Spatial Variation in Transcript and Protein Abundance of Atlantic

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Spatial Variation in Transcript and Protein Abundance of Atlantic Salmon during Feeding Migration in the Baltic Sea Mirella Kanerva,*,† Anni Vehmas,‡ Mikko Nikinmaa,† and Kristiina A. Vuori† †

Laboratory of Animal Physiology, Department of Biology, University of Turku, Turku FI-20014, Finland Translational Proteomics, Turku Centre for Biotechnology, Tykistökatu 6A, Turku FI-20520, Finland

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S Supporting Information *

ABSTRACT: The fitness and reproductive output of fishes can be affected by environmental disturbances. In this study, transcriptomics and label-free proteomics were combined to investigate Atlantic salmon (Salmo salar) sampled from three different field locations within the Baltic Sea (Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS)) during marine migration. The expression of several stress related mRNAs and proteins of xenobiotic metabolism, oxidative stress, DNA damage, and cell death were increased in salmon from GoF compared to salmon from BMB or BS. Respiratory electron chain and ATP synthesis related gene ontology-categories were upregulated in GoF salmon, whereas those associated with RNA processing and synthesis, translation, and protein folding decreased. Differences were seen also in metabolism and immune function related gene expression. Comparisons of the transcriptomic and proteomic profiles between salmon from GoF and salmon from BMB or BS suggest environmental stressors, especially exposure to contaminants, as a main explanation for differences. Salmon feeding in GoF are thus “disturbed by hazardous substances”. The results may also be applied in evaluating the conditions of pelagic ecosystems in the different parts of Baltic Sea.

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INTRODUCTION The fitness and reproductive output of fishes can be affected by a range of interconnected environmental disturbances. For example, animals and plants inhabiting the Baltic Sea are subjected to multiple stressors, from considerable natural spatial, seasonal, and vertical changes in hydrography1 to multiple human impacts including contamination, eutrophication, and temperature increase.2−4 The feeding migration phase of Atlantic salmon (Salmo salar) in the Baltic Sea lasts one to four years, thus the conditions in the sea affect individual survival, health, and reproduction. The main feeding grounds of Baltic S. salar are in the Central and Southern Baltic Main Basin, although a proportion of the fish stay and feed in the northern and northeastern parts of the sea in the Gulfs of Bothnia and Finland.5,6 There are indications that salmon feeding in the Gulf of Finland are exposed to environmental stressors shown as increased oxidative stress, EROD (Ethoxyresorufin-O-deethylase) activity and Aryl hydrocarbon receptor DNA-binding7,8 and higher levels of persistent organochlorine pollutants9 compared to salmon feeding elsewhere in the Baltic Sea. Large-scale gene expression change analyses by “omics” methods facilitate evaluating the effects of environmental stressors. While transcriptomics enable studying mRNAs of genes that are actively transcribed at a certain time, proteomic analyses are closer to the phenotype as they also reflect any effects on translation. Because of post-transcriptional effects, most reports find only a weak correlation between the respective abundances of mRNA and protein.10−12 Further, the correlation © 2014 American Chemical Society

between mRNA and protein level changes is gene-specific. Thus, studying both the transcriptome and the proteome levels help in estimating how environmental changes regulate gene expression (e.g., refs 13−16). In the past, proteomic studies were difficult to perform for nonmodel organisms, but recent “label-free” techniques have enabled greater coverage of the proteome.17−19 To investigate the effects of environmental stressors on salmon feeding in different parts of Baltic Sea, we have analyzed the mRNA and protein expression profiles of representative specimens from three locations: Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS).

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EXPERIMENTAL SECTION Samples. Samples of Atlantic salmon (Salmo salar) were collected from three different locations in the Baltic Sea (Baltic Main Basin, BMB; Bothnian Sea, BS; Gulf of Finland, GoF) during their feeding migration from November 2006 to January 2007 with the help of Finnish fishermen.7 The fish were killed with a blow to the head, liver tissue was taken and frozen in liquid nitrogen on the boat, and stored at −80 °C. Eight representative one-sea-year female fish per location were selected for transcriptomic and proteomic analyses. Details of the samples are given in Supporting Information (SI) 1. Received: Revised: Accepted: Published: 13969

June 18, 2014 October 24, 2014 October 30, 2014 October 30, 2014 dx.doi.org/10.1021/es502956g | Environ. Sci. Technol. 2014, 48, 13969−13977

