Comparative Biochemistry and Physiology, Part D 8 (2013) 32–44

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Comparative Biochemistry and Physiology, Part D

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Concentration–response relationships and temporal patterns in hepatic geneexpression of Chinook salmon (Oncorhynchus tshawytscha) exposed to sewage

H.L. Osachoff a,b, G.C. van Aggelen b, T.P. Mommsen c, C.J. Kennedy a,⁎a Department of Biological Sciences, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6b Environment Canada, Pacific Environmental Science Centre, North Vancouver, B.C., Canada V7H 1B1c Department of Biology, University of Victoria, Victoria, B.C., Canada V8P 3N5

Abbreviations: BOD, biological oxygen demand; STPparts per thousand; NOAEC, no observed adverse effectobserved adverse effect concentration; NOTL, no obserlowest observed transcription level; U, U shaped; inv-U,tration response; inv-CR, inverse-concentration respondent; h, hour; d, day; sd, standard deviation.⁎ Corresponding author at: Department of Biological Sc

Burnaby, B.C., Canada V5A 1S6. Tel.: +1 778 782 5640; faE-mail address: ckennedy@sfu.ca (C.J. Kennedy).

1744-117X/$ – see front matter © 2012 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.cbd.2012.10.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 9 July 2012Received in revised form 13 October 2012Accepted 15 October 2012Available online 22 October 2012

Keywords:ChinookSewageLiverGene expressionConcentration responseTemporal trend

Changes in liver gene expression were examined in juvenile Chinook salmon (Oncorhynchus tshawytscha)exposed in vivo for 8 d to seawater (control) or one of 5 concentrations of sewage (environmentally-relevantdilutions of 0.05%, 0.1%, and 0.7%; 2%, 5% or 10%) and subsequently transferred to clean seawater for an 8-drecovery period. Livers were sampled on days 1, 4, 8 (sewage-exposed) and 16 (8 d of sewage exposureplus 8 d of recovery). A custom cDNA microarray using a universal DNA reference design was used to exam-ine trends of altered gene expression across sewage concentrations, across timepoints, and at the end of therecovery period. Alterations in gene expression followed four distinct concentration-dependent patterns:(1) concentration response (e.g. estrogen receptor alpha), (2) inverse-concentration response (e.g. insulinreceptor beta), U-shaped (e.g. mineralocorticoid receptor), (3) inverse U-shaped (e.g. benzodiazepine recep-tor), and (4) concentration-independent responses (e.g. ubiquitin). Temporal trends included: (1) peak geneexpression at one of the sewage exposure timepoints with recovery to baseline levels after the depuration phase(e.g. vitelline envelope protein beta), (2) gene expression alterations that did not recover (e.g. glucose transporter3), and (3) delayed gene expression alterations initiated only at the recovery timepoint (e.g. insulin-like growthfactor 2). In summary, patterns in gene expression changes were found across sewage concentrations and expo-sure timepoints. This study is the first to show gene expression trends of this nature.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

Sewage, or municipal waste water effluent, contains industrial, do-mestic, and agricultural waste and has been internationally recognizedas posing a high potential risk to human health and the environment.Risks are due to pathogenic microorganisms and/or contaminants thatmay cause a myriad of sub-lethal effects, including endocrine disrup-tion, deformities, organ damage and tumors (CCME, 2006; Hebert etal., 2008). Sewage effluent is discharged to the environment in highvolumes and can result in chronic, low-level exposure of aquatic or-ganisms (Ma et al., 2005; Crane et al., 2006; Corcoran et al., 2010).Sewage commonly contains ‘typical contaminants,’ including: Biologi-cal Oxygen Demand (BOD), Total Suspended Solids (TSS), metals, andnutrients (primarily phosphorus and nitrogenous compounds; CCME,

, sewage treatment plant; ppt,concentration; LOAEC, lowestved transcription level; LOTL,inverse-U shaped; CR, concen-se; CI, concentration indepen-

iences, Simon Fraser University,x: +1 778 782 3496.

rights reserved.

2006). However, chemical compounds are also present in sewage, andcan include natural and synthetic estrogens (Ternes et al., 1999;Spengler et al., 2001; Aerni et al., 2004; Johnson et al., 2005; Williamset al., 2012), pharmaceutical and personal care products (PPCPs;Kuster et al., 2008; Corcoran et al., 2010; Schultz et al., 2010), andpesticides (Kuster et al., 2008). Thus, sewage is a complex mixture ofthousands of substances, whichmakes it a potent and relevant mixtureof concern with the potential for interactions amongst substances(Corcoran et al., 2010). The effects of sewage treatment plant (STP)effluents are often reported from experiments or monitoring programsbased on physiological measurements and observations (Jobling et al.,1998; Jobling et al., 2002; Batty and Lim, 1999; Rodgers-Gray et al.,2001; Afonso et al., 2002; Vajda et al., 2008). These are useful in thatthey can illustrate cause and effect relationships at the whole organismand population levels. However, physiological and/or population leveleffects may be observed when populations and/or the ecosystem havebeen largely adversely affected (Jobling et al., 1998). In recent years, in-vestigations of molecular-level events have been used increasingly toidentify and determine mechanisms underlying physiological alter-ations. A molecular approach can potentially generate informationearly enough to allow preventative measures, as opposed to reactingto large-scale field effects, and provide toxicological assessment foremerging toxicants of interest. At the molecular level, the development

33H.L. Osachoff et al. / Comparative Biochemistry and Physiology, Part D 8 (2013) 32–44

and use of biomarkers has been extensive (e.g. vitellogenin protein as amarker for endocrine disruption (Sumpter and Jobling, 1995; Hansen etal., 1998; Larsson et al., 1999; Pait and Nelson, 2003; Aerni et al., 2004;Jobling et al., 2004; Jiménez et al., 2007)). While the protein level(translational level) covers one aspect of molecular-level research, ourresearch presented here contributes to molecular-level investigationsby examining gene expression (transcriptional level) alterations usinga cDNA microarray as a toxicogenomics tool. Toxicogenomics researchgoes beyond the classical endpoints of toxicology to provide informationon molecular-level endpoints, where assays can be used to detectlow-level chronic as well as acute sublethal effects, and are potentiallyhighly beneficial as predictive tools for identifying future hazardous sub-stances (Andersen et al., 2003; Orphanides, 2003; Miller et al., 2007).

Rather than using commercially-available gene chip arrays, atargeted rainbow trout (Oncorhynchus mykiss) microarray was devel-oped that consisted of 207 cDNA gene transcripts representing broadgene classes, including those involved in the endocrine and immunesystems, binding and transport, embryogenesis, metabolism, onco-genesis, proteolysis, signal transduction, structure, and transcription.This microarray contained highly conserved coding sequences andwas produced using rainbow trout as the source for cDNA fragments(Wiseman et al., 2007). However, material from any Oncorhynchusspecies can be used due to the high level of conservation in codingsequences of oncorhynchids (Rise et al., 2004; von Schalburg et al.,2005). The two-colour microarray design presented here utilized auniversal DNA reference material (UDR) in one channel and a liversample in the other to enable comparisons between timepoints(~24 microarrays), within each year (~100 microarrays) as well asbetween years (~200 microarrays). This reference experimental de-sign allowed for inter-microarray comparisons and is best employedin experiments with large numbers of microarrays (Bowtell, 2003),which was the case for the present study.

The cDNA microarray was used to characterize gene expressionalterations and as a broad screening tool to identify gene classesand genes of interest for further characterization using targetedtoxicogenomics tools. The main purposes of this study included:(1) evaluating concentration-dependent and temporally-related geneexpression alterations, and (2) investigating the recovery of geneexpression alterations initiated by sewage exposure. To evaluateconcentration-dependence, concentration response curveswere gener-ated for the purpose of determining no observed adverse effect concen-trations (NOAEC) and lowest observed adverse effect concentrations(LOAEC). A key objective was to determine if environmentally relevantsewage concentrations (0.05%, 0.1% and 0.7%) caused significant geneexpression alterations, since these sewage concentrations represented2000, 1000 and 143-fold environmental dilutions. Time-dependentchanges in gene expression were examined over the course of an 8 dexposure, and as well as after an 8 d clean water depuration period.

