Environmental Contaminant Mixtures at Ambient Concentrations

Article pubs.acs.org/jpr

Environmental Contaminant Mixtures at Ambient Concentrations Invoke a Metabolic Stress Response in Goldfish Not Predicted from Exposure to Individual Compounds Alone Julia Jordan,† Ava Zare,† Leland J. Jackson,†,‡ Hamid R. Habibi,†,‡ and Aalim M. Weljie*,† †

Department of Biological Sciences and ‡Institute of Environmental Toxicology 2500 University Drive NW, University of Calgary, Calgary, Alberta, Canada T2N 1N4 S Supporting Information *

ABSTRACT: Environmental contaminants from wastewater and industrial or agricultural areas are known to have adverse effects on development, reproduction, and metabolism. However, reliable assessment of environmental contaminant impact at low (i.e., ambient) concentrations using genomics and transcriptomics approaches has proven challenging. A goldfish model was used to investigate the effects of aquatic pollutant exposure in vivo by means of quantitative nuclear magnetic resonance metabolomics in multiple organs to elucidate a system-wide response. Animals were exposed to 4,4′-isopropylidenediphenol (Bisphenol-A, BPA), di-(2-ethylhexyl)-phthalate (DEHP), and nonylphenol (NP). Metabolite-specific spectral analysis combined with pathway-driven bioinformatics indicated changes in energy and lipid metabolism in liver following exposure to individual contaminants and a tertiary mixture. A dissimilar response in testis exposed to DEHP and mixture indicates disrupted AMPK and cAMP signaling. Uniquely, our observations (1) suggest that exposure to a contaminant mixture is characterized by a stress response not predicted from exposure to individual contaminants, even in the absence of other phenotypic features and (2) demonstrate the sensitivity of metabolomics in risk-assessment of environmental toxicant mixtures at ambient concentrations by detecting early stage metabolic dysregulation. These findings have general applicability in the assessment of “benign” compound mixtures in environmental and pharmaceutical development. KEYWORDS: toxicometabolomics, endocrine disruption, environmental toxicology, stress response, chemometrics, 1 H NMR spectroscopy, toxicant mixtures, integrative metabolomics

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INTRODUCTION Increasing evidence links adverse health impacts in animals and humans to environmental contaminants.1−4 Many contaminants, including pesticides (organochlorine insecticides, herbicides and fungicides), industrial chemicals (dioxins, polychlorinated biphenyls (PCBs)), phthalate plasticizers, phenols, alkylphenol surfactants, polynuclear aromatic hydrocarbons (PAHs), pharmaceuticals, phyto-estrogens, and natural hormones have been shown to have endocrine disrupting properties.6 Due to the complexity of biological responses, an “omic” approach (genomics, transcriptomics, proteomics, and/or metabolomics) may help better understand mechanisms of endocrine disruption and toxicological responses.7,8 Metabolomics makes use of proton nuclear magnetic resonance (1H NMR) or mass spectrometry (MS) to detect changes in metabolite concentration in tissue extracts and/or biofluids.9 1 H NMR metabolomics combined with chemometric pattern recognition has originated largely in the toxicology area10 and has been shown to be a reliable method for assessment of biological toxicity due to its robustness and reproducibility.11 Previous metabolomics studies have provided valuable © 2011 American Chemical Society

information regarding single organ and/or biofluid phenotypic changes in response to environmental contaminants. 1H NMR spectroscopy of liver of rats exposed through intragastric administration to environmental endocrine disruptors has demonstrated hepatotoxicity.12 Significant metabolic changes have also been noted in aquatic systems, such as liver of stickleback following the exposure to low levels of polycyclic aryl hydrocarbon dibenzanthracene13 and altered muscle tissue metabolism in chinook salmon on exposure to crude oil.14 Lastly, exposure of rainbow trout to low levels of synthetic contraceptive estrogen in water showed significant changes in blood phospholipid and alanine levels.15 Alternative to single organ studies, analysis of whole earthworms tissue extracts has indicated potential biomarkers of metal toxicity (maltose)16 and pesticide exposure (ratio of alanine to glycine).9 The single organ and whole organism studies have provided valuable insight into metabolic changes from environmental exposure; however, they have not taken advantage of multiorgan integrative Received: August 29, 2011 Published: December 6, 2011 1133

dx.doi.org/10.1021/pr200840b | J. Proteome Res. 2012, 11, 1133−1143

Journal of Proteome Research

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fed the same amount of commercial fish food once a day (HBH Pet Products). After exposure to chemicals for 10 days, fish were sacrificed in accordance with the guidelines of the Canadian Council of Animal Care.

