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Aquatic Toxicology 144– 145 (2013) 172– 185

Contents lists available at ScienceDirect

Aquatic Toxicology

jou rn al hom epage: www.elsev ier .com/ locate /aquatox

ercury contamination in deep-water fish: Transcriptional responsesn tusk (Brosme brosme) from a fjord gradient

ål A. Olsvik ∗, Michael Lindgren, Amund Maageational Institute of Nutrition and Seafood Research, Nordnesboder 1-2, N-5005 Bergen, Norway

r t i c l e i n f o

rticle history:eceived 26 April 2013eceived in revised form9 September 2013ccepted 1 October 2013

eywords:on-model fishioinformaticsext-generation sequencingoxicogenomics

a b s t r a c t

Recent findings have shown that deep-water fish from coastal areas may contain elevated levels of mer-cury (Hg). Tusk (Brosme brosme) was collected from six locations in Hardangerfjord, a fjord system wherethe inner parts are contaminated by metals due to historic industrial activity. ICPMS was used to deter-mine the accumulated levels of metals (Hg, MeHg, Cd, Pb, As, and Se) in the fish, whereas oxidative statusof the liver was assessed by measuring TBARS, vitamin C, vitamin E and catalase activity. To find outwhether accumulated Hg triggers toxicologically relevant transcriptional responses and in order to gaingenomic knowledge from a non-model species, the liver transcriptome of the gadoid fish was sequencedand assembled, and RNA-seq and RT-qPCR were used to screen for effects of Hg. The results showed highlevels of accumulated Hg in tusk liver, probably reflecting an adaptation to deep-water life history, andonly a weak declining outward fjord gradient of Hg concentration in tusk liver. MeHg only accountedfor about 17% of total Hg in liver, suggesting hepatotoxicity of both inorganic and organic Hg. Pathway

analysis suggested an effect of Hg exposure on lipid metabolism and beta-oxidation in liver. Oxidativestress markers glutathione peroxidase 1 and ferritin mRNA, as well as vitamin C and vitamin E (alpha andgamma tocopherol) showed a significant correlation with accumulated levels of Hg. Many transcripts ofgenes encoding established markers for Hg exposure were co-regulated in the fish. In conclusion, tuskfrom Hardangerfjord contains high levels of Hg, with possible hepatic effects on lipid metabolism andoxidative stress.

. Introduction

In recent years it has been documented that marine deep-ater fish species in the North Atlantic Ocean may contain high

oncentrations of mercury (Hg). Elevated levels of Hg have beenound in coastal gadoid species such as tusk (Brosme brosme),reenland halibut (Reinhardtius hippoglossoides) and ling (Molvaolva) (Julshamn et al., 2011; Maage et al., 2012; Kvangarsnes

t al., 2012). Compared to for instance Atlantic cod (Gadus morhua)rom the same areas, these species tend to accumulate higher lev-ls of Hg (Maage et al., 2012). For example, the concentration of Hgs approximately 10 times higher in tusk from Lofoten in North-rn Norway than in cod from the same area. In regard to seafoodafety, large individuals of fish are of special concern, since theyan contain levels of contaminants exceeding the maximum lim-ts allowed for human consumption. A positive correlation between

sh size and Hg content is often observed in deep-water fish speciesKvangarsnes et al., 2012). EU’s and Norway’s maximum limits forg in filet of most fish species for human consumption is 0.5 mg

∗ Corresponding author. Tel.: +47 41459367; fax: +47 55905299.E-mail address: pal.olsvik@nifes.no (P.A. Olsvik).

166-445X/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.aquatox.2013.10.002

© 2013 Elsevier B.V. All rights reserved.

Hg/kg wet weight while for some predatory species such as theAtlantic halibut (Hippoglossus hippoglossus), the upper limit is setat 1.0 mg Hg/kg wet weight. The maximum limits are based on thetotal Hg content. Most of the Hg (80–100%) in muscle tissue ofcontaminated fish is however present as methylmercury (MeHg)(Grieb et al., 1990; Bloom, 1992). MeHg, the most toxic form of Hg,is a common environmental contaminant formed when bacteriamethylate inorganic Hg. In the marine environment, MeHg accu-mulates in organisms and biomagnifies in the aquatic food chains.As a result, the highest concentrations of MeHg are often foundin piscivorous fish and top marine predators. While inorganic Hgtends to accumulate in organs such as kidney, spleen and liver,MeHg is predominantly accumulated in muscle, liver, and kidneyin fish (Kidd and Batchelar, 2012).

Fish consumption represents the main source of MeHg forhumans, with many studies implying a direct correlation betweenbody burden of MeHg and fish consumption (Morel et al., 1998;Chapman and Chan, 2000; Cole et al., 2004; Bjornberg et al., 2005;Diez, 2009). MeHg therefore represents a major concern regarding

seafood safety. In humans, MeHg is a known neurotoxicant, par-ticularly affecting the developing nervous system, and has beenassociated with neurological problems (Davidson et al., 2010). Thecytotoxicity of MeHg has been assigned to three main mechanisms:

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A) effect on intracellular Ca2+ levels, (B) induction of oxidativetress by either production of excessive free reactive oxygen speciesROS) or by depleted oxidative defense capacity and (C) affectingulfhydryl groups thus disrupting function of proteins and peptidesontaining cysteine and methionine (Ceccatelli et al., 2010). Fishtudies suggest that MeHg may disturb reproductive hormones,ediating smaller gonad development or atrophy, delays in spawn-

ng and effects on fecundity (Klaper et al., 2006, 2008), and actender-specifically (Liu et al., 2013). Oxidative stress and apoptosisave been suggested to be two of the main effects of MeHg expo-ure in fish (Gonzalez et al., 2005; Klaper et al., 2008; Berg et al.,010; Cambier et al., 2010; Richter et al., 2011). Conducting stud-

es with Atlantic salmon (Salmo salar), it has recently been shownhat oxidative stress is one of the major effects of MeHg exposureOlsvik et al., 2011a; Nøstbakken et al., 2012a,b). Thus, oxidativetress and apoptosis appear to be two of the major mechanismsffected by MeHg exposure at the cellular level in fish.

Monitoring programs suggest that there may be large spatialnd temporal variation in Hg content in fish in Norwegian watersKvangarsnes et al., 2012). A site with especially elevated mercuryevels in fish has been the Sørfjord, located at the innermost area ofardangerfjord in Western Norway. The Sørfjord has for a long timeeen one of the fjords most strongly affected by metals in Norway,ainly due to earlier industrial activity, with wildlife containing

levated levels of metals such as Pb, Cd and Hg. Earlier it has beeniscovered that deep-water tusk from the Sørfjord was contami-ated with relatively high levels of Hg (Ruus and Green, 2007). Asas shown for Lofoten, tusk from Sørfjord contained considerableigher levels of Hg than Atlantic cod (Ruus et al., 2010). In filet ofusk from the outer Sørfjord average Hg levels of 2.53 mg/kg wereound in 2009, exceeding the allowed maximum upper limit by 5imes (Ruus et al., 2010). The Norwegian food safety authoritiesherefore advise against eating tusk from the Sørfjord, due to thendings documenting that tusk from this fjord may contain levels ofg exceeding the food authorities’ maximum limits (Maage et al.,012). With a total catch of about 29,000 metric tons in 2011 inhe North Atlantic Ocean (www.imr.no), the tusk is a commerciallymportant fish species.

The aim of this study was to study tissue accumulation ofg and whether Hg triggers toxicologically relevant transcrip-

ional responses in tusk collected along the innermost parts ofardangerfjord, in search for molecular markers for Hg exposure

n wild-caught fish. It was hypothesized that accumulation of Hgay affect the transcription of toxicologically relevant genes and

ther markers of oxidative stress in wild-caught tusk.

