Temporal- and dose-dependent hepatic gene expression changes in immature ovariectomized mice following exposure to ethynyl estradiol
D. R. Boverhof1,3,
K. C. Fertuck1,3,
L. D. Burgoon2,3,
J. E. Eckel4,
C. Gennings4 and
T. R. Zacharewski1,3,5
1 Department of Biochemistry and Molecular Biology, 2 Department of Pharmacology and Toxicology, National Food Safety and Toxicology Center and 3 Institute for Environmental Toxicology, Michigan State University, East Lansing, MI 48824, USA and 4 Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA
5 To whom correspondence should be addressed Email: tzachare{at}msu.edu
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Abstract
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Temporal- and dose-dependent changes in hepatic gene expression were examined in immature ovariectomized C57BL/6 mice gavaged with ethynyl estradiol (EE), an orally active estrogen. For temporal analysis, mice were gavaged every 24 h for 3 days with 100 µg/kg EE or vehicle and liver samples were collected at 2, 4, 8, 12, 24 and 72 h. Gene expression was monitored using custom cDNA microarrays containing 3067 genes/ESTs of which 393 exhibited a change at one or more time points. Functional gene annotation extracted from public databases associated temporal gene expression changes with growth and proliferation, cytoskeletal and extracellular matrix responses, microtubule-based processes, oxidative metabolism and stress, and lipid metabolism and transport. In the doseresponse study, hepatic samples were collected 24 h following treatment with 0, 0.1, 1, 10, 100 or 250 µg/kg EE. Thirty-nine of the 79 genes identified as differentially regulated at 24 h in the time course study exhibited a doseresponse relationship with an average ED50 value of 47 ± 3.5 µg/kg. Comparative analysis indicated that many of the identified temporal and dose-dependent hepatic responses are similar to EE-induced uterine responses reported in the literature and in a companion study using the same animals. Results from these studies confirm that the liver is a highly estrogen responsive tissue that exhibits a number of common responses shared with the uterus as well as distinct estrogen-mediated profiles. These data will further aid in the elucidation of the mechanisms of action of estrogens in the liver as well as in other classical and non-classical estrogen responsive tissues.
Abbreviations: Apoe, apolipoprotein E; Ct, threshold cycle; ECM, extracellular matrix; EE, ethynyl estradiol; ER, estrogen receptor; QRT-PCR, quantitative real-time PCR
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Introduction
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Although estrogens are among the most widely prescribed pharmacological agents (1), many aspects of their action following receptor binding remain unresolved. Published research has primarily focused on the biological effects of estrogens on classical estrogen responsive tissues such as the uterus, mammary gland and ovary (2,3). However, estrogens also exert profound effects on other non-classical estrogen responsive tissues including the kidney, bone and liver (4).
Compounds with estrogenic activity, which include endogenous steroids, natural products, industrial chemicals, environmental contaminants and pharmaceutical agents (5), elicit a broad spectrum of physiologic and toxic effects in the liver (3,6). Many of these responses are mediated by
and ß estrogen receptor (ER) isoforms. The liver predominantly expresses ER
(7), although low levels of ERß have been reported (8). In the classic signaling model, estrogen diffuses into cells and binds to the nuclear localized ER resulting in the dissociation of associated proteins. Homodimers of liganded complexes then act as transcription factors by binding to specific estrogen response element (ERE) sequences in the regulatory regions of target genes, evoking a wide range of transcriptional responses. In addition, rapid non-genomic responses mediated by membrane ERs, also stimulate signal transduction pathways (9).
Although the transcriptional actions of estrogen in reproductive tissues are well characterized, less is known about estrogenic responses in the liver. Recent reports utilizing genetically engineered mice that possess ERE-regulated reporter genes indicate that the liver is one of the most estrogen responsive tissues (10,11). While not considered a classical target tissue, accumulating evidence indicates that the liver mounts a multifaceted transcriptional and translational response that includes increased DNA synthesis and the modulation of cell growth (12,13). Estrogens have also been implicated in liver growth during ontogenesis and enhance liver regeneration after partial hepatectomy (14). Moreover, they dramatically alter lipid metabolism and transport and elicit anti-atherosclerotic effects via alterations in the levels and activities of lipid metabolizing enzymes and lipoproteins (15). Estrogens also elicit toxic responses in the liver including cholestasis, oxidative damage, mitotic abnormalities and carcinogenesis (3,6,16,17).
Despite the wide array of physiological and toxic responses, the number of known estrogen-mediated hepatic responses is limited (2). In the present study, cDNA microarrays were utilized to examine the temporal and dose-dependent changes in hepatic gene expression following treatment of immature ovariectomized mice with 17
-ethynyl estradiol (EE), a pharmaceutical agent with enhanced oral bioavailability (18) that elicits a transcriptional response similar to that of 17ß-estradiol (19). Rigorous statistical approaches were used to identify treatment-induced changes in gene expression while accounting for variability between replicates. Comparisons between hepatic responses and those observed in classical estrogen responsive tissues were drawn in order to identify common as well as tissue-specific gene expression responses to further elucidate the mechanisms of action of estrogenic compounds.
