Relating Chloroethene Respiration Rates in Dehalococcoides to

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Relating Chloroethene Respiration Rates in Dehalococcoides to Protein and mRNA Biomarkers Annette R. Rowe,† Gretchen L. Heavner,‡ Cresten B. Mansfeldt,‡ Jeffrey J. Werner,§ and Ruth E. Richardson‡,* †

Field of Microbiology, Cornell University, Ithaca New York 14853, United States Department of Civil and Environmental Engineering, Cornell University, Ithaca New York 14853, United States § Chemistry Department, SUNY Cortland, Cortland New York 13045, United States ‡

S Supporting Information *

ABSTRACT: Molecular biomarkers could provide critical insight into myriad in situ microbial activities. In this study we explore correlations of both mRNA and protein biomarkers with chloroethene respiration rate in Dehalococcoides. In a series of continuously fed dechlorinating mixed-culture microcosm experiments (n = 26), we varied respiratory substrates, substrate ratios and feeding rates. Transcript levels for most biomarkers were responsive down to 0.01× the culture’s maximum respiration rate. The dehalogenase TceA and the Ni−Fe hydrogenase HupL transcripts were positively correlated (Pearson’s r of 0.89 and 0.88, respectively) with respiration rates on log−log plots between 1.5 and 280 μeeq/ L-hr for mRNA abundances of 107 to 1010 transcripts/mL (0.07−230 transcripts/genome). These trends were independent of the types of chloroethene or electron donors fed. Other mRNA target levels plateaued or declined at respiration rates above 5 μeeq/L-hr. Using both relative and absolute protein quantification methods, we found that per-genome protein abundances of most targeted biomarkers did not statistically change over the experimental time frames. However, quantified enzyme levels allowed us to calculate in vivo enzyme-specific rate constants (kcat) for the dehalogenases PceA and TceA: 400 and 22 substrate molecules/enzyme-sec, respectively. Overall, these data support the promise of both mRNA and protein biomarkers for estimating process rates through either empirical (mRNAbased) or kinetic (protein-based) models, but they require follow-up studies in other cultures and at active remediation sites.

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INTRODUCTION Microbes catalyze many environmentally relevant processes, including the transformation or degradation of chemical pollutants. Chlorinated organic compounds, in particular the chloroethenes tetrachloroethene (PCE) and trichloroethene (TCE), are widespread environmental contaminants of international concern. Members of the Dehalococcoides are the only organisms that have been isolated that generate a nontoxic end product (ethene) from these contaminants through organochlorine respiration.1 Cumulatively these organisms have been shown to respire numerous chlorinated contaminants in addition to the chloroethenes.2−5 Because of this proclivity, Dehalococcoides strains have become important organisms in the implementation of chloroethene bioremediation and hold promise for other contaminant classes. Detection of biomarkers has been suggested as a method for documenting in situ activity of environmentally relevant microbes involved in bioremediation.1,6−9 For Dehalococcoides, correlations have been drawn between 16S rRNA gene presence and the generation of ethene in ecosystems remediating chloroethenes.10−13 However, depending on the © 2012 American Chemical Society

abundance and/or specific activity of endemic Dehalococcoides, variation in rates and respiration end products (cis-dichloroethene [cDCE], vinylchloride [VC], ethene) has been observed.10,12,14 Genomic comparisons of Dehalococcoides strains with >97% 16S rRNA gene similarity vary considerably with respect to key metabolic enzymes,15,16 further emphasizing the metabolic variability among strains and suggesting that traditional phylogeny offers limited resolution of specific respiratory capabilities. Genomic sequence analysis has been important for suggesting biomarkers that are either conserved across strains, or thought to be specific to a particular respiratory capacity.8 In addition to requiring halogenated organics as electron acceptors, Dehalococcoides require molecular hydrogen as an electron donor. HupL, the only Dehalococcoides hydrogenase predicted to contain a periplasmic catalytic subunit,17 is abundant at the Received: Revised: Accepted: Published: 9388

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could potentially be used to infer in situ rates from field protein measurements and substrate levels. The results of this work will help not only to further understanding of respiration biomarkers in Dehalococcoides, but inform the potential promise and limitations of these types of biomarkers (mRNA and protein) for assessing microbial activities in complex microbial communities.

protein level in several strains and likely involved in hydrogen oxidation.18−21 On the terminal end of the electron transport chain, reductive dehalogenases (RDases) catalyze the reduction of chlorine-carbon bonds. Of the many different RDases encoded in Dehalococcoides genomes only four have been biochemically characterized: chloroethenes-reducing enzymes TceA, PceA, and VcrA, as well as the chlorobenzene-reducing enzyme CbrA.18,21−26 RDase gene homologues have been monitored and detected at field sites undergoing remediation of TCE or PCE.27−32 However, in studies where RDase mRNA abundance was monitored in conjunction with DNA, gene presence has not always been linked to gene expression.28,33 A number of studies have explored products of gene expression (RNA or protein), rather than gene presence, as preferred molecular biomarker targets.1,7,19,34−40 Work with a mixed culture containing Dehalococcoides mccartyi (formerly Dehalococcoides ethenogenes) str. 195 highlighted up-regulation in five of the 19 putative RDases encoded in the genome (TceA, DET0162, PceA, DET1545, and DET1559), 41 following PCE batch feed. Shotgun proteomics in D. mccartyi was used to confirm presence of all but one (DET0162 which contains a premature stop codon) of the highly transcribed RDase targets in addition to the hydrogenase HupL.19,38 In other mixed cultures, high transcript levels of homologues to TceA,37,42,43 PceA43 and DET154528,42,44 have been demonstrated in addition to culture-specific RDases, BvcA45 and VcrA.42 At a field site undergoing TCE bioremediation, homologues of DET1545 (FtLewis 1638/CBDB1 1638) and bvcA were the dominant RDases represented in RDase cDNA clone libraries.28 In a culturing system developed to supply chlorinated electron acceptors dissolved in media continuously, resulting in steady-state respiration and pseudosteady-state mRNA expression level, D. mccartyi mRNA levels increased linearly with respiration over a limited range (12 feeding rates, all fed butyrate as an electron donor).46,47 These observations raised interest in (1) how these empirical mRNA trends held up over a range of chloroethenes respiration rates and electron donor types and (2) whether corresponding protein levels were changing proportionately to mRNA levels and/or could serve as suitable biomarkers. We addressed three objectives to develop and test chloroethene-respiration biomarkers in D. mccartyi. Our first objective was to resolve empirical relationships between mRNA biomarker levels and steady-state chloroethene respiration rates over a wide range of feeding conditions, extending respiration rates and electron donors used in previous work. Based on observations of bulk biological molecules in other bacteria across various growth rates,48−51 we hypothesized that mRNA as well as protein abundancein bulk as well as at the per-cell levelshould increase with increasing D. mccartyi respiration and growth rates. Our second objective was to quantify corresponding protein biomarker levels over time and in response to changes in respiration rate. As some organisms have been shown to enrich their proteome with proteins involved in respiration,7,52 we hypothesized that quantifying such enrichment could serve as a useful biomarker of respiratory activity. However, as the reaction rates of enzymes are dependent not only on enzyme abundance but also concentration of substrate, our third objective sought to infer in vivo enzyme-specific rate constants for two functionally characterized dehalogenases (TceA and PceA) by fitting data to a Michaelis−Menten kinetic model. Such rate constants