. Materials and methods

.1. Animal sampling

Wild tusk were caught in May–June 2011. Longline fishingechnique with baited hooks at regular intervals was used toollect tusk from six locations situated in the innermost partsf the Hardangerfjord, Western Norway. In addition, deep-waterngling with a fishing rod was used to collect two additionalsh at one of the locations (Lofthus). A detailed descriptionn how the fishing was conducted can be found in Maaget al. (2012). Fishing locations were selected from the outer-ost parts of Sørfjord, and further west toward Langneset. The

ix sampling locations were Lofthus (depth range and longlinetarting latitude/longitude N E) (150–350 m, 60◦20′00 N 6◦37′80 E),

jønno (250–400 m, 60◦29′90 N 6◦58′00 E), Eidfjord (200–360 m,0◦28′10 N 6◦47′00 E), Ålvik (150–470 m, 60◦24′18 N 6◦23′76 E),teinstø (200–800 m, 60◦23′85 N 6◦20′82 E) and east of Samlane-et (200–580 m, 60◦23′77 N 6◦23′20 E). Fig. 1 shows the geographic

44– 145 (2013) 172– 185 173

locations the selected fishing sites. Fishing depth ranged from 150to 800 m, with most of the fish caught at 300–500 m depth. In total,55 individuals were collected and used in the current examina-tion. Total number of tusk collected from the six locations were:Lofthus n = 6, Djønno n = 7, Eidfjord n = 10, Ålvik n = 8, Steinstø n = 9and Samlaneset n = 13. Male–female ratios differed in between thegroups (Lofthus: 4 males/4 females; Djønno: 2 males/5 females;Eidfjord: 6 males/4 females; Ålvik: 4 males/4 females; Steinstø: 6males/3 females; Samlaneset: 6 males/7 females). For the RT-qPCRdata, n = 6 from the Djønno group (1 male/5 females). Average fishsize collected from the six locations was (mean ± SEM), Lofthus(2.5 ± 0.4 kg, 62 ± 3 cm), Djønno (4.2 ± 0.7 kg, 72 ± 4 cm), Eidfjord(3.1 ± 0.4 kg, 64 ± 3 cm), Ålvik (2.7 ± 0.6 kg, 616 ± 5 cm). Steinstø(2.9 ± 0.4 kg, 65 ± 3 cm), and Samlaneset (3.6 ± 0.5 kg, 68 ± 4 cm).

2.2. Tissue sampling

Once onboard the fishing vessel, the fish were killed with a blowat the head. The livers were dissected out, and about 4 g of livertissue from each fish was put on a cryo tube and kept on dry icebefore transport to the laboratory for storage at −80 ◦C. Each samplewas then sectioned into four pieces for downstream analyses.

2.3. Vitamin C, E and TBARS determination

Vitamin C, vitamin E and TBARS were determined as described inOlsvik et al. (2011a). Briefly, vitamin C (total ascorbic acid) contentof the liver (�g/g) was analyzed by means of a reverse phase highperformance liquid chromatography (HPLC) method using elec-trochemical detection. Liver vitamin E was measured as �- and�-tocopherol, and �-tocotrienol, and with HPLC and fluorimetricdetection, and given as �g/g. TBARS analysis was performed on liversamples according to a colorimetric method based on a malondi-aldehyde standard and 2-thiobarbituric acid solution and reportedas nmol/g wet tissue (Schmedes and Holmer, 1989).

2.4. Catalase enzyme activity determination

To analyze catalase enzyme activity samples were weighed(100 mg) into tubes and a tenfold dilution of ice-cold homogeniza-tion buffer (50 mM potassium phosphate, 0.5 mM EDTA, pH 7.2)was added. Samples were homogenized using a metal homogeniza-tion ball (30 shakes per second for 30 s) and centrifuged (15 min,10,000 × g, 4 ◦C), and the supernatant was extracted. The extractswere frozen on dry ice immediately upon preparation and stored at−80 ◦C for a maximum of 1 month before analysis. Catalase activ-ity was analyzed with the commercial kit 707002 (CAT), CaymanChemical Co., Ann Arbor, MI, USA as described by Johansson andBorg (1988) and Wheeler et al. (1990).

2.5. Metal determination by inductively coupled plasma massspectrometry (ICPMS)

Total content of Hg, Cd, Pb, As and Se in individual tusk livertissue was analyzed by ICPMS following microwave-assisted aciddecomposition. In short, approximately 0.5 g wet or 0.2 g dry sam-ple were weighed accurately into 100 ml Teflon digestion vessels(Milestone Inc., Shelton, CT, USA). Two ml HNO3 (65%, Supra-pur; Merck, Darmstadt, Germany) and 0.5 ml H2O2 (30%; Merck,Darmstadt, Germany) were added to the vessels, and the mix-tures were digested in a microwave digestion system (MilestoneMicrowave digestion system MLS-1200 MEGA Microwave Diges-

tion Rotor (MDR 300/10), Milestone, Sorisole, Italy). Total metalconcentrations in all eluted samples were determined by ICPMS(Agilent ICPMS 7500c; Yokogawa analytical systems, Tokyo, Japan)equipped with an autosampler ASX-500 (CETAC Technologies,

174 P.A. Olsvik et al. / Aquatic Toxicology 144– 145 (2013) 172– 185

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ig. 1. Study area in Western Norway. Tusk (Brosme brosme) was collected for me = Djønno, 3 = Eidfjord, 4 = Alvik, 5 = Steinstø and 6 = Samlaneset.

maha, NE, USA). Rhodium was used as an internal standard toorrect for drift of the instrument. The accuracy of the method wasssessed by analysis of two certified reference materials: Tort-2National Research Council, NRC, Canada) and oyster tissue (OT,RM 1566, National Institute of Standards & Technology, NIST).he method is accredited by the Norwegian Accreditation Authorityfter ISO 17025 and published by Julshamn et al. (2007).

The determination of MeHg was done by isotope dilution andas chromatography combined with inductively coupled plasma-ass spectrometry (ID-GC-ICPMS). The GC was used for separation,

he ICPMS for detection and the ID technique was used foruantification. The apparatuses used were an Agilent 6890N gashromatograph coupled to an Agilent 7500a ICPMS instrumentia a heated transfer line. The column in the gas chromatographas a 30 m long capillary column with a stationary phase of 5%henyl methyl siloxane. The injection volumes were either 1.0 or.2 �L, depending on mercury concentration in the sample. Split-

ess injection with temperature of 180 ◦C was used. Tuning of thenstrument was done daily with helium gas containing 1% xenon. Aetailed description of the method can be found in Valdersnes et al.2012).

.6. RNA isolation

Liver tissue from tusk was thoroughly homogenized before RNAxtraction using a Precellys 24 homogenizer by ceramic beads CK28Bertin Technologies, Montigny-le-Bretonneux, France). Total RNA

and transcriptomic analyses from six locations in the Hardangerfjord. 1 = Lofthus,

from tusk was extracted using the BioRobot EZ1 and RNA Tis-sue Mini Kit (Qiagen, Hilden, Germany) and treated with DNaseaccording to the manufacturer’s instructions and eluted in 50 �lRNase-free MilliQ H2O. The RNA was then stored at −80 ◦C beforefurther processing. RNA quality and integrity were assessed withthe NanoDrop ND-1000 UV–vis Spectrophotometer (NanoDropTechnologies, Wilmington, DE, USA) and the Agilent 2100 Bioan-alyzer (Agilent Technologies, Palo Alto, CA, USA). The 260/280 and260/230 nm ratios were 2.08 ± 0.00 and 1.95 ± 0.02 in liver (n = 55),respectively (mean ± SEM). The RNA 6000 Nano LabChip kit (Agi-lent Technologies, Palo Alto, CA, USA) was used to evaluate theRNA integrity of the samples. The RNA integrity number (RIN) was9.5 ± 0.1 (n = 12) in liver (mean ± SEM).

2.7. Direct RNA sequencing (RNA-seq) and de novo transcriptomeassembly

Direct RNA sequencing (RNA-seq) was used to screen for trans-cripts possibly affected by differentially levels of accumulated Hgin the liver of tusk. Three female fish from Djønno, the location withfish containing the highest measured level of MeHg in filet and liver,and three female fish from Samlaneset, the outermost fjord locationwith fish containing some of the lowest levels of MeHg in liver, were

selected for RNA-seq analysis. To be able to compare the groupswith regard to Hg, only female fish were selected for RNA-seq, andthe ones that differed most in accumulated levels of Hg betweenthe two groups. The three fish selected from the Djønno group were

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eighing 3.7 ± 0.6 kg, with the liver containing 10.3 ± 1.4 mg Hg/kgnd 2.7 ± 0.7 mg MeHg/kg wet weight, respectively (mean ± SEM).he three fish from Samlaneset were weighing 1.9 ± 0.4 kg, and theiver contained 2.2 ± 0.6 mg Hg/kg and 0.79 ± 0.11 mg MeHg/kg wet

eight, respectively (mean ± SEM). Thus, the Djønno fish contained.7-fold more total Hg in liver than the fish from Samlaneset, whilehe MeHg difference was 3.4-fold.