metabolomics, which can provide important insight into the systemic metabolic response to toxins.17 There is evidence that chemicals that are commonly known as endocrine disrupting chemicals (EDCs) may impair normal metabolic processes via interaction with endocrine hormone receptors like estrogen receptors (ERs), androgen receptors (ARs), thyroid hormone receptors (TRs), as well as nuclear receptors that are involved in metabolism like aryl hydrocarbon receptors (AhRs), retinoid X receptors (RXR), peroxisome proliferator−activated receptors (PPARs), liver X receptors (LXRs), and farsenoid X receptors (FXRs).18,19 Disruption of androgen, estrogen, and thyroid hormone signaling due to contaminant exposure may result in metabolic disturbances.20,21 Estrogen receptors in particular been shown to play an important role in sugar/glucose balance in males.22 AhR regulates genes involved in various physiological functions, including metabolism.23 PPARs are nutrient sensors with the ability to heterodimerize with Retinoid X Receptors (RXRs) and affect lipid and carbohydrate metabolism, and inflammation.24,25 There is evidence that PPAR subtypes are targeted by phthalates, BPA, perfluoroalkyl compounds and organotins.26−29 Chronic and acute exposures to these contaminants result in disruption of energy balance and adipocyte accumulation.27 Aside from known EDC interference with estrogen, androgen and thyroid hormone receptors (ER, AR, TR, respectively), some EDCs also affect dopamine, serotonin and other neurotransmitter receptor pathways, leading to changes in hypothalamic−pituitary− peripheral axis function.30 We have previously demonstrated the presence and adverse effect of a number of chemicals with estrogen-like activity on sexual development of fish in the Oldman River, Alberta.3,4 In practice, animals and humans exposed to real-life aquatic systems are subjected to mixtures of contaminants, and hence, assessment of the mixture effect is important to elucidate physiological implications of multipollutant exposure. In this study, we investigate the effects of a number of chemicals at the same concentrations measured in the Oldman River32 individually and in a mixture, on the metabolic profile of male goldfish liver and testis. Goldfish is a well-studied organism for neuroendocrine signaling and reproduction.33 The extensive information on goldfish transcriptomics and endocrinology can provide context into the effects of contaminants, including endocrine-disrupting chemicals, on goldfish metabolism. To evaluate the toxicometabolic response of fish to the contaminant treatments, we quantitatively profiled 1 H NMR spectra of tissue extracts,34 an approach complementary to established chemometric methods of analysis.35 Multivariate pattern recognition analysis combined with visualization and pathway-discovery tools, allowed us to illustrate the complex metabolic disruption characteristics of contaminants. Acute exposure of goldfish to low levels of water contaminants generated a metabolic response that is unique in mixtures and may be reflective of processes in early organ damage.

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Exposure to Chemicals

DEHP, BPA, and NP were purchased from Sigma-Aldrich. Exposure concentrations used were based on measured concentrations of these chemicals in the Oldman and Bow Rivers, Alberta, Canada.32 Twenty fish were exposed in tanks treated with DEHP (2050 ng/L), NP (223.5 ng/L), BPA (1550 ng/L) or a mixture of the three chemicals. The control group was exposed to the same concentration of the vehicle (DMSO). The treatment groups were randomly assigned to tanks to avoid bias. Animals were exposed for 10 days in glass aquaria supplied with activated carbon-filtered City of Calgary water (flow rate at 300 mL/min). Due to practical limitations, the treatment aquaria were not replicated; however, it would be valuable to assess whether some of the differences are due to variability in aquatic environment between the tanks. The chemicals were added to the water every 24 h after draining the tanks to ∼10% volume and refilling them with fresh water. Therefore, animals were exposed to declining concentrations of contaminants throughout each day for 10 days. The declining actual concentrations of chemicals were not measured over time. Once sacrificed, sex was determined and liver and gonads were removed from the male goldfish for each treatment group. There were 6−8 male fish per tank and we used six male fish per treatment for the analysis. The tissues were immediately snap-frozen in liquid nitrogen and subsequently kept frozen at −80 °C until use for metabolomics analyses. Metabolite Extraction and 1H NMR Spectroscopy

The metabolite extraction protocol is based on and modified from Atherton et al’s protocol.36 A Bruker Advance 600 spectrometer (Bruker Biospin, Milton, Canada) was used to generate total correlation spectroscopy (2D 1H−13C TOCSY) and heteronuclear single quantum coherence spectroscopy (2D 1 H−13C HSQC). Male liver and testes samples were frozen in liquid nitrogen when collected and stored at −80 °C until extraction. The samples’ tissue weights were not measured. The samples were crushed in a mortar with dry ice. A 2:1 methanolchloroform solution was added to the crushed samples prior to sonication for 5 min. After sonication, 200 μL of a chloroformwater solution (1:1) was added to each sample. The samples were then centrifuged at 13,300 rpm for 7 min at 4 °C where they were centrifuged at 13,500 rpm for 20 min. Following centrifugation, the supernatant was pipetted into a new set of 1.5 mL tubes, then dried for at least 24 h, using Speedvac. Dry aqueous fractions were resuspended in 130 μL of 0.5 M NaH2PO4 buffer ([DSS] = 2.5 mM in D2O, pH = 7.0). Ten microliters of 1 M NaN3 was added and the samples were vortexed for ∼15 s, then pH adjusted to 7.00, followed by the addition of 460 μL of H2O. Samples were then transferred to Norell Standard series 5 mm NMR tubes for 1H NMR analysis. A Bruker Advance 600 spectrometer (Bruker Biospin, Milton, Canada) with a 5 mm TXI probe at 298 K was used for 1 H NMR at 600.22 MHz frequency. Standard Bruker pulse sequence noesypr1d was used to obtain all one-dimensional 1 H NMR spectra of aqueous samples and the residual water peak was irradiated during the relaxation delay of 1.0 s and during 100 ms of mixing time. 63536 data over a spectral width of 12195 Hz with a 90° pulse width and 5s repetition time were acquired into 1024 scans. Prior to Fourier transformation, phasing, and baseline correction, a 0.1 Hz line broadening was

