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The characterization and quantification of methanotrophic bacterial populations in constructed wetland sediments using PCR targeting 16S rRNA gene fragments
Applied Soil Ecology 35 (2007) 648–659 www.elsevier.com/locate/apsoil
The characterization and quantification of methanotrophic bacterial populations in constructed wetland sediments using PCR targeting 16S rRNA gene fragments Todd D. DeJournett, William A. Arnold, Timothy M. LaPara * University of Minnesota, Department of Civil Engineering, 500 Pillsbury Drive SE, Minneapolis, MN 55455-0116, United States Received 21 February 2005; received in revised form 16 August 2006; accepted 5 September 2006
Abstract Three mesocosms were studied to evaluate the effect of wetland plants on the methanotrophic bacterial populations in the sediments of a full-scale constructed wetland. Cores were collected from two vegetated mesocosms and one unvegetated mesocosm from fall 2002 through summer 2003. Competitive quantitative PCR revealed no significant differences in the quantities of either Type I or Type II methanotrophic bacteria between the vegetated and unvegetated mesocosms. Type I methanotroph-biased nested PCR-DGGE resulted in the detection of 23 different populations related to Methylococcus, Methylomonas, Methylobacter, Methylocaldum, and Methylosarcina spp. Type II methanotroph-biased nested PCR-DGGE resulted in the detection of 5 different populations, more than 90% of which were related to previously uncultivated Type II methanotrophs. While wetland vegetation did not affect the structure of either the Type I or Type II methanotrophic communities, the Type I methanotrophic community structure was observed to vary seasonally. This work suggests that wetland plants neither enhanced nor adversely affected the size or structure of methanotrophic communities in our constructed wetland. Substantial quantities of both Type I and Type II methanotrophic populations were detected in both planted and unplanted mesocosms, suggesting that the constructed wetland had substantial potential for xenobiotic bioremediation whether or not plants were present. # 2006 Elsevier B.V. All rights reserved. Keywords: Community analysis; DGGE; Methane oxidation; PCR; Phytoremediation
1. Introduction Constructed wetlands have been used in numerous engineering applications, including the treatment of municipal wastewater (Hammer, 1991; Kadlec and Knight, 1996; Tchobanoglous et al., 2003), agricultural wastes (Hammer, 1991; Ibekwe et al., 2003), landfill leachate (Bulc et al., 1997; Martin and Moshiri, 1994), and acid mine drainage (Karathanasis and Johnson, 2003;
* Corresponding author. Tel.: +1 612 624 6028; fax: +1 612 626 7750. E-mail address: [email protected] (T.M. LaPara). 0929-1393/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.apsoil.2006.09.006
Sikora et al., 2000). Constructed wetlands may also be effective alternatives for remediating sites contaminated with xenobiotic compounds. Some of the potential degradation mechanisms for xenobiotic compounds in wetlands include degradation by bacteria (Lorah and Olsen, 1999; Lorah et al., 2001; Lorah and Voytek, 2004), volatilization through plant aerenchyma tissue (Beckett et al., 2001; Constable et al., 1992), and vascular uptake in the plant transpiration stream (Ma et al., 2004; Burken and Schnoor, 1998; Schnoor et al., 1995). Another mechanism by which constructed wetlands could enhance the biodegradation of xenobiotic compounds is by stimulating the growth of methanotrophic bacteria. The wetland subsurface is typically rich in
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decomposing organic material, resulting in the production of methane. The rhizosphere of emergent wetland plants, therefore, seemingly provides a favorable environment for the growth of methanotrophic bacteria, which can utilize oxygen that diffuses from the roots of wetland plants to catabolize methane from the surrounding anoxic sediment (King, 1994, 1996). The presence of methanotrophic bacteria could enhance the biodegradation of xenobiotic pollutants because they harbor methane monooxygenase (MMO) enzymes that cometabolically oxidize numerous pollutants (Broholm et al., 1992; Chang and Alvarez-Cohen, 1996; Dolan and McCarty, 1995). Methanotrophic bacteria are categorized into three different groups based on the structures of their internal membranes as well as their carbon assimilation pathways (Hanson and Hanson, 1996). Type I methanotrophs produce a membrane-bound (particulate) methane monooxygenase enzyme (pMMO), cluster phylogenetically with the Gammaproteobacteria, and are thought to proliferate under high-oxygen, lowmethane conditions (e.g., in the water columns of lakes and in the upper portion of lake sediment) (Auman and Lidstrom, 2002; Costello et al., 2002; Hanson and Hanson, 1996). Type II methanotrophs produce a pMMO enzyme, but can also produce a more reactive cytoplasmic (soluble) methane monooxygenase enzyme (sMMO) under copper-limiting conditions (Hanson and Hanson, 1996). Type II methanotrophs cluster with the Alphaproteobacteria and proliferate under high methane-to-oxygen ratios (e.g., on the roots of wetland plants) (Hanson and Hanson, 1996). Type X methanotrophs possess characteristics of both Type I and Type II methanotrophs, and cluster phylogenetically with the Gammaproteobacteria (Hanson and Hanson, 1996). The objective of this study was to test the hypothesis that wetland vegetation would stimulate the growth of methanotrophic bacteria. If the present study supported this hypothesis, then the next step in our research would be to discern whether these methanotrophic bacteria improved the potential of constructed wetlands to remediate chlorinated solvents. Previous researchers have demonstrated methanotrophy in association with giant bur reed (Sparganium eurycarpum) roots (King, 1994, 1996) as well as the existence of diverse methanotrophic communities on the roots of submerged rice plants (Horz et al., 2001). A better understanding of the effect of wetland vegetation on methanotrophic populations, therefore, may enhance the applicability of constructed wetlands as protective measures for surface water quality. Quantitative
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competitive polymerase chain reaction (cPCR) was used to quantify the populations of Type I and Type II methanotrophs in sediment samples and a methanotroph-biased nested PCR-DGGE approach was used to examine the structure of the methanotrophic bacterial communities. 2. Materials and methods 2.1. Site description Samples were collected from a constructed wetland located near Lake Minnetonka in Mound, Minnesota. The wetland has a surface area of 0.13 ha and was constructed within a dredged channel. In December 2000, the channel and surrounding areas were re-graded and planted with wetland vegetation. The wetland consists of an open channel, an emergent wetland, and a wet prairie. Wetland hydrology was affected by both surface water elevation in Lake Minnetonka and by groundwater discharge. Local groundwater hydrology was characterized by an upward gradient in the vicinity of the wetland. Sediments were 60% sand, 20% silt, and 20% clay, with slight variations with depth. Soil pH was 7.8, no discernible variation in depth. The fraction of organic matter in the sediments (determined via loss on ignition) was 3–5% from 0 to 45 cm depth. At 60 cm depth, it was 9.5%. Water levels in the mesocosms varied from 45 cm above (spring and early summer) to 30 cm below (late summer and fall) the ground surface. Three 1.6 m 1.6 m 0.8 m deep mesocosm cells were installed in the wetland at the time of construction. The mesocosms consisted of plywood walls embedded in the wetland sediment and are open on the bottom to allow vertical discharge of groundwater. One mesocosm (Mesocosm U) was maintained in an unvegetated condition while the other two (Mesocosm V1; Mesocosm V2) were planted with a mixture of cattail (Typhus latifolia), giant bur-reed (S. eurycarpum), bulrush (Scirpus validus), and bottlebrush sedge (Carex comosd) (Fig. 1). 2.2. Pore water analyses Each mesocosm cell was instrumented with four multi-level sampling arrays (MLSAs). Each MLSA consisted of stainless steel microwells embedded in the sediment at 15-cm intervals to a depth of 75 cm. Sampling was conducted by drawing pore water through Teflon-lined tubing using a glass, gas-tight syringe and a three-way valve.
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Minnesota. The pipe was cut longitudinally and sediment samples (2 g wet weight) were collected at 15-cm intervals along the length of the core. Sediment samples were stored at 20 8C until needed for bacterial community analysis. Additional sediment analyses were performed by the Research Analytical Laboratory (Department of Soil, Water, and Climate; University of Minnesota). 2.4. DNA extraction
Fig. 1. Photograph of the unvegetated (Mesocosm U; left) and vegetated (Mesocosm V1; right) mesocosms sampled in this study. Another vegetated mesocosm (Mesocosm V2; not shown) was also analyzed.
