Towards functional genomics in fish using quantitative proteomics

General and Comparative Endocrinology 164 (2009) 135–141

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Towards functional genomics in fish using quantitative proteomics Christopher J. Martyniuk *, Nancy D. Denslow Center for Environmental and Human Toxicology, Department of Physiological Sciences, University of Florida, P.O. Box 110885, Gainesville, FL 32611-0885, USA

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Article history: Received 25 August 2008 Revised 26 January 2009 Accepted 26 January 2009 Available online 5 February 2009 Keywords: Quantitative proteomics Fathead minnows 17b-trenbolone Genomics

a b s t r a c t Microarray and gene expression analysis have been key in our understanding of molecular pathways underlying physiological responses. Arguably, a large number of microarray based studies in fish have examined steroid nuclear receptor signaling (e.g., estrogens, androgens) in the context of both physiology and toxicology. Following close behind the advances in gene expression analysis, novel proteomic tools are available that have been under utilized in fish endocrinology studies. Quantitative proteomic approaches include both gel based (e.g., 2D gel electrophoresis, 2-D Fluorescence Difference Gel Electrophoresis; DIGE) and non-gel based methods that can be separated further into labeling approaches such as stable isotope labeling (SILAC), isotope coded affinity tags (ICAT), and isobaric tagging (iTRAQÒ) and label-free approaches (e.g., spectral counting and absolute quantitation). This review summarizes quantitative proteomic approaches and describes a successful application of iTRAQÒ to study changes in the liver proteome in fathead minnows in response to the androgen, 17b-trenbolone. The challenge remains to integrate molecular datasets in such a manner as to be able to consider temporal effects and complex regulation at the level of the genome and proteome. Ó 2009 Elsevier Inc. All rights reserved.

1. Functional genomics and proteomics Genomic approaches using microarrays have increased our understanding of molecular pathways regulated in fish tissues by hormones such as reproductive steroids (Larkin et al., 2007; Moens et al., 2007; Santos et al., 2007; Hoffmann et al., 2008; Marlatt et al., 2008). With the vast increase in genomic information, it becomes increasingly important to provide a functional component to the genomic changes observed using methods such as gene knockdown or over-expression studies, mapping regulatory gene networks, or anchoring molecular pathways to physiological or behavioral phenotypes. The concept of functional genomics also includes studying how genomic changes correlate to changes at the protein level as well as to protein activation/deactivation via phosphorylation and phosphatase activities. In general, proteomics is the study of the entire complement of proteins expressed spatially and temporally in an organism, including protein variants and post-translational modifications, and the characterization of protein–protein interactions. The proteome is magnitudes larger than the genome, and some genes, for example neurexins, can have upwards of 1000 protein isoforms (Ullrich et al., 1995). Due to the complexity of the proteome, separation of protein mixtures becomes important for identifying and quantifying proteins.

Along with 2D gel electrophoresis, multidimensional separation techniques using high performance liquid chromatography have facilitated the large scale study of the proteome. Multidimensional Protein Identification Technology (MudPit) separates tryptic peptides obtained from total protein via two high performance liquid chromatography (HPLC) approaches, strong cation exchange (SCX) back-to-back with reversed-phase (RP), to reduce the total complexity of the peptide preparation before MS/MS (Washburn et al., 2001). Combined fractional diagonal chromatography or COFRADICTM (Gevaert et al., 2002) isolates peptides first by RP-HPLC. Then, eluted peptides are modified by chemical or enzymatic means for specific amino acids or functional groups (e.g., methionine or phosphogroups) and separated again by RP-HPLC. The modified peptides exhibit different retention times during RP-HPLC than the original unmodified peptides, sorting the complex mixture. Although these methods act to reduce the complexity of the proteome for study, the proteomic coverage compared to the genome still remains comparatively small. For example, microarray platforms are becoming increasingly dense, containing 20–40 thousand unique elements on a single slide. In comparison, the proteomic coverage obtained by 2D gel electrophoresis is approximately 2000 proteins, amounting to 2% of the total proteome and it currently remains a significant challenge to increase the proteomic coverage. 2. Gel-based separation for protein quantitation

* Corresponding author. Fax: +1 352 392 4707. E-mail address: cmartyni@ufl.edu (C.J. Martyniuk). 0016-6480/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.ygcen.2009.01.023