2. Materials and methods

2.1. Fish

Juvenile chinook salmon (Oncorhynchus tshawytscha)were obtainedunder permit from the Department of Fisheries and Oceans Hatchery(Chilliwack, BC, Canada) 2–3 months prior to being used in the experi-ments. The fish were from the same genetic broodstock, but since theywere obtained one year apart to coincide with the two experiments(outlined below), they were different cohorts. Chinook were main-tained in well water at the Pacific Environmental Science Centre(PESC; North Vancouver, BC, Canada) and acclimated in stages tofiltered saltwater over the course of 6 weeks (in increments of 5 pptper week, up to 27±1 ppt saltwater). At the start of the experiments,fish were approximately 210 d post-hatch and estimates (based on 10fish) of the average masses of the fish were (mean±1 standard devia-tion): 3.35±0.35 g (Year 1) and 4.28±0.76 g (Year 2).

2.2. Sewage

Two experiments were conducted in the month of June but exact-ly one year apart, using untreated sewage (screened to 6 mm) fromthe Capital Regional District in Victoria, British Columbia, Canada.For each experiment (called Year 1 and Year 2), sewage was collectedas grab samples in two batches, thus conforming to EnvironmentCanada's effluent storage policy, which states that effluents olderthan 5 d cannot be used in toxicity testing (Environment Canada,1990). Sewage batch 1 was collected 4 d prior to the start of the expo-sures for toxicogenomics analysis. A 96 h range-finding LC50 experi-ment was conducted (Environment Canada, 1990) using sewagebatch 1 to establish NOAECs and LOAECs for the toxicogenomicsexperiments. A second sewage batch was collected on day 3 (d3) ofthe experiment for use in the 80% solution refreshment that occurredon d4. Fish were fed approximately 2% of their body weight of com-mercial fish pellets (Skretting, Vancouver, BC, Canada) every fourthday, at least 2 h prior to a water change. General water quality pa-rameters (pH, dissolved oxygen and temperature) were measuredevery 1–4 d throughout the experiments from one random tankfor each treatment and were (mean±sd): Year 1, pH=7.8±0.1,dissolved oxygen=7.6±1.2 mg/L and temperature=14.4±1.2 °C;Year 2, pH=7.7±0.1, dissolved oxygen=8.5±0.9 mg/L and tem-perature=14.1±0.5 °C.

2.3. Fish exposures

A rainbow trout 96 h bioassay is an Environment Canada stan-dardized method for evaluating the acute lethality of effluents(Environment Canada, 1990) and used nationally as a key componentof regulatory and compliance monitoring. The Year 1 and Year 2exposures in the present study were based on this standard test;however, additional timepoints were desired. Therefore, a 192 h bio-assay (8 d) was performed with a solution refresh midway through(i.e. on d4). After 8 d of sewage exposure, fish were transferred intonew tanks (retaining treatment group separation) containing nega-tive control seawater. The depuration/recovery phase was 8 d andalso had a solution refresh after 4 d (i.e. on d12).

Chinook salmon were exposed to 5 concentrations of sewage plus anegative control (seawater only). The sewage concentrations (weight/volume) were prepared in large vats by diluting the sewage withclean seawater. The three lowest sewage concentrations (0.05%, 0.1%and 0.7%) were based on predicted environmental concentrations ofsewage in the receiving environment (Hodgins et al., 1998; CRD,2011). The other two sewage concentrations – 2% and 10% (Year 1) or2% and 5% (Year 2) – were based on the pre-experiment, range-finding 96 h bioassays (Year 1 LC50: 13.4%, and Year 2 LC50: 10%)corresponding to the NOAECs and LOAECs of sewage, respectively.

At the start of the experiment, 120 fish were randomly placed ineach treatment (approximately 15 replicate tanks per treatment,n=8 per tank). At defined timepoints (d1, d4, d8 and d16), 30 fishper treatment were euthanized by an overdose of 500 mg/L MS-222(tricaine methanesulfonate; TMS; Syndel Labs, Vancouver, BC, Cana-da) and then dissected on ice. Fish sacrificed at d1 and d4 wereexposed to sewage batch 1. Fish sacrificed at d8 were exposed toboth batches 1 and 2 of sewage. Fish sacrificed at d16 were exposedto batches 1 and 2 of sewage, and 8 d of clean seawater. Liver tissuefor each individual was placed in a 1.5 mL centrifuge tube containing1–1.2 mL of RNALater (Ambion, Austin, TX, USA). Tissues were storedat 4 °C for one day to allow permeation of the tissues by RNALater,and then transferred to −80 °C for storage until RNA extraction.

2.4. RNA Isolation

Total RNA was extracted from the liver tissue of individual fish.Approximately 30 mg of liver was placed in 1 mL of Qiazol buffer

34 H.L. Osachoff et al. / Comparative Biochemistry and Physiology, Part D 8 (2013) 32–44

(Qiagen, Mississauga, ON, Canada) in a 1.5 mL Eppendorf Safe-lock tube(Thermo Fisher Scientific, Toronto, ON, Canada). A 3 mm tungsten-carbide bead (Qiagen) was added to each tube and the liver tissueswere homogenized in a Mixer Mill 300 (Qiagen) at 20 Hz for 4 min(or until no visible liver pieces remained, a maximum of 8 min). TotalRNA was isolated from the homogenized liver tissue using an RNeasyMini kit (Qiagen), which included on-column DNaseI digestion. Themanufacturer's protocol was followed except elution occurred usingtwo 35 μL volumes. Using a NanoDrop® 1000 spectrophotometer(Thermo Fisher Scientific), total RNA concentrations were determinedusing the A260/A280method. RNA integritywas evaluated using eithera Bioanalyzer 2100 (Agilent Technologies Inc., Mississauga, ON) oragarose gel electrophoresis, and then stored at −80 °C.

2.5. Microarrays

2.5.1. Source and printingThis study used the Environment Canada rainbow trout cDNA mi-

croarray. The cDNA pieces for 207 gene transcripts (Table 1 shows the60 genes added to those listed by Wiseman et al. (2007); full gene listprovided in Supplementary material) were prepared for printing asdescribed by Wiseman et al. (2007). The Microarray Facility of The

Table 1Sixty genes added to custom low density cDNA microarray since initial publication by Wise

Gene class

Gene AbbreviationGene name Accession #

Binding/TransportATP1A1A (1) Na/K ATPase alpha subunit isoform 1a AY319391ATP1A1A (2) Na/K ATPase alpha subunit isoform 1a AY319391ATP1A1C Na/K ATPase alpha subunit isoform 1c AY319389ATP1A2 Na/K ATPase alpha subunit isoform 2 AY319387ATP1A3 Na/K ATPase alpha subunit isoform 3 AY319388BZRPL1 Benzodiazepine receptor AY029216CYGB Cytoglobin AJ547802HCN1 Hyperpolarisation-activated cyclic

nucleotide-gated cation channel 1AF421883

IHPK2 Inorganic phosphate uptake stimulator AY210434NGB 1 Neuroglobin 1 AJ547800NGB 2 Neuroglobin 2 AJ547801RBP4 (2) Retinol binding protein 4 (2) AF538329RHAG Rhesus blood group-associated glycoprotein AY207445SLC12A2 Solute carrier family 12

(sodium/potassium/chloride transporters), member 2AJ417891

SLC34A1 (2) Solute carrier family 34 (Na phosphate), member 1 (2) AF297186SLC34A2 (2) Solute carrier family 34 (Na phosphate), member 2 (2) AF297184SLC5A1 Solute carrier family 5

(sodium/glucose cotransporter), member 1AY210435

EndocrineADCYAP1 Adenylate cyclase activating polypeptide 1 AF343977ADRB2 Adrenergic receptor beta-2 AY044093CALCRL Calcitonin receptor-like AJ508554CRH Corticotropin releasing hormone AY156929CYP19B Cytochrome P450, family 19, subfamily B AJ311938DIO2 Deiodinase, type 2 AF312396DRD2 Dopamine receptor D2 AJ347728FSHR Follicle stimulating hormone receptor AF439405GHR Growth hormone receptor AF403539LHCGR Luteinizing hormone receptor AF439404PPARB Peroxisome proliferative activated receptor beta AY356399PRLR Prolactin receptor AF229197

Genomic controlActin genomic Actin genomic AF157514GH1 intron C Growth hormone 1 intron C AF005923IL1B intron Interleukin-1-beta intron 5 AJ298294

Prostate Centre at Vancouver General Hospital (Vancouver, BC, Canada)printed the microarrays on EZ-rays™ aminosilane coated slides (MatrixTechnologies Corp., Hudson, NH, USA)with a BioroboticsMicrogrid IImi-croarray printer (Genomic Solutions, Ann Harbor, MI, USA) equippedwith Microspot 10 K quill pins (Biorobotics, Cambridge, UK) in a 48-pintool that deposited ~0.1 ng cDNA pieces per spot. The cDNA fragmentswere spotted in duplicate in 16 subgrids, with 36 spots per subgrid,and each subgrid also included negative controls (Arabidopsis DNA,empty spots and 3X SSC buffer-only spots) as well as corner markers(a 280 bp cDNA fragment of GFP (Green Fluorescent Protein; GenBankID: U17997)) provided by the Microarray Facility of The Prostate Centreat Vancouver General Hospital, Vancouver, BC, Canada.

