Quantifying Expression of a Dissimilatory (bi)Sulfite ... - Geobacter.org

Microb Ecol (2008) 55:489–499 DOI 10.1007/s00248-007-9294-2

ORIGINAL ARTICLE

Quantifying Expression of a Dissimilatory (bi)Sulfite Reductase Gene in Petroleum-Contaminated Marine Harbor Sediments Kuk-Jeong Chin & Manju L. Sharma & Lyndsey A. Russell & Kathleen R. O’Neill & Derek R. Lovley

Received: 29 November 2006 / Accepted: 12 June 2007 / Published online: 5 September 2007 # Springer Science + Business Media, LLC 2007

Abstract The possibility of quantifying in situ levels of transcripts for dissimilatory (bi)sulfite reductase (dsr) genes to track the activity of sulfate-reducing microorganisms in petroleum-contaminated marine harbor sediments was evaluated. Phylogenetic analysis of the cDNA generated from mRNA for a ca. 1.4 kbp portion of the contiguous dsrA and dsrB genes suggested that Desulfosarcina species, closely related to cultures known to anaerobically oxidize aromatic hydrocarbons, were active sulfate reducers in the sediments. The levels of dsrA transcripts (per μg total mRNA) were quantified in sediments incubated anaerobically at the in situ temperature as well as in sediments incubated at higher temperatures and/or with added acetate to increase the rate of sulfate reduction. Levels of dsrA transcripts were low when there was no sulfate reduction because the sediments were depleted of sulfate or if sulfate reduction was inhibited with added molybdate. There was a direct correlation between dsrA transcript levels and rates of sulfate reduction when sulfate was at ca. 10 mM in the various sediment treatments, but it was also apparent that within a given sediment, dsrA levels increased over time as long as sulfate was available, even when sulfate reduction rates did not increase. These results suggest that phylogenetic analysis of dsr transcript sequences may provide insight into the active sulfate reducers in marine sediments and that quantifying levels of K.-J. Chin : M. L. Sharma : L. A. Russell : K. R. O’Neill : D. R. Lovley Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA K.-J. Chin (*) Department of Biology, Georgia State University, 24 Peachtree Center Ave., Atlanta, GA 30303, USA e-mail: [email protected]

dsrA transcripts can indicate whether sulfate reducers are active in particular sediment. Furthermore, it may only be possible to use dsrA transcript levels to compare the relative rates of sulfate reduction in sediments when sulfate concentrations, and possibly other environmental conditions, are comparable.

Introduction Anaerobic oxidation of aromatic hydrocarbons coupled to the reduction of sulfate can be an important process for the removal of these contaminants in polluted aquatic sediments and subsurface environments [2, 4, 5, 7, 10, 18, 20, 40], but quantifying this activity within sediments has proven difficult. For example, polycyclic aromatic hydrocarbons (PAHs) are anaerobically oxidized with the reduction of sulfate in a diversity of contaminated harbor sediments [14, 15, 36], but estimating the in situ rate of anaerobic PAH degradation with [14C]-labelled PAHs is not possible because there is not a consistent relationship between the rate that the [14C]-labelled PAHs are oxidized to 14CO2 and the rate of degradation of the bulk pool of PAHs [36]. Although the disappearance of PAHs can be monitored over time in anaerobic sediment incubations [36], the long incubation times required to document losses of the PAHs, as well as other factors such as the added [14C]-PAHs do not equilibrate with the PAH pools that are sorbed onto particles [4], may result in incubation conditions that fail to adequately mimic those in situ. The advent of quantitative techniques for estimating the levels of gene transcripts in environments and the possibility that there may be a correspondence in levels of transcripts of genes for metabolic processes and the rates of those

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processes has suggested that monitoring transcript levels for key genes may provide an alternative method for assaying rates of metabolism [21]. For example, in early studies employing this approach, it was found that levels of nahA transcripts were positively correlated with aerobic [14C] naphthalene mineralization rates in hydrocarboncontaminated soil [12], and levels of mRNA for merA were highest in mercury-contaminated water with the highest rates of Hg(II) reduction [28]. More recently, levels of transcripts for gltA, which encodes for a novel, eukaryoticlike citrate synthase in Geobacteraceae, were used to track the activity of Geobacter species during in situ bioremediation of uranium-contaminated groundwater [16]. Analysis of gene transcript levels to track anaerobic degradation of PAHs coupled to sulfate reduction in contaminated harbor sediments requires the ability to effectively extract and quantify mRNA from the sediments as well as knowledge of which genes to monitor. To our knowledge, quantitative analysis of gene transcripts in contaminated harbor sediments has not previously been reported. Analysis of mRNA in such environments may be problematic because of the presence of high concentrations of complex organic matter and metals, which may interfere with extraction and/ or subsequent amplification steps. Furthermore, many of the genes, which encode important steps in anaerobic degradation of PAHs, have yet to be identified. We evaluated the possibility of effectively extracting mRNA from contaminated harbor sediments and relating gene expression to the activity of sulfate reducers in the sediments. The dsrAB genes, which are present in nonsulfate and sulfate-reducing microorganisms, code for the dissimilatory (bi)sulfite reductase in sulfate reduction and are highly conserved among the sulfate-reducing microorganisms [31, 38]. It has been noted in some pure culture studies that levels of mRNA for the dsrAB genes increase in dissimilatory sulfate-reducing microorganisms as rates of sulfate reduction increase, suggesting that rates of sulfate reduction in the environment might be estimated from dsr transcript levels [30]. However, studies on fumarate and Fe(III) respiration in Geobacter sulfurreducens have demonstrated that although there may be a direct correlation between rates of respiration and levels of transcripts for key respiratory genes for a given set of environmental conditions, the number of transcripts at a given rate of respiration can vary dramatically under different environmental conditions [5]. For example, for the same rate of fumarate reduction, the numbers of transcripts for fumarate reductase were threefold higher under electronacceptor limiting conditions versus electron-donor limiting conditions [5]. For a gene related to Fe(III) reduction, the transcript levels differed 20-fold between electrondonor limiting and electron-acceptor limiting conditions [5]. In this paper, we report that mRNA for dsr can effectively be recovered from contaminated harbor sediments and used

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to identify active sulfate reducers in those sediments. Furthermore, levels of dsr transcripts in the sediments could be related to the in situ activity of the sulfate reducers.

