Proteomic analysis of a model fish species exposed to individual pesticides and a binary mixture

Aquatic Toxicology 101 (2011) 196–206

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Aquatic Toxicology journal homepage: www.elsevier.com/locate/aquatox

Proteomic analysis of a model fish species exposed to individual pesticides and a binary mixture Adam D. Biales ∗ , David C. Bencic, Robert L. Flick, Karen A. Blocksom, James M. Lazorchak, David L. Lattier US EPA Office of Research and Development, National Exposure Research Laboratory, United States

a r t i c l e

i n f o

Article history: Received 21 June 2010 Received in revised form 20 September 2010 Accepted 25 September 2010 Keywords: DIGE Mixtures Biomarkers Pimephales promelas Pyrethroid Organophosphate Proteomics

a b s t r a c t Pesticides are nearly ubiquitous in surface waters of the United States, where they often are found as mixtures. The molecular mechanisms underlying the toxic effects of sub-lethal exposure to pesticides as both individual and mixtures are unclear. The current work aims to identify and compare differentially expressed proteins in brains of male fathead minnows (Pimephales promelas) exposed for 72 h to permethrin (7.5 ␮g/L), terbufos (57.5 ␮g/L) and a binary mixture of both. Twenty-four proteins were found to be differentially expressed among all three treatments relative to the control using an ANOVA followed by a Dunnett’s post hoc test (p ≤ 0.05). One protein was found to be differentially expressed among all treatment groups and one protein was in common between the terbufos and the mixture group. Fifteen spots were successfully sequenced using LC–MS/MS sequencing. Proteins associated with the ubiquitin–proteasome system, glycolysis, the cytoskeleton and hypoxia were enriched. As a second objective, we attempted to establish protein expression signatures (PES) for individual permethrin and terbufos exposures. We were unable to generate a useable PES for terbufos; however, the permethrin PES was able to distinguish between control and permethrin-exposed individuals in an independent experiment with an accuracy of 87.5%. This PES also accurately classified permethrin exposed individuals when the exposure occurred as part of a mixture. The identification of proteins differentially expressed as a result of pesticide exposure represent a step forward in the understanding of mechanisms of toxicity of permethrin and terbufos. They also allow a comparison of molecular responses of the binary mixture to single exposures. The permethrin PES is the first step in establishing a method to determine exposures in real-world scenarios. Published by Elsevier B.V.

1. Introduction Pesticides are nearly ubiquitous in receiving waters in the United States, where they are often found in complex mixtures (Gilliom, 2007). The health and reproductive status of aquatic organisms have been shown to be negatively impacted by exposure to these compounds (Baldwin et al., 2009; Dutta et al., 2006). Organophosphate (OP) and pyrethroid insecticides are among the most heavily used classes of pesticides and both are considered highly toxic to fish. Members of these two pesticide classes overlap in crop and geographical usage. Their application often occurs during the dormant season, coinciding with spring rain events, which results in an increased likelihood of run-off into receiving waters (Epstein et al., 2000) where they have been shown to co-occur in

∗ Corresponding author at: AWBERC, 26 W. MLK, MS 592, Cincinnati, OH 45268, United States. Tel.: +1 513 569 7094; fax: +1 513 569 7609. E-mail address: [email protected] (A.D. Biales). 0166-445X/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.aquatox.2010.09.019

waters at concentrations toxic to aquatic organisms (Bacey et al., 2005; Eder et al., 2004). Fish metabolize pyrethroids slowly relative to other taxa (Glickman et al., 1981; Glickman and Lech, 1981) and, possibly as a consequence, exhibit higher sensitivity to pyrethroid exposure than mammals and birds (Bradbury and Coats, 1989). Permethrin is a type 1 pyrethroid, which induces a prolonged open state in brain Na+ channels resulting in hyperactivity of the nervous system (Narahashi, 1992). Permethrin toxicity is manifest as increased levels of agitation and ataxia, reduced equilibrium, and decreased startle response rates (Rebach, 1999; Rice et al., 1997). Permethrin is a suspected endocrine disruptor (Chen et al., 2002) and may interact directly with the estrogen receptor (McCarthy et al., 2006); however, pyrethroids may also affect reproduction through disruption of olfaction (Jaensson et al., 2007). Terbufos is an OP insecticide/nematicide, which is classified as highly toxic to freshwater fish species and is the fourth leading cause of fish kill incidents of all pesticides applied to any crop in the US (USEPA, 2001). Like all OP pesticides, terbufos is

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an acetylcholinesterase (AChE) inhibitor and has been shown to reduce plasma, brain and red blood cell (RBC) cholinesterase activity. Although data for non-AChE effects of terbufos are limited, studies focusing on other members of this class of chemical suggest they may also disrupt endocrine function (Jolly et al., 2009). Considerable uncertainty exists in the underlying mechanisms of toxicity resulting from exposure to sub-lethal concentrations of either OP or pyrethroid pesticides, or a binary mixture of both. Monitoring alterations of the transcriptome or proteome may provide insights into these mechanisms. Alterations in gene and protein expression are often the immediate responses of exposure and may have roles in mediating the exposure. Thus changes on this level may inform mechanism/mode of action (MOA). Several studies have attempted to characterize expression changes in response to OP or pyrethroid pesticide exposure (Imamura et al., 2006; Slotkin et al., 2010); however, these have mainly focused on the transcriptional level, on a select set of genes and generally use mammal models. No global characterization on the translational level exists for pyrethroid or OP pesticide exposed fish species An additional benefit of monitoring the cellular composition of mRNA and proteins is that changes in the specific chemical species may be used to establish an expression signature for a given toxicant or class of toxicant, which, in turn, may have usefulness as an indicator of exposure (Monsinjon and Knigge, 2007). Exposure of aquatic organisms to toxicants can lead to reproductive impairments and mortality and, if left unchecked, may eventually lead to impacts on the population and community levels. Environmental regulators are tasked with responding to ecological disturbances to restore ecosystems to acceptable levels; however identifying the causative agents in the complex environmental milieu is difficult. Traditional chemical analyses can be used to identify and quantify chemical contaminants present at the time of sampling; however, they provide no insight into the bioavailability or relative toxicity of mixture constituents. Conversely, expression signatures may be useful in the identification of the biologically active constituents of a mixture among all the potential exposures (Bradley et al., 2002). This would allow regulators to focus remediation efforts on a subset of chemicals and may provide a way of reducing the complexity of exposure by rank ordering the toxicity of biologically active mixture constituents (Verro et al., 2009). Though there are examples of the development of expression signatures for model chemicals in fish (Hook et al., 2006), few have demonstrated their stability in more complicated exposure scenarios, which is an essential attribute if they are to be a useful tool for environmental exposure assessments. The present study aims to characterize the proteomic response in the brains of an ecologically relevant aquatic vertebrate, the fathead minnow (FHM, Pimephales promelas), to single exposures of the pyrethroid pesticide permethrin and the OP pesticide terbufos. We compare these responses to that of a binary mixture exposure of equal concentrations of both. Our final objective was to establish a unique FHM brain protein expression signature (PES) to either permethrin or terbufos and evaluate the quality of the single chemical PES when exposures occur to single chemicals or as part of a binary mixture.

