ToxSci Advance Access originally published online on November 2, 2005
Toxicological Sciences 2006 89(2):524-534; doi:10.1093/toxsci/kfj033
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Dynamic Gene Expression Changes Precede Dioxin-Induced Liver Pathogenesis in Medaka Fish
* Integrated Toxicology Program and Nicholas School of the Environment and Earth Sciences, Duke University, Durham, North Carolina 27708; and Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina 27606
1 To whom correspondence should be addressed at Division of Environmental Sciences and Policy, Nicholas School of the Environment and Earth Sciences, Duke University, NC 27708-0328. Fax: (919) 684-8741. E-mail: swkull{at}duke.edu.
Received August 25, 2005; accepted October 23, 2005
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ABSTRACT |
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A major challenge for environmental genomics is linking gene expression to cellular toxicity and morphological alteration. Herein, we address complexities related to hepatic gene expression responses after a single injection of the aryl hydrocarbon receptor (AHR) agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) and illustrate an initial stress response followed by cytologic and adaptive changes in the teleost fish medaka. Using a custom 175-gene array, we find that overall hepatic gene expression and histological changes are strongly dependent on dose and time. The most pronounced dioxin-induced gene expression changes occurred early and preceded morphologic alteration in the liver. Following a systematic search for putative Ah response elements (AHREs) (5'-CACGCA-3') within 2000 bp upstream of the predicted transcriptional start site, the majority (87%) of genes screened in this study did not contain an AHRE, suggesting that gene expression was not solely dependent on AHRE-mediated transcription. Moreover, in the highest dosage, we observed gene expression changes associated with adaptation that persisted for almost two weeks, including induction of a gene putatively identified as ependymin that may function in hepatic injury repair. These data suggest that the cellular response to dioxin involves both AHRE- and non-AHRE-mediated transcription, and that coupling gene expression profiling with analysis of morphologic pathogenesis is essential for establishing temporal relationships between transcriptional changes, toxicity, and adaptation to hepatic injury.
Key Words: medaka; dioxin; liver; aryl hydrocarbon receptor; gene expression; transcription.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY DATAREFERENCES |
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Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that has been identified in mammals and a number of bony fishes including medaka (Hahn, 2002
; Kawamura and Yamashita, 2002). Contrary to mammals, fish have two AHR isoforms (AHR1 and AHR2), a genetic trait due to gene duplication early in vertebrate evolution (Hahn, 2002). AHR is a basic-helix-loop-helix Per-ARNT-Sim (bHLH-PAS) transcription factor that mediates normal cellular events as well as xenobiotic-induced toxicity. Upon ligand (dioxin) binding, AHR translocates as a complex from the cytosol into the nucleus via AHR-specific nuclear localization signals (Ikuta et al., 1998). Following dissociation, AHR heterodimerizes with aryl hydrocarbon nuclear translocator (ARNT) and binds to aryl hydrocarbon response elements (AHREs) in promoter regions of "Ah gene battery" target genes such as cytochrome P450 1A (CYP1A) and UDP glucuronosyltransferase (UDPGT) (Nebert et al., 2000). Compared to wild-type mice, embryonic AHR-null mice treated with dioxin display significantly decreased developmental abnormalities, suggesting that AHR activation alone mediates dioxin-induced teratogenicity (Peters et al., 1999). Similarly, morpholino (MO)-based knock-down of AHR2 translation in zebrafish eliminates dioxin-induced developmental abnormalities such as heart malformations and pericardial edema commonly observed by 72 hpf in non-MO-treated control embryos exposed to dioxin (Prasch et al., 2003; Teraoka et al., 2003).
While AHR activation is required to manifest the toxic effects of dioxin, this injury is not always dependent on CYP1A induction. In zebrafish embryos, MO-based knock-down of CYP1A does not prevent TCDD-induced developmental toxicity (Carney et al., 2004), suggesting that alternative AHR-mediated mechanisms are responsible for toxicity. Likewise, Cyp1a–/– or Cyp1a2–/– knock-out mice exhibit increased toxic reactive oxygen species (ROS) and oxidative stress in liver mitochondria similar to dioxin-treated wild-type mice, while dioxin-treated Ahr–/– knock-out mice show levels comparable to untreated wild-type mice (Senft et al., 2002). While these data do not exclude the possibility of functional redundancy between CYP1A1 and CYP1A2, this study equally suggests that dioxin-induced toxicity is dependent on AHR activation but independent of CYP1A transcription. Indeed, as nuclear translocation, ARNT heterodimerization, and DNA binding of AHR are required for dioxin toxicity in vivo (Bunger et al., 2003; Walisser et al., 2004), primary and secondary AHR-mediated transcription of genes other than CYP1A likely contributes a greater effect to downstream toxicity. In addition to its well-characterized function as a transcription factor, AHR can also induce signaling cascades that may elicit downstream transcriptional responses and toxicity. For example, the protein kinase c-Src is specifically activated upon AHR ligand binding, leading to increased protein phosphorylation, signal transduction, and subsequent cellular toxicity (Enan and Matsumura, 1995, 1996; Matsumura, 1994).