Poly (A) mRNA was isolated using magnetic beads with oligodT) from total RNA from six fish. Fragmentation buffer was addedo shred mRNA to short reads. Using these short fragments (about00 bp) as templates, random hexamer primers were applied toynthesize first-strand cDNA. Second-strand cDNA was synthesizedsing buffer, dNTPs, RNaseH, and DNA polymerase I. QiaQuick PCRxtraction kit (Qiagen) was used to purify short double-strandedDNA fragments. These fragments were then resolved with EBuffer for end reparation, added poly (A), and then ligated to theequencing adapters. After agarose gel electrophoresis, the suit-ble fragments were selected for PCR amplification as templates.inally, the libraries were sequenced using Illumina HiSeqTM 2000San Diego, CA, USA).

Since no tusk reference genome was available at the time of anal-sis, transcriptome de novo assembly had to be conducted usingllumina paired-end reads before RNA-seq analyses. RNA from liverf one tusk collected at Samlaneset, the sample with the highestNA quality as determined by the RNA integrity number (RIN),as used to generate the de novo transcriptome assembly. Trans-

riptome de novo assembly was conducted using the short readsssembling software Trinity as described by Grabherr et al. (2011).nigenes were annotated with Blastx (using an e-value cut-offf 10−5) alignment between unigenes and the databases of NR,T, SwissProt, KEGG, COG and GO. The DEseq software package

Anders and Huber, 2010) was used to screen for differentiallyxpressed genes (DEGs; in here and later, gene expression is takens a synonym for gene transcription, although it is acknowledgedhat translation, mRNA and protein stability also influence genexpression). The DESeq package is based on the negative binomialistribution, and provides a method to test for differential expres-ion by use of a shrinkage estimator for the variance. P-adjustment

0.05 and the absolute value of log2 ratio ≥ 1 was used as thehreshold to judge the significance of gene expression difference.ll RNA-seq work was performed by staff at the Beijing Genome

nstitute (BGI, Hong Kong).

.8. Quantitative real-time RT-qPCR

PCR primer sequences used for quantification of the transcrip-ional levels of the evaluated genes are shown in Table 1. Real-timeT-qPCR was conducted according to the MIQE guidelines (Bustint al., 2009) as previously described by Olsvik et al. (2011a). Briefly,

two-step real-time RT-PCR protocol was used to quantify theranscriptional levels of the selected genes. The RT reactions wereun in duplicate using 96-well reaction plates. Twofold serial dilu-ions of total RNA were made for efficiency calculations. Six serialilutions (1000–31 ng RNA) in triplicates were analyzed in sepa-ate sample wells. Total RNA input was 500 ng in each reactionor all genes. No-template controls (ntc) and RT-controls (no-mplification controls, nac) were run for quality assessment forach PCR assay. Reverse transcription was performed at 48 ◦C for0 min by using oligo dT primers (2.5 �M) for all genes in 50 �Lotal volume. Twofold diluted cDNA (2.0 �L cDNA in each RT reac-ion) was transferred to 384-well reaction plates and the qPCR runn 10 �L reactions on the LightCycler 480 Real-Time PCR System

Roche Applied Sciences, Basel, Switzerland). Real-time PCR waserformed using SYBR Green Master Mix (LightCycler 480 SYBRreen master mix kit, Roche Applied Sciences). PCR was achievedith a 5 min activation and denaturing step at 95 ◦C, followed by

44– 145 (2013) 172– 185 175

45 cycles of a 10 s denaturing step at 95 ◦C, a 20 s annealing stepat 60 ◦C and a 30 s synthesis step at 72 ◦C. Target gene mean nor-malized expression (MNE) was determined using a normalizationfactor based upon ubi, uba52 and eef1a, as calculated by the geNormsoftware (Vandesompele et al., 2002). The geNorm stability indexM was less than 0.42 for all reference genes.

2.9. Statistics

The GraphPad Prism 5.0 software (GraphPad Software, Inc.,San Diego, CA, USA) was used for statistical analyses of the geneexpression data. One-way analysis of variance (ANOVA) withTukey’s multiple comparison posthoc test and correlation analysis(Spearman’s rank-order and Pearson’s) were used to compare thetranscriptional levels of the examined genes between the experi-mental groups. In case the Bartlett’s test showed that the variancesdiffered, the mean normalized expression (MNE) data were log-transformed before ANOVA analysis. ROUT (Q = 1.000%) outlier testwas used to screen for outliers. Correlation analysis was performedusing the program Statistica 8.0; 2008 (Statsoft Inc., Tulsa, USA).Contigs were annotated with the Blast2GO software. A significancelevel of P < 0.05 was used for all tests.

3. Results

3.1. Tissue concentrations of mercury and other metals

Fig. 2 shows the concentrations of Hg, MeHg, Cd, Pb, As andSe in liver of tusk collected from six locations in the Hardanger-fjord. Letters above the columns indicates significant differencesbetween the locations. The highest level of total mercury (Hg) inliver was found in fish from Lofthus, the innermost location inthe fjord system, followed by fish from the Eidfjord and Djønnolocations (Fig. 2A). Total Hg levels in the fish liver ranged from14.2 mg/kg in fish from Lofthus to 3.2 mg/kg in fish from Ålvik(lowest). Especially for fish from Lofthus, there was a large indi-vidual variation in the accumulated level of total Hg. Fish from theDjønno location had the highest level of MeHg in liver, followedby fish from Lofthus and Eidfjord (Fig. 2B). MeHg concentrations infish liver ranged from 2.2 mg/kg in fish from Djønno to 0.7 mg/kgin fish from Ålvik. MeHg accounted for only 11–30% of total Hgconcentration in liver. MeHg levels in liver were strongly cor-related with total Hg levels (Spearman’s rank-order correlation,rs = 0.84, P < 0.0001). The MeHg concentrations in liver and mus-cle (data obtained from Maage et al., 2012) were relatively similar,and showed a significant correlation (Spearman’s rank-order cor-relation, rs = 0.76, P < 0.0001). Normalized to length, both total Hgand MeHg showed a significant size-dependent accumulation intusk liver (Spearman’s rank-order correlation, total Hg: rs = 0.43,P = 0.0011; MeHg: rs = 0.37, P = 0.0053).

Surprisingly, fish from the Steinstø contained the highest levelsof hepatic Cd (Fig. 2C). Otherwise, the Cd levels in liver showed adeclining concentration outwards from the inner parts of the fjord.Tusk from Lofthus contained the highest Pb concentration in liver(Fig. 2D), whereas no significant differences were observed for Asconcentrations between the locations (Fig. 2E). Fig. 2F shows theSe concentrations in tusk liver. As with total Hg and Pb, fish fromLofthus contained the highest concentrations of Se. Selenium levelswere positively correlated with both total Hg (Spearman’s rank-order correlation, rs = 0.71, P < 0.001) and MeHg levels (Spearman’s

rank-order correlation, rs = 0.44, P < 0.001) in tusk liver. Accumu-lated levels of Cd and Pb were positively correlated with total Hgand Se, but not with MeHg in the fish liver. Opposed to the Hgand MeHg data, neither Cd, Pb, As nor Se showed any significant

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Toxicology 144– 145 (2013) 172– 185

Table 1Gene symbols and names, PCR primers, accession or contig numbers, amplicon sizes and PCR efficiencies. Potential marker genes for Hg or MeHg exposure were selected based on reported interactions between Hg/MeHg andmRNA or protein levels in animals (CTD, 2013; Olsvik et al., 2011a; Nøstbakken et al., 2012a, 2012b).

