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Aquatic Toxicology 105 (2011) 246– 263

Contents lists available at ScienceDirect

Aquatic Toxicology

jou rn al h om epa ge: www.elsev ier .com/ locate /aquatox

epatic in vitro toxicity assessment of PBDE congeners BDE47, BDE153 andDE154 in Atlantic salmon (Salmo salar L.)

iv Søftelanda,∗, Kjell Petersenb, Anne-Kristin Stavrumc, Terence Wud, Pål A. Olsvika

National Institute of Nutrition and Seafood Research, PO Box 2029 Nordnes, N-5817 Bergen, NorwayComputational Biology Unit, Bergen Centre for Computational Science, Uni Research AS, Thormøhlensgt 55, N5008 Bergen, NorwayDept of Clinical Medicine, University of Bergen, NorwayYale University W.M. Keck Biotechnology Resource, 300 George St., Room G001, New Haven, CT 06511, United States

r t i c l e i n f o

rticle history:eceived 11 November 2010eceived in revised form 11 March 2011ccepted 22 March 2011

eywords:tlantic salmon hepatocytesBDEoxicogenomicsroteomicsYP1ATGP3

a b s t r a c t

The brominated flame retardant congeners BDE47, BDE153 and BDE154 are among the congeners accu-mulating to the highest degree in fish. In order to gain knowledge about the toxicological effects of PBDEsin fish, microarray-based transcriptomic and 2D-DIGE/MALDI-TOF/TOF proteomic approaches were usedto screen for effects in primary Atlantic salmon hepatocytes exposed to these congeners alone or in com-bination (PBDE-MIX). A small set of stress related transcripts and proteins were differentially expressedin the PBDE exposed hepatocytes. The PBDE-MIX, and BDE153 to a lesser degree, seems to have inducedmetabolic disturbances by affecting several pathways related to glucose homeostasis. Further, effects oncell cycle control and proliferation signal pathways in PBDE-MIX-exposed hepatocytes clearly suggestthat the PBDE exposure affected cell proliferation processes. CYP1A was 7.41- and 7.37-fold up-regulatedin hepatocytes exposed to BDE47 and PBDE-MIX, respectively, and was the only biotransformation path-way affected by the PBDE exposure. The factorial design and PLS regression analyses of the effect ofthe PBDE-MIX indicated that BDE47 contributed the most to the observed CYP1A response, suggestingthat this congener should be incorporated in the toxic equivalent (TEQ) concept in future risk assess-

ment of dioxin-like chemicals. Additionally, a significant up-regulation of the ER-responsive genes VTGand ZP3 was observed in cells exposed to BDE47 and PBDE-MIX. Further analyses suggested that BDE47and BDE154 have an estrogenic effect in male fish. The data also suggested an antagonistic interactionbetween BDE153 and BDE154. In conclusion, this study shows that PBDEs can affect several biological sys-tems in Atlantic salmon cells, and demonstrates the need for more studies on the simultaneous exposure

denti

to chemical mixtures to i

. Introduction

The polybrominated diphenyl ether (PBDE) flame retardantsave been used extensively in polyurethane foam, textiles, elec-ronic devices and plastic products, and the global productionf PBDE in 2001 was 67,440 ton (Guerra et al., 2010). Due toncreased amounts of PBDE found in the environment and biotand enhanced toxicological knowledge about Penta- (PeBDE) andcta-PBDE (OBDE) mixture toxicity, the European Union bannednd the main producer in USA voluntarily stopped the productionf these mixtures in 2004. Deca-PBDE mixtures are still being usedBirnbaum and Hubal, 2006) and even though PeBDE and OBDE

ave been banned; the physiochemical properties of PBDEs makeossible that they can leak from deposed PBDE containing productsnd contaminated hot-spots and undergo long range transport (de

∗ Corresponding author. Tel.: +47 41 45 84 95; fax: +47 55 90 52 99.E-mail address: lso@nifes.no (L. Søfteland).

166-445X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.aquatox.2011.03.012

fy combined effects of chemicals.© 2011 Elsevier B.V. All rights reserved.

Wit, 2002; de Wit et al., 2010). In the environment PBDEs can bebroken down to more toxic and persistent lower brominated PBDEcongeners (Birnbaum and Staskal, 2004) and can therefore continueto be an environmental problem in years to come.

Relative high levels of PBDE have been measured in marineorganisms (Darnerud, 2003) and BDE47, BDE153 and BDE154 areamong the congeners accumulated to the highest degree in fish(Hites, 2004; Xia et al., 2011). These congeners occur in high quan-tities in PeBDE mixtures; the most toxic PBDE mixture. PeBDEmixture is not acute toxic but the effects in fish are still notadequately known. Research has revealed that PeBDE have hep-atic toxicity effects (Birnbaum and Hubal, 2006), is carcinogenic(Siddiqi, 2003), act as an endocrine disruptor (Sanderson, 2006;Legler, 2008), is immunotoxic (de Wit, 2002), can cause devel-opmental and reproductive effects (Birnbaum and Hubal, 2006)

and can cause behavioural changes in fish (Timme-Laragy et al.,2006). PeBDE congeners are structurally similar to polyhalogenatedaromatic hydrocarbons like polychlorinated biphenyls and dioxinsand, thus; it has been raised concerns if PeBDE can exert dioxin-

Toxicology 105 (2011) 246– 263 247

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Table 1Overview over the different concentration combinations used for the various PBDEsin the factorial design for microarray and RT-qPCR evaluation.

Exposure no. qPCR BDE 47 (�M) BDE 153 (�M) BDE 154 (�M)

1 0 0 02 1 0 03 0 1 04 1 1 05 0 0 16 1 0 17 0 1 1

L. Søfteland et al. / Aquatic

ike toxicity through binding to AhR with subsequent inductionf CYP1A and the rest of the CYP1A gene battery (Birnbaum andubal, 2006). Compared to the structurally similar PCBs, all PBDEongeners are found to be non-coplanar and therefore poor AhR-gonist (Sanders et al., 2005; Wang et al., 2005). In mammals,eBDEs have been found to be mix-inducers in vivo and in vitroprimary rat hepatocytes), inducing CYP1A, and CYP2B and CYP3Ahrough the constitutive androstane receptor (CAR) and pregnane

receptor (PXR) pathways, respectively. However, induction ofYP2B appears to be the main effect (Sanders et al., 2005; vaner Ven et al., 2008). CYP1A induction has been shown in PeBDEammalian and zebrafish exposure studies (Kuiper et al., 2006;

an der Ven et al., 2008; Wahl et al., 2008), however, dioxins andurans (PCDD/Fs) found in the PeBDE mixtures have been blamedor the observed CYP1A induction (Birnbaum and Hubal, 2006;

ahl et al., 2008). Compared to mammals, fish have low abilitieso induce CYP2B-enzymes, despite the presence of CYP2B-relatedenes like CYP2M and CYP2K that have been detected in salmonids.t present, the CAR has not been identified in fish (Schlenk et al.,008), making it hard to elucidate the exact mechanisms behindBDE-induced toxicity in Atlantic salmon.

The aim of this experiment was to evaluate the effects of chronicow dose exposure of the PeBDE congeners BDE47, BDE153 andDE154, singly and in combination, by using omic methods in ordero gain more toxicological knowledge about the effects of individualeBDE congeners and the combined effects of these chemicals. As

model system, primary hepatocytes from Atlantic salmon weresed.

. Materials and methods

.1. Chemicals

2,2′,4,4′-Tetrabromodiphenyl ether (BDE47; 98.8% pure,ppendix A, Table 5) and 2,2′,4,4′,5,5′-hexabromodiphenyl ether

BDE153; 98.0% pure, Appendix A, Table 5) were purchased fromhiron (Trondheim, Norway) and 2,2′,4,4′,5,6′-hexabromodiphenylther (BDE154; >98% pure) was purchased at Promochem (Wesel,ermany). The different stock solutions were prepared in dimethylulfoxide, DMSO (>99.9% pure, Sigma–Aldrich, Oslo, Norway).

.2. Isolation of primary cultures of hepatocytes

Juvenile Atlantic salmon (Salmo salar) were obtained fromavbruksstasjonen at Matre and kept in 1500 l tank at the ani-al holding facility at the Institute of Marine Research, Bergen,orway. The fish were fed once a day regular commercial feed

rom Skretting, Norway (Spirit 400-50A HH, 6.0 mm). All glass-are, instruments and solutions were autoclaved prior to livererfusion. Hepatocytes were isolated from six Atlantic salmons266–526 g) with a two-step perfusion method earlier described byøfteland et al. (2009). The final cell pellet was resuspended in L-15edium containing 10% FBS, 1% glutamax (Invitrogen, Norway) and

% penicillin–streptomycin–amphotericin (10,000 units/ml potas-ium penicillin 10,000 mcg/ml streptomycin sulfate and 25 �g/mlmphotericin B) (Lonzo, Medprobe, Oslo, Norway). The Trypan Bluexclusion method was performed in accordance with the manufac-urer’s protocol (Lonzo, Medprobe, Oslo, Norway) and was used toetermine cell viability. For further use, the cell viability had to be90%.

