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Phenotypic anchoring of gene expression changes during estrogen-induced uterine growth.

A major challenge in the emerging field of toxicogenomics is to define the relationships between chemically induced changes in gene expression and alterations in conventional toxicologic parameters such as clinical chemistry and histopathology. We have explored these relationships in detail using the rodent uterotrophic assay as a model system. Gene expression levels, uterine weights, and histologic parameters were analyzed 1, 2, 4, 8, 24, 48, and 72 hr after exposure to the reference physiologic estrogen 17[beta]-estradiol ([E.sub.2]). A multistep analysis method, involving unsupervised hierarchical clustering followed by supervised gene ontology--driven clustering, was used to define the transcriptional program associated with [E.sub.2]-induced uterine growth and to identify groups of genes that may drive specific histologic changes in the uterus. This revealed that uterine growth and maturation are preceded and accompanied by a complex, multistage molecular program. The program begins with the induction of genes involved in transcriptional regulation and signal transduction and is followed, sequentially, by the regulation of genes involved in protein biosynthesis, cell proliferation, and epithelial cell differentiation. Furthermore, we have identified genes with common molecular functions that may drive fluid uptake, coordinated cell division, and remodeling of luminal epithelial cells. These data define the mechanism by which an estrogen induces organ growth and tissue maturation, and demonstrate that comparison of temporal changes in gene expression and conventional toxicology end points can facilitate the phenotypic anchoring of toxicogenomic data. Key words: estrogen, gene expression, microarray, phenotypic anchoring, uterus. Environ Health Perspect 112:1589-1606 (2004). doi: 10.1289/txg.7345 available via http://dx.doi.org/[Online 7 October 2004]

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Gene expression profiling, used within the existing framework of toxicologic assessment, promises to advance significantly the mechanistic understanding and prediction of adverse effects. To benefit fully from the opportunities offered by gene expression profiling, we must first understand the relationships between changes in gene expression and alterations in traditional toxicology parameters. The process by which gene expression changes are linked to changes in phenotype has been termed "phenotypic anchoring" (Cunningham et al. 2003; Paules 2003; Schmidt 2003). This approach has been used successfully to identify groups of genes whose expression correlates with specific pathologic changes during griseofulvin-induced chronic liver injury (Gant et al. 2003), renal toxicity (Amin et al. 2004), furan-mediated hepatotoxicity (Hamadeh et al. 2004), and acetaminophen-induced hepatotoxicity (Heinloth et al. 2004). In the present study we used phenotypic anchoring, in conjunction with gene ontology analysis, to define the transcriptional program associated with the response of the rodent uterus to a reference estrogen and to identify groups of genes that may drive specific histologic changes.

The immature mouse uterus is a major estrogen-responsive organ and forms the basis for a reference assay of estrogenic activity of chemicals (Owens and Ashby 2002). The physiologic response of the uterus to exogenous estrogens has been documented in detail (Clark and Mani 1994). The immature mouse uterus is sensitive to elevations in endogenous levels of 17[beta]-estradiol ([E.sub.2]) that occur during puberty. [E.sub.2] releases the immature uterus from quiescence and promotes cell proliferation and differentiation. The initial effects of [E.sub.2] are rapid (4-6 hr) and involve the uptake of fluid resulting from hyperemia and vasodilation of uterine capillaries, which causes the uterus to swell. This phenomenon is termed "water imbibition" and increases the availability of substrates and ions required for growth. Another early event is an increase in overall levels of mRNA and protein synthesis. The uterus then enters a proliferative phase that is responsible, at least in part, for the large increase in uterine weight that occurs 16-30 hr after [E.sub.2] exposure. Later responses mimic the changes in uterine physiology that accompany the onset of puberty and include alterations in the surface of the luminal epithelia.

