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Aquatic Toxicology 126 (2013) 314– 325

Contents lists available at SciVerse ScienceDirect

Aquatic Toxicology

j ourna l ho me p ag e: www.elsev ier .com/ l ocate /aquatox

lobal transcriptome analysis of Atlantic cod (Gadus morhua) liver after in vivoethylmercury exposure suggests effects on energy metabolism pathways

ekadu Yadetiea,b,∗, Odd Andre Karlsena,b, Anders Lanzénc,d, Karin Berga, Pål Olsvike,hrister Hogstrandf, Anders Goksøyrb

Department of Molecular Biology, University of Bergen, Bergen, NorwayDepartment of Biology, University of Bergen, Bergen, NorwayBergen Center for Computational Science, University of Bergen, Bergen, NorwayDepartment of Biology and Centre for Geobiology, University of Bergen, NorwayNational Institute of Nutrition and Seafood Research (NIFES), Bergen, NorwayDiabetes and Nutritional Sciences Division, King’s College London, London, UK

r t i c l e i n f o

rticle history:eceived 14 August 2012eceived in revised form7 September 2012ccepted 23 September 2012

eywords:icroarray

ranscriptionethylmercury

oxicogenomicstlantic codiomarker

a b s t r a c t

Methylmercury (MeHg) is a widely distributed contaminant polluting many aquatic environments, withhealth risks to humans exposed mainly through consumption of seafood. The mechanisms of toxicity ofMeHg are not completely understood. In order to map the range of molecular targets and gain betterinsights into the mechanisms of toxicity, we prepared Atlantic cod (Gadus morhua) 135k oligonucleotidearrays and performed global analysis of transcriptional changes in the liver of fish treated with MeHg (0.5and 2 mg/kg of body weight) for 14 days. Inferring from the observed transcriptional changes, the mainpathways significantly affected by the treatment were energy metabolism, oxidative stress response,immune response and cytoskeleton remodeling. Consistent with known effects of MeHg, many trans-cripts for genes in oxidative stress pathways such as glutathione metabolism and Nrf2 regulation ofoxidative stress response were differentially regulated. Among the differentially regulated genes, therewere disproportionate numbers of genes coding for enzymes involved in metabolism of amino acids, fattyacids and glucose. In particular, many genes coding for enzymes of fatty acid beta-oxidation were up-

regulated. The coordinated effects observed on many transcripts coding for enzymes of energy pathwaysmay suggest disruption of nutrient metabolism by MeHg. Many transcripts for genes coding for enzymesin the synthetic pathways of sulphur containing amino acids were also up-regulated, suggesting adap-tive responses to MeHg toxicity. By this toxicogenomics approach, we were also able to identify manypotential biomarker candidate genes for monitoring environmental MeHg pollution. These results basedon changes on transcript levels, however, need to be confirmed by other methods such as proteomics.

© 2012 Elsevier B.V. All rights reserved.

. Introduction

Mercury is a widely distributed environmental contaminant ofajor concern to human health. Mercury from both natural and

nthropogenic sources such as mercury ores, gold mining activ-ties and discharges from fossil fuel combustion is emitted intohe atmosphere and returns in rain water in oxidized form back tohe Earth’s surface, where it can be re-emitted to the atmosphere

Clarkson, 1997). Inorganic mercury is converted to methylmer-ury (MeHg) by sulfate-reducing bacteria in sediments (Jensen andernelov, 1969). MeHg is one of the most toxic forms of mercury.

∗ Corresponding author at: Department of Biology, University of Bergen, PB 7803,-5020 Bergen, Norway. Tel.: +47 55 58 46 51; fax: +47 55 58 44 50.

E-mail address: Fekadu.Yadetie@mbi.uib.no (F. Yadetie).

166-445X/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.aquatox.2012.09.013

It is readily absorbed and bioaccumulates in fish and other aquaticorganisms. Almost all mercury found in fish muscle is in the form ofMeHg (Bloom, 1992). Consumption of fish and shellfish, particularlylarge predatory fish species is the main source of exposure of MeHgto humans (NRC, 2000). Epidemiological studies have suggestedassociations between prenatal MeHg exposure and neurodevelop-mental deficits in vulnerable communities dependent on fish asstaple food (Clarkson and Magos, 2006).

MeHg is a neurotoxic compound. Human poisoning incidentsby MeHg of industrial sources in Minamata, Japan and in Iraqcaused a range of neurotoxicological manifestations (Bakir et al.,1973; Harada, 1995). Several animal studies have also shown its

toxicity to central nervous system (NRC, 2000). The developingbrain is more susceptible to MeHg neurotoxicity (Shimai and Satoh,1985). MeHg is widely transported in the body and can cross bothblood–brain and blood–placental barriers (Simmons-Willis et al.,

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002). It has been shown to be transported across cellular mem-ranes via L-type amino acid transporters by molecular mimicrySimmons-Willis et al., 2002).

In addition to human health risk caused through consumption ofontaminated seafood, the health of wild populations of fish may beffected by mercury contamination in some aquatic environments.ercury levels that might cause adverse health effects in sensitive

nimals have been detected in several species of fish (Burger andochfeld, 2007; Peterson et al., 2007; Sandheinrich et al., 2011).ercury contamination has also been associated with reproduc-

ive impairment in fish, although the mechanisms involved are notnderstood (Crump and Trudeau, 2009).