Materials and Methods Sediment Collection and Incubation Experiments All studies were conducted on laboratory incubations of Boston Harbor sediments collected in April 2004 from the previously described Island End River site in Everett, MA. These sediments are highly contaminated with petroleum hydrocarbons [8, 14, 36]. Sediment to a depth of about 20 cm was collected in a water depth of 2–3 m with a grab sampler, and black sulfide-containing sediments were filled into quart canning-jars. Seawater was collected from a depth of about 1 m above the sediment. In the laboratory, sediments were transferred to 1,000 ml Pyrex bottles in an anaerobic glove bag (Coy Laboratory Products, Grass Lake, MI) under N2. Seawater was bubbled with N2/CO2 [93:7(v/v)] for 5 min to remove dissolved oxygen. All the vessels were sealed with thick rubber stoppers and stored at 4 °C for ca. 72 h before starting the incubations. For the sediment incubations, 7 g wet sediment and 7 ml seawater were added to 62 ml serum bottles under N2 in a glove bag. The bottles were sealed with butyl rubber stoppers secured with an aluminum cap. The sediments were briefly mixed on a vortex mixer, flushed with N2/CO2 [93:7 (v/v)], and then incubated at 9, 16, or 30 °C in the dark. After 7 days (167 h) of incubation at a given temperature, some of sediments were amended with acetate (10 mM) or molybdate (20.4 mM), a specific inhibitor of sulfate reduction [32]. For each sampling time duplicate bottles were sacrificed for sulfate measurements and extraction of RNA. Extraction of Total RNA and mRNA The procedure for extraction of RNA was a modification of previously described protocols [11, 25]. The modifications, which consisted of adding effective coprecipitants and RNase inhibitor, were applied to aid in quantitative recovery of small amounts of nucleic acids during ethanol precipitations and to maintain the integrity of RNA. The complete procedure was performed at 2 °C or on ice. To obtain sufficient amounts of total RNA, a total of 2.6 ml of the sediment slurry from duplicate sediment incubations was distributed equally into four precooled 2.0-ml reaction tubes containing 0.6 g of glass beads (ca. 0.1-mm diameter), 0.6 ml of cold TPM buffer [50 mM Tris–HCl (pH 7.0), 1.7% (wt/vol) polyvinylpyrrolidone K25 (Sigma-Aldrich), 20 mM MgCl2], and 10 μl of yeast tRNA (5 mg/ml; Ambion). The suspension was shaken for 60 s at 4,200 rpm in a Mini-bead

Expression of Dissimilatory (bi)Sulfite Reductase Gene

beater (Biospec Products; Barthlesville, OK). After 10 min of centrifugation at 13,000×g, the supernatant was transferred to a new reaction tube containing 0.6 ml watersaturated phenol and 2 μl of RNase inhibitor (Ambion). The pellet was suspended in 0.6 ml of phenol-saturated lysis buffer [50 mM Tris–HCl (pH 7.0), 50 mM EDTA, 1% (w/v)] sodium dodecyl sulfate, 6% (v/v) water-saturated phenol, followed by a second round of bead beating. After centrifugation at 13,000×g, the supernatant was transferred to the tube containing phenol and the supernatant of the first bead-beating treatment, and the pooled supernatants were extracted with phenol, phenol–chloroform–isoamylalcohol [25:24:1 (v/v/v)], and chloroform–isoamylalcohol [24:1 (v/ v)]. Total nucleic acids were precipitated (from the aqueous phase) with 1/10 volume sodium acetate (3 M, pH 5.2), 4 μl linear acrylamide (5 mg/ml; Ambion) and 3 vol of cold ethanol at −80 °C for 60 min, and recovered by centrifugation at 13,000×g for 60 min. The pellets were washed with 70% ethanol. After being dried, the pellet was resuspended in 20 μl of RNase-free water and the nucleic acids from the four reaction tubes were pooled. The integrity of the 16S and 23S rRNA fragments of extracted total RNA was checked with denaturing formaldehyde agarose gel electrophoresis. The extracted RNA was further purified by a Sephadex (G100) column filtration as described previously [26] and was treated with RNase-free DNase to remove any contaminating DNA. To further purify and concentrate the RNA, it was precipitated with 3 M sodium acetate, 5 μg glycogen, and 3 vol of ethanol at −80 °C for 30 min. RNA was recovered by centrifugation at 13,000×g for 30 min at 2 °C. The pellet was washed with 70% ethanol, and resuspended in Tris– EDTA buffer (10 mM Tris, 1 mM EDTA; pH 7.6). The mRNA was enriched and isolated by a magnetic bead hybridization method with MICROB Express purification system, and large ribosomal RNA (16S and 23 rRNA) were removed by this protocol part (Ambion, Austin, TX). To remove any residue of small RNAs (including tRNA and 5S rRNA), the enriched mRNA was further purified with the glass fiber-based filtration method with the MEGAclear purification system (Ambion). DNA contamination was checked with agarose gel electrophoresis following reverse transcription-polymerase chain reaction (RT-PCR) by performing control experiments in which no reverse transcriptase was added to extracted RNA before the PCR step. RNA concentration was determined by absorption at 260 nm with a Biophotometer (Eppendorf, Hamburg, Germany). Purified RNA was stored at −80 °C. PCR Primers The primers used in this study were as follows: the forward primers were DSR1F (AC[C/G]CACTGGAAGCACG) and

