Population Proteomics of the European Hake ... - ACS Publications

Oct 10, 2010 - Research Institute, Nea Peramos, Kavala, GR-64007, Greece, and the FishPopTrace Consortium. Received July 2, 2010. We report the novel ...
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Population Proteomics of the European Hake (Merluccius merluccius) Elena G. Gonzalez,*,†,‡ Grigorios Krey,‡,§ Montserrat Espin ˜ eira,‡,| Amalia Diez,†,‡ †,‡ Antonio Puyet, and José M. Bautista*,†,‡ Departamento de Bioquı´mica y Biologı´a Molecular IV, Universidad Complutense de Madrid (UCM), Facultad de Veterinaria, 28040 Madrid, Spain, Area of Molecular Biology and Biotechnology, ANFACO-CECOPESCA, Crta. Colegio Universitario 16, Vigo, 36310, Spain, National Agricultural Research Foundation-Fisheries Research Institute, Nea Peramos, Kavala, GR-64007, Greece, and the FishPopTrace Consortium Received July 2, 2010

We report the novel use of proteomics to investigate protein variation among populations of the European hake (Merluccius merluccius). The liver and brain extracts of 18 hake (N ) 36) captured in the Mediterranean Sea, Cantabrian Sea, and Atlantic Ocean were examined by 2D/DIGE and mass spectrometry. Significant differences in protein expression among populations were revealed by 84 spots obtained in the gels for the liver and 145 spots for the brain. Population groups of samples were defined by multivariate analysis (PCA and hierarchical clustering). According to protein expression levels and the functions of the 55 candidate protein spots identified, which showed significant expression differences, highest population discrimination was rendered by brain proteins involved in cell signaling and metabolism/energy and by liver proteins involved in protein fate. Finally, we present a statistically robust framework to accurately classify individuals according to their population of origin. Thus, purposely identified protein isoforms were found to be competent at discriminating populations. These results suggest the possibility of identifying protein biomarkers related to environmental changes in a nonmodel species such as the hake and pave the way for more extensive research on protein variation among populations of marine fishes. Keywords: DIGE technique • hake • Merluccius merluccius; population proteomics • discriminant analyses • liver • brain

1. Introduction Traditionally, population genetic studies conducted in marine fish species have provided valuable information for understanding their rather complex population structure and dynamics.1,2 The current genetic structure of marine fish populations has been shaped by numerous and diverse factors, * To whom correspondence should be addressed. Dr. Jose M. Bautista (E-mail, [email protected]; Phone: +34 91 3943885, Fax: +34 913943823) and Dra. Elena G. Gonzalez (E-mail, [email protected]; Phone: +34 91 3943885, Fax: +34 913943824), Departamento de Bioquı´mica y Biologı´a Molecular IV, Universidad Complutense de Madrid (UCM), Facultad de Veterinaria, Av. Puerta del Hierro s/n. 28040 Madrid, Spain. † Departamento de Bioquı´mica y Biologı´a Molecular IV, Universidad Complutense de Madrid (UCM), Spain. ‡ The FishPopTrace Consortium (https://fishpoptrace.jrc.ec.europa.eu/) comprises members from the following institutions: Bangor University, UK; Danish Institute for Fisheries Research, Technical University of Denmark, Universidad Complutense de Madrid, Spain; Katholieke Universiteit, Leuven, Belgium; University of Bologna, Italy; University of Bergen, Norway, Joint Research Centre of the European Commision; University of Bremen, Germany; Wildlife DNA Services, UK; De´partement Sciences & Techniques Alimentaires Marines, France; National Agricultural Research Foundation, Greece; Spanish National Foundation of Fish and Seafood Processors; University of Aarhus, Denmark and The Centre of Molecular Genetic Identification, Russia. § National Agricultural Research Foundation-Fisheries Research Institute, Greece. | ANFACO-CECOPESCA, Vigo, Spain.

6392 Journal of Proteome Research 2010, 9, 6392–6404 Published on Web 10/10/2010

such as historical processes (e.g., vicariant events) or life-history features (e.g., phylopatric behavior or local larval retention), coupled to their ecological adaptation to the physical peculiarities of the marine environment. In addition, marine fish species usually show large population sizes and strong migratory behavior that complicates sampling. These characteristics, enhanced by relatively homogeneous habitats that lack an apparent barrier to gene flow, may hinder the detection of genetic differentiation. Considering the above biological and methodological complexities, there is a need for information from sources other than genetic studies if we are to improve our understanding of how selection pressures affect these organisms at the global or cell level. Hence, besides the genome, the proteome could reflect adaptive global changes produced in nonmodel species and proteomics may therefore complement the analysis of their population biology and structure.3,4 As originally coined in 2005,5,6 population proteomics is a novel discipline that was developed to identify cancer-specific biomarkers in human population studies.5-7 However, to date this technique has been scarcely used to examine protein diversity in nonmodel animals.8-12 The present study sought to explore the potential of population proteomics for detecting 10.1021/pr100683k

 2010 American Chemical Society

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Population Proteomics of the European Hake Table 1. Details of Hake Sampling and Protein Extraction for Proteomic Analysis

number of samples

protein extracction (mg)

population

acronym

fishing division

date

liver (L)

brain (B)

North Aegean Sea, (Mediterranean Sea) Cies Islands, (Atlantic Ocean) Bay of Biscay (Cantabrian Sea)

M

37.3.1

40°N; 24°E (40.33 24.55)