MATERIALS AND METHODS

Experimental Animals

Goldfish (Carassius auratus ∼10 cm long, ∼30 g each) were purchased from Aquatic Imports, Calgary, Alberta. The fish were stored and acclimated for 72 h in flow-through glass tanks (49 L) at 17 °C (16−18 fish of unknown gender per tank) and 1134



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was entered into over representation analysis (ORA). The hypergeometric test analyzes the chances of the metabolite set repeating by chance for the compound list and provides metabolic superpathways affected with a one sided P -value.38 Data containing metabolite identifiers (KEGG IDs) and corresponding coefficient values from O2PLS-DA analysis (VIP > 1 metabolites for significant treatment-control comparisons) were uploaded into the Ingenuity Pathways Analysis application (Ingenuity Systems, www.ingenuity.com). Metabolites were overlaid onto a global molecular network developed from information contained in Ingenuity’s Knowledge Base. Networks were then algorithmically generated based on their connectivity. Canonical pathways analysis identified the pathways from the Ingenuity Pathways Analysis library of canonical pathways that were most significant to the data. The significance of the association between the data and the canonical pathway was measured in 2 ways: (1) A ratio of the number of molecules from the data that map to the pathway divided by the total number of molecules that map to the canonical pathway is displayed, and (2) Fisher’s exact test was used to calculate a P-value determining the probability that the association between the genes in the data and the canonical pathway is explained by chance alone. The Functional Analysis identified the biological functions that were most significant to the data. Right-tailed Fisher’s exact test was used to calculate a P-value determining the probability that each biological function assigned to that data set is due to chance alone.

applied to all the spectra. Standard Bruker pulse programs were applied to generate two-dimensional NMR experiments. The following 2D spectroscopy was performed to validate metabolite chemical shift assignments: total correlation spectroscopy (2D 1H−13C TOCSY) and heteronuclear single quantum coherence spectroscopy (2D 1H−13C HSQC). 1

H NMR Data Analysis

Targeted profiling of the resulting 1H NMR spectra was performed with Chenomx NMR Suite 6.0. The spectra for all samples were manually corrected for phase and baseline and then fitted with reference to the DSS peak. Metabolites were identified and quantified using the Chenomx program and its reference literature.34 In order to ensure consistency in fitting, the spectra were fitted in random order and iteratively evaluated several times until a high degree of confidence in the consistency of the metabolite fitting was achieved. Chenomx analysis of sample spectra yielded individual metabolite concentrations that were further internally normalized to account for dilution by calculating the relative abundances of individual metabolites. Concentrations for each metabolite were summed across each sample with exclusion of those whose concentrations were at least 10-fold greater than the rest. This was performed to eliminate the influence of metabolites with excessive contributions to the normalization, however these metabolites were not excluded from the subsequent calculation steps. The relative abundances of all individual metabolites were calculated by dividing the individual concentrations by the sum (previous step). The relative abundances of metabolites were log10 transformed to establish a normal distribution across all the metabolites within each sample.

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RESULTS To identify detectable metabolites and measure their abundance, we used 1H NMR to characterize the aqueous phase from male liver and gonad extracts. Quantitative 1H NMR targeted profiling was used to identify 46 metabolites in liver and 48 in gonad (Table S1, Supporting Information). A sample fitted 1H NMR spectrum is shown in Figure S1 (Supporting Information). Due to subsequent data normalization and analyses, the changes in metabolite abundance spoken of from here on are regarded as changes relative to control metabolite levels.

Statistical Analysis

The transformed data were used in multivariate statistical analysis, as well as to generate z-score plots and heatmaps.37 Individual metabolite z-scores were calculated based on a perorgan control mean and its standard deviation using the formula z-score = ((treatment metabolite abundance − control mean)/standard deviation of control). Multivariate statistical data analysis, including SUS (Shared and Unique structure) plots, was performed on log10-transformed relative abundances with SIMCA-P software. Unsupervised principal component analysis (PCA) was performed on all data to identify the most significant variances and potential outliers. Orthogonal partial least-squares discriminant analysis (O2PLS-DA) identified most significant variations between the treatment groups. Significance of the O2PLS-DA models were assessed based on a cross-validated ANOVA based on a 7-fold cross validation performed during the model building process. CV-ANOVA is a significance test of a null hypothesis that the two compared models have equal cross-validatory residuals (Q2YCV) using the F distribution and a P-value 1 were deemed to be significantly different in the multivariate O2PLS-DA models. Metabolites with VIP > 1 and their corresponding z-scores were used to generate a summary table and heatmaps. VIP > 1 metabolites were used for Metabolic Set Enrichment Analysis (MSEA) and, together with O2PLS-DA coefficients, Ingenuity Pathway Analysis (IPA).

