Quantitative Proteomic Profiles of Androgen Receptor Signaling in the

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Quantitative Proteomic Profiles of Androgen Receptor Signaling in the Liver of Fathead Minnows (Pimephales promelas) Christopher J. Martyniuk,† Sophie Alvarez,‡,§ Scott McClung,‡ Daniel L. Villeneuve,| Gerald T. Ankley,| and Nancy D. Denslow*,† Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida 32611, Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, Florida 32611, and U.S. EPA, ORD, NHEERL, MED, Duluth, Minnesota 55804 Received August 12, 2008

Androgenic chemicals are present in the environment at concentrations that impair reproductive processes in fish. The objective of this experiment was to identify proteins and cell processes mediated through androgen receptor signaling using an androgen receptor agonist (17β-trenbolone) and antagonist (flutamide) in the liver. Female fathead minnows were exposed to nominal concentrations of either 17β-trenbolone (0.05, 0.5, or 5 µg/L), flutamide (50, 150, or 500 µg/L), or a mixture (500 µg flutamide/L and 0.5 µg 17β-trenbolone/L) for 48 h. The iTRAQ method was used to label peptides after protein extraction and trypsin-digestion from livers of untreated controls or from fish treated with 17βtrenbolone (5 µg/L), flutamide (500 µg/L), or a mixture of both compounds. Forty-five proteins were differentially altered by one or more treatments (p < 0.05). Many altered proteins were involved in cellular metabolism (e.g., glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate mutase), general and oxidative stress response (e.g., superoxide dismutase and heat shock proteins), and the regulation of translation (e.g., ribosomal proteins). Cellular pathway analysis identified additional signaling cascades activated or inhibited by flutamide that may not be androgen receptor mediated. We also compared changes in select proteins to changes in their mRNA levels and observed, in general, that proteins and mRNA changes did not correlate, suggesting complex regulation at the level of both the transcriptome and proteome. It is concluded that both transcriptomic and proteomic approaches offer unique and complementary insights into mechanisms of regulation. We demonstrate the utility of proteomic profiling for use on a model species with value to ecotoxicology but having limited genomic information. Keywords: iTRAQ • quantitative proteomics • androgens • antiandrogen • gene expression • oxidative stress • toxicoproteomics

Introduction Endocrine disrupting chemicals (EDCs) that impair androgenmediated signaling pathways are present in the environment but have not received the same attention as environmental estrogens.1 For example, trenbolone acetate is implanted in cattle to promote growth and is effective when hydrolyzed to its active form 17β-trenbolone, which acts as a potent androgen receptor (AR) agonist. Limited studies with water samples from the vicinity of cattle feedlots have demonstrated androgenic activity in the samples (using cell lines transfected with the * To whom correspondence should be addressed. Nancy D. Denslow, Center for Environmental and Human Toxicology, Department of Physiological Sciences, University of Florida, PO Box 110885, Gainesville, Florida 32611-0885. E-mail: [email protected]. Tel: 352-392-2243 × 5563. Fax: 352392-4707. † Department of Physiological Sciences and Center for Environmental and Human Toxicology. ‡ Interdisciplinary Center for Biotechnology Research. § Current address: Donald Danforth Plant Science Center, St Louis, Missouri 63132. | U.S. EPA.

2186 Journal of Proteome Research 2009, 8, 2186–2200 Published on Web 03/06/2009

human androgen receptor), with concentrations of both 17βand 17R-trenbolone that are potentially high enough to adversely affect fish.1-3 In fathead minnow (FHM) (Pimephales promelas), 17β-trenbolone binds to the AR with higher affinity than testosterone (T) and exerts AR-mediated biological effects in females, including decreased T, 17β-estradiol (E2) and vitellogenin (Vtg), the egg yolk protein, in the plasma, and reduced egg production at water concentrations between 0.5-5.0 µg 17β-trenbolone/L.2 Comparable biological effects have been observed in females of other fish species such as zebrafish (Danio rerio) and medaka (Oryzias latipes) after 17βtrenbolone treatments.4 In contrast to 17β-trenbolone, flutamide (Drogenil), a pharmaceutical that is used in the treatment of prostate cancer, acts as an AR antagonist.5,6 While flutamide itself has little environmental relevance, other common environmental contaminants could cause toxicity via antagonism of the AR.1 In FHMs, flutamide also competitively binds the ARs.7 Females exposed to 500 µg flutamide/L for 21 days had a significant elevation in plasma T and Vtg, showed a delay in oocyte 10.1021/pr800627n CCC: $40.75

 2009 American Chemical Society

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Androgen Receptor Signaling in Fathead Minnows maturation, exhibited oocyte atresia, and showed a reduction in the mean number of spawns.8 Leo´n et al.9 demonstrated in medaka that there is ovarian atresia in females after exposure to water concentrations of 0.32 and 3.2 mg flutamide/L. Thus, in female teleost fish there is evidence that both 17β-trenbolone and flutamide disrupt reproductive processes by altering sex steroid and Vtg production. Moreover, these changes in reproductive physiology appear to result in reduced spawning and fecundity. Due to their well-characterized and environmentally relevant mechanisms of action, 17β-trenbolone and flutamide often have been used as model androgenic and antiandrogenic compounds for studying AR-mediated molecular pathways. Toxicogenomic approaches offer potential insights for addressing the mechanisms and modes of EDC action. Gene expression analysis has been used successfully to study the transcriptomic response in fish to estrogens in tissues such as liver,10,11 brain12,13 and gonad14 and androgens in the liver15 and gonad16 and both androgens and antiandrogens in the gonad.17 Complementing transcriptomic approaches, proteomics is a rapidly growing area that has great potential for the study of reproductive and developmental disturbances caused by EDCs. Traditional proteomic techniques, such as 2D gel electrophoresis, have been utilized in fish toxicology research to characterize proteomic fingerprints for E2 and 4-nonylphenol (a weak estrogen) in embryonic zebrafish18 and to identify differentially expressed proteins in the brain of Japanese flounder (Paralichthys olivaceus) after cadmium exposure.19 In addition, novel non-gel based methods utilizing 2Dpeptide fractionation procedures prior to mass spectrometry have the potential to be of significant use in toxicological studies for identifying and characterizing molecular biomarkers (reviewed in Martyniuk and Denslow, in press).20 Isobaric tags for relative and absolute quantitation21 (iTRAQ; Applied Biosystems) has several advantages over other labeling methods. First there is increased coverage of the proteome because peptides are labeled on the amine group, rather than on just one amino acid residue, and a larger number of peptides can be simultaneously labeled, processed, and statistically compared across groups. A second advantage is that multiple samples can be labeled and analyzed in the same experiment (up to 4 or 8 with the iTRAQ 8-plex) allowing direct comparison of samples in the same experiment. The iTRAQ method is relatively new for non-model species but it has been used successfully in Rana catesbeiana, to identify proteomic changes that occur in the tail after thyroid hormone treatment.22 In the Rana study, 15 differentially altered proteins were identified in the tail involved in processes such as apoptosis, immune response, and metabolism. That study utilized homology based searching for peptide-protein identification that was augmented by de novo sequencing and demonstrated that the method can be applied to species in which little genomic data is available. The iTRAQ method has also been used to validate putative biomarkers of hepatocarcinogenicity in rats23 and is proving to be a powerful labeling tool for large scale proteomic studies.24,25 A disadvantage with the labeling technique is that little information is available about post-translational modifications. However, the use of both gel-based and peptide labeling strategies to study the proteome can be complimented with toxicogenomics to improve understanding of molecular cascades activated or disrupted by chemicals such as endocrineactive substances. In the present study, we used the iTRAQ