May 2008

6

6

8.45 ( 1.27 4.51 ( 1.80

A C

27.9.a 27.8.c

42°N; 8°O (42.194, -8.944) June 2008 43°N; 8°O (43.34, -8.937) July 2008

6 6

6 6

9.14 ( 2.03 3.64 ( 0.59 7.18 ( 2.22 5.12 ( 2.68

coordinates

differences in protein expression across hake populations, as a species of great commercial interest. Besides the above-mentioned features of proteomics, further technological advances are helping to fulfill the capability of this novel molecular approach. One of these significant improvements was the introduction of the 2D-fluorescence difference gel electrophoresis (DIGE)13 technique. The DIGE technique achieves multiplexed fluorescent Cy-Dye staining of complex protein mixtures and eliminates to a large extent the problems of technical irreproducibility of earlier proteomics techniques. With this method, two samples are labeled using two different fluorescent cyanine dyes (CyDyes) that differ in their excitation and emission wavelengths and are separated together on a single two-dimensional gel. After consecutive excitation with their respective wavelength, differences in protein expression and mobility (e.g., up- or down regulated proteins or post-translationally modified proteins) between the two samples are identified. Because of the comigration of both samples together with an internal standard, methodological variations in spot positions and protein abundance are avoided, resulting in improved reproducibility and more reliable protein quantification.14,15 The European hake (Merluccius merluccius, Linnaeus 1758) is a demersal marine fish of great interest to fisheries throughout its entire distribution range (the northeastern Atlantic Ocean from Norway to Mauritania and the entire Mediterranean Sea). This wide geographical range determines a considerable variety of environmental conditions to which hake populations must adapt and makes it an attractive target for a proteome-level analysis of biological processes, population adaptation mechanisms and the varying metabolic roles some proteins play in different populations. In a recent study performed on Atlantic and Mediterranean hake populations, significant correlation was detected between allele frequencies and the spatial distribution of salinity and temperature values,16 and these correlations were consistent with the genetic differentiation reported previously between Atlantic and Mediterranean hake populations.17-20 An earlier study proposing the existence of a subspecies in the Mediterranean Sea (M. merluccius mediterraneus) according to meristics and morphological traits also supports the hypothesis of population differentiation between the Atlantic Ocean and Mediterranean Sea.21,22 On the basis of the results of these studies and allozyme frequency data,18 two different stocks have been defined in the Atlantic Ocean for management purposes: a Northern stock distributed between western Norway and the Bay of Biscay and a Southern stock, inhabiting waters south of the Bay of Biscay and around the Iberian Peninsula. These stocks are managed separately from an existent Mediterranean stock. So far, protein-based studies conducted in the hake have focused on identifying species of the family Merluccidae and two populations of grenadier (Macruronus novaezelandiae)23-25 in processed seafood products. These studies have provided valuable information on a group of parvalbumin proteins and

liver (L)

brain (B)

have greatly increased the protein sequences available for this species in public databases. In a recent extensive review26 of proteomic tools used in fish biology research, the potential of this approach for studies exploring fish physiology and developmental biology and the impacts of human pollutants on fish populations was described. However, this review lacks a section addressing studies of protein diversity in natural fish populations,26 indicating a gap in such proteomic studies and altogether pointing out the novelty of the present report. In this study, we assessed protein variation in the liver and brain tissue of hake from the three aforementioned fish stocks using the 2-D electrophoresis (2-DE) technique. Using a comparative proteomics approach, differences in protein abundance and mobility were determined to trace frequency distributions and differential features across populations. Thereafter, these differentially expressed proteins were identified through mass spectrometry and functionally classified. Differences in the frequencies of allozymic variants have proved useful for detecting strong biogeographical structure among brown trout populations.27,28 We extended this idea by determining differences in the expression patterns of protein isoforms among hake populations to identify putative biomarkers of population structure. Moreover, the protein spots routinely examined in DIGE experiments were also quantitatively (relative protein expression in a population) and qualitatively (presence or absence of proteins across populations) analyzed to identify potential biomarkers.

2. Materials and Methods 2.1. Sample Collection and Protein Extraction. Hake specimens were fished from two sites in the Atlantic Ocean (Bay of Biscay, n ) 6; and off of the Cies Islands, n ) 6; Table 1) in an expedition organized by ANFACO-CECOPESCA, the Spanish National Foundation of Fish and Seafood Processors; and one site in the Mediterranean Sea (n ) 6, Table 1), in the North Aegean Sea (sampling performed by the Fisheries Research Institute of the National Agricultural Research Foundation, Greece). To ensure efficient protein extraction avoiding degradation, the specimens were weighed, sexed and processed on board. Each tissue (liver and brain) was excised, immediately snap-frozen in liquid nitrogen and then kept on dry ice (-70 °C) until land was reached (see https://fishpoptrace.jrc.ec.europa. eu/sampling, for more information on the sampling procedure and details of collection). The samples were then kept at -80 °C before protein extraction. To improve the resolution of the 2D proteome maps and for accurate representation of proteins, we used a modified extraction buffer called RIPA.29 RIPA has higher contents of nonionic and zwitterionic detergents than the commercial buffers normally used for protein extraction and increases the amounts of hydrophobic and low abundance proteins extracted, thus improving gel resolution and spot numbers. Journal of Proteome Research • Vol. 9, No. 12, 2010 6393