Pore water samples were transferred to 10 mL (nominal volume) headspace vials. Headspace samples (100 mL) were analyzed for methane using a ThermoQuest Trace GC gas chromatograph equipped with a HS2000 headspace autosampler, a GS Gas-Pro column, and a flame ionization detector. Methane concentrations were compared to a calibration curve of equivalent methane concentrations ranging from 0 to 3 mg/L. Sulfate and dissolved oxygen concentrations in pore water samples were also quantified using CHEMetrics colorimetric test kits (Chemetrics Inc.; Calverton, VA). 2.3. Sediment sampling Sediment cores were collected from the mesocosms in November 2002, April 2003, and June 2003. Cores were collected by driving a 2-in. PVC pipe into the sediment to a depth of at least 1.5 m. The pipe was filled with water, sealed with a butyl rubber cap, and pulled from sediment. The bottom end of the pipe was then capped for immediate transport to the University of
Total genomic DNA was extracted from the sediment samples (0.5 g wet weight) using a bead-beater to enhance cell lysis. Genomic DNA was then purified using the FastDNA Spin Kit for soil (Qbiogene) per manufacturer’s instructions. Total genomic DNA was quantified by staining with Hoechst 33258 dye using a TD-700 fluorometer (Turner Designs; Sunnyvale, CA) using calf thymus DNA as a standard. 2.5. Polymerase chain reaction All PCR reactions were performed using a PTC 100 thermal cycler (MJ Research Inc.; Watertown, MA). PCR reactions contained (volume = 50 mL): 1 PCR buffer (Promega; Madison, WI), 2% bovine serum albumin (BSA), 4 nmol deoxynucleoside triphosphates (each), 25 pmol of forward and reverse primers, 1.25 units of Taq polymerase (Promega), and 1 ng of template DNA. PCR reactions targeting the V3 region of the 16S rRNA gene for all Bacteria were conducted using primers 338f and 518r (Table 1). The PCR protocol included a 5 min initial denaturation at 94 8C, 30 cycles of 92 8C for 30 s, 55 8C for 30 s, and 72 8C for 30 s, and a final extension for 10 min at 72 8C. PCR reactions targeting 16S rRNA gene fragments of Type I methanotrophs were conducted using primers
Table 1 PCR primer sequences used in this study Name
Target
Primer sequences (50 ! 30 )
Reference
8f 338f 518r MethT1dF Meth T1bR MethT2R MethT1dF-cp1 8f-cp3
All Bacteria All Bacteria All Bacteria Type I Methanotrophs Type I Methanotrophs Type II Methanotrophs M. methanica ATCC 51626 M. trichosporium ATCC 49242
AGAGTTTGATCCTGGCTCAG ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGCTGG CCTTCGGGMGCYGACGAGT GATTCYMTSATGTCAAGG CATCTCTGRCSAYCATACCGG ccttcggmgcygacgagtGCGCTAACAGATGAGCCT agagtttgatcctggctcagCCTTCGGTTCGGAATAAC
Edwards et al. (1989) Lane (1991) Muyzer et al. (1993) Wise et al. (1999) Wise et al. (1999) Wise et al. (1999) This Work This work
The portions of primer sequences shown in lower case are other primers used as a tail to generate a competitor for quantitative PCR. All primers target gene fragments that code for 16S rRNA.
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MethTldF and MethTlbR (Table 1). Reaction conditions were (Wise et al., 1999): initial denaturing at 94 8C for 2 min 30 s, followed by: 10 cycles of 94 8C for 30 s, 56 8C for 45 s, and 72 8C for 1 min; 10 cycles of 94 8C for 30 s, 56 8C for 1 min, and 72 8C for 1 min 30 s; and 18 cycles of 94 8C for 30 s, 56 8C for 1 min 15 s, and 72 8C for 2 min 15 s, and final extension at 72 8C for 7 min 30 s. PCR reactions targeting a 16S rRNA gene fragments of Type II methanotrophs were conducted using primers 8f and MethT2R (Table 1). The following touchdown PCR protocol was used: initial denaturing at 94 8C for 2 min 30 s followed by 17 cycles of denaturing at 94 8C for 30 s, annealing at 65 8C for 1 min with a decrease of 0.5 8C per cycle, and extension at 72 8C for 1 min 30 s. This was followed by 23 cycles of 94 8C for 30 s, 57 8C for 1 min 15 s, and 72 8C for 2 min 15 s, and a final extension of 72 8C for 7 min 30 s. Nested PCR was performed by first targeting 16S rRNA genes of either Type I or Type II methanotrophs as previously described. These products were then diluted 101- to 102-fold and used as template for PCR using primers 338f and 518r. Negative controls were included during all PCR reactions to check for background contamination; during nested PCR, a double-negative control was performed. 2.6. Denaturing gradient gel electrophoresis Denaturing gradient gel electrophoresis (DGGE) was performed using a D-Code apparatus (BioRad; Hercules, CA). Samples containing equal amounts of PCR amplicons were loaded onto 8% (w/v) polyacrylamide gels (37.5:1, acrylamide:bisacrylamide) in 0.5 TAE buffer (Sambrook et al., 1989) using a specific denaturing gradient (100% denaturant contains 7 M urea, 40% (v/v) formamide in 0.5 TAE). Electrophoresis was performed at 60 8C, initially at 20 V (15 min) and then at 200 V (180 min). Following electrophoresis, the gel was stained with SYBR Green I (Molecular Probes; Eugene, OR; diluted 1:5000 in 0.5 TAE). Gels were visualized on a UV transilluminator and photographed with a digital CCD camera (BioChemi System; UVP Inc.; Upland, CA). Photographs were enhanced for contrast and brightness using Adobe Photoshop v6.0. Specific PCR-DGGE bands were manually excised from the gel, suspended in 20-mL of sterile water and incubated overnight at room temperature. The PCRDGGE protocol then was repeated using this water as template, until only a single band was detectable. A final PCR step was performed without the GC-clamp attached to the forward primer. The resulting PCR
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products were purified using the Gene Clean II DNA purification system (QBiogene) prior to nucleotide sequence determination. 2.7. Competitive quantitative PCR of Type I and Type II methanotrophs Competitor templates were synthesized via PCR amplification of partial 16S rRNA genes using primer sets MethTldF/MethTlbR (Type I) and 8f/MethT2R (Type II) from bacterial isolates known to contain each gene (Type I: Methylomonas methanica ATCC 51626; Type II: Methylosinus trichosporium ATCC 49242). Based on the nucleotide sequences of these PCR products (data not shown), internal PCR primers (Table 1) were designed for each gene fragment with the original forward primer attached to the 50 -end. PCR with these primers resulted in a 112 nucleotide deletion from the Type I16S rRNA gene fragment and a 110 nucleotide deletion from the Type II 16S rRNA gene fragment. PCR amplicons of the synthesized competitor DNA were then ligated into the pGEM-T Easy cloning vector (Promega, Madison, WI) and transformed into competent E. coli DH5a (Sambrook et al., 1989). Plasmids were then extracted by the alkaline lysis procedure. DNA concentrations in the plasmid extracts were determined by staining with Hoescht dye 33258 and quantified on a TD-700 fluorometer using calf thymus as a standard. Type I and Type II methanotrophs were quantified by cPCR using primers MethTldF/MethTlbR (Type I) and 8f/MethT2R (Type II). For each sample (target), competitive PCR amplifications were performed on six mixtures containing a constant target DNA concentration and decreasing competitor copy numbers. PCR products were resolved on 2% (v/v) agarose gels and stained with ethidium bromide. Band intensities of target DNA and competitor DNA were quantified using LabWorks Image Acquisition software (UVP). The concentration of target gene was determined by computing the y-intercept of a linear regression of the log of the competitor copy number versus the log of the ratio of target to corrected competitor band intensity. Competitor band intensity was multiplied by a correction factor to account for the difference in size of the target and the competitor (Type I: 920/808; Type II: 950/840). 2.8. DNA sequencing Nucleotide sequences were determined at the Advanced Genetic Analysis Center at the University
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of Minnesota using an ABI 3100 Genetic Analyzer (Applied Biosystems; Foster City, CA). Nucleotide sequences were determined fully in both directions for each purified PCR-DGGE band using 338f and 518r as sequencing primers. Reported nucleotide sequences are the consensus of bi-directional sequencing and do not include the original PCR primer sequences. Nucleotide sequences were deposited in the GenBank database under accession numbers AY740742–AY740769. 2.9. Data analysis Phylogenetic trees were constructed using the Neighbor-joining method (Saito and Nei, 1987) using DNAMAN v. 4.1 software (Lynnon Biosoft; VaudreuilDorion, Que.). Reference sequences were obtained from the GenBank database (Benson et al., 2002) and were included in the phylogenetic trees for comparison. All nucleotide sequences were optimally aligned prior to tree construction (Thompson et al., 1994). Band patterns from PCR-DGGE fingerprints were analyzed using unweighted pair group method with arithmetic mean (UPGMA) (Sokal and Michener, 1958) using DNAMAN v. 4.1. Binary sequences were generated for individual fingerprints by determining the number and position of bands compared to the total number of band positions using Lab works software (UVP). Principal component analysis (PCA) was performed on nested PCR fingerprints and competitive PCR data using NTSYSpc software v. 2.11S (Applied Biostatistics, East Setauket, NY). Data were organized into an input matrix, which was then standardized and used to generate a matrix of correlation coefficients. Four principal components were then extracted from the correlation matrix. The standardized data were then projected onto the principal axes, plotted in two and three dimensions, and examined for clustering behavior. Methanotroph biomass densities were computed by dividing the gene copy number by the wet soil weight, computing the area beneath a plot of methanotroph biomass densities versus depth, and then dividing that area by the total depth of the core. 3. Results 3.1. Pore water analysis Substantial concentrations of methane were detected in the pore water during all sampling events (Fig. 2). Methane concentrations ranged from 0.5 to 2.5 mg/L, and either increased (<5-fold) or did not vary as a
Fig. 2. Methane concentration profiles in the wetland sediments of Mesocosm U (*), Mesocosm V1 (&), and Mesocosm V2 (~) during fall 2002 (A), spring 2003 (B), and summer 2003 (C).