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DSR2F (CTGGAAGGA[C/T]GACATCAA) [38], and the reverse primers were DSR4R (GTGTAGCAGTTAC CGCA) [38] and DSRQ2R (GTTGA[C/T]ACGCATGGT [A/G]TG) (this study). Primers were evaluated for quantitative PCR as described earlier [5]. The resulting conditions were experimentally checked using genomic DNA isolated from Desulfovibrio desulfuricans subsp. desulfuricans DSM 642 cultures. All primers were synthesized by Sigma Genosys Oligofactory (The Woodlands, TX). To determine whether the primers were suitable, gene-specific qualitative PCR was performed before quantitative PCR. PCR products were amplified from cDNA generated by RT with the appropriate primers using the following condition: 95 °C (5 min); 40 cycles of 95 °C (40 s); 52 °C (1 min); 72 °C (1 min) followed by a final extension at 72 °C for 10 min. The PCR products were checked using ethidium bromide stained agarose gel, and the specificity of PCR products was verified by sequence analysis of clone libraries. Only the primer combinations, which amplified well, were further used for the real-time PCR quantification and clone libraries. Reverse Transcription-Polymerase Chain Reaction cDNA of the mRNA for DSR genes, was generated with dsrA-specific or dsrAB-specific reverse primers with 0.5 μg of mRNA serving as the template. For RT reactions SuperScript™ III RNase H− Reverse Transcriptase (200 U; Invitrogen Life Technologies, Rockville, MD) was used according to the manufacturer’s instructions. Gene−specific reverse primers (1 μM; Sigma Genosys, The Woodlands, TX) were used for cDNA synthesis. The cDNA synthesis conditions were as follows: incubation at 52 °C for 60 min, and enzyme inactivation at 70 °C for 15 min. To remove RNA complementary to the cDNA, E. coli RNase H (2 U) was added and the reaction was incubated at 37 °C for 20 min, followed by rapid cooling to 4 °C. The cDNA samples were stored at −20 °C before PCR analysis. DNA Extraction and PCR Amplification of 16S rRNA Gene Total DNA was isolated from same sediment samples, which were used for total RNA extraction and taken after 7 days incubation at 9 °C, using FastDNA SPIN Kit for Soil (QBiogene, Irvine, CA) with a modification as follows: the sediment suspension was shaken for 30 S at 2,500 rpm in a Mini-bead beater (Biospec Products) instead of FastPrep Instrument. The 16S rRNA genes were amplified from total DNA extracted from sediment with universal bacterial primers 338F [35] and 926R [17]. PCR was performed with the following conditions: an initial denaturation step at 94 °C for 2 min, followed by 30 cycles of 1 min at 94 °C, 1 min at

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51 °C, and 2 min at 72 °C, and a final extension step of 10 min at 72 °C. Cloning, Sequencing and Phylogenetic Analysis Two clone libraries were generated with RT-PCR amplicons obtained from mRNA, which was isolated from sediment samples taken after 7 days incubation at 9 °C. One was generated with the dsrA-specific RT-PCR amplicons (123 bp length amplified with primers DSR1F and DSRQ2R) to confirm the specificity of the real-time PCR products. Another one was generated with the dsrAB-specific RTPCR amplicons of long fragment (ca. 1.4 kbp length amplified with primers DSR2F and DSR4R) for the phylogenetic analysis. Additionally, another clone library was generated with the 16S rRNA gene amplicon obtained from environmental DNA, which was isolated from same sediment samples mentioned above. Amplicons were cloned into the pCR2.1-TOPO vector with the TOPO TA cloning kit (Invitrogen). A total of 62 clones from each dsr gene clone library and 63 clones from 16S rRNA gene clone library were randomly selected, and plasmid inserts were sequenced with M13 forward and/or reverse primers using an ABI 3730xl DNA Analyzer. Sequences were compared to ones in GenBank with the BLAST program [1]. The phylogenetic analysis of sequence data was performed using the ARB software package (version 2.5b (O. Strunk and W. Ludwig, Technische Universität München, Munich, Germany, http://www.biol.chemie.tu-muenchen.de/pub/ARB/). The ARB_EDIT tool was used for automatic sequence alignment, and the sequences were checked and corrected manually. To ensure the correct phylogenetic placement of the obtained 16S rRNA clone sequences (∼589 nucleotides), new sequences were added to the ARB database consisting in 14,228 complete or partial (>400 nucleotides) bacterial 16S rRNA gene sequences from public databases. Phylogenetic placement was performed in comparison with sequences of Bacteria. Nucleotide base frequency filters were generated to include only nucleotide positions into the phylogenetic analysis with more than 50% invariance to avoid possible treeing artifacts. Tree topology was constructed using maximum-likelihood algorithm and verified by neighborjoining analysis. The obtained partial environmental DSR clone sequences (∼1,168 nucleotides) were added to the database of DSR sequence in the ARB that was constructed in this study and consisted in 924 complete (>2,300 nucleotides) or partial (>360 nucleotides) DSR gene sequences from public databases. Phylogenetic placement of DSR clone sequences was performed in comparison with sequences of Deltaproteobacteria and Desulfotomaculum species. Trees were calculated from aligned nucleotide and deduced DSR amino acid sequences with the ARB software package using neighbor-joining, FITCH, and maximum likelihood

