, even though prokaryotic and mammalian enzymes function in different pathways and have separate evolutionary histories (4, 33).

We did not detect the cbbM gene, which encodes the RuBisCO (ribulose 1,5-bisphosphate carboxylase/oxygenase) form predominantly used by anaerobic bacteria, in any samples. Although the primers we used may not amplify all cbbM genes (20), this result is supported by the lack of visible photosynthetic pigments at the spring and the absence of 16S rRNA gene sequences from photosynthetic organisms in the clone libraries. Previous studies have shown that the Calvin cycle contributes little to bacterial biomass in other Aquificales-dominated springs (W. Zhao, C. S. Romanek, E. A. Burgess, J. Wiegel, G. Mills, C. L. Zhang, presented at the American Geophysical Union Fall Meeting, San Francisco, CA, 11 to 15 December 2006) and that Calvin cycle genes are less abundant than rTCA cycle genes in hydrothermal vents (11). Though all of these environments approach the temperature limit for photosynthesis (75°C) (54), competitive exclusion of photosynthetic organisms may explain the lack of Calvin cycle genes.

Quantification of Aquificales phylotypes and the aclB gene throughout the spring.
Sulfurihydrogenibium and Thermocrinis have been found as dominant members of high-temperature, near-neutral springs worldwide, but they are rarely found together in the same spring (52); so the high numbers of both species throughout Coffee Pots is remarkable. Given the low carbonate in Coffee Pots spring, it is interesting that Thermocrinis was present in every sample because in culture its potential rTCA cycle enzyme activities are among the lowest measured (32). However, Thermocrinis can gain energy and carbon from formate oxidation and feed the CO₂ produced into the rTCA cycle (28, 32), allowing carbon to be metabolized heterotrophically and autotrophically simultaneously. Sulfurihydrogenibium species can use a variety of organic molecules (other than formate) as carbon sources (45), but they gain energy only from inorganic compounds. Although we did not test for metabolic activities, we believe this exploitation of different energy and carbon sources is a factor in the distribution of these species that is worthy of further investigation. Quantification of Thermocrinis and Sulfurihydrogenibium 16S rRNA genes showed that they each had variable populations throughout Coffee Pots. We suspect this can be partially accounted for by temperature as Thermocrinis species have a wider temperature growth range and higher optimal growth temperature than any Sulfurihydrogenibium species. However, the F_ST values for the aclB gene showed that the sequences were significantly different at every sampling point and are on the same order of magnitude for Sulfurihydrogenibium 16S rRNA gene sequences found throughout Yellowstone (63). This level of divergence in a single hot spring suggests that Sulfurihydrogenibium organisms may not be phenotypically or genotypically identical throughout Coffee Pots.

A unified description of microbial spatial patterns has been difficult to construct, especially for thermophilic bacteria and archaea. Some studies have found evidence of locally adapted thermophile populations but have been unable to link their distribution to temperature, alkalinity, or chemical composition (47, 71). Similar problems have arisen in attempts to correlate metabolic capabilities of individual microbes or entire ecosystems to chemical and physical parameters even though many thermophiles require inorganic substrates for energy generation. We have shown that the energy available for different metabolic processes varies greatly in the water overlying just one sample. However, the small size and limited mobility of prokaryotes make it likely that their distribution is affected on an even smaller scale (micrometer to centimeter), especially when communities are fixed in place through filament formation. In this study, we found evidence of significantly different aclB sequences throughout the thermal transect that correspond to spatial variation in the Sulfurihydrogenibium population, which is likely a function of variable microscale conditions. Identifying locally adapted ecotypes and evaluating the genetic and physical parameters that explain their distribution could advance further metabolic characterization of the Aquificales.

 
Geochemical analyses were conducted by W. P. Shanks III and D. K. Nordstrom of the USGS. We appreciate helpful discussions with E. Shock related to thermodynamic calculations performed in the study, as well as insightful and constructive comments from three anonymous reviewers. Brenda Theising, Kimberley Delehaunty, William Courtney, and Michelle O'Laughlin of the Washington University Genome Sequencing Center performed a portion of the sequencing on the project. The Yellowstone Center for Resources (National Park Service) provided inestimable logistical support for the fieldwork associated with this project.

This work was supported by a Biotic Surveys and Inventories grant from the National Science Foundation (02-06773) to C. D. Takacs-Vesbach and by an Initiatives for Minority Student Development grant (GM60201) to M. Werner-Washburne from the National Institute of General Medical Sciences, which provided additional support to J. R. Hall. O. Jackson-Weaver was partially funded by an American Society for Microbiology Undergraduate Research Fellowship. Technical support for sequencing and qPCR was provided by the University of New Mexico's Molecular Biology Facility, which is supported by NIH grant 1P20RR18754 from the Institute Development Award Program of the National Center for Research Resources.

 
* Corresponding author. Mailing address: Department of Biology, University of New Mexico, 167 Castetter Hall, MSC03-2020 1, University of New Mexico, Albuquerque, NM 87131. Phone: (505) 277-3418. Fax: (505) 277-0304. E-mail: cvesbach{at}unm.edu

Published ahead of print on 6 June 2008.

Supplemental material for this article may be found at http://aem.asm.org/.

Present address: Department of Cell Biology and Physiology, University of New Mexico, 232 Biomedical Research Facility, MSC08-4750 1, University of New Mexico, Albuquerque, NM 87131.

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Applied and Environmental Microbiology, August 2008, p. 4910-4922, Vol. 74, No. 15
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