research articles Briefly, for each individual fish, 150 mg of tissue were homogenized in two volumes of RIPA buffer (50 mM Tri-HCl, pH 8; 50 mM NaCl; 3% CHAPS, 0.5% MEGA; protease inhibitors). The disintegrated tissue was then subjected to four cycles of incubation at 37 °C (15 min) and -20 °C (25 min), vortexing the tube between cycles. The resulting homogenate was centrifuged for 30 min at 10,000 RCF (Centrikon T-40 centrifuge) at 4 °C to remove sediment and residual undissolved tissue. The supernatant was then precipitated with the kit 2DClean Up (GE Healthcare) and the pellet solubilized in a DIGEcompatible buffer (8 M urea, 2 M thiourea, 4% CHAPS, and 30 mM Tris-HCl). For protein quantification, the Bradford method30 was used, measuring the samples in triplicate and using bovine serum albumin (BSA) as the standard (Sigma). Correct quantification was confirmed by running 10 µg of each sample on a standard SDS-PAGE gel (10 and 12% for liver and brain tissue, respectively), which was subsequently stained with Coomassie blue. 2.2. DIGE Experimental Design and Data Processing. Six individuals per geographic location, Atlantic Ocean, (A), Cantabrian Sea (C) or Mediterranean Sea (M) (N ) 18), were labeled in a randomized manner with Cy3 or Cy5 to prepare nine replicate gels for comparisons (Figure S1 in Supporting Information). Minimal CyDye (GE Healthcare) labeling was conducted according to the manufacturer’s instructions (400 pmol of Cy dye per 50 µg of protein) with the Cy2 label reserved for the pooled sample and incubated on ice for 30 min (Figure S1 in Supporting Information). The reaction was stopped by the addition of L-lysine (10 mM) and incubation on ice for 15 min. The use of the pool of samples as an internal standard (labeled with Cy2, containing all the proteins included in the experiment) eliminates gel-to-gel variation and therefore normalizes spot intensities across gels. An equal volume of rehydration buffer (8 M Urea, 2 M thiourea, 4% CHAPS, 2% ampholytes pH 3-11, and 200 mM DTT) was added to a mixture of each sample. The final mixtures (containing 150 µg of protein) were cup-loaded onto 24 cm pH 3-11 nonlinear DryStrips (GE Healthcare), which had been rehydrated overnight with a slightly modified rehydration buffer (8 M Urea, 2 M thiourea, 4% CHAPS, 2% ampholytes pH 3-11, and 97 mM DeStreak). The first dimension with denaturing isoelectric focusing was run up to 72000 Vh overnight on a IPGphor II EF system (GE Healthcare) using the following program: 120 V for 1 h, 500 V for 2 h, 1000-5000 V for 18 h. Strips were soaked in reducing buffer (6 M urea, 100 mM TrisHCl at pH 6.8, 30% glycerol (v/v), 2% SDS (w/v), and 0.5% DTT (w/v)) for 12 min followed by another 5 min in the same buffer, which was supplemented with 4.5% iodoacetamide instead of DTT. The second SDS-PAGE (12% T, 2.6% C) dimension was run for 17 h at 2 W per gel at 8 °C. Protein spots were visualized by staining with Coomassie Brilliant Blue and each gel was finally scanned with Typhoon 9400 (GE Healthcare) using the excitation wavelengths corresponding to each CyDye (488 nm, 523 and 633 nm for Cy2, Cy3 and Cy5, respectively) to give three 100 µm pixel size images that were subsequently merged into one (27 images were obtained per experiment). The same methods were followed to prepare both the liver and brain 2-DE protein maps. The protein maps obtained were subjected to a standard analysis process including spot detection, spot volume quantification and volume ratio normalization between samples using the differential in-gel analyses (DIA) module included 6394

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Gonzalez et al. in the 2D-DeCyder software (version 6.5, GE Healthcare). The matching of protein spots among different gels and the identification of proteins showing significant differences was undertaken using the biological variation analysis (BVA) module, also included in the software. All the values obtained are reported in protein volumes based on spot intensities and these values normalized against the internal standard labeled with Cy2. To assign spot boundaries and to calculate the normalized spot boundaries we used the differential in-gel analysis module and tissue-specific criteria for spots selection. Data obtained, were statistically analyzed using the Student’s t-test and oneway ANOVA. Homogeneity-of-variance was verified using the Levene’s test. A 2-fold threshold was set to systematically compare protein expression levels and identify the variables that could best explain differences between each population. Only the significant results (designated spots of interest) of both types of analysis were further selected for mass spectrometry identification and statistical analysis. Among these spots, putative isoforms were also chosen according to their relative position in the 2-DE gel (i.e., spots forming trains that differed only in their charge). Thus, isoforms were here considered when the same protein was identified in a different spot, as shown in Table 2. 2.3. Mass Spectrometry Analysis of Protein Spots. Once the DIGE gels were scanned, the spots showing differences in protein expression and considered to be of interest (including the isoforms) were aligned with the Colloidal Coomassie Blue profile, manually excised using pipet tips and transferred to microcentrifuge tubes. Samples selected were in-gel reduced, alkylated and digested with trypsin according to Sechi and Chait; 1998.31 Briefly, spots were washed twice with water, shrunk 15 min with 100% acetonitrile and dried under vacuum in a Savant SpeedVac for 30 min. The samples were then reduced with 10 mM dithioerytritol in 25 mM ammonium bicarbonate for 30 min at 56 °C and subsequently alkylated with 55 mM iodoacetamide in 25 mM ammonium bicarbonate for 15 min in the dark. Finally, samples were digested with 12.5 ng/mL sequencing grade bovine trypsin (Roche Molecular Biochemicals) in 25 mM ammonium bicarbonate (pH 8.5) overnight at 37 °C. After digestion, the supernatant was collected and one µL was spotted onto a matrix-assisted laser desorption/ionization (MALDI) target plate and allowed to air-dry at room temperature. Next, 0.4 µL of 3 mg/mL of R -cyano-4-hydroxytranscinnamic acid matrix (Sigma) in 50% acetonitrile were added to the dried peptide digest spots and allowed again to air-dry at room temperature. MALDI-TOF MS analyses were performed in a 4800 Proteomics Analyzer MALDI-TOF/TOF mass spectrometer (Applied Biosystems, Framingham, MA) operating in reflector positive ion mode, with an accelerating voltage of 20 000 V. All mass spectra were internally calibrated using peptides from the autodigestion of trypsin. MALDI-TOF/ TOF mass spectrometry generates peptide mass fingerprints and the peptides (with +1 charge) observed with a signal-tonoise ratio greater than 10 were collated and represented as a list of monoisotopic molecular weights, filtering the peaks by using the software GPS Explorer v3.6 (Applied Biosystems, Framingham, MA). Proteins ambiguously identified by peptide mass fingerprints were subjected to MS/MS sequencing using the 4800 Proteomics Analyzer (Applied Biosystems). Then, from the MS spectra,

