Metabolomics for in Situ Environmental Monitoring of Surface Waters

Dec 11, 2013 - Lab-reared male fathead minnows (Pimephales promelas, FHM) were held in mobile monitoring units and exposed on-location to surface ...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/est

Metabolomics for in Situ Environmental Monitoring of Surface Waters Impacted by Contaminants from Both Point and Nonpoint Sources D. M. Skelton,*,† D. R. Ekman,† D. Martinović-Weigelt,‡ G. T. Ankley,§ D. L Villeneuve,§ Q. Teng,† and T. W. Collette*,† †

U.S. EPA, National Exposure Research Laboratory, 960 College Station Rd., Athens, Georgia 30605, United States University of St. Thomas, St. Paul, Minnesota 55105, United States § U.S. EPA, National Health and Environmental Effects Research Laboratory, Duluth, Minnesota 55804, United States ‡

S Supporting Information *

ABSTRACT: We investigated the efficacy of metabolomics for fieldmonitoring of fish exposed to wastewater treatment plant (WWTP) effluents and nonpoint sources of chemical contamination. Lab-reared male fathead minnows (Pimephales promelas, FHM) were held in mobile monitoring units and exposed on-location to surface waters upstream and downstream of the effluent point source, as well as to the actual effluent at three different WWTP sites in Minnesota. After four days of exposure, livers were collected, extracted, and analyzed by 1H NMR spectroscopy and GC-MS to characterize responses of the hepatic metabolome. Multivariate statistical analysis revealed distinct metabolite profile changes in response to effluent exposure from each of the three WWTPs. Differences among locations (i.e., upstream, downstream, and effluent) within each of the three sites were also identified. These observed differences comport with land-use and WWTP characteristics at the study sites. For example, at one of the sites, the metabolomic analyses suggested a positive interactive response from exposure to WWTP effluent and nearby nonpoint (likely agricultural related) contamination. These findings demonstrate the utility of metabolomics as a field-based technique for monitoring the exposure of fish to impacted surface waters.

■

INTRODUCTION Effluent from wastewater treatment plants (WWTP) is frequently a significant source of chemical pollution in aquatic ecosystems. Many components of the influent, such as pharmaceuticals, personal care products, and household and industrial chemicals are not completely removed or degraded by conventional sewage treatment processes and have been detected in waterways receiving WWTP effluent.1,2 A number of these chemicals have been shown to bioaccumulate in fish and/or are considered possible causes of documented adverse impacts on the health and fitness of native fish species.3,4 Additionally, certain land uses, such as urbanization and agricultural activity, have been shown to serve as nonpoint sources of aquatic pollution.5 These multiple sources contribute complex mixtures of contaminants to waterways, making it difficult to attribute specific contaminants to a source and to determine which source(s) are contributors to observed adverse impacts. As a result, there is great demand for improved techniques to assess exposure and resulting effects of aquatic contaminants on fish and other species. Historically, approaches for exposure monitoring typically relied solely on measuring targeted contaminants; unfortunately, this method is often inadequate for fully assessing exposure and risk. For example, some © 2013 American Chemical Society

chemicals (e.g., potent estrogens) may be biologically active at concentrations below their current limit of analytical detection.6 Also, exposure to some harmful contaminants, such as those used in agriculture, may be transient (or pulsed) due to sporadic usage and not present at the time of water sample collection. Furthermore, there are likely many biologically active anthropogenic chemicals that, although not on lists of targeted analytes, nonetheless find their way into the environment. For these and other reasons, effects-based monitoring is being increasingly employed to help address some of these limitations and to serve as a complement to chemical occurrence data. Traditional approaches for effects-based monitoring have typically relied on detection of a small number of biomarkers. For example, enzyme-linked immunosorbent and real-time polymerase chain reaction assays have been employed to detect and quantify levels of specific proteins or genes to monitor the effects of chemical exposure.7,8 The utility of these techniques, however, is limited by their specificity for particular classes of Received: Revised: Accepted: Published: 2395

September 9, 2013 December 10, 2013 December 11, 2013 December 11, 2013 dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

Table 1. Population, Characteristics, and Land Use for Each Site land use (%)

location Site E

Site R

Site H

position relative to WWTP effluenta

drainage area (km2)

total number of people (/km2)

total number of animals

developed

forest

crops

US EF DS

184.9

2

0

1

71

0

240.8

4

0

1

73

0

US EF DS

778.9

106

86 322

15

8

52

807.9

111

87 101

15

8

51

1160

23

1 272 734

6

3

74

1162

23

1 272 749

6

3

74

US EF DS

number of upstream WWTP/ total flow (mgd)

WWTP treatment process

WWTP design flow (mgd)

AS/EA/SF

1.5

3900

19.1

100 000

50/50

5/90

5.4

13 900

68/32

4/0.26

AS/PO

MB

population served by WWTP

WWTP %domestic/ %industrial 100/0

0/0

a

Upstream of effluent (US), downstream of effluent (DS), and at site of WWTP effluent (EF). Table is adapted from Lee et al.17 For the WWTP treatment process, AS = activated sludge, EA = extended aeration, SF = sand filters, PO = pure oxygen, and MB = membrane bioreactor. For WWTP design flow, mgd = million gallons per day.