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methods. The trees constructed with amino acid and nucleotide sequences yielded similar results. Tree topology was constructed from nucleotide sequences using neighborjoining analysis with Jukes-Cantor distance correction method and verified by maximum-likelihood algorithm. The significance levels of interior branch points obtained in neighbor-joining analysis were determined by bootstrap analyses (1,000 data resamplings). Real-time PCR Quantification Dilution series of purified RT-PCR products were used as calibration standards for the real-time PCR quantification. The cDNAs were amplified with the dsrA genespecific primers and the resulting amplicons were purified. The purified RT-PCR products were quantified and prepared for serial dilution as described previously [5], and stored at −20 °C. The detection limits of all PCR assays were determined from four independent measurements with dilution series of purified RT-PCR products (standards) in real-time PCR (108 to 101 target molecules per reaction). All assays had a minimum sensitivity of 100 to 101 target molecules per reaction. The copy numbers of unknown samples were calculated after real-time amplification from the linear regression of the standard curve (Fig. 1). The cDNA generated with dsrA gene-specific primer was quantified with real-time quantitative PCR, using SYBR Green. The levels of dsrA transcripts (per μg mRNA) were determined in the incubated sediments, where the reduction in sulfate was measured. PCR was performed with the primers DSR1F and DSRQ2R as described previously [5]. The temperature profile was composed of an initial incubation step for 2 min at 50 °C (activation of the polymerase) followed by a 10 min denaturation step at 95 °C, 40 cycles of denaturation for

Figure 1 Standard curves for target dsrA amplicons. The symbols represent means±SD of triplicates of PCR amplifications of the standards. The line is the trend line of the linear regression. The lower detection limit was about 3–7 copies of target mRNA


30 s at 95 °C, annealing for 1 min at 52 °C, elongation for 30 s at 65 °C, and a final elongation step for 6 min at 65 °C. Four negative controls (reaction without template) were included in each real-time PCR run. The size of the PCR products was checked with agarose gel electrophoresis, and the specificity of PCR products was verified by sequence analysis of clone library. Quantitative analysis of the cDNA was carried out with the GeneAmp 5700 Sequence Detection System (Applied Biosystem, Foster City, CA) using GeneAmp 5700 SDS software (Version 1.3). The precision and reproducibility of quantification were carefully optimized, and correct lengths of PCR products were checked as described previously [5].

Figure 2 Phylogenetic tree indicating the relationship of selected dsrAB mRNA sequences retrieved from Boston Harbor sediments (BHS-DSR clones, in bold) to those of cultivated sulfate-reducing bacteria and to other environmental clone sequences from aromatic naphthalene-degrading consortia [33] and Guaymas Basin [9]. The tree was constructed using the neighbor-joining and maximum likelihood algorithm. Bootstrap values (1,000 data resamplings) of >50% are shown. The dsrAB gene sequence of Archaeoglobus fulgidus was used as outgroup reference. The scale bar indicates the estimated number of base changes per nucleotide sequence position. GenBank accession numbers of the sequences are indicated

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Nucleotide Sequence Accession Numbers The sequences generated in this study were deposited in the GenBank database under the following accession numbers: dsrAB mRNA clones, BHS-DSR1 to BHS-DSR62 under EF418125 to EF418186, BHS-DSRs1 to BHS-DSRs62 under EF656366 to EF656427, and 16S rRNA gene clones, BHS-16S1 to BHS-16S63 under EF418062 to EF418124. Analytical Techniques Sulfate was measured with a Dionex DX-100 ion chromatograph as described previously [24]. Acetate concentrations were measured by high-pressure liquid chromatography

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(HPLC) on a Hewlett-Packard series 1100 (Agilent Technologies, Albany, NY) with a Bio-Rad Aminex HPX-87H column (300×7.8 mm) and a mobile phase of 8 mM H2SO4 (flow rate 1 ml/min).

Results and Discussion DSR mRNA and 16S rRNA Genes in Petroleum-contaminated Harbor Sediments The phylogeny of sulfate reducers in sediment incubations was evaluated with sediments that had been incubated for 7 days at the in situ temperature of 9 °C. No amplicons were obtained from mRNA or DNA extracted from Boston harbor sediments with the primer pair DSR1F/DSR4R [38] which has previously successfully recovered dsrAB DNA sequences from other environments [9, 19, 29, 34] and amplified the dsrAB sequence from a pure culture of Desulfovibrio desulfuricans in this study. However, the primer pair DSR2F/DSR4R [38], which amplifies a smaller (1.4 kbp) fragment of dsrAB and targets a more limited subset of sulfate reducers, including Desulobacteraceae and Desulfovibrio species, successfully amplified portions of the dsrAB sequences from both mRNA and DNA extracted from the sediments. In clone library generated from mRNA with the DSR2F/DSR4R primer pair, all 62 clones analyzed grouped within the Desulfobacteraceae (Fig. 2). The sequences were most closely related (83–97% identical) to the sequence of Desulfosarcina variabilis, a benzoate-oxidizing sulfatereducer [39], and also to the environmental DSR clone sequence, Nap51, retrieved from an aromatic naphthalenedegrading consortium [33]. Some sequences were most closely related (79.2–89% identical) to the sequence of Desulfosarcina cetonica, a cresol-oxidizing sulfate-reducer [27]. These results suggest that the active sulfate reducers in these contaminated sediments that we detected are related to Desulfobacteraceae. The amplicon of the DSR2F/DSR4R primer set is too large for quantitative PCR. Therefore, a new primer, DSRQ2R, was designed, which when combined with DSR1F targeted the same phylogenetic group of Desulfobacteraceae sequences that were amplified from the sediments with the DSR2F/ DSR4R primer set but provided an amplicon of only 123 bp. All 62 clones from a library generated with the DSR1F/DSRQ2R primer set were in a subgroup of Desulfobacteraceae that were amplified with the DSR2F/ DSR4R primers (data not shown). Analysis of a16S rRNA gene clone library generated with environmental DNA suggested that members of the Desulfobacteraceae were abundant in the sediments. The most abundant microbes were Deltaproteobacteria accounting for ca. 40% of 16S rRNA gene sequences (Fig. 3). Two of the