After decades of research, there is still only limited under-tanding of the mechanisms of toxicity of MeHg. Neurotoxicitiesf MeHg in animal and cell culture studies appear to be related tonduction of oxidative stress (Farina et al., 2011; Kromidas et al.,990). Because of its high affinity for thiol groups, MeHg depleteshe antioxidant glutathione resulting in oxidative stress in cells. Itlso binds to free cysteine groups in proteins, which may lead toheir inactivation. Cellular targets affected have been highlightedn recent toxicogenomics and proteomics studies, and examplesf pathways affected include redox homeostasis, mitochondrialnergy metabolism, protein folding, and steroid biosynthetic pro-ess (Berg et al., 2010; Cambier et al., 2009; Glover et al., 2009;østbakken et al., 2012; Richter et al., 2011). Although the brainppears to be the most sensitive target organ, studies have alsoocumented toxic effects such as oxidative stress and disruption ofitochondrial functions by mercury compounds in multiple organs

n fish such as kidney, liver and skeletal muscle (Berg et al., 2010;ambier et al., 2012; Gonzalez et al., 2005; Nøstbakken et al., 2012;lsvik et al., 2011). As a major organ involved in vital processes

uch as nutrient metabolism, detoxification and protein synthesis,nalysis of transcriptional changes in the liver can provide usefulnformation on toxicity of MeHg. The liver is also one of the mostommonly analyzed organs in biomarker assessment.

The Atlantic cod is an important species in both North Atlanticsheries and in a developing aquaculture industry, and its genomeas been sequenced recently (Star et al., 2011). Atlantic cod can beusceptible to environmental contaminants in coastal regions andt is commonly used in environmental toxicology studies (Amlundt al., 2007; Balk et al., 2011; Bohne-Kjersem et al., 2009; Goksøyrt al., 1987, 1994; Lie et al., 2009; Meier et al., 2007; Olsvik et al.,009). Toxicogenomics studies can be used to illuminate the molec-lar targets and may help to better understand mechanisms ofoxicity of environmental pollutants (Hahn, 2011). Further, new

echanistic and exposure monitoring biomarkers may be devel-ped from such studies (Hahn, 2011). Previous EST sequencingrojects have led to construction of microarrays for analysis ofranscriptional changes in cod (Booman et al., 2011; Edvardsent al., 2011; Lie et al., 2009). Using the newly available G. morhuaranscriptome sequences from the cod genome sequencing projectStar et al., 2011), we have constructed 135k oligonucleotide arrayor analysis of global transcriptional changes. The objective of thistudy was to perform analysis of global transcriptional changesn cod liver in order to map transcriptional targets and possibleellular pathways affected by MeHg and better elucidate toxicityechanisms. Furthermore, we identify potential biomarker candi-

ate transcripts of genes for MeHg exposure and effects.

. Materials and methods

.1. Fish exposure and sampling

Fish exposure and sampling was described before (Berg et al.,010). Briefly, about 1.5 years old mixed gender (males and

logy 126 (2013) 314– 325 315

females) juvenile Atlantic cod (260–530 g body weight/BW) weremaintained at ILAB in Bergen, Norway in 500 l tanks supplied withcontinuously flowing seawater at 10 ◦C, 34‰ salinity and a 12 hlight/dark cycle and fed daily with commercial pellets (EWOS,Bergen, Norway). After acclimation for 6 days, the fish were dividedinto separate tanks and injected (i.p.) with vehicle (20% acetoneand 80% soybean), 0.5 and 2 mg/kg BW methylmercury chloride(Strem Chemicals, Newburyport, USA). The lowest dose used here(0.5 mg/kg BW) is within the range of concentrations of mercuryfound in some organs of wild fish population from contaminatedaquatic environments, e.g. see Sandheinrich et al. (2011). The2 mg/kg BW methylmercury chloride was well tolerated, whereas ahigher dose of 8 mg/kg BW was lethal to the fish (Berg et al., 2010).The doses were given in two aliquots with 1 week interval. After14 days of first injection, the fish were sacrificed and tissue sam-pled and frozen in liquid nitrogen and stored at −80 ◦C until use.The exposure experiment was approved by the National AnimalResearch Authority of Norway.

2.2. RNA extraction

Total RNA was isolated from the frozen liver samples using theRNeasy Mini Kit according to manufacturer’s protocols (QIAGEN,Hilden, Germany). RNA concentration and quality was assessedusing NanoDrop ND-1000 (NanoDrop Technologies, Wilmington,DE). RNA quality was further assessed using Agilent 2100 BioAn-alyzer (Agilent Technologies, Palo Alto, CA). RNA samples fromvehicle control, 0.5 and 2 mg/kg BW methylmercury (MeHg) doses(n = 5 per group) were submitted to Roche NimbleGen for labelingand hybridization.

2.3. Microarray design and hybridization

Custom made Atlantic cod (Gadus morhua) high-density 60-meroligonucleotide arrays were designed from the reference collec-tion of the cod transcriptome from the Cod Genome SequencingConsortium (Star et al., 2011) and manufactured by Roche Nim-blegen (Madison, WI). Probes were designed based on a selectionof 44k cDNA sequences. Only contig sequences (26,060 in total)with (i) a significant similarity (BLASTX bitscore >45) to knownprotein sequences in the UniRef90 database (Suzek et al., 2007) (15March 2010); or (ii) a significant match to the PFAM protein familydatabase (Bateman et al., 2002) with e-value <10−6, aligned usingHMMER (Eddy, 2009) were included in the selection. In addition,18,067 “singleton” sequences, i.e. reads that could not be assem-bled, were included, based on their having significant matches toUniRef90 clusters not included among those matching the con-tig sequences. The NimbleGen Gene Expression 12 × 135K Arraydesign was used. The array contains 125,825 probes derived fromthe G. morhua sequences (3 or more probes per cDNA sequence)and 11,779 Nimblegen control probes. RNA samples from five fishin each group (control, 0.5 and 2 mg/kg BW MeHg doses, 15 fish intotal) were submitted to Roche NimbleGen, where double strandcDNA synthesis and labelling with Cy3 and one-color hybridiza-tion was performed according to protocols in Gene Expression UserGuide (Roche NimbleGen, Madison, WI). Thus, the experiment hada simple design where each dose group (0.5 or 2 mg/kg BW MeHg)was directly compared with the control group (n = 5).