■

known pollutants. More recently, “omic” technologies, such as transcriptomics, proteomics, and metabolomics have been employed to provide more robust and comprehensive methods to detect chemical-induced biological impacts.9−13 Metabolomics, which measures alterations in up to hundreds of endogenous metabolites simultaneously, is particularly attractive for application to a wide variety of ecologically relevant species, partly because it is not hampered by the absence of a sequenced genome.14 While this technique is now relatively commonplace for laboratory application, few studies have employed metabolomics for effects-based monitoring in the field.10,15,16 The ability to relate metabolite profiles to specific WWTP effluent and/or surrounding land use when deployed in the field would be a powerful complement to traditional chemical monitoring and would greatly aid in informing risk assessment. The primary goal of this study was to evaluate the efficacy of metabolomics for effects-based in situ (i.e., in the field) exposure monitoring near WWTPs. To accomplish this, we examined the hepatic metabolite profiles of male fathead minnows (Pimephales promelas, FHM) using a combination of 1 H nuclear magnetic resonance spectroscopy (1H NMR, for the polar metabolites) and gas chromatography mass spectrometry (GC-MS, for the nonpolar metabolites) to characterize biological responses of FHM exposed to surface waters impacted by different WWTP effluents in watersheds with varying land use patterns. Three sites previously characterized by Lee et al.17 were selected for study, in part on the basis of their differing degrees of urbanization and land use (Table 1). The specific aims of this study were to investigate whether metabolite profiles of FHM could be used to (1) detect “downstream exposure effects” from WWTP effluents; (2) distinguish between effluents from WWTPs that use different treatment types and/or receive different types and amounts of waste (e.g., domestic vs industrial); (3) discriminate between geographically separated waterways (e.g., upstream of WWTPs) with different land uses (e.g., urban vs agricultural); and (4) correlate observed impacts with likely source(s) of contaminants (e.g., point vs nonpoint sources).

MATERIALS AND METHODS

Site Selection and Fish Exposure. The site selection criteria and fish exposure setup are described in detail in our companion paper,18 and a complete description of these sites has also been presented by Lee. et al.17 For convenience, the fish exposure setup is also described in the Supporting Information and key site characteristics are summarized in Table 1. Briefly, lab-reared, sexually mature, male FHM were acclimated to the streamwater temperatures for 72 h in the laboratory before being transported to each site in aerated containers. At each site, the fish were maintained in mobile monitoring units supplied with a continual flow (or static renewal in the case of Site E) of water from locations upstream or downstream of an effluent discharge or final treated effluent from the WWTP (prior to discharge into the receiving water). The units allowed the temperature variation and feeding to be controlled, to help ensure that the quality and composition of the water could be attributed as the primary driver of observed perturbations in the fish metabolome. Note that this study also included a single “control group”, which was transported from the laboratory to Site E where they were exposed (in the same fashion as the site-exposed fish) to filtered Lake Superior water acquired from the U.S. EPA facility in Duluth (MN, USA). After 4 days of exposure, fish were anaesthetized and livers were removed and snap-frozen in liquid nitrogen (see the Supporting Information for further sampling details). Metabolite Extraction. Liver samples from individual fish were extracted using a dual phase extraction procedure adapted from Viant19 and were dried using a vacuum concentrator (Thermo Scientific, Waltham, MA, USA). Samples were processed in a randomized fashion to reduce technical bias in the data set. The polar phase was analyzed using NMR, and the nonpolar phase was analyzed using GC-MS. NMR Spectroscopy of Polar Fraction. Prior to NMR analysis, each sample was reconstituted in 165 μL of 0.1 M sodium phosphate buffered deuterium oxide (pH 7.4) containing 50 μM sodium 3-(trimethylsilyl) propionate2,2,3,3-d4 (TSP), vortexed briefly, and centrifuged at 10 600 relative centrifugal force for 15 min at 4 °C to remove insoluble components. The supernatants were pipetted into a 96-well plate containing 500 μL glass inserts (Microliter Analytical 2396

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

rejected; only peaks consistently detected in at least 80% of the samples were retained.27 The remaining missing values were imputed with the k-nearest neighbor algorithm using the knn function in the impute R-package (http://www.r-project.org/).28 Metabolites were identified by comparing the retention index (Δ of ±10) and EI mass spectrum (similarity ≥70%) to those recorded in the NIST02, Wiley 7, and Golm Metabolome Database 29 mass spectral libraries, using tools available in the MetaboliteDetector software.30 Ions unique for each metabolite were used for relative quantification. Artifact peaks arising from plasticizers, column bleed, and MSTFA reagent, as well as peaks observed in our method blanks, were excluded from the final data set. Normalization to a constant sum of all metabolites in each sample was performed prior to statistical analyses. Multivariate Data Analysis. NMR and GC-MS data were imported into SIMCA-P+ version 13.0 (Umetrics, Umea, Sweden) for multivariate data analysis. NMR data were meancentered and Pareto-scaled. GC-MS data were mean-centered and scaled to unit variance. Principal component analysis (PCA) and partial least-squares-discriminant analysis (PLSDA) models were constructed. Note that the exposures were designed to yield an N of 10 for each of the 10 classes. Two fish (not from the same class) were lost due to sampling mishaps. Seven of the remaining 98 fish were found to be outliers in the NMR data with a Hotellings T2 test at the 95% confidence interval using preliminary PCA models and were omitted from further analysis. Fortunately, no more than one outlier per exposure class was omitted, which left an N of 8 (for one class), 9 (for seven classes), or 10 (for 2 classes) for final analyses of the NMR-acquired data set. (No samples were omitted for the GC-MS analysis.) PLS-DA models were validated using 7-fold cross-validation and permutation testing. All optimized PLS-DA models presented here were tested for significance using ANOVA on the cross-validated (CV) residuals and found to have CV-ANOVA p-values (which are presented in Table S1, Supporting Information) less than 0.05.31 SigmaPlot software version 12.3 (Systat Software Inc., San Jose, CA) was used for univariate data analysis by Student’s t test assuming significance at p < 0.05.