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MATERIALS AND METHODS Experimental Conditions and Analysis of Substrates. All experiments were performed with undiluted biomass from a 6 L dechlorinating mixed culture53 that has been maintained for fifteen years on a batch feeding regime supplying doses of PCE and butyrate (as previously described41,54). D. mccartyi is the dominant microorganism in this culture, making up 50−60% of the cell numbers based on FISH.55 Other community members include methanogenic Archaea and syntrophic fermenters, predominantly from the Firmicutes and δ/ε-Proteobacteria divisions.55 These syntrophic fermenters produce hydrogen from butyrate, which is then consumed by D. mccartyi and hydrogenotrophic methanogens in this culture. Experimental subcultures were constructed in 160 mL serum vials with 100 mL culture volume and a 60 mL headspace. In continuous-feed experiments, substrates were fed at controlled rates via syringe pumps as previously described.46,47 Culture volume was maintained by withdrawal of samples and, if necessary, by occasional wasting (culture volume never allowed to increase more than 10%). Experimental conditions varied electron donor [ED] and chlorinated electron acceptor [EA] types, ratios and feeding rates (Parameters listed in Supporting Information (SI) Table S1). Respiration was quantified from headspace concentrations using a gas chromatograph (GC) (Perkin-Elmer) equipped with a flame ionizing detector (FID) (as previously described in ref 41, see SI and refs 46 and 47 for sample calculations). Hydrogen partial pressures were quantified as previously described56 using GCs equipped with a reduced gas detector (RGD) (Trace Analytical) or a thermal conductivity detector (TCD). The hydrogen detection limit using the RGD is 0.06 μM nominal per reactor. Extraction of Nucleic Acids and Proteins. Nucleic acid extractions for qPCR or qRT-PCR were performed on 2-mL culture samples. Samples were pelleted (21,000 g, 5 min, 4 °C) and stored at −80 °C for less than 7 days. Cell lysis was performed as described previously55 using lysozyme, βmercaptoethanol and vigorous vortexing. Luciferase mRNA (Promega) was added at a known concentration during lysis to serve as internal reference standard.57 Isolation and cleanup of RNA and DNA were performed according to the Qiagen RNA/ DNA mini prep kit (Qiagen). Protein extractions were performed from 30−50 mL culture cell pellets (14 000g, 10 min) as previously described.39 In addition to pellets processed in parallel for nucleic acids, 200 μL (equating to 2 mL of culture) of cell lysate was collected following French-press lysis for qPCR and cleaned by the UltraClean Microbial DNA Isolation Kit (MoBio) without bead beating (direct DNA extraction) to quantify cell abundance. Nucleic Acid Quantification. Total DNA was quantified using the Quant-iT Picogreen double stranded DNA assay (Invitrogen). Prior to qPCR, all DNA samples were diluted 1 to 10. RNA samples were run on the Agilent 2100 BioAnlyzer to assess quality and quantity of extracted RNA. DNase treatment, cDNA synthesis, qPCR set up and qPCR run conditions were performed as previously described.46,47 Primers and annealing 9389

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each MRM analysis run and analyzed in duplicate. Four MRM analysis runs were performed over a four month period (March 2010 through June 2010). Standard and sample analyses were performed using MultiQuant 2.0 (ABISciex). Peptide quantities reported were averaged from all injections over this period. Limits of quantification (SI Table S3) were set at 10× the background noise signal at the expected peptide elution time. Calculation of Protein-Specific Kinetic Parameters. Respiration rates and metabolite levels for PCE fed experiments were used to calculate kinetic parameters for the enzyme PceA. TCE-fed and cDCE-fed experimental data were used for TceA parameters. Experimental data sets were limited to those where key substrates were above the GC-FID detection limit (approximately 20−40 nM dissolved concentration). Quantified enzyme levels (Xenzyme) were based on average or experiment-specific protein measurements determined by MRM assays. Nonlinear regression (performed in MATLAB, see below) was used to solve for KS (nmol/L) and kmax (attamol/protein-hr), based on substrate conversion rates (nmol/h) and average substrate concentration Cw (nmol/L). It has previously been shown that hydrogen acts as a second limiting substrate in this culture so we also include a correction term for possible hydrogen limitation using the equation below and previously calculated KS(H2) and hydrogn threshold values.61

temperatures used in this study are listed in SI Table S2. Analysis of qPCR data was performed as outlined previously,46 utilizing luciferase quantities to estimate mRNA recovery.57 Raw fluorescence data was used to calculate R0 values using the DART (Data Analysis for Real Time) methods,58,59 and plasmid or pure culture DNA extracts served as standard curves for each target were used to convert R0 to copies. Proteome Sample Preparation. In preparation for TRAQ protein assays (Applied Biosystems), 100 μg of total protein (quantified using the Agilent 2100 BioAnalyzer Protein 230 Kit) from samples fed at different rates were digested with trypsin (Sequencing grade, Promega) according to the iTRAQ reagent labeling protocol. A single control (time zero or baseline culture sample) was labeled with the 114 isobaric tag. Protein pools for targeted quantitative proteomics (via multiple reaction monitoring [MRM]) were quality-checked via SDSPAGE electrophoresis alongside a E. coli K12 protein standard (supplied by Cornell Proteomics Facility). From each protein sample, 10−20 μg of total protein was treated with 1 mM tris(2-carboxyethyl)phosphine (TCEP HCL) at 37 °C for one hr, followed by 50 mM iodoacetamide in the dark for 15 min. This alkylation reaction was quenched using free L-cysteine. Prior to digestion with trypsin (mass spectrometry grade, Promega) at 37 °C for 12−14 h, the concentration of urea and SDS were diluted to 0.4 M and 0.1%, respectively. Mass Spectrometry for iTRAQ Labeled Samples. Relative protein abundances were quantified using the iTRAQ (Applied Biosystems) isobaric tagging of digest samples. Labels corresponding to reporter ion masses of 114 (control/time zero or baseline culture sample), and 115 through 117 (experimental samples) were combined into one protein pool for shot gun proteomic analysis. Strong cation exchange (Agilent 1100 HPLC with UV detector) fractionation (10×) followed by SPE (Waters SepPak C18 cartridge; 1 mL of 75% ACN eluent) was described previously.39 Shotgun proteomics via nLC-MS/MS was performed as described in refs 19 and 38. Identification of proteins, and statistical analysis of identification (Prot. scores) as well as iTRAQ ratios, and error factors were determined using Protein Pilot 2.0 (ABSciex). Targeted Quantitative Proteomics of Mixed-Culture Peptides by MRM. In addition to previously developed MRM targets,39 biomarker peptide MS/MS transitions for targeted proteomic experiments via MRM were selected based on detection in previous shotgun proteomic experiments using MRMPilot 2.0 (ABSciex) (SI Table S3). Each transition was confirmed via MRM-IDA (described below) with control protein samples. Peptide pools were extracted and 1.5−3 μL aliquots (1−2 μg total protein) were injected for MRM and MRM-IDA nLCMS/MS (as previously described39), using a hybrid triple quadrupole linear ion trap, 4000 Q Trap (Applied Biosystems). MRM-IDA analysis was used for validation of selected fragment ion pairs prior to MRM quantitative analysis. In MRM-IDA, MS/MS transition ions were monitored as in normal MRM mode, but positive detection triggered linear ion trapping of parent ions followed by MS/MS scanning. Spectra were checked to confirm peptide ID. Synthetic peptide standards for MRM targets (SI Table S3) were obtained (purified >95%, Bio Basic Inc.). A dilution series of these standards was constructed in a background matrix of peptides extracted from aerobic soil mixed culture (as previously described39,60). A standard curve was generated for