2. Materials and methods 2.1. Sources of reagents The following reagents were obtained from Sigma–Aldrich (Saint Louis, Missouri): Tris–HCl, anahydrous dimethyl formamide, lysine, acetonitrile, NH4 CO3 , DMSO, bromo-phenol-blue and IAA (MS identification of proteins). The following reagents were obtained from GE Healthcare: IAA (first dimension separation of

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proteins), DTT, pharmalyte, CHAPS, urea, thiourea. Acetic acid was obtained from Fisher Scientific (Fairlawn, NJ) and TFA was obtained from Acros (Geel, Belgium). 2.2. Exposures Exposures and animal handling were conducted in accordance with Institutional Animal Care and Use Committee (IACUC) approved protocols. The present research consisted of two phases. The initial phase (n = 12/treatment), hereafter Phase 1, was used to identify differentially expressed proteins and establish a putative PES for permethrin and terbufos. The second phase, hereafter Phase 2 (n = 5/treatment) was used to evaluate the quality of the PES. Unless otherwise noted, data analysis and discussions are limited to Phase 1 data. Adult male FHMs 6–7 months old were obtained from cultures from an on-site aquatic facility. Fish were size- and aged-matched, with sizes ranging from 3.5 to 4.5 g. Fish were exposed in static exposures to 7.5 ␮g/L permethrin, 57.5 ␮g/L terbufos, a mixture of both (7.5 ␮g/L permethrin, 57.5 ␮g/L terbufos) or a solvent (DMSO, 0.001% final concentration) control (nominal values). Exposure concentrations were selected to be sub-lethal based on values reported at the Pesticide Action Network Pesticide Database (2005 values) (http://www.pesticideinfo.org). Exposure durations were selected to minimize potential pesticide dependent mortality and to capture early de novo protein expression changes. Each exposure chamber (3 L) contained a single male fish exposed for 72 h, with a renewal of at least 75% of the test solution every 24 h. Test individuals were fed 2 mL of concentrated brine shrimp each day. All exposures were conducted under controlled conditions of 25 ◦ C in a 16 h light:8 h dark cycle. 2.3. Tissue isolation At the end of the 72 h exposure, fish were anesthetized in icecold water for 5 min prior to weighing. Excess water was removed from fish and fish were weighed on a Mettler A30 scale (Mettler Toledo, Inc., Columbus, OH) and immediately euthanized by cervical dislocation. Whole intact brains were removed from the cranium and snap frozen in liquid nitrogen. Brain was selected as the tissue of interest because both OP and pyrethroid pesticides have been previously demonstrated to have effects in the nervous system (Wheelock et al., 2005). 2.4. Protein extraction, CyDye protein labeling and 2-dimensional electrophoresis Four hundred microliters of lysis buffer (LB; 30 mM Tris, 2 M thiourea, 7 M urea, 4% CHAPS) was added to each frozen whole tissue sample. Frozen whole brains were homogenized using 3.2 mm stainless steel beads (BioSpec Products, Inc., Bartlesville, OK) and a Retsch MM330 mixer mill (Retsch laboratory Equipment, Newtown, PA) at 30 Hz for 5 min. Homogenates were centrifuged for 5 min at 14,000 × g to remove particulates and protein content was quantified using the EZQ protein quantification kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s protocol. Fluorescent membranes were visualized using a Typhoon 9410 variable mode imager (GE Healthcare, Piscataway, NJ). Images were analyzed using the array analysis module of the ImageQuant TL software (GE Healthcare). Quantities were calculated from a standard curve, run in duplicate, of purified ovalbumin. Two aliquots of each experimental sample, each containing approximately 125 ␮g of protein, were purified using the 2-D Clean-Up Kit (GE Healthcare) according to the manufacturer’s protocol. Protein pellets were re-suspended in 20 ␮L LB and duplicate purifications were pooled. Purified samples were then re-quantified as described above.