Quantitative proteomic approaches can be separated into both gel based and non-gel based methods (Fig. 1). The more classical

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Fig. 1. General classification scheme of label and label-free quantitation techniques available to study teleost proteomes.

gel based methods include 2D gel electrophoresis, with proteins being separated in the first dimension by isoelectric point followed by the second dimension by protein mass on a sodium dodecyl sulfate polyacrylamide gel. Technical experience is a significant factor in the successful application of 2D gel electrophoresis and an obstacle to overcome is gel to gel variation due to running time and separation, which increases the difficulty in correlating spots across gels. In addition, the co-migration of multiple proteins in overlapping spots and proteins with low signals are difficulties despite assistance from automated software programs. To reduce gel to gel variation, difference gel electrophoresis (DIGE) utilizes fluorophores to measure relative protein abundance within a single gel (Unlü et al., 1997). Similar to two dye microarray experiments, cyanine (Cy) 3 and 5 dyes are commonly used to label both control and treatment protein groups. In addition, a third dye (Cy2) is used to label an internal protein standard which is added into each control and treatment protein mix before isoelectric focusing and electrophoresis separation. This allows the researcher to compare protein changes by standardizing across gels. Fluorescent images are then overlaid to identify differentially expressed proteins. Software such as DeCyderTM software (GE Healthcare) uses the information gathered from the Cy2 labeled protein standard across gels to determine which proteins are consistently regulated across multiple gels. An advantage of gel-based approaches is that information on post-translational modifications (PMTs) can be readily extracted. Disadvantages of the gel based methods include limited ability to quantify proteins that (1) are highly acidic/basic, (2) have low/high molecular weight, (3) are membrane bound proteins that are not soluble and readily fractionated, (4) co-migrate with other proteins and (5) are not abundant, although this last point remains a challenge for gel-free methods as well. 3. Non gel-based, labeling approaches for protein quantitation 3.1. Isotope labeling of proteins with SILAC and ICAT methods Both these methods utilize a light and heavy isotope to label different protein mixtures. Stable isotope labeling with amino acids in cell culture (SILAC) incorporates a light or heavy labeled amino acid into the proteome of two different cell populations growing in cell culturing media (e.g., 12C- and 13C-labeled L-lysine or arginine) (Ong et al., 2002). As cells grow and divide, the labeled amino acid is incorporated into proteins without compromising protein function. The cell cultures can then be used to perform pro-

teomic profiling for example, in prostate cell lines during cancer progression (Everley et al., 2004). Krijgsveld et al. (2003) has applied this principle to labeling the proteome of higher organisms, first labeling Escherichia coli in medium enriched with 15N and using the labeled E. coli as a food source for C. elegans. An analysis of the proteome using 2D gel electrophoresis and MALDI-TOF showed 95% incorporation of 15N in the first generation of C. elegans, increasing to 98% in the second generation. The authors also successfully labeled the proteome of D. melanogaster using this method. Isotope coded affinity tags (ICAT) (ICATÒ reagents; Applied Biosystems, Inc., Foster City, CA) was also developed on the principle that differences in isotopically labeled peptides could be resolved and compared between two groups (Gygi et al., 1999a). Free cysteine thiol groups in peptides are targeted by the iodoacetamidebased ICAT reagent under strong reducing conditions with either the heavy or light isotope tag. An advantage of the ICAT reagents is that, after digestion and mixing of the labeled peptides, one can further separate the mixture through a streptavidin column because each ICAT label has a biotin covalently linked to an ethylene glycol linker group. This feature further reduces the complexity of the protein mixture before peptide identification and removes non-labeled peptides from the experimental workflow. ICAT labeled peptide pairs are then distinguished by MS/MS. A limitation of ICAT is that the peptide must have a cysteine present for labeling to occur. Therefore, ICAT may label only relatively few peptides of a protein, leading to fewer peptides available for quantitation. 3.2. Isobaric tagging for relative and absolute quantitation (iTRAQÒ) More recently, Applied Biosystems has developed a strategy called isobaric tagging for relative and absolute quantitation of proteins (iTRAQÒ) (Ross et al., 2004). The functional components of the isobaric tagging system includes a reporter tag with varying mass, a balance to insure labeled peptides from different treatments have identical mass, and a peptide reactive group that chemically tags amine groups of peptides generated from tryptic digests. The 4-plex contains reporter tags 114–117 m/z and more recently, an 8-plex system has been developed with reporter tags 113–119 and 121 m/z to quantify protein expression. Fragmentation of the peptide tag generates the low molecular mass reporter ion and measurement of the intensity of these reporter ions enables relative quantitation across treatment groups using algorithms within programs such as Protein Pilot (Paragon algorithm, Applied Biosystems). Advantages of this method include increased ability to multiplex in a single LC MS/MS experiment and increased coverage of the proteome because all peptides are theoretically labeled in contrast to ICAT which requires the presence of cysteine groups. In addition, multiple peptides can be quantified for a single protein and confidence intervals can be calculated to obtain statistical measures of protein changes. Our group has recently used the iTRAQÒ method successfully in a non-model teleost, the fathead minnow (FHM) (Pimephales promelas), to investigate changes in protein abundance in the liver (Martyniuk, unpublished data). The purpose of the experiment was to investigate the effects of an androgenic chemical, 17b-trenbolone, on proteins in the liver. Female FHM were exposed to a dose of 5.0 lg/L 17b-trenbolone for a 48 h period in a flow through exposure system. 17b-trenbolone is a potent AR agonist that binds the AR with higher affinity than testosterone in FHM (Ankley et al., 2003). Proteins were isolated using RIPA buffer (Pierce, Thermo Fisher Scientific Inc., Rockford, IL, USA) and precipitated with six volumes of acetone from six individuals; three control and three 17b-trenbolone treated animals. Three separate iTRAQÒ labeling reactions (randomly pairing a control and treated sample TM