Environmental Science & Technology

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and a 15 cm long, 75 μm inner diameter analytical column packed with the same C18 particles was used for peptide separation. A 100 min long gradient from 98% solvent A (98% H2O, 2% ACN and 0.2% HCOOH) to 35% solvent B (95% ACN, 5% H2O and 0.2% HCOOH) with a flow rate 0.3 μL/min was used for peptide elution. Sample loading, solvent delivery, and scan functions were controlled by Xcalibur software (version 2.6.0 SP3 Thermo Fisher Scientific). The samples were analyzed in Orbitrap Velos in Data Dependent Acquisition (DDA) mode, where the 15 most intense doubly or triply charged parent masses were automatically selected for fragmentation. Mass scanning (MS1) was performed in positive-ion mode in the Orbitrap mass analyzer, where a low resolution preview scan and subsequent survey scan (MS) at a resolution of 60 000 was performed. The Automatic Gain Control (AGC) of the Orbitrap was set to 106 and the mass range for analysis was from 300 to 2000 m/z. Precursor ions were selected for fragmentation (MS/MS) by collision-induced dissociation (CID) in the ion trap mass analyzer, after which they were added to an exclusion list for 60s to prevent their reanalysis. The database searches for the 22 successfully acquired spectrum files were performed in Proteome Discoverer (version 1.3.0.339 Thermo Fisher Scientific) using the Mascot algorithm. Two files were removed due to instrument instability during the run. The spectra were searched against a Uniprot salmon database20 supplemented with common protein contaminants in laboratory from the common Repository of Adventitious Proteins database (10 039 sequences, accessed July-2011). A reversed version of this database was used in the decoy search performed within Proteome Discoverer by Percolator algorithm. Search parameters were as follows: The enzyme used was trypsin, methionine oxidation was chosen as dynamic and cysteine carbamidomethylation as fixed modification, decoy database search was performed, one missed cleavage site was allowed, accepted precursor mass tolerance was set to 5 ppm, fragment mass tolerance to 0.5 Da, and false discovery rate (FDR) to 1% defined by Percolator algorithm. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium21 via the PRIDE partner repository with the dataset identifier PXD001453. Data Analysis. Microarray Analysis. Array quality was assessed through the use of Agilent control features as well as spike-in controls (Agilent One-Color RNA Spike-in Kit). The mean (standard deviation) Spike-In Detection Limit was 1.58 (0.16). gProcessedSignals, the end result of standard Agilent normalization and background correction procedures, from the Feature Extraction Software (v 10.7.1.) were used for further analysis in the Chipster open source platform (version 1.4).20 The arrays were normalized using the quantile method.23 Probes with missing values and probes that varied least across all observations were filtered away based on the coefficient of variation value 0.2, leaving 18 256 probes for further analyses. Differentially expressed transcripts between samples collected from different sea areas were identified using the empirical Bayes procedure.24 Transcripts showing between group differences at FDR ≤ 0.05 were selected for further analyses. Four samples (out of 24) were removed from analyses of differentially expressed transcripts based on data inspection with nonmetric multidimensional scaling analysis. Proteomic Analyses. Raw spectral data and identification data from the 22 mass spectrometry runs were imported to Progenesis 4.0 for feature detection and quantification.

