Prokaryotic Gene Profiling Assays to Detect Sediment Toxicity

Feb 2, 2007 - Despite their complexity, ecotoxicological measurements using higher level responses remain a major tool in the assessment of ecosystem ...
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Environ. Sci. Technol. 2007, 41, 1790-1796

Prokaryotic Gene Profiling Assays to Detect Sediment Toxicity: Evaluating the Ecotoxicological Relevance of a Cell-Based Assay F . D A R D E N N E , * ,† R . S M O L D E R S , †,‡ W. DE COEN,† AND R. BLUST† Department of Biology, Ecophysiology, Biochemistry and Toxicology Group, University of Antwerp, Groenenborgerlaan 171/U7, B-2020 Antwerp, Belgium, VITO, Environmental Toxicology Group, Boeretang 200, B-2400 Mol, Belgium

Despite their complexity, ecotoxicological measurements using higher level responses remain a major tool in the assessment of ecosystem integrity. Nevertheless, the past decade saw an increasing number of cell based testing systems have found widespread application in ecotoxicology. One such test is bacterial bioreporters carrying a stress sensitive promoter fused to an easily detectable reporter gene. In the presence of a specific toxic stress, the expression cassette is switched on and the reporter gene is produced. This study evaluated the use of 14 different Escherichia coli bioreporter strains sensitive to different types of toxicity in the assessment of the ecological status of a small river basin in Flanders, Belgium. The river is fed at two geographically separate locations by two distinct and welldescribed effluents, one from a household sewage treatment facility and one from the discharge of the wastewater treatment facility of a large chemical plant. The results of the bacterial gene profiling assay were related to active biomonitoring results obtained through higherlevel responses of caged Dreissena polymorpha, Chironomus riparius, and Cyprinus carpio deployed at the locations sampled for the bacterial assay. The results of the gene induction assay and the active biomonitoring data correlated well and corresponded to the flow dilution data, which is used here as a surrogate for the chemical pollution gradient present in the river basin.

1. Introduction Aquatic sediments serve as a sink for pollutants released into the environment; consequently, many fresh water ecosystems are under continuous pollutant stress from various anthropogenic and natural sources. The nature of these pollutants is mainly determined by the surrounding land use and inputs from human activity discharged into the river basin. Several studies addressed the nature and/or impact of pollution on fresh water ecosystems through chemical analysis (1), ecotoxicological monitoring (2), or a combination of both (3). Chemical analysis can add valuable information to cause-effect studies, but it is limited to a previously selected list of chemicals irrespective of bioavail* Corresponding author phone: ++/32/(0)3. 265.35.01; fax ++/ 32/(0)3. 265 44.97; e-mail: [email protected]. † University of Antwerp. ‡ Environmental Toxicology Group. 1790

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ability issues. Biomonitoring approaches (e.g., microcontaminant accumulation levels, species diversity, and abundance) provide direct information about the impact of pollutant stress on an ecosystem, as they take into account differences in bioavailability and interactions of pollutants, going directly to toxicological stress, irrespective of the exact chemical composition or dosage. One of the main problems, however, is that community or ecosystem effects are often detected in a relative late phase of impact, and hence, the need for approaches that allow detection of ecotoxicological impact in theearliest stage possible. Most often, the latter creates the need for extrapolation of test results obtained at lower levels of biological organization to higher levels (4, 5). Many confounding factors hamper predictions between these individual levels, and extrapolation becomes increasingly difficult as the distance between the level of measurement and the target extrapolation level increases (6). Safeguarding and restoration of (polluted) environments require test systems that are straightforward, economically affordable, and at least qualitatively predictive of possible higher level effects. Cell-based assays appeared in ecotoxicology in the early eighties when different studies showed genotoxicity in sediments and/or water column samples using the traditional Ames assay (7), (8). More recently, Johnson et.al (9). used a tandem approach based on the fluorescent marine bacterium Vibrio fischeri, using the commercially available Mutatox and Microtox kit, for assessment of acute and genotoxic sediment toxicity in one setup. The SOS chromotest (10), based on an Escherichia coli strain transformed with a SfiA::lacZ reporter cassette, is probably the most used and best documented cell-based test in ecotoxicology. The test detects genotoxicity through induction of the SOS response system. Bombardier et al. (11) used it to assess PAH driven genotoxicity, and they were able to distinguish among sediments with differing PAH loadings. A similar system is the SOS/Umu test used by Hamer et al. (12); they used an adapted version of this assay spanning more bacterial generations to detect genotoxic activity in environmental samples. Although it is clear that bacterial and eukaryotic (13) test systems are emerging in field studies; in most cases, either overall toxicity or only one specific endpoint is determined. In this study, 13 transgenic E. coli strains with single-copy chromosomal inserts of different promoter:lacZ fusions are used (14) to assess the pollution in a small river basin in Flanders, Belgium. Additionally, the SOS chromotest strain described by Quillardet (15) is employed. The promoters belong to different toxicological endpoint classes and respond in a dose-dependent manner to their inducers (see Supporting Information Table 1). The integration of 14 endpoints in one test enhances the resolution of the assay compared to the traditional oneendpoint tests and, in combination with different extraction methodologies, allows for physicochemical typing of the toxic fraction of a sample improving the toxicity inventory evaluation (TIE) process and consequent choice of remediation strategy, if any. For detailed information on the regulatory mechanism of the different promoters, we refer to specialized literature (16-26). 1.2. Scope of this Study. The aims of this study were 4-fold: (1) Determine whether bacterial gene profiling assays are responsive to toxic stress in both water column and pore water extracts of a river basin in a dose related manner, (2) Demonstrate the added value of a multi-endpoint test system and show that the test system can distinguish different effluents through its multi-endpoint setup, (3) If so, evaluate 10.1021/es062162m CCC: $37.00