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for each reaction) were processed individually according to the manufacturer’s protocol (Applied Biosystems). The process includes trypsin digestion of the proteins prior to labeling. The labeling procedure does not tolerate detergents and must follow the prescribed method set up for optimal labeling by Applied Biosystems. For each reaction, the control sample was labeled with tag 114 and the treated sample was labeled with tag 115. The sample was desalted on a Vydac Silica C18 column (The Nest Group Inc., Southboro, MA) and the eluted peptides were dried and resuspended in 100 ll buffer A (75% 0.01 M ammonium formate and 25% acetonitrile) for off-line SCX fractionation using a polysulfoethylA column (100  2.1 mm, 5 lm, 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. Twenty fractions were collected and pooled into four groups of about equal protein content for each of the iTRAQ labeling experiments. Each of the 12 pools (four pools from each of three iTRAQ reactions) was injected onto a capillary trap LC Packings PepMap (DIONEX, Sunnyvale, CA) and desalted for 5 min with a flow rate of 20 lL/min of 3% ACN, 0.1% acetic acid, 0.01% TFA and loaded onto the in-line LC Packing C18 Pep Map HPLC column (300 lM  5 mm) connected to a QSTAR XL mass spectrometer (Applied Biosystems). The peptides were eluted with a flow rate of 200 nl/min with a 2 h elution gradient starting at 3% solvent B and finishing at 60% solvent B (0.1% acetic acid, 96.9% ACN). The focusing potential and ion spray voltage on the mass spectrometer were set to 275 and 2600 V, respectively. The information-dependent acquisition 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. We detected peptides from approximately 293 liver proteins of which 15 proteins were differentially altered after 17b-trenbolone treatment. Proteins were identified using a homology based-strategy from a tryptic peptide database constructed by us for all rayfinned fish protein sequences available at NCBI. We used Mascot (v2.2, Matrix Science, London, UK) and Protein PilotTM (ParagonTM algorithm, v2.0, Applied Biosystem Inc.) software for this purpose. Only proteins containing a least three high quality MS/MS peptides were used in relative quantitation. A false discovery rate for peptide–protein assignments was determined using both a reverse dataset created by Proteomics System Performance Evaluation Pipeline (ProteomicS PEP) in Protein PilotTM and a decoy database as outlined by Elias and Gygi (2007). A FDR = 5% was considered a positive peptide–protein assignment. The number of total FHM liver proteins identified and quantified by LC MS/MS are comparable to iTRAQÒ studies done in rat (Fig. 2). For example, in a study by Glückmann et al. (2007) 10 wk old Winstar male rats were administered N-nitrosomorpholine to induce liver carcinogenicity and they identified 685 proteins. In that study, the iTRAQÒ labeled peptides were separated only in one dimension using a C18 column (PepMapTM) on an UltiMateTM nanoLC system (Dionex). They then identified the proteins using a MALDI TOF/TOF mass spectrometer (Applied Biosystems 4700/4800 Proteomics Analyzer). Of the identified proteins, 98 were determined to be differentially expressed, but 65% of their identified proteins had only one peptide identified, 7% had 5–10 peptides identified, and 6% of the proteins had greater than 10 peptides assigned to a protein. In our study with FHM liver, 293 proteins were identified, of which 28% had one peptide assigned, 20% had 5–10 peptides assigned, and 14% had >10 peptides identified per protein. This illustrates the importance of multiple dimensional separations as this increases the overall number of identified peptides that can be used to quantify differential expression. Increased protein iden-