RNA Sample Preparation and Array Hybridization. Total RNA was isolated from liver tissue using TRI-reagent (Molecular Research Center, OH, U.S.A.) and an additional purification of the extracted RNA was performed using Nucleospin RNA II (Machinery-Nagel, Germany) according to the manufacturer’s instructions. RNA concentration was quantified using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, DE, U.S.A.), and RNA quality was assessed using an Agilent 2100 bioanalyzer RNA 6000 Nano kit (Agilent Technologies, CA, U.S.A.). The mean RNA integrity number (stdev) of all samples was 9.64 (0.20). RNA labeling, hybridizations, and scanning were performed by the Finnish Microarray and Sequencing Centre in Turku Centre for Biotechnology. Briefly, total RNA (200 ng) was amplified and Cy3-labeled with Agilent’s Low Input Quick Amp Labeling Kit, One Color (Agilent), together with Agilent’s One-Color RNA Spike-in Kit following the manufacturer’s protocols. After the labeling, the cRNA was examined with the Nanodrop ND-1000 and the Agilent’s 2100 bioanalyzer RNA 6000 Nano kit to assess the concentration and quality of the labeling. Each sample (1.65 μg) was hybridized to the Agilent’s 4 × 44K Salmon array (Design ID 020938) at 65 °C overnight using Agilent’s Gene Expression Hybridization kit. Washes were conducted, as recommended by the manufacturer using Agilent’s Gene Expression Wash Pack and using Agilent’s stabilization and drying solution. Arrays were scanned with Agilent Technologies Scanner, model G2565CA. Spot intensities and other quality control features were extracted with Agilent’s Feature Extraction Software version 10.7.1. Microarray data are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-3041. Protein Sample Preparation and LC-MS/MS. For liquid chromatography−tandem mass spectrometry (LC/MS2) analysis, tissues were homogenized in 100 mM K-phosphate buffer with 150 mM KCl, pH 7.4 and protease inhibitors: 500 nM PMSF, 2 ng/mL LAP and 2 ng/mL approteinin. The samples were centrifuged for 15 min at 10 000g, +4 °C. Supernatants were transferred to new Eppendorf tubes, frozen in liquid nitrogen, and stored at −80 °C. Proteins were quantified using the BCA (bicinchoninic acid) protein assay (Pierce, IL, U.S.A.). An aliquot of sample containing 100 μg of protein was taken into a new tube and acetoneprecipitated. Cysteine residues were reduced and alkylated, and proteins were digested with trypsin overnight. Samples were desalted using Empore C18 solid phase extraction cartridges (Sigma-Aldrich, MO, U.S.A.) and the peptide concentrations were estimated by measuring the absorbance in 280 nm by Nanodrop ND-1000 spectrophotometer (version 3.7.1, Thermo Fisher Scientific). The sample preparation details are given in SI 2. The 24 liver peptide samples were analyzed in a randomized order by microcapillary liquid chromatography electrospray ionization−tandem mass spectrometry (μLC/ESI-MS/MS) on an ESI-hybrid Ion Trap-Orbitrap mass spectrometer (LTQ Orbitrap Velos; Thermo Fisher Scientific, Bremen, Germany) coupled to an Easy Nano LC nanoliquid chromatography (nLC) system (Thermo Fisher Scientific, Bremen, Germany). An amount of 200 ng of peptides in 5 μL of 1% HCOOH was injected into the nLC system, where peptides were separated by their hydrophobicity using reversed-phase chromatography. An in-house packed 2.5 cm long, 75 μm inner diameter trap column packed with C18 particles (Magic AQ C18 resin −5 μm/200 Å, Bruker-Michrom, Billerica, MA, U.S.A.) was used for desalting and concentrating of peptides, 13970

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databases. Significant models (p < 0.05), assessed according to a Monte Carlo simulation procedure,30,31 of subnetworks with zero or one intermediate (“missing”) nodes between list members were chosen for further examination. The models were visualized with Cytoscape.29

All LC-MS maps were aligned together, and the feature detection was done by automatic peak picking in default sensitivity mode, maximum charge of precursor was set to 3+, and the retention time window limit to 12 s. In Progenesis, the peptidefeature matches that had less than two hits or had precursor mass tolerance higher than 5 ppm were removed. Moreover, the analysis was restricted to the linear section of the gradient, and the normalization was done against all features. The normalization factors were 0.6−1.2. In the protein data, on average 6648 distinct peptide features could be found in each liver sample, and altogether, 1091 proteins were assigned with unique features and used in quantification. Approximately 80% of the recognized protein sequences could be ID mapped to the microarray probes. Proteins showing between-group differences at q ≤ 0.05 in BMB vs GoF and BS vs GoF comparisons and at q ≤ 0.159 in BMB vs BS comparison were selected for further analyses. The reason for different q-value criteria in BMB vs BS comparison was that although 29 proteins had p-values smaller than 0.01 (equals to q ≤ 0.159), lowest q-value was 0.083. Association between mRNA and Protein Expression. Associations between multivariate descriptors of mRNA (18 256 probes) and protein (754 proteins) expression were inferred via coinertia analysis (CIA), a multivariate method that identifies trends or corelationships in multiple data sets.25 CIA simultaneously finds ordinations from the data sets that are most similar by finding successive axes from the two data sets with maximum covariance. Between-class (between sample group) CIA was performed using the “ade4” package26 implemented in R. Monte Carlo test (1000 permutations) was used for validating the CIA result. Gene Ontology Enrichment Tests. Blast2Go27 was used to determine if differentially transcribed genes or expressed proteins were significantly over- or under-represented by particular functional categories at FDR < 0.05 and FDR < 0.3. Reference set for transcriptomics results analyses consisted of top blast results (E value threshold of 10−6) of all the microarray’s probe sequences obtained from NCBI’s nonredundant databases. After the blast search, the mRNA reference set contained 17 703 unique transcripts out of which 83% had an annotation. 80% of top hits were from teleost species. Salmo salar UniProt proteins were used as reference sets for proteomics results.17 In addition, ClueGO, a Cytoscape plug-in,28 was used for analyzing significantly enriched gene ontology (GO)-categories and their relationships. The results were visualized with Cytoscape.29 Because of the limited annotation information available, salmon transcripts and proteins were matched to their human orthologs using BLAST searches from EMBL database. It is important to note, however, that the function of human protein does not necessarily match its function in fish. Interaction Modeling and Network Analysis. For conducting interaction network analyses, human orthologs of salmon transcripts and proteins were used. Models were inferred with PPI and R spider.30,31 For a given set of genes, PPI spider (available as a web tool via http://www.bioprofiling.de/) identifies subnetworks in a global reference PPI network from the IntAct database that minimize the interaction distances among the genes. R spider (available as a web tool via http://www.bioprofiling.de/) implements the Global Network statistical framework to analyze gene list using as reference knowledge a global gene network constructed by combining signaling and metabolic pathways from Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG)