function of depth. Methane concentrations were generally 2-fold higher in summer 2003 than either fall 2002 or spring 2003. Sulfate concentrations ranged from 30 to 70 mg/L with a peak about 30 cm below the ground surface. Dissolved oxygen concentrations ranged from 0.5 to 2 mg/L, with higher levels at shallow depths. There were no apparent differences between the vegetated and unvegetated mesocosms with respect to sulfate or dissolved oxygen concentrations. 3.2. Bacterial community analysis PCR-DGGE fingerprints of the total bacterial communities were complex during all sample events
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at all depths for both the vegetated and unvegetated mesocosms (data not shown). Visual inspection of the fingerprints suggested substantial variability in community structure as functions of depth and mesocosm type. Statistical analysis using the UPGMA algorithm demonstrated that community fingerprints gradually varied as a function of depth within each mesocosm (data not shown). Pairwise comparison between two different mesocosms demonstrated that community fingerprints from Mesocosm U clustered separately from either Mesocosm V1 or Mesocosm V2. Mesocosms V1 and V2, however, also clustered separately from each other. 3.3. Quantification of methanotrophic bacterial populations Ribosomal RNA gene fragments of putative Type I and Type II methanotrophic bacterial populations were detected at all depths in all three mesocosms during all
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three sampling events (Fig. 3). The quantities of Type I and Type II methanotrophs ranged from 106 to 1010 copy numbers per gram of soil, with no clear trend as a function of depth or season. The average methanotroph concentrations in the vegetated mesocosms were 4.8 108 (S.D. = 4.2 108; n = 6) and 1.4 109 (S.D. = 1.1 109; n = 6) gene copies per gram wet soil for Type I and Type II methanotrophs, respectively. In the unvegetated mesocosm, average methanotroph concentrations were 3.9 108 (S.D. = 1.5 108; n = 3) and 4.1 109 (S.D. = 3.6 109; n = 3) gene copies per gram wet soil for Type I and Type II methanotrophs, respectively. Single-factor ANOVA analysis revealed no statistically significant difference in methanotroph populations between vegetated and unvegetated mesocosms (P = 0.74 and 0.12 for Type I and Type II, respectively). Principal component analysis confirmed the absence of any trends in cPCR data with respect to depth, time, or the presence of vegetation.
Fig. 3. Population profiles for Type I and Type II methanotrophs in the wetland sediments of Mesocosms U, V1, and V2 during fall 2002, spring 2003, and summer 2003. Solid symbols represent Type I methanotrophs and open symbols represent Type II methanotrophs.
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3.4. Methanotroph-biased nested PCR-DGGE Nested PCR-DGGE was used to examine the community structure of both Type I (Fig. 4) and Type II (Fig. 5) methanotrophic bacterial communities. Similar band patterns were observed in the vegetated and unvegetated mesocosms. The Type I methanotrophic community structure typically varied with depth, although no clear trends were observed with respect to shifts from one dominant population to another. The structure of the Type II methanotrophic community changed very little over time, with two bands dominating nearly all the mesocosm samples with very few minor bands. There were no apparent differences in community structure between vegetated and unvegetated mesocosms or as a function of depth. More than 70 bands were excised from denaturing gels containing 16S rRNA gene fragments of Type I methanotrophic bacteria and sequenced, revealing 23 different populations (Fig. 6). The detected populations were related to several methanotrophic genera, including Methylobacter, Methylomonas, Methylomicrobium, Methylocaldum, and Methylosarcina. Methylobacterlike and Methylomonas-like organisms were the most prevalent, being detected in all three mesocosms throughout the study period. Methylosarcina-like organisms were detected almost exclusively in shallowest sediment samples in all three mesocosms throughout the study. Methylocaldum-like organisms were detected in Mesocosms U and V1 during summer and fall, but not in spring. Twenty-nine different bands were excised and sequenced from denaturing gels containing 16S rRNA gene fragments from Type II methanotrophic bacteria, revealing four different Type II methanotrophs and one population that was not closely related to any known methanotrophic population (band 5, Fig. 5). The two populations most commonly detected were two Type II methanotroph-like organisms previously detected without cultivation (Fig. 7). A minor band (band 2, Fig. 5) representing a Methylocystis-like organism was detected in the Mesocosm U during fall 2002. Principal component analysis of nested PCR fingerprints revealed seasonal variations in Type I methanotroph community structure (Fig. 8). The seasonal clustering behavior was strongly influenced Fig. 4. Fingerprints of Type I methanotrophic bacterial communities in wetland sediments as generated by nested PCR-DGGE (denaturing gradient: 35–55%) for fall 2002 (A), spring 2003 (B), and summer 2003 (C). Bands that have a letter designation were excised and subject to nucleotide determination.
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Fig. 6. Neighbor-joining tree depicting phylogenetic relationships of Type I methanotrophic populations detected in wetland sediments by nested PCR-DGGE. Bootstrap values are shown for nodes with > 50% support of 10,000 replicates. The scale bar indicates an estimated change of 5%.
by the presence of population Y (Methylocaldum sp.) in fall 2002, population N and Q (Methylobacter sp.) in spring 2003, and populations S (Methylomonas sp.) and P (Methylobacter sp.) in summer 2003. No trends with respect to depth or presence of vegetation were observed, however. Type II community structure exhibited no trends with respect to time, depth, or the presence of vegetation (data not shown). Fig. 5. Fingerprints of Type II methanotrophic bacterial communities in wetland sediments as generated by nested PCR-DGGE (denaturing gradient: 30–50%) for fall 2002 (A), spring 2003 (B), and summer 2003 (C). Bands that have a number designation were excised and subject to nucleotide determination.
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Fig. 7. Neighbor-joining tree depicting phylogenetic relationships of Type II methanotrophic populations detected in wetland sediments by nested PCR-DGGE. Bootstrap values are shown for nodes with > 50% support of 10,000 replicates. The scale bar indicates an estimated change of 5%.
Fig. 8. Principal component analysis of nested PCR-DGGE fingerprints of Type I and Type II methanotrophic bacterial populations detected in wetland sediments. Each data point is labeled according to sample time (F = fall; SP = spring; SU = summer), the mesocosm from which the population was detected (U; V1; V2), and the sediment depth where the population was detected (0–90 cm). Principal components 1 and 2 capture 55% of the variability in the data.