Supplies, Inc., Suwanee, GA, USA) and run in automated fashion using a push-through direct injection method20 using a standard 1D NOESY pulse sequence. Additional details relating to acquisition of NMR spectra are described in the Supporting Information. NMR Data Processing. Spectra were zero-filled, exponentially line broadened, and Fourier transformed (ACD/1D NMR Manager, Advanced Chemistry Development, Toronto, Canada). Using an automated routine, spectra were then phase- and baseline-corrected, referenced to TSP, and subsequently binned at a width of 0.005 ppm. Several regions of the binned data were excluded to eliminate a residual water peak (4.75−4.95 ppm), a residual methanol peak (3.35−3.37 ppm), and a large resonance from betaine that exhibited extremely high leverage and variability (3.26−3.29 ppm). The remaining bins were normalized to unit total integrated intensity and submitted to multivariate data analysis. To identify and visualize metabolites that were significantly different between field exposure classes and controls, we generated “t test filtered difference spectra” based on the binned, edited, and normalized spectra. To generate these difference spectra, we subtracted, on a pairwise basis, the average spectrum of the control fish from the average spectrum of fish in a given field exposure class. We then applied a t test to each spectral bin, which determined if average differences within a particular bin were significantly different between field-deployed and control fish. If the t test was not significant for a particular bin comparison (i.e., p ≥ 0.05), we set the average difference to zero; otherwise, we reported the average difference. (The method for limiting the false discovery rate for this case of multiple hypothesis testing has been described elsewhere.21) For significantly different bins, we identified metabolite peaks using Chenomx NMR Suite 7.0 (Chenomx Inc., Edmonton, Canada) and previously published metabolite chemical shift values.22−24 GC-MS of Nonpolar Fraction. The dried nonpolar metabolite fractions were reconstituted in 10 μL of n-hexane and 10 μL of pyridine and then derivatized with 80 μL of Nmethyl-N-trimethylsily trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS). The samples were vortexed for 1 min and incubated at 37 °C for 30 min. Derivatized samples were analyzed in random order on an Agilent 6890 N gas chromatograph connected to an Agilent 5973 mass spectrometer using a DB-5MS stationary phase column (30 m length, 0.25 mm i.d., 0.25 μm film thickness, and 10 m Duraguard; Agilent J&W Scientific, Folsom, CA). A volume of 1 μL was injected in pulsed splitless mode at 50 psi for 0.5 min using an empty hot needle technique.25 Helium was used as the carrier gas at a constant flow of 1 mL/min. The injector, transfer line, and ion source were maintained at 230, 280, and 230 °C, respectively. The oven was maintained at 60 °C for 1 min and then increased at 10 °C/min to 325 °C where it was maintained for 5 min. The solvent cutoff time was 6.2 min. The MS was operated in electron ionization mode at 70 eV, and data acquisition was performed in full scan mode from m/z 70 to 550 at 3 scans s−1. A series of n-alkanes (C12, C15, C19, C22, C28, C32, C36) (each at 0.2 mg/mL in pyridine) was analyzed under identical GC-MS parameters to calculate linear retention indices. GC-MS Data Processing. The GC-MS chromatograms were subjected to baseline correction, background and noise reduction, peak smoothing and picking, and retention time alignment using metAlign software (www.metalign.wur.nl).26 Peaks with signal-to-noise (S/N) ratios less than 5 were

■

RESULTS AND DISCUSSION Three sites, each containing a WWTP serving a community with varying degrees of agricultural/industrial activity and urbanization, were chosen for conducting in situ exposures of male FHM. The hepatic metabolite profiles of these fish were measured by NMR (polar fraction) and GC-MS (nonpolar fraction). Our main strategy for comparing the metabolite profiles within and between sites was to construct a series of scores plots from validated PLS-DA models. PLS-DA is a supervised classification method that finds the maximum variance between classes.32 By examining the scores plots of these models, we sought to assess the relative responses of the fish to exposure at the various sites, and we considered these responses in relation to land use and other conditions at the sites. Hepatic Metabolite Profiling of Male FHM within Each Site. Site E. The area containing Site E was expected to have minimal anthropogenic chemical burden due to its low population density, absence of any feedlots or significant agricultural activity, lack of local industry, and absence of upstream WWTPs. (Table 1). Therefore, we hypothesized that the locations upstream (US) and downstream (DS) of the 2397

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

Figure 1. Two component scores plots of polar extracts analyzed with NMR using (A) PCA for Site E, (B) PLS-DA for Site R, and (C) PLS-DA for Site H. Classes are labeled as lab control (CON), upstream of effluent (US), downstream of effluent (DS), and effluent outflow (EF) at the various sites. Each marker is the mean of the score values for each location with its associated standard error.

expect a separation of the EF class from the US and CON classes in the PLS-DA scores space. From the validated PLS-DA scores plot in Figure 1B, we clearly observe that the US and DS classes cluster together for the NMR data (and also for the GC-MS data, Figure S1B, Supporting Information), while the EF class is significantly separated from all other classes along the PLS-DA 1 axis (p = 0.0016 and p = 0.0001 for EF vs US for NMR and GC-MS, respectively). This suggests that the WWTP effluent was, indeed, having a significant impact on the FHM hepatic metabolome. Furthermore, the clustering of the DS and US classes indicates that the impact from the WWTP effluent is mitigated at the DS location, perhaps by the effective dilution of effluent contaminants. Additionally, note that the US class is well separated from the CON class along the PLS-DA 2 axis for the NMR (Figure 1B) and GC-MS (Figure S1B, Supporting Information) data, possibly suggesting upstream contamination, and the close clustering of the DS and US classes suggests further that this contamination is also present at the DS location. These observations are consistent with sources of contamination at this site that are unrelated to the local WWTP. However, we can conclude that these contaminants are less impactful on the FHM metabolome than the undiluted WWTP effluent since US and DS class separations from CON are largely along the PLS-DA 2 axis, which captures less variation than PLS-DA 1. Alternative sources of contamination may include both nonpoint sources (e.g., from the significant agricultural activity in this area) and other upstream point sources (there are five WWTPs, totaling 90 million gallons per day discharge, upstream from this site; see Table 1).17 Site H. The area containing Site H is predominantly agricultural, containing the highest percentage of crop-land and, by far, the greatest number of farmed animals of the three sites in this study (Table 1). Therefore, we expected that the US and DS locations at Site H would likely be impacted by nonpoint sources of pollution and, therefore, produce metabolite responses more distinguishable from CON than those observed for Site R where the WWTP effluent was clearly the predominant source of contamination. Indeed, in Figures 1C and S1C, Supporting Information, we observe that the US class is significantly separated from the CON class along both the PLS-DA 1 and PLS-DA 2 axes for both the NMR and GC-MS data, respectively, for Site H. Also,