Although the events described above have been characterized at the physiologic level, little is known about how [E.sub.2] acting through the estrogen receptors ER-[alpha] and ER-[beta], coordinates at the molecular level the myriad cellular processes involved, despite significant progress in elucidating the molecular mechanisms by which ERs regulate gene expression in vitro (Hall et al. 2001; McKenna and O'Malley 2002; Metivier et al. 2003; Moggs and Orphanides 2001; Moggs et al. 2003; Tremblay and Giguere 2002). Our data reveal the transcriptional program associated with [E.sub.2]-induced uterine growth. We show that [E.sub.2] induces a tightly coordinated transcriptional program that regulates successive and interlinked cellular processes during the uterotrophic response. Moreover, by comparing changes in gene expression with alterations in uterine weight and histology, we have identified classes of genes that may drive specific histologic changes in the uterus, including fluid uptake, coordinated cell division, and remodeling of the luminal epithelial cell layer in preparation fur embryo implantation. Our data also provide novel insights into how [E.sub.2] initiates paracrine signaling events, recruits immune and inflammatory cells, increases mRNA and protein synthesis, and suppresses apoptosis.

These data describe, at an unprecedented level of detail, how [E.sub.2] induces organ growth and maturation and provide a paradigm for understanding the mechanisms of action of other nuclear receptors. Furthermore, this study demonstrates that analysis of the temporal associations between a chemically induced transcriptional program and the accompanying histologic changes can provide valuable insight into the relationships between gene expression changes and phenotypic alterations.

Materials and Methods

Animals

Female Alpk:[Ap.sub.f]CD-1 mice (19 20 days old), weighing no more than 14 g on arrival in the laboratory, were obtained from a barriered animal breeding unit (AstraZeneca, Macclesfield, Cheshire, UK). The animals were housed five per cage in solid-bottom cages and allowed to acclimatize for 24 hr. They were allowed RM1 diet (Rat and Mouse No. 1; Special Diet Services Ltd., Witham, Essex, UK) and water ad libitum for the duration of the study. All animal experimentation described in this article was conducted in accord with accepted standards (local and national regulations) of humane animal care. Group sizes of 10 animals were used for the first two of the three replicate studies. Five animals per group were used in the third replicate study.

Uterotrophic Assays

The mice were given a single subcutaneous injection of [E.sub.2] (400 [micro]g/kg) or arachis oil (AO; vehicle control) using a dosing volume of 5 mL/kg body weight. A single dose of [E.sub.2] was used to avoid the complex transcriptional program that may result from the standard uterotrophic assay exposure regime (i.e., repeated administration on 3 consecutive days; Odum et al. 1997). The relatively high dose level of 400 pg/kg was chosen to ensure a sustained and significant increase in blotted uterine weight during the 72-hr sampling period (Supplemental Data, Figure 1). No overt toxicity was observed during the 72-hr exposure to [E.sub.2] (400 [micro]g/kg). All animals were terminated at the appropriate time using an overdose of halothane (Concord Pharmaceuticals Ltd., Essex, UK) followed by cervical dislocation. Vaginal opening was recorded, and the uterus was then removed, trimmed free of fat, gently blotted, and weighed. Blotted uterine weights were analyzed by covariance with terminal body weights (SAS Institute Inc. 1999). Half of each left uterine horn was fixed in 10% formol saline and processed to paraffin wax for histologic analysis (Odum et al. 1997). The mean thickness of the endometrial and epithelial cell layers, indicators of cellular hypertrophy, were calculated based on the assessment of 10 locations on hemotoxylin-and eosin-stained longitudinal uterine sections for each animal. All hypertrophy data were assessed for statistical significance by analysis of variance (ANOVA). The remainder of the uterus was snap frozen in liquid nitrogen and stored at 70[degrees]C for RNA extraction.

[FIGURE 1 OMITTED]

Mitotic Index

The total number of mitotic figures in each uterus section was counted, noting the tissue location, and the area of the section was measured using a KS400 image analysis system (Imaging Associates, Bicester, UK). The number of mitotic figures per square millimeter was calculated, and the frequency after administration of [E.sub.2] was compared with the frequency seen after the administration of AO using an appropriate statistical procedure. The number of mitoses per square millimeter was considered by a fixed-effects ANOVA allowing for treatment, time, and the treatment by time interaction. Analyses were carried out using the MIXED procedure in SAS, version 8.2 (SAS Institute Inc. 1999). Contrasts within the treatment by time interaction provided estimates of differences in [E.sub.2] and control response at each time point. These were compared statistically using a two-sided Student t-test based on the error mean square in the ANOVA.