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Population Proteomics of the European Hake a

Table 2. List of Liver and Brain Candidate Proteins Identify by 2-D DIGE as Differentially Expressed

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Table 2. Continued

a The spot matched corresponds to the spot number showed in Figure S1 (Supporting Information). Graphics show the average of the protein abundance (in Log) per population (M, C or A) of each isoform identified. The functional classification of the proteins based on the Gene Ontology (GO) Term Finder and the Ruepp et at. method34 is also indicated. b MASCOT score obtained for the proteins identified by peptide mass fingerprint (PMF), combined analysis of PMF and MS/MS and de novo peptide sequencing analices. c Number of peptide mass values matched. A total number of 65 peaks per spectra were sent to analyze, except those cases that were necessary to eliminate contaminant peaks. d Amino acid sequence coverage for the identified proteins by PMF and MS/MS analysis. e Amino acid sequence identified by MS/MS. MASCOT ion score is in parentheses. f Amino acid sequence idntified by de novo peptide sequencing. Ion score (in parentheses) corresponds to Pro BLAST v. 3.6 for homology-based search.

a suitable precursor was selected for MS/MS analyses with CID (using atmospheric gas) in 1 Kv ion reflector mode and using a precursor mass window (5 Da. The plate model and default calibration were optimized for the MS-MS spectra processing. The same searches were performed on both the liver and brain tissue 2-DE maps. All protein lists including mass spectrometry details corresponding to the MS/MS results are freely available

via the PRIDE database (http://www.ebi.ac.uk/pride/),32 PRIDE accession 13163. 2.4. Database Search and Functional Classification of the Identified Proteins. To avoid misidentifications with species more broadly covered in DNA sequence databases, searches were performed against an in-house developed fish protein database restricted to fish sequences available from Journal of Proteome Research • Vol. 9, No. 12, 2010 6397

research articles NCBI (nonredundant protein sequences deposited in NCBI on the search date, September 25, 2009, comprised 257 377 sequences; 83 362 140 residues). Peptide fragments were used to search for protein candidates using a local license of MASCOT 2.1 (Matrix Science) through the Global Protein Server v.3.6 (Applied Biosystems). Carbamidomethyl cystein was considered a fixed modification and oxidized methionine a variable modification, allowing a peptide mass tolerance of (50-100 ppm, one trypsin missed cleavage site, and a MSMS fragments tolerance of (0.3 Da. The parameters for the combined search (peptide mass fingerprinting and MS-MS spectra) were as described above. In all protein identifications, probability scores were greater than the score fixed by MASCOT 2.1 as significant at a p-value < 0.05. De novo sequencing from fragmentation spectra of peptides was performed using DeNovo Explorer v 3.6 software (Applied Biosystems) followed by manual revision and homology searches of the sequences obtained by ProBlast software (Applied Biosystems) using an E-value threshold of 20 and NCBInr DataBase or Blast software from NCBI (http://www.ncbi.nlm. nih.gov/BLAST). Candidate proteins identified were first functionally grouped based on their gene ontology (GO) annotation using the information available at the Swiss-Prot/TrEMBL database (QuickGO tool, www.ebi.ac.uk/QuickGO/)33 and then on the basis of the functional categories described in the MIPS Functional Catalogue.34 The FunCat software (web access at http://www.helmholtz-muenchen.de/en/mips/projects/funcat/ index.html) includes a search tool to browse the functional categories of the queried sequence using the GO annotation scheme.35 Once classified, candidate proteins were sorted into these functional categories. 2.5. Statistical Analysis of Proteins of Interest. The expression of the identified proteins was statistically analyzed to determine possible correlations in their up or down-regulation and molecular functions when comparing each type of tissue (liver and brain). In the Student’s t-test, we used the average changes in observed protein volumes between two populations, which may be positive (indicating their up-regulation) or negative (indicating their down-regulation) and compare these with the protein function variables. To make the number of variables used in the statistical analyses more manageable, the list of functional categories described above was reduced to five, including proteins involved in Cell metabolism and energy, Cell signaling, Protein fate, Protein structure and Protein transport processes. The significance threshold was set at p-value < 0.05. To visualize the pattern of protein expression of the identified spots we performed two multivariate clustering analyses. First, an unsupervised principal component analysis (PCA) was performed on the whole set of spots and also on the differentially expressed spots. Second, spots were also visualized using the hierarchical clustering (HC) and heat mapping methods using the differentially expressed proteins. We used the Pearson correlation coefficient to create the distance matrix for the graphical representation of the data in the heat map. A linear mixed effect-model36 was used to evaluate the effect of the interaction “isoforms-populations” with the protein volume data obtained. To compute the model, the standardized protein volume of the identified isoforms was considered the dependent variable and “isoform” and “population” were included as fixed factors, using the Akaike’s Information 6398

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Criterion (AIC ) as the discriminatory variable. Data were previously normalized by logarithmic transformation. To determine the best combination of individual spots (proteins) that could discriminate between populations, a multivariate linear discriminant analysis38 was performed including all the variables (spots) that showed significant protein-expression differences according to the Student’s t-test and one-way ANOVA results and also the isoforms with a higher power of predicting previously selected with the linear mixed effect-model. Fisher discriminatory functions were calculated for all possible pairs of classes, and the probabilities inferred from these equations were later used to correctly assign each fish individual to one the three populations considered. Statistical analyses were performed using SPSS (v17, SPSS Inc., Chicago, IL) software and the extended data analysis (EDA) module of the DeCyder software. The data for all 18 fishes were included in the statistical tests.