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Deltaproteobacteria clone sequences (Fig. 4) were most closely related to Geobacter metallireducens, an organism known to oxidize aromatic hydrocarbons with the reduction in Fe(III) oxides [22, 23]. The remaining 24 sequences related to Deltaproteobacteria (Fig. 4) were closely related to sequences of dissimilatory sulfate-reducing pure cultures and also to environmental 16S rRNA gene sequences from sulfate-reducing, benzene-mineralizing consortia [33] and oil field microbial communities [37]. Ten clone sequences were closely related to sequences of Desulfosarcina cetonica and Desulfosarcina variabilis, corresponding to the results from the DSR clone library. Three clone sequences were closely related to the sequence of NaphS2, a naphthalene-degrading and sulfate-reducing pure culture [13], for which dsr sequences are not yet available. Previous studies demonstrated that enhanced capacity for naphthalene degradation in Boston Harbor sediments was associated with an enrichment of 16S rRNA gene sequences closely related to NaphS2 [15]. Quantifying dsrA Transcript Levels When the Boston Harbor sediments were incubated at the in situ temperature of 9 °C, sulfate was consumed over time (Fig. 5). With the exception of the data point at 402 h, the loss of sulfate over time suggested that rate of sulfate reduction was relatively steady over the 1,072 h of incubation (20.9±2.1 nmol h−1 ml−1 sediment slurry). DsrA transcripts could be detected at each time point. The number of dsrA transcripts increased linearly over time from ca. 4.1×102 copies at time 0 to ca. 1.1×104 copies at the end of incubation despite the fact that the sulfate reduction rate remained relatively constant (Fig. 5). For example, during the clearly steady rate of sulfate reduction between 570 h and 1,072 h of incubation the level of dsrA transcripts increased ca. threefold. These results indicated that a range of dsrA

Figure 3 Composition and relative proportions of sequences of 16S rRNA gene clone libraries assembled from anoxically incubated Boston Harbor sediments


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BHS-16S49, EF418110 Desulfosarcina cetonica, AF420282 BHS-16S22, EF418083 Desulfosarcina variabilis, M34407 BHS-16S11, EF418072 BHS-16S29, EF418090 BHS-16S12, EF418073 BHS-16S25, EF418086 79 BHS-16S18, EF418079 BHS-16S15, EF418076 benzene mineralizing SB-29, AF029047 BHS-16S62, EF418123 86 BHS-16S10, EF418071 BHS-16S51, EF418112 BHS-16S63, EF418124 BHS-16S44, EF418105 NaphS2, AJ132804 99 Desulfobacterium anilini, AJ237601 Deltaproteobacteria 51 benzene mineralizing SB-21, AF029045 Desulfobacter postgatei, M26633 BHS-16S61, EF418122 Geobacter metallireducens, L07834 52 BHS-16S55, EF418116 BHS-16S14, EF418075 Desulfobacterium catecholicum, AJ237602 BHS-16S43, EF418104 BHS-16S52, EF418113 BHS-16S16, EF418077 Desulfocapsa sulfoexigens, Y13672 BHS-16S5, EF418066 BHS-16S20, EF418081 BHS-16S4, EF418065 BHS-16S21, EF418082 BHS-16S57, EF418118 BHS-16S56, EF418117 78 Desulfobulbus elongatus, X95180 95 Desulfovibrio desulfuricans, M34113 unidentified oil field DVIB104, U46526 BHS-16S46, EF418107 BHS-16S40, EF418101 BHS-16S45, EF418106 BHS-16S50, EF418111 BHS-16S30, EF418091 BHS-16S27, EF418088 BHS-16S36, EF418097 BHS-16S54, EF418115 72 BHS-16S33, EF418094 BHS-16S42, EF418103 Gammaproteobacteria 77 Alcanovorax borkumii, Y12579 Nitrosococcus oceanus, M96398 Anodontia phillipiana gill sym, L25711 BHS-16S28, EF418089 BHS-16S26, EF418087 BHS-16S32, EF418093 54 BHS-16S34, EF418095 BHS-16S1, EF418062 BHS-16S39, EF418100 BHS-16S35, EF418096 90 BHS-16S53, EF418114 BHS-16S47, EF418108 BHS-16S58, EF418119 Alphaproteobacteria BHS-16S60, EF418121 77 Rhodobium marinum, D30791 BHS-16S19, EF418080 BHS-16S23, EF418084 86 BHS-16S59, EF418120 Epsilonroteobacteria BHS-16S38, EF418099 benzene mineralizing SB-17, AF029044 52 Sulfurospirillum arcachonense, Y11561 BHS-16S31, EF418092 Mycobacteriaceae 68 Mycobacterium abscessus, X82235 BHS-16S24, EF418085 Low G+C Gram-Positive unidentified oil field EUBA7, U46533 BHS-16S6 72 Clostridium aminovalericum, X73436 BHS-16S9, EF418070 Planctomyces Planctomyces brasiliensis, X81949 BHS-16S2, EF418063 BHS-16S48, EF418063 Bacteroides putredinis, L16497 Cytophaga fermentans, M58766 BHS-16S13, EF418074 CFB group BHS-16S8, EF418069 benzene mineralizing SB-5, AF029041 BHS-16S7, EF418068 benzene mineralizing SB-1, AF029039 78 BHS-16S41, EF418102 BHS-16S37, EF418098 Spirochaetes Spirochaeta thermophila, X62809 BHS-16S17, EF418078 95 Chloroflexus aurantiacus, M34116 Chloroflexi BHS-16S3, EF418064 benzene mineralizing SB-34, AF029049 Archaeoglobus fulgidus, X05567 Y00275