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Twelve and five fish were exposed in Phases 1 and 2 respectively; however, only 8 and 4 of these were analyzed by two-dimensional gel electrophoresis (2-DE). Individuals selected for 2-DE were randomly selected. Brain protein samples were labeled using the difference in gel electrophoresis system (DIGE, GE Healthcare) (Unlu et al., 1997). This system uses three spectrally resolvable fluorescent amine reactive cyanine dyes (CyDye® ), allowing three samples to be included on the same gel. Experimental samples were always labeled with either Cy3 or Cy5 and the internal standard was labeled with Cy2. The Cy2-labeled standard consisted of a pool of an equal mass of proteins from all experimental samples and was included on every gel within the experiment. Quantity values of experimental samples used in analyses were normalized relative to the internal standard to reduce inter-gel technical variability. Gel assignments of experimental samples were randomized using a random sequence generator (http://www.random.org/sequences). To control for dye effects, each treatment group had an equal number of biological samples labeled with Cy3 and Cy5. Fifty micrograms of each sample in LB was labeled with 300 pmol of a cyanine dye in anhydrous dimethyl formamide (DMF). Labeling reactions were incubated on ice, under dark conditions, for 30 min and terminated by the addition of 1 ␮l of a 10 mM lysine solution under similar conditions for 15 min. Cy3and Cy5-labeled experimental samples and the Cy2-labeled internal standard reference that were to be on a single gel were mixed and an equal volume of 2× sample buffer (7 M urea, 2 M thiourea, 4% CHAPS, with 2% Pharmalyte pH 3–10 and 2% DTT) was added. The volume of the combined samples was adjusted to 450 ␮L with rehydration buffer (7 M urea, 2 M thiourea, 30 mM Tris–Cl, pH 8.5, 4% CHAPS and 1% Pharmalyte pH 3–10 and 1% DTT) for the first dimension iso-electric focusing (IEF). The labeled sample mixture was then loaded onto 24 cm immobilized pH gradient (IPG) strips (pH 4–7, GE Healthcare), and passively rehydrated for 11 h at 20 ◦ C at 80 V using the ETTAN IPGphor II apparatus (GE Healthcare). IEF was performed overnight with the following parameters: 500 V for 500 Vh, 1000 V for 800 Vh, a gradient increase to 8000 V for 13,500 Vh and 8000 V for 45,000 Vh. Following completion of the 1st dimension, IPG strips were either used immediately or stored at −80 ◦ C. IPG strips (allowed to thaw to room temperature if previously frozen) were immediately equilibrated in 100 mM Tris pH 8.0, 30% glycerol, 6 M urea, 2% SDS, with 0.2 mg/mL bromophenol blue, plus 0.5% DTT. After removal of the first solution, the strips were equilibrated a second time in a similar solution, except the DTT was replaced with 4.5% iodoacetamide (IAA). Each equilibration step was performed for 10 min on a rocking table (60 rpm). IEF strips were subsequently rinsed by submersion into running buffer (upper chamber buffer, see below) for 5 sec, overlaid onto precast 24 cm, 1 mm thick 12% polyacrylamide Laemmli gels (Jule Inc., Milford, CT) and sealed with 0.8% agarose in running buffer (25 mM Tris pH 8.3, 192 mM glycine, and 0.2% SDS). Gels were electrophoresed at 10 ◦ C at 2 W per gel for 16.5 h with Tris–glycine running buffer (lower buffer chamber: 25 mM Tris–HCl, 192 mM glycine, 0.2% SDS; upper buffer chamber: 50 mM Tris–HCl, 384 mM glycine, 0.2% SDS) on an Ettan DALTtwelve Separation Unit (GE Healthcare). 2.5. Image acquisition and processing Following electrophoresis, the glass plates were cleaned by alternating rinses of distilled water and 70% ethanol with a final rinse of distilled water. Gels were scanned using a Typhoon 9410 Variable Mode Imager. Gels were pre-scanned at 1000 ␮m in each of the three channels, and laser intensity was adjusted per gel per channel. A final scan in each of the three channels was performed at 100 ␮m. Gel images were cropped using ImageQuant TL v2005 and imported into DeCyder v 6.5 (GE Healthcare) for matching and all subsequent processing and analyses unless otherwise

noted. Images were processed using the Difference in Gel Analysis module (DIA) of the DeCyder software. Processed DIA images were then imported into the Biological Variation Analysis (BVA) module for inter-gel matching. All of the Cy2 standard images were examined and the image that displayed the fewest experimental artifacts (streaking, etc.) and had the highest resolution over the entire gel was selected as the master image. All other gels were subsequently matched to this gel. Before automated gel matching, the Cy2 standard images from all gels were extensively landmarked, targeting edges, to minimize mismatches. Match fidelity was visually confirmed for a subset of spots from areas of high fluorescence and from edges. In the case where automated matching was of low quality, gels were unmatched, additional landmarks added, and then re-matched using the automated matching feature. 2.6. Statistical analysis Following matching, spot data were imported from the BVA into the Extended Data Analysis (EDA) module of the DeCyder v 6.5 software for data analysis. All data analyses were performed on the log standardized abundance values. Data were filtered so that any unassigned spot maps were removed and only those spots that were present in 75% of the spot maps (biological replicates) across all individuals across treatments were included in the analysis. With the exception of the Dunnett’s test (discussed below) all analyses were done using the EDA module. An ANOVA (p ≤ 0.05) was used to identify proteins differentially expressed among groups. The fidelity of matching for these proteins was visually confirmed in the BVA module. Spot data were subsequently exported via the BVA module and imported into SAS (v. 9.2, SAS Institute, Cary, NC), where PROC GLM was used to run a Dunnett’s test to identify proteins that differed between each treatment and the control at an overall significance level of p ≤ 0.05. 2.7. Protein identification For preparative (spot picking) gels, three IPG strips were loaded with 400–1000 ␮g of protein pooled from all biological samples as described above. Following completion of the first dimension and equilibration steps, strips were placed onto a 12% polyacrylamide, Laemmli 24 cm gel, which included reference markers, and an additional well, which was loaded with 25 ␮L of Full Range Rainbow Molecular Weight Markers (GE Healthcare). Preparative gels were electrophoresed under the conditions described above. These gels were then post-stained with Deep Purple Total Protein Stain (GE Healthcare) following the manufacturer’s recommended protocol. Gel plates were cleaned and scanned as described above. Following scanning, gel images were processed using the DIA module and imported into the existing BVA workspace. Proteins to be excised were manually matched between the preparative gel and the master gel. Spots were excised using the Ettan Spot Picker (GE Healthcare), placed into a 96-well plate, sealed and stored at 4 ◦ C. Accuracy of spot picking was evaluated by rescanning the gels. Molecular weights of proteins were calculated in the BVA through comparison to the molecular weight markers. Gel plugs were shipped overnight on dry ice to the University of Florida, Interdisciplinary Center for Biotechnology Research Proteomics Division for protein identification. Gel plugs were washed twice with 200 ␮L of a 50 mM NH4 HCO3 /50% acetonitrile (ACN) solution and then lyophilized briefly. Following drying, 45 mM DTT was added and samples were incubated at 55 ◦ C for 45 min and an additional 45 min following a subsequent addition of 100 mM IAA. The DTT-IAA solution was removed and the samples were washed three times with 200 ␮L of 50 mM NH4 HCO3 /50% ACN solution and lyophilized. Following drying, 5–10 ␮L of Trypsin (Promega, Madison, WI) in 25 mM NH4 HCO3 was added to the gel