Low-level (sublethal level) Environmental Contaminants, Individually and in a Mixture, Directly Impact Metabolism

To comprehensively analyze variation in metabolite levels, various univariate and multivariate statistical and visualization techniques were employed. Univariate z-score plots were generated to visualize the variance in the data based on individual metabolites, demonstrating the changes in metabolite levels due to treatment effects (Figure S2A,B, Supporting Information). We performed a supervised orthogonal partial least squared discriminant analysis (O2PLS-DA) to test whether the observed shift in z-scores of individual metabolites is indicative of significant changes. Metabolite levels are not truly independent due to biological interactions and O2PLSDA multivariate analysis allows for interpretation of concerted metabolite changes. In all O2PLS-DA models (Table S2: statistics values for all models, Supporting Information), significance of both the overall model (P < 0.05 in crossvalidated ANOVA) and individual metabolites, as indicated by variable influence on the projection parameter greater than one (VIP > 1), were assessed as described in the methods. Initially, a pairwise cross-validated (CV) ANOVA test of each treatmenttreatment or treatment-control pair revealed that the liver-derived metabolic profile exposed to either NP or DEHP significantly differs from the control (P = 0.026, P = 0.021, respectively) but not the mixture (P = 0.19, P = 0.37, respectively) (Table 1).

Pathway Analysis

Metabolic Set Enrichment Analysis (MSEA) was performed on the selected differential metabolites for significant treatmentcontrol pairs for liver and gonads. A list of important compounds 1135



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groups were compared to the control and the mixture. In liver, all treatments resulted in significantly different metabolic profiles compared to the control, while in testes only the mixture and DEHP were significantly different from the control (Table 1). The results clearly demonstrate that the effects of chemicals are organ-specific.

To assess the implications of this observation on the overall data, an O2PLS-DA model of all treatments (n = 6 per group for BPA, Table 1. CV-ANOVA (Cross-validated ANOVA) P-values for Every Treatment-to-Treatment Comparison for Male Liver (italic font) and Gonad (not italic) control Control Mixture BPA NP DEHP a

0.016 0.85 0.18 0.050

mixture

BPA

NP

DEHP

0.015a

0.023 0.013

0.026 0.19 0.028

0.021 0.37 0.016 0.046

1 0.036 0.13

0.23 0.090

Metabolic Perturbation from Toxicants Is Highly Organ Specific

To further investigate the effects of the treatments, we identified individual metabolites with VIP > 1 for all treatment groups compared to the control (Table S3, Supporting Information). The results demonstrate that different treatments changed metabolite levels differentially in liver and testis. For example, BPA, DEHP and the mixture increased levels of aspartate in liver, yet decreased it in testes (Table S3, Supporting Information). Opposite changes were seen for a number of metabolites including 3-aminoisobutyrate, acetate, AMP, citrate, glutamate and lactate in certain treatment groups. For liver and gonad, unidirectional change resulted following treatment with BPA (ADP), NP (asparagine), and the mixture (ADP). To visualize differential metabolic alterations, a unique approach was adopted that combined the univariate and multivariate information. Metabolites with VIP > 1 were selected from the multivariate models and their z-scores were then used to generate heatmaps. Figure 2 illustrates relative increases and decreases in different metabolite concentrations determined to be significant in the pair wise multivariate O2PLSDA analysis that compares the mixture to the control for liver (Figure 2A) and gonad (Figure 2B). Similar heatmaps are illustrated for individual toxicants in Figure S4A,B, Supporting Information. The red color indicates an increase in metabolite z-score and the green color indicates a decrease. The control group did not have extreme negative or positive values and appeared darker due to its clustering around the mean (Figure 2), as z-scores were calculated around the control mean. We utilized SUS (Shared and Unique Structure) plots for the VIP > 1 metabolites of mixture-control comparison to assess the organ-specific response (Figure S5A, Supporting Information). In this analysis, metabolites on the diagonals are shared (positive slope = correlated

0.11

P-values 1 metabolites (multivariate O2PLS-DA modeling) for (A) Liver and (B) Gonad. Red represents an increase in metabolites’ z-scores, while green a decrease. The color bars limits are the minimum and the maximum observed z-scores. For example, the minimum and maximum z-score changes for the liver mixture−control comparison were found to be −5.74 (green) and +3.57 (red), respectively.

A Specific Set of Metabolic Pathways Is Impacted in a Tissue-specific Manner

response, negative slope = inverse response), while the metabolites that are close to the vertical perpendicular (pcorr = 0) are uniquely affected by the mixture treatment in gonad tissue, and those that are close to the horizontal (pcorr = 0) are uniquely affected in the liver. In both organs glutamine, lactate and 3-aminoisobutyrate, aspartate are affected in the same direction, while ADP and taurine are affected in opposite direction. Mixture treatment uniquely affects amino acids leucine, alanine, valine, phenylalanine and proline in liver, and creatine and O-phosphocholine in gonad.