effluent (EF) would not be significantly impacted by nonpoint source pollution. As such, we expected the hepatic metabolite profiles of FHM at the US location to be similar to those of the controls (CON), and we expected that these two classes would not have a significant separation in the scores space of the PLSDA model. However, a valid PLS-DA model could not be constructed from the data for the four classes (CON, US, DS, and EF) at Site E. In addition, we constructed a series of all possible two-class PLS-DA models comparing the various locations at Site E (e.g., EF vs US, EF vs CON, US vs DS); none were valid. Note that validation of PLS-DA models fail when there is not statistically significant variation between classes. This observation supports our expectation that there would likely be no nonpoint sources of pollution. Furthermore, it suggests that the impact of the WWTP effluent on the hepatic metabolome was minimal, as well. To further investigate this, we utilized PCA, an unsupervised method (and thus not dependent on valid class separation) to construct scores plots to visualize differences in the metabolite profiles at each location within Site E. PCA differs from PLS-DA in that it finds the maximum variance in the data set regardless of class distinction. While not as effective as PLS-DA for identifying the variables most responsible for class separation, PCA scores plots do provide a useful means for visually assessing similarities between classes. The PCA scores plot for the NMR data for Site E is shown in Figure 1A (GC-MS, Figure S1A, Supporting Information). Using a Student’s t test, we observed no statistically significant separation (at p < 0.05) of any of the classes for either data set (NMR or GC-MS) along either the PC1 or PC2 axis. While the EF class may appear to separate from some of the other classes (e.g., the CON class in Figure 1A), none of these comparisons were statistically significant. Site R. The area containing Site R is the most urbanized of the three sites investigated in this study. It has the highest percentage of developed land and the highest population density but also has a significant amount of agricultural activity (Table 1). In addition, this site receives the highest percentage of industrial input (about 50/50 industrial/domestic) at its WWTP. Therefore, we expected that the effluent might significantly perturb the FHM hepatic metabolome due to the presence of anthropogenic chemicals that are associated with urbanization and industrialization and which may not be removed by WWTP processes. If this were correct, we would 2398

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

Figure 2. Two component PLS-DA scores plots of polar extracts analyzed with NMR for locations (A) upstream, (b) effluent, and (c) downstream. CON = lab control. Each marker is the mean of the score values for each site with its associated standard error.

note that for the NMR data (Figure 1C), the DS class is well seperated from the CON class along PLS-DA 1 in the same direction as is the EF class. But, remarkably, the separation between the CON and DS classes is considerably greater than for the CON and EF classes. In other words, water from the downstream location had a greater impact on the fish’s polar hepatic metabolome (relative to the control class) than effluent from this WWTP. This suggests that the WWTP effluent effect is not mitigated at the DS location with regard to the polar metabolite profile and/or that the contaminant contribution to the DS location was not solely attributable to the local WWTP effluent. The situation with the GC-MS data is a bit different (Figure S1C, Supporting Information), suggesting that the downstream contamination perturbing the polar metabolite profile is not as severe for the nonpolar metabolites. This is not completely surprising. Conditions that affect the polar metabolite profile of the FHM hepatic metabolome (as measured by NMR) may not necessarily affect lipid metabolism (as measured by GC-MS) to the same degree. Nonetheless, even for the GC-MS PLS-DA scores plot (Figure S1C, Supporting Information), we note that (as with the NMR data) the perturbation observed from exposure to the WTTP effluent is not significantly more severe (compared to controls) than that observed by exposure to water from other locations at this site. Collectively, these observations strongly suggest significant sources of contamination other than the local WWTP at the US and DS locations. Some of this contamination could be from the four WWTPs that are found upstream from this site. However, these plants are very small, contributing a total flow of only 0.26 mgd (see Table 1). Thus, the extensive agricultural activity in this area, including over one million animals (Table 1), was likely contributing as a significant nonpoint source of contamination at this site. Hepatic Metabolite Profiling of Male FHM between Sites. After comparing the hepatic metabolite profiles of male FHM within each site, we sought to make comparisons between sites to assess the efficacy of metabolomics for discriminating between different geographical areas with various land use and population characteristics. Upstream Locations. On the basis of the land use characteristics in Table 1 and the intrasite comparisons presented above, we predicted that the US locations for Sites H and R would be considerably more contaminated than the