method to identify putative biomarkers of exposure and effects in fish treated with AR-active chemicals. We measured the proteomic response in the FHM liver to a 48 h exposure to 17β-trenbolone, flutamide, and to a mixture of both compounds. This was done, in part, to determine if there were candidate proteins regulated through AR signaling that may act as putative biomarkers of androgen exposure. In addition to being a key organ for metabolism of xenobiotics, the liver is the site of several important reproductive processes in fish, including the production of Vtg. Bioassays in teleost fish such as the FHM are being actively pursued to identify biomarkers of environmental contaminants that act through AR signaling.7,26 The FHM is a cyprinid fish that has a wide distribution in North America. Due to their amenability to laboratory culture and testing, FHM are commonly used in studies evaluating acute and chronic toxicity of contaminants such as pesticides and pharmaceuticals in aquatic environments.27 Experimental concentrations of 17β-trenbolone and flutamide for this work were selected based on previous observations in FHM females of changes in reproductive parameters such as plasma Vtg and steroid concentrations, abnormal ovarian histology and decreased production of eggs.2,8 We hypothesized that flutamide would block, at least to some degree, the effect of 17β-trenbolone in the liver of fish exposed to a mixture of the two chemicals. For proteomic characterization and relative quantitation, we used the iTRAQ method to label peptides followed by LC-MS/MS protein identification. The isobaric tag consists of a reporter (mass ) 114, 115, 116, and 117) and a balance group to ensure precursor ions enter the collision cell simultaneously. Upon dissociation in the collision cell, the reporter tags yield low mass discriminating MS/MS signatures which can be used to quantitatively evaluate the relative changes between control and treatment groups. Using LC-MS/MS, we also begin to characterize the FHM proteome for future toxicological studies on EDC exposures. This study is the first to demonstrate the utility of the iTRAQ mass spectrometric based approach in an environmentally relevant fish toxicological model.

Materials and Methods Experimental Animals and Design. All exposures were conducted at the U.S. Environmental Protection Agency laboratory in Duluth, Minnesota. Adult female FHM (ca. 6 months old) were tested under continuous flow conditions (45 mL/ min of UV-filtered Lake Superior water) in tanks containing 10 L of water at a density of 6 fish/tank. Fish were held at a temperature of 25 °C, under a photoperiod of 16:8 L:D photoperiod. The FHM were exposed to a single nominal concentration of either flutamide (50, 150, or 500 µg/L), 17βtrenbolone (0.05, 0.5, or 5 µg/L), or a mixture of 500 µg flutamide/L and 0.5 µg 17β-trenbolone/L for 48 h. No carrier solvents were used. The chemicals were dissolved in water at approximately their water solubility levels and were further diluted to achieve the target concentrations. Test concentrations were based on results of previous FHM assays conducted with the two chemicals2,8 concentrations specifically selected for the mixture alter endocrine function in the FHM in shortterm reproduction assays2,8 Exposure initiation times were staggered throughout the day, by replicate (four tanks per treatment), to minimize variation in the exposure duration between the first and last fish sampled. Water from each exposure tank was sampled just prior to the addition of fish (0 h), after 24 h of exposure, and approximately 1 h before Journal of Proteome Research • Vol. 8, No. 5, 2009 2187