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Materials and methods
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Animal treatment
Female C57BL/6 mice, ovariectomized by the vendor on postnatal day 20 and all having body weights within 10% of the average body weight, were obtained from Charles River Laboratories on postnatal day 26 (Raleigh, NC). The mice were housed in polycarbonate cages containing cellulose fiber chips (Aspen Chip Laboratory Bedding, Northeastern Products, Warrensberg, NY) in a 23°C HEPA-filtered environment with 3040% humidity and a 12 h light/dark cycle (07:0019:00 h). Animals were allowed free access to deionized water and Harlan Teklad 22/5 Rodent Diet 8640 (Madison, WI), and acclimatized for 4 days prior to dosing. On the fourth day, animals were weighed, and 17
-EE (Sigma Chemical, St Louis, MO) was dissolved in sesame oil (Loriva, Ronkonkoma, NY) to achieve the desired dose based on the average weight of the animals. For the time course study, animals were treated by gavage with 0.1 ml of sesame oil for a nominal dose of 0 (vehicle control) or 100 µg/kg body wt of EE. Five animals were treated per dose group and time point and groups for each dose and time point were housed in separate cages. Mice were killed 2, 4, 8, 12 and 24 h after dosing. Additional groups of five animals were included, which were dosed for 3 consecutive days with either the vehicle or EE (100 µg/kg body wt) and were killed 24 h after the final dose, referred to here after as the 3x 24 h group. An untreated group of mice was also included, which was killed at time zero, the time at which the other animals were dosed. For the doseresponse study, 5 mice/group were gavaged with 0.1 ml of vehicle or 0.1, 1, 10, 100 or 250 µg/kg EE and killed 24 h after dosing. In both studies, treatment was staggered to ensure exposure times were within 5% of the desired length. Animals were killed by cervical dislocation and a section of the liver was removed and stored in RNA later (Ambion, Austin, TX) at 80°C until further use. All procedures were performed with the approval of the Michigan State University All-University Committee on Animal Use and Care.
RNA isolation
Liver samples (
70 mg) were transferred to 1.0 ml of Trizol (Invitrogen, Carlsbad, CA) in a 2.0 ml microfuge tube and homogenized using a Mixer Mill 300 tissue homogenizer (Retsch, Germany). Total RNA was isolated according to the manufacturer's protocol with an additional phenolchloroform extraction. Isolated RNA was resuspended in RNA storage solution (Ambion), quantified (A260) and assessed for purity by determining the A260:A280 ratio and by visual inspection of 1.0 µg on a denaturing gel.
Experimental design
Temporal changes in gene expression were assessed using cDNA microarrays by comparing EE-treated samples to time-matched vehicle controls using a modified loop design (Figure 1A). One loop utilizes one of the five animals from each time/treatment group, with four independent labelings of each sample, with appropriate dye swaps, for a total of 26 arrays per loop. Three loops, and therefore three biological replicates, were conducted for a total of 78 arrays. Therefore, within a dose group (n = 5) three animals were used to assess temporal changes in gene expression by cDNA microarrays.

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Fig. 1. Microarray experimental designs for (A) temporal and (B) doseresponse studies. Temporal gene expression patterns were analyzed with cDNA microarrays using a modified loop design that included four independent labelings of each sample, with appropriate dye swaps, for a total of 26 arrays per loop. One loop utilizes one of the five animals from each time/treatment group. Three loops, and therefore three biological replicates, were conducted for a total of 78 arrays. Arrow bases represent labeling with Cy3 while arrowheads represent labeling with Cy5. U = untreated animal at time zero, V and T = vehicle and EE-treated animals, respectively; numbers indicate time of death (hours). (B) Dose-dependent changes in gene expression were analyzed 24 h after treatment using a common reference design in which samples from EE-treated mice were co-hybridized with a common vehicle control. This design uses one of the five animals from each dose group with two independent labelings per sample, with appropriate dye swaps, for a total of 10 arrays per replicate. Four biological replicates were conducted for a total of 40 microarrays. Double-headed arrows indicate dye swap (each sample labeled with Cy3 and Cy5 on different arrays). V = vehicle control, doses represent animals treated with the indicated dose of EE.
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Doseresponse changes in gene expression were analyzed using a common reference design in which samples from EE-treated mice are co-hybridized with a common vehicle control (Figure 1B). Each design replicate uses one of the five animals from each dose group with two independent labelings per sample, with appropriate dye swaps, for a total of 10 arrays. Four biological replicates were conducted for a total of 40 microarrays. Therefore, within a dose group (n = 5) four animals were used to assess dose-dependent changes in gene expression by cDNA microarrays.
Microarray analysis of differential gene expression
Detailed protocols for microarray construction, labeling of the cDNA probe, sample hybridization and slide washing can be found at http://dbzach.fst.msu.edu/interfaces/microarray.html. Briefly, PCR amplified DNA was robotically arrayed in duplicate onto epoxy coated glass slides (Quantifoil, Germany) using an Omnigrid arrayer (GeneMachines, San Carlos, CA) equipped with 16 (4 x 4) Chipmaker 2 pins (Telechem) at the Genomics Technology Support Facility at Michigan State University (http://www.genomics.msu.edu). Total RNA (25 µg) was reverse transcribed in the presence of Cy3- or Cy5-dUTP to create fluor-labeled cDNA, which was purified using a Qiagen PCR purification kit (Qiagen, Valencia, CA). Cy3 and Cy5 samples were mixed, vacuum dried and resuspended in 32 µl of hybridization buffer (40% formamide, 4x SSC, 1% SDS) with 20 µg polydA and 20 µg of mouse COT-1 DNA (Invitrogen, Carlsbad, CA) as a competitor. This probe mixture was heated at 95°C for 3 min and was then hybridized on the array under a 22 x 40 mm coverslip (Corning, Corning, NY) in a light protected and humidified hybridization chamber (Corning). Samples were hybridized for 1824 h at 42°C in a water bath. Slides were then washed, dried by centrifugation and scanned at 635 (Cy5) and 535 nm (Cy3) on an Affymetrix 428 Array Scanner (Santa Clara, CA). Images were analyzed for feature and background intensities using AnalyzerDG (MolecularWare, Cambridge, MA).