Gene symbol Gene name Potential marker for Accession no. Forward primer Reverse primer Ampliconsize (bp)

PCRefficiency

mt Metallothionein Hg/oxidative stress Contig682 BB-3 Low TTTGCGTCGCATATCTTGTCTT CTGCGAGTGCTGTCCTTCAG 75 1.81cat Catalase Hg/MeHg/oxidative stress DQ270487 GCCAAGTTGTTTGAGCACGTT CTGGGATCACGCACCGTATC 101 1.99gpx1 Glutathione peroxidase 1 Hg/MeHg/oxidative stress >Contig8023 BB-3 Low GAATGACCCGAAGTGCATCA ACCCTCTGCTGTACCGTCTGA 116 1.98fth1 Ferritin, heavy polypeptide 1 Hg/oxidative stress >Contig124 BB-3 Low TTGCCAAGTTCTTCCGCAAT CTCGCCCACTCATCCTTTTC 133 2.04hmox1 Heme oxygenase 1 Hg/MeHg >isotig02251 57 AGCGCATCGAAGACCTGTATG TCCAGCCCTAATCGGTTCAA 134 2.07nfe2l2 Nuclear factor (erythroid-derived 2)-like 2 MeHg/oxidative stress >Contig2305 BB-3 Low TGATGGAGCACAACGAAAGC CCATCGGCTCCTGTTTGAAT 123 1.89bclx Apoptosis Regulator Bcl-X MeHg/Apoptosis >GJV2SU002HM97T TTCACCGAGATGTCGCATCA CCCCAGTTCACACCGTCTCT 101 2.08bcl2l Bcl-2-like protein MeHg/Apoptosis >GJV2SU002FYWNS ACGCATAGTGGGCCTGTTTG GGTCAATGTGGTTGTCCAGGTA 125 2.00gadd45a Growth arrest and DNA-damage-inducible, alpha MeHg/Apoptosis >GmE100127i27876 CAGCTCCGCCGAGTATCTGT GACGTTGCCCTTCAGATTCAC 115 1.92maob Monoamine oxidase B MeHg >GJV2SU002G2G6P AGCAGTGTGGAGGCATCATG ACCTGTTGGCCCAGCTCTCT 118 2.09tuba8 Tubulin, alpha 8 MeHg EX722933 ATGATCTCCTTGCCGATTGTG TGGAACCCACCGTGATTGAT 136 1.93cyp1a3 Cytochrome P450, family 1, subfamily A,

polypeptide 3Hg >Contig777 BB-3 Low CACCATCCCACACTGCACAA GGTTGAAGGTGGAGGGTTCTT 134 2.03

esr1 Estrogen receptor 1 Hg >Unigene30914 BB-3 Low GGGCATGGCGGAGATCTT AACGAGAAGGCCCCAGAGTT 126 2.08vtga Vitellogenin A Hg >Unigene39461 BB-3 Low GAAGCGCTGCTTCTGCTTGT CAGGAGCCGTCTGTCCAAGA 123 2.05fabp1 Fatty acid binding protein 1 MeHg/lipid metabolism EX723233 GACAGTCACCACCGGCACTA CACGCTTGACCACAGCCTTA 107 2.00fabp3 Fatty acid binding protein 3 MeHg/lipid metabolism >Contig12693 BB-3 Low TCGCAGGCACATGGAACAT ATGATGGTGGTGGGCTTGAT 115 1.88scd Stearoyl-CoA desaturase MeHg/lipid metabolism >Contig332 BB-3 Low GGAGGGCTTCCACAACTACCA CCAGACCGAGGGAACACATG 116 2.01pdgfra Platelet-derived growth factor receptor, alpha

polypeptideMeHg GJV2SU002JE8B0 GTGGTGCTCAGGCCATCCT ATCCTGCGCTCCAACTACGA 101 2.10

sparc Secreted protein, acidic, cysteine-rich(osteonectin)

MeHg >isotig03599 35 TCCACCTCGCACACCTTACC GCGTCAACCCTGTGCAGATT 124 1.99

ywhae Tyrosine 3-monooxygenase/tryptophan5-monooxygenase activation protein, epsilonpolypeptide

MeHg >isotig04310 21 GCAGCAGGAATGAGGTGCTT AAGGGCGGTGAAGACAAACTC 119 2.00

hspd1 Heat shock 60 kDa protein 1 (chaperonin) MeHg >GmE090818c20297 CCGTGAAGGATGGCAAGACT GGCCTTTAGCGGTGTTGATG 105 2.06capn2 Calpain 2 (m/II) large subunit Oxidative stress >GJV2SU002IU02T GAGCGACGGGTCATCAGAGT CTCAGTGAAGGCCATCCAGAA 91 2.00igfbp1 Insulin-like growth factor binding protein 1 Metabolism/anti growth >isotig05644 12 CACTCCACGCCCTCATCAG GGCCGTTGAACCCTAACAGA 114 2.02ubi Ubiquitin refgen CO542553 GGCCGCAAAGATGCAGAT CTGGGCTCGACCTCAAGAGT 69 2.06uba52 Ubiquitin A-52 residue ribosomal protein fusion

product 1refgen >Contig158 BB-3 Low GAGTCGACCCTGCATTTGGT AAGGCGGGCATAGCACTTG 120 1.99

eef1a Eukaryotic translation elongation factor 1 alpha refgen >Contig38 BB-3 Low CACATCGCCTGCAAGTTCAA GCTTGCTCGGGATCATGTTC 120 1.89

P.A. Olsvik et al. / Aquatic Toxicology 144– 145 (2013) 172– 185 177

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ig. 2. Metal concentrations in liver of tusk from 6 locations in the Hardangerfjordignificant differences between the locations (one-way ANOVA with Tukey’s postho

orrelation between size and accumulated levels in the examinedsh.

.2. Antioxidants – TBARS, vitamin C and E, and catalase activity

Selected non-specific antioxidant parameters were determinedn tusk liver, as shown in Fig. 3. No significant difference in alphaocopherol levels, the major form of vitamin E, was detected in tuskiver between the locations (Fig. 3A). Alpha tocopherol levels wereositively correlated with total Hg and MeHg concentrations in

iver (Spearman’s rank-order correlation, total Hg: rs = 0.31; MeHg:s = 0.34, P < 0.05). Gamma tocopherol was present in much lowerevels than alpha tocopherol, with the highest levels detected in fishrom the Ålvik location (Fig. 3B), and with significant higher levels iniver of fish from Ålvik and Steinstø compared to the other locationsANOVA, log-transformed data, P < 0.05). For gamma tocotrienol,esults above the detection level were only obtained from a fewf the evaluated fish, and all these were from the three outermostjord locations (Fig. 3C). Significant differences were also observedor vitamin C, with highest levels detected in liver of fish from the

teinstø and Lofthus locations (Fig. 3D). Vitamin C levels were sig-ificantly higher in liver of fish from Steinstø compared to in fish

rom Djønno and Ålvik (ANOVA, P < 0.05). Levels of vitamin C wereegatively correlated Hg and MeHg concentrations in tusk liver

Hg, (B) MeHg, (C) Cd, (D) Pb, (E) As and (F) Se. Letters above the columns denotes, P < 0.05). Individual measurements from 55 fish. Mean ± SEM.

(Spearman’s rank-order correlation, total Hg: rs = −0.37; MeHg:rs = −0.36, P < 0.05). No difference in TBARS levels in the fish fromthe six locations was observed (ANOVA, log-transformed data),indicating no difference in lipid peroxidation in liver of the col-lected fish (Fig. 3E). Catalase activity showed a significant drop inliver of fish from the Samlaneset location compared to fish from Eid-fjord, Djønno and Steinstø (Fig. 4F (ANOVA, log-transformed data,P < 0.05)).

3.3. Transcriptome de novo assembly

Total RNA extracted from liver of one fish collected at theSamlaneset location was used to sequence the tusk transcrip-tome using the paired-end transcriptome sequencing approach.In total 59,425,332 raw reads and 53,808,272 clean reads weresequenced. The clean reads were assembled into 110,473 contigswith mean length of 282 nucleotides (nts). Of these, 47,054 contigswith mean length of 586 nts mapped to Unigene entries. In addi-tion, 39,897 distinct singletons mapped to Unigene entries. 61.5%of the contigs showed Unigene similarity greater than 80%. By far,

most of the Ungenes mapped to Nile tilapia (Oreochromis niloti-cus) sequences, followed by hits against green-spotted pufferfish(Tetraodon nigriviridis), zebrafish (Danio rerio), European seabass(Dicentrarchus labrax) and Atlantic salmon.

178 P.A. Olsvik et al. / Aquatic Toxicology 144– 145 (2013) 172– 185

Fig. 3. Antioxidant status in liver of tusk from 6 locations in the Hardangerfjord. (A) Alphaand (F) catalase activity. Letters above the columns denotes significant differences betwmeasurements from 55 fish. Mean ± SEM.