 2007 American Chemical Society Published on Web 02/02/2007

FIGURE 1. Exposure and sampling locations and contributions of effluents 1 and 2 to the total flow rate of the stream. Data from July 1999 represent minimal dilution. Data from May 2002 represent average contribution of both effluents during the experimental period. if these assays can be used to quantitatively monitor a documented toxicological gradient, (4) Assess the correspondence of bacterial gene inductions to effects on higher level responses obtained through active biomonitoring.

2. Materials and Methods 2.1. Study Area. The study area is situated in the northern part of Belgium, the Campine, and comprises a small river basin of approximately 12 km2 (Figure 1). We selected five locations that are representative of the basin. Location 1 is the furthest upstream, and 100% of the flow rate is determined by the discharge of a household wastewater treatment plant with a capacity of 6300 inhabitant-equivalents (IE) using a two-step treatment procedure (referred to as effluent 1). The

effluent of this wastewater treatment plant consists mainly of macronutrients such as nitrates and phosphates. A second effluent discharge is situated at exposure location 3 (referred to as effluent 2). This effluent is from an industrial complex, which treats its water on-site in a wastewater treatment plant with a capacity of 43 000 IE using a three-step treatment process before discharge. The effluent has a clear “industrial fingerprint”, showing high conductivity and pH, as well as elevated temperature and chloride levels and low amounts of N-and P-compounds. Before the industrial effluent enters the main river, it flows for about 0.8 km through a brook that enters the river downstream from location 2. The flow of the river is dominated by the two effluent inputs in such a way that VOL. 41, NO. 5, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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(TOT_Eff ) %_Eff_1 + %_Eff_2) during this experiment both effluents had a combined contribution of about 55% to the flow rate at location 5. Contamination in the river basin remains below the Flemish water criteria at all locations. Over the past few years, we studied this small ecosystem using both active and passive biomonitoring with various test organisms. The results were published in several papers (2, 27-32), and they show that over the 4 year time frame of the study, different species show constant responses over time throughout the pollution gradient. Moreover, the basin has a proven historical contamination in the sediment. The bacterial gene-profiling results were related to the results of this series of studies considering whole organism higherlevel responses in invertebrates and fish. 2.2. Sampling and Sample Treatment. Sediment samples were taken in the summer of 2004 in high-density polyethylene containers (2.5 liter) at a maximum sediment depth of 30 cm, taking care of minimal headspace and surface water contamination. Samples were stored as whole sediment in the dark between 8 and 10 °C. Porewater was collected through a nylon sieve and cleared by centrifugation (Avanti J-25 Beckman, Belgium) at 15 000 g for 10 min in Teflon centrifuge tubes. Immediately thereafter, the pore water was processed as described below. Water column samples were taken in 2 liter brown large mouth bottles, allowing easy headspace minimalization and stored like the sediment samples. One liter volumes were extracted as described below. Both water column and pore water samples were extracted by vacuum filtration over 47 mm Empore C18 and SDB-RPS membranes (3M, Belgium), according to the guidelines of the manufacturer (33-36). The Empore C18 membranes consist of bonded silica with a 60 Å pore size to which octadecyl groups are attached, giving the membrane a strongly nonpolar affinity. C18 membranes typically adsorb molecules like organochlorine pesticides (e.g., Aldrin, Toxaphene, dieldrin, ...), Polyaromatic hydrocarbons (e.g., acenaphtene, fluoranthene, chrysene, .....). The SDB-RPS membrane (80 Å pore size) is a poly(styrenedivinylbenzene) copolymer that has been modified with sulfonic acid groups to create a polar affinity. Adsorbed molecules include drugs, polar pesticides and their metabolites, polar organic compounds, amines. After elution from the membrane with methylene chloride, the eluate was dried under N2 and taken up in 3 mL dimethylsulfoxide (DMSO) (Sigma, Belgium). Extracts were analyzed immediately or stored at -20 °C in polypropylene centrifuge tubes. Immediately prior to analysis, extracts were diluted 1/2 with H2O, an additional 1/10 dilution is applied while adding the extracts to the cells. Hence at the highest dose, cells are exposed to a 1/20 dilution of every extract. The final concentration of 5% DMSO is applied to all cells and controls. Negative controls consist of nine cultures per strain exposed only to 5% DMSO. 2.3. Bacterial Strains and Bacterial Gene Profiling Assay. The bacterial strains, except PQ37, and the procedure for the bacterial gene profiling assay are described elsewhere (14). The PQ37 strain is described in White et al. (37). All strains were cultured in Luria Bertani Broth according to standard protocols and kept frozen in 15% v/v glycerol at -80 °C prior to use (38). All chemicals were purchased at UCB, Belgium and of pro analysis grade, unless specified in the text. The bacteria were exposed at the onset of exponential growth for 90 min, after which cells were lysed and the β-galactosidase activity determined. 2.3.1. Calculations. The fold induction at any given dose i for every strain is calculated as the ratio between the β-galactosidase activity at dose i and the average β-galactosidase activity of the non exposed cells (see eq 1). The expression in both denominators (measurements at 420 nm) reflects the β-galactosidase activity, while the expressions in the nominator (measurements at 600 nm) are a correction 1792