Currently, gene array and bioinformatic approaches are revealing dioxin-induced transcriptional events that contribute to hepatic toxicity (Boverhof et al., 2005; Tijet et al., 2005). Moreover, these strategies have been used to identify predictive gene sets specific for AHR ligands (Vezina et al., 2004), as well as detect AHREs within promoter regions of differentially expressed genes (Kel et al., 2004; Tijet et al., 2005). To date, these approaches have largely been restricted to rodent models. Depending on the extent and annotation of teleost genome databases, similar strategies can be employed with fish models to discriminate between AHRE-dependent and AHRE-independent transcriptional gene activation. Indeed, near-complete genome sequences for zebrafish and medaka are now publicly available online, providing the necessary bioinformatic tools to link gene expression and gene promoter analysis with conventional indicators of toxicity.
Temporal relationships among initial dioxin exposure, AHR-induced hepatic transcriptional events, and subsequent liver pathogenesis have not been addressed in teleost fish. In this study, we explored these multi-dimensional complexities through the integration of gene expression with conventional histologic data. We administered a single ip injection of vehicle or dioxin (0.1, 1.0, or 10 µg/kg) to adult male fish (medaka) and sampled at days 1, 5, 9, and 13 for histologic evaluation and differential gene expression using hepatic 175-gene arrays and real-time PCR. Using a previously identified consensus AHRE motif (5'-CACGCA-3') found within the medaka CYP1A promoter (Kim et al., 2004), we identified the presence and number of putative AHREs within 2000 bp upstream of the predicted transcriptional start site for dioxin-responsive genes. Overall, we demonstrate that both AHRE- and non-AHRE-mediated transcriptional events are likely associated with downstream dioxin-induced hepatic alterations observed at the histologic level. Moreover, through statistical filtering of array data, we identify a gene (ependymin) possibly involved in tissue repair that is significantly induced following acute transcriptional changes.
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MATERIALS AND METHODS |
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Chemicals.
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) (99% purity) was purchased from Cambridge Isotope Laboratories, and nominal TCDD stocks (0.035, 0.35, and 3.5 µg/ml) were prepared in 100% high performance liquid chromatography (HPLC)-grade dimethyl sulfoxide (DMSO) and stored at room temperature in the dark.
Test animals.
Medaka (Oryzias latipes) are small (3–5-cm adult length) oviparous freshwater fish native to rice paddies of Japan, Korea, and eastern China. Male fish used for this study were collected from an orange-red line under standard recirculating aquaculture conditions for medaka. All fish were handled and treated according to protocols approved by the Duke Institutional Animal Care and Use Committee (IACUC).
Dioxin exposures.
Two-hundred and forty adult male medaka (6–7 months old) were randomly isolated from four different culture tanks and acclimated in two separate tanks (120 fish per tank) under recirculating conditions for 1 week prior to injection. All fish hatched out as larvae in March 2004, and wet body weight and adult length at mid-September 2004 (experiment initiation) averaged 418 ± 60 mg and 3.7 ± 0.2 cm respectively. While positioned in right lateral recumbancy, cold-anesthetized fish were ip-injected in the abdominal region once with vehicle (DMSO) or TCDD using a sterile, glass 25-µl Hamilton syringe equipped with an ultra-fine needle. At test initiation, fish were injected with 1 µl HPLC-grade DMSO or 1 µl TCDD stock; based on mean adult wet body weight, approximate single TCDD doses delivered were 0.1, 1.0, and 10.0 µg/kg. A total of 54 fish per treatment (216 total fish) were injected and randomly distributed equally into three replicate ethanol-rinsed 2-l glass beakers per treatment containing embryo rearing medium (ERM) (17.1 mM NaCl, 272 µM CaCl2•2H2O, 402 µM KCl, 661 µM MgSO4•7H2O; pH 6.0 ± 0.2) (Kirchen and West, 1976), totaling 18 fish per beaker and 12 beakers across all treatments. Following injection, fish were incubated in fresh, well-aerated ERM at 25°C under 16 h:8 h light:dark conditions for 13 days. ERM was half-renewed daily with fresh, well-aerated ERM to ensure optimal water quality conditions throughout the exposure period. Starting on day 1, fish within each beaker were fed 100 ± 10 mg Otohime B1 aquarium feed (Reed Mariculture) every two days, for a total of five feeding schedules. On post-injection days 1, 5, 9, and 13, fish were sacrificed for cDNA array, real-time PCR, and histologic evaluation. For gene expression analyses (cDNA array and real-time PCR) at each time-point, livers from three fish per replicate beaker were removed as previously described (Volz et al., 2005), pooled, and immediately frozen in liquid nitrogen; this sampling regimen yielded three independent liver pools per dose-time condition, where each tissue pool contained three livers. Livers were stored in nuclease-free cryogenic vials at –80°C for approximately 3 weeks until total RNA isolation. For histological analysis, one fish per replicate beaker was sacrificed at each time-point to yield a total of three fish per dose-time condition.
Histopathologic analysis.
All vehicle- and dioxin-exposed fish were anesthetized in ice-cold ERM and caudal peduncle was transected distal to the anus using clean scissors and forceps. Clean scissors were then used to open the abdominal cavity, permitting fixative to infiltrate all internal viscera. Fish were fixed in 2% paraformaldehyde/phosphate buffered saline (PBS; pH 7.4) for 72 h at 4°C, and stored in 6% sucrose/PBS (pH 7.4) at 4°C until mounting and sectioning. Fish were oriented in lateral recumbency, paraffin-embedded, and 6-µm thick-step sections through the whole body were mounted on glass slides and stained with hematoxylin and eosin. All livers were scored blindly and imaged (60X) with a Nikon Eclipse E600 light microscope, a Nikon DXM 1200 digital camera, and EclipseNet imaging software (Nikon).