4. Discussion Understanding the effect of wetland plants on the ecology of methanotrophic bacteria in wetland sediment is important for evaluating the feasibility of constructed wetlands as a possible remediation technology for environmental contamination with xenobio-
tic compounds. In our study, the presence of plants did not significantly affect the quantity or structure of either Type I or Type II methanotrophic populations growing in a constructed wetland. This result refutes our original hypothesis and is substantially different from previous studies in which higher numbers of methanotrophic bacteria were detected by cultivation-dependent
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approaches in the rhizosphere of rice plants relative to bulk soil (Gilbert and Frenzel, 1998; van Bodegom et al., 2001). From a practical perspective, this suggests that the presence of wetland plants neither enhanced nor adversely affected the potential of the wetland to oxidize xenobiotic pollutants (e.g., chlorinated organic compounds) by increasing the population densities of methanotrophic bacteria harboring non-specific methane monooxygenase enzymes. Very high methanotrophic population densities were detected in all three mesocosms, perhaps indicating that oxygen transfer (or release of other plant-associated nutrients) did not limit growth of methanotrophic bacteria in our constructed wetland. Additionally, the 2-year-old wetland has not yet reached maturity, and thus our results may not reflect the long-term impact of mature wetland vegetation on methanotrophic bacterial communities. Previous studies that used cultivation-dependent techniques to investigate methanotrophic bacterial community structure have detected primarily Type II populations (mainly Methylocystis spp.) in wetland sediments (Gilbert and Frenzel, 1998; Horz et al., 2002; van Bodegom et al., 2001) and on wetland plant roots (Calhoun and King, 1998). In contrast, we detected approximately equal amounts of Type I and Type II methanotrophs in the wetland sediment. Other researchers investigating methanotroph community structure via cultivation-independent techniques have also detected both Type I and Type II methanotrophs in wetland sediments (Horz et al., 2001; Svenning et al., 2003). This suggests that cultivation-based approaches may underestimate the abundance of methanotrophic bacteria in wetland sediments. In contrast, cultivationindependent methods could overestimate methanotrophic bacteria abundance due to mis-priming, the formation of heteroduplexes, and other artifacts that occur during PCR (von Witzengerode et al., 1997). Although Type II methanotrophs are thought to be more competitive in high-methane low-oxygen environments such as wetland soils and wetland plant roots (Hanson and Hanson, 1996), the results of this study suggest that Type I methanotrophs were able to compete effectively with Type II methanotrophs in the wetland mesocosms. The wetland studied in this work had fluctuating water levels throughout the period of study, with unsaturated conditions periodically occurring in the sediment profile (data not shown). Henckel et al. (2001) observed that Type II methanotrophs dominated a flooded rice field, but that Type I methanotrophs proliferated as the field was drained. We hypothesize, therefore, that periodic fluctuations between saturated and unsaturated conditions helped sustain approxi-
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mately equal quantities of Type I and Type II methanotrophs. This study is the first use of cultivation-independent techniques to study the effect of wetland vegetation on the quantity and structure of methanotrophic communities in constructed wetland sediments. Cultivationdependent techniques are well known to be biased towards a small fraction of bacteria capable of growth on laboratory media (Amann et al., 1995). Cultivationindependent approaches, however, can also be biased, when targeted organisms are not detected (e.g., here-tofore unknown methanotrophic bacteria that do not phylogenetically cluster with Type I or Type II populations) or when non-targeted organisms fail to be excluded. The methanotroph-biased PCR approaches used in this study almost exclusively detected Type I and Type II methanotrophic bacteria. In this study, a novel assay was developed to enumerate methanotrophic bacterial populations by quantitative PCR. A specific concern with this new assay is that no statistically significant differences were detected between the planted and unplanted mesocosms, perhaps suggesting that our results may have been an artifact of the technique. Numerous factors could have interfered with our assay and affected our results. Humic acids found in soils and sediments can be co-extracted with genomic DNA and interfere with PCR (Tsai and Olson, 1992; Wilson, 1997). Furthermore, a substantial fraction of the variability of quantitative PCR stems from a lack of reproducibility in the DNA extraction step (Dionisi et al., 2003; Mumy and Findlay, 2004). Even with these possible concerns, however, we detected substantial numbers of methanotrophic bacteria (>106 gene copies per gram) in virtually all sediment samples. In addition, the other analyses of the site support the lack of statistically significant differences between the planted and unplanted mesocosms, specifically with respect to the methane concentration profiles (Fig. 2), the total bacterial community structure as detected by PCR-DGGE (data not shown), or the types of methanotrophic bacterial populations that were detected (Figs. 4 and 5). Although multiple soil cores were collected from each mesocosm over the course of the study, the lack of replication of the unvegetated mesocosm limits our ability to draw conclusions with statistical certainty. Statistical limitations aside, this work provides important information regarding methanotroph population dynamics in a newly-constructed wetland. Our results suggest that the culture-dependent techniques previously used to characterize the size and structure of wetland plant-associated methanotrophic bacterial
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communities may omit important groups of methanotrophic bacteria. The results of this work illustrate the need to further investigate methanotrophic bacterial communities in wetland ecosystems using cultivationindependent techniques. Acknowledgements This work was financially supported by a Graduate Assistantship in Areas of National Needs Fellowship from the US Department of Education to TDJ and Grant 2004MN48B from the United States Geological Survey through the Minnesota Water Resources Center. We thank Steve Nold for providing reference bacterial strains and Don Richard for providing access to the field site. References Amann, R.I., Ludwig, W., Schleifer, K.-H., 1995. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59, 143–169. Auman, A., Lidstrom, M., 2002. Analysis of sMMO-containing Type I methanotrophs in Lake Washington sediment. Environ. Microbiol. 4, 517–524. Beckett, P.M., Armstrong, W., Armstrong, J., 2001. Mathematical modelling of methane transport by Phragmites: the potential for diffusion within the roots and rhizosphere. Aquat. Bot. 69, 293– 312. Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Rapp, B.A., Wheeler, D.L., 2002. GenBank. Nucleic Acids Res. 30, 17– 20. Broholm, K., Christensen, T.H., Jensen, B.K., 1992. Modeling TCE degradation by a mixed culture of methane-oxidizing bacteria. Water Res. 9, 1117–1185. Bulc, T., Vrhovsek, D., Kukanja, V., 1997. The use of constructed wetland for landfill leachate treatment. Water Sci. Technol. 35, 301–306. Burken, J.G., Schnoor, J.L., 1998. Predictive relationships for uptake of organic contaminants by hybrid poplar trees. Environ. Sci. Technol. 32, 3379–3385. Calhoun, A., King, G.M., 1998. Characterization of root-associated methanotrophs from three freshwater macrophytes: Ponederia cor data, Sparganium eurycarpum, and Sagittaria latifolia. Appl. Environ. Microbiol. 64, 1099–1105. Chang, H., Alvarez-Cohen, L., 1996. Biodegradation of individual and multiple chlorinated aliphatic hydrocarbons by methane-oxidizing cultures. Appl. Environ. Microbiol. 62, 3371–3377. Constable, J.V.H., Grace, J.B., Longstreth, D.J., 1992. High carbon dioxide concentrations in aerenchyma of Typha latifolia. Am. J. Bot. 79, 415–418. Costello, A.M., Auman, A.J., Macalady, J.L., Scow, K.M., Lidstrom, M.E., 2002. Estimation of methanotroph abundance in a freshwater lake sediment. Environ. Microbiol. 4, 433–450. Dolan, M.E., McCarty, P.L., 1995. Methanotrophic chloroethene transformation capacities and 1,1-dichloroethene transformation product toxicity. Environ. Sci. Technol. 29, 2741–2747. Dionisi, H.M., Harms, G., Layton, A.C., Gregory, I.R., Parker, J., Hawkins, S.A., Robinson, K.G., Sayler, G.S., 2003. Power ana-
lysis for real-time PCR quantification of genes in activated sludge and analysis of the variability introduced by DNA extraction. Appl. Environ. Microbiol. 69, 6597–6604. Edwards, U., Rogall, T., Blocker, H., Emde, M., Bottger, E.C., 1989. Isolation and direct complete nucleotide determination of entire genes. Characterization of a gene coding for 16S ribosomal RNA. Nucleic Acids Res. 17, 7843–7851. Gilbert, B., Frenzel, P., 1998. Rice roots and CH4 oxidation: the activity of bacteria, their distribution and the microenvironment. Soil Biol. Biochem. 30, 1903–1916. Hammer, D.A., 1991. Constructed Wetlands for Wastewater Treatment: Municipal, Industrial, and Agricultural. Lewis Publishers Inc., Chelsea, MI. Hanson, R.S., Hanson, T.E., 1996. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471. Henckel, T., Jackel, U., Conrad, R., 2001. Vertical distribution of the methanotrophic community after drainage of rice field soil. FEMS Microbiol. Ecol. 34, 279–291. Horz, H.P., Yimga, M.T., Liesack, W., 2001. Detection of memanotroph diversity on roots of submerged rice plants by molecular retrieval of pmoA, mmoX, mxaF, and 16S rRNA and ribosomal DNA, including pmoA-based terminal restriction fragment length polymorphism profiling. Appl. Environ. Microbiol. 67, 4177– 4185. Horz, H., Rghubanshi, A.S., Heyer, J., Kammann, C., Conrad, R., Dunfield, P.F., 2002. Activity and community structure of methane-oxidising bacteria in a wet meadow soil. FEMS Microbiol. Ecol. 41, 247–257. Ibekwe, A.M., Grieve, C.M., Lyon, S.R., 2003. Characterization of microbial communities and composition in constructed dairy wetland wastewater effluent. Appl. Environ. Microbiol. 69, 5060–5069. Kadlec, R.H., Knight, R.L., 1996. Treatment Wetlands. Lewis Publishers, Boca Raton. Karathanasis, A.D., Johnson, C.M., 2003. Metal removal potential by three aquatic plants in an acid mine drainage wetlands. Mine Water Environ. 22, 22–30. King, G.M., 1994. Associations of methanotrophs with the roots and rhizomes of aquatic vegetation. Appl. Environ. Microbiol. 60, 3220–3227. King, G.M., 1996. In situ analyses of methane oxidation associated with the roots and rhizomes of a bur reed, Sparganium eurycarpum, in a Maine wetland. Appl. Environ. Microbiol. 62, 4548– 4555. Lane, D.J., 1991. 16S/23S rRNA sequencing. In: Stackebrandt, E., Goodfellow, M. (Eds.), Nucleic Acid Techniques in Bacterial Systematics. John Wiley & Sons Ltd., West Sussex, United Kingdom, pp. 115–175. Lorah, M.M., Olsen, L.D., 1999. Degradation of 1,1,2,2-tetrachloroethane in a freshwater tidal wetland: field and laboratory evidence. Environ. Sci. Technol. 33, 227–234. Lorah, M.M., Olsen, L.D., Capne, D.G., Baker, J.E., 2001. Biodegradation of trichloroethylene and its anaerobic daughter products in freshwater wetland sediments. Bioremed. J. 5, 101–118. Lorah, M.M., Voytek, M.A., 2004. Degradation of 1,1,2,2-tetrachloroethane and accumulation of vinyl chloride in wetland sediment microcosms and in situ porewater: biochemical controls and associations with microbial communities. J. Contam. Hydrol. 70, 117–145. Ma, X., Richter, A.R., Albers, S., Burken, J.G., 2004. Phytoremediation of MTBE with hybrid poplar trees. Int. J. Phytoremed. 6, 157– 167.