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factor for the amount of cells present in the assay during the exposure phase. The difference between the pre-dose (PD) optical density (OD) and the start of exposure (SE) OD corrects for possible color change as a result of sample addition to the cells.

FIi )

[(

(

PE SE OD420nm - OD420nm

PD (OD600nm × 90 min) +

((OD

PE 600nm

SE - OD600nm )×

)

90 min 2

SE PE OD420nm - OD420nm

PD (OD600nm × 90 min) +

(

PE SE (OD600nm - OD600nm )×

90 min 2

)

)

)

i

blanks

]

(1)

PE is the post exposure, SE is the start exposure ()post dose), PD is the pre-dose, OD is the optical density, blanks is the average activity of non exposed cells. As the volume of pore water extracted on the membrane was dependent on the filterability of the samples, pore water fold inductions were corrected for the extracted volume by means of eq 2.

FIvol ) fold _inductioni - fold _inductionnegative.controls +1 Volumepore.water (2) 2.4. Data Analysis. General calculations were done in Excel 2002 (Microsoft Corp.). Statistical calculations were performed in Minitab Release 14.13 (Minitab Inc.), and STATISTICA version 6 (StatSoft, Inc. (2001), Tulsa, OK). Fold inductions were considered significant based on the following criteria: (1) Presence of a dose response relationship (R2 > 0.50, significant at p < 0.05 for six degrees of freedom) and a positive slope different from 0 (p < 0.05) in a linear model, (2) Signal different from and higher as the blank confirmed by Dunnett’s test (p < 0.05). These criteria were applied before volume correction in case of pore water. All assays are performed in triplicate on seven doses (1/2 dilution series) and three blanks giving 30 data points per dose response. 2.5. Active Biomonitoring. In recent years, the river basin was studied at the ecotoxicological level using active biomonitoring. For this purpose, responses of different organisms were studied on the cellular and physiological level, the data used in this study concern caged Chironomus riparius, Dreissena polymorpha, and Cyprinus carpio. All procedures were published in the references listed above under “2.1. Study Area” (31,32) (see also Table 2 in the Supporting Information).

3. Results 3.1. Water Column. Water column C18 and SDB-RPS extracts from all sampling sites were analyzed on all tester strains (Table 3 in the Supporting Information). Although the FI of the SDB-RPS extracts never exceeded the control value by a factor higher than two, dose responses and fold inductions for Locations 1 and 3 were statistically different from the blank. Locations 4 and 5 further downstream do not show inductions. Location 3, the industrial discharge site, shows the most and highest inductions, although never exceeding twice the signal of the blank. The induced promoters indicate membrane and protein perturbation and DNA damage. Apart from Ada and DinD, the household effluent and the downstream location 2 show DNA damage through induction of RecA, UmuDC, and SfiA. Note that the