Transcript Profiling and Data Analysis

Three independent biologic replicates of the entire time course study for [E.sub.2]-treated and time-matched AO-treated groups of animals were used to generate transcript profiling data and for subsequent statistical analysis. To minimize the effect of any interanimal variability, total RNA was isolated from the pooled uteri for each treatment group (n = 10 in the first two studies; reduced to n = 5 for the last study because of highly similar transcriptional responses being obtained in replicate studies 1 and 2) using RNeasy Midi kits (Qiagen Ltd., Crawley, West Sussex, UK). Biotin-labeled complementary RNAs were synthesized using the Enzo Bioarray HighYield RNA transcript labeling kit and hybridized to Affymetrix routine U74-Av2 GeneChips as described previously (Zhu et al. 2001) and in the Affymetrix GeneChip expression analysis technical manual (Affymetrix, Inc. 2002). Probe arrays were scanned and the intensities were averaged using Microarray Analysis Suite 5.0 (Affymetrix, High Wycombe, UK). The mean signal intensity of each array was globally scaled to a target signal value of 500. To select [E.sub.2]-responsive genes, each gene was subjected to a mixed-model ANOVA allowing for treatment, time, and the treatment by time interaction as fixed effects and replicate study as a random effect. The use of mixed ANOVA models for the analysis of differential gene expression in microarray experiments has been previously described (Churchill 2004; Cui and Churchill 2003). Analyses were carried out using the MIXED procedure in SAS, version 8.2 (SAS Institute Inc. 1999). Contrasts within the treatment by time interaction provided estimates of differences in [E.sub.2] and control response at each time point. These were compared statistically using a two-sided Student t-test based on the error mean square in the ANOVA [Supplemental Data, Table 1 (http://ehp.niehs.nih.gov/txg/ members/2004/7345/supplemental.pdf)]. Data for genes exhibiting significant changes in expression (p, < 0.01, two-sided) at one or more time points were then exported into GeneSpring 6.0 (SiliconGenetics, Redwood City, CA, USA), and a data transformation (values < 0.01 set to 0.01) and per-chip normalization (to the 50th percentile) were applied. Genes that did not have a Present detection call (Affymetrix) in any of the 14 treatment groups were removed from further analysis. Ratios of changes in gene expression were then calculated by normalizing each [E.sub.2]-treated sample to its corresponding time-matched vehicle (AO)-treated control. GeneChip data sets for the three independent biologic replicates were interpreted in log of ratio analysis mode, with biologic replicates being selected as a noncontinuous parameter. A total of 3,538 [E.sub.2]-responsive genes exhibiting a minimum of 1.5-fold up- or down-regulation in at least one rime point were then subjected to gene tree-based hierarchical clustering (Pearson correlation). To identify genes that function in specific biologic pathways, these 3,538 genes were further filtered using functional annotations derived from the NetAffx database, Analysis Center (Liu et al. 2003; http:// www.affymetrix.com/analysis/index.affx), together with manual annotations from published literature, before hierarchical clustering using GeneSpring. Gene names used in this article (see Appendix) were derived by homology searching of nucleotide sequence databases (BLASTn; http://www.ncbi.nih.gov/BLAST/) using Affymetrix probe target sequences and the interrogation of NetAffx (Liu et al. 2003) database. All genes described in the figures and text showed statistically significant alterations in expression in all three replicate studies. MIAME (Minimum information About a Microarray Experiment)-compliant microarray data for the three independent replicate studies are available as supplementary information and have been submitted to the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geol).