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plugs and incubated at 4 ◦ C for 30 min. The enzyme solution was removed and 25 mM NH4 HCO3 was added and incubated overnight at 37 ◦ C. Trypsinization was stopped by the addition of 5% acetic acid. Trypsin-digested peptides were extracted in a 60% ACN/1% trifluoroacetic acid (TFA) solution and sonicated for 5 min. The supernatant was removed and an additional 50 mL ACN was added, followed by 10 min of sonication. The supernatant was lyophilized and resuspended in a solution containing 3% ACN, 1% acetic acid, and 0.1% TFA. The extracted peptides were then injected onto a LC Packings PepMap capillary trap (Dionex, Sunnyvale, CA) and desalted for 5 min with a flow rate of 10 ␮L/min of 0.1% (v/v) acetic acid. Samples were loaded onto an LC Packings® C18 Pep Map HPLC column (Dionex). The elution gradient of the HPLC column started at 3% solvent A (0.1%, v/v acetic acid, 3%, v/v ACN, and 96.9%, v/v water), 97% solvent B (0.1%, v/v acetic acid, 96.9%, v/v ACN, and 3%, v/v water) and finished at 60% solvent A, 40% solvent B for 60 min for protein identification. LC–MS/MS analysis was carried out on a hybrid quadrupole-TOF mass spectrometer (QSTAR, Applied Biosystems, Framingham, MA). The focusing potential and ion spray voltage was set to 275 and 2600 V, respectively. The information-dependent acquisition (IDA) mode of operation was employed in which a survey scan from m/z 400–1200 was acquired followed by collision induced dissociation (CID) of the three most intense ions. Survey and MS/MS spectra for each IDA cycle were accumulated for 1 and 3 s, respectively. Tandem mass spectra were extracted by ABI Analyst version 1.1 (Applied Biosystems) using the default settings, charge state deconvolution and deisotoping were not performed. All MS/MS samples were then analyzed using Mascot (Matrix Science, London, UK; version 2.2.0). Mascot was set up to search NCBI non-redundant database (NCBI nr 2009.04.12; 8,224,370 entries) assuming the digestion enzyme trypsin. Mascot was searched with a fragment ion mass tolerance of 0.30 Da and a precursor ion tolerance of 0.30 Da. IAA derivative of Cys was specified as a fixed modification, and S-carbamoylmethylcysteine cyclization (N-terminus) of the Nterminus, deamidation of asparagine and oxidation of methionine were specified in Mascot as variable modifications. The maximum number of missed cleavages allowed was two. Scaffold (version Scaffold-02-04-00, Proteome Software Inc., Portland, OR) was used to validate MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95% probability as specified by the Peptide Prophet algorithm (Keller et al., 2002). Protein identifications were accepted if they were able to be established at greater than 99.0% probability and contain at least two unique peptides as assigned by the Protein Prophet algorithm (Nesvizhskii et al., 2003). Because the proteome of the study organism is poorly characterized, acceptance thresholds were set to be as stringent as possible while still returning some protein identifications. Protein identifications of human keratin and trypsin were considered experimental artifacts and removed from the protein list. In the case of multiple protein identities for a single spot, the list was reduced based on total sequence coverage and the number of unique peptides used in identification so that only the most likely identification was listed. If multiple accession numbers for the same identification were listed, the one that had the greatest amount of annotation was reported. 2.8. Gene ontology analysis Gene Ontology (GO) analysis was performed to identify biological processes that were important in the mitigation of pesticide exposure. GO analysis was done using the Database for Annotation and Visualization and Integrated Discovery (DAVID) v. 6.7 (Dennis et al., 2003; Huang da et al., 2009). The functional annotation option was used for GO-term enrichment with the default medium classification strategy. The Functional Annotation Clustering Tool

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clusters similar GO terms based on biological functionality to allow researchers to focus on larger biological networks rather than individual genes, generating biologically meaningful modules (Huang da et al., 2009). To increase the biological significance of results, only annotation clusters demonstrating a fold enrichment value of five or greater and a p-value ≤0.05 were considered enriched. 2.9. Establishment of protein expression signatures The establishment of protein expression signatures requires the identification of those proteins that are best able to discriminate between a single-chemical-exposed and a non-exposed individual. These requirements differ from those described above, which aimed only to compare differentially expressed proteins across treatment groups. Therefore, filtering and statistical tests for PES establishment were done independently of the analyses described above because comparisons focused on each single pesticide exposure relative to the control irrespective of other treatment groups. Of the ten individuals for which 2-D data existed, four, two control and two treated, were removed from the Phase 1 data set as a test set prior to identification of the discriminant model. The remaining individuals in the Phase 1 exposure (n = 6/treatment) were used to identify the proteins to be used in the discriminant model. All analyses were done using the EDA module of the Decyder software package. Spot feature selection was conducted on spots that exhibited a value of p ≤ 0.01 (Student’s t test) in the terbufos or permethrin group relative to the control. Discriminant analysis for permethrin or terbufos exposure was then performed on these proteins using the Partial Least Square Search (PLSS) routine as the search method and Regularized Discriminant Analysis (RDA) (alpha = 0.05, gamma = 0.05) as the evaluation method. The classifier was evaluated in a leave-one-out cross validation (LOOCV) using RDA as the classification method. If the classifier was found to be 100% accurate in the LOOCV, it was then evaluated using blinded test sets from Phases 1 and 2, respectively. 3. Results 3.1. Exposure Mortality was assessed on the total number of fish exposed (Phase 1 = 12/treatment; Phase 2 = 5/treatment). A single mortality each was observed in the control and the terbufos exposure groups and six were observed in the mixture exposure for Phase 1. For Phase 2, there were no mortalities in either of the single chemical exposures or the control; however, two were observed in the mixture exposure. Slightly higher mortality rates in the mixture exposures were consistent between Phases 1 (50%) and 2 (40%) (data not shown). There were no significant differences in fish masses among treatment groups (data not shown). 3.2. DIGE analysis Phase 1 consisted of 30 gel images with a total of 2243 identified protein spots. Following filtering (described above), 1052 spots remained and served as the base set for all analyses. Gels resolved protein spots ranging from 11.5 to 80 kDa and pI values from 4.4 to 6.0. Fifty-one protein spots were identified as differentially expressed among all treatment groups in an ANOVA analysis (p ≤ 0.05) and were visually confirmed. Standardized log abundance data for these were exported from the BVA module and a Dunnett’s test was performed to find differences between each treatment group and the control. Twenty-four proteins were differentially expressed at or greater than 1.15 up or down relative to the control (p ≤ 0.05) (Table 1). The MW of these 24 differentially