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Fig. 2. Comparison of the number of peptides assigned to a protein in an iTRAQÒ experiment in rat liver (Glückmann et al., 2007) and fathead minnow liver (Martyniuk, unpublished data). The difference in the number of peptides assigned to a protein in the FHM liver compared to the rat is likely due to the larger number of fractions analyzed in the FHM study and the separation approaches used in each study.

tification and quantitation is likely to improve with database searches specifically to FHM (or the fish model of interest) as more fish genomes are completed. Applications of iTRAQÒ have been successful in other non-model vertebrates, for example Rana catesbeiana to identify protein changes during metamorphosis and tail reabsorption (Domanski and Helbing, 2007). This study and ours demonstrate that this quantitative approach can be used in nonmodel organisms in which genomic data is limited. To further explore the cellular processes altered by 17b-trenbolone in the FHM liver, pathway analysis was done using Pathway StudioÒ (v5.0) (Nikitin et al., 2003; Ariadne Genomics) (Fig. 3). This approach provides novel information on protein–protein interactions, common regulators of differentially modulated proteins, and common targets based on literature searches. Pathways are built by finding the shortest paths between selected entities using databases in Pathway StudioÒ. Cellular pathways altered by 17btrenbolone include glycolysis and cell division and were involved in processes such as liver dysfunction and cancer. These data suggest that these pathways are regulated in part by AR signaling in the FHM liver. Other programs for pathway analysis include KEGG (Kyoto Encyclopedia of Genes and Genomes) and Ingenuity Pathways Analysis (Ingenuity Systems, Redwood City, CA, USA) and are useful tools in placing molecular data into a physiological context. Bioinformatics approaches are critical for integrating genomic and proteomic data and provide additional insight into functional changes that can occur after a treatment, such as androgenic chemicals. 4. Non gel-based, label-free approaches for protein quantitation 4.1. Normalized ion current and peptide ion intensities Label-free approaches for protein quantitation also depend on successful reduction of the proteome using multiple fractionation techniques, such as MudPit. Once fractionation is preformed, one is able to utilize the peptide ion intensity to quantify relative peptide abundance because the intensity of m/z is related to the abundance of the peptide. Thus, peak ion intensities extracted from mass spectrum provide a relative measure of protein abundance (Cutillas and Vanhaesebroeck, 2007). These authors improve the confidence of their quantitation by determining the mean peptide ion intensity for a number of peptides from a given protein, since

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Fig. 3. Pathway studio analyzes of proteins regulated by the androgenic compound 17b-trenbolone in FHM liver. Also included in the figure are the relationships between proteins and the androgen receptor. The purple line represents direct protein binding, the blue line represents a relationship due to expression, and the gray line represents a relationship due to regulation. Cellular pathways altered by 17b-trenbolone include glycolysis and cell division along with proteins implicated in liver dysfunction and cancer. Abbreviations are as follows. AGMAT, agmatine ureohydrolase; AR, androgen receptor isoform 1; CALM3, calmodulin; COL1A1, collagen a1; FABP6, liver fatty acid binding protein; FAH, fumarylacetoacetate hydrolase (fumarylacetoacetase); GAPDH, glyceraldehyde 3-phosphate dehydrogenase; HBA2, hemoglobin alpha chain; HSPD1, heat shock 60 kD protein 1; SOD1, Cu/Zn superoxide dismutase; TPI1, triosephosphate isomerase 1b.