To identify metabolic pathways associated with significantly altered metabolites, the KEGG database (Kyoto Encyclopedia of Genes and Genomes, database) was interrogated, and the role of each metabolite classified in possible pathways and postulated superpathways (Table S3, Supporting Information). All treatments tested affected most or all of amino acid, lipid, carbohydrate, nucleotide and energy metabolic pathways. To establish which pathways were most significantly impacted, Metabolic Set Enrichment Analysis (MSEA) was conducted on the differential metabolites in the pairwise models for liver and gonad as summarized in Figure 3. Results from individual analyses are illustrated in Figure S6A,B, Supporting Information. The top metabolic superpathways were selected using a Pvalues threshold P < 0.05. The results summarize the metabolic pathways that were primarily, but not exclusively, affected by the treatments when compared to the control. Energy and nitrogen metabolism were highly impacted in gonad and liver by exposure to environmental contaminants, while phospholipid metabolism was affected only by DEHP in testis. Interestingly, BPA and the mixture altered major energy and nitrogen pathways in liver including protein biosynthesis, electron transport chain, Krebs cycle, carbohydrate metabolism, ammonia recycling, amino acid metabolism and the urea cycle. It is likely that the observed changes in liver metabolism following treatment with mixtures were caused to a large extent by BPA. In testis, however, the effect of treatment with the mixture was similar to DEHP and caused disruption of protein biosynthesis, carbohydrate metabolism, ammonia recycling and amino acid metabolism (Figure 3). To deduce higher-level biological events that may be mediating the observed alterations in metabolism, further interrogation of the metabolite data from the significant pairwise O2PLSDA models (all liver treatments and DEHP and the mixture in testis) was conducted using Ingenuity Pathway Analysis. This analysis revealed canonical pathways and biological functions affected by treatments as summarized in Figure 4 (Table S4, Supporting Information). Similar to vast metabolic disturbances associated with BPA in liver (Figure 3), most of the major

Mixture Effect Is Not Simply an Additive Contribution of Underlying Constituents

One of the important findings from our analyses is that the net metabolic effect of the mixture is relatively unique between gonad and liver. We hypothesized that in spite of this unique signature a comparison of the most important metabolites from the pairwise models generated by individual contaminants to control versus the mixture to control models would provide some indication of the relative contribution of individual contaminants to the mixture’s effect. We exploited the pairwise comparisons using SUS (Shared and Unique Structure) plots for the VIP > 1 metabolites of each treatment-control pair (Figure S5B, Supporting Information). As mentioned previously, the metabolites on the diagonals are shared while the metabolites that fall on the vertical perpendicular are unique to the individual contaminants and those on the horizontal are unique to the mixture. In liver, tryptophan is unique to the mixture when compared to BPA, π-methylhistidine, leucine and AMP are unique to the mixture when compared to DEHP, and no compounds unique to mixture were identified when compared to NP (Table 2). In gonad, acetate, phenylalanine, glutamine and π-methylhistidine are unique to the mixture when compared to BPA, leucine and π-methylhistidine are unique when compared to DEHP and proline, acetate and π-methylhistidine are unique when compared to NP (Table 2). Therefore, mixture effects are not simply the addition of individual treatment effects. The mixture effects are also organ-specific due to different unique metabolites for each organ (Table 2). Further quantitative characterization of the interaction effects of the various contaminants requires stringent dose−response modeling. 1137



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Table 2. Differential (VIP >1) Metabolites That Are Either Shared between the Individual Treatment and Mixture, or Unique to One of the Twoa shared metabolites organ Liver

treatment DEHP

NP

unidirectional change

unique metabolites opposite change

Aspartate Creatine Proline Fumarate Sarcosine

BPA

Gonad

DEHP

Aspartate 3-Aminoisobutyrate Creatine Lactate O-phosphoethanolamine Proline

NP

BPA

a

Glutamine Sarcosine Phenylalanine

individual treatment Niacinamide

mixture AMP Leucine π-methylhistidine

Alanine Asparagine Acetate Aspartate Citrate Fumarate Lactate Succinate Taurine Acetoacetate Tyrosine

Alanine Aspartate Citrate Fumarate 3-Aminoisobutyrate ADP AMP Lactate Proline Serine Trimethylamine

Tryptophan

Asparagine

π-methylhistidine Leucine

2-Aminobutyrate ADP Creatinine sn-glycerophosphocholine

Acetate Proline π-methylhistidine Acetate Glutamate Phenylalanine π-methylhistidine

Information is derived from the SUS (Shared and Unique Structure) plots.