research articles sampling (47 h). 17β-trenbolone and flutamide concentrations in the water were quantified using methods similar to those described by Ankley et al.2 and Jensen et al.8 The following actual water concentrations were measured: control, no contaminant detected (method detection limits ) 0.03 µg 17βtrenbolone/L; 20 µg flutamide/L); flutamide (53 ( 18, 158 ( 57, and 540 ( 188 µg/L), 17β-trenbolone (0.04 ( 0.005, 0.43 ( 0.03, and 4.4 ( 0.12 µg/L) and the mixture (560 ( 186 µg flutamide/L and 0.47 ( 0.03 µg 17β-trenbolone/L). Other water quality conditions, monitored daily, were maintained within the guidelines established for FHM reproduction tests.28 After exposures, fish were anesthetized with tricaine methanesulfonate (100 mg/L) buffered with NaHCO3 (200 mg/L). Blood samples were collected from the caudal vasculature of individual fish using heparinized microhematocrit tubes for future studies. Samples were centrifuged at 2000 g for 10 min to separate plasma, and plasma was stored at -80 °C for future use. Tissue samples were collected from four fish per tank replicate. The liver along with gonad, brain, and pituitary were rapidly dissected after the treatment, frozen in liquid nitrogen, and stored at -80 °C for future studies. Protein Extraction and Peptide iTRAQ Labeling. Approximately 30-50 mg of liver from each of three FHM individuals per treatment (control, 5.0 µg 17β-trenbolone/L, 500 µg flutamide/L, and mixture-treatment) were mechanically disrupted in 200 µL RIPA Lysis and Extraction Buffer (25 mM Tris-HCl pH 7.6, 150 mM NaCl, 1% nonyl phenoxylpolyethoxylethanol40, 1% sodium deoxycholate and 0.1% SDS) (Pierce, Thermo Fisher Scientific Inc., Rockford, IL) containing 10 µg protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO). Proteins were acetone precipitated with cold acetone with 6 times (1.2 mL) the volume of each sample (200 µL). Precipitated proteins were reconstituted in 20 µL dissolution buffer (iTRAQ kit). Total protein was determined using Coomassie Plus Better Bradford Assay Reagent (Pierce). One hundred µg total protein/sample was used for peptide labeling. Three separate iTRAQ labeling reactions (4 labels of 114-117/ iTRAQ experiment) were processed separately according to the manufacturer’s protocol (Applied Biosystems Inc., Foster City, CA). Briefly, after reducing the protein extract with 50 mM Tris-(2-carboxyethyl) phosphine and blocking the thiol groups using methyl methanethiosulfonate, proteins were trypsin digested in a volume of 35 µL with 10 µg trypsin for 16 h at 37 °C (Sigma-Aldrich) and then labeled with iTRAQ reagents as follows; control, isobaric tag 114; 17βtrenbolone-treated, isobaric tag 115; flutamide-17β-trenbolone mix-treated, isobaric tag 116; flutamide-treated, isobaric tag 117. For each iTRAQ experiment, peptides labeled with each of the four labels (114-117) were pooled before strong cation exchange (SCX). This experiment was performed a total of three times to compare results for FHM biological replicates for each exposure condition. 2D-LC-MS/MS Analysis. The following protocol was performed on each of the three individual iTRAQ experiments separately. The pooled iTRAQ sample (from above) was desalted using a macrospin column Vydac Silica C18 (The Nest Group Inc., Southboro, MA) and dried to completeness. Peptides were resuspended in 100 µL buffer A (75% 0.01 M ammonium formate and 25% acetonitrile; ACN) for off-line SCX fractionation on a polysulfoethylA column (100 × 2.1 mm, 5 µm, 300 Å). Peptides were eluted using a linear gradient of 0-20% buffer B (75% 0.5 M ammonium formate, 25% ACN) over 40 min, followed by a gradient of 20-100% buffer B for 5 min. Peptide peaks were monitored by UV at 280 nm. Twenty 2188

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Martyniuk et al. peptide fractions were collected and pooled into four fractions of equal peptide content for mass spectrometry analysis. The majority of peptides were eluted in the first 12 fractions so these were pooled into three fractions (1-4, 5-8, 9-12) and the fourth fraction consisted of 8 peptide fractions (12-20). Each fraction was injected onto a capillary trap LC Packings PepMap (DIONEX, Sunnyvale, CA) and desalted for 5 min with a flow rate of 20 µL/min of 3% ACN, 0.1% acetic acid, 0.01% trifluoroacetic acid (TFA), and then continued onto an in-line LC Packing C18 Pep Map HPLC column (300 µM × 5 mm) connected to the mass spectrometer with a flow rate of 200 nl/min. The elution gradient of the nano-HPLC column started at 3% solvent B and finished at 60% solvent B and proceeded for 2 h. Solvent B consisted of 0.1% acetic acid and 96.9% ACN. LC-MS/MS analysis was carried out on a hybrid quadrupoleTOF mass spectrometer, QSTAR XL (Applied Biosystems). 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. Protein Search Database. Tandem mass spectra were extracted by Analyst (v1.1, Applied Biosystems). To better assign peptides to proteins, a ray-finned fish database containing trypsin digested peptides was created by extraction of protein sequence information from National Center for Biotechnology Information (NCBI) (08/07/2008; # entries ) 121 756) and searched using MS/MS data interpretation algorithms within Protein Pilot (Paragon algorithm, v 2.0, Applied Biosystems). The Paragon algorithm from Protein Pilot was set up to search iTRAQ 4-plex samples as variable modifications with methyl methanethiosulfonate as a fixed modification. The Protein Pilot algorithm was selected to search automatically for biological modifications such as homocysteines. Additional information on this algorithm is found in Shilov et al.29 The confidence level for protein identification was set up to 1.3 (95%) which is the default for the detected protein threshold in a Paragon method. To calculate a false discovery rate (FDR) for peptide-protein assignments, Proteomics System Performance Evaluation Pipeline (ProteomicS PEP, Applied Biosystems) in Protein Pilot was used to create a reversed ray-finned fish database in which to search. We obtained a FDR ) 9.6% for peptide-protein assignments (CI 95%) using ProteomicS PEP reverse database and performed a second analysis to increase our confidence in peptide-protein identification. In the second analysis, only proteins with at least one peptide at a confidence level equal or greater than 90% were used. A FDR ) 5.1% was calculated as using twice the total identification spectra above 90% confidence from the decoy database as outlined by Elias and Gygi.30 Between the two algorithms, we observed high agreement in protein ID assignment and quantitation and five proteins from the original data set of differentially regulated proteins were removed after the second analysis. The differential expression ratios for protein quantitation were obtained from Protein Pilot which calculates protein ratios using only ratios from the spectra that are distinct to each protein, excluding the shared peptides of protein isoforms. Peptides with low spectral counts were also excluded from the calculation of averages by setting the intensity threshold for the sum of the signal-to-noise ratio for all the peak pairs at >9. All the quantitative ratios were then corrected for bias auto-

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Table 1. Primers Used to Evaluate Relative Gene Expression Changes in the Liver Using Real-Time PCR gene

forward primer 5′-3′

reverse primer 5′-3′

Betaine homocysteine methyltransferase Catalase Elongation factor 1a GAPDH Superoxide dismutase Ribosomal protein L23a Ribosomal protein 18s

CTG GAG AAC AGA GGC AAC AAA CAA CAC CCC CAT CTT CTT TAT C CCC TCC TGG CTT TCA CTC T CTT CCC ACA AAC GAG GAC AC GCA CTT CAA CCC TCA CAC AC AGG CAG CCC AAG TAC CC CGG TTC TAT TTT GTG GGT TTC T

TGA TCG CTG AGT ACT TTG AGC A GGT TTG CGT CCT GAA TCG GGA TGG AAG GTT GAG CGT AA TGT TCA AGT ATG ACT CCA CCC A GGT AAG GGA GGC AAT GAA GA ACA AAA TTG GCA TCA TCT AAA CTG CAA GAA CGA AAG TCG GAG G

a

Note that each primer set is listed in a 5′ to 3′ orientation.