2.4. Microarray data analysis

After control spots were removed, the normalized arrays were

filtered as follows. Probes with mean intensity values less than 300(average intensity of negative control probes) in both control andtreated samples were removed. For analysis of differential regula-tion, expression ratios were calculated for each probe by dividing

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16 F. Yadetie et al. / Aquatic T

he fluorescence intensity value in the treated fish by the meanuorescence intensity value in the control fish. Probes with meanxpression ratios (treated/control for 2 mg/kg BW MeHg) greaterhan 0.67 and less than 1.5 were removed. Differentially regulatedenes were identified using Significance Analysis of MicroarraysSAM) (Tusher et al., 2001) implemented in J-Express version009 (Dysvik and Jonassen, 2001) (Molmine, Bergen, Norway).hen replicate probes representing the same gene are present,

he probe with the most significant differential expression waselected. Log2-transformed ratio values from each of the treatmentoses (5 fish per group) and control fish (n = 5) were comparedsing unpaired t-test. For the 2 mg/kg BW MeHg dose and con-rol, unpaired t-test was performed using SAM (400 permutations)nd the top scoring differentially regulated 650 genes with FDR < 5%ere used in pathway analysis. The 0.5 mg/kg BW MeHg dose didot result in significantly regulated genes (with expression changes

n the same direction as the 2 mg/kg BW dose group) compared toontrols using SAM with the same cut-off (FDR < 5%). Heat-mapsnd principal component analyses of the differentially regulatedenes were performed in Qlucore Omics Explorer 2.0 (Qlucore AB,und, Sweden) software. The array data have been deposited inCBI’s Gene Expression Omnibus (GEO) database (GEO accessionSE38746).

.5. Annotation and pathway analysis

The Atlantic cod genes were mapped to putative humanrthologs for functional and pathway analyses. Each cod cDNAequence was annotated with the best hit in the human proteomen SwissProt NCBI database (BLASTX e-value < 10−6). Mappingsh genes to putative mammalian orthologs enables usinghe well-annotated databases for mammalian model organismsGarcia-Reyero et al., 2008; Wang et al., 2010), despite limitations ofhe mapping due to the extra genome duplication events in teleostsh and species differences in gene functions and pathways. Path-ay and functional analyses were performed in DAVID (KEGG andO) (Dennis et al., 2003). Enrichment analyses for GeneGo func-

ional ontologies Pathway Maps, Map Folders, Process Networks,nd Drug and Xenobiotic Metabolism Enzymes, as well as Interac-ome analyses were performed in MetaCore (GeneGo, St. Joseph,

I) (Ekins et al., 2006). Default settings (FDR < 0.05) were used innrichment analyses unless stated otherwise.

.6. Quantitative real-time PCR (qPCR)

For each RNA sample, total RNA (1.0 �g) was reverse-ranscribed using SuperScript III First-Strand Synthesis System forT-PCR in 20 �L reaction as described in the manufacturer’s pro-ocols (Invitrogen). Then the reaction was diluted 1:10, and 5 �L ofhe cDNA was used in 20 �L amplification reaction using FastStartYBR Green Master Mix according manufacturer’s protocols (Roche,asel, Switzerland), and qPCR was performed using LightCycler80 Real-Time PCR System (Roche). The thermal cycling conditionsere as follows: an initial denaturation of 95 ◦C for 10 min, followed

y 45 cycles of denaturation at 95 ◦C for 10 s and an annealing tem-erature of 55 ◦C for 20 s and elongation at 72 ◦C for 30 s. Negativeontrols with no reverse transcriptase enzyme were run for eachrimer pair and each sample was amplified in duplicate. Beta-actinnd RPB11a genes were used as a housekeeping reference forormalization of RNA levels. The mRNA expression levels of thesewo genes were not changing between control and treated groups.or each primer pair, serial dilutions of gel-purified PCR product

as amplified in the same plate and used to construct a standard

urve from which the relative concentrations of mRNA levels in thendividual samples were determined. Post-amplification meltingurve analysis was performed to check specificity of products. The

logy 126 (2013) 314– 325

PCR products were also analyzed by agarose gel electrophoresisto verify the amplification of a single product of the right size.Normalized relative mRNA expression level for each gene wasexpressed as fold-change relative to the control.

3. Results

3.1. Genes differentially regulated by MeHg

MeHg (2 mg/kg BW dose) resulted in differential regulationof transcripts for 650 genes (384 up-regulated and 266 down-regulated) (Table S1). The mean expression values for the groupwith low dose MeHg (0.5 mg/kg BW) are also presented for com-parison (Table S1), although they did not pass threshold usedfor differential regulation (FDR < 5%). However, there is a dose-response trend between the two doses (0.5 mg and 2 mg/kg BW)with levels changing in the same direction for more than 75% of thedifferentially regulated genes (Table S1). Hierarchical clustering ofthe differentially regulated genes also shows some dose-responsetrend although not all of the low dose (0.5 mg/kg BW) samples arewell separated from the control samples (Fig. 1A). Principal compo-nent analysis (PCA) shows a similar trend with the low dose groupclustering close to the control and a well separated high dose group(Fig. 1B). To perform functional analyses, the differentially regu-lated genes were mapped to the better annotated human proteomein Swissprot database (see below). All the differentially regulatedgenes were then subjected to various functional analyses such asGene Ontology and pathway enrichment analyses using DAVID andMetaCore (GeneGo).