RNA isolation.
Pooled livers were homogenized with 1 ml RNA Bee (TelTest) using a stainless steel Polytron homogenizer (Kinematica) cleaned with RNaseZAP (Sigma), DEPC-treated water, and sterile de-ionized water. Following homogenization, total RNA was isolated per manufacturer's instructions using an RNeasy Mini Kit (Qiagen). Prior to sample elution, each sample was on-column-digested with DNase to eliminate DNA contamination using an RNase-free DNase Set according to manufacturer's instructions (Qiagen), and then eluted with 30 µl warmed (52°C) RNase-free water. RNA quantity and 260/280 ratios were verified using a NanoDrop ND-1000 spectrophotometer. Total RNA was stored at –80°C for approximately 3 months until cDNA array hybridizations.
cDNA arrays.
175-gene cDNA membrane arrays were constructed using subtraction libraries generated from 6–7-month-old male orange-red medaka liver, brain, or testis following a 48-h single-dose exposure to TCDD (Volz et al., 2005); in addition, subtracted cDNAs from male fish liver, brain, or testis treated for 48 h with ciprofibrate, and liver treated for 48 h with 17ß-estradiol were also used for array construction (unpublished data). Approximately 93% (163/175) of the cDNAs spotted on the arrays were derived from medaka liver, and 46% (80/175) and 54% (95/175) of the cDNAs spotted were derived from forward and reverse subtraction libraries respectively. Gene names, gene ontology, subtraction library origin, BLAST E-values, and mapped genome locations based on medaka genome assembly revision 200406 (see URLs) for these arrays can be found as Table 1 in the Supplementary Material online. In addition, using a previously identified consensus AHRE motif (5'-CACGCA-3') found within the promoter for medaka CYP1A (Kim et al., 2004), the presence and number of AHREs within 2000 bp upstream of the predicted transcriptional start site for each gene are provided in Table 1 in the Supplementary Material online. For this study, we limited our search for AHREs within 2000 bp of upstream sequence (rather than the typical 5000–10000 bp) because five AHRE motifs (5'-CACGCA-3') are within 2000-bp of upstream medaka CYP1A promoter sequence (Kim et al., 2004). However, once the medaka genome is fully assembled and annotated, a more exhaustive search for AHRE motifs within promoter regions of these screened genes may reveal additional response elements not discovered in this study.
To construct cDNA membrane arrays, stock colonies from all subtraction libraries were individually transferred with sterile toothpicks to two 96-well microplates containing 100-µl LB media with ampicillin (100 µg/ml) per well and re-grown overnight at 37°C with constant agitation at 150 rpm. Following overnight growth, all new stock colonies were stored in 60% glycerol at –80°C until PCR amplification. All cDNA clones were simultaneously PCR-amplified in duplicate using two 96-well PCR plates. For each 50-µl PCR reaction, cDNA clones were PCR-amplified using 4 µl bacterial growth, 5.0 µl 10X Advantage 2 buffer, 1.5 µl 10 µM NP-1 primer, 1.5 µl 10 µM NP-2R primer, 1.0 µl 10 µM dNTPs, 36.5 µl sterile water, and 0.5 µl 50X Advantage 2 Polymerase Mix (BD Biosciences). PCR reaction conditions were: 94°C for 30 s followed by 35 cycles of 95°C for 10 s and 68°C for 3 min. PCR products were checked for purity by resolving nine randomly selected wells from each PCR plate (18 total PCR products) on a 1.2% agarose gel. Once quality was established, PCR-amplified cDNAs were individually purified using a QIAquick PCR Purification Kit (Qiagen) and quantified in 1-µl aliquots using a NanoDrop ND-1000 UV/Vis Spectrophotometer (NanoDrop Technologies). For each cDNA membrane array, 20 ng of purified cDNA was denatured at 65°C for 5 min with 0.6 M NaOH and chilled on ice for 2–3 min. For each well, 10 ng of each cDNA were duplicate-spotted onto positively charged nylon membranes (75 mm x 115 mm) (Roche Diagnostics) using a sterile 96-pin Slot Pin Multi-Blot Replicator (V&P Scientific) with a 2-µl delivery volume. Excluding array controls, the final arrays contained 175 cDNAs from different clones and subtraction libraries; 28 (16%), 34 (19%), and 113 (65%) cDNAs were derived from ciprofibrate-, 17ß-estradiol-, and TCDD-treated medaka subtraction libraries respectively (Table 1 in the Supplementary Material online). To assess intra- and inter-array variability, array controls were randomly spotted (10 ng/spot) on each membrane array. Array controls included: Arabidopsis Cab1 cDNA (Accession ID: X56062) (6 spots/array) (Spot Report-3 Array Validation System, Stratagene) and salmon sperm DNA (28 spots/array) (Sigma-Aldrich). Spotted membranes were neutralized in 0.5 M Tris/1.5 M NaCl for 30 s, rinsed in water for 2 min, and cDNAs UV-cross-linked to membranes prior to drying and storage. As two arrays per replicate RNA pool were used for analysis, a total of 96 identical membrane arrays (6 arrays per dose-time condition) were spotted and stored in a dry, sealed environment at room temperature until hybridizations.