Microarray data normalization
Data normalization and identification of treatment-related effects were performed using the general linear mixed model.
 | (1) |
where yijkbmqsr denotes the base-2 logarithm-transformed uncorrected median signal intensity of the ith gene (i = 1, ..., J), jth array (j = 1, ..., J), kth dye (k = 1, 2), bth block (1,..., 32), mth treatment (m = UR, V, T), qth time (q = 0, 2, 4, 8, 12, 24, 72 h; not used in the dose response), sth spot (s = 1, 2) and the rth design replicate (r = 1, ..., R), µ is the overall mean, Dk is a fixed effect associated with the kth dye, Aj is a random effect associated with the jth array, (AD)jk is a random effect associated with the array by dye interaction, Bb is a random effect associated with the bth block, and (AB)jb is a random effect associated with the array by block interaction (Eckel and Gennings, submitted for publication).
The residuals from the normalization model are defined as
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and are used as the normalized response for the gene model. Specific treatment effects on genes relative to vehicle effects were determined using the gene model:
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where rijkmqsr is the normalized response as defined in equation (2), µi is the overall mean for the ith gene, Aij is a random effect associated with the jth array for the ith gene, Dik is a fixed effect associated with the kth dye for the ith gene, Hiq is a fixed effect associated with the qth time point for the ith gene, T(H)im(q) is a fixed effect associated with the mth treatment nested within the qth time point for the ith gene, Sis is a fixed effect associated with the sth spot for the ith gene, Rir is a fixed effect associated with the rth design replicate for the ith gene, (AR)ijr is a random effect associated with the array by design replicate interaction for the ith gene, and (AS)ijs is a random effect associated with the array by spot interaction for the ith gene. The model for the doseresponse experiment is similar to the temporal model, except there is no effect for the qth time point. This model provides estimates of the treatment effect, relative to the vehicle effect, which was used in the data-filtering step.
Data filtering and analysis
For analysis purposes, a reduced data set was desired in order to remove those genes with highly variable responses that are potentially unrelated to the treatment. A model-based t-statistic (MBT) was used to rank gene expression changes based on absolute t-score values in order to initially prioritize treatment related effects for subsequent analysis. The MBT was calculated based on the results of the general linear mixed model for the gene-specific treatment effects (equation 3). The MBT is defined as
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where
is the contrast estimate from the gene-specific treatment model, and
is the standard error of
. For the time-course experiment all values are based on the ith gene and the qth time point.
To focus subsequent analyses and data interpretation on the most reproducible differentially regulated genes, a stringent t-score threshold of 3.3 was used to obtain a subset of differentially regulated genes. This threshold is an absolute t-score value that accounts for changes in gene expression on both tails of the t-distribution. Gene expression changes that passed the threshold were subsequently analyzed using K-means clustering (GeneSpring 6.0, Silicon Genetics, Redwood City, CA). Doseresponse analysis was performed using Graph Pad Prism 4.0 (GraphPad Software, San Diego, CA).
Quantitative real-time PCR
For each sample, 1.0 µg of total RNA was reverse transcribed by SuperScript II using an anchored oligo-dT primer as described by the manufacturer (Invitrogen, Carlsbad, CA). The cDNA (1.0 µl) was used as a template in a 30 µl PCR reaction containing 0.1 µM each of forward and reverse gene-specific primers designed using Primer3 (20), 3 mM MgCl2, 1.0 mM dNTPs, 0.025 IU AmpliTaq Gold and 1x SYBR Green PCR buffer (Applied Biosystems, Foster City, CA). Gene names, accession numbers, forward and reverse primer sequences and amplicon sizes are listed in Table I. PCR amplification was conducted in MicroAmp Optical 96-well reaction plates (Applied Biosystems) on an Applied Biosystems PRISM 7000 Sequence Detection System using the following conditions: initial denaturation and enzyme activation for 10 min at 95°C, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. A dissociation protocol was performed to assess the specificity of the primers and the uniformity of the PCR generated products. Each plate contained duplicate standards of purified PCR products of known template concentration covering seven orders of magnitude to interpolate relative template concentrations of the samples from the standard curves of log copy number versus threshold cycle (Ct). No template controls (NTC) were also included on each plate. Samples with a Ct value within 2 SD of the mean Ct values for the NTCs were considered below the limits of detection. The copy number of each unknown sample for each gene was standardized to the geometric mean of three house-keeping genes (Bactin, Gapd and Hprt) to control for differences in RNA loading, quality and cDNA synthesis. Statistical significance of induced or repressed genes was determined using the t-test. For graphing purposes, the relative expression levels were scaled such that the expression level of the time-matched control group was equal to 1.
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Results
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Experimental quality assurance
The production of accurate and precise microarray results requires repeated measures of individual samples as well as biological replication in order to minimize noise associated with the experimental method and its biological samples. The experimental designs utilized address these issues by incorporating multiple independent labelings of each sample as well as completing biological replicates for each study (Figure 1). To assess image quality, raw microarray data for each dye was monitored for (i) background signal intensity, (ii) feature signal intensity, (iii) feature/background signal intensity ratios, (iv) the number of features with background intensities greater than the feature intensity for each array, and (v) relationships between feature and background signal intensities (Table II). Background signal intensities between time-course and doseresponse studies were very similar despite the chance occurrence of some areas of a few arrays having background signal intensities that approached saturation. All parameters within and across the two studies (i.e. 3 x 26 arrays for time course; 4 x 10 arrays for doseresponse) were highly consistent, which facilitated the identification of temporal and doseresponse associations.