matically by Protein Pilot when processing the data to create the Pro Group algorithm results. Each protein that was quantified was identified by a minimum of three spectra. To calculate differential expression ratios, all identified spectra from a protein were used to obtain an average protein ratio relative to the control label (i.e., fold change). The error factor (EF) is a measure of the variation between the different iTRAQ ratios (the greater the variation, the greater the uncertainty) and represents the 95% uncertainty range for a reported ratio. The p-value is calculated based on the 95% confidence interval. Gene Ontology and Pathway Studio. Gene ontology was used to further characterize proteins identified in the liver of FHMs. Biological process, molecular function, and cellular components of all proteins identified by tandem MS/MS were assigned using Blast2Go.31 BlastP (NCBI) was used to identify homologous proteins in the database and default parameters in Blast2Go were used for GO term assignment and annotation. For proteins with altered abundance, pathway analysis was done to further identify putative cellular processes disrupted by 17β-trenbolone and flutamide using Pathway Studio (v5.0) (Ariadne Genomics, Rockville, MD).32 Human homologues (NCBI) for FHM proteins were obtained (RefSeq in NCBI) and Entrez Gene identifiers were retrieved using the ID mapping service in Pathway Studio to MD Anderson GeneLink (University of Texas, Houston, TX). Pathways were built by finding the shortest paths between selected entities using the Resnet5 database in Pathway Studio. All entities were used to identify common cellular processes that were modulated by 17βtrenbolone or flutamide. RNA Extraction and cDNA Synthesis. Additional FHM livers from the same exposures were homogenized in STAT 60 (TelTest Inc., Friendswood, TX) and RNA was extracted as per the manufacturer’s protocol. Concentration and A260/A280 ratios for total RNA in each sample were measured using a NanoDrop spectrophotometer (ND1000; Eppendorf, Westbury, NY). Total RNA was further purified and DNase treated on-column using the RNeasy Mini Kit (Qiagen, Valencia, CA, USA) as per manufacturer’s protocol. First-strand cDNA synthesis was done with Superscript-II (Invitrogen, Carlsbad, CA, USA). Briefly, 3 µg total RNA from FHM liver samples were incubated with 50 ng random hexamers (Invitrogen) and 1 µL 10 mM dNTPs. The reaction was heated to 70 °C for 5 min, quickly chilled on ice, and centrifuged briefly. Four microliters of 5× reaction buffer, 2 µL 0.1 M DTT, and 0.5 µL RNase inhibitor (40 U/µL) (Invitrogen) were added, gently mixed, and heated at 42 °C for 2 min. One µL Superscript II RNase H- Reverse Transcriptase (200 U) (Invitrogen) was then added and the reaction was allowed to continue at 42 °C for 70 min. The reaction was inactivated at 70 °C for 10 min and stored at -20 °C until used for real-time PCR. SYBR Green Real-Time PCR. We elected to investigate the mRNA steady state levels for proteins that represented the

general biological functions identified in this study such as catalase and SOD (stress), EF1a (translation control), and GAPDH (cellular respiration). All primers for real-time PCR were designed using Primer333 and synthesized by MWG Biotech (Eurofin MWG Operon, Huntsville, AL) (Table 1). Optimal annealing temperature was between 58-60 °C and primers were designed to amplify sequences between 100 to 250 bp. Primers were initially tested using FHM liver cDNA and the resultant amplicons were cloned and sequenced to confirm specificity (ICBR, University of Florida, Gainsville, FL). Gene cloning was done using the pGEM-T Easy Vector (Promega, Madison, WI) and transformed in One Shot TOP10 E. coli (Invitrogen). Accession numbers for cloned FHM genes are as follows; catalase (FJ030936), Cu/Zn superoxide dismutase; SOD (FJ030938), betaine-homocysteine methyltransferase; BHMT (FJ030935), ribosomal protein L23a; RPL23A (FJ030937). Elongation factor 1a; EF1a (AY643400) was previously cloned and real-time primers were designed from this available sequence. Serial dilutions (n ) 8) from a calculated starting copy number of 109 (to 102) were used to construct standard curves for each gene of interest. The equation to determine transcript copy number was as follows; number of copies ) (X * 6.022 × 1023)/ (Y * 1 × 109 * 650), where X is the template amount (ng of vector + insert), Y is the template length (bp vector + insert), and 650 (kDa) is the average weight of a base pair. Reaction efficiencies for real-time PCR reactions ranged between 99.8-102.9% and R2 > 0.995. Real-time PCR analysis of gene expression was carried out on first-strand cDNA derived from DNase treated RNA samples from control and treatment groups. Real-time PCR reactions were done using 1× iQ SYBR Green Supermix (Bio-Rad), 1 µL 10 mM gene specific primers, and 100-150 ng first-strand cDNA derived from DNase treated RNA samples. The two step thermal cycling parameters were as follows: initial 1 cycle Taq activation at 95 °C for 3 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. After 40 cycles, a dissociation curve was produced starting at 55 °C (+1 °C/30 s) to 95 °C. Realtime PCR expression was assayed on an iCycler (Bio-Rad). FHM 18S rRNA (AY855349) was used to normalize gene copy number. A one-way analysis of variance (ANOVA) followed by post hoc Dunnett’s pairwise multiple comparison was performed on log transformed expression data. All analyses including simple linear regressions were performed using JMP Genomics (SAS, NC).

Results and Discussion Identification of Proteins in FHM liver. A complete list of proteins (n ) 293) identified in this study is provided in Supporting Information (Appendix 1). This table also includes the NCBI protein accession number and protein name, % coverage of protein, ray-finned species identified as showing Journal of Proteome Research • Vol. 8, No. 5, 2009 2189

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Figure 1. Representative bar graphs for gene ontology terms (biological processes (A), molecular function (B)) for identified proteins by LC-MS/MS. Protein sequences were annotated and GO terms assigned using Blast2GO. Note that some proteins were assigned multiple GO terms based on biological process and molecular process.

the highest homology to the query, ratios of labels 115-117 to control (114), p value for all proteins, and the error factor. All raw peptide data are also present in Appendix 1 and includes the peptide sequence and the confidence (%) of the peptide, and information indicating whether or not the peptide was used in the quantitation of the protein. In summary, the analysis used a cutoff of >1.3 (95% confidence). The total number of distinct peptides detected in this study was 2148. The total number of spectra detected was 4655 and 53.7% of the spectra were identified. Thus, 46.3% of the spectra were not identified by homology searching. We acknowledge that this is a limitation in using a species for which the entire genome is not complete. Currently, 250 000 EST sequences exist for the FHM and we are working with bioinformatics experts to curate this data to make it useful for future proteomics 2190