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Table 1 LC–MS/MS identification of proteins differentially expressed relative to the control (Dunnet’s test p < 0.05). Asterisk (*) denotes no acceptable sequences generated. Spot number

Protein name

Theoretical MW (kDa)/pI

Observed MW (kDa)/pI

Number of unique peptides

Percentage sequence coverage

391 534

NA 45501264

NA 102.7/5.9

66.7/5.1 63.3/5.8

NA 15

NA 17.90%

610* 618 623* 885 976

NA NA NA NA 42542740

NA NA NA NA 53.6/6.3

61.9/5.9 61.5/5.4 61.6/5.6 55.6/6.0 53.2/5.7

NA NA NA NA 8

NA NA NA NA 18.00%

984 985 1060

NA 122890758 157888752

NA 68.4/5.2 62.4/5.6

53.1/5.3 53.0/5.3 50.9/5.8

NA 6 9

1064 1129 1190

66472750 193788703 37595424

61.5/5.9 65.3587/4.4 47.9/4.7

50.7/5.4 48.9/4.7 47.8/5.4

1319 1385

NA 47551317

NA 47.5/6.1

1394

148725626

1408

50344782

1523

41388972

1675

113679255

1693 1706 1857

28277619 47085833 41282130

NA Hexokinase 1 NA NA NA NA Vat1 Protein NA ATP6V1AL Novel protein similar to Dpysl2 Dpysl3 P4Hb Tubulin alpha 6 NA Enolase 3 Novel protein similar to Efcbp2 Proteasome 26S Subunit Pgk1 protein Phosphotriesterase related LDH-B4 GAPDH Annexin A13 NA NA

1874* 1993*

NA NA

Mixture

Terbufos

Permethrin

– 1.19

−1.27 –

– −1.58 – – −1.23

– – 1.21 – –

−1.22 – – 1.15 –

NA 11.70% 20.40%

1.15 1.18 −1.16

– – –

7 4 17

17.10% 10.00% 54.50%

– −1.18 –

– – 1.16

1.28 – ––

44.9/4.6 43.5/6.0

NA 5

NA 18.50%

– –

– –

−1.18 1.25

47.9/4.8

43.4/4.9

5

11.20%

–

1.31

–

45.3/5.1

43.1/5.0

14

51.20%

−1.2

−1.16

–

44.7/6.3

39.5/5.8

5

14.60%

–

1.23

–

39.2/6.0

35.3/5.9

5

20.30%

−1.28

–

–

36.2/6.3 36.1/6.4 35.3/4.3

34.7/5.9 34.5/5.9 31.2/4.5

2 2 2

7.78% 9.85% 10.80%

−1.6 −1.48 –

– – –

–

NA NA

30.1/4.9 25.3/5.3

NA NA

NA NA

−1.65 1.29

– –

−1.66 –

– – –

−1.44 −1.44 –

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ACC number

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3.3. Protein identification

Fig. 1. Representative 2-D gel with proteins differentially expressed relative to control identified. Proteins were identified as differentially expressed among the single exposures of permethrin and terbufos and the binary mixture of both and the control using an ANOVA (p < 0.05). This was followed by a Dunnett’s post hoc test to identify those proteins that differed relative to the control group. The image represents the standard (Cy2) image from the master gel. Spots highlighted in white are included in the protein expression signature for permethrin and were identified through a t test between permethrin and the control (p ≤ 0.01).

expressed proteins ranged from 25.3 to 66.7 kDa, with an average MW 47.2 kDa and pIs of 4.6–6.0 (Fig. 1). Seven proteins were found to be differentially expressed following terbufos exposure, with only two proteins being down-regulated (Table 1). There were eight and 12 proteins differentially expressed in the permethrin and mixture exposure groups and 5 and 9 were down-regulated, respectively. The magnitude of change up or down among all groups was relatively small, with a maximum of 1.66-fold downregulation in spot 1874 and 1.31-fold up-regulation in spot 1394, both were in the terbufos exposure. There was little overlap of differentially expressed proteins among the treatment groups (Fig. 2). Only one protein, spot 1874, which was down-regulated in all the treatment groups, was common across all treatments. No other proteins were differentially expressed in common between the permethrin and terbufos groups. One spot was in common between terbufos and the mixture, spot 1408, and none between permethrin and the mixture.

Fig. 2. Venn diagram of differentially expressed proteins. Proteins were identified as differentially expressed relative to control through a Dunnett’s test (p ≤ 0.05).