.3. Chemical exposure

A MTT-based in vitro toxicity assay kit was used to determinehich concentrations of the different PBDE congeners to use in theicroarray and 2D-DIGE analysis, and the MTT was performed in

8 1 1 19 0.5 0.5 0.5

accordance with the manufacturer’s protocol (Sigma–Aldrich, Oslo,Norway). Cells from one mature male Atlantic salmon (fish no. 1)were used to determine dose-dependent toxicity of the PBDE con-gener. 0.16 × 106 hepatocytes per well were plated out in 96-wellplates, and the cells were cultured for 36–40 h prior to chemicalexposure with an exchange of medium after 18–20 h. Cells wereexposed in triplicates, in a dose-dependent manner for BDE-47,BDE-153 and BDE-154 (0.01, 0.1, 1, 10, 100 �M) and 0.1% DMSO(control). The old exposure medium was exchanged with newexposure medium after 18–20 h and the chemical exposure wassustained for another 24 h. The hepatocytes were kept at 10 ◦Cin a sterile incubator (Sanyo, CFC FREE, Etten Leur, Netherlands)without additional O2/CO2. The male Atlantic salmon used in thisstudy had prior to the in vitro PBDE exposure experiment poten-tially been exposed to very low concentrations of PBDE via thecommercial fish feed used in this study. However, the levels inthe fish are low. In 2008 the mean calculated level of the

∑PBDE

(BDE28, BDE47, BDE99, BDE100, BDE153 and BDE154) in fillet ofAtlantic salmon was 1.3 �g/kg (N = 70; www.nifes.no). The concen-trations of the different congeners were measured in the exposuremedium at the end of the experiment. The following PBDE con-centrations were detected: BDE47 stock: 27 ng/ml BDE47; BDE153stock: 3.1 ng/ml BDE153; BDE154 stock: 27.19 ng/ml BDE154 and0.24 ng/ml BDE99; PBDE-MIX stock: 28.36 ng/ml BDE47, 2.06 ng/mlBDE99, 33.4 ng/ml BDE154 and 6.01 ng/ml BDE153. The concen-tration was evaluated with a standard accredited PBDE analyticalmethod according to NS-EN ISO/IEC 17025, routinely used forthe documentation and surveillance program at NIFES, Seafooddatabase for undesirables (www.nifes.no).

The MTT test is based on spectrophotometrical determinationof the cell number as a function of mitochondrial activity in livingcells. The 3-[4,5-domethylthiazol-2-yl]-2,5-diphenyl tetrazoliumbromide (MTT) solution was dissolved in PBS. The absorbance wasmeasured after 4 h incubation at 570 nm using an iEMS reader (Lab-systems I EMS Reader MF, Helsinki, Finland).

In order to detect possible estrogenic responses in BDE47,BDE153 and BDE154 exposed cells, the primary hepatocytes wereisolated from male Atlantic salmon. The cells were exposed for48 h to single and a simple mixture of these chemicals accordingto a full factorial design with two levels (low and high concen-trations) which included a zero point (0.1% DMSO control) andone center points in order to evaluate linearity (Table 1). For themicroarray analyses the primary hepatocytes were isolated fromfive Atlantic salmon (N = 5; Fish no. 2–6), and 4.52 × 106 cells perwell (in 4 ml complete L-15 medium) were plated on laminin(1.8 �g/cm2; Sigma–Aldrich, Oslo, Norway) coated 6 plates (TPP,Trasadingen, Switzerland). Due to the use of a direct hybridizationmicroarray design, from each fish, eight cell cultures were culti-vated; four with the different PBDE exposures (exposure no. 2, 3,

5, 8) and four unexposed 0.1% DMSO control (exposure no. 1). Themicroarray results were further analyzed with RT-qPCR using thefull factorial design. RT-qPCR results for exposure no. 1, 2, 3, 5, 8,

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re obtained from five biological replicates (N = 5; fish no. 2–6) andxposure no. 4, 6, 7 and 9 are obtained from three biological repli-ates (N = 3; fish no. 2–4) exposed in laminin coated 12 well culturelates (1.83 × 106 cells per well in 2 ml complete L-15 medium).xposure no. 1 (0.1% DMSO control; primary hepatocytes isolatedrom two Atlantic salmon (N = 2; fish no. 2 and 4)) and 8 (MIX; pri-

ary hepatocytes isolated from three Atlantic salmon (N = 3; fisho. 2–4)) were also run on 2-D DIGE gels (exposed in 6 well cul-ure plates). The hepatocytes used for the genomic and proteomicnalysis originate from same fish but were exposed separately.

.4. RNA extraction

The RNAeasy Plus mini kit was used to extract total RNAccording to the manufacturer’s protocol. RNA was eluted in 50 �lNase-free MilliQ H2O and stored at −80 ◦C (Qiagen, Crawley,K). The RNA quality was assessed with the NanoDrop® ND-1000V-Vis Spectrophotometer (NanoDrop Technologies, Wilmington,E, USA) and the Agilent 2100 Bioanalyzer (Agilent Technologies,alo Alto, CA, USA) pursuant to the manufacturer’s instructions.he integrity of the RNA was evaluated with the RNA 6000 NanoabChip® kit (Agilent Technologies, Palo Alto, CA, USA). The sam-les used in this experiment had 260/280 nm absorbance ratios thataried between 1.74 and 2.03, 260/230 nm ratios above 2 and RNAntegrity number (RIN) values above 9.5 which indicate pure RNAamples (Schroeder et al., 2006).

.5. Protein extraction

To harvest the exposed hepatocytes for the 2D-DIGE gel elec-rophoresis and MALDI-TOF/TOF, the cells were washed with PBS,rypsinated (0.025%, Sigma–Aldrich, Oslo, Norway) and centrifugedor 5 min 300 × g at 4 ◦C. The supernatant was decanted and theell pellet was re-suspended in 1 ml PBS. The cells were pelleted at2,000 × g for 4 min at 4 ◦C. The re-suspension of the pellet and theentrifugation were repeated 3 times and after the third time theell pellet was suspended in 1 ml of lysis/labelling buffer (7 M urea,

M thiourea, 4% CHAPS, in 25 mM tris, pH 8.6, @ 4 ◦C). A sonifieras used to dissolve the cells (Branson, Danbury, CT USA, model

450D equipped with a 102c transducer with a micro-tip). Theonication was performed on ice five times using 50% power, 20 sonication pulses with 1 min breaks. Samples are precipitated using

“2D clean-up kit” (GE Healthcare, 80-6484-51), and resuspendedn the lysis/labelling buffer. Protein concentration was determinedsing a thiourea and CHAPS tolerant protein assay (2-D Quant Kit,.E. Healthcare, 80-6483-56).

.6. 2D-DIGE gel electrophoresis and MALDI-TOF/TOF

Samples were diluted with labelling buffer to match concentra-ions, and each sample extract was labelled with Cy2, Cy3 and Cy5-hydroxysuccinimidyl ester dyes (GE Healthcare, Piscataway, NJ,SA). To label, 100 �g of each sample was incubated for 30 min,

n the dark on ice, with 400 pmol of dye, respectively and thenuenched by the addition of a 50 fold excess of free lysine to dye for

further 10 min. Samples were combined, and suspended in bufferontaining 8 M urea, 4% CHAPS, 2 mg/ml DTT 1% (v/v) PharmalyteH 3–10, and a trace amount of bromophenyl blue dye, to a finalolume of 400 �l.

Samples are incubated with 24 cm Immobiline (IPG) DrystripsGE Healthcare, Piscataway, NJ, USA) for 11 h, and isoelectric focus-ng was performed for a total of ∼60 kVh using an Ettan IPGphor

apparatus (GE Healthcare, Piscataway, NJ, USA). Upon comple-ion of isoelectric focusing, the strips were equilibrated in 10 mMris pH 8.8, 6 M urea, 30% (w/v) glycerol, 1% SDS (w/v), and 33 mMTT, after 10 min this buffer was removed and replaced with buffer

logy 105 (2011) 246– 263

of the same composition without DTT but with 240 mM iodoac-etamide. The strips were then applied to SDS polyacrylamide gelelectrophoresis, which was run on a 12.5%, 24 cm wide × 20 cmtall × 1.0 mm thick gel (Jule Inc., Milford, CT, USA) with one glassplate side coated with bind-silane (PlusOne, GE Healthcare, Piscat-away, NJ, USA), at 40 mA/gel, 15 ◦C.

Immediately after SDS PAGE, the gel was sequentially scannedat appropriate excitation and emission wavelengths using a GEHealthcare Typhoon 9410 Imager (Piscataway, NJ, USA). After scan-ning, 100 �m resolutions, 16 bit tiff files of each channel wereexported for image analysis using the differential in-gel analysismodule of the DeCyder 2D GE Healthcare (Piscataway, NJ, USA)software package. The unbound glass plate was removed and thegel fixed (10%, v/v methanol, 7.5% acetic acid for 20 min), stainedwith Sypro Ruby dye (Invitrogen, Eugene, OA, USA), and re-scannedbefore spots were picked for identification.

For image analysis, the differential in-gel analysis (DIA) modeof the DeCyder 2D (v 6.5) software was used to quantify the gelimage by establishing spot boundaries and combining the Cy2, Cy3and Cy5 images for the calculation of normalized spot volumes andto identify a “pick list” of differentially expressed protein spots.The protein spot pick list was transferred to the Ettan Spot Pickerinstrument (GE Healthcare, Piscataway, NJ, USA) which excisedthe selected protein spots from the gel and transferred them intoa 96-well plate. The excised protein spots were then subjectedto automated in-gel tryptic digestion on the Ettan TA Digester.An aliquot of each digest was spotted (along with matrix) onto aMALDI-MS target.

The digested proteins were then subjected to MS-based pro-tein identification. High mass accuracy, automated MALDI-MS/MSspectra were acquired on each target (performed on the KeckLaboratory’s Applied Biosystems/Sciex, Foster City, CA, USA)(4800 Tof/Tof) and the resulting peptide masses subjected todatabase searching using Mascot algorithms. Data in XML for-mat was ported through the Yale Protein Expression Database(YPED) for web browsing of the data (for information, seehttp://medicine.yale.edu/keck/proteomics/).

2.7. Microarray experiment

For the paired direct hybridization design used in the microar-ray experiment, the four unexposed samples from one individualwere pooled and used as a common reference for the four exposedsamples from the same individual. I.e., the exposed samples withexposure nos. 2, 3, 5, 8 were individually hybridized with the unex-posed pooled reference from the same fish. Full dye-swap wasconducted using Cy5 and Cy3 and on each slide the sample andreference were labelled with different dyes. In total 40 hybridiza-tions were performed. A Tecan HS 4800TM hybridization station(Tecan Group Ltd., Männedorf, Switzerland) was used to hybridizeRNA to 16K cGRASP v. 2.0 arrays (von Schalburg et al., 2005) and aTecan LS Reloaded scanner (Tecan Group Ltd.) was utilized to scanthe arrays which were further analyzed with the Axon GenePix 5.1software (MDS Inc., Toronto, Canada).