Microarray analysis of EE-induced temporal changes in hepatic gene expression
A model-based t-test identified 447 microarray features, representing 419 annotated clones and 393 unique genes, which were differentially expressed (t > |3.33|), relative to time-matched vehicle controls, at one or more time points. The data revealed that the 2 and 3x 24 h time points were the most active based on the number of significant changes in gene expression (Figure 2). Sample data were also compared with the untreated control at time zero (time of dosing) to assess potential vehicle or circadian effects; while this was not used as a filtering criterion, it was considered during data interpretation.

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Fig. 2. Microarray data analysis and filtering. Microarray data were analyzed for significant changes in gene expression using a model-based t-statistic by comparing EE-treated samples to the corresponding time-matched vehicle controls. Significant changes in gene expression at each time point were combined, filtered for redundancy and ranked according to an absolute t-score value (3.3) that accounted for changes in gene expression on both tails of the t-distribution. Published literature and LocusLink identifiers were then used to associate genes with functional categories. Additional genes from the microarrays were considered for analysis provided that the gene approached the t-score threshold and was associated with a functional category based on published literature or functional annotation extracted from public databases.
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K-means analysis of the 419 annotated genes indicated that five clusters accurately and concisely described the data (Figure 3). The clusters consisted of up- and down-regulated early and late responses as well as an up-regulated early/sustained group, consistent with recently published reports of temporal transcriptional responses to estrogen in the uterus and MCF-7 cells (21,22). In general, there was an equal distribution between up- and down-regulated expression patterns although the magnitude of the response was greater for induced transcripts.

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Fig. 3. K-means clustering of significant temporal gene responses. Five K-means clusters corresponding to (A) up-regulated early, (B) down-regulated early, (C) up-regulated late, (D) down-regulated late and (E) up-regulated early/sustained responses best described the selected genes. The number of genes in each cluster is indicated. Graphs are expressed as log2 expression ratios relative to time-matched vehicle controls. A pseudogene line is drawn in bold to illustrate the representative response that defines the pattern in each cluster. See online supplementary material for colour version of this figure.
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Functional annotation extracted from public databases and the literature revealed that many genes exhibited functions associated with cell cycle, growth and proliferation, cytoskeleton and extracellular matrix (ECM), microtubule-based processes, oxidative stress and metabolism, and lipid transport and metabolism (Table III). Many of the immediate early responses are involved in growth and proliferation and are classical estrogen responsive genes. These genes exhibited significant changes in mRNA levels at 2 and 4 h (primarily clusters A and E) and included FBJ osteosarcoma oncogene (Fos), Jun-B oncogene (Junb), myelocytomatosis oncogene (Myc) and cysteine-rich protein 61 (Cyr61). Genes involved in microtubule-based processes were induced at early to mid-phases of the time course (cluster A) while those involved with cytoskeleton and ECM, oxidative stress and metabolism, and lipid metabolism and transport displayed induction or repression at the later time points (primarily clusters C and D). Apolipoprotein E (Apoe) and Junb were misclassified by K-means clustering as down-regulated early and late, respectively (clusters B and D), due to a non-significant repression at these time points. Inappropriate cluster assignments occur due to the inability of K-means clustering to consider the statistical significance of a change in gene expression at multiple points within a time course.
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Table III. Functional categorization and temporal regulation of select hepatic genes identified as differentially regulated in response to EE
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Quantitative real-time PCR (QRT-PCR) was used to verify changes in transcript levels for a selected subset of genes (Figure 4). In total, 25 of the 29 genes examined by QRT-PCR exhibited a pattern of gene expression comparable with the microarray results (see Supplementary data). In general, fold-change ratios of mRNA expression levels were lower for the microarrays when compared with QRT-PCR. For example, microarray analysis revealed that signal transducer and activator of transcription 5A (Stat5a) was maximally induced 3-fold at 4 h, while QRT-PCR analysis measured an 8-fold increase. Data compression has been documented previously when comparing microarray data to other, more gene specific, measurement techniques (23).

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Fig. 4. Verification of time-course microarray data using QRT-PCR. RNA from the same sample that was used for cDNA microarray analysis was examined using QRT-PCR. All fold changes were calculated relative to time-matched vehicle controls. Bars (left axis) and lines (right axis) represent data obtained by QRT-PCR and cDNA microarrays, respectively, while the x-axis represents the time points (h). Genes are indicated by official gene symbols and results are the average of three biological replicates. Letters represent the cluster designation for each gene. Error bars represent the SEM for the average fold change. *P < 0.05 for QRT-PCR. See online supplementary material for colour version of this figure.
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Microarray analysis of dose-dependent changes in gene expression induced by EE
Of the 79 genes that were differentially expressed at 24 h in the time course study, 39 exhibited a dose-dependent pattern of expression. Cytoplasmic gamma actin (Actg), procollagen IV alpha 1 (Col4a1) and mitotic arrest deficient, homolog-like 1 (Mad2l1) also displayed dose-dependent expression; although the time course study clearly indicated that 24 h was not the optimal induction time for these genes (Table IV). It is probable that other genes identified in the time course study, including some of those identified at the 24-h time point, would have also displayed doseresponse kinetics had additional optimal exposure times been investigated. These observations demonstrate the complexity of conducting and interpreting doseresponse experiments due to the fact that gene expression is not static.