substrate conversion rate k max EAXenzymeCw EA (Cw(H2) − H 2threshold) = × KS(EA) + Cw EA KS(H2) + (Cw(H2) − H2threshold)

Statistical Analysis. JMP 8 (SAS institute inc.) was used to calculate t tests, P-values, analysis of variance and 95% confidence intervals. Nonlinear least-squares analysis of kinetic parameters was performed with the Curve Fitting Toolbox in MATLAB (Math Works).

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RESULTS AND DISCUSSION Correlations between mRNA Biomarkers and Respiration Rate. To relate D. mccartyi biomarkers to respiration rates, subcultures were taken from an anaerobic mixed culture and fed a range of different EDs (butyrate, hydrogen, yeast extract, fermented yeast extract, lactate, or none [endogenous decay]) and chloroethene EAs (PCE, TCE, or cDCE). The majority (n = 26) of treatments were continuously fed both donors and acceptors in order to produce steady-state respiration rates (conditions outlined in SI Table S1). In continuously fed cultures, over the course of these experiments (24−185 h; up to one hydraulic residence time) nucleic acid biomarkers maintained a pseudosteady-state concentration across replicate cultures. This stabilization in mRNA concentration was observed previously46,47 after an initial response period for up-regulation (between four and six hrs). Candidate biomarkers from D. mccartyi demonstrated a variety of trends in terms of pseudosteady-state mRNA abundance in response to steady-state respiration rate (Figure 1). On log−log plots the types of trends included: nearly linear relationships over the full range of feeding conditions (TceA and HupL), a relationship that plateaus at a low respiration rate (PceA and DET1559), and an inverted u-shaped trend peaking at a low rate (DET1545) (Figure 1A−E). Ribosome content (per mL) remained relatively constant except at the highest feeding rates where variability in ribosomes content (Figure 1F) was observed (also see ref 46). Observed patterns were 9390

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Figure 1. Steady-state respiration rates (μeeq/L-hr) vs pseudosteady-state mRNA concentrations (copies per mL) of specific D. mccartyi targets: hydrogenase DET0110 HupL (A); reductive dehalogenases DET0079 TceA (B), DET0318 PceA (C), DET1559 (D), and DET 1545 (E); and 16S rRNA subunit (F). Error bars represent standard errors of average respiration rates in replicate reactors (X-error bars) and standard errors of pseudosteady-state mRNA measurements over time for replicate reactors (Y-error bars). Different symbols are used to group experiments by different electron donors (No Donor, Hydrogen, Butyrate, or Lactate) and/or electron acceptors (PCE, TCE, cDCE). Specific experimental parameter combinations listed in SI Table S1. Power law trend lines (solid black line) displayed for HupL (A) and TceA (B) along with 95% confidence intervals around this trend (dashed red line) excluding data for respiration below 1 μeeq/L-hr (see Table 1 for slope and correlation statistics). 9391

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Table 1. mRNA Regression Values and Correlation Scores (r) for Selected D. mccartyi Biomarker Targetsa gene ID DET_DE16S DET0079 DET0110 DET0318 DET1545 DET1559

gene Name 16S rRNA TceA HupL PceA DET 1545 DET 1559

power law slope for log mRNA vs log respiration regression

16S rRNA

NA

reductive dehalogenase [Ni/Fe] hydrogenase, group 1, large subunit (EC:1.12.99.6) reductive dehalogenase reductive dehalogenase, putative reductive dehalogenase, putative

annotation

r

RNA exponential decay coefficient per hr (active)

r

RNA exponetial decay coefficient per hr (endogenous)

NA

0.014

r

0.70

0.008

0.74

0.95 1.13

0.89 0.88

0.076 0.069

0.95 0.98

0.017 0.014

0.84 0.89

NA NA

0.48 0.55

0.049 0.033

0.98 0.98

0.016 0.019

0.94 0.94

NA

0.50

0.058

0.95

0.019

0.93

a Power law slopes calculated from Log (mRNA per mL) vs. Log (respiration rate) plots (Displayed in Figure 1). First-order specific decay coefficients calculated from regression of Ln (mRNA copies per 16SrDNA copies) vs. time (hours). Raw data (per mL) is displayed in SI Figure S2 for mRNA degradation post PCE feed (starting ∼6 h post feed, active) and post purge (3days post feed, endogenous). NA refers to not applicable or not calculated due to statistical significance.

respiration rate was below a threshold for investment in mRNA production. Exponential decay of transcripts per DNA copy has previously been observed in TceA transcripts at 0.11 per hr, followed by a slower decay (0.01 per hr) once mRNA reaches a background level (around 10−1 per gene after 48 h).37 Our observations are consistent with this result in that, after PCE is completely consumed in batch fed reactors, mRNA degradation occurs rapidly, initially at 0.06 per hr for TceA transcripts (SI Figure S2; Table 1). A slower decay (endogenous) occurs at approximately one-third the rate after TceA mRNA reached a concentration of 106 copies/mL culture (less than 10−1 transcripts/genome copy). Similar initial decay rates per genome copy (0.03−0.06 per hr) and endogenous rates (0.014−0.009 per hr) were seen in the other transcripts we monitored. These data suggest mRNA half-lives range from 10 to 20 h for D. mccartyi in mixed communities. This is comparable to the ∼6 h half-lives reported for the Dehalococcoides containing BDI culture.63 RNA decay rates were observed to occur slowly (Table 1) compared to organisms like E. coli (3−8 min for ∼80% of E. coli transcripts 65 ). Nonetheless, these biomarkers actively decay on a time scale amenable to field applications (< 1 day half-lives, see SI Figure S2, Tables 1). Consistency of Relative Protein Abundance across Respiration Rates. Relative protein quantitation results (iTRAQ) demonstrated that many biomarker metabolic protein abundances increased with respiration rates at moderate values (∼10−50 μeeq/L-hr; SI Figure S3). Fewer peptides were detected for RDases DET1545 and DET1559 than for other reporter proteins resulting in wider 95% confidence intervals for iTRAQ ratios, obscuring potential trends for these two RDases. However, D. mccartyi structural and house-keeping proteins (cellular chaperone GroEL, translation elongation factor EF-TU, ribosomal protein rpL7/L12, and a putative Slayer cell wall protein) also increased in abundance with respiration rate, suggesting a higher D. mccartyi cell abundance per μg of total culture protein. This is likely due to slower growth of other bacterial populations under the higher PCE feeding conditions, resulting in a relative enrichment of D. mccartyi in the metaproteome. Normalizing to structural D. mccartyi targets like the S-Layer cell wall protein (Figure 2) to account for potential increases in cell mass showed no statistically significant differences in metabolic protein abundances. These observations suggest that protein abundan-