FIGURE 2. Induction of MicF (A), ClpB (B), and UmuDC (C) after 90 min exposure to C18(X) and SDB-RPS (X′) Empore membrane extracted porewater from all 5 locations. The error bars designate the standard error (n ) 3). Bars per dilution from left to right designate locations 1 through 5. latter promoter only shows a significant signal at the top dose exposure of locations 1 and 2. The C18 extracts show higher inductions. Location 1, the household effluent (Figure 1 in the Supporting Information) shows the clearest signal of all water column extracts, with MicF (membrane damage) and UmuDC and Ada (DNA damage) as main effects. Unexpectedly, at location 2 (downstream of location 1), more promoters are induced. Note that both MicF and OsmY only show a significant response at the undiluted exposure. At location 3 and further downstream at 4, most inductions disappear. Location 5, however, again shows significant inductions, albeit generally very low. 3.2. Porewater. 3.2.1. Induction Signals Versus Pollution Gradient. The SDB-RPS extract of the sewage water treatment facility (Figure 2 in this manuscript and Figure 1 in the Supporting Information) showed relatively low inductions on OsmY, UspA, RecA, Zwf, UmuDC, and Ada, while the industrial effluent shows high inductions of MicF, ClpB, UmuDC, Ada, and Nfo. The latter profile indicates protein and membrane perturbation in concurrence with DNA damage. Further downstream at location 4, only the UmuDC signal remains high. At location 5, remaining inductions are low (see Table 4 in the Supporting Information). The C18 extracts (Figure 2 in this manuscript and Table 4 in the Supporting Information) showed MicF and ClpB inductions throughout the river basin and diminishing downstream. The UmuDC promoter is induced only by the industrial effluent and, likewise, decreases with the pollution gradient. The other induced promoters only showed low inductions. The differences between both discharges become apparent comparing the SDB-RPS profiles to the C18 signals. The majority of the household sewage pollutants are retained only on the C18 membrane, while at least part of the industrial effluent contaminants is retained on both. Although the individual inductions of the promoters at the different locations are not always significantly different from one another, the inductions decrease along the pollution gradient. This is illustrated in Figure 2, showing both for the MicF and ClpB fusion a high induction at effluent 1 (Location 1), a lower signal at the downstream sampling site at location 2, again an increased signal at the industrial effluent (location 3), followed by a downstream gradient toward locations 4 and 5. The same trend is observed for the UmuDC fusion, albeit that at location 1 and 2, no induction of any genotoxic

TABLE 1. Multilinear Regression Analysis (FI ) aEff1 + bEff2 + c) and Linear Regression Analysis (FI) aEff1 + c or FI) bEff2 + c) of Top Dose Inductions against the Effluent Contributions (%)a fold induction

R2

a (p)

ClpB ClpB (Eff1) ClpB (Eff2) MicF MicF (Eff1) MicF (Eff2) UmuDC UmuDC (Eff2) UmuDC (Eff1)

0.992 0.500 0.173 0.978 0.0 0.214 0.846 0.846 0.651

0.108 (0.005) 0.004 (0.910) na 0.114 (0.014) 0.0009 (0.982) na 0.000 (0.999) na -0.021 (0.099)

b (p)

c (p)

0.119 (0.004) - 5.89 (0.010) na 2.871 (0.233) 0.024 (0.487) 2.243 (0.234) 0.129 (0.011) -6.59 (0.026) na 2.959 (0.256) 0.029 (0.433) 2.008 (0.301) 0.024 (0.253) 0.26 (0.845) 0.024 (0.027) 0.257 (0.441) na 1.996 (0.029)

a na: not applicable. Significant correlations are listed in bold in the FI column. (n ) 5).

marker is measured in the porewater extracts. To verify whether a correlation exists between these induction signals and the effluent contributions (Figure 1) to the total river flow, multilinear regression (FI ) aEff1 (%) + bEff2 (%) + c) analysis (n ) 5) was performed. The results are shown in Table 1. The outcome of the regression analysis clearly showed that both the MicF and ClpB signals are strongly correlated to the flow dilution data which can be used as a surrogate for the chemical pollution gradient. They cannot be explained by one of the two effluents alone. The UmuDC signal is only linked to the industrial discharge; taking both effluents into the equation does not explain the data. Simple linear regression showed a good relationship between the UmuDC induction and the industrial discharge, while regression versus the sewage effluent did not give a significant relationship. The negative slope indicates that dilution of the industrial discharge with the household effluent will reduce the genotoxicity signal. 3.2.2. Gene Activation Signals Versus Active Biomonitoring. All higher effect level biomarker responses were shown to follow the pollution gradient as described by the effluent concentrations (%) (see Figure 2 and Table 5 in the Supporting Information). At location 5, the furthest downstream sampling point, all biomarker responses are comparable to the values measured at a nearby reference site. The linear regression data of the gene induction signals and the individual higher level biomarker responses versus VOL. 41, NO. 5, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Linear Regression Data of the Responses: Gene Induction Signals (Porewater C18 Extracts) and Scope for Growth (SFG), Cellular Energy Allocation (CEA) of Dreissena polymorpha, Lipid Budget (LB) of Cyprinus carpio and Growth of Chironomus riparius, versus the Total Effluent Contribution in the River Basina R2 MicF ClpB UmuDC CEA LB SFG Growth

a (p)

b (p)

0.922 0.121 (0.028) -6.66 (0.010) 0.958 0.113 (0.004) -5.94 (0.013) 0.049 0.011 (0.719) 0.15 (0.954) 0.732 -39.2 (0.064) 1817 (0.202) 0.873 278.1 (0.020) -13403 (0.075) 0.673 2.68 × 10-4 (0.089) 0.049 (0.011) 0.931 0.186 (0.008) -3.99 (0.193)

a Response ) a(TOT Eff (%)) + b, p value between brackets, n ) 5. Significant correlations are listed in boldface in the left column.