Despite the data compression, gene expression patterns across doses were comparable between microarray and QRT-PCR assays (Figure 5). Moreover, there was strong concordance between the time course and doseresponse studies. For example, cytochrome P450 17 (Cyp17), leukemia inhibitory factor receptor (Lifr) and transglutaminase 2, C polypeptide (Tgm2) were up-regulated 2.1-, 5.9- and 3.8-fold, respectively, in the time course study and 2.4-, 4.3- and 4.4-fold at the same dose in the doseresponse study. Similarly, carbonic anhydrase 3 (Car3), insulin-like growth factor 1 (Igf1) and hepatic lipase (Lipc) were down-regulated 0.63-, 0.62- and 0.49-fold, respectively, in the time course study, and 0.50-, 0.77- and 0.67-fold in the doseresponse study. These results indicate the reproducibility of the responses in independent experiments. This, combined with the observed doseresponse relationships for these and other genes, provides strong evidence that these genes are either primary or downstream estrogen target genes.

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Fig. 5. Verification of dose-response microarray data using QRT-PCR. RNA from the same sample was used for both cDNA microarray and QRT-PCR analysis. All fold changes were calculated relative to vehicle controls. Bars (left axis) and lines (right axis) represent data obtained by QRTPCR and cDNA microarrays, respectively, while the x-axis represents the dose groups (µg/kg). Genes are indicated by official gene symbols and results are the average of four biological replicates. Letters represent the cluster designation for each gene as determined in the time course analysis. Error bars represent the SEM for the average fold change. *P < 0.05 for QRT-PCR. See online supplementary material for colour version of this figure.
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ED50 values for dose-dependent changes in gene expression ranged from
1.4 µg/kg to >250 µg/kg with an average ED50 of 47 ± 3.5 µg/kg (Table IV). This average ED50 value for gene expression responses is comparable with the ED50 for induction of uterine weight in mice treated by gavage with EE (24,25) indicating that the transcriptional responses observed in the liver display similar dose kinetics to known EE elicited physiological endpoints. Furthermore, most of the genes for which the ED50 could not be determined (ED50>250) belonged to cluster A (Table IV), which is likely due to the non-optimal sampling times for these transcripts.
Comparison of hepatic and uterine responses
In order to evaluate EE-mediated hepatic and uterine gene expression responses, microarray data from these tissues, collected from the same mice, were compared. Changes in uterine gene expression elicited by EE were assessed using Affymetrix Mu11KSubA GeneChips as described (26). Common genes represented on the Affymetrix Mu11KSubA GeneChip and the cDNA microarrays were determined using LocusLink identifiers. Of the 1318 genes common between the two platforms, 680 exhibited a change in expression in the hepatic or uterine samples at one or more time points. Ninety-three of these genes exhibited changes in gene expression in both tissues at one or more time points (Figure 6). Of the 587 genes that exhibited a gene expression change in only one tissue, 130 were significant only in the liver while 457 were significant only in the uterus (see Supplemental data).

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Fig. 6. Comparison of hepatic and uterine gene expression responses. Hepatic gene expression data were obtained using cDNA microarrays while uterine data were obtained using Affymetrix Mu11KSubA GeneChip arrays (see ref. 26 for details). Genes in common between the two platforms were identified using LocusLink numbers. Statistical cut-offs utilized were t > |3.33| for the hepatic study and p1z >0.90 for the uterine study.
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Although many classical estrogen responsive genes were not represented on the Affymetrix Mu11K SubA GeneChip, similar expression patterns for known estrogen responsive genes were identified by comparing results between the two studies based on published reports. For example, comparable gene expression patterns were observed for established estrogen responsive genes such as Actg, Cyr61, Stat5a, and hypoxia inducible factor 1, alpha subunit (Hif1
), which were represented on both arrays as well as for Fos, Jun and Myc which were absent on the Affymetrix Mu11K SubA GeneChip but could be confirmed based on published reports. In addition, similar hepatic and uterine expression patterns for novel estrogen responsive genes such as Car3, cyclin-dependent kinase inhibitor 1A (Cdkn1a) and Tgm2 were identified and verified by QRT-PCR (Figure 7). Car3 displayed down-regulated transcript levels in both the uterus and the liver. Cdkn1a (aka p21) transcript levels were induced rapidly in both the uterus and the liver (28 h) and again at 24 h in the liver samples only. Interestingly, the induction of Cdkn1a in the uterus lagged compared with hepatic expression, with maximal induction observed at 8 and 2 h, respectively. Tgm2 was induced at nearly all time points for both the uterus and the liver. It is important to note that differences in induction kinetics between tissues can be influenced by a number of factors including route of exposure, blood flow, tissue vascularity and lipid content (27) as well as by other tissue and cell-specific factors that affect transcription and mRNA stability.

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Fig. 7. Car3, Cdkn1a and Tgm2 expression in uterine and hepatic tissues. Uterine and hepatic gene expression changes obtained using Affymetrix Mu11K SubA GeneChips, cDNA microarrays and QRT-PCR were compared. All fold changes were calculated relative to vehicle controls. Car3 (A), Cdkn1a (B) and Tgm2 (C) represent genes that displayed a similar expression pattern between the two tissues. Generally, expression kinetics across the tissues are similar, although uterine responses are lagging compared with hepatic responses, which may be due to differences in blood flow and vascularization, or lipid content of the tissue as well as other factors including pharmacokinetics, pharmacodynamics and factors that could alter mRNA stability. For QRT-PCR results, error bars represent the SEM for the average fold change.