T.D. DeJournett et al. / Applied Soil Ecology 35 (2007) 648–659 Martin, C.D., Moshiri, G.A., 1994. Nutrient reduction in an in-series constructed wetland system treating landfill leachate. Water Sci. Technol. 29, 267–272. Mumy, K.L., Findlay, R.H., 2004. Convenient determination of DNA extraction efficiency using an external DNA recovery standard and quantitative-competitive PCR. J. Microbiol. Methods 57, 259– 268. Muyzer, G., Dewaal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis—analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700. Saito, N., Nei, M., 1987. The neighbor-joining method: a new method for constructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. Sambrook, J., Fritsch, E.F., Maniatis, T., 1989. Molecular Cloning: A Laboratory Manual, second ed. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY. Schnoor, J.L., Licht, L.A., McCutcheon, S.C., Wolfe, N.L., Carreira, L.H., 1995. Pliytoremediation of organic and nutrient contaminants. Environ. Sci. Technol. 29, 318A–323A. Sikora, F.J., Behrends, L.L., Brodie, G.A., Taylor, H.N., 2000. Design criteria and required chemistry for removing manganese in acid mine drainage using subsurface flow wetlands. Water Environ. Res. 72, 536–544. Sokal, R.R., Michener, C.D., 1958. A statistical method for evaluating systematic relationships. Kansas Univ. Sci. Bull. 38, 1409–1438.
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Svenning, M.M., Wartiainen, I., Hestnes, A.G., Binnerup, S.J., 2003. Isolation of methane oxidising bacteria from soil by use of a soil substrate membrane system. FEMS Microbiol. Ecol. 44, 347–354. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering Treatment and Reuse, fourth ed. Metcalf and Eddy Inc., Boston, MA. Thompson, J.D., Higgins, D.G., Gibson, T.J., 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680. Tsai, Y., Olson, B.H., 1992. Rapid method for separation of bacterial DNA from humic substances for polymerase chain reaction. Appl. Environ. Microbiol. 58, 2292–2295. van Bodegom, P., Stams, F., Mollema, L., Boeke, S., Leffelaar, P., 2001. Methane oxidation and competition for oxygen in the rice rhizosphere. Appl. Environ. Microbiol. 67, 3586–3597. von Witzengerode, F., Go¨bel, U.B., Stackebrandt, E., 1997. Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol. Rev. 21, 213–229. Wilson, I.G., 1997. Inhibition and facilitation of nucleic acid amplification. Appl. Environ. Microbiol. 63, 3741–3751. Wise, M.G., McArthur, J.V., Shimkets, L.J., 1999. Methanotroph diversity in landfill soil: isolation of novel Type I and Type II methanotrophs whose presence was suggested by culture-independent 16S ribosomal DNA analysis. Appl. Environ. Microbiol. 65, 4887–4897.
The characterization and quantification of methanotrophic bacterial populations in constructed wetland sediments using PCR targeting 16S rRNA gene fragments Todd D. DeJournett, William A. Arnold, Timothy M. LaPara * University of Minnesota, Department of Civil Engineering, 500 Pillsbury Drive SE, Minneapolis, MN 55455-0116, United States Received 21 February 2005; received in revised form 16 August 2006; accepted 5 September 2006
Abstract Three mesocosms were studied to evaluate the effect of wetland plants on the methanotrophic bacterial populations in the sediments of a full-scale constructed wetland. Cores were collected from two vegetated mesocosms and one unvegetated mesocosm from fall 2002 through summer 2003. Competitive quantitative PCR revealed no significant differences in the quantities of either Type I or Type II methanotrophic bacteria between the vegetated and unvegetated mesocosms. Type I methanotroph-biased nested PCR-DGGE resulted in the detection of 23 different populations related to Methylococcus, Methylomonas, Methylobacter, Methylocaldum, and Methylosarcina spp. Type II methanotroph-biased nested PCR-DGGE resulted in the detection of 5 different populations, more than 90% of which were related to previously uncultivated Type II methanotrophs. While wetland vegetation did not affect the structure of either the Type I or Type II methanotrophic communities, the Type I methanotrophic community structure was observed to vary seasonally. This work suggests that wetland plants neither enhanced nor adversely affected the size or structure of methanotrophic communities in our constructed wetland. Substantial quantities of both Type I and Type II methanotrophic populations were detected in both planted and unplanted mesocosms, suggesting that the constructed wetland had substantial potential for xenobiotic bioremediation whether or not plants were present. # 2006 Elsevier B.V. All rights reserved. Keywords: Community analysis; DGGE; Methane oxidation; PCR; Phytoremediation
1. Introduction Constructed wetlands have been used in numerous engineering applications, including the treatment of municipal wastewater (Hammer, 1991; Kadlec and Knight, 1996; Tchobanoglous et al., 2003), agricultural wastes (Hammer, 1991; Ibekwe et al., 2003), landfill leachate (Bulc et al., 1997; Martin and Moshiri, 1994), and acid mine drainage (Karathanasis and Johnson, 2003;
* Corresponding author. Tel.: +1 612 624 6028; fax: +1 612 626 7750. E-mail address: [email protected] (T.M. LaPara). 0929-1393/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.apsoil.2006.09.006
Sikora et al., 2000). Constructed wetlands may also be effective alternatives for remediating sites contaminated with xenobiotic compounds. Some of the potential degradation mechanisms for xenobiotic compounds in wetlands include degradation by bacteria (Lorah and Olsen, 1999; Lorah et al., 2001; Lorah and Voytek, 2004), volatilization through plant aerenchyma tissue (Beckett et al., 2001; Constable et al., 1992), and vascular uptake in the plant transpiration stream (Ma et al., 2004; Burken and Schnoor, 1998; Schnoor et al., 1995). Another mechanism by which constructed wetlands could enhance the biodegradation of xenobiotic compounds is by stimulating the growth of methanotrophic bacteria. The wetland subsurface is typically rich in
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decomposing organic material, resulting in the production of methane. The rhizosphere of emergent wetland plants, therefore, seemingly provides a favorable environment for the growth of methanotrophic bacteria, which can utilize oxygen that diffuses from the roots of wetland plants to catabolize methane from the surrounding anoxic sediment (King, 1994, 1996). The presence of methanotrophic bacteria could enhance the biodegradation of xenobiotic pollutants because they harbor methane monooxygenase (MMO) enzymes that cometabolically oxidize numerous pollutants (Broholm et al., 1992; Chang and Alvarez-Cohen, 1996; Dolan and McCarty, 1995). Methanotrophic bacteria are categorized into three different groups based on the structures of their internal membranes as well as their carbon assimilation pathways (Hanson and Hanson, 1996). Type I methanotrophs produce a membrane-bound (particulate) methane monooxygenase enzyme (pMMO), cluster phylogenetically with the Gammaproteobacteria, and are thought to proliferate under high-oxygen, lowmethane conditions (e.g., in the water columns of lakes and in the upper portion of lake sediment) (Auman and Lidstrom, 2002; Costello et al., 