Fig. 4. Heat map correlation between samples of RNA-seq DEGs. Samlaneset: Brosmebrosme 1 (BB1)–BB3. Djønno: BB4–BB6. Color code: from white for rejection to bluefor support. (For interpretation of the references to color in this figure legend, thereader is referred to the web version of the article.)

tocopherol, (B) gamma tocopherol, (C) gamma tocotrienol, (D) vitamin C, (E) TBARS,een the locations (one-way ANOVA with Tukey’s posthoc test, P < 0.05). Individual

3.4. RNA-seq

In average, 12,184,521 ± 92,553 total reads were sequencedfrom the samples (n = 6, mean ± SEM). Total mapped reads were10,409,175 ± 138,755 (n = 6, mean ± SEM), representing 85.4% ofthe total reads. According to the differentially expressed genes(DEGs) analysis, the heat map separated fish from the two locationsinto two clusters based on location (Fig. 4). The heat map suggestedthat one of the fish from Djønno (termed BB6) differed slightly fromthe other two samples from this group. This sample was collectedfrom the largest individual fish used for RNA-seq from Djønno,weighing 4.63 kg as compared to 3.31 ± 0.17 kg (mean ± SEM) forthe other two females from this location. Comparison of the highand low Hg groups with gene expression difference analysis (DEG)was therefore performed with and without this sample, as wellas in between the fish from Djønno (Fig. 5). Differences in geneexpression between the fish collected at Djønno and Samlanesetwere identified by pairwise comparisons (BB1–3 Samlaneset versus

BB4–6 Djønno) (Additional file 1). The BB6 sample was included inthe final DEG analysis because the top affected pathways remainedthe same either by including or excluding the sample, however allcomparisons are listed in Additional file 1.

P.A. Olsvik et al. / Aquatic Toxicology 1

Fig. 5. Changes in gene expression profiles between Samlaneset (BB1–BB3) andDjønno (BB4–BB6) tusk using different number of samples from the Djønno group.Tvv

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he numbers of up- and down-regulated genes in comparisons of the BB1–BB3ersus BB4–BB6, BB1–BB3 versus BB4–BB5, BB1–BB3 versus BB6, and BB4–BB5ersus BB6.

Using P ≤ 0.05 and the absolute value of log2 ratio ≥ 1, the listonsisted of 1295 contigs, of which a majority has no Blast hitsgainst the GenBank nr database. Some contigs have redundantnnotations. Comparing fish from the two locations, 1164 contigsere higher expressed in the Hg-enriched fish while 131 were

ower expressed (Fig. 5). Of the transcripts with positive identifica-ion, vitellogenin A (vtga), a reverse transcriptase-like protein anderoxisomal enoyl CoA hydratase 1 (delta(3,5)-delta(2,4)-dienoyl-oA isomerase, mitochondrial) (ech1) were highest expressed in

iver of the Djønno fish compared to the Samlaneset fish. Peroxi-ome proliferator-activated receptor gamma coactivator 1-alphappargc1a), carbonic anhydrase 4 (ca4) and zinc finger homeoboxrotein 4 (zfhx4) were the top three transcripts highest expressed

n liver of the Samlaneset fish.

.5. Gene ontology (GO) and pathway enrichment analysis

In order to interpret the transcriptional changes possiblyffected by Hg exposure in wild tusk, differentially regulated genesn liver of fish from Djønno and Samlaneset were subjected toO enrichment analysis. Significantly enriched GO terms are listed

n Table 2. GO enrichment analysis indicated that only relativelyew GO categories differed between the two groups of tusk. Topnriched GO categories under Biological processes were hexoseiosynthetic process, gluconeogenesis, positive regulation of lipidatabolic process and monosaccharide biosynthetic process, whilehe corresponding top enriched GO categories under molecularunction were sequence-specific DNA binding, nucleic acid bindingranscription factor activity, sequence-specific DNA binding tran-cription factor activity and phosphoenolpyruvate carboxykinasectivity.

Pathway enrichment analysis on differentially regulated genesn tusk from the two locations indicated that Hg exposure mightffect 17 pathways (Table 3). Pathways of adipocytokine signalingathway, proximal tubule bicarbonate reclamation, PPAR signalingathway and insulin signaling pathway were most significantlyifferent between the two groups of fish.

.6. RT-qPCR analysis

One of the main aims with the de novo sequencing of the tuskepatic transcriptome was to generate sequence knowledge for

species with virtually no available genomic information. PCRrimers of a set of known target genes for Hg and MeHg exposureere generated from the transcriptome sequences. In addition, rel-

vant assays previously designed for the gadoid species Atlantic

44– 145 (2013) 172– 185 179

cod and burbot (Lota lota) were tested, of which some worked wellwith tusk RNA. For validation of the RNA-seq data, three genesfrom the gene list (vtga, fabp1 and scd) were selected for directverification, and a few genes from the top affected pathways forRT-qPCR analysis. The results obtained with the two methods cor-related well. The Spearman’s rank-order correlation coefficient wasrs = 1.00 between the RNA-seq and RT-qPCR data for the three genesanalyzed in the six females used for RNA-seq. Many of the evalu-ated genes were differentially regulated at the transcriptional levelbetween the fish from the six locations. Fig. 6 shows the transcrip-tional levels of the target genes listed in Table 1 in liver of tuskfrom the six studied locations in Hardangerfjord. Nine out of the 23target genes were differentially regulated in the fish. These werehspd1 (Fig. 6A), fabp1 (Fig. 6B), cat (Fig. 6C), hmox1 (Fig. 6H), igfbp1(Fig. 6I), pdgfra (Fig. 6J), sparc (Fig. 6K) and capn2 (Fig. 6L). Twoof the transcripts selected for verification of the RNA-seq data, vtga(Fig. 6V) and esr1 (Fig. 6W), showed a strong gender-specific expres-sion, with significantly higher levels in females than in males (vtga,t-test, P < 0.0001; esr1, t-test, P < 0.0001). Although with large varia-tion, vtga and esr1 were higher expressed in liver of female fish fromthe Djønno and Eidfjord locations, compared to females from theother four locations. Males from Djønno, Eidfjord and Steinstø alsoappeared to have elevated levels of vtga, while males from Lofthusappeared to have elevated levels of esr1 (not significant probablydue to the low n).

Additional file 2 shows the correlation matrix between all exam-ined parameters with exact Spearman rank rho (rs) coefficients,including accumulated levels of five metals in filet as publishedearlier by Maage et al. (2012). Several genes were signifi-cantly correlated with fish weight in tusk liver. Pdgfra (rs = 0.39),sparc (rs = 0.41), capn2 (rs = 0.46), nef2l2 (rs = 0.37), mt (rs = 0.39),cyp1a3 (rs = 0.33) and tuba (rs = 0.28) were all positively corre-lated with fish weight, while igfbp1 (rs = −0.39), scd (rs = −0.31)and vtga (rs = −0.29) were negatively correlated with fish weight(Spearman’s rank-order correlation, P < 0.05). Esr1 (rs = 0.35) waspositively correlated with total Hg in liver, while igfbp1 (rs = −0.48),ywhae (rs = −0.42), fth1 (rs = −0.35), gpx1 (rs = −0.31) and fabp3(rs = −0.29) were negatively correlated with hepatic Hg content(P < 0.05). Except for fth1, the same gene transcripts showed asimilar correlation to hepatic MeHg content. In addition, vtgashowed a positive correlation with MeHg in tusk liver (rs = 0.38,P < 0.05). The evaluated antioxidants showed fewer significant cor-relations with gene transcription. TBARS was negatively correlatedwith MT (rs = 0.38, P < 0.05), while catalase activity only showeda weak negative correlation with catalase transcription (rs = 0.28,P < 0.05). Alpha tocopherol showed a negative correlation with fth1(rs = −0.28), fabp3 (rs = −0.33) and scd (rs = −0.29), while gammatocopherol was positively correlated with hmox1 (rs = 0.48) andnegatively correlated with pdgfra (rs = −0.27). Vitamin C levels werepositively correlated with igfbp1 (rs = 0.59) and hmox1 (rs = 0.31)transcription, and negatively correlated with hspd1 (rs = −0.30),capn2 (rs = −0.36), vtga (rs = −0.32) and esr1 (rs = −0.41). Includedas a marker of growth and metabolism, igfbp1 showed a negativecorrelation with concentrations of Hg in filet (rs = −0.52) and liver(rs = −0.48), and with MeHg in liver (rs = −0.40, all P < 0.05).