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Figure 4 Phylogenetic tree indicating the relationship of selected bacterial 16S rRNA gene clone sequences retrieved from Boston Harbor sediments (BHS-16S clones, in bold) to those of cultivated sulfate-reducing bacteria, and to other environmental clone sequences from aromatic benzene-mineralizing consortia [33] and from oil field samples [37]. The tree was constructed using the neighbor-joining and

maximum likelihood algorithm. Bootstrap values (1,000 data resamplings) of >50% are shown. The 16S rRNA gene sequence of Archaeoglobus fulgidus was used as outgroup reference. The scale bar indicates the estimated number of base changes per nucleotide sequence position. GenBank accession numbers of the sequences are indicated

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in the sediments without added acetate and also increased over time, with the exception of the final time point. At 1,072 h, as sulfate concentrations dropped below 3 mM, the level of dsrA transcripts declined substantially (Fig. 5b). The addition of molybdate at 167 h of incubation inhibited sulfate reduction (Figs. 5c, 6c and 7c). The dsrA transcript levels were low when sulfate reduction was inhibited in this manner (Figs. 5c, 6c and 7c).

Figure 5 Expression of dsrA gene and sulfate concentration during incubation of Boston Harbor sediment slurries at 9 °C without addition (a) and with addition of acetate (b) or molybdate (c). Data are means±SD of triplicate determinations on a pooled sample from duplicate sediment incubations. Arrows indicate addition of acetate or molybdate at 167 h

transcript levels could be associated with a similar rate of sulfate reduction in these sediments. In an attempt to evaluate dsrA transcript levels at another rate of sulfate reduction, 10 mM acetate was added to the sediments at 167 h of incubation to stimulate the rate of sulfate reduction (Fig. 5b). Rates of sulfate depletion were only slightly faster than in the sediments without added acetate. Levels of dsrA transcripts were comparable to those



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these higher temperatures (Figs. 6b and 7b), and the rates were 50.1±5.9 and 54.5±8.6 nmol h−1 ml−1 sediment slurry for 16 and 30 °C, respectively. As was observed in the sediments incubated at 9 °C, there was an initial increase in dsrA transcript levels over time in the sediments incubated at 16 and 30 °C. The dsrA transcript levels initially increased at a faster rate at these temperatures than in the sediments incubated at 9 °C, and in the sediments incubated at 30 °C, the highest levels of dsrA transcripts were greater than in the sediments incubated at the cooler temperatures. However, as sediment incubations at 16 and 30 °C were continued, levels of dsrA transcripts became lower as sulfate was depleted from the sediments. These results indicate that expression of dsrA in these sediments is indicative of active sulfate reduction. Previous studies have suggested that levels of transcripts for genes involved in anaerobic respiration might be influenced not only by the rate of respiration, but also by the concentration of electron acceptor that is available [5]. Therefore, to evaluate levels of dsrA transcripts under similar conditions of sulfate availability, the rate of sulfate reduction when sulfate concentrations were ca. 10 mM were calculated. This concentration was chosen because rates of sulfate depletion at concentrations near 10 mM appeared to be linear in all sediment incubations, which made it possible to calculate a rate of sulfate reduction from the differences in sulfate concentrations over time. Also included in this comparison (Fig. 8) was data on dsrA transcript levels when sulfate reduction stopped due to sulfate depletion to reflect the fact that dsrA does not appear to be expressed when


Sediments were incubated at elevated temperatures to provide additional rates of sulfate reduction. Incubation at 16 °C, the in situ temperature in October, or at 30 °C, resulted in higher rates of sulfate reduction (Figs. 6a and 7a). Rates of sulfate reduction calculated from the initial linear loss of sulfate in these sediments were 17.5±3.0 and 49.5±5.6 nmol h−1 ml−1 sediment slurry for 16 and 30 °C, respectively. Addition of acetate had a greater impact on the rates of sulfate reduction at

Figure 8 Relationship between sulfate reduction rates when sulfate concentrations were ca. 10 mM and levels of dsrA transcripts in Boston Harbor sediments. The sulfate reduction rates were calculated from the linear depletion of sulfate reduction over time. Levels of dsrA transcripts were calculated as the midpoint of the regression lines during the linear increase of dsrA mRNA levels, when sulfate concentration reached ca. 10 mM. Data points (filled circles) originated from the following: (c) 9 °C control; d) 9 °C + acetate; (e) 16 °C control; (f) 16 °C + acetate; (g) 30 °C control; (h) 30 °C + acetate. Data points (a, b) at sulfate reduction rate 0 are originated from the sulfate depletion phase after continued incubation at 16 and 30 °C with added acetate, respectively