2.5.2. Experimental designThis study incorporated a reference experimental design approach

using an in-house produced Universal DNA Reference (UDR) material.The UDRwas a fluorescently labelled (Cy3)mixture of all genes printedon the microarray. It was hybridized to every microarray slide alongwith the experimental sample (Cy5-labelled liver cDNA from exposedor control fish). The end result was a two-colour microarray with refer-ence material in the green channel and fish liver material in the redchannel. For each treatment within a timepoint, four individual fish

man et al. (2007).

Gene class

Gene AbbreviationGene name Accession #

ReferenceRPL23A Ribosomal protein L23a AF281334RPS5 Ribosomal protein S5 AF543539RPS6 Ribosomal protein S6 AF009665

ImmuneC1R (1) Complement component 1r (1) AJ519930C1R (2) Complement component 1r (2) AJ519930C4B Complement component 4B AJ544262GPR4 G protein-coupled receptor 4 AJ519929SERPING1 Serine (or cysteine) proteinase inhibitor,

clade G (C1 inhibitor), member 1TLR5 Toll-like receptor 5 AB062504

MetabolismCYP1A1 Cytochrome P450, family 1A, polypeptide 1 AF059711CYP1A2 (2) Cytochrome P450, family 1A, polypeptide 2 (2) U62797CYP2K4 Cytochrome P450, family 2 K, polypeptide 4 AF043296CYP3A27 (1) Cytochrome P450, family 3A, polypeptide 27 (1) U96077CYP3A27 (2) Cytochrome P450, family 3A, polypeptide 27 (2) U96077DBT Dihydropoamide branched chain transacylase AB050595NME Nucleoside diphosphate kinase AF350241NOS2A (2) Nitric oxide synthase 2A (2) AJ300555SOD1 Superoxide dismutase 1, soluble

(amyotrophic lateral sclerosis 1 (adult))AF469663

TYR Tyrosinase AB117622Proteolysis

CTSL Cathepsin L AF358668PSMC3 Proteasome (prosome, macropain)

26S subunit, ATPase, 3AF281342

Signal transductionCHRNA9 Cholinergic receptor, nicotinic, alpha polypeptide 9 AY037940CLOCK Clock protein AF266745LDLR Low density lipoprotein receptor

(familial hypercholesterolemia)AF542091

OPN1MW Opsin 1 (cone pigments), medium-wave-sensitive AF425076OPN1SW Opsin 1 (cone pigments), short-wave-sensitive AF425074PPARG Peroxisome proliferative activated

receptor gammaAJ416952

RHO Rhodopsin (opsin 2, rod pigment) AF425072

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were evaluated (one permicroarray). Each timepointwas hybridized asa batch of arrays to allow for direct comparisons between control andtreatments. Each year had four timepoints and six treatments, resultingin aminimum of 96microarrays. The reference design allowed for com-parison between treatments and timepoints within a year aswell as be-tween Years 1 and 2.

2.5.3. Preparation of fluorescently-labelled cDNA

2.5.3.1. Reverse transcription of liver RNA. Superscript Direct cDNALabeling System kits (Invitrogen, Burlington, ON, Canada) wereused to prepare Cy5-dUTP labelled cDNA from fish liver RNA. Foreach liver, 25 μg of total RNA was combined with DEPC-treatedwater (Ambion) to make a final volume of 12 μL in amber-coloured 1.5 mL centrifuge tubes (VWR, Mississauga, ON, Canada),which were used for all steps to minimize light exposure. AnchorTprimer (2 μL) provided in the kit was added to each tube and theRNA-AnchorT mixture was heated at 70 °C for 10 min to removesecondary structure in the RNA. After the heat treatment, thetubes were immediately placed on ice for 3 min, and then cen-trifuged briefly to remove water vapour from the lids. Using re-agents provided in the Invitrogen kit, two Master Mixes (MM)were prepared as per the manufacturer's protocol. MM #1 includ-ed: First Strand Buffer (5X), dNTPs for dUTP labelling, anddithiothreitol (DTT; 0.1 M). MM #2 included: Superscript III en-zyme and RNaseOUT. To each RNA-AnchorT mixture, 12 μL of MM#1, 3 μL of MM #2, and 3 nmol of Cy5-dUTP dye (Amersham, BaieD'Urfe, QC, Canada) were added and the mixtures were incubatedat 46 °C for 3 h. To stop the reverse transcription reaction andhydrolyze the RNA, 15 μL of 0.1 M NaOH was added and the tubeswere incubated at 70 °C for 30 min. After snap-cooling and briefcentrifugation, 15 μL of 0.1 M HCl was added to neutralize the reac-tion. To remove unincorporated reagent from the Cy5-labelledcDNA samples, Cyscribe GFX Purification kits (Amersham) were usedas per themanufacturer's protocol, except: two 60 μL aliquots of elutionbuffer were usedwith a 5 min incubation at room temperature for eachelution step. The purified Cy5-labelled cDNA samples were stored over-night in amber 1.5 mL tubes, wrapped in aluminum foil, at −20 °C.

2.5.3.2. Reference material (UDR). The UDR stock solution was preparedby pooling the purified cDNA pieces (in 3X SSC buffer) that were previ-ously generated to print microarray slides. Preparation of the stock UDRoccurred once and was stored at 4 °C. For each microarray slide, aCy3-labelled UDR cDNA sample was made from 600 ng of UDR stockmaterial using 1 nmol of Cy3-dCTP dye (Amersham) and Ready-To-Gobeads (Amersham) as per the manufacturer's protocol, except a 2 h in-cubation at 37 °Cwas used tomaximize yield. The reactionwas stoppedwith 2 μL of 0.5 M EDTA (pH 8.0) and then purified using the CyscribeGFX Purification kit (Amersham). To elute off the Cyscribe GFX columns,two 60 μL aliquots of elution buffer, with 5 min incubations at roomtemperature, were used tomaximize yield. Each Cy3-labelled UDR sam-ple was eluted directly into a 1.5 mL amber tube containing one Cy5-labelled cDNA sample. The Cy3-labelled UDR samples and Cy5-labelledcDNA samples were combined at this step in order to co-precipitatethe materials that were applied to one microarray slide. To precipitatethe materials, the following reagents were added: 20 μg of glycogen(beef liver; Thermo Fisher Scientific) inwater, 0.1 vol. of 3 M sodium ac-etate, and 2.5 vol. of 98% ice-cold ethanol. The precipitation occurred fora minimum of 1 h (maximum of overnight) at−80 °C in aluminum foilwrapped tubes.

2.5.4. Hybridization

2.5.4.1. Preparation of hybridization material. The labelled nucleic acids(Cy5-labelled liver cDNA and Cy3-labelledUDRmixtures)were pelleted

by centrifugation at 13000 g at 4 °C for 20 min, the supernatant was re-moved, and 500 μL of 70% ethanol prepared with DEPC-treated water(Ambion) was added. The tubes were then mixed by vortexing andcentrifuged at 13,000 g for 5 min. All traces of supernatant were re-moved and the pellet was air dried for 10 min at room temperature.During this drying time, preparation of a 50% formamide based hybrid-ization cocktail occurred by making a Master Mix based upon theamounts for a single 50 μL aliquot: 25 μL formamide (Thermo FisherScientific), 12.5 μL of 20X SSC (Thermo Fisher Scientific), 0.5 μL of 10%SDS (Thermo Fisher Scientific), 4 μL of 5 mg/mL polyA (Invitrogen),4 μL of 2 mg/mL BSA fractionV (BioLynx, Brockville, ON, Canada), and4 μL of 10 mg/mL yeast tRNA (Invitrogen). Hybridization cocktail(50 μL) was added to each dry pellet of cDNA–UDR in a 1.5 mL ambertube. The tubes were vortexed gently, heated at 95 °C for 3 min andthen held at 65 °C (maximum 30 min) until ready for application tothe prepared microarray slides.