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Several genes including various tRNA synthetases, ornithine decarboxylase (Odc), thymidine kinase (Tk1) and cyclin B1 and D2 (Ccnb1 and Ccnd2) were up-regulated only in the uterus while significant expression changes of cytochrome P-450 enzymes and glutathione transferases were specific to hepatic samples. Odc, Tk1, Ccnb1 and Ccnd2 are known to play integral roles in cell cycle progression (26) and the inability to detect changes in the liver may be due to hepatic cells actively cycling in response to normal circulating factors. In contrast, uterine cell growth and proliferation is highly inducible in the immature, ovariectomized mouse, as the tissue is estrogen starved and most cells are arrested in G0 prior to treatment. However, following exposure to EE, these uterine cells are synchronously activated and simultaneously enter into the cell cycle resulting in a pronounced response. Conversely, changes in the expression of the cytochrome P-450 s and glutathione transferases are limited to the liver, which is consistent with their important roles in xenobiotic metabolism in hepatic physiology.
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Discussion
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Although not considered a classical estrogen responsive tissue, the liver evoked a number of temporal- and dose-dependent changes in hepatic gene expression in response to EE. Transcript levels of many novel and known estrogen responsive genes, reported previously to be ER regulated in classical estrogen responsive tissues, were affected by EE in the liver. Functional annotation obtained from public databases indicated that many of the changes in gene expression may contribute to growth and proliferation, cytoskeleton and ECM reorganization, microtubule-based processes, oxidative metabolism and stress, as well as lipid metabolism and transport, which will be described in detail below.
Cell proliferation and growth
Induction of the early immediate genes Fos, Junb, Myc and E26 avian leukemia oncogene 2 (Ets2) was observed at 2 and 4 h, consistent with their roles in growth and proliferation and their known ER-mediated regulation in classical estrogen responsive tissues (28,29). Although some studies suggest that hepatic Fos and Myc are not affected by estrogens (29,30), the induction of Fos in primary rat hepatocytes and Myc in rats by 17ß-estradiol (E2) and EE, respectively (12,31), corroborate the observations of this study. EE induction of Junb and Ets2 also extends the list of estrogen inducible hepatic protocongenes and is consistent with their estrogen-mediated regulation in other estrogen responsive tissues (32).
Cyr61, zuotin related factor 2 (Zrf2) and insulin-like growth factor binding protein 1 (Igfbp1) were also temporally and dose-dependently induced. Cyr61, a member of the ctgf/cyr1/nov (CCN) gene family, which is critical for estrogen-dependent DNA synthesis, MCF-7 cell proliferation and the uterotrophic response in ovariectomized rats (33,34), was induced at 2 and 4 h in the liver. Zrf2 mRNA, which has not been reported previously to be estrogen regulated, was significantly induced at 2 and 24 h. The Zrf2 protein interacts with Id proteins and plays an important role in the promotion of cell growth, cell cycle progression and DNA synthesis (35). Igfbp1, a member of a group of proteins that bind and modulate the signaling of insulin-like growth factors (IGFs), was significantly induced between 2 and 8 h and again at 24 h. Igfbp1 is induced rapidly after partial hepatectomy and plays an important role in liver regeneration (36). Furthermore, estrogen is known to promote liver regeneration after partial hepatectomy (14), which may involve the induction of Igfbp1.
Immediate early-induced transcription factors included Hif1a, E2F transcription factor 1 (E2f1), and signal transducer and activator of transcription 3, 5a and 5b (Stat3, Stat5a and Stat5b). Hif1a is implicated in angiogenesis, apoptosis and energy metabolism and is induced by estrogens in the mouse uterus (26) by an undefined mechanism (37). E2f transcription factors are critical to G1/S progression. E2F is inducible by estrogen in MCF-7 cells as mediated by ER and Sp1 (38). Stat3, 5a and 5b were all induced within 4 h after estrogen treatment and the induction of Stat5a and 5b is consistent with their induction in the mouse kidney (39). These Stat gene products are phosphorylated by receptor-associated kinases, which facilitate the formation of transcriptionally active homo- or heterodimers. Stat5 and 3 are downstream targets for non-genomic effects of estrogen that contribute to growth regulation and may be involved in carcinogenesis (40). Stat5a has also recently been reported to be estrogen regulated in the liver (41) while there are no published reports of hepatic Stat 3 and 5b induction by estrogen.
EE-mediated down-regulated genes included basic helixloophelix domain class B2, transcription factor 4, and transcription factor 12 (Bhlhb2, Tcf4 and Tcf12), which act as repressors and co-repressors of transcription (42,43). Bhlhb2 specifically inhibits Myc-mediated transcription, an important regulator of growth and proliferation (42). Down-regulation of these repressors provides further evidence that EE creates an environment supportive of hepatic growth and proliferation, which is consistent with the effects of estrogen on cultured rat hepatocytes and MCF-7 cells (22,44,45).
Collectively, induction of Fos, Junb, Myc, Ets2, Cyr61, Zrf2, Igfbp1, Hif1a, E2f1, and Stat 3/5a/5b, as well as the down-regulation of Bhlhb2, Tcf4 and Tcf12 are supportive of a proliferative environment (6,31). Products of these genes act as effectors of estrogen signaling by modulating the expression of downstream targets that support cell cycle progression and proliferation (28). Consequently, chronic deregulated expression of these genes may contribute to the hepatocarcinogenic effects of estrogens (28,34,46). Interestingly, many of these genes have been implicated in estrogen-induced endometrial and breast cancers (46,47). Although no increase in liver weight was observed in the present study (data not shown), previously reported hepatotrophic effects used significantly larger doses of estrogens [5 mg/kg EE (31)], alternate dosing regimens and longer exposure periods. However, increases in DNA synthesis have been reported at doses as low as 0.1 µg/rat when administered subcutaneously (48).