Of the twenty-four spots that were shown to be differentially expressed relative to the control, 19 were excised and sequenced. Fifteen spots were successfully sequenced and identified through comparison to the NCBI nr database (Table 1). Eleven of the 19 spots were sequenced at least twice with spots used for sequencing coming from different preparative gels. On average, 7.1 unique peptides were used for identification and sequence coverage was 19.5%. All identity matches were from the zebrafish (Danio rerio) genome. There was general agreement between theoretical and observed MW, with the average difference being 8.2 kDa. Hexokinase, spot 534, displayed the largest discrepancy, 39.4 kDa; however, hexokinase had a theoretical MW of 102.7 kDa and this difference was likely due to compression in the high MW range. The average difference between theoretical and observed pI was 0.08. GO-term enrichment analysis was accomplished using DAVID. Of the 12 differentially expressed proteins observed in the mixtures treatment group, seven had GO terms contained in the DAVID database. Two significantly enriched annotation clusters were observed in the mixtures treatment. Cluster 1 contained the terms NADP binding domain (p ≤ .002) and oxidoreductase activity (p ≤ .004) group, and Cluster 2 contained generation of precursor metabolites and energy (p ≤ .004) (Table 2). Five of the differentially expressed genes in the terbufos exposure group were found in the DAVID database and a single annotation cluster was observed; catabolic processes (p ≤ 0.013). Only three proteins from the permethrin group were in the DAVID database and no enriched clusters were observed. 3.4. Discriminant analysis The goal of the discriminant analysis was to establish a PES for permethrin and terbufos that could be used to discriminate exposed from non-exposed individuals. To this end, all analyses were conducted de novo and independently for each treatment group, namely terbufos and permethrin. Phase 2 gel images were matched to the Phase 1 master in the BVA module prior to discriminant analysis in the EDA module. For the permethrin PES, an EDA workspace consisting of Phases 1 and 2 gel images of permethrinexposed and control individuals was created. Two individuals from the Phase 1 permethrin exposure and two from the control group were randomly removed to be used as a test set. The remaining Phase 1 gels (6 treated and 6 controls) were used to generate the discriminant model. Spot data in the remaining gel images were filtered so that only those spots found in at least 75% of the gels were used for further analysis, leaving 898 spots. A Student’s t-test was conducted between the permethrin exposure group and the control group to identify differentially expressed proteins. Seven spots were identified (p ≤ 0.01) and visually confirmed. A PCA was performed using these spots, which showed a clear separation of the experimental groups, with 70.9% of the variability explained on the first axis (Fig. 3A). These seven spots were then used for marker selection using the PLSS search algorithm and Regularized Discriminant Analysis (RDA) for evaluation. The discriminant model was evaluated, using the RDA classification method in a LOOCV with 5folds and was found to classify individuals with 100% ± 0 accuracy (Supplementary Fig. 1). The performance of the classifier was then evaluated against the Phase 1 test set. The model correctly classified the two permethrin samples, but incorrectly classified both control individuals as permethrin exposed (Supplementary Table 1). The classifier was then tested against the Phase 2 images, and correctly classified seven of eight images (Supplementary Table 1). To determine if the classifier was able to identify individuals exposed to permethrin as part of a mixture, a second EDA workspace was created containing two individuals exposed to the

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Table 2 Functional annotation clusters for identified proteins calculated using DAVID software. Mixture Annotation cluster 1

Enrichment score: 1.94 Term IPR016040:NAD(P)-binding domain Genes GAPDH LDHB4 VAT1 GO:0016491∼oxidoreductase activity Genes GAPDH LDHB4 VAT1 P4HB

Annotation cluster 2

Terbufos Annotation cluster 1

Enrichment score: 1.09 Term GO:0006091∼generation of precursor metabolites and energy Genes ATP6V1AL LDH-B4 GAPDH Enrichment score: 0.87 Term GO:0009056∼catabolic process Genes Hexokinase 1 PGK1 Proteasome 26S subunit

binary mixture during Phase 2. The model classified both of the mixtures images as permethrin, suggesting that the permethrin PES persisted in the mixture exposure (Supplementary Table 1). In this analysis, the model again incorrectly assigned one control image to the permethrin group; however, this was the same image that was misclassified previously. We next attempted to demonstrate a terbufos classifier for brain tissue using the methods described above. Twelve proteins were identified with the Student’s t-test (p < 0.01) and visually confirmed. A PCA was performed (Fig. 3B) and 87.5% of the variability was explained in the first axis. The performance of the classifier was evaluated in a LOOCV as described for the permethrin PES and was found to be 95% ± 11.2 accurate (Supplementary Fig. 2). As with the permethrin PES, the terbufos PES was also evaluated in its ability to correctly classify Phases 1 and 2 test sets. The terbufos PES incorrectly classified one of the control and one of the terbufos of the four Phase 1 unknown gels, yielding an accuracy of 50% (Supplementary Table 2). The terbufos was then evaluated against the Phase 2 unknowns and incorrectly classified one of the terbufos exposed fish as control and two of the control fish as exposed, yielding an accuracy of 62.5% (Supplementary Table 2). 4. Discussion 4.1. Proteomic profiling of single pesticide and mixture exposures The results of the current work demonstrate that both permethrin and terbufos, acting independently, are capable of eliciting a proteomic response in the brains of fathead minnows exposed for 72 h. The proteomic response to the binary mixture was largely different from that observed in either of the individual exposures. Of the 12 proteins found to be differentially expressed in the mixture exposure, ten were unique to the mixture (Fig. 2). This pattern has been noted elsewhere in a number of taxa (Vandenbrouck et al., 2009) including fish (Finne et al., 2007).

p-value 0.002

Fold enrichment 37.9

0.004

9.5

p-value 0.004

Fold enrichment 26.1

p-value 0.013

Fold enrichment 12.3

The underlying reasons for the minimal overlap between the individual exposures and the mixture are unclear; however it may be due to the higher toxicity observed in the mixture treatment group. The concentrations used in the single chemical exposures were equivalent to approximately 25% of the reported 96 h LC50 for permethrin and terbufos; however, values were not adjusted to account for constituent interaction in the mixture. The joint toxicity of OP and pyrethroid pesticides in fish has been shown previously to be greater than additive (Denton et al., 2003). The mechanisms for the synergistic toxic responses elicited by these two pesticide classes has been attributed to the OP-dependent inactivation of carboxylesterase enzymes that hydrolyze pyrethroids (Gaughan et al., 1980, reviewed in Casida and Quistad, 2004). Thus the toxicity of the mixture was anticipated to be greater than that of the single chemical exposures. This was evident in the higher mortality observed in the mixtures exposure for both Phases 1 and 2 exposures. The higher toxicity of the mixture may have initiated a different protein expression response. Concentration-dependent expression profiles have been noted elsewhere. For example, kidneys of rats exposed to low doses of the antibiotic cephaloridine expressed genes primarily associated with detoxification and antioxidant defense, whereas high doses modulated genes involved in cell proliferation (Rokushima et al., 2008). Alternatively, the mixtures expression profile may represent a unique or unanticipated MOA. A novel MOA, not predicted from single exposure characterization, could have implications in modeling the toxicity of environmental mixtures as these models assume the response curves are known for the toxicants included in the mixture model (Dardenne et al., 2008). However, if a new MOA is elicited due to mixture constituent interactions, then these response curves are no longer valid, resulting in a flawed model built on faulty assumptions. Overall, the level of protein expression changes observed in the present study was relatively small, a maximum of 1.66-fold upand 1.31-fold down-regulation relative to the control. It should be noted that the degree of response may not reflect its importance