3.2. Possible effects of MeHg on energy metabolism

The differentially regulated gene list was used to examineenriched GO and KEGG pathways in DAVID (Dennis et al., 2003).PANTHER Gene Ontology (GO) Biological Process (BP) and KEGGpathway analysis showed that the energy metabolism pathways,amino acid metabolism, glycolysis/gluconeogenesis, acyl-CoA andfatty acid metabolism were significantly enriched, suggesting theirmodulation in MeHg treated fish (Table 1A and B). Of the 20 genesinvolved in PANTHER GO BP Amino acid metabolism (which includesAmino acid catabolism), the mRNA levels of 16 were up-regulatedand the remaining four genes (SDHL, TY3H, XCT and LPP60)were down-regulated (Table S2A). Among the genes in Aminoacid metabolism pathway were 4 genes (CBS, AATM, AATC, SPYA)involved in cysteine and methionine metabolism (Table S2A, Fig. 2).One gene in this pathway encoding cystathionine beta synthase(CBS) is known to be involved in MeHg detoxification (Yoshida et al.,2011). Among the 10 genes in the KEGG pathway Valine, leucine andisoleucine degradation (Table S2F) 7 genes are shared with GO BP andKEGG pathway Fatty acid metabolism (Table S2C and D) and 2 areshared with GO BP Amino acid metabolism (Table S2A). Interestingly,all genes except one (KPYM) involved in the GO biological processGlycolysis and KEGG pathway Glycolysis/Gluconeogenesis were up-regulated (Fig. 3, Table S2B and E). Fourteen of the 16 genes involvedin GO BP Fatty acid metabolism (that includes Acyl-CoA metabolism)(Table S2C) and the 10 genes involved in KEGG pathway Fatty acidmetabolism (Table S2D) were also up-regulated.

The most consistent GO biological process (BP) term andpathway significantly affected by MeHg is related to Acyl-CoAmetabolism or Fatty acid metabolism (Table 1A and B). Fur-ther, acyl-CoA dehydrogenase is the only significantly enrichedprotein domain using INTERPRO, PFAM, PANTHER SUBFAMILY

and PROSITE databases (Table 1C). The genes with Acyl-CoAoxidase/dehydrogenase domains (INTERPRO, PFAM) and PAN-THER SUBFAMILY domains (ELECTRON TRANSPORT) (Table 1C) arethe subset of Acyl-CoA metabolism genes in the GO BP Fatty acid

F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325 317

F es diffr ormeda

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ig. 1. Hierarchical clustering (A) and principal component analysis (PCA) (B) of genegulated by MeHg (2 mg/kg BW) based on log2-transformed ratio values, was perfnd columns represent samples (individual fish and treatment received).

etabolism and include short-, medium- and long-chain acyl-CoAehydrogenases (Table S2C). Thus, acyl-CoA metabolism was at theop of pathways affected as revealed by the GO, KEGG pathways androtein domain analyses.

A proprietary database and analyses tool in MetaCore (GeneGo)as also used to analyze the differentially regulated transcripts.

ools in MetaCore enable more detailed analyses such as enrich-ent of pathways, networks, GO, toxicity and interactome for the

ene list. Glycolysis and other energy pathways were also in the

op pathways enriched in GeneGO pathway maps (Table 2A) and

ap folders (Table 2B). Consistent with KEGG pathway analyses,he top enriched pathways in GeneGo Metabolic Networks werearbohydrate, amino acid and lipid metabolism (Table 2C).

able 1ignificantly enriched gene ontology terms (A), KEGG pathways (B) and protein domains (Cates (FDR) are indicated.

Count

(A) Gene ontology termAmino acid catabolism 9

Amino acid metabolism 20

Glycolysis 8

Acyl-CoA metabolism 6

Fatty acid metabolism 16

(B) KEGG pathwayFatty acid metabolism 10

Glycolysis/gluconeogenesis 12

Valine, leucine and isoleucine degradation 10

Glutathione metabolism 10

(C) Protein domainsPFAM: Acyl-CoA dh M 8

PANTHER: Electron transport Oxidoreductase 8

PFAM: Acyl-CoA dhydrogenases 7

PROSITES: Acyl-CoA dehydrogenases 6

PF: Acyl-CoA dehydrogenase 6

bbreviations: PF, PFAM; PTHR, PANTHER FAMILY; PS, PROSITE. Redundant domain term

erentially regulated by MeHg. Hierarchical clustering and PCA of genes differentially in Qlucore Omics Explorer 2.0 (Qlucore AB, Lund, Sweden). Rows represent genes

3.3. Possible effects of MeHg on oxidative stress

Among the top KEGG pathways significantly enriched (using thelist of differentially regulated 650 genes as input) was also Glu-tathione metabolism, which is related to oxidative stress response(Table 1B). The mRNA levels of the 10 genes in the KEGG path-way Glutathione metabolism, which include transcripts for fourglutathione S-transferases (GSTs) were up-regulated (Table S2G)suggesting increased synthesis of anti-oxidant enzymes. Similarly,

the significantly enriched top GeneGo pathways include NRF2 reg-ulation of oxidative stress response (Table 2A). The map for thispathway with the MeHg affected genes indicated is shown inFig. 4. These genes include many anti-oxidant genes (phase II drug

). Uncorrected p-values, Benjamini-Hochberg corrected p-values and false discovery

p-Value Benjamini FDR

1.7E−04 2.9E−02 0.22.0E−04 1.7E−02 0.25.6E−04 3.2E−02 0.78.1E−04 3.5E−02 1.08.9E−04 3.1E−02 1.1

3.4E−05 5.5E−03 0.03.6E−05 2.9E−03 0.07.6E−05 4.1E−03 0.12.2E−04 8.7E−03 0.3

2.6E−07 3.0E−04 4.1E−043.2E−07 1.4E−04 4.5E−042.1E−06 1.2E−03 3.4E−039.9E−06 6.2E−03 1.5E−022.9E−05 8.5E−03 4.7E−02

s were removed.