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DISCUSSION We used a metabolomics approach to investigate the effects of exposure to environmentally relevant low concentrations of BPA, NP, DEHP and a mixture of the contaminants on metabolism of male goldfish. To ascertain the global organismal and organspecific effects we used liver and testis of male goldfish, since the two organs are primary regulatory and metabolic hubs at the midrecrudescence (gonadal development) stage and are responsive to hormonal signals.39 The results provide novel information on changes and possible dysregulation in metabolism of goldfish due to acute exposure to low concentrations of environmental contaminants tested, and identified unique features not previously demonstrated by other experimental approaches. All contaminants significantly affected protein biosynthesis and amino acid, carbohydrate, energy, lipid and nucleotide metabolism. We determined unique features that help identify specific metabolite signatures, indicating that the contaminants tested are not simply estrogen mimics and can disrupt physiological functions by altering multiple pathways, including the endocrine system.

canonical pathways and biological functions were possibly perturbed by BPA including interleukin-1 signaling, DNA repair and protein kinase signaling (Figure 4). Thus, the mixture effect in this case is also similar to BPA along several pathways (Figure 4). Interestingly, the overall metabolic profile observed following treatment with the mixture was significantly different from the BPA-treated group in liver (Table 1). The pathway perturbations described in Figure 3 and Figure 4, however, suggest that the net outcome of the exposure to the mixture is similar to that of BPA. This implies that the contaminant mixture affects similar pathways in liver but to a different extent than BPA alone. Hence we observed significant differences in metabolite profiles between the mixture and BPA in liver. Unlike liver, the mixture’s effect in testis was similar to DEHP regarding the end point pathways perturbed (Figure.4) and were not significantly different from DEHP (Table 1). This suggests that the mixture of contaminants affected similar pathways and most of the same metabolites altered by to DEHP. In both liver and testis mixture effect is linked to DNA repair, cellular maintenance and protein trafficking (Figure 4). A number of the indicated processes are known to be under hormonal control, and may affect further downstream physiological functions related to growth, gonadal development, oxidative stress and overall health of the organism (Figure S7A,B, Supporting Information).

Metabolic Changes Not Necessarily Due to Endocrine Disruptive Effects

Although BPA, NP and DEHP are commonly known as endocrine disrupting chemicals, they have also been shown to affect nuclear receptors other than estrogen receptor (ER), androgen receptor (AR) and thyroid hormone receptor (TR). Examples of these 1138



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Figure 3. Metabolic disturbances due to treatments based on the selected differential metabolites and their superpathways (P < 0.05, greatest number of hits, MSEA analysis).

receptors are PPAR and AhR.18,19 Previous studies indicate an upregulation of PPARγ by BPA and that PPARγ agonists may promote fatty acid storage, increase insulin sensitivity and decrease glucose production by liver.26 As such, PPARγ may play an important role in regulation of energy metabolism.25 Exposure to BPA was shown to increase serum glucose levels and alter resistin, leptin and adiponectin levels thereby changing availability of energy.40 This is consistent with the present results, demonstrating lipid metabolism disruption by BPA through changes in concentrations of creatinine, threonine, O-phosphocholine and valine in liver extracts, and succinate and choline in testicular extracts. Previously, a dose-dependent exposure to NP was shown to cause hyperinsulemia and hypoglycemia,41 which may lead to a

Figure 4. Canonical pathways and biological functions that are significantly (above threshold) implicated based on the networks generated by IPA (www.ingenuity.com) for liver (BPA, DEHP, NP and mixture) and gonad (DEHP and mixture).

development of metabolic syndrome and obesity. In the present study, we demonstrate that low-level acute exposure to NP caused changes in concentrations of O-phosphocholine in liver, a 1139



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compound associated with lipid metabolism. Exposure to DEHP’s metabolite mono- (2-ethylhexyl)-phthalate (MEHP) was shown to affect PPARα, thereby altering lipid metabolism. In the present study, DEHP−induced alteration in acetoacetate and Ophosphocholine levels in liver, and choline levels in testis provide evidence for disruption of lipid metabolism. Carbohydrate and amino acid metabolism were also affected in all goldfish liver and testis treatments, indicating significant impairment of energy metabolism.

useful tool to detect endocrine disrupting actions (androgenic/ estrogenic) of common contaminants following 21 days of exposure.42 However, whether the histological, hormonal or phenotypical disruptive effects are seen may depend on the species, the animal size, the duration of exposure, the contaminant concentration and other factors.43,44 Often, no significant changes are seen in Vtg transcripts of males exposed to EDCs, when metabolic disturbances are noted. For example, Vtg transcript levels were not upregulated by DEHP as a result of the same experiment (personal communication, Ava Zare and Hamid R. Habibi), while the liver metabolome is impacted by the same contaminant (present study). Hence, it is important to utilize a sensitive tool that can detect the relatively early stages of disruption no matter the phenotypical manifestations. Here, we demonstrate that 1H NMR metabolomics is critical in detecting nonphenotypic perturbations in an organism acutely exposed to a mixture of low levels of contaminants. As such, we propose that our observations are reflective of early stage changes, which would be exacerbated at higher doses or with chronic exposure. For example, effects on the lipid metabolism (O-phosphocholine, sn-gycero-3-phosphocholine, choline) and TCA cycle intermediates (fumarate, succinate) in liver due to individual contaminants and mixture suggests early manifestations of hepatotoxic response, analogous to what has been previously observed in 1H NMR toxicity studies. In these separate 1H NMR toxicity studies, similar metabolic effects of a given hepatotoxin on rat liver were noted: when administered in relatively high amounts, it leads to histological45,46 and metabolic45−47 manifestations of hepatic lipidosis due to impaired fatty acid and glucose metabolism. Here, we demonstrate that possibly early stage manifestations of these metabolic effects in liver are generated at low dose acute contaminant exposure in our goldfish model. Further exploration of this phenomenon is possible by broadening the analytical approach, such as incorporating gas-chromatography mass spectrometry, which we have previously used in environmental toxicology work in combination with NMR.48 Additionally, it would be valuable to do a temporal study that addresses the usefulness of genomic and metabolomic methods in detecting the physiological effects of contaminants at different exposure times.