2002; Hanson and Hanson, 1996). Type II methanotrophs produce a pMMO enzyme, but can also produce a more reactive cytoplasmic (soluble) methane monooxygenase enzyme (sMMO) under copper-limiting conditions (Hanson and Hanson, 1996). Type II methanotrophs cluster with the Alphaproteobacteria and proliferate under high methane-to-oxygen ratios (e.g., on the roots of wetland plants) (Hanson and Hanson, 1996). Type X methanotrophs possess characteristics of both Type I and Type II methanotrophs, and cluster phylogenetically with the Gammaproteobacteria (Hanson and Hanson, 1996). The objective of this study was to test the hypothesis that wetland vegetation would stimulate the growth of methanotrophic bacteria. If the present study supported this hypothesis, then the next step in our research would be to discern whether these methanotrophic bacteria improved the potential of constructed wetlands to remediate chlorinated solvents. Previous researchers have demonstrated methanotrophy in association with giant bur reed (Sparganium eurycarpum) roots (King, 1994, 1996) as well as the existence of diverse methanotrophic communities on the roots of submerged rice plants (Horz et al., 2001). A better understanding of the effect of wetland vegetation on methanotrophic populations, therefore, may enhance the applicability of constructed wetlands as protective measures for surface water quality. Quantitative
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competitive polymerase chain reaction (cPCR) was used to quantify the populations of Type I and Type II methanotrophs in sediment samples and a methanotroph-biased nested PCR-DGGE approach was used to examine the structure of the methanotrophic bacterial communities. 2. Materials and methods 2.1. Site description Samples were collected from a constructed wetland located near Lake Minnetonka in Mound, Minnesota. The wetland has a surface area of 0.13 ha and was constructed within a dredged channel. In December 2000, the channel and surrounding areas were re-graded and planted with wetland vegetation. The wetland consists of an open channel, an emergent wetland, and a wet prairie. Wetland hydrology was affected by both surface water elevation in Lake Minnetonka and by groundwater discharge. Local groundwater hydrology was characterized by an upward gradient in the vicinity of the wetland. Sediments were 60% sand, 20% silt, and 20% clay, with slight variations with depth. Soil pH was 7.8, no discernible variation in depth. The fraction of organic matter in the sediments (determined via loss on ignition) was 3–5% from 0 to 45 cm depth. At 60 cm depth, it was 9.5%. Water levels in the mesocosms varied from 45 cm above (spring and early summer) to 30 cm below (late summer and fall) the ground surface. Three 1.6 m 1.6 m 0.8 m deep mesocosm cells were installed in the wetland at the time of construction. The mesocosms consisted of plywood walls embedded in the wetland sediment and are open on the bottom to allow vertical discharge of groundwater. One mesocosm (Mesocosm U) was maintained in an unvegetated condition while the other two (Mesocosm V1; Mesocosm V2) were planted with a mixture of cattail (Typhus latifolia), giant bur-reed (S. eurycarpum), bulrush (Scirpus validus), and bottlebrush sedge (Carex comosd) (Fig. 1). 2.2. Pore water analyses Each mesocosm cell was instrumented with four multi-level sampling arrays (MLSAs). Each MLSA consisted of stainless steel microwells embedded in the sediment at 15-cm intervals to a depth of 75 cm. Sampling was conducted by drawing pore water through Teflon-lined tubing using a glass, gas-tight syringe and a three-way valve.
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Minnesota. The pipe was cut longitudinally and sediment samples (2 g wet weight) were collected at 15-cm intervals along the length of the core. Sediment samples were stored at 20 8C until needed for bacterial community analysis. Additional sediment analyses were performed by the Research Analytical Laboratory (Department of Soil, Water, and Climate; University of Minnesota). 2.4. DNA extraction
Fig. 1. Photograph of the unvegetated (Mesocosm U; left) and vegetated (Mesocosm V1; right) mesocosms sampled in this study. Another vegetated mesocosm (Mesocosm V2; not shown) was also analyzed.
Pore water samples were transferred to 10 mL (nominal volume) headspace vials. Headspace samples (100 mL) were analyzed for methane using a ThermoQuest Trace GC gas chromatograph equipped with a HS2000 headspace autosampler, a GS Gas-Pro column, and a flame ionization detector. Methane concentrations were compared to a calibration curve of equivalent methane concentrations ranging from 0 to 3 mg/L. Sulfate and dissolved oxygen concentrations in pore water samples were also quantified using CHEMetrics colorimetric test kits (Chemetrics Inc.; Calverton, VA). 2.3. Sediment sampling Sediment cores were collected from the mesocosms in November 2002, April 2003, and June 2003. Cores were collected by driving a 2-in. PVC pipe into the sediment to a depth of at least 1.5 m. The pipe was filled with water, sealed with a butyl rubber cap, and pulled from sediment. The bottom end of the pipe was then capped for immediate transport to the University of
Total genomic DNA was extracted from the sediment samples (0.5 g wet weight) using a bead-beater to enhance cell lysis. Genomic DNA was then purified using the FastDNA Spin Kit for soil (Qbiogene) per manufacturer’s instructions. Total genomic DNA was quantified by staining with Hoechst 33258 dye using a TD-700 fluorometer (Turner Designs; Sunnyvale, CA) using calf thymus DNA as a standard. 2.5. Polymerase chain reaction All PCR reactions were performed using a PTC 100 thermal cycler (MJ Research Inc.; Watertown, MA). PCR reactions contained (volume = 50 mL): 1 PCR buffer (Promega; Madison, WI), 2% bovine serum albumin (BSA), 4 nmol deoxynucleoside triphosphates (each), 25 pmol of forward and reverse primers, 1.25 units of Taq polymerase (Promega), and 1 ng of template DNA. PCR reactions targeting the V3 region of the 16S rRNA gene for all Bacteria were conducted using primers 338f and 518r (Table 1). The PCR protocol included a 5 min initial denaturation at 94 8C, 30 cycles of 92 8C for 30 s, 55 8C for 30 s, and 72 8C for 30 s, and a final extension for 10 min at 72 8C. PCR reactions targeting 16S rRNA gene fragments of Type I methanotrophs were conducted using primers
Table 1 PCR primer sequences used in this study Name
Target
Primer sequences (50 ! 30 )
Reference
8f 338f 518r MethT1dF Meth T1bR MethT2R MethT1dF-cp1 8f-cp3
All Bacteria All Bacteria All Bacteria Type I Methanotrophs Type I Methanotrophs Type II Methanotrophs M. methanica ATCC 51626 M. trichosporium ATCC 49242
AGAGTTTGATCCTGGCTCAG ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGCTGG CCTTCGGGMGCYGACGAGT GATTCYMTSATGTCAAGG CATCTCTGRCSAYCATACCGG ccttcggmgcygacgagtGCGCTAACAGATGAGCCT agagtttgatcctggctcagCCTTCGGTTCGGAATAAC
Edwards et al. (1989) Lane (1991) Muyzer et al. (1993) Wise et al. (1999) Wise et al. (1999) Wise et al. (1999) This Work This work
The portions of primer sequences shown in lower case are other primers used as a tail to generate a competitor for quantitative PCR. All primers target gene fragments that code for 16S rRNA.