3. Results and Discussion This study sought to assess the capacity of this proteomic technique to identify protein biomarkers that are differentially expressed in natural populations of the European hake (as a nonmodel species). Since we tried to detect possible correlations between ecologically (life-history)-determined variations in hake populations and protein diversity, we examined protein expression in individuals caught in the wild and thus exposed to different environmental conditions. This approach makes the study of protein diversity in fishes particularly challenging, since all physiological factors and environmental variables are difficult to control. Liver and brain tissue were chosen because they represent two extremes in the way environmental changes may influence local adaptation at the molecular level. The brain is likely to be less affected by environmental variables, whereas the liver should be more affected by factors such as nutrition, salinity, or temperature. Since the tissue-specific responses to environmental changes shown by functional groups of proteins could be attributed to homeostasis, natural selection or phenotypic plasticity, our initial hypothesis for separating populations was based on filtering spot variation according to protein function in these two very different tissues in order to highlight tissue-specific population variance. Moreover, our use of 2-DE coupled to DIGE experimental procedures will considerably reduce technical and analytical variations since the use of a pooled internal standard eliminates gel-to-gel variation and false positives, resulting in highly reproducible data.14 In addition, when we compared our results with published data from experimental studies that mostly examined fishes reared in an artificially controlled environment,9,10,39,40 agreement between some of the putative proteins identified was good. This suggests that proteomic analysis of natural populations could be promising tool to gain insight into protein diversity in fish. 3.1. Protein Extraction, Gel Image Acquisition and Quantitative Analyses. Nine gel images representing 18 biological samples (six replicates for each sampling site) per tissue type were used in the analyses, totaling 18 gels and 36 samples analyzed. The DIGE experimental design is described in Figure S1 (in the Supporting Information). Liver and brain extracts were obtained using the RIPA buffer.29 The quality and quantity of each sample extraction process was assessed using the Bradford method30 and the results were checked on a SDS-PAGE gel (Table 1, Figure S2 in the Supporting Information). The protein concentrations re-

Population Proteomics of the European Hake turned (in terms of mg of protein per mg of fresh tissue ( standard deviation, SD) were similar among sampling sites for the same tissue (Table 1). On the other hand, there was some disparity between tissues in terms of average protein concentrations, which were 8.25 ( 1.95 for liver and 4.43 ( 1.89 for brain. The protein extracts were then analyzed by 2-DE (Figure S1, Supporting Information). Spots were homogeneously distributed across the entire pI range, although a significant cluster of high molecular weight proteins (of around 40-70 kDa) appeared in the gels for both tissue types (Figure S1, Supporting Information). A similar pattern has also been observed in the extracted proteomes of other marine fish species.41 The proteomic profile showed an average of 2104 and 3558 spots detected in the liver and brain gels, respectively. The average number of matched spots was 1472 (coefficient of variation, CV in % was 16.79) and 2474 (CV ) 15.60%) for liver and brain, respectively. When we compared sampling sites, CVs increased considerably (59.83% and 68.78%). In liver, no private spots (defined as a protein that is expressed exclusively in one population) were detected. However, 45 spots (2.1% of the 2104 spots studied) were found only in fish from two of the sampling sites: Atlantic Ocean and Cantabrian Sea. Similarly, in brain tissue, 66 spots out of 3558 (1.8%) were exclusive to the same Atlantic and Cantabrian populations, suggesting some differentiation of the Mediterranean population from the rest. As in the liver, brain tissue failed to show any private proteins in a single population. These proportions are similar to those reported for a marine snail, Littorina saxatilis, when the proteomes of individuals of two different ecotypes were compared,8 and are also below the probability of protein differences due to chance (0.2%) reported by the authors. Through CyDye protein labeling and visualization, we were able to detect 84 spots for liver and 145 spots for brain that indicated significant protein expression variation among populations (average spot volume ratio exceeding 2-fold, Student’s t-test p < 0.01 and one-way ANOVA) (Figure S1, Supporting Information). Among these, sets of nine spots for liver (master numbers: 426, 429, 759, 760, 857, 864, 1032, 1162, 1850) and 10 spots for brain (master numbers: 719, 805, 883, 920, 1163, 1612, 1799, 1802, 2171, 2253) were selected and later confirmed as isoforms (Table 2, Figure S1 and Table S1 in Supporting Information). 3.2. Identification of Differentially Expressed Proteins Associated with Adaptation. Despite the low representation of fish genomes in sequence databases and specifically those of the European hake, whose complete genome is not available, we were able to identify a relatively high number of proteins by using MS/MS combined with BLAST searching from homologue sequences for other fish species. On average, of the 67 proteins analyzed by MALDI-TOF MS, 55 proteins were unambiguously identified in both tissues (20 in liver and 35 in brain, respectively) after discarding the spots that were below the threshold of MS detection (9 spots) and possible contaminations with human keratin proteins (3 spots). Moreover, one spot (liver #767) contained more than one protein. In this case, the individual protein was identified by de novo peptide sequencing to determine that the protein mix included transferrin and a contamination of keratin, which was subsequently discarded from the MASCOT searches. The rest of the proteins were unambiguously identified. The identified proteins were assigned to a variety of fish families, Salmonidae and Cyrpinidae being the most broadly represented (21 and 22% of