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research. While the number of changed proteins is underrepresented, the group we have identified encompasses a broad array of proteins with diverse biological processes, molecular functions, and cellular compartments, enabling us to make conclusions relative to the effects of trenbolone and flutamide in FHM. Using the QSTAR hybrid quadrupole-TOF mass spectrometer, we were able to detect 293 proteins in the FHM liver. Biological processes that contained the greatest number of proteins included cellular (260 proteins) and metabolic processes (232 proteins) (Figure 1A). Identified proteins were involved in other biological processes such as response to stimuli (50 proteins), reproduction (10 proteins), biological adhesion (9 proteins), and growth (7 proteins). When categorized by metabolic function, the greatest numbers of proteins

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research articles of approximately 2-fold (p < 0.05) was found between the control group (114), the mix- (116) and flutamide- (117) treated groups, reflected by the difference in peak intensity of the labels 114, 116, and 117 (inset box).

Figure 2. Representative MS/MS spectrum of the fragmented (b/y series) peptide VDALGNPIDGK from the FHM protein ATP synthase H+ transporting, mitochondrial F1 complex. The lower m/z range of the spectrum was enlarged to show the reporter ion intensity correlated to the peptide abundance from control group (label 114) and 17β-trenbolone- (label 115), mix- (label 116) and flutamide- (label 117) treated groups. This protein had 13 spectra used in quantitation.

were characterized as having binding functions (237 proteins) and catalytic activity (148 proteins) (Figure 1B). Other metabolic functions with relatively high protein representation included transporter activity (30 proteins), molecular transduction activity (14 proteins), and antioxidant activity (6 proteins). Cellular components included proteins located in the cytoplasm (221 proteins), ribosome (66 proteins), mitochondrial (30 proteins), cytoplasmic vesicle (23 proteins), endoplasmic reticulum (19 proteins), and Golgi membrane (6 proteins) (graph not shown). Therefore, we identified a diverse range of proteins in the FHM liver. The major gene ontology categories of proteins identified in this study is comparable to the major categories identified by Wang et al.34 These authors surveyed the zebrafish liver and identified 1204 proteins in the cytosolic component of the zebrafish liver proteome using LC-ESI-MS/MS combined with trypsin digestion and microwave-assisted acid hydrolysis. A significant number of proteins identified in the zebrafish liver were involved in general metabolism and energy pathways or protein metabolism. Both the present study and Wang et al.34 detected a large number of proteins in the liver that had molecular functions of binding and catalytic activity or were structural molecules. Similar to our study in the FHM liver, Wang et al.34 also identified important biomarkers of toxicity that included superoxide dismutase, heat shock proteins, and Vtg. Therefore non-gel based proteomic approaches show great promise in detecting and characterizing biomarkers in fish for use in ecotoxicology. Quantitative Assessment of Proteins. A significant advantage of detecting multiple labeled-peptides from a single protein using the iTRAQ method is that one is able to statistically evaluate relative protein changes. Figure 2 depicts a representative MS/MS fragmentation (b/y ion series) of one peptide VDALGNPIDGK from ATP synthase H+ transporting, mitochondrial F1 complex, alpha subunit 1. This protein had 13 spectra that were used in protein quantitation. For this particular protein a significant change in protein abundance

A total of 98 identified proteins had at least 3 spectra with iTRAQ reporter ions for quantitation. Of those, 45 proteins were differentially regulated in either one or all three treatment groups at p < 0.05 but for comparison, the table also includes 10 proteins that were significantly different among groups at p < 0.10 (Table 2). The table is organized by first presenting all the proteins in which peptides were identified in all three iTRAQ experiments, followed by those proteins in which peptides were identified in two of the three iTRAQ experiments, followed by those proteins in which peptides were identified in one of the three iTRAQ experiments. The total number of proteins identified in each iTRAQ experiment were as follows; 136, 214, and 138. Interestingly, 19 proteins were detected in all three iTRAQ experiments, followed by 18 proteins for two of three iTRAQ experiments, followed by 18 for one of the three iTRAQ experiments. High variability in protein identification for each experiment was expected since we used data dependent acquisition on the Q-STAR mass spectrometer with only the 3 most intense peaks receiving complete MS/MS scans per second. In addition, we expected some biological variation in expressed proteins among the FHM livers. For example, AHNAK nucleoprotein, was only detected in one of the three experiments but with a surprisingly large number of peptides (46 peptides). There is more confidence in the protein data that identifies proteins consistently across experiments. Cellular Processes Affected through AR Signaling. We hypothesized that proteins mediated through AR signaling would show an intermediate fold-change in mixture of both 17β-trenbolone and flutamide when compared to either compound alone, since the two compounds would antagonize each other. We graphed all proteins by relative fold change over control to compare the overall response of protein changes across each of the treatment groups. To achieve a broader view of changes in the liver proteome, we used all 293 proteins that had peptides detected by LC-MS/MS, including proteins that had a single peptide identified as well as proteins that had multiple peptides identified. We organized all proteins by the 17β-trenbolone group from decreasing to increasing fold change because our interest was to determine whether the antiandrogen flutamide blocked the effects of 17β-trenbolone in the mixture (Figure 3). In general, the overall response of the liver proteome to a mixture appeared to be more similar to flutamide alone than to 17β-trenbolone alone, suggesting that there are more proteins in the mix that are acting through flutamide-mediated pathways than via 17β-trenbolone-mediated pathways. Simple linear regression analysis confirms that the overall response in the mixture group is more similar to the flutamidethan trenbolone-treated animals (R2 ) 0.79; p < 0.001) (Figure 4). Interestingly, regression analysis also revealed that there was a weak but significant correlation between 17β-trenbolone and mix-treatment groups and 17β-trenbolone and flutamidetreated group (data not shown). Relative peptide fold change from the 17β-trenbolone treatment was significantly correlated to the mixture (R2 ) 0.07; p < 0.01), similar to the correlation of relative peptide fold change between 17β-trenbolone and flutamide-treatment groups (R2 ) 0.03; p < 0.01). This suggests that there are some proteins in the mixture that are acting through 17β-trenbolone mediated pathways but these are few Journal of Proteome Research • Vol. 8, No. 5, 2009 2191