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RESULTS AND DISCUSSION CIA revealed a significant covariation between transcriptional and proteomic data (Figure 1) showing that globally changes in

Figure 1. Co-inertia analysis of between-group ordinations of log2 transcript (red triangles) and protein abundance (blue circles) of Atlantic salmon (Salmo salar) samples collected from Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS). Global similarity (coinertia) between transcriptome and proteomic data sets is 56% (p = 0.022). Eighty-eight % of total coinertia was captured by the first axis (x-axis) which separates salmon from BMB and GoF. The second coinertia axis (y-axis) captured 12% of total (co)variation in the data, and describe differences between BMB and BS salmon.

transcription are reflected in translation. The RV coefficient, a measure of global correlation between the data matrices, was 0.56 (p = 0.022). 88% of coinertia is described by the first axis representing mainly differences between samples from BMB and GoF. A second coinertia axis captured 12% of total (co)variation in the data, and described differences between BMB and BS salmon. In total, 1616 differentially transcribed (FDR ≤ 0.05) probes, out of which 47% (761) unique mRNAs could be identified with a blast search from nr database and 338 differentially expressed proteins were found from comparisons of samples collected from different sea areas (SI 3). In line with the results of CIA, the amounts of differentially expressed mRNAs and proteins were higher in comparison between GoF salmon and other sample groups. Enrichment analysis was done with Blast2Go using salmon mRNA/protein IDs and with human ortholog data in ClueGO (SI 4). None of the enriched mRNA categories between BMB and GoF or BMB and BS fish, and 21 between BS and GoF fish were significant at FDR < 0.05 in Blast2Go. In contrast, significant (FDR < 0.05) GO terms were found from all comparisons using ClueGO; 80 from BMB vs GoF comparison, 66 from BMB vs BS comparison and 268 from Bs vs GoF 13971



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Table 1. Overview of the Major Themes Found from Comparisons of Differentially Expressed mRNAs and Proteins in Atlantic Salmon (Salmo salar) Samples Collected from Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS) Using Information from Enrichment [Blast2GO (B2GO), ClueGO] and Interaction Model [Bioprofiling (Bioprf); ppi (ppi); and r Spider (r), ClueGO] Analysesa,b,c,d,e, f,g

a X Over (over) or under (under) represented enriched category/categories at FDR < 0.05. bX Over (over) or under (under) represented enriched category/categories at FDR < 0.3. cmajority of mRNAs, proteins or ClueGO nodes upregulated (↑) or downregulated (↓) in significant Bioprofiling (ppi or r spider) or ClueGO models. dthree or less mRNAs, proteins or ClueGO nodes upregulated (↑) or downregulated (↓) in significant Bioprofiling (ppi or r spider) or ClueGO models. e↑↓ or ↑↓ no clear direction of regulation. f− not found in the analysis. gna, the analysis resulted in no enriched categories.