318 F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325

Fig. 2. Network for aminoacid metabolism alanine,glycine,cysteine metabolism and transport. This network is one of the top enriched GeneGo metabolic networks (Table 2C).M lue cirr entialt

mGTtoGt

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rfds

any genes were up-regulated (red circles). Only one gene was down-regulated (bespectively. Network objects were rendered grey to enhance visibility of the differhe reader is referred to the web version of this article.)

etabolism genes GSTM3 and GSTP1) that were also enriched ineneGo Drug and Xenobiotic Metabolism Enzymes pathway (Fig. 4,able 3). The mRNA levels of many genes such as heat shock pro-ein genes and molecular chaperones that appear to be related toxidative stress responses were also up-regulated in the enrichedeneGo process network Protein folding Response to unfolded pro-

eins (Table S3, Fig. 5).

.4. Other pathways enriched

Metacore analysis using the list of differentially regulatedenes showed that other pathways such as cytoskeleton remod-ling and immune responses were also enriched (Table 2A and B,able S3, Fig. S1). Enrichment analyses in MetaCore for Drug andenobiotic Metabolism Enzymes also identified many transcripts for

iver phase I, phase II and phase III biotransformation enzyme genesTable 3). The phase II enzyme genes are SULT2B1 and GSTs (alsohown in Fig. 4). The phase III proteins consist of genes for trans-orters (Table 3). Among them are ABCC2, known to be involved

n hepatobiliary elimination of MeHg (Bridges and Zalups, 2010)nd two copper-transporting ATPases. Notably, transcripts for allhe genes in the pathway Drug and Xenobiotic Metabolism Enzymesere up-regulated except SLC22A5 and SLC7A11, which were down-

egulated (Table 3).

.5. Interactome networks enriched

Networks were built using the Transcription Regulation algo-

ithm in Metacore to see most significantly enriched transcriptionactors involved in the regulation of MeHg modulated genes,irectly or indirectly. SP1 and HNF4-alpha hubs were the top andecond transcription factor sub-networks generated, respectively

cle). Green, red and grey arrows indicate positive, negative and unspecified effects,ly regulated genes. (For interpretation of references to colour in this figure legend,

(Table S4). The transcription factors SREBP2 and PPAR-alpha,known to be involved in lipid metabolism (Brown and Goldstein,1997; Wahli et al., 1995) were also among the top enrichedsub-networks (Table S4), consistent with differential regulationof mRNAs for many lipid metabolism genes. SP1 is a transcriptionfactor that regulates several genes involved in many cellular pro-cesses such as cell growth, apoptosis, differentiation and immuneresponses. Many of the differentially transcribed genes involvedin energy metabolism, oxidative stress response and Xenobi-otic Metabolism appear in the SP1 and HNF4-alpha networks,suggesting their regulation by these factors (Figs. S2 and 3).

3.6. Enrichment analysis of liver specific functional ontologies

The Toxicity Analysis Workflow tool in MetaCore was usedfor enrichment analysis of organ-centered functional ontologiesof the liver. The organ-centered ontology combines normal andpathological processes with organ-specific gene markers. The topsignificantly enriched Liver Toxic Pathology Biomarkers, Drug andXenobiotic Metabolism, and Liver toxicity endpoint processes areshown in Table S5A-C. Genes for liver phase I, II and III Drug andXenobiotic Metabolism enzymes affected by MeHg are shown inTable 3. The top enriched liver specific toxicity ontology termssuch as peroxisomal proliferation induction, steatosis and cholesta-sis development (Table S5C) appear to be related to pathologicalchanges resulting from possible perturbation of lipid metabolismpathways by MeHg.

3.7. qPCR assays

qPCR assays were performed to validate microarray results andalso to evaluate selected biomarker candidates genes on a larger

F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325 319

F e of tho indicT ences

sdTr

ig. 3. Map of glycolysis and gluconeogenesis pathway affected by MeHg. This is onn the maps as thermometer-like figures. Up-ward thermometers have red color andhe differentially regulated genes are encircled for clarity. (For interpretation of refer

ample size. To validate microarray results, the mRNA levels of 19ifferentially regulated genes (Table S1) were also assayed by qPCR.en of the 19 genes were selected randomly from the differentiallyegulated genes. Nine genes (ANXA6, FAAA, DNJC3, GSTO1, GSTP1,

e top enriched GeneGo pathways (Table 2A). Genes affected by MeHg are visualizedate up-regulated genes and down-ward (blue) ones indicate down-regulated genes.to colour in this figure legend, the reader is referred to the web version of this article.)

MCAT, PLXB2, MGST3 and TRI25) were selected manually to evaluatetheir potential as biomarker candidates (see below). The microarrayand qPCR methods generally showed good agreement, with mRNAlevels for the majority of the genes showing positive correlations

320 F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325

Fig. 4. Map of NRF2 regulation of oxidative stress response pathway activated by MeHg. This is one of the top enriched GeneGo pathways (Table 2A). Genes affected byM ters hd See Figt

(swog

gumuaaTaiTd

eHg are visualized on the maps as thermometer-like figures. Up-ward thermomeown-regulated genes. The differentially regulated genes are encircled for clarity.

his figure legend, the reader is referred to the web version of this article.)

Fig. S4). Out of the 19 genes, only 3 genes (TRI25, CO7 and G6PD)howed negative correlation, and one gene (ASGL1) showed veryeak correlation (r = 0.05) in mRNA levels between the two meth-

ds (Fig. S4). Full names of the genes and primer sequences areiven in Table S6.