TABLE 3. Linear Regression Data of the Responses: Gene Induction Signals (Porewater C18 Extracts) and Scope for Growth (SFG), Cellular Energy Allocation (CEA) of Dreissena Polymorpha, Lipid Budget (LB) of Cyprinus Carpio and Growth of Chironomus Riparius Larvae versus the Total Effluent Contribution in the River Basina MicF

ClpB

UmuDC

growth a (p) 1.63 (0.041) b 6.71 (0.021) (p) 0.797 R2

1.55 (0.023) 6.06 (0.019) 0.863

1.09 (0.633) 9.63 (0.046) 0.086

LB

a (p) 1903 (0.098) b 3038 (0.390) (p) 0.653 R2

2174 (0.073) 2104 (0.530) 0.710

8632 (0.165) 109 (0.976) 0.000

CEA

a (p) -285 (0.115) b -449 (0.426) (p) 0.619 R2

-313 (0.112) -347 (0.554) 0.623

-283 (0.602) -1004 (0.241) 0.101

SFG

a (p) -0.002 (0.101) -0.002 (0.081) 0.0008 (0.842) b 0.034 (0.002) 0.035 (0.002) 0.0270 (0.014) (p) 0.647 0.690 0.016 R2

a Response ) a(FI) + b, p value between brackets, n ) 5. Significant correlations are listed in boldface.

the total effluent contribution in the river are shown in Table 2. Both the lipid budget of Cyprinus carpio and the growth of Chironomus riparius larvae correlate well to the total effluent concentration in the stream. The R2 and p values for CEA (0.732 and 0.064) and SFG (0.673 and 0.089) show that, although the p values are slightly above 0.05, a causative relationship between the effluent concentration and the responses measured also exists for these parameters. In the same way it is confirmed that both ClpB and MicF are determined by both the sewage and the industrial effluent, while the UmuDC signal is not. The latter signal does not show any clear-cut correlation to any of the biomarker signals. Table 3 lists the linear regression results of the gene inductions to the higher level responses. MicF and ClpB both correlate significantly to the growth of the Chironomus larvae (R2, respectively, ) 0.797 and 0.863, p < 0.05). The R2 values of the MicF and ClpB correlations to CEA, LB, and SFG indicate that, although the p values are above 0.05, a minimum of 62% (MicF - CEA), and a maximum of 71% (ClpB - LB) of the variability in the datasets is accounted for in all combinations. UmuDC does not show any significant relationship to the other responses. 1794

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4. Discussion It is clear that ecotoxicology needs assays complementary to whole organism tests to quickly and reliably assess possible adverse pollutant effects on ecosystems. Over the past years a number of bacterium-based tests have found their way to ecotoxicological applications. Most detect a single endpoint, such as different types of genotoxicity, e.g., Ames (39), Vitotox (40), Umu-test (41), SOS-chromotest (10), mutatox (42), or measure general (cyto)toxicity, e.g., microtox (Beckman Inc.,1977), (43). The above and similar test systems have been used to monitor pollution of field samples or extracts thereof (44), Nevertheless, only a few studies link cell-based assay data to effects on organismal responses, e.g., macroinvertebrates and diatoms (45), (46). One of the possible reasons for this data gap is that most cellular assays are single endpoint tests and very often measure general toxicity, e.g., growth inhibition. Eggen et al. (47) state that mechanismbased bioassays, like the combination of markers used in this study, can interconnect exposure and effect, as they not only indicate the presence of a toxicant but also provide information on the mode of action. Houtman et al. (13) and van Beelen et al. (48) share the view that microbial toxicity tests can be used to identify the causative classes of compounds if the extraction methodology is cautiously considered. The data presented here show that pore water extraction via different affinity membranes, i.e., the Empore C18 and SDB-RPS membrane and consequent gene induction analysis on a cell-based system, can help to clarify both the physicochemical aspects and the toxicological mode of action of a sample. Our results show that a 14-endpoint bacterial assay system based on stress promoter controlled reporter gene inductions detects pollutant stress in surface water and pore water extracts from the site under study. The obtained gene profiles show mode of action of and differentiate between the household and industrial effluent. Both effluents induce the protein perturbation (ClpB) and membrane damage (MicF) markers, while only the industrial discharge shows genotoxicity (UmuDC). Indeed the genotoxicity signal is not present at locations 1 and 2, yet it does show at location 4, further downstream of location 3, where the industrial discharge is added to the stream. Moreover, the chemical fraction causing the induction ClpB, MicF, and UmuDC by the industrial effluent is adsorbed both to the hydrophilic SDB-RPS membrane and to the hydrophobic C18 membrane, while the household effluent toxicity is only retained on the latter. This clearly shows the applicability of this approach to TIE formats and the added resolving power of multi-endpoint testing as opposed to assays based on one (stress) promoter. Moreover the induction profiles quantitatively follow a documented toxicological gradient as represented by flow dilution data. Multilinear regression (FI ) aEff1 + bEff2 + c) of the reporter gene inductions of both the ClpB and the MicF marker with the effluent concentrations in the river basin proved to be highly significant, describing more than 95% of the variation observed. This clearly indicates the correlation between the discharges and the observed reporter gene induction. The signal of the UmuDC promoter was solely linked to the industrial discharge site quickly disappearing along the down-flow of the river which is less than 5 kilometers. As described in Smolders et al. (27) the response times of the active biomonitoring measurements rank from 3 to 4 days up to 28 days depending on the response that is measured. The bacterial assay showed significant inductions within 1 day and proved to be correlated to responses at more complex levels of biological organization in different test species. The best correlations, significant at the 0.05 confidence level, are found between ClpB and MicF and the growth of Chironomus riparius larvae. The same parameters