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MethTldF and MethTlbR (Table 1). Reaction conditions were (Wise et al., 1999): initial denaturing at 94 8C for 2 min 30 s, followed by: 10 cycles of 94 8C for 30 s, 56 8C for 45 s, and 72 8C for 1 min; 10 cycles of 94 8C for 30 s, 56 8C for 1 min, and 72 8C for 1 min 30 s; and 18 cycles of 94 8C for 30 s, 56 8C for 1 min 15 s, and 72 8C for 2 min 15 s, and final extension at 72 8C for 7 min 30 s. PCR reactions targeting a 16S rRNA gene fragments of Type II methanotrophs were conducted using primers 8f and MethT2R (Table 1). The following touchdown PCR protocol was used: initial denaturing at 94 8C for 2 min 30 s followed by 17 cycles of denaturing at 94 8C for 30 s, annealing at 65 8C for 1 min with a decrease of 0.5 8C per cycle, and extension at 72 8C for 1 min 30 s. This was followed by 23 cycles of 94 8C for 30 s, 57 8C for 1 min 15 s, and 72 8C for 2 min 15 s, and a final extension of 72 8C for 7 min 30 s. Nested PCR was performed by first targeting 16S rRNA genes of either Type I or Type II methanotrophs as previously described. These products were then diluted 101- to 102-fold and used as template for PCR using primers 338f and 518r. Negative controls were included during all PCR reactions to check for background contamination; during nested PCR, a double-negative control was performed. 2.6. Denaturing gradient gel electrophoresis Denaturing gradient gel electrophoresis (DGGE) was performed using a D-Code apparatus (BioRad; Hercules, CA). Samples containing equal amounts of PCR amplicons were loaded onto 8% (w/v) polyacrylamide gels (37.5:1, acrylamide:bisacrylamide) in 0.5 TAE buffer (Sambrook et al., 1989) using a specific denaturing gradient (100% denaturant contains 7 M urea, 40% (v/v) formamide in 0.5 TAE). Electrophoresis was performed at 60 8C, initially at 20 V (15 min) and then at 200 V (180 min). Following electrophoresis, the gel was stained with SYBR Green I (Molecular Probes; Eugene, OR; diluted 1:5000 in 0.5 TAE). Gels were visualized on a UV transilluminator and photographed with a digital CCD camera (BioChemi System; UVP Inc.; Upland, CA). Photographs were enhanced for contrast and brightness using Adobe Photoshop v6.0. Specific PCR-DGGE bands were manually excised from the gel, suspended in 20-mL of sterile water and incubated overnight at room temperature. The PCRDGGE protocol then was repeated using this water as template, until only a single band was detectable. A final PCR step was performed without the GC-clamp attached to the forward primer. The resulting PCR
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products were purified using the Gene Clean II DNA purification system (QBiogene) prior to nucleotide sequence determination. 2.7. Competitive quantitative PCR of Type I and Type II methanotrophs Competitor templates were synthesized via PCR amplification of partial 16S rRNA genes using primer sets MethTldF/MethTlbR (Type I) and 8f/MethT2R (Type II) from bacterial isolates known to contain each gene (Type I: Methylomonas methanica ATCC 51626; Type II: Methylosinus trichosporium ATCC 49242). Based on the nucleotide sequences of these PCR products (data not shown), internal PCR primers (Table 1) were designed for each gene fragment with the original forward primer attached to the 50 -end. PCR with these primers resulted in a 112 nucleotide deletion from the Type I16S rRNA gene fragment and a 110 nucleotide deletion from the Type II 16S rRNA gene fragment. PCR amplicons of the synthesized competitor DNA were then ligated into the pGEM-T Easy cloning vector (Promega, Madison, WI) and transformed into competent E. coli DH5a (Sambrook et al., 1989). Plasmids were then extracted by the alkaline lysis procedure. DNA concentrations in the plasmid extracts were determined by staining with Hoescht dye 33258 and quantified on a TD-700 fluorometer using calf thymus as a standard. Type I and Type II methanotrophs were quantified by cPCR using primers MethTldF/MethTlbR (Type I) and 8f/MethT2R (Type II). For each sample (target), competitive PCR amplifications were performed on six mixtures containing a constant target DNA concentration and decreasing competitor copy numbers. PCR products were resolved on 2% (v/v) agarose gels and stained with ethidium bromide. Band intensities of target DNA and competitor DNA were quantified using LabWorks Image Acquisition software (UVP). The concentration of target gene was determined by computing the y-intercept of a linear regression of the log of the competitor copy number versus the log of the ratio of target to corrected competitor band intensity. Competitor band intensity was multiplied by a correction factor to account for the difference in size of the target and the competitor (Type I: 920/808; Type II: 950/840). 2.8. DNA sequencing Nucleotide sequences were determined at the Advanced Genetic Analysis Center at the University
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of Minnesota using an ABI 3100 Genetic Analyzer (Applied Biosystems; Foster City, CA). Nucleotide sequences were determined fully in both directions for each purified PCR-DGGE band using 338f and 518r as sequencing primers. Reported nucleotide sequences are the consensus of bi-directional sequencing and do not include the original PCR primer sequences. Nucleotide sequences were deposited in the GenBank database under accession numbers AY740742–AY740769. 2.9. Data analysis Phylogenetic trees were constructed using the Neighbor-joining method (Saito and Nei, 1987) using DNAMAN v. 4.1 software (Lynnon Biosoft; VaudreuilDorion, Que.). Reference sequences were obtained from the GenBank database (Benson et al., 2002) and were included in the phylogenetic trees for comparison. All nucleotide sequences were optimally aligned prior to tree construction (Thompson et al., 1994). Band patterns from PCR-DGGE fingerprints were analyzed using unweighted pair group method with arithmetic mean (UPGMA) (Sokal and Michener, 1958) using DNAMAN v. 4.1. Binary sequences were generated for individual fingerprints by determining the number and position of bands compared to the total number of band positions using Lab works software (UVP). Principal component analysis (PCA) was performed on nested PCR fingerprints and competitive PCR data using NTSYSpc software v. 2.11S (Applied Biostatistics, East Setauket, NY). Data were organized into an input matrix, which was then standardized and used to generate a matrix of correlation coefficients. Four principal components were then extracted from the correlation matrix. The standardized data were then projected onto the principal axes, plotted in two and three dimensions, and examined for clustering behavior. Methanotroph biomass densities were computed by dividing the gene copy number by the wet soil weight, computing the area beneath a plot of methanotroph biomass densities versus depth, and then dividing that area by the total depth of the core. 3. Results 3.1. Pore water analysis Substantial concentrations of methane were detected in the pore water during all sampling events (Fig. 2). Methane concentrations ranged from 0.5 to 2.5 mg/L, and either increased (<5-fold) or did not vary as a
Fig. 2. Methane concentration profiles in the wetland sediments of Mesocosm U (*), Mesocosm V1 (&), and Mesocosm V2 (~) during fall 2002 (A), spring 2003 (B), and summer 2003 (C).
function of depth. Methane concentrations were generally 2-fold higher in summer 2003 than either fall 2002 or spring 2003. Sulfate concentrations ranged from 30 to 70 mg/L with a peak about 30 cm below the ground surface. Dissolved oxygen concentrations ranged from 0.5 to 2 mg/L, with higher levels at shallow depths. There were no apparent differences between the vegetated and unvegetated mesocosms with respect to sulfate or dissolved oxygen concentrations. 3.2. Bacterial community analysis PCR-DGGE fingerprints of the total bacterial communities were complex during all sample events
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at all depths for both the vegetated and unvegetated mesocosms (data not shown). Visual inspection of the fingerprints suggested substantial variability in community structure as functions of depth and mesocosm type. Statistical analysis using the UPGMA algorithm demonstrated that community fingerprints gradually varied as a function of depth within each mesocosm (data not shown). Pairwise comparison between two different mesocosms demonstrated that community fingerprints from Mesocosm U clustered separately from either Mesocosm V1 or Mesocosm V2. Mesocosms V1 and V2, however, also clustered separately from each other. 3.3. Quantification of methanotrophic bacterial populations Ribosomal RNA gene fragments of putative Type I and Type II methanotrophic bacterial populations were detected at all depths in all three mesocosms during all
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three sampling events (Fig. 3). The quantities of Type I and Type II methanotrophs ranged from 106 to 1010 copy numbers per gram of soil, with no clear trend as a function of depth or season. The average methanotroph concentrations in the vegetated mesocosms were 4.8 108 (S.D. = 4.2 108; n = 6) and 1.4 109 (S.D. = 1.1 109; n = 6) gene copies per gram wet soil for Type I and Type II methanotrophs, respectively. In the unvegetated mesocosm, average methanotroph concentrations were 3.9 108 (S.D. = 1.5 108; n = 3) and 4.1 109 (S.D. = 3.6 109; n = 3) gene copies per gram wet soil for Type I and Type II methanotrophs, respectively. Single-factor ANOVA analysis revealed no statistically significant difference in methanotroph populations between vegetated and unvegetated mesocosms (P = 0.74 and 0.12 for Type I and Type II, respectively). Principal component analysis confirmed the absence of any trends in cPCR data with respect to depth, time, or the presence of vegetation.
Fig. 3. Population profiles for Type I and Type II methanotrophs in the wetland sediments of Mesocosms U, V1, and V2 during fall 2002, spring 2003, and summer 2003. Solid symbols represent Type I methanotrophs and open symbols represent Type II methanotrophs.