independent of type of ED or EA provided. Cell densities (based on 16S rRNA gene copies per mL) did not change significantly within experiments, though they varied across experiments performed over time, and are expected to vary across different Dehalococcoides-containing cultures and environmental samples. To make trends more comparable and applicable to other systems, measurements of mRNA were also normalized either to an internal RNA marker (16S rRNA copies) or genome copies (single copy 16S rRNA gene) extracted from the same sample. These normalized values demonstrated similar overall trends (SI Figure S1). In HupL and TceA, positive trends between mRNA per mL (Figure 1A, B) or per genome (SI Figure S1) were linear with respiration (1.5−280 μeeq/L-hr or 3−375 femto-eeq/genome-hr) on a log−log scale (power relationship). Correlation scores (Pearson’s r) for TceA and HupL were 0.89 and 0.88 (for mRNA per mL data) (Table 1) and 0.85 and 0.83 (for per genome data), respectively, over almost 3 orders of magnitude of respiration rate and nearly 3 orders of magnitude of transcript abundances. The 95% confidence intervals around the power law model lines for HupL and TceA suggest predictive power within 1 order of magnitude of respiration rate for a given, measured mRNA level (Figure 1A-B). Notably, a linear trend between HupL abundance and chemostat growth rate was observed using hybridization of mixed-culture mRNA pools to a hydrogenase Gene Chip.62 These data, along with the variability in RDase content across Dehalococcoides strains,15 support the potential for HupL to serve as a general Dehalococcoides respiration biomarker. For other biomarkers in this work, above a low to moderate respiration rate (∼5 μeeq/L-hr) negative (DET1545) or poor correlation scores (R < 0.5, PceA and DET1559) were observed (Table 1). Though a wide range of transcript abundances were observed for the various targets monitored over various conditions, these values fell within values observed for Dehalococcoides in other systems.36,37,63,64 Specifically, transcript per gene ratios rarely exceed 10 and can fall below one per genome (SI Figure S1).36,37,63,64 mRNA Biomarker Decay. For the majority of transcripts monitored (except DET1559), the lowest experimental feeding rate tested (0.9 μeeq PCE/L-hr or 3.8 femto-eeq PCE/ genome-hr) resulted in expression patterns similar to endogenous mRNA decay (SI Figure S2) suggesting this 9392

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Figure 2. Relative protein abundance for D. mccartyi targets assessed using iTRAQ isobaric tags and normalized to putative S-Layer cell wall protein (as a proxy for cell abundance). Relative protein abundance is based on ratio of peak areas of iTRAQ tags for samples fed 9, 28, and 52 μeeq PCE/Lhr relative to S-Layer ratio for each sample. Error bars reflect standard deviations of reporter ion ratios for all spectra matching the target protein and S-Layer normalizing protein. Error bars for structural proteins, GroEL, EF-TU and L7 rp are thick and black, thin and black, and thin and gray, respectively.

(n = 3−4 measurements) was: TceA (1095 ± 337 proteins/ genome), followed by HupL (167 ± 121 proteins/genome), PceA (85 ± 8 proteins/genome) and DET1545 (6.7 ± 4.2 proteins/genome) (Figure 3A). Only one peptide from DET1559 could confidently be quantified (SI Table S3) and therefore protein quantities were not assessed, due to the minimum criteria of two peptides per protein.66−68 Structural and house-keeping proteins were more abundant than metabolic targets, with the exception of TceA. For the targets that overlapped between this work and previous data,39 the rank order of D. mccartyi targets was conserved with the exception of PceA, which was more abundant on average in this study, but statistically indistinguishable from HupL. Ribosomal protein levels measured by MRM were similar in abundance to 16S rRNA copies measured by qPCR: both on the order of 100 ribosomes per genome copy. The fact that cultures grown under continuous-feed conditions (Figure 3B) illustrated remarkably similar proteins levels compared to one another and to time zero culture samples (Figure 3A) highlights that per-genome protein abundances alone do not appear to be a useful indicator of respiration rate over the subset of conditions tested. Therefore, the different respiration rates observed are assumed to directly rely on the concentrations of limiting substrates as modeled by Michaelis−Menten enzyme kinetics. In vivo Rate Parameters for PceA and TceA. The consistent per-cell (per-genome) protein levels observed over experimental time-courses allow us to use our corresponding metabolite data (chloroethenes, Hydrogen) to infer in vivo rate constants for the enzymes known to convert PCE to TCE (DET0318, PceA) and TCE to cDCE/cDCE to VC (DET0079, TceA).22,23 Nonlinear regressions of per enzyme respiration rates, based on average enzyme levels (enzymes/ genome × genomes/mL) compared with average substrate

ces on a per cell basis did not distinguishably change. This deviates from observations from other bacteria that have been shown to enrich their proteomes in metabolic proteins during periods of active growth and respiration.7,52 However, this lack of plasticity at the proteome level, which has previously been suggested in proteomic comparisons of pure vs mixed culture D. mccartyi,38 may be a function of organism specialization, which is consistent with the small genomes of Dehalococcoides strains. Consistency of Proteins per Cell measured via Absolute quantification. To measure absolute rather than relative protein abundances, MRM was employed to quantify preselected proteotypic peptides for the target enzymes of interest. Synthetic peptide standard curves showed a high degree of reproducibility (see SI Tables S5−S6). Because mixed cultures by definition contain more than one population, we first employed MRM to quantify variations in protein levels in our inoculum stock culture over the course of several months (“Time Zero” samples) then compared these levels to those observed after a full hydraulic residence time for replicate cultures that were continuously fed PCE at 40 or 120 μeeq/Lhr. Few statistically significant differences were noted in our mixed culture D. mccartyi proteome quantified via MRM. This included both Time Zero inoculum samples, and the continuously fed culture samples grown for one hydraulic residence time (SI Figure S4). As cell density varied significantly (for example, Time Zero samples ranged from: 3.6 ± 0.7 × 108, 1.2 ± 0.2 × 109, 3.7 ± 0.1 × 108 16S rRNA gene copies/mL when averaging biological replicates from the same experimental set), using DNA copies as a correction for cell density helped account for the variation in peptide abundance observed on a per μg total protein basis (Figure 3). The ranked order abundance of metabolic targets based on the mean ± standard error of protein measurements 9393