2.8. Quantitative real-time RT-PCR

The transcriptional levels of the target genes were quantifiedwith a two-step real-time RT-PCR protocol. A serial dilution curveof a total RNA with six points in triplicates between 1000 and31 ng were made for PCR efficiency calculations. 250 ng of total RNAwas added to the reaction for each sample, and reverse transcrip-

tion (RT) reactions were run in duplicates. No amplification control(nac) and no template control (ntc) reactions were run for qualityassessment for every gene assay. A 50 �l RT reaction was per-formed at 48 ◦C for 60 min utilizing a GeneAmp PCR 9700 machine

L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263 249

Table 2PCR primers, GenBank accession numbers, amplicon sizes and efficiency.

Gene Accession no. Forward primer (5′–3′) Reverse primer (5′–3′) Product size (bp) Efficiency

CYP1A AF364076 TGGAGATCTTCCGGCACTCT CAGGTGTCCTTGGGAATGGA 101 1.94Tob1 CA059883 TGTGCTTGGTTGGCAAAAGA TGACTTTGCCCCCTTGAGTT 114 2.07GRP94 CA057152 CGGTGAGGTCCACGTTCATA TGCTGTTTGAGACTGCCACACT 111 2.1ZP3 CK991165 CTGCGGCTGAATTGGGTTAC ACTCCCAGTTCATGCCTCGTT 101 2.1Enol1 NM 001139894 ATCCAGGTGGTGGGTGATGA CGGAGCCGATCTGGTTGA 112 2.03DLD BT045027 GTGACAGCGGTGGAGTTCCT TTGGTGCCCAGCTTGAACTT 116 1.89CaM2 BT060375 GGACATGGCCGACCAACTAA TCTGTCCCAGCGACCTCATC 131 1.93NROB2 CB510335 CAAACAGATTCGCCTGACCAA GACCCTTCACCCAGGAGACTT 131 1.96PSCA CA057830 TCCATGCCTCACTGACTCACA AGGGACAGCTCTCGCAAAAA 135 1.98VPTA CA062691 TTCCCTCCAGAATGCTTTCAA TCATCATGGAGGGCCTGTCT 129 2.10VTG DY802177 AAGCCACCTCCAATGTCATC TCTCACTAAACGGAGCAGGAT 122 2.03

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Applied Biosystems, Foster City, CA, USA). Individual RT reactionsontained 1× TaqMan RT buffer (10×), 5.5 mM MgCl2, 500 �M dNTPof each), oligo dT primers (2.5 �M), 0.4 U/�l RNase inhibitor and.25 U/�l Multiscribe Reverse Transcriptase (N808-0234, Appliediosystems) and RNase-free water.

For every gene analyzed, real-time qPCR was run in 20 �l reac-ions on a LightCycler® 480 Real-Time PCR System (Roche Appliedciences, Basel, Switzerland) containing 2.0 �l cDNA. The real-ime qPCR was carried out in two 96-well reaction plates usingYBR Green Master Mix (LightCycler 480 SYBR Green master mixit, Roche Applied Sciences, Basel, Switzerland) containing gene-pecific primers and FastStart DNA polymerase. PCR runs wereerformed with a 5 min activation and denaturizing step at 95 ◦C,0 cycles of a 15 s denaturing step at 95 ◦C, a 60 s annealing stepnd finally a 30 s synthesis step at 72 ◦C. The primer pairs had annnealing temperature of 60 ◦C, see Table 2 for primer sequences,mplicon sizes and GenBank accession numbers. For the primers, anal concentration of 500 nM was used. For confirmation of ampli-cation of gene-specific products, a melting curve analysis waspplied and the second derivative maximum method (Tellmann,006) was used to determine crossing point (CT) values using theightcycler 480 Software. To calculate the mean normalized expres-ion (MNE) of the target genes, the geNorm VBA applet for Microsoftxcel version 3.4 was used to calculate a normalization factor basedn three reference genes by using gene-specific efficiencies cal-ulated from the standard curves, the CT values are convertednto quantities (Vandesompele et al., 2002). Elongation factor 1 ABEF1AB) and acidic ribosomal protein (ARP) were the selected refer-nce genes employed in this experiment. The reference genes weretable with gene expression stability (M) values of 0.38.

.9. Data analysis

.9.1. Microarray analysesFiltering and normalization of the raw data as well as the

ownstream data analysis were performed with JExpress Pro v.2.8Stavrum et al., 2008). Control spots and spots that were empty,aturated or flagged were filtered. The median foreground intensityignals were normalized with the nonlinear global lowess methodCleveland and Devlin, 1988). Further, weak spots were filtered outf the foreground signal intensity were less than 1.5 times the stan-ard deviations above of the background signal intensity level. Aingle expression profile matrix with all the arrays was made withhe log ratios of corresponding dyeswap hybridizations averaged.Simpute adaptive method (Bo et al., 2004) was used to estimatend replace missing values, but expressed sequence tag (EST) clones

ith more than 80% missing values were excluded from the analy-

is. The dataset was further divided into smaller subsets containingnly the matrices for one exposure type. For each sub-dataset, allenes having more than 20% missing values were removed.

CACGGCCCACAGGTACT 59 2.03CTTCCCACGCAAGGACAGA 101 1.98

Correspondence analysis (CA) (Fellenberg et al., 2001) and hier-archical clustering were used to look at global trends in the data,and Rank Product analysis (Breitling et al., 2004) was used for thestatistical assessment of the microarray dataset (J-Express). GSEA(Subramanian et al., 2005), also implemented in J-Express, was usedto look for sets of differentially expressed genes sharing commoncharacteristics based on Gene Ontology (GO). Compilation of genesets—genes sets were created on the basis of the GO, by mapping theGO annotations in the cGRASP v.2.0 annotation file (dated 13.02.08)to the GO accession numbers in the Gene Ontology OBO v1.0 filedated 12.07.07. Parameters of GSEA—analysis was run at probe level,assuming probes represent individual genes in the uncharacterizedSalmon salar genome. Gene sets smaller than 10 or larger than 500were excluded from the analysis. GSEA was run with t-test as theranking statistic. Significance of the gene set analysis was tested bypermuting the scores over the genes (1000 iterations).

The microarray experiment has been fully documented innationalBASE2 database of the Norwegian Microarray Consor-tium and FUGE Bioinformatics platform to meet the terms ofthe Minimum Information about Microarray Experiment (MIAME)guidelines (Brazma et al., 2001). The data and documentation hasbeen deposited in ArrayExpress for public access (accession num-ber pending).

GraphPad Prism 5.0 software (GraphPad Software Inc., PaloAlto, CA, USA) was used for the statistical analyses of the MTTdose–response curves using one-way analysis of variance togetherwith a Dunnett’s post hoc test (p < 0.05) to detect treatment varia-tion in PBDE-exposed hepatocytes. Mean ± SE were calculated forthree replicates. For the statistical analyses of the RT-qPCR data,Student’s t-test (p < 0.05) were used to detect significant differencebetween the mean FC ratios calculated for PBDE-exposed hepa-tocytes and the 0.1% DMSO control hepatocyte cell cultures fromfive replicates (N = 5). Further, Pearson correlation was used to seehow well the FC values obtained by the microarray and RT-qPCRcorresponded.

Principal component analysis (PCA) (Jackson, 1991) was con-ducted with Simca-P 11.0 (Umetrics, Umeå, Sweden) on the datamatrix containing all target genes for the evaluation of responsesimilarities between genes. Prior to the PCA the data were meancentered.

Regression was performed with Projections to Latent Structures(PLS) (Wold et al., 1984) to correlate the design matrix to the CYP1Aand VTG responses. Modde 7.0 (Umetrics, Umeå, Sweden) was usedfor the experimental design and the PLS analysis. Before the PLSanalysis the blend matrix was augmented with interaction terms,the data were scaled to unit variance and mean centered.

The PCA and PLS models were validated with respect toexplained variance and goodness of prediction (shown as Q2), the

latter obtained after cross validation (Wold, 1978). The PLS modelwas in addition evaluated with respect to goodness of fit (R2).

250 L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263

Fig. 1. MTT in vitro cytotoxicity test in primary Atlantic salmon hepatocytes exposedfor 48 h to BDE47, BDE153 and BDE154 (0.01–100 �M). The values represent themd

3

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Fig. 2. Correspondence analysis plot show the global gene expression separationof different PBDE exposed groups. The plot explained 12.99% of retained variancein the dataset, principal components 1 (PC1) and 2 (PC2), explained 6.8% and 6.2%of the variation, respectively. Colored lines are plotted from point of origin andthrough different groups of median, represented by the color of the PBDE group.

groups, Rank Product test (Breitling et al., 2004) was chosen since

ean ± S.E. of three replicates from one individual. The analyses showed significantifference between the control and the exposed group indicated by *p < 0.05.

. Results

.1. MTT

None of the applied BDE47 concentrations (0.01–100 �M)howed cytotoxic effects, measured with the MTT assay, in exposed

The black line represents the global mean. PBDE-MIX is a mixture composed ofBDE147, BDE153 and BDE154. (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of the article.)

hepatocytes compared to the 0.1% DMSO control (Fig. 1A). However,in BDE154 exposed hepatocytes, the cell viability was significantlyreduced at the highest concentration (100 �M) (Fig. 1C). Fur-ther, the cell viability was slightly but significantly higher in cellsexposed to the lowest BDE153 concentration (0.01 �M) comparedto the control (Fig. 1B). Based on these results, to limit potentialcombined cytotoxic effects in hepatocytes when exposed to a sim-ple mixture of BDE47, BDE153 and BDE154, 1 �M was chosen as theindividual exposure concentration for each PBDE congener used inthis exposure study.