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Table 2. Differentially Expressed Proteins Identified by iTRAQ and LC-MS/MS (QSTAR) accession

gi|51701879 gi|47939362 gi|42734425 gi|5689156 gi|116325975 gi|92087016 gi|81097752 gi|50980344 gi|46520169 gi|47085885 gi|119943230 gi|110589604 gi|41351240 gi|110351024 gi|44890340 gi|47227151 gi|56790262 gi|78098990 gi|47271422 gi|41152144 gi|57526731 gi|50370041 gi|47085861 gi|62202562 gi|126211569 gi|125805987 gi|41152334 gi|71564498 gi|62550923 gi|41054569 gi|46329692 gi|94732278 gi|81097694 gi|50539996 gi|125822940 gi|50344972 gi|4572552

protein

% coverage # spectra fold change fold change fold change protein ID quantitation 17β-trenbolone mix flutamide

Peptides for proteins identified in all three iTRAQ experiments 60S ribosomal protein L8 27.76 8 Acyl-Coenzyme A dehydrogenase, long chain 11.54 3 Aldolase b, fructose-bisphosphate 48.35 24 ATP synthase beta-subunit 32.63 32 ATP synthase, H+ transporting, mitochondrial F1 25.41 13 complex,alpha subunit 1, cardiac muscle Beta-Actin-2 40 26 Betaine-homocysteine methyltransferase 47.75 67 Elongation factor 1-alpha 53.46 31 Endozepine 75.86 17 Fructose-1,6-bisphosphatase 1 26.41 13 Glyceraldehyde 3-phosphate dehydrogenase 54.65 33 Hemoglobin alpha chain 59.15 22 Keratin 18 41.3 7 Liver fatty acid binding protein 10 81.36 120 Phosphoglycerate mutase 1 14.57 6 Ribosomal protein L23a 38.06 10 Superoxide dismutase 1, soluble 37.66 15 Translationally controlled tumor protein 8.24 3 Triosephosphate isomerase 1b 66.53 78 Peptides for proteins identified in two of three iTRAQ experiments 40S ribosomal protein S27 17.86 5 40S ribosomal protein S28 57.97 6 60S Ribosomal protein L13 33.65 6 60S Ribosomal protein L26 30.34 8 60S Ribosomal protein L6 22.26 3 60S Ribosomal protein L7 27.35 6 Agmatine ureohydrolase 19.95 9 ATP synthase, H+ transporting,mitochondrial F0 complex, 18.63 4 subunit d Beta thymosin-like protein 79.55 7 Cu/Zn superoxide dismutase 64.18 11 Fumarylacetoacetate hydrolase 17.82 9 Heat shock 60kD protein 1 18.43 7 Heat shock 70 kDa protein 8 22.34 12 Leucyl aminopeptidase 26.1 7 Peroxiredoxin 2 42.13 12 PREDICTED: similar to SORD protein 30.79 10 Reticulocalbin 3, EF-hand calcium binding domain 21.84 7 Vitellogenin precursor 20.46 14

-1.02 -1.12 1.19 -1.15 1.03

2.9 -1.05 1.78d -1.26 2.24c

2.52d -1.75b 1.37c -1.65d 1.86c

-1.01 -1.11 1.12 1.06 -1.28 1.86d 1.45c 1.27 -1.71d 1.07 1.31 -1.61 -1.78 -1.20c

2.42c -1.64d 2.00d -1.98d -1.15 1.25 1.59c 3.72 -2.20d -1 -2.76d -1.58 -3.69d -1.30d

2.55d -2.24d 1.67d -2.58d -2.23b -1.18 2.23d 3.25b -2.08d -1.98c -2.25d -2.59d -3.15 -2.20d

1.41c -2.07d 1.19 1 1.1 1.2 -1.68d -1.89b

4.61d -2.49d 2.44b 3.29b 4.40d 5.7 -1.27 -1.09

2.85d -2.73c 2.40b 2.62c 3.20c -1.43 -1.83c

1.08 -1.67d -1.31c -1.61b 1.33 -1.17 1.1 1.53 1.08 -

-2.57d 1.08 1 -1.23 3.79c -1.41c -1.31b 3.90c 2.87b 3.62

-1.82d -1.60d -1.43b -1.51 2.42c -1.97d -2.07d 1.49 1.74 2.39c

-2.25c 5.05c 7.81c -1.38 -1.39 2.62b -3.69b 2.21b -2.1c -1.65b* -1.47d 1.63 -2.07 -2.85d

-2.81b 3.28b 8.58c -1.92d -1.76d 1.56b -6.39c 1.39 -2.41d -2.42d 2.63b -2.09b -2.96c

1.21b -2.17d -1.24b -1.33b

-1.11 -2.74d -1.60c -1.82d

Peptides for proteins identified in one of three iTRAQ experiments 40S ribosomal protein S12 36.36 10 -1 40S ribosomal protein S8 12.02 4 -1 60S ribosomal protein L28 38.41 3 1.53 Activated RNA polymerase II transcription cofactor 4 30.89 4 -1.03 Calmodulin 48.85 14 1.16b Calreticulin 7.88 3 1.09 Collagen a1 10.56 8 -6.35c Electron-transfer-flavoprotein, beta polypeptide 9.45 3 1.16 Enoyl Coenzyme A hydratase 1, peroxisomal 8.77 4 -2.1 FABP1B; Fatty acid binding protein 1B 41.67 7 -1.06 Ferritin heavy chain 33.71 11 1.08 Histone H2B 1/2 31.71 5 1.28 Nucleolin 26.22 15 1.03 PREDICTED: similar to additional sex 4.36 8 -2.72d combs like 2 isoform 1 gi|125834993 PREDICTED: similar to AHNAK nucleoprotein 23.57 46 1.44d gi|58801526 SET translocation (myeloid leukemia-associated) B 11.27 6 1.07 gi|82185638 Small ubiquitin-related modifier 3-A precursor 26.6 4 1.45 gi|86370988 Ubiquitin C 86.98 20 -1.08 gi|15294035 gi|54039530 gi|49619151 gi|66472668 gi|47221709 gi|38325823 gi|14164347 gi|47085679 gi|54400638 gi|41152406 gi|125812710 gi|47198055 gi|33949944 gi|47221235

a Proteins are arranged first by the number of iTRAQ experiments in which peptides were identified for that protein, followed by alphabetical order. All fold changes are relative to control isobaric label 114. b Significant difference at p < 0.10. c Significant difference (p < 0.05). d Significant difference (p < 0.01). A line in the column is because multiple peptides for the protein were not detected in that treatment. Accession numbers are NCBI protein accessions.