2.5.4.2. Pre-hybridization slide preparation. Using coplin jars, themicroarray slides were washed by inversion with Wash Solution 2(0.1X SSC) and then transferred to new coplin jars containingwarm (48 °C) pre-hyb buffer (0.22 μm filtered solution of 5X SSC,0.1% SDS, and 0.2% BSA fraction V (all supplied by Thermo FisherScientific)). The slides were incubated in the warm pre-hyb bufferfor 45 min in a 48 °C waterbath. The slides were then transferredto a new coplin jar containing room temperature de-ionized waterand mixed by inversion. This process was repeated and then the slideswere allowed to cool for 8 min. The slides were dried by immersion inisopropanol (HPLC-grade, Thermo Fisher Scientific) followed by centri-fugation at 1000 g for 3 min in sterile 50 mL polypropylene centrifugetubes (Sarstedt, Montreal, QC, Canada). After centrifugation, the slideswere placed in Corning hybridization chambers (Thermo FisherScientific).

2.5.4.3. Hybridization and washing. A Lifter-Slip (VWR) silanized withSigmaCote (Sigma-Aldrich, Oakville, ON, Canada) was placed in thecenter of each microarray slide and the pre-warmed cDNA–UDR mix-ture was added to each slide in 10 μL aliquots. Immediately after ad-dition of the cDNA–UDR mixture, the hybridization chamber wasassembled and placed in a 42.4 °C hybridization oven. Microarrayswere hybridized for 16 to 19 h.

After overnight hybridization, slides were soaked in pre-warmed(48 °C for 10 min) Wash Solution 1 (1X SSC, 0.1% SDS) to remove theLifterSlip. The slides were then transferred to a coplin jar containingwarmed Wash Solution 1 and washed by gentle shaking for 4 min.The slides were transferred to a clean coplin jar containing room tem-perature Wash Solution 1 and shaken for an additional 4 min. Thiscycle was repeated twice more with room temperature Wash Solution2 (0.1X SSC). The slides were dried by centrifugation at 1000 g for3 min in clean 50 mL polypropylene tubes. Dry slides were transferredto dark storage boxes and then scanned using a microarray scanner. Ifscanning did not occur immediately after hybridization, the slideswere stored in a desiccator.

2.5.5. ScanningDual-laser scanners were used to scan the microarray slides: laser 1

was 543 nm for Cy3 (green channel) and laser 2 was 633 nm for Cy5(red channel). Microarray slides from Experiment 1 were scanned atThe Microarray Facility of The Prostate Research Centre at VancouverGeneral Hospital (Vancouver, BC, Canada) on a ScanArray Express(PerkinElmer, Woodbridge, ON, Canada) at 10 μm resolution. Thelaserwas set to 90%power, and the PMT gainwas 62 for the green chan-nel and 66–68 for the red channel. Experiment 2microarray slideswerescanned at PESC on a ProScanArray (PerkinElmer) at 10 μm resolution.The laser was set to 90% power, and the PMT gain was 54 for the greenchannel and 61 for the red channel.

36 H.L. Osachoff et al. / Comparative Biochemistry and Physiology, Part D 8 (2013) 32–44

2.5.6. Data analysisImage TIFF files from the scanned microarrays were imported into

ImaGene software version 6.1 (BioDiscovery Inc., El Segundo, CA) forquantification of the signal for each spot on the microarray. Spot find-ing was performed using the automated spot finding algorithm andeach sub-grid was manually inspected to ensure the appropriatesub-grids and spots were selected.

ImaGene output files were imported into GeneSpring softwareversion 7.3.1 (Agilent Technologies Inc.) for statistical analysis. Eachtimepoint for each experiment was treated as a unique block andanalyzed independently of other timepoints. Four microarrays werehybridized for each treatment within a timepoint unless a microarrayhad to be repeated or was excluded. A microarray was excluded fromGeneSpring analysis only when (1) the slide failed the sensitivitycalibration scan (meaning the scanner could not read the microarrayto optimize the scan conditions), or (2) if during the manual inspec-tion, at the ImaGene analysis step, a sub-grid was found to be badlysmeared or missing, which occurred only once in each experiment.

The following steps occurred sequentially in GeneSpring: (1) if a rawsignal value (after background correction) for a genewas less than 0.01,a value of 0.01was assigned (to preventmathematical calculationswitha value of zero). (2) A ratio was determined for each gene of the signalminus background in the red channel (Cy5-labelled cDNA) to the signalminus background in the green channel (Cy3-labelled UDR). The back-ground levels used in the ratio calculationwere based upon the negativecontrols (empty spots andbuffer-only spots). (3) The datawere normal-ized to the overall median values for all genes across the microarrayswithin each timepoint. Statistical analysis of this normalized data wasperformed using one-way ANOVA, and then Welch t-tests (variancesnot assumed equal) were performed to compare the gene expressionsin a sewage treatment versus the control with significant differencesnoted when pb0.05. These data were deposited into the Gene Expres-sion Omnibus (platform GEO ID: GLP15697; series GEO ID: GSE38925).GeneSpring data (fold changes and p-values) were exported to Excelsoftware (Microsoft, Redmond, WA, USA) for organization and furtherevaluation to examine patterns or relationships between gene expres-sion changes in sewage treatments and/or timepoints.

The fold change data were graphed and the resulting R2 valuesfrom non-linear regression trend analysis aided with classificationof the type of gene expression response occurring. R2 values greaterthan 0.7 were chosen to be diagnostic of concentration response (CR)or inverse-concentration response (inv-CR) trends. For concentration-independent (CI), U shaped (U) and inverse-U shaped (inv-U) curves,very low R2 values (b0.05) aided with trend analysis but R2 valueswere not necessarily solely indicative of trends, because low R2 valueswere also obtained for genes with no discernible pattern.

3. Results

3.1. Concentration–response patterns

Large portions of genes (75% and 72%, respectively) showedno apparent trend in their expression across sewage treatmentswithin each timepoint (Fig. 1A). The remainder of the genes hadconcentration-related trends (CR=concentration response; inv-CR=inverse-concentration response; U=U shaped; inv-U=inverse-Ushaped; or C-I=concentration independent) shown in Fig. 1B–F usingspecific gene examples. Year 1 had a large percentage of U responses(16%), and then the order from highest to lower percentages were(Fig. 1A): inv-CR (4%), inv-U (2%), and C-I (1%) or CR (1%); in Year 2,CR patterns were the most common (10%), followed by: inv-CR (7%),U (6%), inv-U (4%), and C-I (2%).

All genes significantly different from the control treatment in atleast one of five sewage treatments within a timepoint (d1, d4, d8or d16) are listed for Year 1 (Table 2) and Year 2 (Table 3). Withthe exception of vitelline envelope protein α (VEPA), which had a U

shaped concentration response in three out of four timepoints in Year1 (Table 2), we found no concentration response specific to any partic-ular gene class or gene. However, VEPA had completely different con-centration patterns in Year 2 (Table 3): inv-CR at d1, CR at d4, was notsignificantly altered at d8, and had no trend at d16. The classes ofgenes consistently altered (in three out of four timepoints in bothexperiments) by the sewage treatment included: binding/transport, en-docrine, immune, metabolism, oncogene, proteolysis, and signal trans-duction (Tables 2 and 3). Overall for Year 1, the number of genessignificantly altered at each timepoint was 30 genes at d1 (Table 2A),30 genes at d4 (Table 2B), 32 genes at d8 (Table 2C), and 42 genes atd16 (Table 2D). Overall for Year 2, the number of genes significantly al-tered at each timepoint was: 38 genes at d1 (Table 3A), 47 genes at d4(Table 3B), 48 genes at d8 (Table 3C), and 33 genes at d16 (Table 3D).