individual peptides ionize with different efficiencies. An important consideration is that ion intensities must first be normalized to an internal standard. This method has also been applied to identify phosphorylation sites (Steen et al., 2005). 4.2. Spectral counting and absolute quantitation (AQUA) Spectral counting is a semi-quantitative method that exploits the correlation between the number of observed peptides identified for each protein by LC MS/MS and the abundance of the protein (Gao et al., 2005). Also known as the peptide hit technique; spectral counting requires some calibration and adjustment due to the length of each protein to adjust the spectral counts. In general, the ratio of a protein is equal to [(nB + 1)(tA/tB)/(nA + 1)] where nB and nA are the total number of spectra identified for a particular protein in sample B and A, respectively. The normalization factor tA/tB utilizes the total number of identified spectra for all proteins in A or B and acts as a weighted adjustment. This technique is optimal with genomes that are completely sequenced so in practice, all peptide spectra can be assigned to a particular protein. This avoids bias from spectra that are detected but cannot be assigned. This method may not be ideal for non-model species where limited genomic information is available. Absolute quantitation (AQUA) is also a label-free method that utilizes a heavy labeled peptide standard mixed with the tryptic protein digest of the sample for quantitation (Gerber et al.,

2003). A standard probe or ‘‘spike” peptide of a specific tryptic peptide from the protein of interest is synthesized with one amino acid containing a heavy isotope. The spike peptide must be ionized and detected consistently in a complex protein mixture and must be unique to the protein of interest. A standard curve is then generated with known amounts of the heavy peptide. Standard AQUA curves can be extremely sensitive and accurate, down to the fentomol range with a high R2 = 0.998 (Ottens et al., 2007). To quantify a protein, a known amount of the reference or spike is added to a complex protein mixture and the ion intensity of the spike (heavy isotope) is compared to the ion intensity of the unknown sample (light isotope). A standard curve bracketing the concentrations of the unknown sample is constructed for precise quantitation. Theoretically, one could generate multiple AQUA probes to spike a sample and quantify many proteins within the same mixture. However, a limitation to this method is the multiple individual standard curves that must be generated for each protein to be quantified. Another limitation is that often low abundant proteins maybe undetectable in a complex mixture. This problem can circumvented by enriching for the protein of interest (Warren et al., 2005). 5. Comparison of quantitative proteomic techniques An important question to address is how different proteomic methods compare to each other in the number of proteins identi-

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fied and quantified. Glückmann et al. (2007) compared data from both 2D gel electrophoresis (2DE/MS) and iTRAQÒ methods after rats were treated with the carcinogen N-nitrosomorpholine. Using 2DE/MS, 57 proteins were identified as differentially expressed while the iTRAQÒ method identified 98 proteins. Of the biomarkers identified, 63% (36) of the proteins were identified by both techniques and the expression of 26 proteins were confirmed by both 2DE/MS and iTRAQÒ. This suggests that both methods work well and are complementary in the number of proteins identified. Using both ICAT and iTRAQÒ labeling, DeSouza et al. (2005) identified common protein markers in uterine tissue with endometrial carcinoma, and showed that the proteomic information generated by the two methods was largely complementary and identified similar number of housekeeping and metabolic proteins. However, there were some differences in the functional classes of proteins identified between the two methods. iTRAQÒ identified approximately twice the number of proteins involved in transcription/translation while ICAT identified approximately twice the number of proteins involved in cell signaling, when compared to each other. This was most likely because the iTRAQÒ method identifies a larger percentage of the ribosomal proteins than ICAT. Schmidt et al. (2004) compared ICAT-LC/MS with 2DE/MS to identify proteins in M. tuberculosis. The authors found that each method was biased towards a particular functional class of proteins. The ICAT method preferentially identified proteins involved in intermediary metabolism, non-ribosomal peptide synthesis and transport/binding proteins while the 2DE/MS method identified more proteins involved in lipid biosynthesis, chaperones/ heat-shock proteins, and protein secretion. In the same study, 2DE/MS identified 108 proteins and ICAT identified 280 proteins with 27 proteins identified using both techniques. Thus, although some methods such as iTRAQÒ provide increased protein identification and greater range in expression changes compared to 2DE/ MS (Choe et al., 2005), each method provides additional information that increases overall understanding of the underlying biology. 6. How does the proteome relate to the transcriptome? One important aspect of functional genomics is to determine how gene expression is correlated to protein translation. Currently, it is poorly understood how well an increase in mRNA steady state abundance correlates with increased protein abundance. Each mRNA and protein has intrinsic steady state levels that depend on both synthesis and degradation and these half lives may not be comparable. Some proteins may be more long lived than their corresponding mRNAs and these proteins may be informative biomarkers. This is active area of research in environmental toxicology and studies are beginning to consider changes at both the genome and proteome in fish toxicological studies (Martyniuk, unpublished data; De Wit et al., 2008). Biomarkers for contaminant exposures may be resolved or improved by considering both transcriptomic and proteomic changes. Pearson and Spearman-Rank correlations have been used to measure the response of the transcriptome in relation to the proteome. Hack (2004) did a survey of published literature that compared genomic and proteomic data sets using Pearson and Spearman-Rank correlations. Both Serial Analysis of Gene Expression (SAGE) and microarray data in general showed between 45– 75% similarity when compared to quantitative proteomic data. Gygi et al. (1999b) investigated the correlation between protein and mRNA levels for 106 genes in yeast growing in log phase with glucose as a carbon source. The Pearson product moment correlation coefficient was r = 0.935 for all 106 genes and proteins. However, this estimate was biased by small numbers of genes/proteins with high abundance. When considering only the lowest expressed