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Figure 3. Absolute peptide biomarker abundances in three samples taken from the 6 L stock culture reactor over the course of six months (A). Absolute protein abundances in final time-point samples for replicate continuously fed cultures fed PCE at two rates (specifically, 120 and 40 μeeq/L-hr) for one hydraulic residence time (B). Presented abundances were averaged from three to four MRM runs for a given peptide (see SI Table S3 for peptide identities) and error bars indicate standard deviation of replicate (n = 3−4) measurements.

levels allowed estimation of in vivo Michaelis−Menten rate parameters (Figure 4, Table 2). Given that under continuous butyrate feeding conditions average hydrogen concentrations are generally around the KS(H2) (0.1 μM; (56)) and are rarely observed above 1 μM (Cw) (SI Table S1), hydrogen levels measured during these experiments were used to correct reaction rates for donor limitation according to the previously presented model.61 Using the measured PceA content of approximately 90 proteins/genome we obtained a kcat of 400 PCE molecules/PceA-sec (Table 2). TceA, which is a more abundant enzyme (∼1000 proteins/genome), has a lower kcat for the conversion of TCE and cDCE; both were calculated at 22 TCE molecules/TceA-sec. To our knowledge, this is the first application of MRM-based quantification to explore in vivo enzyme kinetics. For comparisons with previous work, our kcat values are 2−10× higher than those observed during biochemical characterization on purified enzymes, which are 19, 5.4, and 13 substrate molecules/protein-sec for PceA, TceA (TCE to cDCE), and TceA (cDCE to VC), respectively.22,23 Another biochemically characterized reductive dehalogenases, VcrA, which is not possessed by D. mccartyi str.195, has an estimated kcat of 0.9 substrate molecules/protein-sec.24 Previously reported assays were performed post extraction and purification of these O2sensitive proteins, and there is potential that some loss of activity occurred due to processing and/or disruption of protein

Figure 4. Nonlinear regression plots of in vivo Michaelis−Menten parameters including a term correcting for hydrogen concentration as a limiting substrate. Nonlinear regressions for PceA (using only PCE experiments) and TceA (using only TCE or only cDCE experiments) calculated from substrate levels and respiration rates observed in pseudosteady-state data sets. Protein levels for each study were calculated by multiplying measured 16S rDNA/mL and protein-pergenome values (see Table 2 for calculated values).

complexes. These differences could also be the result of in vivo versus in vitro activity. Interestingly TceA has a low kcat relative to PceA, though functionally the Vmax rates (substrate/cell-hr) for the given dehalogenation steps they perform are similar (300 and 250 attamole substrate/genome-hr for PceA and TcA respectively). TceA’s abundance compensates for its low processivity. In terms of whole-culture Vmax values reported for dechlorinating cultures, our values are near the high end of reported TCE through VC rates (∼0.2 to 330 attamol/cell-hr69,70) and PCE to TCE rates (∼1 attamol/cell-hr 70). With the exception of TceA (for TCE to cDCE) our calculated Km values fall within the reported KS values for various chloroethenes reduction reactions: ranging from 0.5 to 12 μM.69,70 Enzyme-specific kinetic parameters may help to resolve differences among rates of reactions observed between strains with different protein profiles or strains with variable proteomes. Experiments in 9394

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Table 2. Enzyme-Specific Kinetic Parameters Calculated for D. mccartyi str. 195 Enzymes TceA and PceAa k max(EA)Cw(EA) substrate conversion rate = ⎛ (Cw(H2 − H2threshold) ⎞ Cw(EA) + K m ⎟ Xenzyme⎜ K ⎝ S(H2) + (Cw(H2 − H2threshold) ⎠

k max(EA)Cw(EA) substrate conversion rate = Xenzyme Cw(EA) + K m

kinetic parameters including hydrogen term

kinetic parameters omitting hydrogen term

enzyme

reaction

kcat ± std. dev. (substrate/enzyme-sec)

Km ± std. dev. (nM)

kcat ± std. dev. (substrate/enzyme-sec)

Km ± std. dev. (nM)

PceA TceA TceA

(PCE→TCE) (TCE→cDCE) (cDCE→VC)

400 ± 50 22 ± 1.8 22 ± 1.8

10 000 ± 4400 180 ± 70 2900 ± 1400

260 ± 48 10 ± 1 24 ± 2.4

36 000 ± 14 000 190 ± 74 2900 ± 1400

a

Calculations based on average measured enzyme proteins per cell, then used 16S rDNA copies measured for individual experiments, as well as pseudo-steady-state respiration rates and average substrate concentrations measured during continuous-feed experiments (listed in SI Table S1). Parameter values calculated using a term correcting for hydrogen concentration or using the traditional one limiting substrate Michaelis-Menten model. Corresponding equations used for calculation are displayed. kmax converted into kcat with units of substrate/enzyme-sec.

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other cultures and at field sites will be important for determining broader applicability of these rate parameters and are under way. Implications. This work provides evidence for the utility of both RNA and protein biomarkers though there are pros and cons to each type of biomarker. mRNA levels may serve as an important instantaneous indicator of growth/activity, but very clear differences in mRNA relationships with respiration demand a posteriori empirical culture tests before models could be applied in a new biological system. While mRNA quantification techniques are currently more widely used and more thoroughly tested, protein techniques are appealing in that they avoid some of the potential limitations of RNA, investigate the catalysts themselves, and can be multiplexed to monitor dozens of biomarker peptides in a single analytical run. However, representative recovery of heterogeneous proteins from environmental matrices remains a significant challenge compared to recovery of nucleic acids. Additional studies that test these approaches in other Dehalococcoides strains and at field sites undergoing dehalorespiration will be vital to further exploring utility of these approaches in site remediation monitoring and management.

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AUTHOR INFORMATION

Corresponding Author

*Phone: (607) 255-3233; e-mail: [email protected]. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge Celeste Ptak and Sheng Zhang at the Cornell Proteomics and Mass Spectrometry core facility for proteomic sample analysis. We also thank James Gossett and Stephen Zinder for their expert advice and editorial comments for this manuscript. This work was funded through research grants from the National Science Foundation CBET Program (CB1ET-0731169) and the Department of Defense Army Research Office (W911NF-07-1-0249).