3.2. Microarray analysis

3.2.1. Global trendsThe microarray data set contained 11,998 transcripts after the

pre-processing step. Correspondence analysis (CA) was employedto determine if the global gene expression of different PBDE expo-sure groups separated in to distinct groups by estimating how muchof the variability is caused by common factors. The total variationretained in the plot was 12.99%, and principal component 1 (PC1)explained 6.8% and PC2 explained 6.2% of the variation. Although allsamples in each exposure group gathered on one side of the origin(the global mean) no clear separation between all four groups couldbe observed at the global level. However, BDE47 and the PBDE-MIXseemed to separate from the BDE153 and BDE154 groups (Fig. 2).The same global trend of separation between the BDE47 and PBDE-MIX groups and the PBDE153 and 154 was seen with the used ofHierarchical clustering (HC, data not shown). Both the CA and theHC analysis of the samples did not revealed any systematic bias.Since the outliers in the BDE47 group was not the same in CA andHC, none of the outliers were removed.

3.2.2. Rank ProductTo calculate differential expression of transcript between

it is a proven robust method for microarray experimental designswith few replicates (Jeffery et al., 2006) and the analysis giveq-values for assessment of statistical significance of the results. q-

L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263 251

Table 3Top rank product list of differently expressed transcript with q-value below 5% in Atlantic salmon hepatocytes exposed to PBDEs.

Exposure ID Gene Microarray RT-qPCR

Fold change q-ValueA Fold change p-ValueB

BDE47 CB497960 Rainbow trout (S. gairdneri) cytochromeP450IA1 mRNA, complete cds

4.26 0 7.41 0.001

BDE47 CB505556 Cytochrome P450 1A3 4.03 0 – –BDE47 CA044359 Cytochrome P450 1A3 3.08 0 – –BDE47 CB501070 Cytochrome P450 1A3 2.34 0 – –BDE47 CA051849 Cell differentiation protein rcd1 2.08 0 – –BDE47 CA051633 Vitellogenin precursor 1.59 3.57E−004 1.78 0.018BDE47 CB494318 Myosin heavy chain, fast skeletal muscle 1.49 3.57E−004 – –BDE47 CB515390 Vitellogenin precursor 1.55 0 – –BDE47 CA059883 Protein Tob1 1.36 0.02 1.25 >0.05BDE47 CA049955 UNKNOWN 1.4 0.03 – –BDE47 CA057830 PREDICTED: similar to prostate stem cell

antigen precursor-like [Danio rerio]1.4 0.03 3.16 >0.05

BDE47 CK990250 Protein Tob1 1.35 0.03 – –BDE47 CB501396 UNKNOWN −1.89 0 – –BDE153 CB509815 Protein KIAA0020 1.54 0 – –BDE153 CB511967 IFI6: Interferon-induced protein 6-16

precursor1.38 0.03 – –

BDE153 CB501396 UNKNOWN −1.46 0.04 – –BDE154 CA042636 pfam05805, L6 membrane, L6

membrane protein.1.42 0.03 – –

BDE154 CB498716 14-3-3 protein beta/alpha-1 1.41 0.03 – –BDE154 CB501396 UNKNOWN −3.07 0 – –BDE154 CA770037 Hypothetical 18K protein—goldfish

mitochondrion−1.33 0.02 – –

BDE154 CB508108 Oncorhynchus masou gene foralpha-glycoprotein subunit 1, 5′ flankingregion

−1.25 0.02 – –

BDE154 CB502944 Neuroblastoma suppressor oftumourigenicity 1 precursor

−1.42 0.02 – –

BDE154 CA042008 UNKNOWN −1.37 0.04 – –BDE154 CB499796 Chroococcidiopsis sp. CC1 16S ribosomal

RNA gene, complete sequence−1.36 0.04 – –

PBDE-MIX CA044359 Cytochrome P450 1A3 4.13 0 7.37 0.0010PBDE-MIX CB497960 Rainbow trout (S. gairdneri) cytochrome

P450IA1 mRNA, complete cds3.79 0 – –

PBDE-MIX CB505556 Cytochrome P450 1A3 3.94 0 – –PBDE-MIX CA051849 Cell differentiation protein rcd1 3.51 0 – –PBDE-MIX CB501070 Cytochrome P450 1A3 2.61 0 – –PBDE-MIX CA051633 Vitellogenin precursor 2.03 0 2.94 0.0267PBDE-MIX CK991165 Zona pellucida sperm-binding protein 3

precursor1.66 0 1.88 0.0499

PBDE-MIX CB510335 Nuclear receptor 0B2 1.55 0 3.17 0.0015PBDE-MIX CB497747 Protein Tob1 1.53 1.92E−004 2.11 0.0065PBDE-MIX CA062870 Protein Tob1 1.51 1.92E−004 – –PBDE-MIX CB515390 Vitellogenin precursor 1.58 1.92E−004 – –PBDE-MIX CA062691 Vacuolar proton translocating ATPase

116 kDa subunit a isoform 11.52 1.92E−004 1.35 >0.05

PBDE-MIX CA059883 Protein Tob1 1.05 1.92E−004 – –PBDE-MIX CB494318 Myosin heavy chain, fast skeletal muscle 1.47 0 – –PBDE-MIX CB509964 Troponin I, slow skeletal muscle 1.45 0 – –PBDE-MIX CA060454 Protein Tob1 1.44 0 – –PBDE-MIX CK990250 Protein Tob1 1.45 0 – –PBDE-MIX CB502507 Leukocyte elastase inhibitor ELA2 1.35 0.02 – –PBDE-MIX CB492457 Protein Tob1 1.35 0.04 – –PBDE-MIX CB503486 Enolase 1 −1.45 0.01 −1.08 >0.05PBDE-MIX CA052095 Unknown −1.44 0.01 – –

T compP -test b

Vmrtras(rrd

he table shows the microarray data and genes validated by RT-qPCR. PBDE-MIX isroduct test is denoted by letter A (N = 5) and statistic significance calculated with t

alues are false discovery rate (FDR) values and have correction forultiple testing built in. In total 45 transcripts were significantly

egulated with q-values below 5%, see Table 3. The PBDE-MIX (21ranscripts) and BDE47 (13 transcripts) had the most significantegulated transcripts compared to BDE153 and BDE154 with only 3nd 8 genes, respectively. Most genes were up-regulated and tran-cript with the strongest fold change up-regulation was CYP1A1

CB197960) with a fold change of 4.26. Few genes were down-egulated. BDE154 had the most with 6 transcripts being negativelyegulated. Transcript CB501396, an unknown, was the strongestown-regulated transcript with a FC of −3.07.

osed of BDE47, BDE153 and BDE154. Statistical significance calculated with Ranky letter B (N = 5).

Despite that small level of BDE99 detected in the exposuremedium at the end of the experiment, no changes in gene expres-sion related to debromination or transformation were significant inthe rank list and these data were therefore not further evaluated.

3.2.3. Gene set enrichment analysisThe data was further analyzed with gene set enrichment anal-

ysis (GSEA) to detect differential expression of related set of genes(Subramanian et al., 2005). BDE154 got the highest number ofenriched gene sets followed by BDE153 and PBDE-MIX with thelowest number of enriched gene sets (FDR < 20%). GSEA of BDE47

252 L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263

Fig. 3. (A) Correlation between fold changes of 11 differently expressed transcripts obtained by microarray and RT-qPCR analysis. The measured RT-qPCR expression levelswere in good accordance to the microarray result (Person correlation test r = 0.94, p < 0.0001). (B) PCA loading bi-plot of mean fold change levels for the 11 genes (CYP1A,V of Atl( and 8( espec

defitp“trBc

3

tecibtM

TG, ZP3, PSCA, Tob1, GRP94, NROB2, VPTA, DLD and enol1), evaluated in culturesP154) and PBDE-MIX (MIX) accordingly to the factorial design (exposure no. 1–3, 5PC1 and PC2), explaining 82.1% and 15.2% of the observed variation in the model, r

id not reveal any gene sets with FDR < 20%. Enriched differentialxpressed gene sets with Gene Ontology (GO) terms with FDR < 20%or PBDE-MIX and FDR < 10% for BDE153 and BDE154 are presentedn Appendix A, Table 6. The enriched gene sets detected con-ained following GO-terms: PBDE-MIX; “negative regulation of cellroliferation”, “responses to organic substances”, “cell cycle” andregulation of transcription” and “angiogenesis”, BDE-154; “induc-ion of apoptosis”, “positive regulation of apoptosis” and “positiveegulation of programmed cell death” and “immune response” andDE-153; “immune response”, “glycolysis”, “glucose catabolic pro-ess” and “lipid transport”.

.3. 2D-DIGE analysis

The 2D-DIGE gels demonstrated high protein resolutionhroughout the size and pH range. Few spots showed fold differ-nces between PBDE-MIX exposed hepatocytes and the DMSO 0.1%ontrol hepatocytes. Due to large gel to gel variation and biolog-

cal variability among the samples, statistical analysis could note performed with the proteomic data. However, since some ofhe protein spots showed clear differences between the PBDE-

IX and the control, and because proteins can give additional

antic salmon hepatocytes (N = 5) exposed to BDE47 (P47), BDE153 (P153), BDE154). The model was good (R2 = 0.97 and Q2 = 0.61) and contained two PCA componentstively (N = 5).

knowledge about PBDE toxicity in exposed Atlantic salmon hep-atocytes, we decided to go further and identify interesting proteinspots. 166 protein spots were listed in a “pick list” (not pre-sented), and of them five spots were selected for identification withMALDI-TOF/TOF. The proteins that were identified were calmod-ulin 2 (CaM2), glucose-regulated protein 94 (GRP 94), dihydrolipoyldehydrogenase (DLD) and two protein spots, with some homologywith glyoxylate reductase/hydroxypyruvate reductase (GRHPR).The fold differences of the identified spots varied between −10.52and 3.82. The expression levels of three proteins were reduced andtwo were increased in the PBDE-MIX exposed hepatocytes com-pared to the control (Table 4). Additional information about theidentified proteins, e.g. pI and MW are presented in Appendix A,Table 7.