4. Discussion

The innermost sampling station in this study, Lofthus, is situ-ated in the Sørfjord, a fjord known to be contaminated with Hg(Ruus and Green, 2007), and with previously documented elevated

levels of Hg in deep-water fish (Ruus et al., 2010; Kvangarsneset al., 2012; Maage et al., 2012). The Sørfjord is a 40 km long out-let in the inner part of the Hardangerfjord, and the high levels ofHg found in tusk captured further out in the fjord system in this

180 P.A. Olsvik et al. / Aquatic Toxicology 144– 145 (2013) 172– 185

Fig. 6. Mean normalized expression (MNE) of 23 potential marker genes of He exposure in liver of tusk. (A) hspd1, (B) fabp1, (C) cat, (D) gadd45a, (E) tuba, (F) bcl2l, (G) maob,(H) hmox1, (I) igfbp1, (J) pdgfra, (K) sparc, (L) capn2, (M) ywhae, (N) bclx, (O) nfe2l2, (P) mt, (Q) fth1, (R) fabp3, (S) scd, (T) gpx1, (U) cyp1a3, (V) vtga, and (W) esr1. For thelatter two, gender-specific expression is shown. Letters above the column indicates significant differences between the locations (one-way ANOVA with Tukey’s posthoc test,P < 0.05). Individual measurements from 54 fish. Mean ± SEM.

P.A. Olsvik et al. / Aquatic Toxicology 144– 145 (2013) 172– 185 181

Table 2Gene ontology terms with significant different molecular function between Djønno and Samlaneset fish.

# Gene ontology term (biological process) Cluster frequency Genome frequency of use CorrectedP-value

1 Hexose biosynthetic process 9 out of 282 genes, 3.2% 50 out of 14,994 genes, 0.3% 0.000442 Gluconeogenesis 8 out of 282 genes, 2.8% 39 out of 14,994 genes, 0.3% 0.000713 Positive regulation of lipid catabolic process 6 out of 282 genes, 2.1% 20 out of 14,994 genes, 0.1% 0.001764 Monosaccharide biosynthetic process 9 out of 282 genes, 3.2% 65 out of 14,994 genes, 0.4% 0.004465 Pyruvate metabolic process 8 out of 282 genes, 2.8% 50 out of 14,994 genes, 0.3% 0.005206 Positive regulation of fatty acid beta-oxidation 5 out of 282 genes, 1.8% 14 out of 14,994 genes, 0.1% 0.005367 Regulation of cellular biosynthetic process 72 out of 282 genes, 25.5% 2294 out of 14,994 genes, 15.3% 0.006478 Germ-line stem cell division 3 out of 282 genes, 1.1% 3 out of 14,994 genes, 0.0% 0.008929 Male germ-line stem cell division 3 out of 282 genes, 1.1% 3 out of 14,994 genes, 0.0% 0.00892

10 Regulation of biosynthetic process 72 out of 282 genes, 25.5% 2327 out of 14,994 genes, 15.5% 0.0107911 Positive regulation of fatty acid oxidation 5 out of 282 genes, 1.8% 16 out of 14,994 genes, 0.1% 0.0113512 Regulation of cellular macromolecule biosynthetic process 68 out of 282 genes, 24.1% 2160 out of 14,994 genes, 14.4% 0.0118113 Regulation of transcription, DNA-dependent 61 out of 282 genes, 21.6% 1866 out of 14,994 genes, 12.4% 0.0123914 Glucose metabolic process 16 out of 282 genes, 5.7% 236 out of 14,994 genes, 1.6% 0.0140415 Positive regulation of cellular biosynthetic process 34 out of 282 genes, 12.1% 817 out of 14,994 genes, 5.4% 0.0156116 Regulation of transcription 61 out of 282 genes, 21.6% 1880 out of 14,994 genes, 12.5% 0.0156517 Regulation of cellular metabolic process 94 out of 282 genes, 33.3% 3362 out of 14,994 genes, 22.4% 0.0199218 Regulation of macromolecule biosynthetic process 68 out of 282 genes, 24.1% 2199 out of 14,994 genes, 14.7% 0.0215419 Positive regulation of biosynthetic process 34 out of 282 genes, 12.1% 834 out of 14,994 genes, 5.6% 0.0239720 Monocarboxylic acid metabolic process 18 out of 282 genes, 6.4% 305 out of 14,994 genes, 2.0% 0.0264021 Cellular carbohydrate biosynthetic process 11 out of 282 genes, 3.9% 123 out of 14,994 genes, 0.8% 0.0277622 Regulation of RNA metabolic process 62 out of 282 genes, 22.0% 1958 out of 14,994 genes, 13.1% 0.0278623 Regulation of metabolic process 101 out of 282 genes, 35.8% 3725 out of 14,994 genes, 24.8% 0.0303324 Organic acid transport 13 out of 282 genes, 4.6% 173 out of 14,994 genes, 1.2% 0.0326225 Carboxylic acid transport 13 out of 282 genes, 4.6% 173 out of 14,994 genes, 1.2% 0.0326226 Asymmetric cell division 3 out of 282 genes, 1.1% 4 out of 14,994 genes, 0.0% 0.0352027 Alcohol biosynthetic process 9 out of 282 genes, 3.2% 84 out of 14,994 genes, 0.6% 0.03768

# Gene ontology term (molecular function) Cluster frequency Genome frequency of use CorrectedP-value

1 Sequence-specific DNA binding 32 out of 286 genes, 11.2% 475 out of 15,869 genes, 3.0% 4.01e−082 Nucleic acid binding transcription factor activity 36 out of 286 genes, 12.6% 629 out of 15,869 genes, 4.0% 2.41e−073 Sequence-specific DNA binding transcription factor activity 36 out of 286 genes, 12.6% 629 out of 15,869 genes, 4.0% 2.41e−074 Phosphoenolpyruvate carboxykinase activity 5 out of 286 genes, 1.7% 7 out of 15,869 genes, 0.0% 1.08e−055 Phosphoenolpyruvate carboxykinase (GTP) activity 5 out of 286 genes, 1.7% 7 out of 15,869 genes, 0.0% 1.08e−056 DNA binding 52 out of 286 genes, 18.2% 1485 out of 15,869 genes, 9.4% 0.000647 Carboxylic acid transmembrane transporter activity 12 out of 286 genes, 4.2% 125 out of 15,869 genes, 0.8% 0.000778 Organic acid transmembrane transporter activity 12 out of 286 genes, 4.2% 132 out of 15,869 genes, 0.8% 0.00137

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tudy was unexpected. A weaker than expected reduction in theg content in tusk liver was observed outward in the fjord system,ith lower Hg levels outward in the fjord system away from the

nown point-source contamination site in the inner section of the

able 3athways with differential expression (P < 0.05) between three females from Djønno and thNA-seq data. The table shows pathways, DEGs (differentially expressed genes) with patathway ID.

# Pathway DEGs withpathwayannotation

1 Adipocytokine signaling pathway 21 (5.34%)

2 Proximal tubule bicarbonate reclamation 9 (2.29%)

3 PPAR signaling pathway 14 (3.56%)

4 Insulin signaling pathway 24 (6.11%)

5 Glycolysis/Gluconeogenesis 8 (2.04%)

6 Transcriptional misregulation in cancer 18 (4.58%)

7 MAPK signaling pathway 22 (5.6%)

8 Aldosterone-regulated sodium reabsorption 7 (1.78%)

9 Carbohydrate digestion and absorption 6 (1.53%)

10 Pathways in cancer 28 (7.12%)

11 Prostate cancer 12 (3.05%)

12 Fatty acid metabolism 5 (1.27%)

13 Antigen processing and presentation 6 (1.53%)

14 Alpha-Linolenic acid metabolism 3 (0.76%)

15 Galactose metabolism 4 (1.02%)

16 Pyruvate metabolism 5 (1.27%)

f 286 genes, 1.4% 14 out of 15,869 genes, 0.1% 0.02600of 286 genes, 10.8% 829 out of 15,869 genes, 5.2% 0.02816f 286 genes, 1.4% 15 out of 15,869 genes, 0.1% 0.03496

Sørfjord. However, long-range atmospheric transport and deposi-tion is a major source of Hg contamination, with anthropogenicand natural emissions believed to contribute equally to the atmo-spheric pool of Hg (Lindberg et al., 2007). It is therefore possible that

ree males from Samlaneset. N = 3. One pathway irrelevant for fish has been omitted.hway annotation, all genes with pathway annotation, P-values, Q-values and Kegg

All genes withpathwayannotation

P value Q value Pathway ID

219 (1.07%) 1.711353e−09 3.217344e−07 ko0492055 (0.27%) 9.553892e−07 8.980658e−05 ko04964

179 (0.88%) 1.003468e−05 6.288399e−04 ko03320507 (2.48%) 5.201352e−05 2.444635e−03 ko04910112 (0.55%) 0.001455831 5.473925e−02 ko00010478 (2.34%) 0.005450697 1.463901e−01 ko05202638 (3.12%) 0.006308384 1.482470e−01 ko04010118 (0.58%) 0.007849488 1.603861e−01 ko04960

94 (0.46%) 0.009593259 1.603861e−01 ko04973910 (4.45%) 0.01008074 1.603861e−01 ko05200288 (1.41%) 0.01023741 1.603861e−01 ko05215

77 (0.38%) 0.01637449 2.368003e−01 ko00071116 (0.57%) 0.02470507 3.317538e−01 ko04612

40 (0.2%) 0.04129169 5.153683e−01 ko0059269 (0.34%) 0.04386113 5.153683e−01 ko00052

103 (0.5%) 0.04875418 5.391639e−01 ko00620

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he high Hg concentrations in deep-water fish in Hardangerfjord inart rely on more evenly spread global atmospheric transport andeposition, or by circulation and fjord currents resulting in outwardediment transport and deposition along the deep-sea floor. Deep-ater fish may be more prone to Hg accumulation than fish living inore shallow waters due to their adaptations to a deep-water life

istory, i.e. adaptation to limited food supply, singularities of deep-ea food chains, sluggish and sedentary behavior, slow growth andhysiological adaptations such as homeoviscous adaptation.