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Figure 3. Comparison of all proteins identified in each treatment organized by fold change compared to control. Proteins are first organized from the most decreased to the most increased fold change for the 17β trenbolone-treated group and then compared to the flutamide- and the mix-treated group groups (i.e., protein positions remain the same in each graph). Note the higher concentration of flutamide in the mix-treated group appears to have reversed or reduced the effect of 17β-trenbolone.

Figure 4. Regression analysis of all relative protein changes between the flutamide-treated and mix-treated groups. Relative protein changes in the mix-treated group were significantly and highly correlated to protein changes in the flutamide-treated group (R2 ) 0.79, p < 0.001).

compared to flutamide-mediated proteins. There were 24 proteins that showed intermediate fold-changes in the mixture (i.e., putatively regulated through the AR). The majority of proteins in the mixture were most likely being affected only through flutamide mediated pathways (Figures 3 and 4). To identify cell pathways that may be regulated in part by AR, significantly regulated proteins that showed an intermediate fold change in the mix-treatment group were further

analyzed using Pathway Studio (Figure 5). Green indicates a reduction in protein abundance while red indicates an increase in protein abundance in the mix-treatment compared to control. Cell processes putatively affected through AR signaling included growth rate, cell division and differentiation (G1 and G2 phases, S phase), pregnancy, secretion, endocytosis, glycolysis, and proliferation. Interestingly, Garcia-Reyero17 identified similar biological processes in the ovary in a mixture of 17βtrenbolone and flutamide using gene expression analysis and functional enrichment of gene ontology. These included DNA replication, nucleus localization, and mitotic spindle organization (i.e., cell division and differentiation) and parturition (pregnancy). Proteins that are regulated through the AR are candidate specific biomarkers for environmental exposures to androgens. These proteins included betaine homocysteine methyl-transferase, endozepine, fatty acid binding protein 1b, ferritin heavy chain, and GAPDH (Figure 5). A major goal of this study was to identify proteins altered by 17β-trenbolone-treatment that were blocked by adding flutamide (mix-treated group), providing evidence for modulation of the protein through AR mediated pathways. However, it is difficult to ascertain whether protein levels were altered via direct AR signaling or whether there were additional molecular pathways involved. For example, there were proteins that were altered to a similar fold change and direction by both 17β-trenbolone and flutamide (and in the mixture of both). This may be the result of activation of a common toxicological or stress response pathway in the liver, rather than direct regulation through the AR. Examples included transcripts and Journal of Proteome Research • Vol. 8, No. 5, 2009 2193

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Figure 5. Pathway analyses of significant proteins that showed an intermediate fold change in abundance in the mix. These proteins and cellular processes are putatively regulated through AR signaling and are potential biomarkers for androgenic exposures. Cell processes identified were cell mitosis, differentiation, and cell growth (e.g., G1 phase, G2m-transition, G2 phase, and growth rate). To simplify the figure, a subset of cellular processes was excluded. FHM proteins were assigned to the best of our ability to the closest human homologue, and in some cases, proteins identified in FHM liver are represented in the pathway analysis by their closest relative. Abbreviations follow those reported in Figure 6.

proteins with a role in general and oxidative stress such as catalase mRNA (induced in all treatments), SOD mRNA and protein (generally decreased in all treatments), and HSP70 protein 8 (increased in all treatments). In this case, the hepatic proteome may be responding to a chemical exposure rather than to a specific chemical mode of action. A second mechanism that is not directly involved in classical nuclear AR signaling is that of membrane-bound AR signaling. Similar to reports with estrogens,35,36 androgens can also rapidly activate G-protein coupled membrane bound receptors resulting in increased kinase activity and calcium signaling.37 Membrane-bound receptor signaling compared to nuclear receptor signaling may evoke very different effects at the translational level, as increased transcriptional activity is not a necessary prerequisite for protein synthesis or degradation. For example, measures of steady state mRNA abundance and changes in protein abundance may be the result of increased stability of the mRNA as opposed to increases in transcription. Moreover, there is the possibility that flutamide may (1) act to stimulate or inhibit additional receptors and pathways that are not activated by androgens and/or (2) interact with unknown proteins involved in other cellular processes. In support of this, flutamide treatment of androgen-independent prostate cancer cells increases the expression of protein kinase C isoforms in the absence of ARs.38 In the liver of female Sprague-Dawley rats, Coe et al.39 used microarray analysis to compare the effect of flutamide to classical aryl hydrocarbon receptor (AhR) agonists such as benzo[a]pyrene and 3-methylcholanthrene. The authors demonstrated that flutamide is an activator of the AhR in vivo based on gene expression com2194

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parisons with cytochrome P450 1A inducers, suggesting interactions with other receptor systems. Cellular Processes Affected by 17β-Trenbolone and Flutamide Alone. We further investigated the cellular processes affected by 17β-trenbolone and flutamide alone using pathway analysis (Figure 6). These analyses also included proteins that showed intermediate responses in the mixture group (e.g., GAPDH appears in both the 17β-trenbolone and mixture pathway analyses but not in the flutamide group). Major cellular pathways identified as being affected by 17β-trenbolone alone included mutagenesis, catabolism, apoptosis, and cell division (Figure 6A). The major cellular pathways identified as being affected by flutamide alone were cell survival (and apoptosis), proliferation, differentiation (G1 phase, G2 phase, S phase, DNA replication), protein degradation and proteolysis, and oxidative stress (Figure 6B). Therefore, compared to 17βtrenbolone, flutamide affects different cell processes, suggesting flutamide is acting through additional molecular pathways. However, there were also common cellular pathways affected by both 17β-trenbolone and flutamide and these included cell division (G1 phase), mutagenesis, and apoptosis. Characteristics of Specific Proteins Altered by the Treatments. In general, the majority of proteins affected in the liver were involved in cellular respiration, the oxidative stress response, and translation machinery. Each of these three categories is further discussed in detail below. However, there was also some evidence that 17β-trenbolone and flutamide treatments affected proteins involved in fatty acid mobilization. For example, fatty acid binding proteins (FABPs) belong to a diverse family of intracellular lipid binding proteins. FABP10 was decreased in all three treatments by 1.7-2.2-fold (p
50% of total nuclear transcription in growing cells, underscoring the importance of protein production.53 Based on our data, it is difficult to speculate on whether specific ribosomal proteins were directly regulated through AR signaling. However there is good evidence showing that other androgens such as testosterone have pronounced effects on translational machinery.54,55 There is also evidence that cell stress impairs protein synthesis. H2O2 treatment and free radical production resulted in a dose-dependent inhibition of protein synthesis in yeast S. cerevisiae, studied by preparing extracts from polysomes containing actively transcribing ribosomes and comparing ribosomal subunit composition in untreated and treated yeast.56 Relationship Between Protein Changes and Transcript Changes. In general, the genomic response to AR modulators for select genes did not correlate strongly to proteomic changes (Figure 7A-F). Additional studies suggest that the correlation between transcript and protein levels can vary from 45 to 75%.57 This study highlights the complexity of interactions between the genomic and proteomic responses and one must consider that the transcriptional response will not always parallel the proteomic responses due to temporal differences, transcript and/or protein stability, multiple regulatory pathways, and post-translational modifications. It is difficult to speculate in the present study on the mechanisms underlying changes in transcripts-proteins and it is plausible that the lack of correlation is due to the single time-point examined. However, this study emphasizes the need for carefully designed, temporally intensive studies to fully understand the nature of changes in gene versus protein expression. Integrative approaches using algorithms to consider both genomic and proteomic data are recently developed and have demonstrated improved ability to predict physiological responses58 and biomarker integration will be paramount in improving predictions of toxicant effects in aquatic toxicology.