Cytoskeleton and ECM
Changes in gene expression that favor hepatic cell growth and proliferation were followed by alterations in the expression of many structural genes including Actg, myosin, heavy polypeptide 3 (Myh3), myosin light chain (Myln), Col4a1, and fibronectin 1 (Fn1). Moreover, these genes exhibited dose-dependent increases in expression at 24 h. Actg, a major structural component in eukaryotic cells with roles in cytoskeletal maintenance, intracellular motility and cytokinesis, is known to be estrogen regulated (49). Non-muscle myosins are also involved in cytoskeletal maintenance as well as cell migration and proliferation and are regulated by estrogen in the rabbit endometrium as well as in smooth muscle cells (50,51). Col4a1 is known to serve an important function in estrogen-induced uterine hypertrophy (52). Fibronectin, an ECM component critical for development and wound healing, is also induced by estrogen in rat cardiac fibroblasts and in the mouse mammary gland (53,54). The regulation of these cytoskeletal and ECM genes in the EE-treated liver are likely a response to mitogenic changes in gene expression in preparation for cell division and growth. Their sequential response suggests that the expression of these genes is dependent on the induction of other signaling molecules, which is supported by data indicating that Actg induction is blocked by protein synthesis inhibitors (55).
Microtubule-related processes
Estrogenic compounds increase hepatic mitotic activity and the number of cells in metaphase and anaphase that exhibit spindle disturbances (3,56). Hepatocytes exposed to estrogens also exhibit abnormal mitosis as well as alterations in cytoplasmic microtubules and disarrangement of chromosomes (3,57). Consistent with these observations, microtubule-associated protein 1 light chain 3 (Map11c3), microtubule-associated protein 2 (Map2), mitotic arrest deficient, homolog-like 1 (Mad2l1), pericentrin 2 (Pcnt2), and dynein, cytoplasmic, light chain 1(Dnclc1) were induced by EE. These gene products are involved in microtubule stabilization, organization and centrosome attachment, as well as spindle assembly checkpoint and mitotic movement (5863). Although not shown previously to be estrogen regulated, their induction may be part of a larger cascade of events in preparation for cellular division. Alternatively, their inappropriate induction may contribute to abnormal hepatic mitotic features as observed after estrogen exposure.
Oxidative metabolism and stress
Although the hepatocarcinogenic effects of estrogens have been attributed to stimulated growth and proliferation, cytochrome P450 (Cyp)-mediated formation of genotoxic catechol and quinone metabolites and the resulting oxidative damage to DNA, proteins and lipids, may also be a contributing factor (17,64). Hepatic induction of Cyp2b19 and Cyp17 as well as the pronounced down-regulation of Cyp2b10 was observed in the present study. Cyp2b19 is a relatively uncharacterized enzyme that is involved in the metabolism of arachidonic acid (65), while hepatic Cyp17 is involved in estrogen biosynthesis during development by ensuring the conversion of circulating progestogens to estrogen (66). The down-regulation of Cyp2b10 contradicts studies reporting induction in the liver, but is consistent with its ER-mediated regulation (67). Gamma-glutamyl transpeptidase (Ggt1) was also induced by EE and may play a pro-oxidant and hepatocarcinogenic role via the generation of reactive oxygen species (68). Increases in Ggt1 positive foci in the rat liver after EE administration have been reported and are thought to be ER dependent (48).
The glutathione S-transferase (GST) family of enzymes facilitates the conjugation of glutathione with exogenous and endogenous compounds (69). Genetic polymorphisms in these genes have been attributed to increased cancer susceptibility (70). Glutathione S-transferase isoforms alpha 1 (Gsta1), alpha 4 (Gsta4), mu 5 (Gstm5) and theta 2 (Gstt2) were all repressed at 3x 24 h, with concomitant up-regulation of the pi 2 isoform (Gstp2). Many of these transcripts were also dose-dependently regulated at 24 h. E2 is known to block the immunoreactivity of the alpha and mu class isoforms in epithelial cells of the vas deferens in Syrian hamsters (71) while significant down-regulation of Gstt2 transcripts in the mouse uterus has been reported previously (72). Overall, down-regulation of GST enzymes would reduce conjugation and elimination of the oxidative catechol and quinone metabolites, thus increasing susceptibility to oxidative stress and genotoxicity, which may contribute to the hepatocarcinogenic effects of EE in addition to its promotion of cell growth and proliferation.
Lipid metabolism and transport
Estrogens modulate lipid metabolism and transport and elicit anti-atherosclerotic effects in mammals (15,73) primarily through alterations in hepatic lipase and plasma lipoprotein levels, which lead to decreases in plasma VLDL and concomitant increases in HDL (74,75). Apolipoprotein C-II (Apoc2), which plays an important role in plasma lipid clearance and when deficient results in hypertriglyceridemia (76), was temporally and dose-dependently induced, consistent with its ER-mediated induction in primate liver and human HepG2 cells (74,75). Temporal- and dose-dependent induction of apolipoprotein A-IV (Apoa4) transcript was also detected. Apoa4 is involved in cholesterol transport and increasing HDL plasma levels and exerts atherosclerotic protective effects (77). Both Apoc2 and Apoa4 are cofactors that modulate key enzyme activities involved in lipoprotein metabolism including lecithin:cholesterol acyltransferase and lipoprotein lipase, which clear plasma chylomicrons and decrease plasma VLDL levels (76,78).