3.4. RT-qPCR validation

To validate the result from microarray and 2D-DIGE and MALDI-

TOF/TOF analysis, significantly (q < 5%) up- and down-regulatedtranscript from eight genes in the PBDE-MIX and BDE47 Rank prod-uct lists and three of the identified proteins were evaluated withRT-qPCR. The measured RT-qPCR expression level (FC) was in good

L. Søfteland

et al.

/ A

quatic Toxicology

105 (2011) 246– 263253

Table 4Differentially expressed proteins separated by 2D-DIGE gels and identified by MALDI-TOF/TOF in PBDE-MIX and 0.1% DMSO (control) exposed Atlantic salmon hepatocytes.

Gel/spot Accession no Protein id Control PBDE-MIX 2D-DIGEFold change RT-qPCRAFold change MicroarrayAFold change

1047/1584 gi|47216117 Unnamed protein productSome homology to GRHPR

3.82 – –

1047/625 gi|110226526 GRP94 −1.98 1.36 1.32

1047/1610 gi|47216117 Unnamed protein productSome homology to GRHPR

−11.15 – –

1096/1147 gi|47209763 DLD 2.05 −1.07 –

254 L. Søfteland et al. / Aquatic Toxico

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logy 105 (2011) 246– 263

accordance to the microarray results; all transcript changed accor-dant to the microarray results (Pearson correlation test r = 0.94,p < 0.0001) (Fig. 3A). By comparing the PBDE-exposed cells withthe 0.1% DMSO control, seven transcripts out of eleven were signif-icantly higher expressed (Student’s t-test, p < 0.05) and thereforeconfirming the microarray results. The quantified FC levels are pre-sented in Table 3.

The RT-qPCR expression levels of three proteins, DLD, GRP94,and CaM2 corresponded to a low degree to the fold differencesquantified with the 2D-DIGE (Pearson correlation test r = −0.5,p < 0.5; correlation plot is not presented). CaM2 was the only pro-tein where the transcript levels changed according to the proteinlevels, while the fold differences measured in RT-qPCR and 2D-DIGE for GRP94 and DLD went in opposite direction. Both CaM2 andGRP94 were in the top 1000 rank list of PBDE-MIX, but their RankProduct scores were not significant (q-value of 11% and 8%, respec-tively) and were therefore not presented in the PBDE-MIX ranklist. When comparing the expression levels for GRP94 and CaM2obtained with the microarray and RT-qPCR analyzes, the transcriptlevels were found to correspond well when included in the correla-tion matrix for validation of the microarray results (Fig. 3A) thougha smaller FC were measured with RT-qPCR. However, none of thetranscripts levels of the identified proteins quantified with RT-qPCRwere significantly differentially expressed (Student’s t-test, p < 0.5)compared to the control (Table 4).

3.5. PCA for all genes

The mean FC levels for all 11 genes, evaluated with RT-qPCRfor confirmation of the omic results, were further analyzed withPCA. The model had two PCA components, which explained 82.1%and 15.2% of the variation, respectively (Fig. 3B). The model’s R2-value was 0.97 and Q2 = 0.61. Vitellogenin (VTG), zona pellucida3 (ZP3), Tob1, GRP94, CaM2, prostate stem cell antigen (PSCA) andNROB2 genes grouped together with sample PBDE-MIX (MIX) in thebottom right corner of the PCA plot. Enol1 was the only gene clus-tered with BDE153 (P153) and BDE154 (P154) opposite of CYP1A,indicating that it is negatively correlated with CYP1A. Further thecontrol grouped together with the samples exposed to BDE153 andBDE154 in the bi-plot, indicating that transcription levels in theBDE153 and BDE154 samples were in most cases close to the basallevels measured in the control sample, in line with the microar-ray results suggesting that the BDE47 and PBDE-MIX induced thestrongest response. PSCA and VPTA were in the microarray experi-ment significantly differentially expressed in hepatocytes exposedto the BDE47 and PBDE-MIX, respectively. However, they groupedopposite in the PCA-plot.

3.6. PLS analysis of the CYP1A and VTG expression levels

PLS analysis was performed on the CYP1A and VTG expres-sion levels (MNE) obtained in cells exposed to BDE47, BDE153 andBDE154 using a factorial design in order to determine possiblechemical interactions. CYP1A and VTG were chosen as biomark-ers due to their expression levels and a clear link to toxicity (vander Oost et al., 2003; Walker, 2007). The best CYP1A model hadone PLS component (Fig. 4A) and contained four PLS regressioncoefficients. The model had three positive linear terms for BDE47,BDE153 and BDE154 which is an indication that all chemicals con-tributed to the transcriptional up-regulation of CYP1A, howeveronly the linear term for BDE47 was significant (p = 0.00554). Themodel also contained a not significant negative interaction term

for BDE47 and BDE153, indicating occurrence of an antagonism orresponse additivity effect (Berenbaum, 1989; Kodell and Pounds,1990) on the CYP1A response when two chemicals are exposedin combination to primary hepatocytes. The model’s R2-value was

L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263 255

Fig. 4. (A) Scaled and centered PLS regression coefficients with 95% confidence intervals for CYP1A mRNA levels measured in primary Atlantic salmon hepatocytes exposedto BDE47, BDE153 and BDE154 accordingly to the factorial design (exposure no. 1–3, 5 and 8 (N = 5) and 4, 6, 7 and 9 (N = 3)). The model is based on normalized expression( as go 2 2

O YP1A mo

0g

acrpccte

tbe

c((tttlctwa

MNE) values of nine experimental objects, and had one PLS component. The model wnly the linear term for BDE47 was significant. (B) Predicted versus the measured Cbjects.

.88 and the Q2-value was 0.66; the factorial design PLS model hasood prediction capabilities (Lundstedt et al., 1998).

The predicted versus the observed mean CYP1A levels (MNE)re presented in Fig. 4B (obtained after cross-validation). Only theenter points, nine of the experimental points, diverged from theegression line which means that the response was more com-lex than the factorial design model can reveal. Factorial designan only detect main responses and interaction between chemi-als in mixtures. However, there were good coherence betweenhe models prediction ability and the actual measured CYP1A lev-ls.

A contour plot analysis of the negative interaction term, iden-ified in the CYP1A PLS model, indicated an additive responseetween the two chemicals, BDE47 and BDE153, in the PBDE-xposed hepatocytes (data not shown).

The best PLS model for the analysis of the measured VTG levelsontained one PLS component and four PLS regression coefficientsFig. 5A). The model had two significant linear terms for BDE47p = 0.019) and BDE154 (p = 0.038) and one not-significant nega-ive linear term for BDE153 (p = 0.8219). The two significant linearerms for BDE47 and BDE154 indicated that both congeners con-ributed to the transcriptional up-regulation of VTG. BDE47 has aarger regression coefficient than BDE154, and thus, have a larger

ontribution to the VTG up-regulation than BDE154. Furthermore,he best model had one negative interaction term (p = 0.126), whichas not significant. The model’s R2-and the Q2-values were 0.87

nd 0.55, respectively.

od (R = 0.88 and Q = 0.66), containing three linear terms and one interaction term.RNA MNE levels for the best obtained PLS model containing all nine experimental

Fig. 5B shows the predicted and the measured VTG expressionvalues and all nine experimental points were included in the model.The exposure points diverged somewhat from the straight line(R2 = 0.87), illustrates that there are a higher degree of uncertaintyin this model compared to the CYP1A model since and that themodel has a lower R2 and Q2-values, even though, no outliers wereidentified and eliminated from the model.

The contour plots in Fig. 6 show equipotent concentration ofBDE153 and BDE154 with BDE47 kept constant. The plot had antag-onistic isobols for BDE153 and BDE154 illustrates that BDE153 andBDE154 have and antagonistic combined effect on the measuredVTG mRNA levels in PBDE-MIX exposed hepatocytes.

4. Discussion

In a worldwide assessment of the PBDE concentrations foundin biota and in the environment, done by Hites (2004), the mean∑

PBDE concentrations measured in river sediment samples and inambient air were found to be 203 ng/g lipid weight and 76.8 pg/m3,respectively. Hites (2004) further found that the North American∑

PBDE concentration was on average 10 times higher (1050 ng/glipid weight) than calculated for European freshwater and marinefish (119 ng/g lipid weight), suggesting that North American fish in

general are exposed to higher levels of PBDE than those in Europe.However, as different agencies and authors often include differ-ent congeners in their

∑PBDE mix, these data are uncertain. Like

for rest of Europe, generally low PBDE concentrations have been

256 L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263

Fig. 5. (A) Scaled and centered PLS regression coefficients with 95% confidence intervals for VTG mRNA levels measured in primary Atlantic salmon hepatocytes exposedt 3, 5 an( el wat rsus tn

mHsiMtt(

Fv

o BDE47, BDE153 and BDE154 accordingly to the factorial design (exposure no. 1–MNE) values of nine experimental objects, and had one PLS component. The moderm. Only the linear terms for BDE47 and BDE154 were significant. (B) Predicted veine experimental objects.

easured in fillet of Norwegian Atlantic salmon (www.nifes.no).owever, extremely high PBDE concentrations have been mea-

ured in burbot whole fish (∑

PBDE 19,675 ng/g lipid weight) andn fillet of trout (

∑PBDE 6281 ng/g lipid weight) caught in the lake

jøsa, Norway (Mariussen et al., 2008). The 1 �M exposure concen-

ration used for the different PBDE congeners in this in vitro study isherefore relevant for the environmental levels found in some fishMariussen et al., 2008).

ig. 6. Contour plots of the VTG mRNA mean normalized expression (MNE) values as a funalues in the plot represent VTG levels for the different stratification beddings (isoboles).