Quantitative Real-Time Polymerase Chain Reaction

Uterine RNA was isolated and purified from all [E.sub.2]-treated and time-matched vehicle control groups (each consisting of pooled uteri) in all three replicate time course studies using the Qiagen RNeasy Midi kit (Qiagen). Before reverse transcription, RNA was treated with Dnase I (DNA-free kit; Ambion, Huntington, UK) to remove any contaminating genomic DNA. For each pool, 2 [micro]g total RNA was reverse transcribed in a 25-[micro]L reaction using SuperScript II (Invitrogen, Paisley, UK) and oligo-dT primer according to the manufacturer's instructions. Polymerase chain reaction (PCR; 25 [micro]L) containing 2 [micro]L first-strand eDNA (1:10 dilution), 12.5 [micro]L of SYBR Green PCR Master Mix (Applied Biosystems, Warrington, UK), and 0.3 [micro]M each of forward and reverse primers were run For 40 amplification cycles in an ABI PRISM 7700 Sequence Detection System (Applied Biosystems). Cycling conditions were 50[degrees]C for 2 min, 9[degrees]C for 10 rain, 95[degrees]C for 15 sec, and 60[degrees]C for 1 min. All reactions were run in triplicate.

Real-time (RT) PCR primers for FOS (5'-CTGTGGCCTCCCTGGATTTG-3 "and 5'-TGAGAAGGGGCAGGGTGAAG-3'), LTF (5'-CGGGGGCCTTCAGACCATC3' and 5'-CTAAAGTGACAGCAGGG AGTG-3'), and the control gene RPBI (5'-GTTCTGGACCCCATTTTT GATAGGC-3' and 5'-CAGGGGACTGGCAGGGTAACAA-3') were designed using Primer Express software (version 1.5; Applied Biosystems) to generate amplicons within their corresponding Affymetrix probe set target sequences.

Results

Histologic Changes and Increases in Uterine Weight

Our aim was to identify the genes and molecular networks associated with the uterotrophic response and to define the relationships between gene expression changes and histologic alterations. To this end, we gave immature female mice a single subcutaneous injection of [E.sub.2] (400 [micro]g/kg) or vehicle and used DNA microarrays to measure uterine gene expression profiles at seven different times (1, 2, 4, 8, 24, 48, and 72 hr) after exposure. To facilitate the phenotypic anchoring of expression changes, we also measured blotted uterine weights and determined the average heights of the luminal epithelium and stromal endometrium for each animal. Three independent replicate experiments were carried out to allow a rigorous statistical analysis of the gene expression data (see "Materials and Methods"). We chose to use a single dose of [E.sub.2] to avoid the complex transcriptional program that may result from the standard uterotrophic assay exposure regime in which test compound is dosed by repeated administration on 3 consecutive days (Odum et al. 1997). This dose induced a sustained increase in blotted uterine weight that was similar in the three replicate experiments (Figure 1A). in each replicate experiment, a significant increase (p < 0.01) in uterine weight was observed 4 hr after exposure to [E.sub.2] and reached maximal levels between 24 and 72 hr (Figure 1A).

Histologic analysis of uterine sections revealed the cellular changes associated with the increase in uterine weight between 1 and 72 hr (Figure 2A). Consistent with previous reports (Clark and Mani 1994), the weight increase that occurred within 4 hr of exposure (Figure 1A) was associated with thickening of the stromal endometrium (Figure 2B) resulting from the uptake of fluid. The larger increase in uterine weight that occurred between 8 and 24 hr was due to hypertrophy and cell proliferation (Kaye et al. 1971; Quarmby and Korach 1984), which caused an increase in thickness of the luminal epithelium between 8 and 24 hr (Figure 2C). We conclude that the single dose of [E.sub.2] used induced a conventional uterotrophic response. Furthermore, the expression profiles of two classical [E.sub.2]-responsive genes, lactotransferrin (LTF; Liu and Teng 1992) and the proto-oncogene C-FOS (Weisz and Bresciani 1988), demonstrate that [E.sub.2] elicited a robust transcriptional response that was similar in the three replicate experiments (Figure 1B).