To evaluate their potential as biomarkers, we selected 18enes for qPCR assay on a larger number of samples (n = 8) thansed in for microarray analysis (n = 5). Out of these, 9 genesentioned above were selected from the list of genes significantly

p-regulated by microarray (Table S1). The other 9 genes werelso slightly up-regulated by microarray (not shown) but were notmong the top list of differentially regulated genes in Table S1.he main criteria used in the selection were high expression levels

nd high fold changes. Some genes were selected for their knownnvolvement in oxidative stress responses (e.g. GCLM and GSTs).he mRNA levels for most of the genes were up-regulated asetected by qPCR, with apparent dose-response trends (Fig. S5).

ave red color and indicate up-regulated genes and down-ward (blue) ones indicate. 3 legend for details of the symbols. (For interpretation of references to colour in

However, transcripts for only seven genes (BLM, TGM5, DNAJC3,MCAT, MGST3 and FAAA) were significantly up-regulated by at leastone dose of MeHg (p < 0.05, one-sided Student’s t-test) (Fig. S5).

4. Discussion

In this study, we have used a newly constructed 135k oligonu-cleotide array based on cDNA collection of the cod genomesequencing project (Star et al., 2011) to map genome-wide trans-criptional responses in the liver of Atlantic cod treated with MeHg.The ubiquitous environmental contaminant MeHg modulated thetranscript levels of several genes in energy metabolism, oxidativestress, immune response and cytoskeleton remodeling pathways

in the liver of Atlantic cod. However, further experiments usingother methods such as proteomics are needed to confirm the pos-sible effects on the pathways suggested by transcriptomics analysishere. Pathway analysis showed that particularly many energy

F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325 321

F ne of

s e, nege ces to

pwMtrfrtspbcet

4

iaiacc11gwMttst

ig. 5. Network for protein folding response to unfolded proteins. This network is ohown in red circles (all up-regulated). Green, red and grey arrows indicate positivnhance visibility of the differentially regulated genes. (For interpretation of referen

athways, mainly fatty acid, amino acid and glucose metabolismere significantly enriched, suggesting their perturbation byeHg. Up-regulation of mRNA levels for genes involved in oxida-

ive stress responses observed here appears to be an adaptiveesponse to the possible disturbance of the redox balance resultingrom binding of thiol groups of glutathione by MeHg, as previouslyeported (Ercal et al., 2001; Stohs and Bagchi, 1995). In addition tohe apparent adaptive responses to counter specific MeHg effects,everal genes differentially regulated in many of the other enrichedathways such as energy metabolism and immune response mighte related to secondary effects from general toxicity. Compensatoryhanges in mRNA expression of genes resulting from adverse gen-ral toxic stress responses are less predictive of mechanisms ofoxicity than adaptive responses (Denslow et al., 2007).

.1. Possible effects of MeHg on energy pathways

Among the energy pathways enriched, amino acid metabolisms one of the most affected with 20 genes involved (Table 1And B, Table S1A). These include pathways for sulphur contain-ng amino acids involved in glutathione synthesis. For example,

gene encoding cystathionine beta synthase (CBS) involved inysteine synthesis was up-regulated (Table S1). The amino acid,ysteine, is a rate-limiting substrate in glutathione synthesis (Lu,999). Another highly enriched pathway is lipid metabolism, with6 genes involved (Table 1A). Among lipid metabolism enzymeenes, particularly many mitochondrial acyl-CoA dehydrogenasesere up-regulated in the present study (Table 1A, Table S2C and D).eHg may inactivate proteins by binding to thiol groups of cys-

eine residues. For example, MeHg has been shown to inhibit thehioredoxin enzyme system by binding to thiol groups at activeites of enzymes (Carvalho et al., 2008). Interestingly, it was shownhat a cysteine residue that is essential for the enzymatic activity

the top enriched GeneGo process networks (Table S3). Genes affected by MeHg areative and unspecified effects, respectively. Network objects were rendered grey to

colour in this figure legend, the reader is referred to the web version of this article.)

is located in the active site of acyl-CoA dehydrogenases from ratliver mitochondria (Okamuraikeda et al., 1985). Thus, it is possiblethat acyl-CoA dehydrogenase enzymes are particularly susceptibleto inactivation by MeHg. Since genes encoding acyl-CoA dehydro-genase enzymes were up-regulated in MeHg treated fish in ourexperiment (Table S2C), the increased transcription of these genescould be a compensatory response to MeHg inactivated enzymes.Although previous studies in the brain of fish, including proteomicsstudies of the same fish used in this experiment, have also identi-fied energy pathways as targets of MeHg (Berg et al., 2010; Krameret al., 1992), our analysis of the liver transcriptome suggests amore concerted effect on metabolism of the major energy sub-strates. This may also be because the liver has a wider role innutrient metabolism than the brain. Acute exposure to inorganicmercury also affected gluconeogenesis, oxidative phosphorylationand lipid metabolism in zebrafish liver (Ung et al., 2010). Enrich-ment analysis of liver specific functional and toxicity ontologiesalso suggested differential regulation of genes related to develop-ment of liver diseases (Table S5A and C). Taken together, theseresults suggest that the liver is a sensitive target organ to tox-icity of mercury compounds. Although MeHg may interfere withenergy metabolism by inactivating the enzymes involved, we can-not exclude any possibility that the energy pathways were affectedindirectly by general toxicity leading to reduced food consumptionin the treated fish. In the present experiment, the fish were dailyfed but food consumption was not measured. The mRNA expres-sion of many genes involved in lipolysis, fatty acid beta oxidation,amino acid catabolism and gluconeogenesis have been reported tobe affected by food deprivation in the liver of both mammals and

fish (Bauer et al., 2004; Drew et al., 2008; Wang et al., 2006). Furtherexperiments will be needed to determine whether the apparenteffects on energy pathways are MeHg specific or related to generaltoxicity.

322 F. Yadetie et al. / Aquatic Toxicology 126 (2013) 314– 325

Table 2Significantly enriched GeneGo Pathway Maps (A), Map Folders (B) and MetabolicNetworks (C).