d 8 (N = 5) and 4, 6, 7 and 9 (N = 3)). The model is based on normalized expressions good (R2 = 0.87 and Q2 = 0.55), containing three linear terms and one interactionhe measured VTG mRNA MNE levels for the best obtained PLS model containing all

This experiment attempted to unravel the toxic mechanismsof PBDEs, alone or in combination, in Atlantic salmon using tran-scriptomic and proteomic analysis of primary Atlantic salmonhepatocytes exposed to three of the environmental most abun-dant PBDE congeners. The analysis revealed the regulation of a

small set of stress related transcripts and proteins in primaryhepatocytes isolated from male Atlantic salmon. Both correspon-dence analysis and hierarchical clustering separated the BDE47 and

ction of BDE153 (�M) and BDE154 (�M), keeping BDE47 constant. The highlighted

Toxico

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L. Søfteland et al. / Aquatic

BDE-MIX treated groups from the BDE153 and BDE154 treatedroups. According to the number of genes with significant expres-ion changes and indicated by the PCA plot, BDE47 and PBDE-MIXxposure had the strongest effect. The GSEA and PLS analysis indi-ate that BDE153 and BDE154 exposure produced a similar effects BDE47 and PBDE-MIX in the cells. Even though that BDE47 andBDE-MIX exposure gave the strongest effect, the GSEA revealedhat BDE154 exposure affected the highest number of gene sets.he reason for this might be that the cGRASP array has a largeumber of genes annotated as unknown which were excluded

rom the functional enrichment analyses, and therefore reduced theomprehensiveness of the functional classification of the enrichedata. This is in line with the findings of Tröße et al. (2009), wholso used the 16K cGRASP array. In species with poorly annotatedenomes, it can sometimes be difficult to characterize differentiallyxpressed proteins identified with mass spectrometry methods likeALDI-TOF/TOF. However the method is capable to identify highly

onserved proteins with peptide sequences matching homologousroteins in other species. The obtained transcriptomic and theroteomic data complemented each other nicely. The differentlyxpressed transcripts and proteins detected in cells exposed toBDEs belong mainly to biological processes related to xenobioticiotransformation, regulation of cell cycle control and proliferation,ndocrine metabolism and glucose homeostasis regulation. How-ver, no correlation was observed between the identified proteinsnd their transcriptional levels. The correlation between proteinnd transcript levels can be low (Denslow et al., 2006), especiallyor long-lived proteins. Transcriptional and post-transcriptional asell as secretion and degradation events determines the protein

evels in a cell, and due to the time-difference between mRNA androtein expression, an exact correlation between mRNA and pro-ein levels is often not likely (Conrads et al., 2005; Kuo et al., 2005;ratt et al., 2002). However Cox and Mann (2007) found, by com-aring an Affymetrix microarray data set with a proteomic dataet from exposed HeLa cells, a large overlap between the expres-ion profiles of the two data sets. Nevertheless vast quantities werenly expressed as a protein or a transcript. Further investigationevealed that a large number of the corresponding genes of thedentified proteins were not on the microarray chip. This suggestshat with further development of proteomic and microarray tech-iques, and enhanced gene annotation for non-model species liketlantic salmon, the data from proteomic and transcriptomic exam-

nations may correspond to a higher degree in the future.Both acute and chronic stress to environmental contaminates

an result in mobilization of energy reserves by activation ofipolysis, gluconeogenesis and glycolysis pathways as part of theecondary stress responses to restore homeostasis in fish (Iwamat al., 2007; Wendlaar Bonga, 1997). Increased expression levelsf transcripts coding for proteins involved in energy metabolismathways like glycolysis have previously been found in Atlanticod (Gadus morhua) exposed to low levels of alkylphenols (Liet al., 2009). NROB2 and Enol1 transcripts, as well as GRHPR andLD proteins, all linked to glucose metabolism, were differentiallyxpressed in the PBDE-MIX treated and the GSEA revealed that theglycolysis” and glucose catabolic process” gene sets were effectedn BDE153 treated cells. Peroxisome proliferator-activated recep-ors (PPARs) are involved in fatty acid and glucose metabolismKliewer et al., 1999). NROB2 is found to enhance the level ofPAR� in mammalian cells (Nishizawa et al., 2002) and is furthern important receptor inhibitor for the maintenance of glucose andile acid homeostasis and hepatic glucocorticoid action (Lu et al.,000); responses regulated through the glucocorticoid receptor

GR) pathway (Borgius et al., 2002). GRHPR, a key enzyme in thelyoxylate cycle contributing to glucose synthesis, is regulated byPAR� in rodents under physiological stress like fasting (Genolett al., 2005). Two proteins with some homology with GRHPR were

logy 105 (2011) 246– 263 257

differentially regulated in the present study, one with a FC of 3.82and the other with a FC of −11 in hepatocytes exposed to thePBDE-MIX. DLD (2.05 fold up-regulated) is on the other hand anelement of the mitochondrial pyruvate dehydrogenase (PDH) com-plex involved in conversion of pyruvate to acetyl-CoA under thecontrol of hypoxia inducible factor 1 (HIF-1) receptor in mammals(Ferreira and Campos, 2009). The results indicate that exposure tothe PBDE-MIX, and possibly also to BDE153, mediate a disturbanceof glucose metabolism in primary Atlantic salmon hepatocytes.

For sustainability of cellular homeostasis it is important to main-tain a balance between cell proliferation and apoptosis processesto prevent cancer development (Gregus, 2008). In mammals, PBDEsare possibly acting as carcinogens (Siddiqi, 2003). BDE47 has beenfound to induce DNA damage in mammalian model system (Barnes-Nkrumah et al., 2006; He et al., 2008) in the concentration rangeused in the present study. In association with the DNA damage,Barnes-Nkrumah et al. (2006) found increased cell viability after48 h exposure to BDE47 due to induction of cell proliferation. Asimilar concentration dependent increase in the cell viability (MTT)was also observed in the present study in cells exposed to BDE47(0.01–100 �M), however not significant. Only BDE153 exposureyielded a significant increase at the lowest concentration (0.01 �M),however the reason for this is not known. The transcriptomic andproteomic analysis of Atlantic salmon hepatocytes exposed to thePBDE-MIX also revealed different expressions of several proteinsand transcripts as well as enriched gene sets involved in regu-lation of cell proliferation processes. CaM2 and GRP94 were twoof the proteins found differentially regulated in the proteomicexamination in cells exposed to the PBDE-MIX. CaMs are impor-tant calcium-binding transducer of calcium signals (Stull, 2001)involved in cell proliferation stimulation and shortening of the cellcycle (Rasmusen and Means, 1990). The stress protein GRP94 assistsin folding and assembly of proteins in the endoplasmic reticulumand is known to inhibit apoptosis by sustaining cells calcium home-ostasis (Chen et al., 2002). Tob1, on the other hand, is a tumoursuppressor genes which suppress cell proliferation (Suzuki et al.,2001; Miyasaka et al., 2008) by making cells go into a cell cyclearrest, preventing mutations from passing on to daughter cells byletting the cells to carry out DNA repair (Gregus, 2008). Tob1 hasbeen documented to be transcriptionally up-regulated in TCDDexposed mammals (Boverhof et al., 2006) and in EE2 exposed rain-bow trout (Oncorhynchus mykiss) hepatocytes (Finne et al., 2007)and was one of the transcripts with elevated expression in cellsexposed to the PBDE-MIX. In contrast, the neuroblastoma sup-pressor of tumourgenecity 1 precursor was down-regulated in theBDE154 treated group. BDE154 was the only PBDE congener thathad a significant increase in the cell cytotoxicity at the highestconcentration (100 �M) and to have enriched gene set related tothe induction of apoptosis pathways (all with FDR < 1%). There hasbeen some speculation that cell proliferation in the liver can be anadaptive response to toxicity to restore injured tissue, a responseobserved in TCDD exposed animals (Mandal, 2005; Van Veld andNacci, 2008). The effects seen on cell cycle control and prolifera-tion processes in the PBDE-MIX-exposed hepatocytes, suggest thatPBDE affected cell proliferation processes as previously shown forTCDD exposure.

The liver is the main target organ of PBDEs in the fish. PBDE-exposed fish have been found to accumulate higher levels of BDE47compared to rats due to a lower biotransformation rate (van derVen et al., 2008). In juvenile carp, Stapleton et al. (2004) foundthe bioaccumulation factor of BDE47 (1.36) to be significantlyhigher than calculated for different PCBs, which can imply that

fish exposed to moderate levels of BDE47 in the environmentmight reach accumulation levels of concern (Stapleton et al., 2004).Most organic xenobiotics are excreted via biotransformation mech-anisms in the liver, with cytochrome P450 (phase I) and phase

2 Toxico

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a2sittPobCR2twta

58 L. Søfteland et al. / Aquatic

I enzymes as the most important effectors. Since no PCDD/Fs-mpurities were detected in the BDE47 and BDE153 stocks usedn this study (see Appendix A, Table 5) and having in mind thatDE47 is a CYP2B inducer in mammals, it was surprising to find

significant transcriptional up-regulation of CYP1A in the BDE47nd PBDE-MIX-treated cells. We cannot rule out that impuritiesn the PBDE stocks caused the detected CYP1A induction, dueo the inability of the gas chromatography coupled mass spec-rometry (GC–MS) method used to analyze the PBDE standardso discriminate between PBDEs from impurities; especially furannalogs (Stapleton, 2006). However, some studies indicate thatYP1A induction in animals exposed to PBDEs cannot be causedy PCDD/F-impurities detected in the PBDE stocks. In an expo-ure study with rats (Rattus norwegicus) the PCDD/F concentrationuantified in the PeBDE stock was too low to cause the detectedYP1A induction (van der Ven et al., 2008). In zebrafish exposedo purified PeBDE, BDE47 being the most prominent PBDE con-ener in the mixture, CYP1A was induced in liver after one monthf exposure (Kuiper et al., 2006). Further, CYP1A was significantlyp-regulated in F344 rats exposed to high BDE47 concentrationSanders et al., 2005), indicating that BDE47 might be a weak CYP1Anducer. Potent CYP1A inducer like PCDD is known to only up-egulate CYP1A, repressing the estrogenic receptor (ER)-pathwayTilton et al., 2006; Søfteland et al., 2010) in teleosts. If possibleCDD/F-impurities in the BDE47 stock caused the CYP1A induc-ion observed in this study; we would expect a down-regulationf estrogenic related genes. Weak CYP1A inducers may act dif-erently in fish test system since regulation of CYP1A in fish isifferent from mammals, e.g. fish having several AhRs (Hahn andestermann, 2008) and low CYP2K/CYP2M and CYP3A inductionbilities. The current microarray analyses of PBDE exposed hepato-ytes revealed that the fish CYP3A and CYP2B-like enzymes CYP2Knd CYP2M were not significantly differently expressed. Phenobar-ital (PB) has been used as a model toxin for CYP2B induction inammals (Schlenk et al., 2008). PB exposure did not up-regulate