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3.4. Methanotroph-biased nested PCR-DGGE Nested PCR-DGGE was used to examine the community structure of both Type I (Fig. 4) and Type II (Fig. 5) methanotrophic bacterial communities. Similar band patterns were observed in the vegetated and unvegetated mesocosms. The Type I methanotrophic community structure typically varied with depth, although no clear trends were observed with respect to shifts from one dominant population to another. The structure of the Type II methanotrophic community changed very little over time, with two bands dominating nearly all the mesocosm samples with very few minor bands. There were no apparent differences in community structure between vegetated and unvegetated mesocosms or as a function of depth. More than 70 bands were excised from denaturing gels containing 16S rRNA gene fragments of Type I methanotrophic bacteria and sequenced, revealing 23 different populations (Fig. 6). The detected populations were related to several methanotrophic genera, including Methylobacter, Methylomonas, Methylomicrobium, Methylocaldum, and Methylosarcina. Methylobacterlike and Methylomonas-like organisms were the most prevalent, being detected in all three mesocosms throughout the study period. Methylosarcina-like organisms were detected almost exclusively in shallowest sediment samples in all three mesocosms throughout the study. Methylocaldum-like organisms were detected in Mesocosms U and V1 during summer and fall, but not in spring. Twenty-nine different bands were excised and sequenced from denaturing gels containing 16S rRNA gene fragments from Type II methanotrophic bacteria, revealing four different Type II methanotrophs and one population that was not closely related to any known methanotrophic population (band 5, Fig. 5). The two populations most commonly detected were two Type II methanotroph-like organisms previously detected without cultivation (Fig. 7). A minor band (band 2, Fig. 5) representing a Methylocystis-like organism was detected in the Mesocosm U during fall 2002. Principal component analysis of nested PCR fingerprints revealed seasonal variations in Type I methanotroph community structure (Fig. 8). The seasonal clustering behavior was strongly influenced Fig. 4. Fingerprints of Type I methanotrophic bacterial communities in wetland sediments as generated by nested PCR-DGGE (denaturing gradient: 35–55%) for fall 2002 (A), spring 2003 (B), and summer 2003 (C). Bands that have a letter designation were excised and subject to nucleotide determination.
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Fig. 6. Neighbor-joining tree depicting phylogenetic relationships of Type I methanotrophic populations detected in wetland sediments by nested PCR-DGGE. Bootstrap values are shown for nodes with > 50% support of 10,000 replicates. The scale bar indicates an estimated change of 5%.
by the presence of population Y (Methylocaldum sp.) in fall 2002, population N and Q (Methylobacter sp.) in spring 2003, and populations S (Methylomonas sp.) and P (Methylobacter sp.) in summer 2003. No trends with respect to depth or presence of vegetation were observed, however. Type II community structure exhibited no trends with respect to time, depth, or the presence of vegetation (data not shown). Fig. 5. Fingerprints of Type II methanotrophic bacterial communities in wetland sediments as generated by nested PCR-DGGE (denaturing gradient: 30–50%) for fall 2002 (A), spring 2003 (B), and summer 2003 (C). Bands that have a number designation were excised and subject to nucleotide determination.
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Fig. 7. Neighbor-joining tree depicting phylogenetic relationships of Type II methanotrophic populations detected in wetland sediments by nested PCR-DGGE. Bootstrap values are shown for nodes with > 50% support of 10,000 replicates. The scale bar indicates an estimated change of 5%.
Fig. 8. Principal component analysis of nested PCR-DGGE fingerprints of Type I and Type II methanotrophic bacterial populations detected in wetland sediments. Each data point is labeled according to sample time (F = fall; SP = spring; SU = summer), the mesocosm from which the population was detected (U; V1; V2), and the sediment depth where the population was detected (0–90 cm). Principal components 1 and 2 capture 55% of the variability in the data.
4. Discussion Understanding the effect of wetland plants on the ecology of methanotrophic bacteria in wetland sediment is important for evaluating the feasibility of constructed wetlands as a possible remediation technology for environmental contamination with xenobio-
tic compounds. In our study, the presence of plants did not significantly affect the quantity or structure of either Type I or Type II methanotrophic populations growing in a constructed wetland. This result refutes our original hypothesis and is substantially different from previous studies in which higher numbers of methanotrophic bacteria were detected by cultivation-dependent
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approaches in the rhizosphere of rice plants relative to bulk soil (Gilbert and Frenzel, 1998; van Bodegom et al., 2001). From a practical perspective, this suggests that the presence of wetland plants neither enhanced nor adversely affected the potential of the wetland to oxidize xenobiotic pollutants (e.g., chlorinated organic compounds) by increasing the population densities of methanotrophic bacteria harboring non-specific methane monooxygenase enzymes. Very high methanotrophic population densities were detected in all three mesocosms, perhaps indicating that oxygen transfer (or release of other plant-associated nutrients) did not limit growth of methanotrophic bacteria in our constructed wetland. Additionally, the 2-year-old wetland has not yet reached maturity, and thus our results may not reflect the long-term impact of mature wetland vegetation on methanotrophic bacterial communities. Previous studies that used cultivation-dependent techniques to investigate methanotrophic bacterial community structure have detected primarily Type II populations (mainly Methylocystis spp.) in wetland sediments (Gilbert and Frenzel, 1998; Horz et al., 2002; van Bodegom et al., 2001) and on wetland plant roots (Calhoun and King, 1998). In contrast, we detected approximately equal amounts of Type I and Type II methanotrophs in the wetland sediment. Other researchers investigating methanotroph community structure via cultivation-independent techniques have also detected both Type I and Type II methanotrophs in wetland sediments (Horz et al., 2001; Svenning et al., 2003). This suggests that cultivation-based approaches may underestimate the abundance of methanotrophic bacteria in wetland sediments. In contrast, cultivationindependent methods could overestimate methanotrophic bacteria abundance due to mis-priming, the formation of heteroduplexes, and other artifacts that occur during PCR (von Witzengerode et al., 1997). Although Type II methanotrophs are thought to be more competitive in high-methane low-oxygen environments such as wetland soils and wetland plant roots (Hanson and Hanson, 1996), the results of this study suggest that Type I methanotrophs were able to compete effectively with Type II methanotrophs in the wetland mesocosms. The wetland studied in this work had fluctuating water levels throughout the period of study, with unsaturated conditions periodically occurring in the sediment profile (data not shown). Henckel et al. (2001) observed that Type II methanotrophs dominated a flooded rice field, but that Type I methanotrophs proliferated as the field was drained. We hypothesize, therefore, that periodic fluctuations between saturated and unsaturated conditions helped sustain approxi-
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mately equal quantities of Type I and Type II methanotrophs. This study is the first use of cultivation-independent techniques to study the effect of wetland vegetation on the quantity and structure of methanotrophic communities in constructed wetland sediments. Cultivationdependent techniques are well known to be biased towards a small fraction of bacteria capable of growth on laboratory media (Amann et al., 1995). Cultivationindependent approaches, however, can also be biased, when targeted organisms are not detected (e.g., here-tofore unknown methanotrophic bacteria that do not phylogenetically cluster with Type I or Type II populations) or when non-targeted organisms fail to be excluded. The methanotroph-biased PCR approaches used in this study almost exclusively detected Type I and Type II methanotrophic bacteria. In this study, a novel assay was developed to enumerate methanotrophic bacterial populations by quantitative PCR. A specific concern with this new assay is that no statistically significant differences were detected between the planted and unplanted mesocosms, perhaps suggesting that our results may have been an artifact of the technique. Numerous factors could have interfered with our assay and affected our results. Humic acids found in soils and sediments can be co-extracted with genomic DNA and interfere with PCR (Tsai and Olson, 1992; Wilson, 1997). Furthermore, a substantial fraction of the variability of quantitative PCR stems from a lack of reproducibility in the DNA extraction step (Dionisi et al., 2003; Mumy and Findlay, 2004). Even with these possible concerns, however, we detected substantial numbers of methanotrophic bacteria (>106 gene copies per gram) in virtually all sediment samples. In addition, the other analyses of the site support the lack of statistically significant differences between the planted and unplanted mesocosms, specifically with respect to the methane concentration profiles (Fig. 2), the total bacterial community structure as detected by PCR-DGGE (data not shown), or the types of methanotrophic bacterial populations that were detected (Figs. 4 and 5). Although multiple soil cores were collected from each mesocosm over the course of the study, the lack of replication of the unvegetated mesocosm limits our ability to draw conclusions with statistical certainty. Statistical limitations aside, this work provides important information regarding methanotroph population dynamics in a newly-constructed wetland. Our results suggest that the culture-dependent techniques previously used to characterize the size and structure of wetland plant-associated methanotrophic bacterial
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