32P-labeled cDNA probes for array hybridizations were generated in a labeling reaction from array control mRNA (Arabidopsis Cab1 mRNA) (Stratagene) and total sample RNA (DMSO- or TCDD-treated liver RNA); Arabidopsis Cab1 mRNA was used as an internal standard to control for cDNA labeling and hybridization efficiency. For reverse transcriptase (RT) labeling reactions, 2 µg total RNA, 1 ng Arabidopsis Cab1 mRNA, and 4 µl 10 µM oligo(dT) primer were mixed and denatured at 65°C for 5 min. After cooling to room temperature for 5 min, 8 µl 5X First Strand buffer (Invitrogen), 4 µl 0.1 M DTT, 2 µl RNaseOUT Ribonuclease inhibitor (40 U/µl) (Invitrogen), 2 µl modified dNTPs (10 mM dGTP, 10 mM dCTP, 10 mM dTTP, and 0.05 mM dATP), 40 µCi [
32P]dATP (Amersham Biosciences), and 1 µl M-MLV RT (200 U/µl) (Invitrogen) were mixed thoroughly with the above, and the final mixture incubated at 42°C for 1.5 h. Following incubation, all 32P-labeled cDNAs were purified using Microspin G-25 columns (Amersham Biosciences), and probe radioactivity quantified using a bench-top Beckman Coulter LS 6500 scintillation counter (Beckman Coulter). Using glass hybridization tubes, membrane arrays were individually pre-hybridized with 5 ml hybridization buffer (ExpressHyb Hybridization Solution, BD Biosciences) and 100 µl blocking solution (10X SSC/5.5 mg/ml denatured salmon sperm DNA) at 72°C while rotating for 1.5 h. Based on 32P-specific radioactive counts for each triplicate set of labeled probes, equal quantities of denatured 32P-labeled cDNA probe from each treatment replicate labeling reaction diluted in 100 µl blocking solution were hybridized to appropriate membrane arrays overnight while rotating at 72°C. Our sampling regimen yielded three independent liver RNA pools (3 fish livers per pool) for each dose-time condition (16 total conditions) to account for biological variation, yielding 48 independent RNA samples for array analysis. For each RNA sample, two replicate membranes containing duplicate spots per membrane were hybridized with 32P-labeled cDNAs in separate hybridization tubes and ovens to control for random technical errors. As a result, six membranes were hybridized for each dose-time condition, totaling 96 membrane hybridizations for the entire experiment and 12 replicate spots per dose-time condition for each gene. Following hybridization, membrane arrays were washed 4 times with 2X SSC/0.5% SDS for 30 min/wash, and 2 times with 0.2X SSC/0.5% SDS for 30 min/wash. Following washes, hybridized membranes were plastic-wrapped, exposed onto 35X43 cm phosphor screens for 72 h, and scanned using a Storm 860 Phosphoimager (Molecular Dynamics).
Real-time PCR.
First-strand cDNAs were generated from the same RNA pool used for cDNA arrays; a total of 48 RNA samples were used for first-strand cDNA synthesis. Using a 96-well PCR plate, total RNA (1–3 µg) was diluted with RNase-free water to a final volume of 10 µl, and 1 µl oligo(dT)15 (500 µg/ml; Promega) and 1 µl 10 mM dNTPs were mixed with diluted RNA to yield a final volume of 20 µl. The mix was heated to 65°C for 5 min and chilled on ice for 2 min. Following centrifugation, 4 µl 5X first-strand buffer (Invitrogen), 2 µl 0.1 M DDT, and 1 µl RNase OUT Inhibitor (40 U/µl; Invitrogen) were added to each reaction and heated to 37°C. Following 2 min incubation, 1 µl Superscript Reverse Transcriptase (200 U/µl; Invitrogen) was added to each reaction and mRNA reverse transcribed at 37°C for 1 h. All reactions were then inactivated by incubating at 70°C for 15 min. First-strand cDNAs were stored at –20°C until real-time PCR.
Relative levels of CYP1A, ependymin, and ß-actin transcripts in DMSO- and TCDD-treated livers were measured using real-time PCR. The following medaka-specific real-time PCR primers were designed using PrimerQuest (Integrated DNA Technologies): CYP1A, forward primer 5'-ACATCGGCCTGAACCGAAATCCTA-3', reverse primer 5'-TGCTTCATTGTGAGCCCGTACTCT-3'; ependymin, forward primer 5'-AACATGAAGCTGGCAGTGGTGTTG-3', reverse primer 5'- AAATTGCCCATCTCAAAGAGCCGC-3'; and ß-actin, forward primer 5'-ACAACGGATCTGGCATGTGCAAAG-3', reverse primer 5'- AGGGCTGTGATCTCCTTCTGCATT-3'. CYP1A, ependymin, and ß-actin cDNAs were PCR-amplified separately in duplicate using a 96-well PCR plate and an ABI PRISM 7000 Sequence Detection System (Applied Biosystems). For each 25-µl real-time PCR reaction, first-strand cDNAs were amplified using 2 µl (100–300 ng) first-strand cDNA, 9 µl RNase-free water, 0.75 µl 10 µM forward primer (0.3 µM), 0.75 µl 10 µM reverse primer (0.3 µM), and 12.5 µl 2X QuantiTect SYBR Green PCR Master Mix (Qiagen). Real-time PCR reaction conditions were: 95°C for 15 min followed by 41 cycles of 94°C for 15 s, 55°C for 30 s, and 72°C for 1 min. Relative quantitation of gene expression within each reaction was calculated following manufacturer's instructions (User Bulletin #2, ABI PRISM 7700 Sequence Detection System, Applied Biosystems). For each sample, the threshold cycle for reference (ß-actin) amplification (Ct,ß-actin) was subtracted from the threshold cycle for target amplification (Ct,CYP1A or ependymin) to yield a 
Ct. The threshold cycle represents the cycle number at which the fluorescence signal is significantly above the baseline. The mean or standard deviation of Ct for DMSO-treated samples was subtracted from the mean or standard deviation of Ct for TCDD-treated samples to yield a mean and standard deviation of Ct for each target. Fold induction relative to DMSO-treated organs and 95% confidence intervals were calculated using 2–Ct.