3.2. Temporal patterns

Years 1 and 2 both had large proportions (mean across sewageconcentrations of 65% and 79%, respectively) of a Type 1 temporaltrend: genes significantly altered during the sewage treatment phase(timepoints d1, d4 or d8) that recovered to control levels during thedepuration phase and were not significantly different at d16 (Fig. 2A).The second largest portion of genes in Years 1 and 2 (mean across sew-age concentrations of 32% and 16%, respectively) had a Type 3 trend:significantly altered at d16 only compared with controls and with thepreceding exposure time (Fig. 2A). The least common temporal trendin Year 1 and 2 (mean across sewage concentrations of 4% and 6%, re-spectively) was a Type 2 trend: significantly altered during the sewagetreatment phase (timepoints d1, d4 or d8), which remained significant-ly altered at d16 (Fig. 2A). Each temporal trend is shownusing an exam-ple gene (Fig. 2B–F). The Type 1 temporal trendwas further refined intothree categories: genes with peak induction or repression at d1(Fig. 2B), genes that had peak induction or repression at d4 (Fig. 2C),and genes that had peak expression at d8 (Fig. 2D).

All genes with Type 1 trends are listed for Years 1 and 2 (Table 4). Wenoted relatively consistent levels of genes with peak induction or repres-sion at each of d1, d4 or d8, and there was not one sewage concentrationthat consistently had the highest number of altered genes. However, 0.7%sewage repeatedly had the fewest number of altered genes. Some genesonly appeared in Table 4 once or twice, but often the same gene appearedacross sewage concentrations within a timepoint, indicating that thesame temporal expression eventwas occurring in that gene. Examples in-clude:α1 globin (HBA1) in Year 1, peak at d1, Repression category (4 outof 5 sewage concentrations); vitelline envelope protein β (VEPB) in Year1, peak at d8, Induction category (4 out of 5 sewage concentrations); andfollicle stimulating hormone receptor (FSHR) in Year 2, peak at d8, Re-pression category (at all sewage concentrations).

Genes with Type 2 and 3 trends are listed for Year 1 and Year 2(Table 5). As can be seen, few genes remained significantly altered atd16, and these genes were further refined into two categories — geneswith the same significantly altered trend at d16 as that which occurredat d1, d4 or d8, and geneswith the opposite trend at d16 comparedwithd1, d4 or d8 (Type 2 in Table 5). Surprisingly, and likely indicating somelatency effects, many genes were significantly induced at d16 only andhad not been altered during the previous sewage exposure timepoints(Type 3 in Table 5). This pattern was observed in both experiments,with more occurrences in Year 1 (53) than in Year 2 (26).

4. Discussion

4.1. Overall gene expression alterations

Since our rainbow trout microarray was a targeted or designermicroarray, with genes selected for their known function, a relativelylarge proportion (maximum of 23% in Year 2, d8 (Table 3C)) of the207 genes printed were significantly altered. The proportion of altered

Fig. 1. (A.) Summation of sewage treatment dose-related trends for Year 1 and 2. Example relationships depicted (±SEM) with R2 values from Year 2, d1 or d8 timepoints for (B.)concentration-independent (C-I) trend for ubiquitin (UBQ), (C.) concentration response (CR) trend for estrogen receptor α (ERA), (D.) inverse-concentration response (Inv-CR)trend for insulin receptor β (INSRB), (E.) U shaped trend (U) for mineralocorticoid receptor (NR3C2 MCR), and (F.) Inverse-U shaped (Inv-U) trend for POU domain, class 1, tran-scription factor 1 (POU1F1).

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genes is much higher than what would be expected from microarrayscontaining thousands of genes because larger microarrays are usuallycomprised of gene pieces from numerous tissue or development-stage li-braries. Thus, in experiments evaluating a single tissue, only a fraction of apercentage of the geneswould be significantly altered (Chua et al., 2006).For example, Finne et al. (2007) used the GRASP (Genomic Research onAll Salmon Project) 16,000 salmonid genes microarray (von Schalburget al., 2005) to assess paraquat treatment in rainbow trout, and the max-imum proportion of significantly altered genes (exceeding 1.5 fold intheir experiment) was 0.9%.

Significant changes in gene expression were apparent at all concen-trations of sewage and at all timepoints in both years (Tables 2 and 3).Thus, the threshold level of gene expression alterations was below ourlowest sewage concentration (0.05%) in both experiments. Therefore,NOTLs (NoObserved Transcription Levels) and LOTLs (Lowest ObservedTranscription Levels) could not be determined using these concentra-tion series and future studies should evaluate sewage concentrationsbelow 0.05%. The lowest sewage concentrations (0.05%, 0.1% and0.7%) were environmentally relevant dilutions of sewage (predictedreceiving water concentrations in the ecosystem where the sewage isdischarged; Hodgins et al., 1998; CRD, 2011). These concentrations aremuch lower than those utilized elsewhere (Hansen et al., 1998;

Rodgers-Gray et al., 2001; Aerni et al., 2004; Jobling et al., 2004; Ingset al., 2011). Such low dilutions at this site are a result of the outfallsdischarging to the highly dynamic Juan de Fuca Strait (CRD, 2011). Com-monly, sewage effects on fish are examined in situ through caged expo-sures ormesocosm-type experiments (Hansen et al., 1998; Larsson et al.,1999; Nichols et al., 1999; Aerni et al., 2004; Allard et al., 2004; Jobling etal., 2004; Diniz et al., 2005; Vajda et al., 2008; Garcia-Reyero et al., 2009;Ings et al., 2011). In situ experiments are valuable because they accountfor all variables impinging on fish (Roberts et al., 2005) and are thereforemore realistic than laboratory-based studies; however, in situ experi-ments can be logistically difficult (Ma et al., 2005). Now that effectshave been shown in the current laboratory-based study, an in situ ap-proach could be utilized at this sewage location to elucidate potentialreal-world effects.

4.2. Concentration-related patterns

For the majority of significantly altered genes, our data showed noobvious relationship between gene expression levels and sewageconcentrations (75% for Year 1 and 72% for Year 2; Fig. 1A). However,for the remaining portion of significantly altered genes, genes fromnumerous gene classes exhibited one of five defined relationships

Table 2Year 1 genes that were significantly different (Welch t-test, pb0.05) from the control treatment in at least one out of five sewage treatments within a timepoint (Day 1, 4, 8 or 16).A large number of genes had no evident trend, but a portion of genes had dose-related trends: concentration-independent (C-I), dose response (D), inverse-dose response (Inv.-D),U-shaped trend (U), or inverse-U shaped (Inv.-U).

A. Day 1 Trend B. Day 4 Trend C. Day 8 Trend D. Day 16 Trend

Gene Class Gene class Gene class Gene class

Gene Gene Gene Gene

Binding/transport Binding/transport Binding/transport Binding/transportAPOA1 ATP1A1A (1) ATP1A1C CR ATP1A1CHBA1 ATP1A1A (2) HBA1 BZRPHBB BZRP Inv-U LGALS9 HBG1HBG1 NGB 2 NGB 2 U IHPK2RHAG RBP4 (1) RBP4 (2) RBP4 (1)SLC34A2 (1) VLDLR (1) U SLC2A1 SLC2A1

Embryogenic VLDLR (2) Endocrine EndocrineCSRP2 U Endocrine ADCYAP1 U AR

Endocrine CRH IGF2 CRHGNRHR2 GHR INSRA GNRH1 UINSRC Inv-CR MTNR1A PMCH GNRH2 UPPARB CR THRA Inv-CR SST Inv-U IGF1THRB U VEPA VEPA U IGF2TSHB VEPB U VEPB U NR3C1 (GCR)VEPA U VEPG VEPG POMC B

Immune Metabolism Reference PRLC1R (2) ATP6V1B2 RPS5 VEPA UMX2 CYP3A27 (1) Immune VTGVIG1 NOS2A (1) U C1R (1) Reference

Metabolism PTGS2 Metabolism RPS5CKB SOX9 CYP1A2 (2) ImmuneCYP1A2 (1) SOXLZ CYP1A2 (1) Inv-CR CBLN4CYP3A27 (1) Oncogene CYP2K1 CCK1CYP3A27 (2) MYC CYP2M1 MX1GCK Proteolysis GLUD1 MetabolismGLUL2 MMP2 U GLUL2 Inv-U CYP1A2 (2)HSD3B1 Inv-CR SENP1 GLUL3 CYP2K4PTGS2 Signal Transduction MAOA GLUL3SOD1 HSP70C NME CI NMESOX9 LDLR U PEPCK NOS2A (1)

Oncogene RHO SOX9 PKKRAS U Structural Signal Transduction SOX23

Proteolysis ACTA1 FGF6 TYRMMP1 COL1A2 U HSP70C Inv-CR Oncogene

Signal Transduction MYH1 HSP90 TP53CHRNA9 U Transcription PPARG Inv-CR Proteolysis