show the best correlation to the total effluent contribution in the river basin, explaining between 92 and 96% of the variation in those datasets. Both the MicF and ClpB gene inductions also correlate with the other higher level responses (CEA, SFG, and LB), where still between 62 and 71% of the variation is accounted for, although the p values are above 0.05, ranging up to 0.115. The UmuDC signal does not correlate to the higher level responses measured in this study. Possibly because the UmuDC promoter is part of the SOS response of Escherichia coli and is completely dedicated to DNA damage, a toxicological endpoint not present, nor directly involved with the higher level responses measured in the river basin. Moreover the UmuDC induction is solely attributable to the industrial discharge, quickly diminishing downstream and, therefore, has limited data points useful for correlation to the other parameters. Although our results show a good connectivity between bacterial gene expression and the effects on various higher level biomarkers in traditional ecotoxicological test species, other and more complex river basins need to be evaluated before conclusions about a more general applicability can be made. We feel that our data showed that bacterial geneexpression-based tests represent an important asset as a fast and cost efficient sentinel system, within a broader scope of assays to monitor ecosystem stress.

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Supporting Information Available Tables showing stress gene promoters fused to the lacZ gene and their major inducers, a description of cellular and physiological parameters determined on caged organisms along the pollution gradient, fold inductions of the water column C18 and SDB-RPS extracts, fold inductions of the pore water C18 SDB-RPS extracts, and effluent contributions to the river flow. Two figures show the stress gene profile of all location extracts and the scope for growth and cellular energy allocation of Dreissena polymorpha, lipid budget of Cyprinus carpio, and growth of Chironmus riparius larvae exposed in cages. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Long, J. L. A.; House, W. A.; Parker, A.; Rae, J. E. Micro-organic compounds associated with sediments in the Humber rivers. Sci. Total Environ. 1998, 210 (1-6), 229-253. (2) Smolders, R.; Bervoets, L.; Blust, R. In situ and laboratory bioassays to evaluate the impact of effluent discharges on receiving aquatic ecosystems. Environ. Pollut. 2004, 132 (2), 231-243. (3) De Lange, H. J.; De Jonge, J.; Den Besten, P. J.; Oosterbaan, J.; Peeters, E. T. H. M. Sediment pollution and predation affect structure and production of benthic macroinvertebrate communities in the. J. North Am. Benthological Soc. 2004, 23 (3), 557-579. (4) Munns, W. R. Axes of extrapolation in risk assessment. Hum. Ecol. Risk Assess. 2002, 8 (1), 19-29. (5) De Kruijf, H. A. M. Extrapolation through hierarchical levels. Comp. Biochem. Physiol. 1991, 100C (1/2), 291-299. (6) Munkittrick, K. R.; McCarty, L. S. An integrated approach to aquatic ecosystem health: Top-down, bottom-up or middleout. J. Aquat. Ecosys. Health 1995, 4, 77-90. (7) Grabow, W. O. K.; Vanrossum, P. G.; Grabow, N. A.; Denkhaus, R. Relationship of the raw water-quality to mutagens detectable by the ames salmonella-microsome assay in a drinking-water supply. Water Res. 1981, 15 (9), 1037-1043. (8) Sato, T.; Momma, T.; Ose, Y.; Ishikawa, T.; Kato, K. Mutagenicity of Nagara River sediment. Mutat. Res. 1983, 118 (4), 257-267. (9) Johnson, B. T.; Long, E. R. Rapid toxicity assessment of sediments from estuarine ecosystems: A new tandem in vitro testing approach. Environ. Toxicol. Chem. 1998, 17 (6), 1099-1106. (10) Quillardet, P.; Debellecombe, C.; Hofnung, M. The sos chromotest, a colorimetric bacterial assay for genotoxinssvalidationstudy with 83 compounds. Mutat. Res. 1985, 147 (3), 79-95. (11) Bombardier, M.; Bermingham, N.; Legault, R.; Fouquet, A. Evaluation of an SOS-Chromotest-based approach for the

(20)

(21) (22)

(23)

(24)

(25) (26) (27) (28)

(29) (30)

(31)

(32)