Statistical analyses.
Raw spot density data for each membrane were individually quantified by ArrayGauge v2.1 (FugiFilm), exported as a spreadsheet into Microsoft Excel, and coded by gene, day, dose, dose replicate, membrane replicate, and spot replicate. These hierarchical levels of identification were necessary for appropriate statistical analyses outlined below. For each membrane, spot intensity values were normalized by dividing mean intensity values for Arabidopsis Cab1 on the same membrane. As such, for each membrane, raw intensity values were individually adjusted for differences in cDNA labeling or hybridization among membranes. All Cab1-normalized values were then multiplied by 1000, and log2 transformations of these adjusted values were performed to ensure model assumptions of linearity and equal variance. Log2-transformed gene expression data were clustered by gene using uncentered correlation with average linkage clustering in Eisen's Cluster and TreeView (Eisen et al., 1998). To test for statistical significance, log2-transformed data were imported into SAS v9.1 (SAS Institute, Inc.) and tested for main effects of day or dose and day-dose interaction effects using a mixed linear model ( = 0.05) (a MIXED procedure in SAS) (Wolfinger et al., 2001). This model considers both fixed- and random-effects parameters associated with known a priori-assigned variables (gene, day, and dose) and unknown random variables (dose replicate, membrane replicate, and spot replicate), respectively, assumed to impact the variability of the data; thus, since these data were generated using a nested experimental design, we used a mixed linear model to account for variability linked with each level of hierarchy. Bonferonni-adjusted multiple comparisons of least square means were used to account for high false-positive rates associated with post-hoc pair-wise comparisons. Mixed linear models and multiple comparisons were applied at the level of gene expression pattern, individual gene response, and ontological groupings to test significance at different organizational hierarchies in the data. All significant differences were reported at the 95% confidence level (p < 0.05). To evaluate entire gene expression patterns across dose and time, principal component analysis (PCA) was performed using the PRINCOMP procedure ( = 0.05) in SAS, and was based on time-matched treatment-to-control ratios calculated from mean normalized data for each gene. Mean gene expression ratios used for clustering and PCA are available in Table 2 in the Supplementary Material online.
URLs.
The Medaka Genome Browser (DNA Sequencing Center, National Institute of Genetics, Japan) is available at http://dolphin.lab.nig.ac.jp/medaka/. Eisen's Cluster and TreeView are available at http://rana.lbl.gov/EisenSoftware.htm. Transcription Element Search System (TESS) is available at http://www.cbil.upenn.edu/tess/.
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RESULTS |
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Animal survival for the entire 13-day exposure period was >85% across all treatments (93 ± 8%, 100 ± 0%, 94 ± 6%, and 85 ± 8% in vehicle, 0.1, 1.0, and 10.0 µg/kg dioxin, respectively). Based on array-wide normalized spot intensity data, there were significant main effects of dose and day and significant dose-day interaction effects when comparing entire gene expression profiles (Fig. 1A). Following multiple comparisons of array patterns across dose-time groups, overall hepatic transcriptional changes were most pronounced on day 1 in dioxin-treated fish (Fig. 1A). Similar temporal data trends were evident from PCA: the first principal component accounted for 35% of the data variability and was largely influenced by the strong day-1 response in all dioxin treatments (Fig. 1B). After considering the day-1 contribution to the variation in gene expression data, the second principal component was influenced by dose for the remainder of the time, accounting for 21% of the total data variability (Fig. 1B). Within day-1, there was a dose response in overall gene expression data: hepatic gene expression patterns from 1.0 and 10.0 µg/kg dioxin treatments were similar to each other but significantly different from 0.1 µg/kg-treated fish (Figs. 1A and 1B). Similarly, when analyzed on a gene-by-gene basis relative to day 1 controls, there were 78, 161, and 158 significant transcript responses on day 1 in fish treated with 0.1, 1.0, and 10.0 µg/kg respectively (Fig. 2A and Table 2 in the Supplementary Material online). These significant responses were represented by roughly 87% (27/31) of ontological categories in the lowest dose (0.1 µg/kg) and 93% (28/31) of ontological categories in the two higher doses (1.0 and 10.0 µg/kg) (Fig. 2B), demonstrating that dioxin affected a wide range of cellular processes by 24 h in the liver. Only 13% of significantly responsive genes (including CYP1A) in each dose contained at least one AHRE within 2000 bp upstream of the predicted transcriptional start site (Fig. 2A and Table 1 in the Supplementary Material online). Based on array and real-time PCR data, CYP1A transcripts were increased in a dose- and time-dependent manner over time (Fig. 1A, Fig. 3A, and Table 2 in the Supplementary Material online), demonstrating that AHR:AHRE-driven transcription persisted for the entire 13-day exposure period.