US location for Site E. Indeed, the validated PLS-DA scores plots in Figures 2A and S2A, Supporting Information, support our predictions. In these plots, the Sites H and R US classes cluster separately from the Site E US class and from CON, while the Site E and CON classes cluster together. Furthermore, Site H clusters farther from the CON (p = 0.00002 and p = 6.9 × 10−8 for NMR and GC-MS data, respectively) than does Site R (p = 0.0027 and p = 9.95 × 10−6 for NMR and GC-MS data, respectively) along the PLS-DA 1 axis. This might be expected given the higher farmed animal population and agricultural activity upstream at Site H compared to Site R. Effluent. Our intrasite comparisons showed that the WWTP effluent from Sites H and R had a significant impact on the hepatic FHM metabolome, while the Site E WWTP effluent did not. These observations are reinforced by the validated PLS-DA scores plots in Figures 2B and S2B, Supporting Information. While Sites H and R were well separated from CON along PLS-DA 1, they were also significantly separated from each other along PLS-DA 2 for both the NMR (p = 0.0027) and GC-MS (p = 1.8 × 10−6) data. This indicates that, while the extent of impact from the WWTP effluents at these sites was similar, the nature of the impact was significantly different. As we have discussed, the area containing Site H is predominantly agricultural, while the area containing Site R is more urban. Therefore, the WTTP at Site R serves a much larger human population, has a higher percentage of industrial influent, is designed to handle significantly more flow, and also uses a different type of treatment process than does the WWTP at Site H (Table 1). Given these differences in WWTP characteristics, we might expect the makeup of the effluents to be considerably different, thus perturbing the fish hepatic metabolome in observably different ways. Downstream Locations. Our intrasite comparisons indicated that any WWTP effluent impact on the metabolome was mitigated in the DS locations at Sites E and R while the DS location at Site H was apparently impacted by a combination of WWTP effluent and nonpoint source stressors. These observations are reinforced by the validated PLS-DA scores plots in Figure 2C and S2C, Supporting Information, where we see that the Site E class is not significantly separated from the CON class along either axis for both NMR and GC-MS data, respectively. Additionally, we observe that the Site H DS class is significantly separated from all other classes and furthest 2399

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

Figure 3. Average difference spectra (field-exposed minus control) generated both to assess the overall impact of exposure and to determine changes in hepatic metabolite profiles of the male fathead minnows exposed at the (top) upstream, (middle) effluent outflow, or (bottom) downstream location at Site H. Note that all difference spectra are displayed using the same Y scale; therefore, differences in the magnitude of metabolite changes across groups are meaningful. Shaded bars are used to emphasis peaks discussed in the text. The letters ED at the top of a bar indicates a peak change observed in the difference spectrum for both the effluent outflow and the downstream (but not the upstream) location. The letters UD at the top of a bar indicate a peak change observed in the difference spectrum for both the upstream and the downstream (but not the effluent outflow) location. Tentative peak assignments appear at the bottom of the bars.

location, suggesting contributions from both the local WWTP effluent and other, likely nonpoint, sources. On the other hand, the metabolomic and transcriptomic results are notably different for Site E. Both the NMR and the GC-MS metabolomic analyses indicated little or no impact at any of the Site E locations. Even fish exposed to the WWTP effluent at this site were not statistically distinguishable from controls in PCA plots generated from the NMR and GC-MS analyses (Figures 1A and S1A, Supporting Information, respectively). However, as shown in Figure S3A, Supporting Information, the transcriptomic data indicate a significant impact from the WWTP effluent that is also observed at the DS location. Specific transcript analysis, as discussed in our companion paper,18 suggests that much of this impact is attributable to estrogenic properties of the WWTP effluent. It is not clear how these results can be reconciled in light of the NMR and GC-MS analyses (or vice versa). However, the time scale for impacts at the transcript and the metabolite level could well differ, and changes in gene expression due to stressors do not always manifest themselves as changes in endogenous metabolites.33 One of the most striking results from the transcriptomic analysis18 is that for all sites a substantial portion of genes that were over- or under-expressed in the WWTP effluent-exposed fish were found to persist for fish exposed to the downstream water but were not altered at the upstream location. This result provided compelling evidence that some of the biological effects from the exposure to the WWTP effluents were retained at the downstream locations. The metabolomic data also provided evidence of this situation, particularly in the case of Site H. To illustrate, Figure 3 displays t test filtered difference spectra from the NMR data collected from fish deployed at Site H. To generate these difference spectra, the average spectrum

removed from the CON class, along PLS-DA 1. As we discussed previously, this is possibly due to both an unmitigated effect from the WWTP at this site and/or to contamination not attributable to this site’s WWTP (e.g., runoff from the many animal feedlots in the area). Comparison of Metabolomic and Transcriptomic Responses. Note that the FHM male livers used to generate these samples for metabolomic analysis were split at the time of dissection, and a fraction was used for transcriptomic analysis. These results are described in our companion paper.18 This gave us an opportunity to directly compare the utility and extent of agreement between transcriptomics and metabolomics for this application of effects-based monitoring. In Figure S3, Supporting Information, we present PCA and validated PLS-DA plots from the intrasite transcriptomic analyses in the same format that our metabolomic results are presented here for NMR (Figure 1) and GC-MS (Figure S1, Supporting Information) analyses. In the main, our metabolomic results are quite similar to the transcriptomic results for Sites H and R when comparing relative impacts at the various locations within a site. For example, at Site R, the transcriptomic PLS-DA plot (Figure S3, Supporting Information) indicates a considerable impact from the WWTP effluent, which to some degree, is mitigated at the DS location. Furthermore, the DS and US classes are clustered and located in the scores plot in a way that suggests an alternative, albeit less impactful, source of contamination similarly affecting fish from these two locations. This is in good agreement with both the NMR and GC-MS results (Figures 1B and S1B, Supporting Information, respectively). With regard to Site H, from both the transcriptomic (Figure S3C, Supporting Information) and the NMR-based metabolomic (Figure 1C) analyses, the DS location appears to be significantly more impacted than the US 2400