research articles sequences, respectively), and Salmo salar, Oncorhynchus mykiss, and Danio rerio the species grouping the majority of the identified spots. Protein names, accession numbers, biological functions, and other MS data are summarized in Table 2 and Supplementary Table S1 (Supporting Information). Candidate proteins identified were functionally classified34 into the following groups: Cell metabolism and energy (11 proteins in liver, 18 proteins in brain), Cell signaling (2 in liver and 11 in brain), Protein fate (3 proteins in liver), Protein structure (4 in liver, 2 in brain) and Protein transport (4 proteins in brain). Proteins assigned to the Cell metabolism and energy group differed most among populations (Figure 1). The 19 spots that were deliberately searched and confirmed as isoforms showed slight differences in their Mr, probably due to posttranslational modifications such as phosphorylation or proteolysis (Supplementary Table S1, Supporting Information). Two additional spots (spots 1615 and 1619) were also identified as protein isoforms, both of creatine kinase (CK), making this protein the most abundant (4 isoforms) among the identified proteins. Several authors have reported global changes in protein expression in response to changes in several ecological (e.g., temperature,10 salinity,9 levels of oxygen42-44 or aquatic toxicity11,41) and morphological-adaptive (e.g., fertility and reproduction success39 or muscle growth40) variables, but see Forne´ et al. 200926 for a complete review. Numerous muscle isoforms of CK were also found in carp, Cyprinus carpio, when individuals acclimatized to cold temperatures.10 In the latter study, CK fragments were more prominent in skeletal muscle cooled to 10 °C probably due to proteolysis of the enzyme. The enzyme CK catalyzes the reversible transfer of the N-phosphoryl group from phosphocreatine (PCr) to ADP to regenerate ATP, and plays a key role in the energy homeostasis of cells with intermittently high-energy requirements.45 According to the authors, this temperature-dependent activity of CK isoforms explains why the protein and its isoforms undergo significant changes following thermal acclimation.10 Interestingly, our Mediterranean Sea hake population showed greatest changes in the expression of the four CK isoenzymes identified (Spot 1612, Figure 1). Sea temperature data at the time of sampling are not available, but it should be noted that temperature near the sea bottom, where this species dwells, is fairly constant at all the sites. However, in future work the impacts of factors such annual fluctuations in sea bottom temperatures in Mediterranean and Atlantic waters on the expression levels of CK and other proteins need to be assessed. Other proteins identified in the carp10 as biomarkers for temperature are the heat shock protein (HSP) 90-R and AMPdeaminase, which were not identified in our study. However, we did detect another molecular chaperone, the brain heat shock cognate 70, HSC70 (spots 801 and 805, Table 2) which has been assigned to the HSP family due to homology with other HSPs.46 HSC70 belongs to a highly conserved family of chaperone regulators involved in the appropriate folding and trafficking of newly synthesized proteins in the cell,46 but other functions of this protein have been recently discovered (e.g., inhibition of cell proliferation,47 etc.). The activity of HSC70 is brought on by stress and heat shock46 such that it plays an essential role in protecting organisms from environmental and genetic stress.48 Hence, it is not surprising that fishes from our different sampling sites showed differences in HSC70 expression levels. Interestingly, in a very complete study based on the use of cDNA (cDNA) microarray technology,49 it was Journal of Proteome Research • Vol. 9, No. 12, 2010 6399

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Figure 1. Pairwise comparisons of (A) liver and (B) brain protein expression values between populations Each bars represent the spot expression average ratio comparison among two populations as follows: Atlantic Ocean versus Cantabrian Sea in blue; Atlantic Ocean versus Mediterranean Sea in red; and Cantabrian versus Mediterranean Sea in green. The values are referred to the denominator of each comparison and thus highlight protein expression difference between populations. A negative value indicates that the average in protein expression for the denominator population is lower (down regulated) in comparison with the other population. On the contrary, a positive value indicates that the average in protein expression for the denominator population is higher (up regulated) in comparison with the other population. Spot numbers on the right side of the figure refer to candidate proteins identified in Table 2 and Figure S1 (Supporting Information).

observed that the expression levels of the constitutive gene HSC70-1 varied among Salmo trutta individuals, in that higher transcript levels were detected in migratory populations than sedentary populations, suggesting a role for this gene in the responses to other types of abiotic stress. 3.3. Protein Expression of the Identified Proteins. The expression levels of the 20 liver and 35 brain proteins identified were examined. Average ratios were calculated by dividing the mean protein volume per spot corresponding to one sampling site by the mean protein volume per spot corresponding to another sampling site (for the three possible population comparisons) (Figure 1). Thus, expression levels of 30 of the protein spots (17 in liver and 13 in brain) differed among sampling sites. However, variations in the expression of the liver proteins were more equally distributed between their up- and down- regulation, whereas in the brain, down-regulation was the prominent trend. To compare protein expression differences across the functional classes established, Student’s t-test was used. This test revealed the existence of different patterns of regulation depending on the different protein function, and these differences were more marked in the brain than liver (Figure 1). Specifically, brain proteins involved in Cell metabolism and energy, Cell signaling and Protein structure were significantly down-regulated (p < 0.05, in both the parametric and nonparametic test) in most of the populations pairwise comparisons, whereas only the liver proteins involved in Protein fate were significantly up-regulated (in this case, probably due to the effect of protein spot 760, identified as disulfide-isomerase A6). Among the down-regulated brain proteins were those involved in energy metabolism, such as phosphoglycerate kinase, glyceraldehyde 3-phosphate dehy6400