Transcription MYOD1 Transcription CST3SPARC MYOG U FZR1

PSMC3 USENP1

Signal TransductionAHRCLOCKHSP70IHSP70COPN1SWRHO

StructuralACTA1NUP62 U

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between gene expression levels and sewage concentrations (Tables 2and 3). Concentration response (CR) and inverse-concentration re-sponse (inv-CR) relationships were the most common patterns inYear 2 (10% for CR and 7% for inv-CR=17% total; Fig. 1A), while Ushaped (U) and inverse-U shaped (inv-U) prevailed in Year 1 (16% forU and 2% for inv-U=18 % total; Fig. 1A). Only one gene, vitellineenvelope protein α (VEPA), was significantly altered at the exactsame timepoint in both years (d1), albeit with opposing patterns: U inYear 1 and CR in Year 2. Some genes significantly changed at onetimepoint in Year 1 but were significantly altered in another timepointin Year 2, and none of them exhibited the same pattern. For example,KRAS (Year 1, d1: U, but Year 2, d8: CR), vitelline envelope protein β(VEPB; Year 1, d8: U, but Year 2, d4: CR), and gonadotropin releasinghormone receptor 2 (GNRHR2; Year 1, d16: U, but Year 2, d8: CR;Tables 2 and 3). The cause of such differences between Year 1 and 2

was most likely the constituents or profile of the sewage. The sewageeffluent from this location varies due to fluctuations in parameterssuch as population/tourism and weather (Stubblefield et al., 2006).Therefore, it was not surprising that the sewage batches used in thecurrent study gave rise to different and inconsistent results at themolecular level.

In toxicological studies, concentration or dose response curves aretypically sigmoidal (Calabrese and Baldwin, 2001) allowing, in general,for the establishment of NOAECs and LOAECs. However, U shapedcurves and their physiological relevance have garnered increased atten-tion in the literature in the last decade (Calabrese, 2008). Studies withendocrine disrupting substances have shown non-sigmoidal results.For example, in studies with xenoestrogens, Giesy et al. (2000) notedinverse-U shaped responses for egg production and plasma levels of vi-tellogenin and 17β-estradiol in fatheadminnows (Pimephales promelas)

Table 3Year 2 genes that were significantly different (Welch t-test, pb0.05) from the control treatment in at least one out of five sewage treatments within a timepoint (days 1, 4, 8 or 16).A large number of genes had no evident trend, but a portion of genes had a dose-related trend: concentration-independent (C-I), concentration response (CR),inverse-concentration response (Inv-CR), U-shaped trend (U), or inverse-U shaped (Inv-U). * Bold genes were used as examples in Fig. 1B–F.

A. Day 1 B. Day 4 C. Day 8 D. Day 16

Gene Class Gene Class Gene Class Gene Class

Gene Trend Gene Trend Gene Trend Gene Trend

Binding/transport Binding/transport Binding/transport Binding/transportAPOA1 HBB Inv-U DNTT APOEATP1A2 MT1A FABP3 ATP1A1A (2)HPX RBP4 (2) HBA1 HBG1 Inv-CRSLC34A1 (1) SLC12A2 HBB MTND6SLC34A2 (2) Inv-CR SLC34A1 (1) HPX SLC12A2TF SLC4A1 MTND6 Endocrine

Endocrine Endocrine NGB 1 CR CRH CRADCYAP1 U CRH RHAG GNRH2AR CR GH1 Endocrine IGF2CALCRL GNRHR2 CR DIO2 INSRCCCK U IGF1 FSHR MTNR1A Inv-CR*ERA CR PRL U GNRHR2 CR STARGNRH2 CR TGFB1 *NR3C2 (MCR) U VEPAGNRHR2 Inv-CR VEPA CR PMCH VEPG U*INSRB Inv-CR VEPB CR POMC A Inv-CR ImmuneTGFB1 VEPG Reference C1R (1)VEPA Inv-CR Reference RPL23A PSMB9VEPB RPL23A CR Immune RAG1

Immune RPS6 BF MetabolismB2M Inv-CR Immune C1R (1) CR CYP1A2 (1)PSMB9 Inv-U C1R (1) CR C4B CYP3A27 (1) CR

Metabolism C4B CCR9 CYP3A27 (2)ATP6V1B2 Inv-CR CCK1 CXCR4 LPLCAD2 CR MHC1 MHC1 NOS2A (2)CYP11B1 SERPING1 MX1 OncogeneG6PC Metabolism PSMB9 KRASGAD2 AANAT RAG1 RB1GLUL1 CI CYP1A1 Metabolism ProteolysisNME Inv-U CYP2K1 AANAT CTSLPK CYP2K5 CYP17A1 FZR1

Oncogene GLUD1 U CYP1A1 MMP2 CIMYC HSD3B1 CYP1A2 (1) SENP1

Proteolysis MAOA CYP1A2 (2) Signal TransductionMMP2 NOS2A (2) CYP2K1 HSP70C Inv-U

Signal Transduction SOX9 CYP2M1 HSP90 UHSP70I Inv-U Oncogene CYP3A27 (1) StructuralID1 KRAS G6PC COL1A2RHO Inv-CR MYC GLUD1 CR MYH6TNF Proteolysis GLUL1 U NUP62

Structural MMP1 NOS2A (1) OMP1ACTB U PSMC3 CR SOXP1GDF8 SENP1 OncogeneMYH6 Signal Transduction KRAS CROMP1 AHR Proteolysis

Transcription ARNT U CST3SPARC FGF6 *UBQ CI

HSP70C Signal TransductionRHO ARNTTNF HSP70CUCN HSP90

Structural StructuralACTA1 TUBA1GDF8 TranscriptionMYH1 MYOD1NUP62 MYOG Inv-CR

*POU1F1 Inv-USPARC

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exposed to nonylphenol, while Jobling et al. (2004) described inverse-Ushaped results for the reproductive output of snails (Potamopyrgusantipodarum) and fathead minnows exposed to treated sewage effluent.Themechanism of non-sigmoidal response is likely a result of the effectsof endocrine disruptors on hormone receptor-mediated pathways,which have been shown to exhibit U shaped or inverse-U shapedconcentration-responsive curves (estrogen (Calabrese, 2001a) andandrogen (Calabrese, 2001b)). In the present study, some hormonereceptors showed U or inv-U shaped curves, namely thyroid hormone

receptor (THRB; Table 2) and mineralocorticoid receptor (NR3C2(MCR); Table 3). However, these patterns did not apply to all hormonereceptors included on our cDNA microarray, since estrogen receptoralpha (ERA) had a CR in Year 2 (d1, Table 3) and androgen receptor(AR) showed no distinct pattern in Year 1 (d16, Table 2) but exhibiteda CR in Year 2 (d1, Table 3). Thus, hormone receptors may have U re-sponses but they can also have no trend or sigmoidal responses. Hor-mone receptor-mediated pathways are not the only endpoints thatmight result in U shaped curves— apoptotic (Calabrese, 2001c) and nitric

Fig. 2. (A.) Summation of sewage treatment temporal trends for Year 1 and 2: Type 1 (significant alterations at d1, d4 or d8 that recover by d16), Type 2 (significant alterations at d1,d4 or d8 that remain at d16) and Type 3 (significant alteration only at d16). Example relationships depicted (±SEM) for temporal trends from Year 1, 0.1% or 10% sewage. * pb0.05,Welch t-test. SOX9=SRY (sex determining region Y)-box 9; PTGS2=prostaglandin D2 Synthase; MMP2=matrix metalloproteinase 2 (gelatinase A); LDLR=vitellogenin receptor(1); VEPB=vitelline envelope protein b; FGF6=fibroblast growth factor 6; GLUL3=glutamine synthetase 3; IGF2=insulin-like growth factor 2.