YP2K1 mRNA levels and PROD enzyme activity in primary rain-ow trout hepatocytes (Sadar et al., 1996). However, like PBDE,B has been shown to induce CYP1A mRNA and EROD activity inainbow trout hepatocytes with CYP1A induction being both tran-criptional and post-transcriptional regulated (Sadar et al., 1996).on-dioxin-like PCBs and other chemicals that have low affinity

or AhR have previously been shown to induce CYP1A in mammalsAix et al., 1994; McFarland and Clarke, 1989; Puga et al., 1992).ince BDE47 have low affinity for the AhR, the CYP1A up-regulationbserved in the present study may be due to BDE47 modulationf AhR by altering the phosphorylation state or other com-onents in the transcriptional-activation-machinery of CYP1A1

nduction (Sadar et al., 1996) or due to cross-talk with other signalathways.

The lack of CYP1A induction observed in zebrafish (Danio rerio)nd salmonids exposed to BDE47 and PBDE mixtures (Boon et al.,002; Hook et al., 2006) may be explained by a general tran-criptional down-regulation due to hepatocellular apoptotic cellnjury. In a study by Hook et al. (2006), most genes were lowerranscribed in rainbow trout exposed to BDE47. A similar observa-ion has been done in male rats exposed to high concentrations ofeBDE (van der Ven et al., 2008). Contrary to the observed elevationf CYP1A in BDE47 exposed primary Atlantic salmon and rain-ow trout hepatocytes (Nakari and Pessala, 2005), a correspondingYP1A induction has not been detected in the rainbow trout cell lineLT-W1 and other mammalian cells exposed to BDE47 (Chen et al.,001; Kuiper et al., 2006). Primary mammalian hepatocyte cell cul-

ures have previously shown increased CYP1A induction sensitivityhen exposed to PBDE compared to mammalian cell lines. In con-

rast to primary cell cultures, mammalian cell lines are in generalcknowledged to lack CYP2B and CYP3A expression (Wahl et al.,

logy 105 (2011) 246– 263

2008). Further, rainbow trout RLT-W1 cells are known to be defi-cient in VTG expression (Bols et al., 2006), which may explain theincreased sensitivity of CYP1A induction in PBDE exposed primaryAtlantic salmon and rainbow trout hepatocyte cell cultures.

The phase II enzyme UDP-glucuronosyl transferase (UGT) haspreviously been found to be transcriptionally up-regulated in com-bination with CYP1A by potent CYP1A inducers in salmonid primaryhepatocytes. Other genes associated with the AhR-pathway suchas glutathione S-transferase and aldehyde dehydrogenase, are notnecessarily co-regulated with CYP1A in salmonids (Finne et al.,2007; Søfteland et al., 2009). Neither of these genes were up-regulated in BDE47 nor PBDE-MIX exposed cells in the presentstudy.

Since toxicity testing of mixtures will not provide informationabout combined actions and/or interactions between individualcomponents, the CYP1A response was further evaluated in hepa-tocytes exposed in a full factorial design experiment and evaluatedwith PLS analysis. The resulting PLS model showed high predictabil-ity. Previous mixture studies have shown antagonistic effects onCYP1A induction in mammalian cell lines when BDE47 is given incombination with potent CYP1A inducers like TCDD (Peters et al.,2006), suggesting that the toxic action of BDE47 is different in fishand mammals. The toxic equivalent (TEQ) approach has tradition-ally been used in risk evaluation of dioxins (Van Den Berg et al.,2006). Like for non-dioxin-like PCBs, PBDE toxicity is thought tobe induced by other mechanisms than those activated via the AhR,and would therefore not be included in toxicity assessments usingthe TEQ method. The CYP1A induction observed in cells treatedwith BDE47 in the current examination, indicates that this PBDEcongener contribute to fish specific TEQ and should therefore beaccounted for in future risk assessments.

The egg-yolk protein precursor VTG and the eggshell membraneprotein ZP, are produced in hepatocytes in female fish under estro-genic stimulation of ovarian follicle development (Hinton et al.,2008). Both became highly up-regulated in EE2 exposed rainbowtrout (Gunnarson et al., 2007) and in primary Atlantic salmon hepa-tocytes exposed to the estrogenic like chemical endosulfan (Krøvelet al., 2010). The VTG transcript was significantly elevated in cellsexposed to both the PBDE-MIX and BDE47, with fold differences of2.94 and 1.78, respectively, whereas ZP3 was 1.88 fold up-regulatedin cells exposed to the PBDE-MIX. VTG and ZP were not found to bedifferently expressed in PeBDE and OBDE exposed juvenile Atlanticsalmon and European flounder (Platichthys flesus) (Boon et al., 2002;Kuiper et al., 2008). However, BDE47 has been shown to be weakestrogenic in both mammalian in vitro models (Hamers et al., 2006;Kojima et al., 2009) and primary rainbow trout hepatocyte cell cul-tures (Nakari and Pessala, 2005).

To be able to identify interaction effects between the examinedPBDE congeners, the VTG response was further analyzed with PLSas described for CYP1A above. The VTG PLS model showed thatboth BDE47 and BDE154 had a significant contribution to the VTGresponse; acting additive in the PBDE-MIX. BDE153 has previouslybeen shown to have an antiestrogenic effect in exposed mammaliancell lines (Hamers et al., 2006; Kojima et al., 2009; Meerts et al.,2001), in line with our finding suggesting that BDE153 acted antag-onistic in co-exposure with BDE154. The mechanism behind theantagonism of BDE153 might be cross talk between the AhR and ERpathways, e.g. that AhR could act as a repressor of ER or reduce ERsbinding to the estrogenic-responsive element (Meerts et al., 2001).

The BDE99 congener detected in the BDE154 and PBDE-MIXexposure medium at the end of the experiment has previously beenshown to increase EROD activity and the synthesis and secretion ofvitellogenin in primary rainbow trout hepatocytes. The low BDE99concentrations can therefore have contributed to the CYP1A and

VTG and ZP3 responses induced in BDE154 and PBDE-MIX exposedprimary Atlantic salmon hepatocytes.

L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263 259

Table 5Impurities detected with gas chromatography coupled mass spectrometry (GC–MS) in BDE47 and BDE153 stocks.

PBDE stock Peak GC–MS chromatograph(% in stock)

Compounds

BDE47 Peak 1 0.206 Probably acetylated dibromophenolBDE47 Peak 2 0.119 Tribromodiphenyl etherBDE47 Peak 3 0.051 Unknown but not dioxins or furans likeBDE47 Peak 4 0.045 Unknown, but not dioxins or furans likeBDE47 Peak 5 0.006 Unknown, but not dioxins or furans likeBDE47 Peak 6 99.222 BDE47BDE47 Peak 7 0.082 Methoxy tetrabromodiphenyl etherBDE47 Peak 8 0.019 Chloro tetrabromodiphenyl etherBDE47 Peak 9 0.249 Tetrabromodiphenyl ether (isomer of BDE47)BDE153 Peak 1 0.210 Unknown, but not dioxins or furans likeBDE153 Peak 2 98.650 BDE 153BDE153 Peak 3 1.140 Heptabromodiphenyl ether

The PBDE stocks were analyzed with GC–MS by Chiron.

Table 6Enriched gene sets in GSEA of PBDE-exposed Atlantic salmon hepatocytes.

Exposure Regulation Gene Set Size p-Value FDR%

BDE153 Up Endoplasmic reticulum 77 0.0 2.96BDE153 Up Protein complex 11 0.0 4.54BDE153 Up Macromolecular complex 11 0.0 3.03BDE153 Up Circulation 25 0.0 2.65BDE153 Up Hemopoiesis 12 0.0 4.81BDE153 Up Immune system development 12 0.0 4.01BDE153 Up Hemopoietic or lymphoid organ development 12 0.0 3.43BDE153 Up Hexose catabolic process 11 0.0 4.44BDE153 Up Glycolysis 11 0.0 3.95BDE153 Up Glucose catabolic process 11 0.0 3.55BDE153 Up Lipid transport 16 0.0 4.18BDE153 Up Angiogenesis 13 0.0 4.85BDE153 Up Myeloid leukocyte differentiation 10 0.0 4.83BDE153 Up Myeloid cell differentiation 10 0.0 4.49BDE153 Up Alcohol catabolic process 13 0.0 5.86BDE153 Up Monosaccharide catabolic process 13 0.0 5.49BDE153 Up Cellular catabolic process 36 0.0 5.27BDE153 Up Cell migration 12 0.01 8.52BDE153 Up Protein homodimerization activity 17 0.01 9.1BDE153 Up Lipid transporter activity 18 0.01 9.22BDE153 Up SH3/SH2 adaptor activity 12 0.01 10.02BDE154 Up Molecular adaptor activity 13 0.0 1.45BDE154 Up Induction of apoptosis 23 0.0 0.95BDE154 Up Negative regulation of cell proliferation 19 0.0 0.67BDE154 Up Protein dimerization activity 27 0.0 0.57BDE154 Up Nucleobase, nucleoside, nucleotide and nucleic