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drogenase and several proteins involved in cytoskeleton regulation, such as creatine kinase. 3.4. Principal Component Analysis (PCA) and Hierarchical Clustering. Principal component analysis (PCA) is a useful method of categorization, since it separates the dominating features in the data set and tries to reduce complexity by replacing these with a limited number of components. In our case, PCA analysis of the significant differences detected in protein expression (81 spots for liver and 154 for brain), yielded two principal components that were able to explain more than 75% of the data variance (accounting for 60.2% + 15.4% ) 75.6% of the total variance in liver; and 56.7% + 21.1% ) 77.8% of total variance in brain). This allowed for a clear distinction of all samples. Thus, in a plot based on only two components, we observed clear clustering between all six biological replicates for each sampling site, highlighting the differences between the populations examined (Figures 2A and 3A). The first component provided straightforward discrimination between the Atlantic Ocean (A) and Cantabrian Sea (C) populations within the liver gels (Figure 2A) and between the Mediterranean (M) and Cantabrian Sea (C) populations for the gels loaded with brain extracts (Figure 3A). For the whole set of differential spots (2104 and 3558 spots for liver and brain, respectively), separation by PCA was also clear, but there was some overlapping of the ellipses possibly due to the inclusion of outliers in the analysis (not shown). Differential spots were also clustered using a hierarchical clustering (HC) method and the level of normalized protein expression (heat map) represented in the map as negative values (-0.5) in green and positive values (+0.5) in red (Figures 2B and 3B). The heat map and the hierarchical tree constructed using protein expression pattern similarities were able to define

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Population Proteomics of the European Hake

Figure 2. Unsupervised multivariate analysis of DIGE results for liver proteins. (A) Principal component analysis clustered the samples into groups corresponding to the Mediterranean Sea (M, indicated in green), Cantabrian Sea (C, indicated in red) and Atlantic Ocean (A, indicated in blue), differentiated by two principal components that distinguish the variance. (B) Unsupervised hierarchical clustering of individual proteins (below) with heat map representation (down-regulated proteins are represented in green and up-regulated proteins in red). The origin of the population sample is listed in the left-hand axis of the graph (see Table 1).

two groups for the liver and three for the brain, a fairly similar pattern to that rendered by the PCA analysis. 3.5. Classification of Isoforms. The protein volumes obtained for the identified isoforms were analyzed using SPSS software (v.17.0, SPSS Inc., Chicago, IL) according to a linear mixed-effect model to test whether isoform, population or the interaction isoform × population could explain some of the differences in protein variability. The results in Supplementary Table S2 (Supporting Information) indicate a significant effect of isoform × population in all the comparisons. Thus, in future comparative proteomic studies in the hake, the factor isoform could be a useful candidate to discriminate among different populations. The isoforms showing the lowest AIC and highest F test (F) values in the one-way ANOVA (Tables S2, S3 and S4 in Supporting Information) were phosphoenolpyruvate carboxykinase (spot 420), lactate dehydrogenase b (spot 1162) and fatty acid binding protein H8 (spot 1850) in the case of liver. The brain protein isoforms that met these conditions were ATP synthase (spot 1144), voltage-dependent anion channel protein (spot 2090) and 1-3-3 protein (spot 2249). These isoforms also showed drastic abundance differences among populations (Table 2) and could thus be used to classify individuals by their population of origin. 3.6. Classification of Individuals Using Linear Discriminant Analysis. Fisher’s linear discriminant coefficients calculated for the selected spots were able to define each population as listed in Table 3. These coefficients can be used to calculate

Fisher discriminant functions that characterize each population analyzed in this study. On initial analysis of the data, we were able to select three spots as the minimum number of variables that could correctly discriminate individuals of each population with a high percentage of successful classifications (with a discriminant score of 90% of higher), and could therefore reduce the degrees of freedom in the analysis. This approach involved a multivariate stepwise discriminant analysis that served to identify the variables (spots) that were better at classifying individuals according to their population of origin. For liver, these were spots 753-821-847 and for brain spots 1373-1572-1576. Two of these proteins were identified in liver as disulfide-isomerase A6 (753) and beta actin (847). In a further linear discriminant analysis of the abovementioned isoforms (spots 420-1162-1850 for liver and 1144-2090-2249 for brain), these proteins were again able to correctly distinguish 100% of the individuals according to their population of origin. Accordingly, these proteins could serve as diagnostic markers to identify individuals from the three sampling sites examined here, and could perhaps also discriminate between other hake populations.

4. Conclusions Recent developments in protein-based techniques, such as the use of fluorescent dyes in 2-DE gels, have led to the discovery of reliable biomarkers in a large number of animal species. In this study, we explore for the first time, changes in protein abundances among three natural populations of hake Journal of Proteome Research • Vol. 9, No. 12, 2010 6401

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Figure 3. Unsupervised multivariate analysis of DIGE results for brain proteins. (A) Principal component analysis clustered the samples into groups corresponding to the Mediterranean Sea (M, indicated in green), Cantabrian Sea (C, indicated in red) and Atlantic Ocean (A, indicated in blue), differentiated by two principal components that distinguish the variance. (B) Unsupervised hierarchical clustering of individual proteins (below) with heat map representation (down-regulated proteins are represented in green and up-regulated proteins in red). The origin of the population sample is listed in the left-hand axis of the graph (see Table 1). Table 3. Fisher’s Linear Discriminant Function for Liver and Brain

tissue

spot

Mediterranean Sea

Liver Liver Liver Liver Liver Liver Liver Liver Brain Brain Brain Brain Brain Brain Brain Brain

753 847 821 Constant 420 1146 1850 Constant 1373 1572 1576 Constant 1144 2090 2249 Constant

7,601 5,211 20,39 -13,69 13,597 14,045 16,754 -24,058 78,899 22,154 53,563 -57,573 5,475 28,52 48,349 -40,287

population Cantabrian Sea

30,51 33,74 5,915 -74,91 21,51 -2,339 38,812 -41,537 157,95 13,664 29,902 -114,325 25,761 11,349 3,784 -12,925

Atlantic Oc.