[FIGURE 2 OMITTED]

Multistep Method for Analysis of Gene Expression Changes

Uterine RNA from the seven time points for each of the [E.sub.2]-treated and time-matched vehicle control groups was analyzed using Affymetrix MG-U74Av2 GeneChips. A total of 42 microarray data sets were collected for the three replicate experiments. We used a multistep method to analyze the microarray gene expression data (Figure 3A). First, data were filtered and subjected to statistical analyses to identify the 3,538 genes with altered expression in [E.sub.2]-treated mice (o < 0.01 and > 1.5-fold) during at least one time point (see "Materials and Methods"). Unsupervised hierarchical clustering was then used to group these genes into co-regulated clusters (Quackenbush 2002; Figure 3B), revealing a complex multistage transcriptional response to [E.sub.2] in the uterus (gene clusters A-I in Figure 3B). To gain an overview of the predominant molecular functions and biologic pathways that were regulated at the transcriptional level during the uterotrophic response to [E.sub.2], we interrogated the 3,538 [E.sub.2]-responsive genes using the GOStat gone ontology mining tool (http://gostat.wehi.edu.au) (Beissbarth and Speed 2004). This approach revealed that [E.sub.2] targets predominantly genes involved in protein metabolism, cell cycle, cell proliferation, DNA replication, RNA metabolism, mRNA transcription, and blood vessel development [Supplemental Data, Table 2 (http://ehp.niehs.nih.gov/txg/ members/2004/7345/supplemental.pdf)]. Next, we used a supervised clustering approach using customized gene ontology definitions (see "Materials and Methods") to identify gene functions that were predominant in each co-regulated cluster in Figure 3B. This revealed that [E.sub.2] regulates each class of gene during a narrow window of time and suggests that [E.sub.2] induces uterine growth and maturation by regulating successively the activities of different biologic pathways (described below). Finally, we analyzed the temporal associations between the gene expression program and alterations in uterine weight and histology to anchor the gene expression changes to alterations in uterine phenotype. These associations are described below.

[FIGURE 3 OMITTED]

Phase 1: Rapid Induction of Transcriptional Regulators and Signaling Components by [E.sub.2]

The first 4 hr of the uterotrophic response is characterized by the influx into the uterus of fluid that provides the nutrients and ions required for growth (Clark and Mani 1994). This leads to decompaction of stromal cells (Figure 4A) and thickening of the stromal endometrial layer at 4 hr (Figure 2B). This first phase of the uterotrophic response is accompanied by the rapid and transient regulation of genes encoding components of intra- and intercellular signaling pathways (Figure 4B) and sequence-specific transcriptional regulators (Figure 4C). Most of these genes show maximal expression between 1 and 4 hr, suggesting that the transcriptional effects of [E.sub.2], mediated via ER-[alpha] and ER-[beta], are amplified rapidly through the induction or modulation of multiple transcriptional and nontranscriptional signaling pathways.

[FIGURE 4 OMITTED]

Signaling Genes

The signaling genes rapidly up-regulated by [E.sub.2] function in a broad array of signal transduction pathways (Figure 4B). These genes include protein kinases (AKT, MEK1, PIM3), growth factors (VEGF, PLGF), GTPases (RHOC, RAB11A, DEXRAS1), cytokine signaling proteins (MCP1, SOCS1, SOCS3, WSB1, IL17R), and a Wnt signaling factor (WNT4). Several [E.sub.2]-induced genes may act to attenuate initial signaling events (e.g., the protein phosphatase MKP1 negatively modulates MAP kinase activity). Strikingly, many of the signaling genes induced within 4 hr of [E.sub.2] exposure have roles in the regulation of vascular permeability in other tissues, suggesting that they may be involved directly in initiating the influx of fluid into the uterus at this time (Figure 4B). These genes include angiogenic/vascular cell growth factors (VEGF, PLGF, ADM, ANGPT2, TGFB2), vasoactive serine proteases (KLK2, KLK6, KLK9, KLK22), and vascular endothelial receptors (IL17R, BDKRB1, ENG, GNA13). Furthermore, the vascular growth factor receptors TIE1 and TIE2 are rapidly down-regulated in response to [E.sub.2] (Figure 4B), which may serve to attenuate the uptake of fluid after 4 hr. Collectively, these genes shed light on the mechanism by which [E.sub.2] promotes fluid uptake in the uterus and provide a clear link between gene expression changes and histologic changes occurring at this time.