Effects of Low Levels of Contaminants Individually and in Mixture Are Indicative of Early Stage Toxicity and Stress Response

In nature, organisms are exposed to chemicals in mixtures rather than individually. The present study provides novel information on the effects of BPA, NP and DEHP at environmentally relevant low concentrations on metabolic changes in goldfish with an attempt to mimic this context, albeit incompletely. Dose−response study assessing the effects of varying concentrations of contaminants in mixture would be helpful in understanding the interplay of the individual chemicals when combined. In this study we focus only on the environmentally relevant concentrations of contaminants and their effects in mixture. In liver, the contaminant mixture caused a response that differed from BPA, yet was similar in some aspects to DEHP and NP individually. The top superpathway dysregulated by the mixture is protein biosynthesis, suggesting that an overall stress response is generated in the organism. This result might indicate that the compounds interact metabolically when in mixture, and that there is a “push−pull” antagonism between pathways generating an overall stress response that is not observed for individual chemicals. In testis, the mixture’s effect is similar to BPA and DEHP, but significantly different from NP with respect to overall profile. The results suggest that disruption of energy availability and nucleotide metabolism were again possibly caused by induction of oxidative stress response. The effect of individual contaminants and the mixture on liver metabolic profile differed from those observed in testis, indicating that the contaminantmediated response is organ and tissue specific, most likely due to differential (tissue-specific) hormone receptor and receptor subtypes distribution and/or interaction with other organ-specific receptors. Although the mechanisms of metabolic changes are yet to be established, we can make certain conjectures regarding contaminants modes of action on the basis of our findings. In liver, the individual compounds and the mixture affect mainly the amino acid metabolism, and do so to similar extent. However, BPA appears to have a greater effect on carbohydrate, lipid and nucleotide metabolism when compared to NP and DEHP. Aside from amino acid metabolism disruption, NP affects mainly carbohydrate metabolism with no effects directly seen in lipid metabolism. DEHP affects carbohydrate and lipid metabolism equally. Meanwhile, the mixture effect appears to be more global than that of individual compounds, as its effects are seen in all major metabolic pathways in liver. In gonad, similarly to liver, DEHP and mixture mainly interfere with amino acid metabolism, but unlike in liver, they exert equal effects on carbohydrate and nucleotide metabolism. Since we see the noted perturbations at low contaminant concentrations after 10-day exposure, we may infer that at a prolonged or higher concentration exposure might result in greater metabolic disruptions, not exclusive of phenotypic and histological disturbances. For instance, van der Ven et al. (2003) have demonstrated that histopathology might be a

Observed Response Cannot Be Attributed to Estrogenic Signaling Alone

On the basis of IPA analysis, the canonical pathways that are significantly different in testis between the mixture and the control include protein degradation pathway, cAMP signaling, AMPK signaling and DNA mismatch repair, which are important cellular mediators of various hormones and growth factors. Contaminants individually, however, were without these significant effects on canonical pathways. The results demonstrate more overlap between the mixture and BPA in liver than in testis. In the liver, we observed greater differences between mixture and DEHP and NP. These results provide strong support for the hypothesis that DEHP, BPA and NP affect multiple pathways and their disruptive actions cannot be explained only through disruption of estrogenic pathways. Using physiological reproductive parameters a previous study observed that a mixture of natural and synthetic estrogen with NP and BPA had a more pronounced effect than individual contaminants, which to some extent supports the present results.49 Our findings are also consistent with previous studies in mammalian cells that demonstrate that contaminant mixture effects are not simply additive, but may be synergistic or even antagonistic in some cases.50 Importantly, while DEHP (unlike NP and BPA) has a significant metabolic effect on gonad metabolism, the tertiary mixture also has a significant effect, in a manner clearly 1140



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disruption and changes in metabolic pathways responsible for the observed adverse heath impact of aquatic organisms. The novel metabolomic data provided in the present study are important not only to explain effects observed in the environment, such as feminization of male fish, but also potentially toward understanding the progression from the metabolic disturbances seen at low level acute exposures to the phenotypic changes seen at chronic or higher concentration exposures. Lastly, this study illustrates the sensitivity of metabolomics as a tool to gain insight into early manifestations of integral and organ-specific disruptive effects of acute exposure to low level of contaminants on other organisms and human health. Such approaches are important in the evaluation of compounds beyond environmental toxicants, such as pharmaceuticals and natural health products, which may be considered to be present at benign levels.