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PC2

A

Spot Maps (Score Plot) 3.2

Control Permethrin

0

-3

-6 -5 -4 -3 -2 -1 0

1

2

3

4

5

6

PC1

PC2

B

Spot Maps (Score Plot) 0.32

Control Terbufos

0

-0.32

-1

0

PC1

1

Fig. 3. Principle component analyses of pesticide exposed or control individuals. Proteins exhibiting differential expression (p < 0.01) between (A) permethrin or (B) terbufos exposed fish and controls were used in a PCA analysis.

(Tusher et al., 2001). The relatively low magnitude of the proteomic response may be due to an averaging effect resulting from the use of whole brain. The brain is a highly complex organ with well-characterized regional differences in terms of cellular composition, density, and functionality. A proteomic response to a toxic insult may involve only a small proportion of cells, thus pooling of the whole tissue may have effectively diluted out these changes. Regional specificity in RNA and protein expression in brains in response to pesticides has been observed elsewhere (Dayal et al., 2001). Comparison of expression within specific brain regions would allow a finer characterization of the proteomic response of the brain. Fifteen of the 24 spots were successfully identified (Table 1). Identifications came solely from the zebrafish genome, a closely related fish species. The use of a surrogate species for protein identifications was necessitated due to database limitations of ecotoxicologically important, yet largely uncharacterized organisms, such as the FHM (Monsinjon and Knigge, 2007). At the time of this study, only 119 entries in the NCBI protein database existed for FHM. Cross-species comparisons may have resulted in some of the differences between the observed and theoretical MW. Proteins associated with the ubiquitin proteasome system (UPS) were down-regulated in all three treatment groups. Downregulation of Annexin 13A was observed in the permethrin exposure group (Table 1). Annexin 13A is a Ca2+ - dependent phospholipid binding protein and has been shown to interact with an

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E3 ubiquitin-ligase protein, namely neural precursor cell expressed, developmentally down-regulated 4 (NEDD4) (Plant et al., 2000). In both the terbufos and mixture exposure groups, down-regulation of the proteosome 26S subunit, part of the UPS, was observed. A reduction in UPS activity may indicate a central role of the UPS system in pesticide toxicity or mediation. A variety of pesticide classes have been shown to reduce UPS activity (Wang et al., 2006), which has been linked to sporadic Parkinson’s disease (PD) in humans (Lim and Tan, 2007). UPS-related subunits have been shown to be primarily down-regulated in the substantia nigra of individuals with PD, suggesting a reduction of UPS activity may be, at least in part, dependent on expression levels of UPS subunits (Duke et al., 2006). The role of the UPS in PD may be multifaceted as the UPS has been shown to influence a number of processes. Neuronal cell death is evident in both pesticide exposure and PD and may be due to the accumulation of unwanted proteins through reduction of UPS proteolytic activity (Bedford et al., 2008). The UPS may also indirectly regulate synaptic transmission by controlling the expression levels of other regulatory factors, such as synaptotagmin (Huynh et al., 2003). The mitigation of pesticide insults results in an energetic cost to recipient organisms (Calow, 1991), which is evident as reduced brain glycogen content (Sarin and Gill, 1999). Two strategies exist to deal with increased energy demands, down-regulation of non-essential/energetically demanding pathways or increased ATP production (Lutz and Nilsson, 2004). The GO term generation of precursor metabolites and energy was enriched 26.1-fold in the mixture exposure group, as was the GO term catabolic processes in the terbufos group (Table 2). Together, these suggest a need for an increase in available energy to mitigate pesticide exposure. A relatively large percentage of the identified proteins are either directly or indirectly associated with the glycolytic pathway, suggesting a central role of this pathway. Glycolytic proteins that demonstrated differential expression were found in all three treatment groups and included; enolase 3, hexokinase 1, phosphoglycerate kinase, lactate dehyrdogenase, and glyceraldehyde-3-phosphate dehyrdogenase. In both single exposure groups there was an up-regulation of glycolytic proteins. An increase in enzymatic activity of hexokinase, as well as other members of this pathway, has been previously observed in the brains of OP-exposed rats (Matin and Husain, 1987) and may underlie the observation that OP pesticides increase brain glucose levels (Pellet-Gondret and Mailly, 1988). In contrast, glycolytic proteins display down-regulation in the mixture group. This may suggest that the increased toxicity of the mixture group has led to an alternative metabolic strategy, where exposed organisms are favoring a reduction in non-essential processes rather than an increase in ATP production to satisfy increased stress-related energy demands. Proteins associated with cytoskeletal dynamics were differentially expressed in all three treatment groups. Members of the dihydropyrimidinase family of proteins (Dpysl) were shown to be differentially regulated in both the permethrin (Dpysl3) and mixtures exposure groups (Dpysl2). Dpysl proteins are highly expressed in the developing nervous system (Schweitzer et al., 2005) and, though they are largely down-regulated in adults, they are expressed in areas of the adult brain that undergo neurogenesis and plasticity, such as the olfactory bulb (Veyrac et al., 2005) and cerebellum (Wang and Strittmatter, 1996). Dpysl protein activity is associated with axonal growth and guidance in neurons (Nishimura et al., 2003) possibly through interaction with cytoskelatal structures. They have been shown to interact with tubulin dimers (Fukata et al., 2002) and other microtubule associated proteins such as microtubule associated protein-2 (MAP-2) (Su et al., 2007). Dysregulation of Dpysl activity is connected with neuropathies such as Alzheimer’s disease (AD) (Petratos et al., 2008) and PD (Barzilai et al., 2001).