[FIGURE 4 OMITTED]

Transcriptional Regulators

The sequence-specific transcription factors induced during the first 4 hr of the response can be divided into four main classes (Figure 4C). The first contains members of the Jun, Fos, and ATF subgroups of transcription factors (C-FOS, FOSB, C-JUN, JUNB, ATF3, ATF4, ATF5) that form AP-1 dimers implicated in the regulation of cell proliferation and survival (Shaulian and Karin 2001). The second class contains genes that control cell differentiation during the development of a number of tissues (SOX11, SOX18, HEY1, CART1, PRX2, SMAD7, ID1). The early induction of members of this class suggests that [E.sub.2] deploys a diverse range of gene expression networks to control cell growth and differentiation in the uterus. The third class contains two genes that encode coregulators for nuclear receptors (RIP140, NCOP2), suggesting that these may act to modulate ER-mediated responses to [E.sub.2] in the uterus. The fourth class of genes encodes presumed transcriptional regulators of unknown function (e.g., GIF).

We conclude that the initial response to [E.sub.2] serves to a) modulate the activities of intra- and intercellular signaling pathways that, among other functions, promote vascular permeability and fluid uptake and b) up-regulate the expression levels of transcription factors that promote growth and differentiation. These early gene expression changes facilitate the amplification of the originating hormonal signal and set into motion the series of events that result in uterine growth and differentiation.

Phase 2: Coordinated Induction of Genes Required for mRNA and Protein Synthesis

No increase in uterine weight or obvious changes in uterine histology occur between 4 and 8 hr (Figures 1 and 2). Nevertheless, our data reveal that this phase is associated with the induction of a large cluster of genes (Figure 5). Most are induced 2 hr after [E.sub.2] administration, reach maximal expression at 4 or 8 hr, and return to control or subcontrol levels by 48 hr (Figure 5B). Most of these genes play roles in mRNA and protein synthesis, demonstrating that the bulk of transcriptional activity occurring at this time functions to increase the capacity of the uterus for new protein synthesis. This is consistent with earlier observations that exposure to [E.sub.2] results in a rapid increase in the mRNA and protein content of the uterus (Clark and Mani 1994). Our data define the molecular basis for these prior observations and identify the genes targeted by ERs to induce these effects.

In a broad sense, protein synthesis includes the interlinked processes of transcription, mRNA processing, mRNA export into the cytoplasm, protein translation, and protein folding (Orphanides and Reinberg 2002, and references therein; Figure 5G). Our data reveal the coordinated induction of genes involved in each of these processes (Figure 5A-F). These genes include a) components of the RNAP II general transcription machinery (RPB8, RPB10, TAF10; Figure 5A); b) transcription termination and polyadenylation factors (NSAP1; Figure 5A); c) mRNA splicing factors (SFPQ, U2AF1, RNPS1; Figure 5A); d) mRNA export proteins (NXF1; Figure 5C); e) protein translation factors (EIF1A, EIF2A, EIF2B, EIF3; ribosomal proteins RPL11, RPL12, RPL20, RPL52, RPS18b, and tRNA synthetases VALRS, GLURS, PHERS; Figure 5D), and f) protein folding factors (FKBP4, CCT3, CCT6a, CCT7, CCT8; Figure 5E). The down-regulation of several genes associated with transcriptional repression (HDA1, TGIF, MAD4, EZH1) and mRNA degradation (AUH; Figure 5B) may also contribute to the general elevation of mRNA synthesis. We also note a concurrent increase in the expression of components of the ubiquitin-proteasome proteolytic pathway (PAD1, SUG1; Figure 5F) and genes whose products are required for the nuclear import and export of proteins (IMPORTIN [alpha] 2, IMPORTIN [alpha] 3, RAE1, G3BP2; Figure 5C), indicating that [E.sub.2] additionally elevates proteasome levels and nuclear-cytoplasmic protein transport activity at this time. We conclude that [E.sub.2] is able to increase protein synthesis activity in the uterus by altering the expression of genes involved in all aspects of the protein biosynthesis pathway.