6,242 -2,67 48,75 -45,96 30,459 10,533 3,181 -24,641 98,986 62,667 79,542 -145,954 40,375 31,953 14,424 -50,038

(Merluccius merluccius). Liver and brain extracts from eighteen hake individuals captured in the Mediterranean Sea, Cantabrian Sea, and Atlantic Ocean were examined by 2-DE, DIGE and mass spectrometry (MS). This new approach highlights the potential of proteomics as a new discipline to identify protein biomarkers of populations in nonmodel species such as the hake. 6402

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Through a comparative analysis of qualitative data (present or absence of protein spots in gels), the Atlantic Ocean and Cantabrian Sea populations were found to have more proteins in common compared to the Mediterranean Sea population. However, quantitative analyses (hierarchical clustering and principal component analysis) revealed two or three groups, for the liver and brain extracts, respectively, that clustered most of the individuals belonging to each of the three sampling sites, indicating the overall differentiation of the protein profiles of the hake populations examined. In terms of protein expression, we observed that two of the brain protein functions (cell signaling and metabolism-energy) were significantly more discriminating of the three populations, while according to the liver proteins detected, population variability in protein expression was more equally distributed across the different protein functions. Such differential tissuespecific variation supports the hypothesis that brain protein profiles might better reflect the functional adaptation of populations, making this tissue of greater interest for future studies. The cause of these differences among tissues was not investigated here, though we speculate it has something to do with the nature of the tissue itself (how each tissue is affected by ecological variables) and with proteome plasticity in response to environmental changes. In future work, we will test the validity of the biomarkers identified by increasing the number of hake specimens used and resampling the same sites to investigate the temporal stability of protein diversity in these regions. Moreover, by

Population Proteomics of the European Hake including ecological variables in our analysis, we should be able to track the response of a given protein to environmental changes.

Acknowledgment. This research was funded by the European Community’s Seventh Framework Programme (FP7/ 2007-2013) under grant agreement KBBE-212399 (FishPopTrace). E.G.G. holds a postdoctoral contract for the FPT project. We thank Montserrat Martı´nez-Gomariz, Ma Luisa Herna´ez and Ma Dolores Gutierrez for their technical support. The DIGE and MALDI-TOF/TOF analyses were conducted at the Proteomics Facility of the UCM-PCM, a component of the ProteoRed network. Supporting Information Available: Figure S1. Representative proteome maps for liver (A) and brain (B) hake tissues. Proteins were resolved using the 3-11 (nonlinear) pH range on the first dimension and 12% T, 2.6% C acrylamidePDA on the second dimension. Gels were Coomassie stained. Proteins differentially expressed are shown in the gels by spot master number (Table 2). Tables below each image indicate the DIGE experimental design (with the labeling combination for each of the nine gel replicates). Figure S2. SDS-PAGE 10% gel for liver (A) and brain (B) hake tissues. Gels were loaded with 10 µg of each protein for each sample. The designation of each sample is also indicated. Table S1. List of liver and brain candidate proteins identify by 2-D DIGE as differentially expressed. Table S2. Results of the linear mixed-effect model. The table indicates the variance component analyses and the effect of the interaction “isoform × population” on the distribution of protein abundances. Table S3. One-way ANOVA of the results obtained in the analysis of the liver proteins of interest. Table S4. One-way ANOVA of the results obtained in the analysis of the brain proteins of interest. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Gonzalez, E. G.; Beerli, P.; Zardoya, R. Genetic structuring and migration patterns of Atlantic bigeye tuna, Thunnus obesus (Lowe, 1839). BMC Evol. Biol. 2008, 8, 252. (2) Gonzalez, E. G.; Zardoya, R. Relative role of life-history traits and historical factors in shaping genetic population structure of sardines (Sardina pilchardus). BMC Evol. Biol. 2007, 7, 197. (3) Karr, T. L. Application of proteomics to ecology and population biology. Heredity 2008, 100 (2), 200–6. (4) Biron, D. G.; Loxdale, H. D.; Ponton, F.; Moura, H.; Marche, L.; Brugidou, C.; Thomas, F. Population proteomics: an emerging discipline to study metapopulation ecology. Proteomics 2006, 6 (6), 1712–5. (5) Nedelkov, D. Population proteomics: addressing protein diversity in humans. Expert Rev. Proteomics 2005, 2 (3), 315–24. (6) Nedelkov, D.; Kiernan, U. A.; Niederkofler, E. E.; Tubbs, K. A.; Nelson, R. W. Population proteomics: the concept, attributes, and potential for cancer biomarker research. Mol. Cell. Proteomics 2006, 5 (10), 1811–8. (7) Nedelkov, D. Population proteomics: investigation of protein diversity in human populations. Proteomics 2008, 8 (4), 779–86. (8) Martinez-Fernandez, M.; Rodriguez-Pineiro, A. M.; Oliveira, E.; Paez de la Cadena, M.; Rolan-Alvarez, E. Proteomic comparison between two marine snail ecotypes reveals details about the biochemistry of adaptation. J. Proteome Res. 2008, 7 (11), 4926– 34. (9) Ky, C. L.; de Lorgeril, J.; Hirtz, C.; Sommerer, N.; Rossignol, M.; Bonhomme, F. The effect of environmental salinity on the proteome of the sea bass (Dicentrarchus labrax L.). Anim. Genet. 2007, 38 (6), 601–8. (10) McLean, L.; Young, I. S.; Doherty, M. K.; Robertson, D. H.; Cossins, A. R.; Gracey, A. Y.; Beynon, R. J.; Whitfield, P. D. Global cooling: cold acclimation and the expression of soluble proteins in carp skeletal muscle. Proteomics 2007, 7 (15), 2667–81.

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