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oxide (Calabrese and Baldwin, 2001) receptor-mediated processes canalso generate U shaped curves. Cells can also respond to exogenousagents or environmental conditions by increasing their restorative pro-teins or stress resistance proteins in a U-shaped concentration responsemanner including heat shock proteins (HSP), antioxidant enzymes (e.g.superoxide dismutase (SOD) and glutathione peroxidase), andinsulin-like growth factors (Mattson, 2008). For all of these proteins,the microarray used in the present study contained the correspondinggene and some showed significant changes ((e.g.) SOD1, nitric oxidesynthase 2A (NOS2A), HSP70C, HSP90; Tables 4 and 5) but only threewere altered in a U shaped manner (NOS2A, d4 [Table 2]; HSP70I, d1andHSP90, d16 [Table 3]). Ings et al. (2011) evaluated the effects of sew-age on rainbow trout using a cDNA microarray and found indications ofan impaired cellular stress threshold (due to HSP70C and HSP90), both

of which were up- and down-regulated in the current experiment in dif-ferent sewage concentrations at differing time points (Tables 4 and 5).Thus, rainbow trout in the current experimentmay have had altered cel-lular stress threshold, although there was no particular association be-tween sewage concentration and an up- or down-regulated geneexpression pattern.

Since the microarray measures the expression of receptors them-selves, as well as the downstream products in pathways controlled byreceptors, U and inv-U results were anticipated for non-receptorgenes and indeed a number of these were obtained in this study (20in Year 1 and 15 in Year 2). Calabrese and Baldwin (2001) suggestedthat onewould findU shaped curves for nearly all studies if the concen-tration series or study design contained sufficient ultra-low doses. Inthe case of this research, we found U shaped curves likely due to the

Table 4Year 1 and 2 genes that were significantly different (Welch t-test, pb0.05) from the control treatment in at least one out of four timepoints (days 1, 4, 8 or 16) within a sewagetreatment (0.05%, 0.1%, 0.7%, 2%, 5/10%) showing one form of a Type 1 Temporal trend (significant alteration of gene expression during sewage treatment timepoints (d1, 4 or 8) withrecovery to control levels at d16). * bold genes were used as examples in Fig. 2B–D.

Year 1 Year 2

0.05% 0.1% 0.7% 2% 10% 0.05% 0.1% 0.7% 2% 5%

Peak at Day 1 Induction APOA1 CYP3A27 (2) APOA1 SOD1 CYP1A2 (1) GNRHR2 GNRHR2 AR MMP2 ADCYAP1CYP3A27 (1) CYP3A27 (1) GLUL2 GLUL2 TF TNF TNF MYH6 ERAINSRC SPARC SPARC HBG1 HSP70I GDF8KRAS *SOX9 MMP1VIG1 PPARB

Repression CHRNA9 GCK CKB C1R (2) CHRNA9 CCK ATP6V1B2 CYP11B1 CYP11B1 ACTBHBA1 HSD3B1 HBA1 HBA1 HBA1 ID1 APOA1 APOA1 APOA1GNRHR2 MX2 SLC34A2 (1) GNRHR2 RHO CALCRL CALCRL GLUL1

HBB HBB HBB TGFB1 HPX GAD2 SLC34A1 (1)*PTGS2 CSRP2 VEPA NME

RHAG OMP1THRBTSHB

Peak at Day 4 Induction ATP1A1 (2) ATP6V1B2 NGB 2 ATP1A1 (1) VEPG CCK1 VEPG VEPA VEPBACTA1 MYC MYC *MMP2 NOS2A (2) ACTA1 ACTA1

HSP70C ` PRL PRLTNF GLUD1

Repression MYH1 GHR PTGS2 MYH1 MYH1 C4B UCN CYP1A1 CYP2K5 CYP2K5COL1A2 RBP4 (1) SOXLZ *LDLR IGF1 GH1 HBB GDF8 GH1MTNR1A SENP1 THRA SOX9 MHC1 SLC34A1 (1) SLC34A1 (1) GNRHR2 MHC1MYOD1 VLDLR (2) NUP62 HSD3B1 NUP62 HSP70CVLDLR (1) MT1A MAOA MYC

PSMC3 PSMC3RBP4 (2) SLC4A1SENP1

Peak at Day 8 Induction VEPA SST ADCYAP1 RPS5 VEPA SOXP1 CCR9 NGB 1 CCR9 SOXP1VEPB VEPB VEPB *VEPB CYP2K1 CYP2K1 CYP2K1MYOG CYP2M1 VEPG HSP70C AANAT NR3C2 (MCR)

ATP1A1C HSP90 ARNT RHAGCYP2K1 POMCA PMCH PMCHIGF2 POU1F1

Repression RBP4 (2) CYP1A2 (1) CYP1A2 (1) LGALS9 LGALS9 CYP1A1 HBA1 CYP1A2 (2) CYP1A2 (2) BFFGF6 FGF6 GLUL2 FGF6 *FGF6 CST3 HBB MX1 CST3 C4BNGB 2 HSP70C GLUL3 C1R (1) NGB 2 FSHR FSHR FSHR FSHR FSHR

PMCH INSRA CYP1A2 (2) MYOD1 HPX NOS2A (1) HPX HPXPPARG MAOA MAOA C1R (1)

GLUD1 CYP17A1HSP90 CYP1A2 (1)

DNTTFABP3GLUD1

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utilization of five sewage concentrations that spanned a relatively widerange (200-fold). This approach is not commonly used but was a keyfactor in the findings of the present study: gene expression responsescan exhibit a variety of concentration–response relationships as wellas not have any apparent pattern relating to increasing sewage concen-tration. This has implications for gene expression studies where aconcentration- or dose–response is not evident and such results mayunfortunately go unpublished. As shown here, non-classical geneexpression concentration–response relationships are valid and impor-tant findings, and contribute to the knowledge of elucidating toxicantor complex mixture effects. Thus we suggest that future gene expres-sion studies span a wide range and include at least five concentrationsof toxicant to enable the identification of relationships.

4.3. Temporal patterns

The most common trend across timepoints in both experimentswas that of significant gene expression induction or repression duringsewage exposure, with recovery to control fish levels at d16 (Type 1trend): 65% Year 1, and 79% Year 2 (Fig. 2A). Within the Type 1 trend,further distinctions were made as to whether the gene expressionpeaked at d1, d4 or d8 (for induction and repression; Table 4). It was

expected that the initially induced genes (d1) to the sewage would begenes necessary for the fish to adapt to the chemical compounds and/ormicroorganisms in the sewage in a rapid manner, and thus geneswould probably be from the gene classes encompassing metabolism,immune, signal transduction, and binding/transport (similar classesreported in Ings et al. (2011)). That is not to exclude these genesfrom later timepoints, or the fact that contaminants in the sewagecould cause effects unrelated to adaptation, but the rapid responseof adaptation would be expected as soon as the fish were placed in di-luted sewage. The majority of the genes with peak induction at d1 inYear 1 were indeed from these gene classes, but in Year 2, the majorityof genes with peak up-regulation at d1 (Table 4) fell into the endocrineclass of genes, indicating that the two sewages, as expected, were likelyquite different in chemical composition. Genes with peak up- or down-regulation at d8were potentially indicative of longer term effects of thesewage, although sewage batches were refreshed between the d4 andd8 timepoints which somewhat confounds the data interpretation atd8. For Year 1, the vitelline envelope proteins (VEPA, VEPB, VEPG)had peak up-regulation at d8, but for Year 2, the VEPs had peak up-regulation at d4 and not d8 (Table 4). Thus, the second batch of sewagein Year 2 was likely less estrogenic than the first batch as VEPs areknown as highly sensitive, estrogen-responsive genes (Arukwe and

Table 5Year 1 and 2 genes that were significantly different (Welch t-test, pb0.05) from the control treatment at day 16 (recovery timepoint) within a sewage treatment (0.05%, 0.1%, 0.7%,2%, 5/10%) showing either a Type 2 Temporal trend (significant alteration of gene expression during sewage treatment timepoints (d1, 4 or 8) that remains significant at d16; Ops. =opposite), or Type 3 Temporal trend (significant alteration of gene expression at day 16 only). * Bold genes were used as examples in Fig. 2E–F.