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

be enriched in fish from the EF location at Site E (relative to both the US and DS locations), the DS and EF locations at Site H (relative to the US location), the EF location at Site R (relative to both the US and DS locations), and also the US location at Site R relative to the DS location.18 In Figure 4, we have plotted the relative levels of cholesterol as measured by GC-MS in the lipid fraction of the liver extracts

for the CON class was subtracted from that of an average “location class” (US, or EF, or DS), as described in the Materials and Methods section. These difference spectra facilitate the identification of metabolites that are changing in response to the various exposures. Specifically, peaks above the baseline correspond to metabolites that increased (relative to controls) upon exposure, while peaks below the baseline correspond to those that decreased. As shown in Figure 3, there are many features common to the difference spectra for the EF and DS exposures that are absent in that for the US exposure at Site H. This includes peaks that correspond to an increase in glycogen at 5.40 and 3.55−3.68 ppm, a decrease in choline and phosphocholine between 3.22 and 3.27 ppm, an increase in lysine at 1.71−1.76 ppm, and an increase in the branched chain amino acids (i.e., valine, leucine, and isoleucine) at 0.93−1.06 ppm. As with the transcriptomic data,18 this presents a compelling case that the WWTP effluent has an impact on downstream water quality and that this impact can induce measurable biological effects in fish at this site. Interestingly, in Figure 3, we note that some peaks discussed above, for example, those corresponding to an increase in glycogen, are actually larger in the difference spectrum for the downstream location than in that for the effluent exposure. This suggests, as did the scores plot of Figure 1C, that biological responses to contamination at the DS location reflect a combination of WWTP effluent and some other unrelated source(s) that are causing positive interactive effects consistent, possibly, with additivity. However, we might expect to see an indication of these other sources of contamination upstream since the US and DS locations lie in close proximity and have similar land use characteristics. But, in the case of glycogen elevation, this is not observed. On the other hand, there are some features present in all three difference spectra (US, EF, and DS) that are, indeed, largest in the spectrum for the DS location. This includes peaks corresponding to NAD+ at 4.20− 4.58 ppm. Furthermore, there are several peaks present in both the US and DS difference spectra that are absent in the EF spectrum. These include glucose at 5.24 ppm, unassigned resonances at 2.68−2.75 ppm, glutamate at 2.34−2.41 ppm, and taurocholic acid at 0.71−0.85 ppm. Collectively, these observations strongly suggest that impacts observed at the DS location are due to both the local WWTP and other unrelated sources. Impacts on Liver Cholesterol Biosynthesis. Historical chemical analysis at some of these sites has revealed the presence of a suite of endocrine active chemicals (EACs),17 and the water at some of these locations has, in the past, been found estrogenic on the basis of in vitro assays.8 For these reasons, we explored in our companion paper18 whether genes that were differentially expressed in these fish were indicative of known responses of EACs and whether they could impact pathways involved in the brain−pituitary−gonadal−hepatic (BPGH) axis functioning. Toward that end, we conducted gene set enrichment analysis (GSEA) using gene sets corresponding to features represented within the conceptual model of the teleost BPGH axis that was recently described by Villeneuve et al.34 One of the gene sets found most often to be enriched in these fish was “liver cholesterol biosynthesis” (LCB). This is an important process that is critical for proper BPGH functioning, and it has been shown to be significantly impacted by certain EACs.35 Specifically, on the basis of binomial comparisons conducted separately for each site, we found LCB gene sets to

Figure 4. Cholesterol levels identified by GC-MS analysis of nonpolar liver extracts. Each bar represents the mean of the normalized peak abundance for each location at each site with its associated standard error. ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001 relative to CON based on a Student’s t test. Bars without asterisks indicate p > 0.05.

for fish deployed at all of the locations in this study, including the controls. Interestingly, at Sites H and R, we see a significant decrease in hepatic cholesterol levels in fish from most of the locations that were reported (in our companion paper) to have enriched LCB gene sets. For example, cholesterol was significantly lower in fish deployed at the EF location at Site R, as compared to controls, and also as compared to the US and DS locations at this site. Incidentally, we note that this is in agreement with the overall trends we observed in PLS-DA scores plots, a large impact from effluent at Site R that is significantly mitigated at the DS location. Furthermore, note in Figure 4 that we observe the lowest level of cholesterol in the fish deployed at the DS location at Site H. However, unsupported by GSEA results, we also see a depression (relative to controls) in the level of cholesterol at the US location at this site. While only in partial agreement with observations from the gene enrichment analysis, both the GSEA and metabolomic data support our argument that the DS (and, to a lesser extent, the US) location at Site H is significantly impacted by sources unrelated to the local WWTP. On the other hand, in sharp contrast to the GSEA results, we again find (with metabolomics) no effects from exposure at any of the locations at Site E. Indeed, levels of cholesterol at all locations at Site E (Figure 4) are only slightly depressed (and by about the same degree), and none of these are significantly different from controls. In closing, this study clearly demonstrates the efficacy of metabolomics for effects-based in situ exposure monitoring of aquatic organisms residing in impacted surface waters using both NMR and GC-MS. Our results show that exposure of fish to surface waters near WWTPs causes perturbation of the hepatic metabolome that results in distinct metabolite profiles. 2401