decisive for the observed mRNA/protein level changes. Further information on the temperature, salinity, and oxygen levels in the study sites is given in SI 1. According to HELCOM’s Integrated classification of hazardous substances in offshore areas,3 most assessment units in Gulf of Finland and Northern and Central Main Basin are classified “poor” or “bad” in contrast to units in the Gulf of Bothnia and Southern Main Basin, which are classified as “moderate”. All Baltic Sea open sea units are, however, “areas disturbed by hazardous substances”. Salmon from Northern Baltic Sea, 32 but especially from the Gulf of Finland,9 in general, contain more persistent organoclorine pollutants than fish from Southern Main Basin. Environmental Stress Responses. Several stress related (xenobiotic metabolism, oxidative stress, DNA damage, and cell death) mRNA and protein levels were higher in salmon from GoF than in salmon from BMB or BS (Table 1, Figures 2 and 3). Upregulated ClueGO categories found in BMB vs GoF proteomics comparison include xenobiotic/drug metabolism, hydrogen peroxide-, superoxide- and glutathione metabolism, and DNA damage response (Figure 3). ClueGO results of BS vs GoF comparison at transcriptomic level suggest enhancement of xenobiotic/drug metabolism and response to corticosteroid stimulus categories (Figure 2) and at the proteomic level an increase of fatty acid oxidation, response to oxygen and hydrogen peroxide metabolism associated GOs in salmon from GoF (Figure 3). Significantly increased proteins in BMB vs GoF comparison, known to be associated with environmental stress responses, include those involved in redox regulation, SOD2, CAT, and two peroxiredoxins (PRDX1 and 5), and biotransformation associated proteins CYP3A4, GSTP1, GSTT2B, and UGT2A1 (SI 4−5). Peroxiredoxin 3 protein level decreased in opposition to PRDX1 and 5. SOD2 and CAT proteins increased in GoF salmon compared to BS salmon. No significantly changed redox regulation or biotransformation associated transcripts were found from BMB vs GoF comparison,

comparison (SI 4). According to Blast2Go, 176, 4, and 12 enriched protein categories were significant at FDR < 0.05 in BMB vs GoF, BMB vs BS, and BS vs GoF comparisons, respectively (SI 4). In ClueGO analyses, significant enriched protein categories of the same comparisons were 368, 22, and 65 (SI 4). ClueGO gives more significantly enriched GO-terms than Blast2GO, as human sequences are better annotated than those of nonmodel organism orthologs. For interpreting the results, information from different sources were used; enrichment (Blast2Go, ClueGO) and interaction model (Bioprofiling ppi and r spider, ClueGO) analyses. Overview of the major themes and directions of regulation found from comparisons of differentially expressed mRNAs and proteins are given in Table 1. In general, the results from different analysis software agree on the major themes, although some differences in the direction of change were found between Bioprofiling and ClueGO models. While the transcriptomics and proteomics data give changes in the same direction in the case of many genes and gene groups, for some genes or groups the transcript and protein data do not match. Since the protein is the ultimate product of gene expression, the protein data are in these cases more biologically relevant than the mRNA data. It is also possible that the differences of protein and mRNA data are due to large variation in the field data. The analyses and interpretation of proteomics data were clearer than mRNA data as the percentage of identified mRNA sequences was lower than that of protein sequences. Additionally, the proteomics results were more uniform than transcriptomics results in pointing out the direction of change in a certain GO-group. The molecular states of the samples collected from the field may be affected by complex combination of abiotic and biotic environmental factors including contamination. There are, however, no reported data that temperature or salinity differences between the sampling areas of this study could be 13972



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Figure 2. Cytoscape visualization of ClueGo clustering results of significantly enriched gene ontology (GO)-categories (nodes) in pairwise comparisons of differentially expressed mRNAs in Atlantic salmon (Salmo salar) samples collected from Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS); (A) BMB vs GoF, (B) BMB vs BS, and (C) BS vs GoF. Enrichment of a certain GO-category is expressed by node size and the lines connecting different nodes indicate that these categories share gene(s). The direction of change is given by colors: red, increased and green, decreased in the latter group. The group name is the leading group term based on highest significance (p-value corrected with BenjaminiHochberg).