acid metabolic process35 0.0 0.84

BDE154 Up Protein domain specific binding 12 0.0 0.9BDE154 Up Positive regulation of apoptosis 24 0.0 0.87BDE154 Up Positive regulation of programmed cell death 24 0.0 0.76BDE154 Up Transcription 14 0.0 0.76BDE154 Up Negative regulation of cell growth 13 0.0 0.79BDE154 Up Negative regulation of cell size 13 0.0 0.72BDE154 Up SH3/SH2 adaptor activity 11 0.0 0.66BDE154 Up Cellular structure morphogenesis 34 0.0 0.76BDE154 Up Cell morphogenesis 34 0.0 0.71BDE154 Up Regulation of cell size 17 0.0 0.88BDE154 Up Regulation of transcription 12 0.0 0.84BDE154 Up Hemopoiesis 12 0.0 0.94BDE154 Up Immune system development 12 0.0 0.89BDE154 Up Hemopoietic or lymphoid organ development 12 0.0 0.84BDE154 Up Cell migration 12 0.0 1.03BDE154 Up Protein complex 10 0.0 1.51BDE154 Up Macromolecular complex 10 0.0 1.44BDE154 Up Cell motility 28 0.0 1.59BDE154 Up Localization of cell 28 0.0 1.52BDE154 Up Microtubule cytoskeleton 14 0.0 1.63BDE154 Up Cytoskeleton-dependent intracellular transport 10 0.0 1.67BDE154 Up Chromosomal part 15 0.0 1.61BDE154 Up Macromolecule localization 34 0.0 1.73BDE154 Up Cell surface receptor linked signal transduction 42 0.0 2.31BDE154 Up Protein localization 30 0.0 2.26BDE154 Up Negative regulation of protein metabolic process 15 0.0 2.34BDE154 Up Myeloid leukocyte differentiation 10 0.01 2.47BDE154 Up Myeloid cell differentiation 10 0.01 2.39BDE154 Up Immune response 21 0.0 2.44

260 L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263

Table 6 (Continued )

Exposure Regulation Gene Set Size p-Value FDR%

BDE154 Up Angiogenesis 13 0.0 2.46BDE154 Up Blood vessel morphogenesis 13 0.0 2.39BDE154 Up Regulation of protein metabolic process 19 0.0 2.46BDE154 Up Protein metabolic process 19 0.0 2.4BDE154 Up Membrane fraction 36 0.0 2.4BDE154 Up Sphingolipid metabolic process 11 0.01 2.53BDE154 Up Protein transport 14 0.01 2.5BDE154 Up Protein heterodimerization activity 17 0.01 2.62BDE154 Up Intracellular non-membrane-bound organelle 98 0.0 2.78BDE154 Up Cytoskeleton organization and biogenesis 30 0.01 3.55BDE154 Up Cytoskeleton 76 0.0 3.75BDE154 Up Blood vessel development 17 0.01 4.09BDE154 Up Vasculature development 17 0.01 4.01BDE154 Up Actin filament-based process 23 0.01 4.18BDE154 Up Actin cytoskeleton organization and biogenesis 23 0.01 4.09BDE154 Up Intracellular transport 80 0.0 4.39BDE154 Up Establishment of protein localization 18 0.02 4.58BDE154 Up Positive regulation of cellular process 51 0.0 5.33BDE154 Up Response to organic substance 12 0.02 5.48BDE154 Up Actin binding 13 0.02 5.69BDE154 Up Protein homodimerization activity 15 0.01 6.08BDE154 Up Nucleosome 11 0.02 6.13BDE154 Up Chromatin 11 0.02 6.02BDE154 Up Cell fraction 52 0.0 5.94BDE154 Up Phosphoric diester hydrolase activity 15 0.02 6.34BDE154 Up Microsome 24 0.01 6.33BDE154 Up Vesicular fraction 24 0.01 6.23BDE154 Up Response to protein stimulus 12 0.03 6.3BDE154 Up Response to unfolded protein 12 0.03 6.2BDE154 Up Leading edge 19 0.03 6.97BDE154 Up Regulation of progression through cell cycle 27 0.02 7.05BDE154 Up Regulation of cell cycle 27 0.02 6.95BDE154 Up Proteolysis 22 0.02 7.33BDE154 Up Regulation of cellular process 129 0.0 7.42BDE154 Up Establishment of cellular localization 88 0.01 7.46BDE154 Up Membrane lipid metabolic process 15 0.03 7.71BDE154 Up Nucleotide metabolic process 12 0.03 7.64BDE154 Up Chromosome 24 0.03 8.02BDE154 Up Cellular localization 91 0.01 7.96BDE154 Up Cellular component organization and biogenesis 88 0.0 9.07BDE154 Up Nucleosome assembly 19 0.04 9.57BDE154 Up Protein-DNA complex assembly 19 0.04 9.44BDE154 Down Structural constituent of ribosome 86 0.0 0.12BDE154 Down Structural molecule activity 103 0.0 0.12BDE154 Down Eukaryotic 48S initiation complex 43 0.0 0.87BDE154 Down Cytosolic small ribosomal subunit (sensu

Eukaryota)43 0.0 0.65

BDE154 Down Translation 114 0.0 0.73BDE154 Down Eukaryotic 43S preinitiation complex 13 0.0 2.69BDE154 Down Eukaryotic translation initiation factor 3 complex 10 0.0 2.43BDE154 Down Cytosolic large ribosomal subunit (sensu

Eukaryota)41 0.0 5.23

BDE154 Down Cytosolic ribosome (sensu Eukaryota) 41 0.0 4.65BDE154 Down Cytosolic part 41 0.0 4.18BDE154 Down Nucleobase, nucleoside, nucleotide kinase activity 10 0.01 7.78BDE154 Down Translational initiation 29 0.01 7.44PBDE-MIX Up Molecular adaptor activity 17 0.0 0.56PBDE-MIX Up SH3/SH2 adaptor activity 14 0.0 0.7PBDE-MIX Up Negative regulation of cell proliferation 26 0.0 1.24PBDE-MIX Up protein domain specific binding 15 0.0 10.3PBDE-MIX Up Response to organic substance 14 0.0 12.06PBDE-MIX Up Leading edge 23 0.0 15.32PBDE-MIX Up Cell cycle 16 0.0 18.36PBDE-MIX Up Intermediate filament cytoskeleton 10 0.01 16.38PBDE-MIX Up Intermediate filament 10 0.01 14.56PBDE-MIX Up Second-messenger-mediated signaling 11 0.0 13.86PBDE-MIX Up Anatomical structure morphogenesis 10 0.01 14.75PBDE-MIX Up Microtubule cytoskeleton 17 0.01 14.74PBDE-MIX Up Regulation of transcription 19 0.01 16.95PBDE-MIX Up Angiogenesis 19 0.01 18.39PBDE-MIX Up Blood vessel morphogenesis 19 0.01 17.24PBDE-MIX Up Ruffle 17 0.01 17.9PBDE-MIX Up Transcription 20 0.01 16.98PBDE-MIX Up Cytoskeleton 94 0.0 18.91PBDE-MIX Up Cell migration 17 0.01 19.52

PBDE-MIX is composed of BDE47, BDE153 and BDE154. The table shows top ranked gene sets obtained with gene set enrichment analysis (GSEA) for BDE153 and BDE154with a FDR < 10% and PBDE-MIX with FDR < 20%.

L. Søfteland et al. / Aquatic Toxicology 105 (2011) 246– 263 261

Table 7Differentially expressed proteins identified in Atlantic salmon hepatocytes exposed to PBDE-MIX and 0.1% DMSO. Protein information is obtained by Mascot analysis againstthe NCBInr database data viewed using the Yale protein expression database (YPED) web browser.

YPEDspot no.

Accession no. Protein Identified Species Protein MW pI DB searchscore

Total ionscore

Percentcoverage

Peptide no

625 gi|110226526 GRP94 Paralichthys olivaceus 92066,2891 4,69999981 457 402 27 201584 gi|47216117 Unnamed, some

homology to GRHPRTetraodon nigroviridis 39546,9492 8,38000011 279 249 25 8

1610 gi|47216117 Unnamed, somehomology to GRHPR

Tetraodon nigroviridis 39546,9492 8,38000011 255 225 26 8

1147 gi|47209763 DLD Tetraodon nigroviridis 49693,4883 6,67999983 174 119 32 12546,1

G tase:

5

aibmPoltsc

A

cmpotA(tbRG

A

R

A

B

BB

B

B

B

B

2304 gi|74219094 CaM2 Mus musculus 21

lucose-regulated protein 94: GRP94; glyoxylate reductase/hydroxypyruvate reduc

. Conclusion

The omic analysis of Atlantic salmon hepatocytes exposed singlynd in combination to low concentration of the environmentalmportant PBDEs affected biological processes related to xeno-iotic biotransformation, regulation of proliferation, endocrineetabolism and glucose homeostasis regulation. Evaluation of the

BDE-MIX responses suggested that BDE47 with its up-regulationf CYP1A should be included in future risk assessment of dioxin-ike chemicals (TEQ) and that BDE47 and BDE154 contributes tohe observed estrogenic responses in male Atlantic salmon. Thistudy highlights the importance with combine effect analysis ofhemicals in mixtures.

cknowledgments

The authors like to thank Synnøve Winterthun NIFES, for herontribution with the cell cultures and Elisabeth Holen, NIFES, foranaging the strategic institute research program (SIP) which this

roject is part of. Ben F. Koop and Willie Davidson of the Consortiumf genome research on All salmon project at the University of Vic-oria, Canada (cGrasp) are thanked for providing the microarrays.tle van Beelen Granlund at the Norwegian Microarray Consortium

NMC) is acknowledged for performing the microarray hybridiza-ions at the national technology platform in Trondheim, supportedy the Functional Genomics Program (FUGE) in the Norwegianesearch Council (NRC). This work was funded by the NRC, NFRrant 173534/I30 and NIFES, Norway.

ppendix A.

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