Conclusions We demonstrate that iTRAQ methodology is a powerful approach for toxicological studies by characterizing proteomic 2198

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Martyniuk et al. responses in the liver of FHM exposed to chemicals that interact with the vertebrate AR. We show that the response of the proteome to a mixture of flutamide and 17β-trenbolone is more similar to flutamide alone than to 17β-trenbolone, at the concentrations tested. There were additional cellular pathways regulated by flutamide that were not regulated by 17β-trenbolone, suggesting additional non-AR signaling mechanisms activated by flutamide.

Acknowledgment. The research was supported by NSERC postdoctoral fellowship to C.J.M. and by the Environmental Protection Agency STAR grant, EPA (R831848). This work was also supported by the Superfund Basic Research Program from the National Institute of Environmental Health Sciences RO1 ES015449 to N.D. We also thank Li Lu for discussions involving database searches. L. Blake, E. Durhan, M. Kahl, K. Jensen and E. Makynen provided valuable technical assistance for this work. Supporting Information Available: Supplementary data can be found in Appendix 1. This appendix contains all raw peptide identification information, protein identification, and protein quantitation. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Gray, L. E., Jr.; Wilson, V. S.; Stoker, T.; Lambright, C.; Furr, J.; Noriega, N.; Howdeshell, K.; Ankley, G. T.; Guillette, L. Adverse effects of environmental anti-androgens and androgens on reproductive development in mammals. Int. J. Androl. 2006, 29, 96– 104. (2) Ankley, G. T.; Jensen, K. M.; Makynen, E. A.; Kahl, M. D.; Korte, J. J.; Hornung, M. W.; Henry, T. R.; Denny, J. S.; Leino, R. L.; Wilson, V. S.; Cardon, M. C.; Hartig, P. C.; Gray, L. E. Effects of the androgenic growth promoter 17β-trenbolone on fecundity and reproductive endocrinology of the fathead minnow. Environ. Toxicol. Chem. 2003, 22, 1350–1360. (3) Durhan, E. J.; Lambright, C. S.; Makynen, E. A.; Lazorchak, J.; Hartig, P. C.; Wilson, V. S.; Gray, L. E.; Ankley, G. T. Identification of metabolites of trenbolone acetate in androgenic runoff from a beef feedlot. Environ. Health Perspect. 2006, 114 (1), 65–68. (4) Seki, M.; Fujishima, S.; Nozaka, T.; Maeda, M.; Kobayashi, K. Comparison of response to 17β-estradiol and 17β-trenbolone among three small fish species. Environ. Toxicol. Chem. 2006, 25, 2742–2752. (5) Hartig, P. C.; Bobseine, K. L.; Britt, B. H.; Cardon, M. C.; Lambright, C. R.; Wilson, V. S.; Gray, L. E., Jr. Development of two androgen receptor assays using adenoviral transduction of MMTV-luc reporter and/or hAR for endocrine screening. Toxicol. Sci. 2002, 66, 82–90. (6) Singh, S. M.; Gauthier, S.; Labrie, F. Androgen receptor antagonists (antiandrogens): structure-activity relationships. Curr. Med. Chem. 2000, 7, 211–247. (7) Ankley, G. T.; DeFoe, D. L.; Kahl, M. D.; Jensen, K. M.; Makynen, E. A.; Miracle, A.; Hartig, P.; Gray, L. E.; Cardon, M.; Wilson, V. Evaluation of the model anti-androgen flutamide for assessing the mechanistic basis of responses to an androgen in the fathead minnow (Pimephales promelas). Environ. Sci. Technol. 2004, 38, 6322–6327. (8) Jensen, K. M.; Kahl, M. D.; Makynen, E. A.; Korte, J. J.; Leino, R. L.; Butterworth, B. C.; Ankley, G. T. Characterization of responses to the antiandrogen flutamide in a short-term reproduction assay with the fathead minnow. Aquat. Toxicol. 2004, 70, 99–110. (9) Leo´n, A.; Teh, S. J.; Hall, L. C.; Teh, F. C. Androgen disruption of early development in Qurt strain medaka (Oryzias latipes). Aquat. Toxicol. 2007, 82, 195–203. (10) Larkin, P.; Villeneuve, D. L.; Knoebl, I.; Miracle, A. L.; Carter, B. J.; Liu, L.; Denslow, N. D.; Ankley, G. T. Development and validation of a 2,000-gene microarray for the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 2007, 26, 1497–1506. (11) Gunnarsson, L.; Kristiansson, E.; Förlin, L.; Nerman, O.; Larsson, D. G. Sensitive and robust gene expression changes in fish exposed to estrogen--a microarray approach. BMC Genomics 2007, 8, 149. (12) Martyniuk, C. J.; Xiong, H.; Crump, K.; Chiu, S.; Sardana, R.; Nadler, A.; Gerrie, E. R.; Xia, X.; Trudeau, V. L. Gene expression profiling

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