# p-Valuea Count

(A) Pathway maps1 Cytoskeleton remodeling TGF, WNT and

cytoskeletal remodeling1.5E−06 14

2 Immune response Alternative complementpathway

7.4E−06 8

3 Glycolysis and gluconeogenesis (shortmap)

9.4E−06 10

4 Integrin-mediated cell adhesion andmigration

3.7E−05 8

5 Immune response Lectin inducedcomplement pathway

4.3E−05 8

6 Immune response Classical complementpathway

6.7E−05 8

7 Cytoskeleton remodeling Cytoskeletonremodeling

8.9E−05 11

8 GTP-XTP metabolism 1.5E−04 109 Phenylalanine metabolism/Rodent version 3.7E−04 810 Development Glucocorticoid receptor

signaling3.8E−04 5

11 Glycolysis and gluconeogenesis p.3 3.8E−04 512 Phenylalanine metabolism 4.1E−04 813 Influence of Ras and Rho proteins on G1/S

Transition5.1E−04 7

14 NRF2 regulation of oxidative stressresponse

5.7E−04 7

15 CTP/UTP metabolism 6.4E−04 1016 Tyrosine metabolism p.2 (melanin) 1.7E−03 817 Development BMP signaling 1.8E−03 518 ATP/ITP metabolism 1.8E−03 1019 Pyruvate metabolism 1.9E−03 620 G-protein signaling RhoA regulation

pathway2.0E−03 5

# (B) Map folders1 Tissue remodeling and wound repair 1.9E−042 Immune system response 6.7E−043 Inflammatory response 1.3E−034 Protein degradation 6.3E−035 Myogenesis regulation 7.2E−036 Energy metabolism and its regulation 1.7E−027 Aminoacid metabolism and its regulation 2.0E−028 Apoptosis 2.2E−029 Oxidative stress regulation 2.2E−0210 Mitogenic signaling 2.6E−02

# (C) Metabolic networks1 Glycine pathway 3.5E−04 102 Carbohydrate metabolism Pyruvate

metabolism and transport new4.4E−03 7

3 Aminoacidmetabolism Alanine,Glycine,Cysteinemetabolism and transport

4.9E−03 10

4 Carbohydrate metabolism Glycolisys,Glucogenesis and glucose transport

7.4E−03 10

5 Lipid metabolism Triacylglycerolmetabolism

9.7E−03 8

a

m

4

gs(ogwi2h

Table 3Liver phase I, II and III Drug and Xenobiotic Metabolism genes affected by MeHg.

Gene symbol Name Ratioa

Xenobiotic Metabolism: Phase IADH5 Alcohol dehydrogenase class-3 3.0AKR1A1 Alcohol dehydrogenase [NADP + ] 2.7ALDH3A2 Fatty aldehyde dehydrogenase 2.0ALDH4A1 Delta-1-pyrroline-5-carboxylate

dehydrogenase, mitochondrial3.8

ALDH9A1 4-trimethylaminobutyraldehydedehydrogenase

2.7

EPHX2 Epoxide hydrolase 2 2.8PLD2 Phospholipase D2 2.2XDH Xanthine dehydrogenase/oxidase 2.3

Xenobiotic Metabolism: Phase IIGSTM3 Glutathione S-transferase Mu 3 2.1GSTP1 Glutathione S-transferase P 1.9GSTT1 Glutathione S-transferase theta-1 2.3SULT2B1 Sulfotransferase family cytosolic 2B

member 12.3

Xenobiotic Metabolism: Phase IIIABCC2 Canalicular multispecific organic anion

transporter 12.6

ATP7A Copper-transporting ATPase 1 2.6ATP7B Copper-transporting ATPase 2 2.5NPC1 Niemann-Pick C1 protein 3.1OSCP1 Protein OSCP1 2.7SLC22A5 Solute carrier family 22 member 5 0.2SLC22A7 Solute carrier family 22 member 7 2.3SLC47A1 Multidrug and toxin extrusion protein 1 3.3SLC7A11 Cystine/glutamate transporter 0.4

Only top significant pathway maps (FDR < 0.05), maps folders (FDR < 0.1) andetabolic networks (FDR < 0.2) are shown.

.2. Possible effects of MeHg on oxidative stress pathways

MeHg is able to deplete cellular antioxidant systems such aslutathione leading to increased concentration of reactive oxygenpecies (ROS) that can cause damage to cellular macromoleculesErcal et al., 2001; Stohs and Bagchi, 1995). In the present study,xidative stress response pathways related to Nrf2 signalling andlutathione metabolism were enriched. The Nrf2 signalling path-

ay up-regulates many antioxidant genes such as HO-1 and GSTs,

n response to oxidative stress (Ercal et al., 2001; Kensler et al.,007; Stohs and Bagchi, 1995). Previous studies in mouse primaryepatocytes showed involvement of Nrf2 in cellular protection

a Expression ratio for 2 mg/kg MeHg dose.

against MeHg induced oxidative stress (Toyama et al., 2007). Inthe brain primary microglial cells, MeHg activated Nrf2 regulatedanti-oxidant enzyme genes resulting in reduced cytotoxicity (Niet al., 2010). HO-1 has been shown to be induced in the liver of codexposed to Hg contaminated sediment (Olsvik et al., 2011). Stud-ies in salmon using proteomics have also shown that MeHg affectsthe Nrf2 pathway in kidney (Nøstbakken et al., 2012). These studiesare consistent with activation of the Nrf2 pathway observed hereand collectively suggest that the Nrf2 mediated oxidative stressresponses are induced in a wide variety of cells in both mammalsand fish. Among Nrf2 regulated anti-oxidant genes, two GSTs areshown in Fig. 4. Anti-oxidant enzymes such as GSTs are among themost consistently up-regulated genes by MeHg (Di Simplicio et al.,1990; Yu et al., 2010).