Type 2 Year 1 Year 2

0.05% 0.1% 0.7% 2% 10% 0.05% 0.1% 0.7% 2% 5%

Remain Sig. at Day 16 Same SLC2A1 *GLUL3 PSMB9 SLC12A2NOS2A (1) SENP1 VEPA

VEPGOps. HSP70C CRH KRAS KRAS KRAS KRAS

RHO MTND6 CRH C1R (1)HSP90

Type 3Peak at day 16 Induction AR PK CLOCK PK PK FZR1 CYP3A27 CYP3A27 (2)

CCK1 TYR CRH CCK1 TYR HBG1 MMP2 MMP2CBLN4 CBLN4 GNRH1 BZRPL1 CYP1A2 CYP1A2 (1)CYP1A2 (2) GNRH2 GNRH2 MTND6 MTND6PRL PRL PRL PRL STARHBG1 IGF1 PSMC3 IGF1SENP1 CST3 SENP1 SENP1NUP62 *IGF2 FZR1VTG TP53 AHR

HSP70CIHPK2MX1NOS2AOPN1SWPOMCRBP4 (1)

Repression SOX23 ATP1A1C SOX23 ACTA1 ATP1A1A (2) ATP1A1A (2) RB1 APOE MYH6NR3C1 HSP70I RPS5 RAG1 IGF2 NUP62 COL1A2 NUP62

NME SLC12A2 MTNR1A SENP1 MTNR1ASLC2A1 CTSLVEPA

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Goksoyr, 2003; Thomas-Jones et al., 2003). Chemical analysis of thesewage (unpublished data) supported such a finding because therewas no detectable estrone, the only detected estrogen hormone inany of the sewage batches, in the second batch of Year 2 sewage (thefirst batch of Year 2 sewage had 6 ng/L in 5% sewage); whereas, Year1 had estrone in both batches of sewage (6–7 ng/L measured in 10%sewage). The chemical detection of estrone and the alteration ofestrogen-responsive genes (ERA and VEPs) indicated that both yearsof sewage were indeed estrogenic effluents. This level of estrone wassimilar to other reports for Canadian and German (Ternes et al., 1999),British (Williams et al., 2012) and Swiss (Aerni et al., 2004) sewageeffluents.

In both years, the majority of genes that recovered by d16 weredown-regulated during the sewage exposure timepoints (59% forYear 1, 53% for Year 2; Table 4). Many genes in each experimentwere altered at one timepoint or another but very few genes wereconsistently altered or in common between the two experiments.An example of one gene down-regulated in both years was C1R(complement component 1R) in the 2% sewage concentration. C1R is animmune gene and, among other effects, sewage and/or xenoestrogensare known to alter immune endpoints, often in a repressive manner(Yamaguchi et al., 2001; Cuesta et al., 2007; Muller et al., 2009; Wengeret al., 2011; Shelley et al., 2012). In the present study, both down- andup-regulated immune genes were detected (Table 4; results similar toJin et al., 2010). For all of the Type 1 genes, follicle stimulating hormonereceptor (FSHR) was the only gene to be affected across all concentra-tions: it was down-regulated in all 5 sewage treatments at d8 of Year 2.It is possible that repression of hepatic FSHR could have an impact onfish maturation, if lower levels of FSHR protein resulted, as this receptoris central to endocrine system signalling during the female process ofvitellogenesis and zonagenesis (Arukwe and Goksoyr, 2003), and it hasa role in male spermatogonia proliferation (Thomas, 2008).

The Type 2 trend has implications regarding the potential of thesewage to cause permanent or long-term effects. For example, KRAS,in Year 2 only, was significantly repressed in 4 out of 5 sewage

concentrations during the sewage exposure period, and then signifi-cantly induced in all 5 sewage treatment groups at the end of thedepuration period (Table 5). This could indicate a long-term effect asup-regulating KRAS, an oncogene, at d16 may result in increased recruit-ment and activation of proteins necessary for cell growth, division, matu-ration or differentiation. TP53 (p53), another oncogenewas up-regulated(a protective response) at d16: in Year 1, 0.1%. This potentially signifiesgenotoxic stress in this sewage treatment as TP53 has been shown to beactivated in response to DNA damage (Lee et al., 2008; Liu et al., 2011).

4.4. Recovery

In both years, some genes did not recover to baseline levels by d16,after 8 days of recovery in cleanwater, implying that the affected genesrepresent potential long-term or permanent effects of the sewage onthe fish. The Type 3 (significantly induced or repressed at d16 only)temporal pattern occurred with a greater number of genes than Type2, particularly in Year 1 where numerous genes were up-regulated atd16 only (Table 5). A Type 3 trend in gene expression alteration waslikely due to cascade effects of gene expression changes initiated duringthe sewage exposure timepoints. Altering gene expression can be acomplex process, especially because transcription for many genes isunder receptor-ligand control. If a receptor's gene expression wasaltered by the sewage, translation into a receptor protein will taketime, resulting in the receptor-mediated processes, including alteringgene expression of genes in that particular pathway, occurring subse-quently (Flouriot et al., 1996). In addition, chemicals in the sewagemay have accumulated in tissues during exposures and subsequentlybeen eliminated over time. For some compounds that were recalcitrantto biotransformation, clearance may only occur slowly (Schlenk et al.,2008). During the time of recovery (between d8 and d16), compoundsmay have been released from certain tissues during redistribution andelimination (via the liver; Kleinow et al., 2008), where gene expressionalterations were initiated. For example, cytochrome p450 detoxifyingenzymes were up-regulated at d16: CYP1A2 in 0.05% sewage, Year 1;

43H.L. Osachoff et al. / Comparative Biochemistry and Physiology, Part D 8 (2013) 32–44

CYP2K4 in 2% sewage, Year 1; CYP3A27 in 0.7% and 5%, Year 2 (Table 5).It is also possible that fish at d16 were recovering from, or respondingto, infection that occurred during the sewage exposure period, thus,genes may have been altered.

Providing fish an opportunity to recover from sewage effluentexposure, with subsequent evaluation, is not an approach that is com-monly studied. Yet, it is an important concept that may provideinsight into the environmental effects on resident or transient fishat the sewage discharge site. In the present study, our results showedthat gene expression alterations can recover within 8 d of sewageeffluent cessation, but also that gene expression alterations can beinitiated during the recovery period, thereby indicating that fishhave been impacted, although specific health implications remain tobe determined.

4.5. Implications for fish health

It is challenging to draw conclusions as to what gene expressionalterations such as those shown in the present study may mean atthe population level. There are indications from the present studythat long term, chronic exposure could be deleterious since somegenes were up-regulated in expression as the duration of sewageexposure increased (peaking at d4 or d8). As well, a depuration peri-od equal to the sewage exposure time did not enable alterations ingene expression to return to, or remain at, baseline levels, as shownby the genes significantly altered at d16. Thus, fish swimming inand out of sewage discharge areas may experience gene expressionalterations during the sewage exposure time as well as for a periodof time after leaving the area. The fact that hepatic gene expressionalterations were detected using a 2000-fold dilution of sewage dem-onstrates the utility of toxicogenomics techniques to identify poten-tially hazardous substances. Future studies could identify additionaleffects of the sewage on fish since the present study indicated thatalterations at the molecular level occurred, which may potentiallybe translated into adverse health effects at the individual and/orpopulation levels.

5. Conclusions

Using microarray technology, gene expression alterations in rain-bow trout caused by sewage exposure at ultra-low environmentallyrelevant concentrations were characterized. Between Years 1 and 2there were similarities in: (1) the gene classes affected by the sewage,(2) the fact that the majority of genes altered during the sewageexposures recovered to control fish levels after the depurationphase, and (3) that the threshold level of gene expression alterationswas below the environmentally relevant 0.05% sewage concentration.Three genes were consistent in their gene expression changes acrosstimepoints or within a sewage concentration: KRAS, FSHR, and VEPA.Since the environmentally relevant dilutions caused significant geneexpression alterations, the presence of this sewage in the environ-ment could cause gene expression changes in feral fish, with implica-tions for overall fish health.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.cbd.2012.10.002.

Acknowledgements

Funding support was provided by the Georgia Basin Action Plan, anEnvironment Canada program. The authors gratefully acknowledge theCapital Regional District (Victoria, B.C., Canada) staff who aided withsewage collection and C. Lowe for revisions. We thank all of the staff inthe Chemistry and Environmental Toxicology Sections at EnvironmentCanada's Pacific Environmental Science Centre in North Vancouver,B.C., Canada, for help on various aspects of the study, in particular: N.

Berke, L. Brown, J. Bruno, C. Buday, G. Schroeder, R. Skirrow and M.Linssen-Sauvé.

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Glossary

type 1 pattern: genes significantly altered during the sewage treatment phase (d1–d8)that recovered to control levels during the depuration phase and were not signif-icantly different at d16;

type 2 pattern: genes significantly altered during the sewage treatment phase (d1–d8)that remained significantly altered after the recovery phase (at d16);

type 3 pattern: genes significantly altered at d16 only (i.e. not during the precedingsewage exposure time).