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

(7) Falciani, F.; Diab, A. M.; Sabine, V.; Williams, T. D.; Ortega, F.; George, S. G.; Chipman, J. K. Hepatic transcriptomic profiles of European flounder (Platichthys f lesus) from field sites and computational approaches to predict site from stress gene responses following exposure to model toxicants. Aquat. Toxicol. 2008, 90 (2), 92−101. (8) Garcia-Reyero, N.; Adelman, I. R.; Martinovic, D.; Liu, L.; Denslow, N. D. Site-specific impacts on gene expression and behavior in fathead minnows (Pimephales promelas) exposed in situ to streams adjacent to sewage treatment plants. BMC Bioinf. 2009, 10, S11. (9) Zhang, Y.; Zhang, X.; Wu, B.; Cheng, S. Evaluating the transcriptomic and metabolic profile of mice exposed to source drinking water. Environ. Sci. Technol. 2011, 46 (1), 78−83. (10) Williams, T. D.; Wu, H.; Santos, E. M.; Ball, J.; Katsiadaki, I.; Brown, M. M.; Baker, P.; Ortega, F.; Falciani, F.; Craft, J. A.; Tyler, C. R.; Chipman, J. K.; Viant, M. R. Hepatic transcriptomic and metabolomic responses in the stickleback (Gasterosteus aculeatus) exposed to environmentally relevant concentrations of dibenzanthracene. Environ. Sci. Technol. 2009, 43 (16), 6341−6348. (11) Jones, O. A. H.; Swain, S. C.; Svendsen, C.; Griffin, J. L.; Sturzenbaum, S. R.; Spurgeon, D. J. Potential new method of mixture effects testing using metabolomics and Caenorhabditis elegans. J. Proteome Res. 2012, 11 (2), 1446−1453. (12) Samuelsson, L. M.; Björlenius, B.; Förlin, L.; Larsson, D. G. J. Reproducible 1H NMR-based metabolomic responses in fish exposed to different sewage effluents in two separate studies. Environ. Sci. Technol. 2011, 45 (4), 1703−1710. (13) Sellin Jeffries, M. K.; Mehinto, A. C.; Carter, B. J.; Denslow, N. D.; Kolok, A. S. Taking microarrays to the field: Differential hepatic gene expression of caged fathead minnows from Nebraska watersheds. Environ. Sci. Technol. 2011, 46 (3), 1877−1885. (14) Fiehn, O. Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48 (1−2), 155−171. (15) Bundy, J. G.; Keun, H. C.; Sidhu, J. K.; Spurgeon, D. J.; Svendsen, C.; Kille, P.; Morgan, A. J. Metabolic profile biomarkers of metal contamination in a sentinel terrestrial species are applicable across multiple sites. Environ. Sci. Technol. 2007, 41 (12), 4458−4464. (16) Viant, M. R.; Rosenblum, E. S.; Tjeerdema, R. S. NMR-based metabolomics: A powerful approach for characterizing the effects of environmental stressors on organism health. Environ. Sci. Technol. 2003, 37 (21), 4982−4989. (17) Lee, K. E.; Langer, S. K.; Barber, L. B.; Writer, J. H.; Ferrey, M. L.; Schoenfuss, H. L.; Furlong, E. T.; Foreman, W. T.; Gray, J. L.; ReVello, R. C.; Martinovic, D.; Woodruff, O. P.; Keefe, S. H.; Brown, G. K.; Taylor, H. E.; Ferrer, I.; Thurman, E. M. Endocrine active chemicals, pharmaceuticals, and other chemicals of concern in surface water, wastewater-treatment plant effluent, and bed sediment, and biological characteristics in selected streams. Minnesota-design, methods, and data, 2009: U.S. Geological Survey Data Series 575; U.S. Geological Survey: Reston, VA, 2011; 54 p., with appendixes. (18) Martinović-Weigelt, D.; Mehinto, A.; Ankley, G. T.; Denslow, N.; Barber, L. B.; Lee, K.; King, R.; Schoenfuss, H. L.; Villeneuve, D. L. Transcriptomic-based effects monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution. Environ. Sci. Technol. DOI: 10.1021/es404027n. (19) Viant, M. R. Revealing the metabolome of animal tissues using H-1 nuclear magnetic resonance spectroscopy. In Methods in Molecular Biology; Weckwerth, W., Ed.; Humana Press Inc: Totowa, NJ, 2007; Vol. 358, pp 229−246. (20) Teng, Q.; Ekman, D. R.; Huang, W. L.; Collette, T. W. Pushthrough direct injection NMR: An optimized automation method applied to metabolomics. Analyst 2012, 137 (9), 2226−2232. (21) Collette, T. W.; Teng, Q.; Jensen, K. M.; Kahl, M. D.; Makynen, E. A.; Durhan, E. J.; Villeneuve, D. L.; Martinovic-Weigelt, D.; Ankley, G. T.; Ekman, D. R. Impacts of an anti-androgen and an androgen/ anti-androgen mixture on the metabolite profile of male fathead minnow urine. Environ. Sci. Technol. 2010, 44 (17), 6881−6886. (22) Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D. D.; Psychogios, N.; Dong, E.; Bouatra, S.; Mandal, R.; Sinelnikov, I.; Xia, J. G.; Jia, L.; Cruz, J. A.; Lim, E.;

These profiles differentiate between WWTPs that utilize different wastewater treatment methods and/or receive different amounts of influent generated from different sources (e.g., domestic vs industrial) and also differentiate between different types of land use (e.g., urban vs agricultural). The results also demonstrate that these distinct metabolite profiles correlate with likely point and/or nonpoint source(s) of contaminants. Furthermore, our metabolomic results generally comport well with the transcriptomic data presented in a companion paper18 and demonstrate the utility of combining these techniques for characterizing the biological responses of fish exposed to impacted surface waters.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional experimental information and methods, figures of two component scores plots, and a table of CV-ANOVA pvalues for the PLS-DA models. This information is available free of charge via the Internet at http://pubs.acs.org/.