Increased plasma Apoe levels are also associated with decreased risk for atherosclerosis (76). Translationally mediated increases in plasma Apoe have been reported in male mice treated with estrogen for 5 consecutive days at 3 mg/kg (15). However, in this study Apoe transcripts were repressed at 24 and 3x 24 h suggesting that transcriptional regulation may also exist. The directional discrepancy may be due to differences in dose, time of death, sex and route of exposure. Lipc mRNA levels also exhibited temporal- and dose-dependent repression, in agreement with published studies using HepG2 cells (79). Lipc is an important enzyme in lipoprotein metabolism and there is an inverse correlation between its activity and plasma levels of HDL cholesterol (80). Collectively, these alterations in lipid transport and metabolism mRNAs illustrate the complex role that estrogen plays in the regulation of transcripts that modulate plasma lipid profiles, and provide supporting evidence for estrogen-mediated decreases in plasma VLDL levels with concomitant increases in HDL levels that are associated with a reduced risk for atherosclerosis.
Doseresponse analysis
Conducting doseresponse experiments allows for further interpretation of the sensitivity of the responses to the chemical agent. A majority of the 39 doseresponsive genes identified in this study exhibited ED50 values comparable (10100 µg/kg) to the uterotrophic ED50 values reported for EE in the literature (24,25) (Table IV). However, a number of transcripts had ED50 values <10 µg/kg indicating that changes in gene expression can occur at doses that may not elicit a physiological effect. Therefore, these responses could serve as molecular markers for exposure that are more sensitive than the physiological response. Several genes also had ED50 values >250 µg/kg, which may represent weak estrogenic responses or an effect due to an inappropriate sampling time for these responses. Alternatively, these genes may represent ER-independent responses to suprapharmacological doses that may contribute to EE toxicity.
Comparison of uterine and hepatic responses
Intuitively, differences in gene expression in response to EE are expected between tissues. By harvesting multiple tissues from the same animal, this study provided an opportunity to comparatively assess EE elicited shared and tissue-specific gene expression responses. Despite differences in data analysis (i.e. empirical Bayes analysis of two replicates in uterine study versus model t-statistic of three replicates in hepatic study), and the array platform (i.e. Affymetrix Mu11KSubA GeneChips for uterine study versus cDNA/EST microarrays for hepatic study) used in the two studies, 93 of the 1318 genes in common, were significantly affected in both tissues. For example, Car3 was down-regulated in both tissues whereas Car1 was up-regulated, indicating a common isoform-specific regulation. Carbonic anhydrases catalyze the reversible hydration of CO2, which serves functions in cellular homeostasis related to energy metabolism and the maintenance of pH, both of which are likely to be important in tissue responses to estrogen (81). Although typically associated with cell cycle arrest; Cdkn1a (aka p21) induction in both tissues in response to estrogen stimulation suggests a more complex role in the cell cycle (82) as does its immediate early induction at both the mRNA and protein level (21). These data suggest that Cdkn1a may play a regulatory role in S-phase progression during cell growth and proliferation. Likewise, Tgm2 was also induced in both tissues and is likely involved in maintaining structural integrity during tissue growth (83). Other genes that displayed similar expression profiles included Stat5a, Hif1a, Actg and Cyr61 suggesting that these gene products may serve common functions and have common mechanisms of regulation. Although these genes shared similar expression patterns, there were noticeable variations in expression kinetics that may be due to differences in blood flow (i.e. first passed through liver), tissue vascularity and lipid content (27) and other tissue and cell-specific factors that may affect transcription and mRNA stability.
Although there is a high degree of similarity in the responses observed in the liver to those reported for reproductive tissues, there are also examples of genes that displayed opposite regulation. For example, Igf1, a well-characterized estrogen inducible gene in the uterus (21,84), that is involved in proliferation, was down-regulated in the liver. Conversely, Igfbp1 is known to be down-regulated in the uterus (84) but was induced over 8-fold in the liver. The products of these genes play important roles in estrogen-mediated proliferation in the uterus. Their opposite regulation in the liver suggests that they may be involved in liver-specific responses such as mediating the mito-inhibitory effect that is observed after a more prolonged exposure of the liver to estrogen (85). Cyp17 exhibited a dose-dependent induction in the liver while dose-dependent repression has been reported in the uterus and ovary (86). This differential regulation of these genes likely involves tissue-specific co-activator/co-repressor expression or may be a function of chromatin structure and promoter accessibility (87). Their tissue-specific regulation and the manner in which their functions contribute to the unique physiology of these tissues, requires further investigation.
General conclusions
Results from these studies clearly establish the liver as an estrogen responsive tissue. Temporal and doseresponse studies not only extended the number and classes of estrogen regulated genes but also further elucidated potential mechanisms associated with known hepatic physiologic responses to estrogen. Furthermore, as a result of comparative studies, common estrogen elicited expression profiles across tissues as well as potential tissue-specific biomarkers of exposure were identified that may support drug development as well as the assessment of the endocrine disrupting activities of xenobiotics and natural products. However, additional studies using complementary technologies are needed to establish causal relationships between changes in gene expression and physiological outcomes. Furthermore, other estrogen responsive tissues will need to be examined in order to more clearly define the shared and tissue-specific effects of estrogens.
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Supplementary material
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Supplementary material can be found at: http://www.carcin.oupjournals.org/
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Acknowledgments
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Assistance with animal handling from Dr Yan Sun and helpful comments regarding the manuscript from Dr Don Jump, Dr Kris Chan, Dr John Lapres, Dr Mark Fielden, Dr Jeremy Burt and Cora Fong. Apologies are also extended to those authors whose publications could not be referenced due to space considerations. This work was funded by NIEHS grant ES011271.
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Received November 22, 2003;
revised February 2, 2004;
accepted February 4, 2004.