different from that of the individual chemical DEHP indicating a nontrivial contribution from NP and BPA. It is therefore possible that BPA and NP cause metabolic changes in testis when in mixture, contributing to the observed differences between the mixture and the individual chemicals. This finding is also consistent with the observation that low concentrations estrogen-like individual contaminants cause a significant response in male fish vitellogenin induction when in mixture, but not individually.51 Although the mixture’s effect may be due to synergy between individual compounds, the mixture’s composition and potency have to be considered because many similar acting chemicals still exhibit antagonistic effects.53 Studying the effect of mixtures is therefore very important since it allows evaluation of the net outcome of synergy and antagonism between contaminants and is highly relevant to ecological risk assessment of field-based populations where an understanding of a population’s long-term potential viability are desired.

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ASSOCIATED CONTENT

S Supporting Information *

Consequences of Metabolic Disruption on Reproduction

Table S1. List of metabolites in liver and gonad detected using 1H NMR spectroscopy. Table S2. Statistics values for the models presented in the manuscript. Table S3. List of VIP > 1 metabolites and associated metabolic pathways when treatments compared to control. Table S4. Components of significantly affected biological networks as per Ingenuity Pathway Analysis. Figure S1. Illustrative 1 H NMR spectra overlay of BPA and control. Figure S2. Control mean-based z-score plots of individual metabolites when each treatment is compared to control. Figure S3. Supervised O2PLSDA analysis of all treatments metabolic profiles for liver and gonad. Figure S4. Heatmaps of control mean-based z-scores. Figure S5. Shared and Unique Structure plots for mixture effects in liver compared to gonad (A); and individual treatment compared to mixture in liver and gonad (B). Figure S6. Metabolic pathways significantly affected as per MSEA analysis. Figure S7. Biological pathways significantly affected as per Ingenuity Pathway Analysis. This material is available free of charge via the Internet at http:// pubs.acs.org.

The fish studied in this experiment were in midrecrudescence stage, characterized by a peak in vitellogenin (Vtg) levels in females. Previous work has demonstrated that estrogenmimicking compounds induce a demonstrable increase in Vtg synthesis in males.54 This is likely related to the metabolic alterations observed in this study, as vitellogenin is an egg-yolk precursor protein, and significant organismal resources are devoted to oocyte development and lipogenesis during peak reproductive periods.55 Additionally, Samuelsson et al. (2006) observed connections between the blood lipid and alanine levels and increase in Vtg production in rainbow trout due to estrogenic effects of ethinylestradiol.15 This is consistent with our findings that energy and lipid metabolism were significantly impacted by the contaminants tested. Interestingly, in liver we observe significant increase in O-phosphocholine and decrease in sn-glycero-3-phosphocholine levels due to BPA, DEHP and mixture effects. In testes, however, only NP causes significant decrease of O-phosphocholine and increase of sn-glycero-3phosphocholine levels in gonad. Choline levels are only affected in gonad: BPA and DEHP cause significant increase. Given the fact that Vtg is a macromolecule with 50% of phospholipid content, we may infer that the contaminants might play a role in affecting metabolic routes that lead to Vtg production in male fish and do so in contaminant- and organ-specific manner. As such, it is also important to further consider the differential effect of seasonality on the consequences of EDC metabolic changes, which will affect the response of field-based populations and is the subject of other ongoing investigations.55

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AUTHOR INFORMATION

Corresponding Author

*Mailing address: Department of Biological Sciences, 2500 University Drive NW, University of Calgary, Calgary, AB, Canada T2N 1N4. Phone/Fax: (403) 220-3556. E-mail: aweljie@ ucalgary.ca.

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ACKNOWLEDGMENTS This study was supported by a strategic project grant from National Science and Engineering Research Council of Canada (NSERC) to H.R.H. and L.J.J. and a discovery grant to A.M.W. Special thanks to Flora Pang, Rustem Shakyutdinov, Gavin Duggan and Alsu Nazyirova for their technical support.

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CONCLUSION In summary, our results demonstrated significant organ-specific metabolic changes by low environmental concentrations of BPA, NP and DEHP in goldfish. This carries an impact since there may be uncertainty about harmful level of contaminants in the receiving rivers, especially given current weakness in testing for toxicant mixtures. Demonstration of significant disruptive effects under controlled laboratory conditions provides strong evidence that adverse environmental impacts are possible in aquatic systems due to exposure to low-level mixtures of BPA, NP and DEHP. Our findings provide experimental evidence that ecological risk assessment should be based on the effects of contaminants in mixtures rather than individually. Furthermore, the present study provides a framework to better understand mechanisms of endocrine

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REFERENCES

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Environmental Contaminant Mixtures at Ambient Concentrations

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