In fish, most of the Hg is present in organic form. Compared toarlier reported levels of methylated Hg in fish tissues, the per-entage of MeHg to total Hg found in tusk liver of about 11–30%mean 17%) is relatively low (Drevnick et al., 2008; Chumchal et al.,011). MeHg accounted for 100% of total Hg in filet of the same fishMaage et al., 2012). In yellow pearch (Perca flavescens) from an Hgotspot in Nova Scotia, the percentage of MeHg in liver tissue variedetween 3 and 86%, and with 97% MeHg in muscle tissue (Batchelart al., 2013). Chronic exposure to MeHg via the diet is thought toepresent the main source for Hg in fish (Kidd and Batchelar, 2012),ince this form is effectively biomagnified up aquatic food websWiener et al., 2003). Both direct uptake of inorganic Hg and depo-ition in liver cells, or active hepatic demethylation may explain thending. A temporal increase in the proportion of inorganic to totalg has been shown in fish liver, suggesting an active demethyl-tion process (Baatrup and Danscher, 1987; Cizdziel et al., 2003;onzalez et al., 2005). Accordingly, demethylation is a possibleechanistic explanation of the relatively low level of MeHg found

n tusk liver. Another possible explanation is continuous intake ofnorganic mercury from bottom-dwelling organisms.

The sequencing of the tusk transcriptome opens for functionalenomics studies in a marine deep-water fish species, and therebyechanistic assessment of toxicity in fish from contaminated areas.

usk is a widely distributed fish species in the North Atlantic, ands regularly collected in seafood surveillance programs. For exam-le, tusk is collected annually near the wreckage of the Germanubmarine U-864, situated on the seabed at 150 m depth northf Bergen, Norway. U-864 contained 65 metric tons of Hg whenunken at the end of World War 2, and represents a potential envi-onmental hazard (Olsvik et al., 2011b). In the current work it wasvaluated whether hepatic transcription of genes can be used torofile the potential negative effects of chronic Hg exposure inarine fish. Several gene transcripts showed a significant corre-

ation with accumulated levels of Hg in tusk liver. These includedarkers for oxidative stress (gpx1, fth1), cell signaling (ywhae), lipidetabolism (fabp3) and metabolism (igfpb1). It was, however, not

ossible clearly to distinguish between the studied locations inardangerfjord in terms of transcription of genes correlated to Hgoncentrations.

Pathway analysis suggests that adipocytokine signaling, Pparnd insulin signaling, and fatty acid metabolism were among theifferentially regulated mechanisms in the female tusk selectedor RNA-seq screening. Perturbation of processes linked to car-ohydrate, especially lipid, metabolism seems to be one of theain effects of He accumulation in tusk liver, which is in accor-

ance with previous findings in zebrafish exposed to the metale.g. Ung et al., 2010; Cambier et al., 2010; Richter et al., 2011).everal of the genes that were selected for RT-qPCR analysis inhis study encode lipid metabolism proteins, including three fromhe Ppar signaling pathway. Of these, fabp3 was negatively corre-ated with Hg and MeHg concentrations in tusk liver. In an earlierhree-month feeding trial with Atlantic salmon, it was found that

mg/kg MeHg affected the transcription of cpt1a in liver (Olsvik

t al., 2011a). Yadetie et al. (2013) also observed up-regulation ofany transcripts linked to fatty acid beta-oxidation in Atlantic cod

iver after 14 days of exposure to MeHg. Thus, perturbation of pro-esses related to lipid metabolism and particularly beta-oxidation

44– 145 (2013) 172– 185

seems to be a possible effect of chronic Hg exposure in wildtusk.

Chronic toxicity of both inorganic Hg and MeHg in fish has beenlinked to a number of biological processes, including reproduction,growth and oxidative stress (Kidd and Batchelar, 2012; Batchelaret al., 2013). Endocrine-disrupting chemicals such as Hg can havea profound effect on reproduction. Our data indicate an endocrine-disruption effect on males, with elevated levels of vtga mRNA inmales from Djønno and Eidfjord, and of esr1 mRNA in males fromLofthus. Female tusk from Djønno had higher levels vtga mRNAin liver than females from Samlaneset. This could be due to dif-ferences in gonad development and maturational status, since thefish from Djønno used for RNA-seq were larger than fish fromSamlaneset. Klaper et al. (2006) showed that MeHg affect the tran-scription of VTG gene in rainbow trout (Oncorhynchus mykiss), withincreases in males and decreases in females. Studies evaluatinggenome-wide transcriptional responses in fish from environmentscontaminated by metals are scarce. Moran et al. (2007), studyingrainbow trout from high-elevation lakes in western Washington,suggested a role for Hg in disrupting the metabolic and reproduc-tive pathways in wild fish. However, since only a few males werecollected from these locations in this examination, further studiesshould be conducted to verify possible endocrine-disrupting effectsof Hg exposure in wild fish from Hardangerfjord.

Linking exposure of chemicals to metabolism and ultimatelygrowth can be challenging in wild-caught animals. In this workthe gene encoding the insulin-like growth factor binding protein 1(IGFBP1) was included as a transcriptional marker for metabolismand anti-growth, based on the assumption that increased mRNAlevel resulted in increased synthesis of IGFBP1 in liver which willaffect growth by binding up IGF1. IGFBPs are plasma proteins thatbind IGFs and regulate their turnover, transport and tissue availabil-ity (Lee et al., 1997). Since only free un-bound IGF1 is biologicallyactive, the abundance of IGFBPs modulates many physiologicaleffects of IGF1, including cellular growth, survival, proliferation,differentiation, cell motility and glucose uptake (Rutkute andNikolova-Karakashian, 2007). According to the Comparative Tox-icogenomic Database, many chemicals show interactions withIGFBP1 (CTD, 2013). Thus, IGFBP1 is produced in the liver inresponse to stress. Transcription of igfbp1 (ortholog igfbp1b) wasinversely correlated with Hg concentration in tusk liver, suggest-ing an inverse relationship between accumulated levels of Hg andmetabolism in the fish. The observed negative correlation betweenaccumulated levels of Hg and igfbp1 mRNA may, however, partlydepend on the size of the fish, as igfbp1 transcript level also showeda negative correlation with fish weight. More research is thereforeneeded to examine the potential phenotypic effect of Hg exposureon deep-water fish from Hardangerfjord.

Many chemicals act by inducing cellular oxidative stress in fish,including inorganic Hg and MeHg (Di Toyama et al., 2007; Di Giulioand Meyer, 2008; Ung et al., 2010). Of the evaluated oxidativestress markers, gpx1 and fth1 gene transcripts showed a signifi-cant correlation with total Hg in tusk liver. Gpx1 transcript alsoshowed a significant correlation with MeHg, while fth1 transcriptwas not correlated with MeHg, suggesting that fth1 expression ismost strongly linked to the effects of inorganic Hg. FTH1 is themajor iron homeostasis protein in eukaryotic cells, and preventsfree iron released from breakdown of heme groups from participat-ing in iron-mediated free radical reactions and subsequent cellularoxidative stress (Anderson and Frazer, 2005). Transcription of bothFTH1 and GPX1 is regulated by NRF2 through antioxidant responseelements (AREs) in the regulatory regions of target genes. Inter-

estingly, both fth1 and gpx1 expression was correlated with nfe2l2transcript in tusk liver. NFE2L2, or NRF2, is a transcription factorthat regulates the environmental stress response and activates theexpression of genes for antioxidants and detoxification enzymes.