(33)

isolation and detection of sediment-associated genotoxins. Chemosphere 2001, 42 (8), 931-944. Hamer, B.; Bihari, N.; Reifferscheid, G.; Zahn, R. K.; Muller, W. E. G.; Batel, R. Evaluation of the SOS/umu-test post-treatment assay for the detection of genotoxic activities of pure compounds and complex environmental mixtures. Mutat. Res., Genet. Toxicol. Environ. Mutat. 2000, 466 (2), 161-171. Houtman, C. J.; Cenijn, P. H.; Hamers, T.; Lamoree, M. H.; Legler, J.; Murk, A. J.; Brouwer, A. Toxicological profiling of sediments using in vitro bioassays, with emphasis on endocrine disruption. Environ. Toxicol. Chem. 2004, 23 (1), 32-40. Orser, C. S.; Foong, F. C. F.; capaldi, S. R.; Nalezny, W.; Mackay, W.; Benjamin, M.; Farr, S. B. Use of prokaryotic stress promotors as indicators of the mechanisms of chemical toxicity. In Vitro Toxicol. 1995, 8 (1), 71-85. Quillardet, P.; Huisman, O.; Dari, R.; Hofnung, M. Sos chromotest, a direct assay of induction of an sos function in escherichia-coli k-12 to measure genotoxicity. Proc. Natl. Acad. Sci., Biol. Sci 1982, 79 (19), 5971-5975. Nunoshiba, T.; Hidalgo, E.; Cuevas, A.; Demple, B. Two stage control of an oxidative stress regulon: the Escherichia coli SoxR protein triggers redox inducible expression of the soxS regulatory gene. J. Bacteriol. 1992 (Oct), 6054-6060. Aslund, F.; Zengh, M.; Beckwith, Storz G. Regulation of the oxyR transcription factor by hydrogen peroxide and the cellular thioldisulfide status. Proc. Natl. Acad. Sci. 1999, 96, 6161-6165. Kvint, K.; Nachin, L.; Diez, A.; Nystrom, T. The bacterial universal stress protein: function and regulation. Curr. Opin. Microbiol. 2003, 6 (2), 140-145. Delihas, N.; Forst, S. MicF: An antisense RNA gene involved in response of Escherichia coli to global stress factors. J. Mol. Biol. 2001, 313 (1), 1-12. Fawcett, W. P.; Wolf, R. E. Genetic definition of the EscherichiaColi ZWF soxbox, the DNA-binding site for soxS-mediated induction of glucose-6-phosphate-dehydrogenase in response to superoxide. J. Bacter. 1995, 177 (7), 1742-1750. Yim, H. H.; Villarejo, M.; Osmy, A. New hyperosmotically inducible gene, encodes a periplasmic protein in Escherichiacoli. J. Bacteriol. 1992, 174 (11), 3637-3644. Kato, T.; Watanabe, M.; Ohta, T. Induction of the sos response and mutations by reactive oxygen-generating compounds in various Escherichia-coli mutants defective in the mutm, muty or soxRS loci. Mutagenesis 1994, 9 (3), 245-251. VanDyk, T. K.; REED, T. R.; Vollmer, A. C.; LaRossa, R. A. Synergistic induction of the heat-shock response in Escherichiacoli by simultaneous treatment with chemical inducers. J. Bacteriol. 1995, 177 (20), 6001-6004. Diez, A.; Gustavsson, N.; Nystrom, T. The universal stress protein A of Escherichia coli is required for resistance to DNA damaging agents and is regulated by a RecA/FtsK-dependent regulatory pathway. Mol. Microbiol. 2000, 36 (6), 1494-1503. Cuevas, A.; Demple, B. Molecular characterization of the soxRS genes of Escherichia coli: Two genes control a superoxide stress regulon. Nucleic Acids Res. 1991, 19 4479-4484. Demple, B. Redox Signalling and gene control in the Escherichia coli soxRS oxidative stress regulonsa review. Gene. 1996, 179 53-57. Smolders, R.; Bervoets, L.; Wepener, V.; Blust, R. A conceptual framework for using mussels as biomonitors in whole effluent toxicity. Hum. Ecol. Risk Assess. 2003, 9 (3), 741-760. Smolders, R.; Bervoets, L.; De Boeck, G.; Blust, R. Integrated condition indices as a measure of whole effluent toxicity in zebrafish (Danio rerio). Environ. Toxicol. Chem. 2002, 21 (1), 87-93. Smolders, R.; De Boeck, G.; Blust, R. Changes in cellular energy budget as a measure of whole effluent toxicity in zebrafish (Danio rerio). Environ. Toxicol. Chem. 2003, 22 (4), 890-899. Smolders, R.; De Coen, W.; Blust, R. An ecologically relevant exposure assessment for a polluted river using an integrated multivariate PLS approach. Environ. Pollut. 2004, 132 (2), 245263. Smolders, R.; Bervoets, L.; De Coen, W.; Blust, R. Cellular energy allocation in zebra mussels exposed along a pollution gradient: linking cellular effects to higher levels of biological organization. Environ. Pollut. 2004, 129 (1), 99-112. Smolders, R.; Bervoets, L.; Blust, R. Transplanted zebra mussels (Dreissena polymorpha) as active biomonitors in an effluentdominated river. Environ. Toxicol. Chem. 2002, 21 (9), 18891896. Senseman, S. A.; Mueller, T. C.; Riley, M. B.; Wauchope, R. D.; Clegg, C.; Young, R. W.; Southwick, L. M.; Moye, H. A.; Dumas, J. A.; Mersie, W.; Mattice, J. D.; Leidy, R. B. Interlaboratory