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An up-regulation of ␣-6-tubulin was observed in the terbufos exposure group (Table 1). OP inhibition of microtubule polymerization in neurons has been previously noted (Prendergast et al., 2007) and alterations in axonal transport have been suggested as a possible alternative MOA for OP pesticides (Terry et al., 2003). The role of ␣-6-tubulin is unclear since it appears that ␣-tubulin abundance does not change (Sachana et al., 2005); however, both ␣- and ␤-tubulins have been shown to be extensively phosphorylated following OP exposure (Choudhary et al., 2001). Extensive post-translational modifications would be manifest as a MW shift and may have been interpreted as up-regulation or novel protein spot. Pesticides have been shown to affect respiration in fish species resulting in a decrease in oxygen uptake (Thomaz et al., 2009). Significant overlap was observed between proteins identified as differentially expressed in the present study and those shown to be differentially regulated in response to anoxic or hypoxic conditions. For example, Smith et al. (2009) identified ten proteins in the brains of carp in anoxic conditions, four of which were also identified here, including; LDH, GAPDH, Vat-1 and Dpysl3. In addition to these proteins, prolyl 4-hydroxylase (P4HB) was also shown to be down-regulated in the mixture exposure group. The prolyl 4hydroxylases act as a negative regulator of the hypoxia inducible factor-1 (HIF-1) when under hypoxic conditions (Siddiq et al., 2009). Up-regulation of phosphoglycerate kinase 1 (PGK1) was also observed in the terbufos exposure group, consistent with what was seen in zebrafish under mild and severe hypoxic conditions (Roesner et al., 2006). Taken together the differential regulation of these proteins strongly suggests a pesticide-dependent hypoxia. Pesticide dependent hypoxia has been seen previously with both OP and carbamate pesticides (McKim et al., 1987). 4.2. Protein expression signatures We have attempted to establish a PES for both permethrin and terbufos in brains of adult male FHMs. The terbufos PES was unable to correctly discriminate the terbufos from the control exposed individuals in either the Phase 1 or 2 test sets, thus was considered too variable to be useful in discriminant analysis. Toxicants exhibit tissue specificity (Vioque-Fernandez et al., 2009); therefore, proteomic interrogation of an alternative tissue may still yield a useful PES for terbufos. Permethrin, however, did yield a useful PES. The quality of the PES was initially evaluated in a test set consisting of four individuals which were processed concomitant with those used for PES establishment (Phase 1). The PES misclassified the two controls as permethrin exposed; however, these results may reflect variability resulting from the small size of the test set. The PES was also evaluated using a larger test from an independent experiment (Phase 2). In this test set, the PES accurately classified seven of eight individuals. Animals used between Phases 1 and 2 were from different broods and sample labeling and electrophoresis was done using different lot numbers. Therefore, it was assumed that this test set would be subject to, and account for, all experimental variability that would be expected in an experiment done de novo. For these reasons, we took the Phase 2 assessment as more indicative of the true accuracy of the PES. Ideally, this PES could be used as a tool to identify fish exposed to pyrethroids in environmental assessments. The use of expression signatures has been demonstrated successfully in a number of ecologically relevant taxa (Poynton et al., 2008). Because organisms are frequently exposed to mixtures of pesticides simultaneously (Gilliom, 2007), a useful PES must remain useable when exposure occurs as part of a mixture. If this was satisfied, then the PES could be used as a means of identifying a toxicant exposure in organisms among all the potential exposures (Snell et al., 2003). To this end, the ability of the PES to correctly discriminate permethrin- from

non-exposed organisms when exposures occurred as part of a mixture was evaluated. The PES correctly classified the two individuals exposed to a binary mixture as having been exposed to permethrin. We interpreted this result in two ways: (1) the PES remained usable in a more complex, albeit still simplistic, mixture scenario, and (2) the proteins selected for the permethrin PES were largely unaffected by terbufos exposure. Because OP and pyrethroid pesticides are likely to co-occur in receiving waters (Epstein et al., 2000), this is a useful attribute for a real-world application. It should be noted that this evaluation also employed a small test set and thus may indicate an unrealistically optimistic result. Validation of the PES through time-course and dose response experiments would serve to further strengthen the linkage between permethrin exposure and the PES. With the exception of enolase 3, spot 1385, no other proteins contained in the PES were able to be identified. The identification of these proteins is not essential in their use in characterizing environmental exposure (Monsinjon and Knigge, 2007); however, their identification is important in understanding the fathead minnow response to permethrin exposure. Further, if the proteins contained in the PES are to be linked to a disease state for use as a biomarker, identification of proteins and understanding their role in the development of the disease state would be critical. Identification of the PES proteins would also be useful in the development of a more exportable experimental platform, such as an ELISA. Future work will aim to identify the proteins included in the permethrin brain PES. 4.3. Conclusions The present study identified 24 proteins differentially expressed among all of the treatment groups: terbufos, permethrin or a binary mixture of both relative to unexposed control organisms. Fifteen of these proteins were unambiguously identified using LC–MS/MS. Little overlap in differentially expressed proteins was observed among treatments. However, proteins involved in glycolysis, hypoxia, the UPS and cytoskeletal dynamics were seen in all three groups suggested a potential role of these cellular activities in pesticide toxicity. Moreover, many of these processes have been previously associated with neuropathologies, such as PD. We have also established a PES for permethrin-exposed fish, which classified exposed fish with 87.5% accuracy in an independent experiment. Upon further development, this PES may prove useful in characterizing environmental exposures of non-target aquatic organisms to permethrin and may provide environmental risk assessors a means of reducing the complexity of real-world exposure scenarios. Conflict of interest The authors declare that no conflicts of interest exist in regards to the present manuscript. Acknowledgements The United States Environmental Protection Agency through its Office of Research and Development partially funded and collaborated on the research described here. It has been subjected to Agency review and approved for publication. The authors would like to acknowledge CB, FM and ZD for their dedicated work on this project as well as their encouragement. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.aquatox.2010.09.019.

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