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sulfate is not being reduced. Within these parameters, there was a direct relationship between levels of dsrA transcripts and rates of sulfate reduction (Fig. 8). Implications To our knowledge, these results demonstrate for the first time that it is possible to monitor the gene expression of sulfatereducing microorganisms living in contaminated marine harbor sediments. The phylogeny of the dsr transcripts suggest wthat the active sulfate reducers in petroleum-contaminated Boston Harbor sediments are sulfate reducers that are closely related to sulfate reducers available in pure culture capable of metabolizing aromatic compounds. Thus, information on the physiology of these pure cultures may aid in better understanding the factors controlling the metabolism of the sulfate reducers involved in contaminant degradation in situ. The results also demonstrate that expression of dsrA indicates that sulfate reduction is likely to be taking place in sediment slurries. However, there were not specific levels of dsrA transcripts that were consistently related with a specific rate of sulfate reduction. This may be a common phenomenon with respiratory genes. For example, there was substantially higher expression of genes for fumarate reductase or a gene encoding a cytochrome required for Fe (III) reduction in Geobacter sulfurreducens under electronacceptor limiting conditions than under conditions under which the electron-donor limited metabolism [5]. Thus, it may not be surprising that the levels of dsrA transcripts increased as sulfate concentrations declined, although rates of sulfate reduction remained relatively constant. However, the results also suggest that it may be possible to estimate relative rates of sulfate reduction from dsrA transcript levels when important environmental conditions, such as sulfate availability, are comparable. Expression of genes that are linked to the central metabolism of electron donors may be more closely linked to metabolic rates than genes encoding respiratory proteins. For example, there was a good correlation between levels of transcripts for the gene encoding the citrate synthase gene of G. sulfurreducens and rates of electron transfer to Fe(III) or electrodes [16]. Furthermore, in situ levels of Geobacteraceae citrate synthase gene transcripts closely tracked the availability of the primary electron donor, acetate, during in situ bioremediation of uranium-contaminated groundwater [16]. In contrast, levels of transcripts for a diversity of housekeeping genes remained relatively constant relative to total RNA at different metabolic rates [16]. Genomic sequencing of pure culture and contaminated environments will provide information on key genes involved in the degradation of important contaminants, such as polycyclic aromatic hydrocarbons. Focusing on genes encoding enzymes involved in key steps in the degradation of

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contaminants in sulfate-reducing sediments is also likely to provide a more direct measure of contaminant degradation by sulfate reducers. Unfortunately, the key genes involved in the degradation of important contaminants, such as polycyclic aromatic hydrocarbons have yet to be elucidated. Acknowledgement This research was funded by the Office of Naval Research (Grant N00014-03-1-0315). We would like to thank Captain Peter Edwards of the University of Massachusetts, Boston and Richard Glaven for their assistance with the sampling in Boston Harbor, MA.

References 1. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410 2. Anderson RT, Lovley DR (2000) Anaerobic bioremediation of benzene under sulfate-reducing conditions in a petroleumcontaminated aquifer. Environ Sci Technol 34:2261–2266 3. Carmichael LM, Christman RF, Pfaender FK (1997) Desorption and mineralization kinetics of phenanthrene and chrysene in contaminated soils. Environ Sci Technol 31:126–132 4. Chang BV, Shiung LC, Yuan SY (2002) Anaerobic biodegradation of polycyclic aromatic hydrocarbon in soil. Chemosphere 48:717–724 5. Chin KJ, Esteve-Nunez A, Leang C, Lovley DR (2004) Direct correlation between rates of anaerobic respiration and levels of mRNA for key respiratory genes in Geobacter sulfurreducens. Appl Environ Microbiol 70:5183–5189 6. Coates JD, Anderson RT, Lovley DR (1996) Oxidation of polycyclic aromatic hydrocarbons under sulfate-reducing conditions. Appl Environ Microbiol 62:1099–1101 7. Coates JD, Woodward J, Allen J, Philp, P, Lovley DR (1997) Anaerobic degradation of polycyclic aromatic hydrocarbons and alkanes in petroleum-contaminated marine harbor sediments. Appl Environ Microbiol 63:3589–3593 8. Darby D (1989) Everett, former Eastern Gas and Fuel, Market Street Site Inspection Report. Department of Environmental Quality Engineering, Boston 9. Dhillon A, Teske A, Dillon J, Stahl DA, Sogin ML (2003) Molecular characterization of sulfate-reducing bacteria in the Guaymas Basin. Appl Environ Microbiol 69:2765–2772 10. Evans WC, Fuchs G (1988) Anaerobic degradation of aromatic compounds. Annu Rev Microbiol 42:289–317 11. Felske A, Engelen B, Nubel U, Backhaus H (1996) Direct ribosome isolation from soil to extract bacterial rRNA for community analysis. Appl Environ Microbiol 62:4162–4167 12. Fleming JT, Sanseverino J, Sayler GS (1993) Quantitative relationship between naphthalene catabolic gene frequency and expression in predicting PAH degradation in soils at town gas manufacturing sites. Environ SciTechnol 27:1068–1074 13. Galushko A, Minz D, Schink B, Widdel F (1999) Anaerobic degradation of naphthalene by a pure culture of a novel type of marine sulphate-reducing bacterium. Environ Microbiol 1:415– 420 14. Hayes LA, Nevin KP, Lovley DR (1999) Role of prior exposure on anaerobic degradation of naphthalene and phenanthrene in marine harbor sediments. Org Geochem 30:937–945 15. Hayes LA, Lovley DR (2002) Specific 16S rDNA sequences associated with naphthalene degradation under sulfate-reducing conditions in harbor sediments. Microb Ecol 43:134–145 16. Holmes DE, Nevin KP, O’Neil RA, Ward JE, Adams LA, Woodward TL, Vrionis HA, Lovley DR (2005) Potential for quantifying expression of the Geobacteraceae citrate synthase gene


17.