whereas several transcripts (e.g., CYP1A1, CYP4B1, GSTT1, MGST3, and UGT2A1) associated with biotransformation processes and glutathione were significantly upregulated in GoF compared to BS samples (SI 4−5). The direction of change of these mRNAs is the same but not significant when compared to BMB. mRNA levels of glutathione reductase (GSR) and −peroxidase 3 (GPX3) were significantly lower in GoF than in BS. Several earlier studies have noted changes in the mRNA or protein levels of oxidative stress or detoxification-related genes in samples collected from contaminated field sites. Wang et al. found an increase in detoxification and oxidative stress related mRNAs33 and proteins34 of goldfish (Carassius auratus). Similar observations were reported by Falciani et al.35 and Williams et al.36 for flounders (Platichthys f lesus). Galland et al.37 found that GST protein level was higher in flounders from a polluted than control site. Asker et al.38 found upregulation of DNA damage related mRNAs in eelpouts (Zoarces viviparus) collected from a polluted site, but on contrary downregulation of detoxification related mRNAs. The increased transcription of Aryl hydrocarbon receptor (AhR) and CYP1A has earlier been shown to correlate with the organochlorine toxicant load in field collected Baltic salmon.39

Unfortunately, we do not have any data on the contaminant levels of the salmon used in our study, but the increased EROD activity, the most commonly used biomarker of exposure to planar aromatic compounds such as dioxins, PCBs and PAHs,40 increased DNA-binding of AhR,7 and earlier measured organochlorine concentrations in salmon from GoF9 indicate that salmon from GoF are affected more by contamination than salmon feeding elsewhere in the Baltic Sea. GoF is also more eutrophied than BS and Southern Main Basin 2 and cyanobacterial toxins may induce oxidative stress.41 Although it is possible that part of the observed transcriptomic and proteomic changes reflect the difference in genetic lineage between the stocks feeding in the GoF and BMB/BS,42 differential expression of mRNAs and proteins related to, for example, xenobiotic/drug metabolism, oxidative stress, DNA damage, and cell death are considered indicative of contamination or environmental stress induced effects, but not generally found in studies focusing on between-population comparisons of gene expression.43−46 While data of GoF fish suggest responses to contaminants, transcriptomic and proteomic differences between other groups did not specifically indicate exposure to contaminants. Upregulation of glutathione metabolism and NADPH generation 13973



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Figure 3. Cytoscape visualization of ClueGo clustering results of significantly enriched gene ontology (GO)-categories (nodes) in pairwise comparisons of differentially expressed proteins in Atlantic salmon (Salmo salar) samples collected from Baltic Main Basin (BMB), Gulf of Finland (GoF), and Bothnian Sea (BS): (A) BMB vs GoF; (B) BMB vs BS; and (C) BS vs GoF. Enrichment of a certain GO-category is expressed by node size and the lines connecting different nodes indicate that these categories share gene(s). The direction of change is given by colors: red, increased and green, decreased in the latter group. The group name is the leading group term based on highest significance (p-value corrected with BenjaminiHochberg).

Upregulation of respiratory chain related mRNA and proteins have been connected to contamination of the environment. Williams et al.36 suggested that it could reflect the elevated energy demand by enhanced xenobiotic metabolism. Studies done on natural populations (goldfish, flounder, and eelpout) with varying levels of pollution (mixture of polycyclic aromatic hydrocarbons and heavy metals) show mainly transcriptional and translational induction of respiratory chain genes.33,35,37,38 Studies done in the laboratory give more varying results: e.g., induction in rainbow trout (Oncorchynchus mykiss) exposed to carbon tetrachloride or pyrene,48 zebrafish (Danio rerio) cells exposed to methyl parathion,49 fathead minnow (Gobiocypris promelas) exposed to paper mill effluents50 and repression in rare minnow (Gobiocypris rarus) exposed to perfluorooctanoic acid,51 and largemouth bass (Micropterus salmoides) exposed to cadmium or atrazine.52 RNA Synthesis and Processing, Translation, and Protein Folding. Transcripts and proteins of genes associated with RNA processing, RNA synthesis, translation, and protein folding had lower levels in GoF than in BMB salmon (Table 1, Figures 2 and 3, SI 5). The decrease was evident especially at the proteomic level, where it was also significant when compared to salmon from BS. Proteins involved in ribosome biogenesis were downregulated and the transcription of ribosomal proteins altered also in BMB vs BS comparison, but the change is much smaller than in BMB vs GoF comparison. Earlier field studies have shown changes in the above GO-categories to be associated with contamination. Asker et al.38 found decreased transcription of genes involved in protein synthesis, unfolded protein response, protein folding, protein transport and ER stress, and aminoacyl tRNA biosynthesis in