4.3. Other pathways possibly affected by MeHg

Pathways related to immune response and cytoskeleton remod-eling were also significantly enriched (Table 2A and Table S3),suggesting effects by MeHg. Previous studies have documentedimmunotoxic effects of MeHg and other mercury compounds(Jayashankar et al., 2011; Moszczynski, 1997; Sweet and Zelikoff,2001; Wolfe et al., 1998). For example, MeHg increased pro-inflammatory cytokine release in LPS-stimulated human peripheralblood mononuclear cells (Gardner et al., 2009). Mercury com-pounds have also been shown to be involved in immunesuppression and induction of autoimmunity (Havarinasab andHultman, 2005). The differential regulation of mRNA levels of genesin cytoskeleton remodeling and related pathways may be a conse-quence of cell injury resulting from MeHg induced oxidative stress,or direct inactivation of proteins. Indeed, MeHg has been shown

to bind to thiol groups of tubulin thereby inhibiting microtubuleassembly (Vogel et al., 1985). Many cytoskeletal proteins such asalpha tubulin were also differentially regulated by MeHg in thebrain proteome of the fish analyzed here (Berg et al., 2010).

oxico

4

bcsFMmwsMGacssoaEawsd2gAsoemaaw(ptstZgi(tcteaidicgp

5

drbopeb

F. Yadetie et al. / Aquatic T

.4. Biomarker candidates

We inspected the list of differentially regulated genes foriomarker candidates for exposure to MeHg. Among the biomarkerandidates genes tested in qPCR assays using a larger number ofamples, transcripts for BLM, TGM5, DNAJC3, MCAT, MGST3 andAAA were significantly up-regulated (Fig. S5). The genes FAAA,CAT and MGST3 are involved in amino acid, lipid and glutathioneetabolism, respectively and they are all represented in the path-ays enriched here. These genes may be evaluated in further

tudies including at the protein levels as potential biomarkers.any stress response genes were up-regulated by MeHg. DNJC3,

RP78, ENPL (a chaperone), MP2K4 (a component of the stress-ctivated protein kinase) and PDIA6 (a protein disulfide isomerasehaperone), all represented in the GeneGO pathway map Apopto-is and survival Endoplasmic reticulum stress response pathway (nothown) were up-regulated (Table S1, Fig. S5). Possible activationf the endoplasmic reticulum stress response pathway might be

consequence of MeHg induced oxidative stress. DNJC3, GRP78,NPL and four heat shock proteins (HSP22, HSP90 beta, HSP90nd HSP70) were also represented in the GeneGO process net-ork Response to unfolded proteins (Fig. 5). Some of the HSP genes

uch as HSP70 are commonly used as biomarkers of exposure toiverse pollutants including mercury compounds (Olsvik et al.,011, 2009) and their up-regulation by MeHg here further sug-ests their usefulness as biomarkers of exposure to MeHg in fish.nother induced gene coding for CBS, which metabolises a crucialtep in the synthesis of cysteine, may be evaluated as a biomarkerf MeHg exposure. CBS is involved in MeHg detoxification (Yoshidat al., 2011). The other genes in the sulphur containing amino acidsetabolism pathway (CGL, AATM, AATC, SPYA) may also be evalu-

ted as candidate biomarkers. Other possible biomarker candidatesre genes significantly enriched in the Xenobiotic Metabolism path-ays in the liver, particularly the GSTs and phase III transporters

Table 3). It is thought that MeHg-glutathione conjugates are trans-orted from hepatocytes into the biliary canaliculus by glutathioneransporters (Dutczak and Ballatori, 1994). Indeed, one of the genestrongly induced by MeHg here, ABCC2 (MRP2) (Table 3) is thoughto be involved in hepatobiliary elimination of MeHg (Bridges andalups, 2010). ABCC2 is also involved in elimination of arsenic-lutathione complexes (Kala et al., 2000). ABCC2 expression isnduced by several xenobiotic compounds in mammalian cellsGerk and Vore, 2002). Thus, its induction in fish, if confirmed athe protein level, can be a general biomarker of exposure for manyompounds including MeHg. Interestingly, the genes for copper-ransporting ATPases ATP7A and ATP7B that mediate excretion ofxcess copper into the bile (La Fontaine and Mercer, 2007) werelso transcriptionally up-regulated in our study (Table 3), suggest-ng their possible involvement in transport of MeHg. The aboveiscussed genes may be useful biomarkers in combination for mon-

toring exposure to MeHg. Whether some of the candidate genesan be MeHg specific expression biomarkers remains to be investi-ated in further studies, which also need to include analyses of therotein levels.

. Conclusions

The modulation of transcript levels of many genes iniverse pathways such as energy metabolism, oxidative stressesponse, immune response, cytoskeleton remodeling and Xeno-iotic Metabolism and drug transport was observed in the liver

f fish treated with MeHg. Remarkably, many energy metabolismathways appear to be affected in fish treated with MeHg. Whileffects related to the oxidative stress and drug transport are possi-le adaptive responses to mediate detoxification MeHg, many of the

logy 126 (2013) 314– 325 323

apparent effects on the other pathways such as energy metabolismand immune response, may represent less specific toxic responses,and the mechanisms involved are not clear. The transcriptionalchanges need to be confirmed by other methods such as proteomicsbefore making sound conclusions about functional consequenceson these pathways. Many genes that can be mechanistically relatedto direct MeHg effects such as genes mediating oxidative stressresponses and xenobiotic transporters should be further evaluatedas candidate biomarkers.

Funding

The project (iCOD) is funded by the Norwegian Research Council(project 192441/I30).

Acknowledgements

The authors thank the Genofisk Consortium and the CodGenome Sequencing Project Team for sharing data in advance ofthe public release of the cod genome data.

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.aquatox.2012.09.013.

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