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) denote significant difference between treatment array mean from another time-matched treatment array mean. Cytochrome P450 1A (CYP1A) induction is highlighted (arrow) as a positive control for dioxin-induced gene expression changes. Gene names and genome map locations are provided in Table 1 of the Supplementary Material online. Raw data used for hierarchical cluster are provided in Table 2 of the Supplementary Material online. (B) Principal component analysis (PCA) of entire gene expression data set shown in Panel A. Time-points are distinguished by color, and dioxin doses (µg/kg) are labeled adjacent to appropriate day-specific symbols. Principal components 1, 2, and 3 accounted for 35, 21, and 14%, respectively, of the data variability. All three principal components accounted for approximately 70% of the variation in the data. Raw data used for PCA are provided in Table 2 of the Supplementary Material online.
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While there were strong transcriptional responses on day 1, no pathologic changes were observed on day-1 in livers from dioxin-treated fish (Fig. 4). However, there were dose-dependent changes in liver pathogenesis for the remainder of the time-points sampled. Fish exposed to 0.1 µg/kg exhibited mild (grade 1) hepatocellular atrophy on day 5 with no adverse cytologic changes at the remaining time-points (data not shown). Conversely, mild-to-moderate (grade 2) fatty liver and moderate (grade 3) hepatocellular atrophy was observed on day 9 in fish exposed to 1.0 µg/kg (data not shown). The most acute effects were observed in fish exposed to 10.0 µg/kg dioxin: severe (grade 4) fatty liver on day 5, mild (grade 2) hepatocellular atrophy on day 9, and severe (grade 4) perivascular inflammation on day 13 (Fig. 4). Coupled with these histologic findings, the day-1 gene expression data demonstrate that both primary and secondary AHR-mediated transcriptional responses were associated with fatty liver and hepatocellular atrophy in fish treated with 1.0 and 10.0 µg/kg dioxin. However, although these two treatments exhibited similar gene expression profiles on day 1, the rate and severity of histologic alterations differed, showing that the day-1 gene expression response was saturated within the two highest doses.
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Gene expression changes in all treatments on days 5, 9, and 13 were significantly decreased relative to day-1. By day 5, there was an 85, 94, and 89% decrease in the number of significant transcriptional responses in fish treated with 0.1, 1.0, and 10.0 µg/kg respectively (Figs. 1A and 2A). By day 9, gene expression patterns approached controls in fish exposed to 0.1 and 1.0 µg/kg dioxin (Figs. 1 and 2), with only one significant transcript response on days 9 and 13 in both treatments (Fig. 2B). However, in the highest dose (10.0 µg/kg) on day 9, we detected 53 significant transcript responses represented by a range of cellular processes (24 of 31 ontological categories) (Figs. 2A and 2B). These day-9 transcriptional events in livers treated with 10.0 µg/kg dioxin preceded perivascular inflammation observed on day 13 (Fig. 4). Similar to day 1, only 11% of these significantly responsive genes contained at least one AHRE within 2000 bp upstream of the transcriptional start site (Fig. 2A).
Lastly, we identified consistent individual transcriptional changes that persisted after the day-1 acute gene expression response. Since livers treated with 10.0 µg/kg dioxin had more than one significant transcriptional change at each time-point following day 1, we identified genes in this treatment that exhibited a statistically significant response on days 5, 9, and 13. This final list consisted of 9 target genes, including classic markers of AHR activation (CYP1A and UDPGT) (Fig. 5A). Four of nine targets identified contained at least one AHRE, including the anti-microbial peptide hepcidin. Similar to CYP1A, there was a dose-dependent induction of hepcidin mRNA at each time-point. In addition, a gene putatively identified as ependymin was strongly induced after day 1 (Fig. 5). Using the medaka genome browser, this unique sequence was mapped to scaffold 1113:16202–18624 (Table 1 in the Supplementary Material online). Ependymin transcripts were 4–9-fold higher than controls in livers treated with the highest TCDD dose (10.0 µg/kg) on days 5, 9, and 13 (Fig. 5B and Table 2 in the Supplementary Material online); similar to CYP1A, slight fluctuations in ependymin gene expression were observed across these sampling time-points (Fig. 5B).
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DISCUSSION |
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In this study, we used custom hepatic 175-gene arrays to assess dioxin-induced transcriptional changes on post-injection days 1, 5, 9, and 13, and related these temporal gene expression patterns to cytologic alterations using conventional histologic approaches. Our study demonstrated that a single ip-injected dose of dioxin resulted in significant early transcriptional events that preceded downstream histopathological changes in medaka liver. Based on the genes surveyed, the most pronounced changes in gene expression occurred on day 1 and were associated with 27–28 different biological processes in the liver (Figs. 1 and 2). These data suggest that dioxin affected a wide range of cellular mechanisms by 24 h post-exposure. However, at this same time-point, there were no histological differences observed between vehicle control and dioxin-treated livers (Fig. 4). In the highest dose (10.0 µg/kg), livers were highly vacuolated (fatty liver) by day 5 while significant transcriptional changes were decreased by almost 90%. However, by day 9, there was an increase in gene expression that preceded inflammation on day 13. In the lower doses tested (0.1 and 1.0 µg/kg), only one significant transcript response occurred on days 9 and 13 (Fig. 2A), and there were no inflammatory responses following initial hepatocyte injury. Thus, as illustrated with the 10.0 µg/kg dose, transcriptional changes preceded both hepatocellular toxicity (fatty liver) and adaptation (inflammation) at distinct time-points following initial dioxin exposure. Through statistical filtering of gene array data, we also identified 9 target genes in the highest dose that had significantly different expression levels from vehicle controls on days 5, 9, and 13, including dose-dependent induction of CYP1A on each day measured (Fig. 5A). In parallel with the overall day-9 transcriptional response in the highest dose, expression of these genes was probably linked to the influx of immune cells and/or hepatic repair. Interestingly, four of these genes including CYP1A contained at least one AHRE within 2000 bp upstream of the transcriptional start site. These findings suggest persistent dioxin-induced AHR activation for almost 2 weeks even after the initial acute day-1 transcriptional response. This was most likely due to the well-known biological persistence of dioxin. For example, in hamsters, CYP enzyme activity was significantly increased for up to 35 days following a single ip injection of 10 µg/kg TCDD (Gasiewicz et al., 1986
). Lastly, a gene putatively identified as ependymin was highly induced after the day-1 transcriptional response in the highest dose, suggesting that this gene may have been involved in host adaptation following acute hepatocyte injury.