by pentose phosphate pathway associated GO-terms at protein level and stress related p53- and MAPKKK-signaling pathways terms at mRNA level were found in ClueGO analyses of salmon from BS compared to salmon from BMB. This is in line with earlier studies, as Vuori et al.7 found no differences in EROD-activity or AhR DNA-binding in between salmon sampled from BS and BMB. As an indication of exposure to environmental stressors, increased activities of enzymes involved in antioxidant defense were found in BS salmon,7 but there were no signs of more severe oxidative stress as measured by reduced/ oxidized glutathione ratio7 or lipid peroxidation.8 Regardless of their role in functional responses to contamination, transcriptome, and protein expression changes can be good biomarkers of contaminant exposure. 47 Notably, for some genes involved in the regulation of redox balance, our results do not suggest clear correspondence between protein and mRNA level changes. This may indicate that their gene expression is post-transcriptionally regulated, enabling more rapid changes to take place than if the regulation were transcriptional. Such regulation could be beneficial for gene products involved in handling unpredictable disturbances. Respiratory Chain. The enrichment and interaction model analyses show both transcriptional (mRNA) and translational (protein) induction of respiratory chain and ATP synthesis component genes in salmon from GoF when compared to salmon from BMB (Table 1, Figures 2 and 3, SI 4−5). When comparing GoF to BS, there were both transcriptional induction and repression, but only translational induction. There were almost no differences between salmon from BMB and BS. 13974



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eelpout samples from contaminated areas. Studies on flounders from contaminated field sites suggest downregulation of nucleic acid metabolism and chaperones involved in protein folding.35,36 Interestingly, Bioprofiling and ClueGO models indicated that the level of five chaperonin TRiC/CCT proteins involved in protein folding to be decreased in GoF salmon. Provided that the change is associated with contamination, it is in agreement with the results of Williams et al.36 where mRNAs of genes encoding four chaperonin TRiC/CCTs decreased in most contaminated sites. Wang et al.33 found ribosomal protein L13a transcript variant 2 upregulated but ribosomal protein L13 (rpl13) mRNA downregulated in goldfish collected from contaminated area. The gene encoding RPL13A, a component of the ribosomal 60S subunit and a repressor of inflammatory genes, was significantly repressed both at transcriptional and translational levels in GoF compared to BMB salmon. Amino Acid, Lipid, and Carbohydrate Metabolism. The genes involved in amino acid metabolism were induced both at transcriptional and proteomic levels in GoF when compared to BS and BMB (Table 1, Figures 2 and 3, SI 4−5). Between BMB and BS there was no clear transcriptional effect, but the proteins of amino acid metabolism were increased in BS. Differences in lipid metabolism occur between BS and GoF (Table 1, Figures 2 and 3, SI 4−5). Here, the results of Bioprofiling models give opposite changes for transcription and protein. In the fish from GoF mRNAs of several genes involved in cholesterol metabolism have decreased levels, but the protein levels of fatty acid metabolism are increased. The analyses also indicate both down- and upregulation in the transcripts of genes involved in cholesterol and lipid biosynthesis but only a decrease in the level of fatty-acid related proteins in salmon from BS when compared to those from BMB. In the comparison between BMB and GoF there were no differences. The protein levels of carbohydrate metabolism of salmon from BMB differ both from GoF and BS fish, and there was a decrease when compared to GoF and increase when compared to BS (Table 1, Figures 2 and 3, SI 4−5). Any responses occurring are post-transcriptional, as no differences can be seen in the mRNA level between salmon from the different areas. Pollution affects amino acid, lipid, and carbohydrate metabolism, but the direction of change varies, our data support this hypothesis. Falciani35 noticed both induction and repression in mRNAs of the lipid and fatty acid metabolism related GO terms in flounders collected from differentially polluted sites. Wang et al.33 did not find any changes in transcription of genes involved in lipid metabolism, but observed protein level alterations34 in goldfish collected from the wild. The level of protein Lb-FABP, which acts in lipid metabolism, decreased in goldfish,34 but in our salmon, showed both transcriptional induction and protein level increase. Falciani et al.35 found both up- and downregulation in the mRNAs of genes involved in amino acid metabolism and over-representation in the upregulated GO terms. For carbohydrate metabolism results are varying, both down-35 and upregulation53 have been found. The majority of salmon feeding in GoF originate from a different genetic lineage compared to salmon feeding in BS or BMB.42 A large proportion (13−17%) of differentially transcribed genes were related to genes encoding proteins of carbohydrate or lipid metabolism in a study of stock-wise transcriptional differences between wild stocks or wild vs hatchery stocks.43 Additionally, Morais et al.54 found that genotypically different salmon, lean and fat, had different fatty acid synthesis and carbohydrate metabolism. However, the