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he NRF2-mediated environmental stress response protects cellsgainst variety of stressors including environmental pollutantsuch as electrophiles and oxidizing agents (Kensler et al., 2007;u et al., 2010). NRF2 may also play an important role in reduc-

ion of MeHg toxicity. Activation of NRF2 by MeHg is essential foreduction of MeHg by facilitating its excretion into extracellularpace (Di Toyama et al., 2007). Our findings suggest that elevatedevels of Hg in tusk may impose an oxidative stress response in theiver possibly via disturbance of iron metabolism. Iron can act as

strong pro-oxidant that may induce oxidative stress by facilitat-ng the production of free-radical species via Fenton-like reactionsArosio and Levi, 2010).

Several non-specific biomarkers for oxidative stress were exam-ned in this work. Vitamin C was included because MeHg exposurean have a significant effect on vitamin C levels in fish liver (Olsvikt al., 2011a). Reduced vitamin C content has also been observedn fish tissues after exposure to inorganic Hg (Verma and Tonk,983). Vitamin C is an important water-soluble antioxidant thatan effectively neutralize ROS and reduce oxidative stress (Yu et al.,008), and has been shown to be more effective than vitamin E

n combating ROS-induced toxicity (Wang et al., 2002). The nega-ive correlation between vitamin C and Hg observed in wild-caughtusk liver suggests a role for vitamin C in the cellular defensegainst Hg-induced toxicity in fish liver. Vitamin C levels in tuskiver were negatively correlated with TBARS, and showed a posi-ive correlation with the transcriptional level of igfbp1. The latternding suggests a link between the cellular antioxidative defensend metabolism. Of the other non-specific antioxidant biomarkersvitamin E and TBARS) evaluated in this study, alpha tocopherol wasositively correlated with Hg concentration, while gamma tocoph-rol was negatively correlated to the accumulated level of Hg. Alphaocopherol was also positively correlated with the levels of Cd, Pbnd Se in tusk liver. Catalase activity showed a weak significantegative correlation with catalase mRNA, but no correlation withg concentration or with any of the other 22 target genes. A pos-

ible link between oxidative stress and lipid metabolism is alsouggested by the data, as the transcription of genes encoding CATnd FTH1 was positively correlated with fabp1 and fabp3, while scdranscription was negatively correlated with nfe2l2.

Interestingly, metallothionein (MT) transcription in tusk liveras not correlated to the accumulated levels of any of the studiedetals. Instead, mt transcription was positively correlated with fish

ize, and negatively correlated with lipid peroxidation (TBARS) inhe liver. Both Hg and Cd bind strongly to MT protein in cells, andhe sequestration of these non-essential metals by MT is consideredo be one of the most important detoxifying mechanisms in liverells (McGeer et al., 2012). MTs, in collaboration with glutathionend ferritins, act as antioxidants, and protect the cells against metaloxicity by reducing the cellular levels of free metal ions throughequestration and scavenging of ROS (Roesijadi, 1996).

Based on the findings in the greater surveillance program fromhere the examined tusk was collected, the Norwegian Fooduthority has imposed new restrictions on commercial sale of tuskollected in these parts of the Hardangerfjord. The Hg levels in filet,specially in fish collected at Djønno, exceeded the Norwegian anduropean limits for allowable levels of Hg in fish (0.5 mg/kg, 1.0or certain larger predatory species). As a consequence of the rela-ively high Hg content in tusk from all six locations, the outermosttation became less distinct and suitable as a low-concentrationontrol along the Hg-contaminated fjord gradient.

Surprisingly, elevated levels of Cd were found in liver of tuskrom Steinstø. At present, the source for this contamination is

nknown. Intracellular detoxification of Cd is primarily mediatedy glutathione and MTs (McGeer et al., 2012). Of the studied generanscripts, those of hmox1, igfbp1, and vtga in males, were elevatedn fish from Steinstø. Numerous studies in mammalian species have

44– 145 (2013) 172– 185 183

shown that Cd exposure mediate induction of hmox1 (CTD, 2013).Previously it has been shown that Cd exposure induces increasedtranscription of hmox1 in Atlantic cod hepatocytes (Søfteland et al.,2010). However, the concentration of Cd was not significantly cor-related with hmox1 transcription in the nine fish from Steinstø.Instead, Cd concentration showed a strong negative correlationwith the transcript of the gene encoding Ca-dependent proteasecalpain 2 (capn2) (r = −0.79, P = 0.015, n = 9), one of the apopto-sis markers included in the current study. It is well known thatCd modulates expression of genes encoding proteins involved inantioxidant defenses, followed by apoptotic cell death mediatedby calpains or caspases (Lee et al., 2006), suggesting a caspase-independent apoptotic response in liver of tusk from Steinstø.

Also surprisingly, elevated levels of gamma tocopherol werefound in liver of tusk from the outermost locations, particularlyin fish from Ålvik. Gamma tocopherol is predominantly present inplants like soyabean and corn, and levels in wild fish are normallybelow detection limits. It can be speculated that the presence ofgamma tocopherol in a deep-water fish species in the Hardanger-fjord implies an anthropogenic source, and that the fish have fed onsurplus feed pellets from a nearby and the only salmon fish farmin the study area. Modern salmon feeds are partly made of plantraw materials. The Hardangerfjord area is known for rather densesalmonid aquaculture, with the innermost sea cages located closeto Ålvik. This could possibly explain the observed elevated levelsof gamma tocopherol and gamma tocotrienols in tusk from theselocations.

To which degree the differentially regulated genes in femaletusk from Djønno and Samlaneset represent a molecular responseto Hg exposure in the fish remains uncertain. It is always difficultto link cause-effect of contaminants in environmental samples, butchronic toxicity of Hg has been observed in fish at tissue concen-trations well below the levels reported here for tusk liver (Beckvaret al., 2005; Sandheinrich and Wiener, 2010; Depew et al., 2012;Batchelar et al., 2013). With mainly females collected from Djønno,and with fish spanning a relative large size range, differentiallysized females were selected for the evaluation Hg/MeHg exposurein tusk. The unequal size of the fish used for RNA-seq may affect theinterpretation of the deep-sequencing data. Size and age of animals,as well as annual reproductive status and nutritional status, mayaffect the hepatic transcriptome. In addition, wild fish can be highlyoutbred (Brown et al., 2011), and teleostean gene expression oftenshows large individual variation. Tusk from the innermost locationsare probably also affected by other contaminants, further compli-cating the search for biomarkers of Hg exposure. Elevated levelsof PCBs and DDT have been found in tusk collected in the Sørfjord(Ruus et al., 2010). Interpretation of deep-sequencing data fromwild-caught fish will therefore always be uncertain, and should bevalidated by independent methods and elucidated in lieu of sizeand gender.

By comparing the DEGs between the three fish from the Djønnogroup used for RNA-seq, gene ontology analysis suggests thatone individual differed mainly in steroid hydroxylase activity,heme binding and oxidoreductase activity. Transcripts that dif-fered in expression between these fish included those of cyp7a1and cyp27a1, suggesting that mechanisms linked to bile acid syn-thesis and removal of cholesterol was activated in the larger fish.Comparison of the RNA-seq data shows, however, that by excludingthe largest fish from the Djønno group, 6 out of 8 most significantaffected biological processes remained the same according to theGO analysis. It was therefore decided to include this individual inthe RNA-seq data analysis.

Here the first functional genomics study of the North Atlanticdeep-water fish species tusk is reported, from a fjord contaminatedby Hg. High Hg levels were found in liver of tusk from Hardan-gerfjord. The low percentage of MeHg of total Hg in liver suggests

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epatotoxic effects of both inorganic Hg and MeHg. Our data sug-est that transcription of genes related to lipid metabolism andxidative stress may be affected by Hg exposure in the fish.

cknowledgments

The authors want to thank Otte Bjelland (IMR), Ørjan Mo, Nord-eimsund, Tore Hartvig Kristensen, Rosendal, and Eva MykkeltvedtNIFES), for help with longline fishing and fish sampling. Stigaldersnes, Hui-shan Tung, Nina Wollertsen, Anne-Karin Syversen,erit Solli and Jaap Wessels (all NIFES) are thanked for methodo-

ogical and analytical help and Svenn Helge Grindhaug (CBU, UiB)or bioinformatics assistance (setting up Gaffa database).

ppendix A. Supplementary data

Supplementary data associated with this article can beound, in the online version, at http://dx.doi.org/10.1016/.aquatox.2013.10.002.

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