■

AUTHOR INFORMATION

Corresponding Authors

*Phone: 706-355-8257; fax: 706-355-8302; e-mail: skelton. [email protected]. *Phone: 706-355-8211; fax: 706-355-8302; e-mail: collette. [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors thank Kathy Lee (USGS) and Heiko Schoenfuss (St. Cloud State University) and their co-workers for their efforts in collecting samples that were used in this study, as well as the Minnesota Clean Water Fund and the Minnesota Pollution Control Agency. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

■

REFERENCES

(1) Schwarzenbach, R. P.; Escher, B. I.; Fenner, K.; Hofstetter, T. B.; Johnson, C. A.; von Gunten, U.; Wehrli, B. The challenge of micropollutants in aquatic systems. Science 2006, 313 (5790), 1072− 1077. (2) la Farre, M.; Perez, S.; Kantiani, L.; Barcelo, D. Fate and toxicity of emerging pollutants, their metabolites and transformation products in the aquatic environment. TrAC, Trends Anal. Chem. 2008, 27 (11), 991−1007. (3) Al-Salhi, R.; Abdul-Sada, A.; Lange, A.; Tyler, C. R.; Hill, E. M. The xenometabolome and novel contaminant markers in fish exposed to a wastewater treatment works effluent. Environ. Sci. Technol. 2012, 46 (16), 9080−9088. (4) Barber, L. B.; Lee, K. E.; Swackhamer, D. L.; Schoenfuss, H. L. Reproductive responses of male fathead minnows exposed to wastewater treatment plant effluent, effluent treated with XAD8 resin, and an environmentally relevant mixture of alkylphenol compounds. Aquat. Toxicol. 2007, 82 (1), 36−46. (5) Crago, J.; Corsi, S. R.; Weber, D.; Bannerman, R.; Klaper, R. Linking biomarkers to reproductive success of caged fathead minnows in streams with increasing urbanization. Chemosphere 2011, 82 (11), 1669−1674. (6) Parrott, J. L.; Blunt, B. R. Life-cycle exposure of fathead minnows (Pimephales promelas) to an ethinylestradiol concentration below 1 ng/L reduces egg fertilization success and demasculinizes males. Environ. Toxicol. 2005, 20 (2), 131−141. 2402

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403

Environmental Science & Technology

Article

Sobsey, C. A.; Shrivastava, S.; Huang, P.; Liu, P.; Fang, L.; Peng, J.; Fradette, R.; Cheng, D.; Tzur, D.; Clements, M.; Lewis, A.; De Souza, A.; Zuniga, A.; Dawe, M.; Xiong, Y. P.; Clive, D.; Greiner, R.; Nazyrova, A.; Shaykhutdinov, R.; Li, L.; Vogel, H. J.; Forsythe, I. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 2009, 37, D603−D610. (23) Teng, Q.; Huang, W. L.; Collette, T. W.; Ekman, D. R.; Tan, C. A direct cell quenching method for cell-culture based metabolomics. Metabolomics 2009, 5 (2), 199−208. (24) Ekman, D. R.; Teng, Q.; Jensen, K. M.; Martinovic, D.; Villeneuve, D. L.; Ankley, G. T.; Collette, T. W. NMR analysis of male fathead minnow urinary metabolites: A potential approach for studying impacts of chemical exposures. Aquat. Toxicol. 2007, 85 (2), 104−112. (25) Grob, K.; Rennhard, S. Evaluation of syringe handling techniques for injections into vaporizing GC injectors. J. High Resolution Chromatogr. 1980, 3 (12), 627−633. (26) Lommen, A.; Kools, H. J. MetAlign 3.0: Performance enhancement by efficient use of advances in computer hardware. Metabolomics 2012, 8 (4), 719−726. (27) Bijlsma, S.; Bobeldijk, L.; Verheij, E. R.; Ramaker, R.; Kochhar, S.; Macdonald, I. A.; van Ommen, B.; Smilde, A. K. Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Anal. Chem. 2006, 78 (2), 567−574. (28) Hrydziuszko, O.; Viant, M. Missing values in mass spectrometry based metabolomics: An undervalued step in the data processing pipeline. Metabolomics 2012, 8 (1), 161−174. (29) Kopka, J.; Schauer, N.; Krueger, S.; Birkemeyer, C.; Usadel, B.; Bergmuller, E.; Dormann, P.; Weckwerth, W.; Gibon, Y.; Stitt, M.; Willmitzer, L.; Fernie, A. R.; Steinhauser, D. [email protected]: The Golm Metabolome Database. Bioinformatics 2005, 21 (8), 1635−1638. (30) Hiller, K.; Hangebrauk, J.; Jager, C.; Spura, J.; Schreiber, K.; Schomburg, D. MetaboliteDetector: Comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal. Chem. 2009, 81 (9), 3429−3439. (31) Eriksson, L.; Trygg, J.; Wold, S. CV-ANOVA for significance testing of PLS and OPLS (R) models. J. Chemom. 2008, 22 (11−12), 594−600. (32) Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in metabonomics. J. Proteome Res. 2006, 6 (2), 469−479. (33) Nicholson, J. K.; Holmes, E.; Lindon, J. C.; Wilson, I. D. The challenges of modeling mammalian biocomplexity. Nat. Biotechnol. 2004, 22 (10), 1268−1274. (34) Villeneuve, D. L.; Garcia-Reyero, N.; Martinovic-Weigelt, D.; Li, Z. H.; Watanabe, K. H.; Orlando, E. F.; LaLone, C. A.; Edwards, S. W.; Burgoon, L. D.; Denslow, N. D.; Perkins, E. J.; Ankley, G. T. A graphical systems model and tissue-specific functional gene sets to aid transcriptomic analysis of chemical impacts on the female teleost reproductive axis. Mutat. Res., Genet. Toxicol. Environ. Mutagen. 2012, 746 (2), 151−162. (35) Ekman, D. R.; Teng, Q. N.; Villeneuve, D. L.; Kahl, M. D.; Jensen, K. M.; Durhan, E. J.; Ankley, G. T.; Collette, T. W. Profiling lipid metabolites yields unique information on sex- and timedependent responses of fathead minnows (Pimephales promelas) exposed to 17 alpha-ethynylestradiol. Metabolomics 2009, 5 (1), 22− 32.

2403

dx.doi.org/10.1021/es404021f | Environ. Sci. Technol. 2014, 48, 2395−2403