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ABBREVIATIONS PCE Perchloroethene TCE Tetrachloroethene cDCE cis-Dichloroethene VC vinylchloride MRM multiple reaction monitoring ED electron donor EA electron acceptor eeq electron equivalent

ASSOCIATED CONTENT

S Supporting Information *

Extended Materials and Methods with sample calculations of respiration rates, Supporting Results and Discussion of MRM reproducibility, and protein per-cell estimates. Raw data and supplemental analyses are also supplied in the following tables and figures: Table S1. List of experimental parameters for experiments used in study. Table S2. List of mRNA biomarker targets with primer sequence, annealing temperature, and observed active and endogenous decay rates. Table S3. List of peptide biomarker targets monitored by MRM analysis. Table S4. Coefficients of variation of replicate experimental MRM injections, digests, extractions and analysis runs. Table S5. Comparison of protein abundances per genome based on DNA extraction approach. Figure S1. Pseudosteady-state mRNA biomarkers normalized to (i) mL of culture, (ii) D. mccartyi rRNA copy, and (iii) D. mccartyi rRNA gene copy. Figure S2. D. mccartyi mRNA decay. Figure S3. iTRAQ protein abundance relative to 100 μg total protein. Figure S4. Peptide abundance per μg total protein quantified via multiple reaction monitoring. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

(1) Maphosa, F.; de Vos, W. M.; Smidt, H. Exploiting the ecogenomics toolbox for environmental diagnostics of organohaliderespiring bacteria. Trends biotechnol. 2010, 28, 308−316. (2) Adrian, L.; Szewzyk, U.; Wecke, J.; Gorisch, H. Bacterial dehalorespiration with chlorinated benzenes. Nature 2000, 408, 580− 583. (3) Bunge, M.; Adrian, L.; Kraus, A.; Opel, M.; Lorenz, W. G.; Andreesen, J. R.; Gorisch, H.; Lechner, U. Reductive dehalogenation of chlorinated dioxins by an anaerobic bacterium. Nature 2003, 421, 357−360. (4) Fennell, D. E.; Nijenhuis, I.; Wilson, S. F.; Zinder, S. H.; Haggblom, M. M. Dehalococcoides ethenogenes strain 195 reductively dechlorinates diverse chlorinated aromatic pollutants. Environ. Sci. Technol. 2004, 38, 2075−2081. (5) Maymo Gatell, X.; Chien, Y. T.; Gossett, J. M.; Zinder, S. H. Isolation of a bacterium that reductively dechlorinates tetrachloroethene to ethene. Science (Washington D C) 1997, 276, 1568−1571. 9395

dx.doi.org/10.1021/es300996c | Environ. Sci. Technol. 2012, 46, 9388−9397

Environmental Science & Technology

Article

(6) Miletto, M.; Williams, K. H.; N’Guessan, A. L.; Lovley, D. R. Molecular analysis of the metabolic rates of discrete subsurface populations of sulfate reducers. Appl. Environ. Microbiol. 2011, 77, 6502−6509 ST - Molecular Analysis of the Metaboli.. (7) Yun, J.; Ueki, T.; Miletto, M.; Lovley, D. R. Monitoring the metabolic status of geobacter species in contaminated groundwater by quantifying key metabolic proteins with geobacter-specific antibodies. Appl. Environ. Microbiol. 2011, 77, 4597−4602 ST - Monitoring the Metabolic Status of.. (8) Lovley, D. R. Cleaning up with genomics: Applying molecular biology to bioremediation. Nat. Rev. Microbiol. 2003, 1, 35−44. (9) Holmes, D. E.; Nevin, K. P.; O’Neil, R. A.; Ward, J. E.; Adams, L. A.; Woodard, T. L.; Vrionis, H. A.; Lovley, D. R. Potential for quantifying expression of the Geobacteraceae citrate synthase gene to assess the activity of Geobacteraceae in the subsurface and on currentharvesting electrodes. Appl. Environ. Microbiol. 2005, 71, 6870−6877 ST - Potential for quantifying expressi.. (10) Major, D. W.; McMaster, M. L.; Cox, E. E.; Edwards, E. A.; Dworatzek, S. M.; Hendrickson, E. R.; Starr, M. G.; Payne, J. A.; Buonamici, L. W. Field demonstration of successful bioaugmentation to achieve dechlorination of tetrachloroethene to ethene. Environ. Sci. Technol. 2002, 36, 5106−5116. (11) Lu, X.; Wilson, J. T.; Kampbell, D. H. Relationship between geochemical parameters and the occurrence of Dehalococcoides DNA in contaminated aquifers. Water Resour. Res. 2006, 42, W08427− W08427. (12) Lendvay, J. M.; Loffler, F. E.; Dollhopf, M.; Aiello, M. R.; Daniels, G.; Fathepure, B. Z.; Gebhard, M.; Heine, R.; Helton, R.; Shi, J.; Krajmalnik-Brown, R.; Major, C. L.; Barcelona, M. J.; Petrovskis, E.; Hickey, R.; Tiedje, J. M.; Adriaens, P. Bioreactive barriers: A comparison of bioaugmentation and biostimulation for chlorinated solvent remediation. Environ. Sci. Technol. 2003, 37, 1422−1431. (13) Hendrickson, E. R.; Payne, J. A.; Young, R. M.; Starr, M. G.; Perry, M. P.; Fahnestock, S.; Ellis, D. E.; Ebersole, R. C. Molecular analysis of Dehalococcoides 16S ribosomal DNA from chloroethenecontaminated sites throughout north America and Europe. Appl. Environ. Microbiol. 2002, 68, 485−495. (14) Ellis, D. E.; Lutz, E. J.; Odom, J. M.; Buchanan, R. J.; Bartlett, C. L.; Lee, M. D.; Harkness, M. R.; DeWeerd, K. A. Bioaugmentation for accelerated in situ anaerobic bioremediation. Environ. Sci. Technol. 2000, 34, 2254−2260. (15) McMurdie, P. J.; Behrens, S. F.; Mueller, J. A.; Goeke, J.; Ritalahti, K. M.; Wagner, R.; Goltsman, E.; Lapidus, A.; Holmes, S.; Loeffler, F. E.; Spormann, A. M. Localized plasticity in the streamlined genomes of vinyl chloride respiring Dehalococcoides. Plos Gen. 2009, 5, e1000714−e1000714. (16) Kube, M.; Beck, A.; Zinder, S. H.; Kuhl, H.; Reinhardt, R.; Adrian, L. Genome sequence of the chlorinated compound respiring bacterium Dehalococcoides species strain CBDB1. Nature biotechnology 2005, 23, 1269−1273. (17) Seshadri, R.; Adrian, L.; Fouts, D. E.; Eisen, J. A.; Phillippy, A. M.; Methe, B. A.; Ward, N. L.; Nelson, W. C.; Deboy, R. T.; Khouri, H. M.; Kolonay, J. F.; Dodson, R. J.; Daugherty, S. C.; Brinkac, L. M.; Sullivan, S. A.; Madupu, R.; Nelson, K. T.; Kang, K. H.; Impraim, M.; Tran, K.; Robinson, J. M.; Forberger, H. A.; Fraser, C. M.; Zinder, S. H.; Heidelberg, J. F. Genome sequence of the PCE-dechlorinating bacterium Dehalococcoides ethenogenes. Science 2005, 307, 105−108. (18) Hoelscher, T.; Goerisch, H.; Adrian, L. Reductive dehalogenation of chlorobenzene congeners in cell extracts of Dehalococcoides sp. strain CBDB1. Appl. Environ. Microbiol. 2003, 69, 2999−3001. (19) Morris, R. M.; Fung, J. M.; Rahm, B. G.; Zhang, S.; Freedman, D. L.; Zinder, S. H.; Richardson, R. E. Comparative proteomics of Dehalococcoides spp. reveals strain-specific peptides associated with activity. Appl. Environ. Microbiol. 2007, 73, 320−326. (20) Nijenhuis, I.; Zinder, S. H. Characterization of hydrogenase and reductive dehalogenase activities of Dehalococcoides ethenogenes strain 195. Appl. Environ. Microbiol. 2005, 71, 1664−1667.