As AHR-null mice and AHR-null fish are resistant to dioxin toxicity (Peters et al., 1999; Prasch et al., 2003), AHR activation was most likely required for downstream hepatocellular toxicity in this study. However, since the majority (87%) of dioxin-responsive genes on day 1 did not contain AHREs based on our search criteria (Fig. 2A), gene expression was probably largely due to secondary transcription factors downstream of AHR activation and AHRE-driven transcription. In addition, as inhibition of the AHR-interacting protein kinase c-Src significantly decreases dioxin-mediated toxicity (Backlund and Ingelman-Sundberg, 2005), protein kinases such as c-Src likely played a central role in communicating signals downstream of activated AHR to initiate transcription. The genomic response to AHR ligand exposure comprises complex AHR-regulated biological networks that include the expression and interaction of primary AHRE-driven target genes, genes secondary to AHRE-driven transcription, and genes downstream of AHR-activated signals such as protein kinases (Johnson et al., 2004). In this study, signaling cascades secondary to AHR activation and/or AHRE-driven transcription probably triggered a coordinated network of transcriptional responses by 24 h that coincided with the expression of classic AHR:AHRE target genes. Interestingly, these early gene expression profiles provided sensitive indicators of histopathologic alterations that occurred days later in the exposure period.
In addition to a classic dose-response in CYP1A induction at each time-point, this study found that CYP1A mRNA levels at all doses oscillated with time (Fig. 3). Within each dose, CYP1A transcripts were equally high on days 1 and 9 and equally low on days 5 and 13 (Fig. 3), a similar trend observed by array analysis (Fig. 1A and Table 2 in the Supplementary Material online). These temporal data suggest a negative and positive feedback regulatory mechanism for CYP1A. In mice, hepatic reactive oxygen species (ROS) are significantly increased by 12 h following a single oral dose of dioxin (62.5 µg/kg) (Bagchi et al., 2002). Moreover, ROS has been shown to down-regulate CYP1A transcription in human HepG2 and rat H4 hepatoma cells (Morel and Barouki, 1998). Although we did not measure ROS in this study, the day-1 gene expression profiles suggest that there was a dioxin-induced acute response that preceded or followed increased cellular ROS production. Consequently, a cellular ROS burst may have initiated a decrease in AHR activation and CYP1A mRNA levels by day 5. Alternatively, aryl hydrocarbon receptor repressor (AHRR) may have decreased CYP1A transcription through interaction with AHR since AHRR also heterodimerizes with ARNT (Mimura et al., 1999) and negatively regulates AHR-driven gene expression by transcriptional repression (Karchner et al., 2002; Mimura et al., 1999). In addition, proteosomal degradation of dioxin-activated AHR may have also resulted in decreased AHR-driven CYP1A transcription (Davarinos and Pollenz, 1999). However, these models do not explain the increase in CYP1A mRNA on day 9, a change likely due to endogenous feedforward/feedback regulation of AHR:AHRE-mediated transcription even in the presence of persistent dioxin exposure. Overall, these data highlight the importance of evaluating temporal transcriptional responses to chemical exposure as well as including time-matched vehicle controls in gene array-based studies.
In fish treated with the highest dioxin dose, severe histologic changes included fatty liver on day 5 and inflammation on day 13 (Fig. 4). Chemically induced fatty liver often results from an overproduction of fatty acids, interference with the triclyceride cycle, decreased fatty acid oxidation, or decreased secretion of very low density lipoproteins. Using a microarray strategy, Boverhof et al. (2005) recently demonstrated in mice that dioxin-induced fatty liver is likely a result of increased expression of genes associated with hepatic fatty acid uptake. Since our arrays only contained 175 genes, we did not detect induction of genes associated with fatty acid uptake and production. However, expression of liver basic fatty acid binding protein (L-FABP) was significantly decreased on days 5, 9, and 13 (Fig. 5A). Teleost L-FABP is a small cytosolic protein responsible for intracellular trafficking and metabolism of fatty acids (Denovan-Wright et al., 2000). Significant dioxin-induced down-regulation of this gene suggests that disruption of L-FABP expression may have contributed to decreased fatty acid export and increased fat accumulation within hepatocytes.