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40–95 proteins, the correlation values ranged between r = 0.1–0.4. In another study, Minagawa et al. (2008) compared the genomic and proteomic responses in human patients that had hepatocellular carcinoma (HCC). The authors utilized a 2D DIGE approach and identified 125 proteins using an electrospray ionization ion-trap mass spectrometer (LCQ Deca XP). The average HCC/non-HCC expression ratios of 93 proteins were plotted against mRNA ratios and the Pearson’s correlation between the two was 0.73. Further insight was gained by separating proteins first by function and comparing this to gene expression. There was a high correlation (r = 0.90) for transcripts and proteins involved in cell metabolism while the remaining proteins showed less of a relationship to transcript levels (r = 0.36). Thus, it appears as though there is some correlation between the steady state mRNA abundance and protein abundance. However, the analytical methods used, the categorical function of genes/proteins, complex transcriptional/translational regulation, post-translational modification of proteins and temporal effects influence the relationship between genome and proteome. 7. OMICS and reproductive steroid receptor regulation in fish Microarray based studies have provided a significant amount of information on the molecular events underlying steroid signaling in fish. This includes studies using E2, pharmaceutical estrogens and androgens (e.g., 17a-ethinylestradiol, 17a-methyldihydrotestosterone, dihydrotestosterone), or chemicals that are steroid mimics (bisphenol A, nonyphenol). Genomic approaches for studying reproductive steroid effects in fish are growing and have been investigated in tissues that include the hypothalamus (Martyniuk et al., 2006; Marlatt et al., 2008), telencephalon (Martyniuk et al., 2007), liver (Moens et al., 2007; Benninghoff and Williams, 2008; Hoffmann et al., 2006, 2008), and gonad (Santos et al., 2007). Transcriptomic studies suggest that fish tissues have unique genomic responses to estrogens (Martyniuk et al., 2007; Garcia-Reyero et al., 2008). However, there are common cellular processes affected in multiple tissues that can be regulated, for example lipid metabolism and transport, cellular respiration and metabolism, electron transport, DNA/protein binding, and protein synthesis by estrogens. Despite the significant genomic advances, quantitative proteomic-based studies in fish are few and have been limited to issues in aquatic toxicology. 2DE/MS analysis identified 15 proteins in the rainbow trout liver proteome that were altered after sewage treatment water effluent (Albertsson et al., 2007). In another study, E2 and 4-nonylphenol exposures of embryonic zebrafish had unique proteomic fingerprints as analyzed by 2DE/MS but the proteins were not identified (Shrader et al., 2003). More recently developed techniques such as DIGE and ICAT have been applied successfully to study the proteomic response to hypoxia in medaka (Oryzias latipes) (Oehlers et al., 2007) and disease infection in Atlantic salmon (Salmo salar) (Booy et al., 2005) respectively. However, the effects of reproductive steroids and environmental mimics on the fish proteome remain largely an open area of research. 8. Summary Fish physiology and toxicology are moving towards the integration of genomic, proteomic, and metabolomic data sets to better understand the underlying physiology and how animals interact with their environment. In fish, studies are beginning to characterize the proteome of tissues such as liver for further study (Wang et al., 2007). Bioinformatics approaches such as pathway analysis will continue to be important in providing functional insight into genomics and proteomics.

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