(21) Jayachandran, G.; Gorisch, H.; Adrian, L. Studies on hydrogenase activity and chlorobenzene respiration in Dehalococcoides sp. strain CBDB1. Arch. Microbiol. 2004, 182, 498−504. (22) Magnuson, J. K.; Stern, R. V.; Gossett, J. M.; Zinder, S. H.; Burris, D. R. Reductive dechlorination of tetrachloroethene to ethene by two-component enzyme pathway. Appl. Environ. Microbiol. 1998, 64, 1270−1275. (23) Magnuson, J. K.; Romine, M. F.; Burris, D. R.; Kingsley, M. T. Trichloroethene reductive dehalogenase from Dehalococcoides ethenogenes: Sequence of tceA and substrate range characterization. Appl. Environ. Microbiol. 2000, 66, 5141−5147. (24) Muller, J. A.; Rosner, B. M.; von Abendroth, G.; MeshulamSimon, G.; McCarty, P. L.; Spormann, A. M. Molecular identification of the catabolic vinyl chloride reductase from Dehalococcoides sp. strain VS and its environmental distribution. Appl. Environ. Microbiol. 2004, 70, 4880−4888. (25) Adrian, L.; Goerisch, H. Microbial transformation of chlorinated benzenes under anaerobic conditions. Res. Microbiol. 2002, 153, 131− 137. (26) Adrian, L.; Hansen, S. K.; Fung, J. M.; Gorisch, H.; Zinder, S. H. Growth of Dehalococcoides strains with chlorophenols as electron acceptors. Environ. Sci. Technol. 2007, 41, 2318−2323. (27) Daprato, R. C.; Loffler, F. E.; Hughes, J. B. Comparative analysis of three tetrachloroethene to ethene halorespiring consortia suggests functional redundancy. Environ. Sci. Technol. 2007, 41, 2261−2269. (28) Lee, P. K. H.; Macbeth, T. W.; Sorenson, K. S., Jr.; Deeb, R. A.; Alvarez-Cohen, L. Quantifying genes and transcripts to assess the in situ physiology of “Dehalococcoides” spp. in a trichloroethenecontaminated groundwater site. Appl. Environ. Microbiol. 2008, 74, 2728−2739. (29) Ritalahti, K. M.; Hatt, J. K.; Lugmayr, V.; Henn, L.; Petrovskis, E. A.; Ogles, D. M.; Davis, G. A.; Yeager, C. M.; Lebron, C. A.; Loeffler, F. E. Comparing on-site to off-site biomass collection for Dehalococcoides biomarker gene quantification to predict in situ chlorinated ethene detoxification potential. Environ. Sci. Technol. 2010, 44, 5127−5133. (30) Nishimura, M.; Ebisawa, M.; Sakihara, S.; Kobayashi, A.; Nakama, T.; Okochi, M.; Yohda, M. Detection and identification of Dehalococcoides species responsible for in situ dechlorination of trichloroethene to ethene enhanced by hydrogen-releasing compounds. Biotechnol. Appl. Biochem. 2008, 51, 1−7. (31) Scheutz, C.; Durant, N. D.; Dennis, P.; Hansen, M. H.; Jorgensen, T.; Jakobsen, R.; Cox, E. E.; Bjerg, P. L. Concurrent ethene generation and growth of dehalococcoides containing vinyl chloride reductive dehalogenase genes during an enhanced reductive dechlorination field demonstration. Environ. Sci. Technol. 2008, 42, 9302−9309. (32) Holscher, T.; Krajmalnik-Brown, R.; Ritalahti, K. M.; von Wintzingerode, F.; Gorisch, H.; Loffler, F. E.; Adrian, L. Multiple nonidentical reductive-dehalogenase-homologous genes are common in Dehalococcoides. Appl. Environ. Microbiol. 2004, 70, 5290−5297. (33) Da Silva, M. L. B.; Alvarez, P. J. J. Exploring the correlation between halorespirer biomarker concentrations and TCE dechlorination rates. J. Environ. Eng. Div. (Am. Soc. Civ. Eng.) 2008, 134, 895− 901. (34) Chow, W. L.; Cheng, D.; Wang, S.; He, J. Identification and transcriptional analysis of trans-DCE-producing reductive dehalogenases in Dehalococcoides species. ISME J. 2010, 4, 1020−1030. (35) Fung, J. M.; Morris, R. M.; Adrian, L.; Zinder, S. H. Expression of reductive dehalogenase genes in Dehalococcoides ethenogenes strain 195 growing on tetrachloroethene, trichloroethene, or 2,3-dichlorophenol. Appl. Environ. Microbiol. 2007, 73, 4439−4445. (36) Johnson, D. R.; Lee, P. K. H.; Holmes, V. F.; Fortin, A. C.; Alvarez-Cohen, L. Transcriptional expression of the tceA gene in a Dehalococcoides-containing microbial enrichment. Appl. Environ. Microbiol. 2005, 71, 7145−7151. (37) Lee, P. K. H.; Johnson, D. R.; Holmes, V. F.; He, J. Z.; AlvarezCohen, L. Reductive dehalogenase gene expression as a biomarker for 9396