Following severe fatty liver on day 5 and hepatocellular atrophy on day 9, fish treated with 10.0 µg/kg dioxin exhibited extensive perivascular inflammation by day 13 (Fig. 4). As noted above, the significant day-9 gene expression responses were probably associated with the secondary inflammatory response 4 days later. Interestingly, we detected significant induction of known host defense genes (DnaJ, matrix metalloproteinase, and hepcidin) that were similarly regulated at each time-point measured (Fig. 5A). DnaJ is a heat shock protein (HSP) that is strongly induced following carbon tetrachloride-induced liver damage in rats (Lee et al., 2004). As in mammals, this gene is likely involved in the protection of hepatocytes following toxic injury in fish. Matrix metalloproteinases (MMP) are well-studied collagenases important for remodeling the extracellular matrix during wound repair (Benyon and Arthur, 2001). Since MMP induction also coincides with inflammation (Knittel et al., 2000), significant MMP up-regulation in the highest dose throughout the entire exposure period suggests an involvement in inflammation and hepatic tissue repair. Lastly, we found that the anti-microbial peptide hepcidin was induced in a dose-dependent manner at each time-point. Coupled with the presence of an AHRE, hepcidin may be regulated by direct AHR:AHRE-mediated transcription. Since hepcidin is strongly induced following bacterial infection in bony fish (Lauth et al., 2005), hepcidin was likely involved in host defense following dioxin exposure.
An interesting finding of this study was the identification of a gene (putatively named ependymin) that may also be involved in hepatic tissue remodeling. Based on array and real-time PCR data, ependymin was strongly upregulated starting on day 5 and induction persisted for the remaining time-points (Fig. 5). Originally identified as a cell-adhesion glycoprotein in the epithelial lining (ependymal zone) of goldfish brain (Schmidt and Shashoua, 1981), ependymin has also been identified in invertebrates (Suarez-Castillo et al., 2004), mice (Apostolopoulos et al., 2001), and humans (Apostolopoulos et al., 2001), and is thought to participate in organ regeneration. Interestingly, low temperatures induce ependymin expression in zebrafish and carp brain (Tang et al., 1999), and ependymin is highly expressed in human colon cancer cells (Nimmrich et al., 2001) and hematopoietic progenitor cells (Gregorio-King et al., 2002). Since ependymin mRNA levels were not different from controls on day 1 and no AHREs were identified within 2000 bp upstream of the predicted transcriptional start site, these data suggest that ependymin transcription was not AHRE-mediated but was rather associated with adaptation following acute hepatocellular toxicity. Based on a combined site search in Transcription Element Search System (TESS) (see URLs) for consensus transcription factor binding sites 8000 bp upstream of the predicted transcriptional start site for ependymin, we identified putative response elements for activating protein-1 (AP-1) (8 sites), CCAAT/enhancer binding protein-ß (C/EBPß) (8 sites), nuclear factor 
B (NF-B) (8 sites), and hepatocyte nuclear factor-3 (HNF-3) (23 sites) (data not shown), all well established liver-enriched transcription factors involved in initiating liver regeneration (Costa et al., 2003). As in mammals, teleost liver has an uncanny ability to rapidly regenerate following acute physical or chemical injury. Thus, similar to the central nervous system, ependymin may be involved in "priming" the liver for regeneration and repair following hepatocyte injury.
Correlating transcript changes with phenotypic or pathologic alterations remains a major challenge for understanding the timing and interplay of environmental stress and gene expression in disease causation. Using rodent models, major strides have been made in integrating multiple platforms of traditional toxicological data with expression profiling data (Boverhof et al., 2005; Hamadeh et al., 2004; Heinloth et al., 2004). Moreover, AHR ligand-specific gene sets and biological networks are increasingly being used as reference tools for predictive toxicology (Johnson et al., 2004; Vezina et al., 2004). In this study, we uncovered temporal relationships between hepatic gene expression profiles and dioxin-induced pathologic changes in teleost liver. Overall, our studies demonstrated that both AHRE- and non-AHRE-mediated transcriptional events were associated with downstream dioxin-induced hepatic alterations observed at the histologic level. However, future functional studies are necessary to determine whether differential expression of individual genes or groups of genes identified in this study is required for dioxin-induced pathologic alterations and adaptive responses in medaka liver.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONSUPPLEMENTARY DATAREFERENCES |
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Supplementary data are available online at www.toxsci.oxfordjournals.org.
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ACKNOWLEDGMENTS |
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This work was supported in part by a U.S. Environmental Protection Agency (EPA) Science to Achieve Results (STAR) Graduate Fellowship (FP916422) to D.C.V., the National Institute of Environmental Health Sciences (NIEHS)-funded Duke University Integrated Toxicology Program (NIH/NIEHS T32 ES07031), University of California – Davis Superfund Basic Research Program (NIH/NIEHS 5P42 ES004699 with partial funding by the U.S. EPA), NIH National Center for Research Resources (NCRR) (NIH/NCRR 1R01 RR018583-03), and Mount Desert Island Biological Laboratory Center for Membrane Toxicity Studies (NIH/NIEHS P30 ES03828). We thank Jonathan Freedman for use of the NanoDrop ND-1000 UV/Vis Spectrophotometer, David Bencic for technical assistance with gene arrays, Pei-Jen Chen and Michael Carney for assistance with fish culture and handling, Sandra Horton for histopathology services, and Mark Delong and Fred Dietrich for assistance with identification of medaka-specific expressed sequence tags (ESTs). We also thank Marjorie Oleksiak for critical review of an early draft of this manuscript.
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