differences were attenuated by diet. The differences in freshwater vs marine characteristics and eutrophication status result in differences in the food web between the three sampling locations and, accordingly, feeding salmon have been shown to have location-specific differences in their trophic positions and diets9 which may contribute to the metabolismrelated gene expression. Immune Responses. Immune response related mRNA and protein expression were affected in all comparisons mainly in the ClueGO models (Table 1, Figures 2 and 3). The betweenfeeding area differences in immune functions related transcripts may be a result of general differences in environmental conditions and pathogen exposure, but also affected by degree of exposure to environmental stressors. The transcription of immune function-related genes have been found to be affected in fish collected from contaminated field sites,33,35,36,38 and exposed to contaminants in laboratory (for example, ref 55). The downregulated proteins assigned to immune functionrelated ClueGO terms in GoF salmon, e.g., antigen processing and presentation of exogenous peptide antigen via MHC class I, included five proteasomal subunit proteins involved in the function of immunoproteasome. Williams et al.36 found coordinated transcriptional repression of genes encoding proteasomal subunit proteins. The effect was especially prominent at one of the contaminated sites and may represent a halogenated aromatic hydrocarbon-related response. General Remarks. Most of the available publications on fish gene expression changes in response to environmental variables are based on laboratory experiments. Aquatic ecosystems are under unprecedented anthropogenic pressure, and it is important to understand the effects of environmental changes also in the wild, although the environmental variability makes interpretation of field data much more complex and challenging than of data collected from laboratory experiments. Further, cDNA microarrays cannot give a picture of functional molecules (= proteins) as gene expression can be regulated at post-transcriptional stages. Therefore, in this study, transcriptomics and label-free proteomics were combined to obtain a comprehensive view of the molecular states of Baltic salmon sampled from three different field locations during feeding migration. HELCOM Baltic Sea Action Plan identifies pollution by hazardous substances as one the main issues requiring action to improve the health of the Baltic Sea. A strategic goal “Baltic Sea with life undisturbed by hazardous substances” is set in the Action Plan.3 Comparisons of the transcriptomic and proteomic profiles between salmon feeding in GoF and in BMB or BS with the previous work done on gene expression change in fish collected from contaminated field sites and the extent of change suggest local conditions and environmental stressors, including contamination, as a main explanation for differences. The transcriptomic and proteomic changes, together with previously reported results on biomarkers of contamination, thus indicate that salmon feeding in GoF are “disturbed by hazardous substances”. It should be noted that whether the changes are just compensation or a sign of damage, molecular and biochemical biomarkers can be good indicators of contaminant exposure. Although there is not currently direct evidence available on connections to organism- or population-level end points, the extent of transcriptomic and proteomic change in GoF fish could indicate effects on fitness. The postsmolt survival in the Gulf of Finland has been very low in the last ten years and occurrences of early life-stage mortality syndrome M74 have 13975



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been reported up to 2007.56 Although the situation may not be alarming for stocked salmon, it may affect the survival of wild salmon from small threatened populations in GoF. In addition, the results could be interpreted as a general estimate of the conditions of the ecosystems in the different parts of the Baltic Sea.

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ASSOCIATED CONTENT

S Supporting Information *

Salmon sample details; protein sample preparation; Venn diagram representation of differentially expressed mRNAs and proteins; results of enrichment analyses; Bioprofiling models of differentially transcribed mRNAs and expressed proteins. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +35823336263; e-mail: mmkane@utu.fi. Author Contributions

The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript. Funding

The Kone foundation (M.K.), the Sigrid Juselius Foundation (A.V.) and the Academy of Finland (Project Nos. 128754, 278058, and 128712). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research was supported by the Kone foundation, the Sigrid Juselius Foundation and the Academy of Finland (Project Nos. 128754, 278058, and 128712). We thank Scott McCairns for helping with CIA in R. We like to thank the CBT Proteomics Facility for the technical assistance.

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REFERENCES

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Spatial Variation in Transcript and Protein Abundance of Atlantic

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