dx.doi.org/10.1021/es300996c | Environ. Sci. Technol. 2012, 46, 9388−9397

Environmental Science & Technology

Article

physiological activity of Dehalococcoides spp. Appl. Environ. Microbiol. 2006, 72, 6161−6168. (38) Morris, R. M.; Sowell, S.; Barofsky, D.; Zinder, S.; Richardson, R. Transcription and mass-spectroscopic proteomic studies of electron transport oxidoreductases in Dehalococcoides ethenogenes. Environ. Microbiol. 2006, 8, 1499−1509. (39) Werner, J. J.; Ptak, A. C.; Rahm, B. G.; Zhang, S.; Richardson, R. E. Absolute quantification of Dehalococcoides proteins: Enzyme bioindicators of chlorinated ethene dehalorespiration. Environ. Microbiol. 2009, 11, 2687−2697. (40) Chin, K. J.; Esteve-Nunez, A.; Leang, C.; Lovley, D. R. Direct correlation between rates of anaerobic respiration and levels of mRNA for key respiratory genes in Geobacter sulfurreducens. Appl. Environ. Microbiol. 2004, 70, 5183−5189. (41) Rahm, B. G.; Morris, R. M.; Richardson, R. E. Temporal expression of respiratory genes in an enrichment culture containing Dehalococcoides ethenogenes. Appl. Environ. Microbiol. 2006, 72, 5486− 5491. (42) Waller, A. S.; Krajmalnik-Brown, R.; Loffler, F. E.; Edwards, E. A. Multiple reductive-dehalogenase-homologous genes are simultaneously transcribed during dechlorination by Dehalococcoides-containing cultures. Appl. Environ. Microbiol. 2005, 71, 8257−8264. (43) Behrens, S.; Azizian, M. F.; McMurdie, P. J.; Sabalowsky, A.; Dolan, M. E.; Semprini, L.; Spormann, A. M. Monitoring abundance and expression of “Dehalococcoides” species chloroethene-reductive dehalogenases in a tetrachloroethene-dechlorinating flow column. Appl. Environ. Microbiol. 2008, 74, 5695−5703. (44) Futamata, H.; Kaiya, S.; Sugawara, M.; Hiraishi, A. Phylogenetic and Transcriptional Analyses of a Tetrachloroethene-dechlorinating “Dehalococcoides” enrichment culture TUT2264 and its reductivedehalogenase genes. Microbes and Environments 2009, 24, 330−337. (45) Krajmalnik-Brown, R.; Holscher, T.; Thomson, I. N.; Saunders, F. M.; Ritalahti, K. M.; Loffler, F. E. Genetic identification of a putative vinyl chloride reductase in Dehalococcoides sp strain BAV1. Appl. Environ. Microbiol. 2004, 70, 6347−6351. (46) Rahm, B. G.; Richardson, R. E. Dehalococcoides’ gene transcripts as quantitative bioindicators of PCE, TCE and cDCE dehalorespiration rates: Trends and limitations. Environ. Sci. Technol. 2008, 42, 5099−5105. (47) Rahm, B. G.; Richardson, R. E. Correlation of respiratory gene expression levels and pseudo-steady-state PCE respiration rates in Dehalococcoides ethenogenes. Environ. Sci. Technol. 2008, 42, 416−421. (48) Dethlefsen, L.; Schmidt, T. M. Performance of the translational apparatus varies with the ecological strategies of bacteria. J. Bacteriol. 2007, 189, 3237−3245. (49) Dethlefsen, L.; Schmidt, T. M. Differences in codon bias cannot explain differences in translational power among microbes. BMC Bioinf. 2005, 6, 3. (50) Kemp, P. F.; Lee, S.; LARoche, J. Estimating the growth rate of slowly growing marine bacteria from RNA content. Appl. Environ. Microbiol. 1993, 59, 2594−2601. (51) Lee, P. S.; Shaw, L. B.; Choe, L. H.; Mehra, A.; Hatzimanikatis, V.; Lee, K. H. Insights into the relation between mRNA and protein expression patterns: II. Experimental observations in Escherichia coli. Biotechnol. Bioeng. 2003, 84, 834−841. (52) Schaechter O., M. . M.; Kjeldgaard, N. O. Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium. Journal of General Microbiology 1958, 19, 592−606. (53) Freedman, D. L.; Gossett, J. M. Biological Reductive dechlorination of tetrachloroethylene and trichloroethylene to ethylene under methanogenic conditions. Appl. Environ. Microbiol. 1989, 55, 2144−2151. (54) Fennell, D. E.; Gossett, J. M.; Zinder, S. H. Comparison of butyric acid, ethanol, lactic acid, and propionic acid as hydrogen donors for the reductive dechlorination of tetrachloroethene. Environ. Sci. Technol. 1997, 31, 918−926. (55) Rowe, A. R.; Lazar, B. J.; Morris, R. M.; Richardson, R. E. Characterization of the community structure of a dechlorinating mixed

culture and comparisons of gene expression in planktonic and bioflocassociated “Dehalococcoides” and Methanospirillum species. Appl. Environ. Microbiol. 2008, 74, 6709−6719. (56) Smatlak, C. R.; Gossett, J. M.; Zinder, S. H. Comparative kinetics of hydrogen utilization for reductive dechlorination of tetrachloroethene and methanogenesis in an anaerobic enrichment culture. Environ. Sci. Technol. 1996, 30, 2850−2858. (57) Johnson, D. R.; Lee, P. K. H.; Holmes, V. F.; Alvarez-Cohen, L. An internal reference technique for accurately quantifying specific mRNAs by real-time PCR with a application to the tceA reductive dehalogenase gene. Appl. Environ. Microbiol. 2005, 71, 3866−3871. (58) Peirson, S. N.; Butler, J. N.; Foster, R. G. Experimental validation of novel and conventional approaches to quantitative realtime PCR data analysis. Nucl. Acids Res. 2003, 31, e73. (59) Schefe, J.; Lehmann, K.; Buschmann, I.; Unger, T.; FunkeKaiser, H. Quantitative real-time RT-PCR data analysis: Current concepts and the novel ΓÇ £gene expressionΓÇ Ö s C T differenceΓÇ ¥ formula. J. Mol. Med. 2006, 84, 901−910. (60) Benndorf, D.; Balcke, G. U.; Harms, H.; von Bergen, M. Functional metaproteome analysis of protein extracts from contaminated soil and groundwater. ISME J. 2007, 1, 224−234. (61) Fennell, D. E.; Gossett, J. M. Modeling the production of and competition for hydrogen in a dechlorinating culture. Environ. Sci. Technol. 1998, 32, 2450−2460. (62) Marshall, I. P.; Berggren, D. R.; Azizian, M. F.; Burow, L. C.; Semprini, L.; Spormann, A. M. The hydrogenase chip: A tiling oligonucleotide DNA microarray technique for characterizing hydrogen-producing and -consuming microbes in microbial communities. ISME J. 2011, 6, 814−826. (63) Amos, B. K.; Ritalahti, K. M.; Cruz-Garcia, C.; Padilla-Crespo, E.; Loeffler, F. E. Oxygen effect on Dehalococcoides viability and biomarker quantification. Environ. Sci. Technol. 2008, 42, 5718−5726. (64) Wagner, A.; Adrian, L.; Kleinsteuber, S.; Andreesen, J. R.; Lechner, U. Transcription analysis of genes encoding homologues of reductive dehalogenases in “Dehalococcoides” sp. strain CBDB1 by using terminal restriction fragment length polymorphism and quantitative PCR. Appl. Environ. Microbiol. 2009, 75, 1876−84. (65) Bernstein, J. A.; Khodursky, A. B.; Lin, P. H.; Lin-Chao, S.; Cohen, S. N. Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 9697−9702. (66) Domon, B.; Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 2010, 28, 710−721. (67) Lange, V.; Picotti, P.; Domon, B.; Aebersold, R. Selected reaction monitoring for quantitative proteomics: A tutorial. Mol. Syst. Biol. 2008, 4, 222. (68) Duncan, M. W.; Aebersold, R.; Caprioli, R. M. The pros and cons of peptide-centric proteomics. Nat. Biotechnol. 2010, 28, 659− 664. (69) Cupples, A. M.; Spormann, A. M.; McCarty, P. L. Vinyl chloride and cis-dichloroethene dechlorination kinetics and microorganism growth under substrate limiting conditions. Environ. Sci. Technol. 2004, 38, 1102−1107. (70) Yu, S. H.; Dolan, M. E.; Semprini, L. Kinetics and inhibition of reductive dechlorination of chlorinated ethylenes by two different mixed cultures. Environ. Sci. Technol. 2005, 39, 195−205.

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dx.doi.org/10.1021/es300996c | Environ. Sci. Technol. 2012, 46, 9388−9397