VOL. 41, NO. 5, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1795

(34)

(35)

(36)

(37)

(38) (39) (40)

(41)

comparison of extraction efficiency of pesticides from surface and laboratory water using solid-phase extraction disks. J. Agric. Food Chem. 2003, 51 (13), 3748-3752. Vrana, B.; Mills, G.; Greenwood, R.; Knutsson, J.; Svenssone, K.; Morrison, G. Performance optimisation of a passive sampler for monitoring hydrophobic organic pollutants in water. J. Environ. Monit. 2005, 7 (6), 612-620. Ozhan, G.; Ozden, S.; Alpertunga, B. Determination of commonly used herbicides in surface water using solid-phase extraction and dual-column HPLC-DAD. J. Environ. Sci. Health 2005, 40 (6), 827-840. Stephens, B. S.; Kapernick, A.; Eaglesham, G.; Mueller, J. Aquatic passive sampling of herbicides on naked particle loaded membranes: Accelerated measurement and empirical estimation of kinetic parameters. Environ. Sci. Technol. 2005, 39 (22), 8891-8897. White, P. A.; Rasmussen, J. B.; Blaise, C. A. semi-automated, microplate version of the SOS Chromotest for the analysis of complex environmental extracts. Mutat. Res., Environ. Mutagen. Relat. Subjects 1996, 360 (1), 51-74. Sambrook, J.; Fritsch, E. F.; Maniatis, T. Molecular Cloning, A Laboratory Manual; Cold Spring Harbor Press: Woodbury, NY, 1989; Vol. 2. Maron, D. M.; Ames, B. N. Revised Methods For The Salmonella Mutagenicity Test. Mutat. Res. 1983, 113 (3-4), 173-215. Verschaeve, L.; Van Gompel, J.; Thilemans, L.; Regniers, L.; Vanparys, P.; van der Lelie, D. VITOTOX (R) bacterial genotoxicity and toxicity test for the rapid screening of chemicals. Environ. Mol. Mutat. 1999, 33 (3), 240-248. Reifferscheid, G.; Hell, J. Validation of the SOS/umu test using test results of 486 chemicals and comparison with the Ames test and carcinogenicity data. Mutat. Res., Gen. Toxicol. 1996, 369 (3-4), 129-145.

1796

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 41, NO. 5, 2007

(42) Legault, R.; Blaise, C.; Rokosh, D.; Chongkit, R. Comparativeassessment of the sos chromotest kit and the mutatox test with the salmonella plate incorporation (ames test) and fluctuation tests for screening genotoxic agents. Environ. Toxicol. Water Qual. 1994, 9 (1), 45-57. (43) Doherty, F. G. A review of the Microtox (R) toxicity test system for assessing the toxicity of sediments and soils. Water Qual. Res. J. Can. 2001, 36 (3), 475-518. (44) Kemble, N. E.; Hardesty, D. G.; Ingersoll, C. G.; Johnson, B. T.; Dwyer, F. T.; MacDonald, D. D. An evaluation of the toxicity of contaminated sediments from Waukegan Harbor, Illinois, following remediation. Arch. Environ. Contam. Toxicol. 2000, 39 (4), 452-461. (45) Dewhurst, R. E.; Wheeler, J. R.; Chummun, K. S.; Mather, J. D.; Callaghan, A.; Crane, M. The comparison of rapid bioassays for the assessment of urban groundwater quality. Chemosphere 2002, 47 (5), 547-554. (46) Cheung, Y. H.; Neller, A.; Chu, K. H.; Tam, N. F. Y.; Wong, C. K.; Wong, Y. S.; Wong, M. H. Assessment of sediment toxicity using different trophic organisms. Arch. Environ. Contam. Toxicol. 1997, 32 (3), 260-267. (47) Eggen, R. I. L.; Segner, H. The potential of mechanism-based bioanalytical tools in ecotoxicological exposure and effect assessment. Anal. Bioanal. Chem. 2003, 377 (3), 386-396. (48) Van Beelen, P. A review on the application of microbial toxicity tests for deriving sediment quality guidelines. Chemosphere 2003, 53 (8), 795-808.

Received for review September 11, 2006. Revised manuscript received December 12, 2006. Accepted January 4, 2007. ES062162M