18.

19.

20. 21. 22.

23.

24.

25.

26.

27.

28.

29.

to assess the activity of Geobacteraceae in the subsurface and on current-harvesting electrodes. Appl Environ Microbiol 71:6870– 6877 Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR (1985) Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci USA 82:6955–6959 Lei L, Khodadoust AP, Suidan MT, Tabak HH (2005) Biodegradation of sediment-bound PAHs in field-contaminated sediment. Water Res 39:349–361 Leloup J, Quillet L, Oger C, Boust D, Petit F (2004) Molecular quantification of sulfate-reducing microorganisms (carrying dsrAB genes) by competitive PCR in estuarine sediments. FEMS Microbiol Ecol 47:207–214 Lovley DR (2000) Anaerobic benzene degradation. Biodegradation 11:107–116 Lovley DR (2003) Cleaning up with genomics: Applying molecular biology to bioremediation. Nat Rev Microbiol 1:35–44 Lovley DR, Baedecker MJ, Lonergan DJ, Cozzarelli IM, Phillips EJP, Siegel DI (1989) Oxidation of aromatic contaminants coupled to microbial iron reduction. Nature 339:297–299 Lovley DR, Lonergan DJ (1990) Anaerobic oxidation of toluene, phenol, and p-cresol by the dissimilatory iron-reducing organism GS-15. Appl Environ Microbiol 56:1858–1864 Lovley DR, Phillips EJP (1994) Novel processes for anaerobic sulfate production from elemental sulfur by sulfate-reducing bacteria. Appl Environ Microbiol 60:2394–2399 Ludemann H, Arth I, Liesack W (2000) Spatial changes in the bacterial community structure along a vertical oxygen gradient in flooded paddy soil cores. Appl Environ Microbiol 66:754–762 Moran MA, Torsvik VL, Torsvik T, Hodson RE (1993) Direct extraction and purification of rRNA for ecological studies. Appl Environ Microbiol 59:915–918 Muller JA, Galushko AS, Kappler A, Schink B (1999) Anaerobic degradation of m-cresol by Desulfobacterium cetonicum is initiated by formation of 3-hydroxybenzylsuccinate. Arch Microbiol 172: 287–294 Nazaret S, Jeffrey WH, Saouter ER, Von Haven R, Barkay T (1994) merA gene expression in aquatic environments measured by mRNA production and Hg(II) volatilization. Appl Environ Microbiol 60:4059–4065 Nercessian O, Bienvenu N, Moreira D, Prieur D, Jeanthon C (2005) Diversity of functional genes of methanogens, methano-

499

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

trophs and sulfate reducers in deep-sea hydrothermal environments. Environ Microbiol 7:118–132 Neretin LN, Schippers A, Pernthaler A, Hamann K, Amann R, Jorgensen BB (2003) Quantification of dissimilatory (bi)sulphite reductase gene expression in Desulfobacterium autotrophicum using real-time RT-PCR. Environ Microbiol 5:660–671 Odom JM, Peck HD, Jr (1984) Hydrogenase, electron transfer proteins, and energy coupling in the sulfate-reducing bacteria Desulfovibrio. Annu Rev Microbiol 38:551–592 Oremland RS, Capone DG (1988) Use of “specific inhibitors” in biogeochemistry and microbial ecology. Adv Microb Ecol 10:285–383 Perez-Jimenez JR, Young LY, Kerkhof LJ (2001) Molecular characterization of sulfate-reducing bacteria in anaerobic hydrocarbon-degrading consortia and pure cultures using the dissimilatory sulfite reductase (dsrAB) genes. FEMS Microbiol Ecol 35:145–150 Perez-Jimenez JR, Kerkhof LJ (2005) Phylogeography of sulfatereducing bacteria among disturbed sediments, disclosed by analysis of the dissimilatory sulfite reductase genes (dsrAB). Appl Environ Microbiol 71:1004–1011 Stahl DA, Amann R (1991) Development and application of nucleic acid probes. In: Stackebrandt E, Goodfellow M (eds) Nucleic acid techniques in bacterial systematics, John Wiley and Sons, Chichester, England, pp 205–248 Rothermich MM, Hayes LA, Lovley DR (2002) Anaerobic, sulfate-dependent degradation of polycyclic aromatic hydrocarbons in petroleum-contaminated harbor sediment. Environ Sci Technol 36:4811–4817 Voordouw G, Armstrong SM, Reimer MF, Fouts B, Telang AJ, Shen Y, Gevertz D (1996) Characterization of 16S rRNA genes from oil field microbial communities indicates the presence of a variety of sulfate-reducing, fermentative, and sulfide-oxidizing bacteria. Appl Environ Microbiol 62:1623–1629 Wagner M, Roger AJ, Flax JL, Brusseau GA, Stahl DA (1998) Phylogeny of dissimilatory sulfite reductases supports an early origin of sulfate respiration. J Bacteriol 180:2975–2982 Widdel F (1980) Anaerober Abbau von Fettsäuren und Benzoesäure durch neu isolierte Arten Sulfat-reduzierender Bakterien. Ph. D. dissertation. University of Göttingen, Göttingen, Germany Widdel F, Rabus R (2001) Anaerobic biodegradation of saturated and aromatic hydrocarbons. Curr Opin Biotechnol 12:259–276