Therefore, during the first two phases of the transcriptional program, [E.sub.2] induces the expression of a battery of sequence-specific transcriptional regulators (phase 1; Figure 4C) and then induces the expression of genes in the protein synthesis pathway (phase 2; Figure 5). It appears, therefore, that, during phase 1, [E.sub.2] specifies the gene expression networks that will be active, and then ensures during phase 2 that these networks have sufficient mRNA and protein synthesis capacity to operate. In addition the increased expression of components of the RNA and protein synthesis machinery is likely to be a prerequisite for proliferation in the uterus because cells must increase their mass before division to provide sufficient cellular components required for survival of the daughter cells (Norbury and Nurse 1992). Consistent with this, we note that induction of protein synthesis components immediately precedes the up-regulation of genes required for proliferation (Figure 6; see below). An additional function of the increased uterine capacity for protein synthesis may be to facilitate the production of the abundant cytoarchitectural and secreted proteins induced at the end of the uterotrophic response (see below).

Phase 3: Coordinated Regulation of Genes Controlling Chromosome Replication and the Cell Cycle

The next phase in the urerotrophic response occurs between 8 and 24 hr and involves an approximate doubling in uterine weight (Figure 1A) and a large increase in the thickness of the luminal epithelium (Figures 2C, 6A). A quantitative histologic analysis of mitotic figures in the uterine cells ("Materials and Methods") revealed a clear and statistically significant (p < 0.01) increase with [E.sub.2] at 24 hr, whereas no [E.sub.2]-dependent increase was observed at 8, 48, or 72 hr (Table 1, Figure 6A). These observations are consistent with previous studies showing that most cells in the immature rodent uterus are stimulated to leave their quiescent state and divide synchronously under the influence of [E.sub.2] (Kaye et al. 1971; Quarmby and Korach 1984).

We found that genes required for the replication of chromosomal DNA (PCNA, FEN1, CDC6, MCM2, MCM3, MCM4, MCM5, ORC1, ORC6, RRM1, RRM2) and genes required for postreplicative phases of the cell division cycle (e.g., CCNB1, PLK1) are coordinately induced and reach maximal expression levels between 8 and 24 hr (Figure 6B), consistent with the timing of the histologic changes observed in Figure 6A. Genes required for maintaining genome integrity (CHK1, CKS1, GEMININ) and the epigenetic status of newly replicated DNA (CAF-1 p60, AHCY) are also up-regulated at 8 and/or 24 hr (Figure 6B). It is striking that after their induction during the proliferative phase (8-24 hr), the expression levels of most genes that regulate chromosome replication and cell division are reduced to levels well below those of control animals (Figure 6B). This suggests that mechanisms exist for the active repression of these genes to prevent further rounds of proliferation.

Declining [E.sub.2] levels in mice 48 hr after a single subcutaneous injection may also contribute to the cessation of proliferation. Together, these data provide a molecular explanation for the changes in uterine weight and histology that occur between 8 and 24 hr (Figures 1A, 2, and 6A) and support the assertion that tire early increase in weight seen at 4 hr is due to fluid uptake. Furthermore, these gene expression changes demonstrate that cell proliferation is restricted to a narrow window of time between 8 and 24 hr by the coordinated regulation of chromosome replication and cell division genes.

Regulation of Cell Division

Our data also provide insight into the mechanisms by which [E.sub.2] releases cells of the immature uterus from quiescence and promotes cell division. The [E.sub.2]-induced expression profile of E2F1, a key transcriptional regulator of DNA replication genes (Ohtani 1999), closely parallels the induction of the chromosome replication genes (Figure 6B), consistent with the proposal that E2F1 regulates the expression of components of the DNA replication fork in human breast cancer cell lines exposed to [E.sub.2] (Lobenhofer et al. 2002). Our data indicate that release from quiescence also involves the [E.sub.2]-induced down-regulation of genes that maintain cells in a growth-arrested state (KIP1, CCNG2, CCNG1). The principle way in which mitogens induce proliferation of quiescent cells involves a reduction in levels of the Kip1 protein, which inhibits the activities of cyclin-cdk complexes and induces cell cycle arrest (Olashaw and Pledger 2002). We found that KIP1 was down-regulated within 1 hr of [E.sub.2] exposure and remains repressed over a period of at least 24 hr, only reaching control levels when cell proliferation has ceased (Figure 6C). Furthermore, [E.sub.2] may promote degradation of the Kip1 protein via the induction of CDC34 (Figure 6C), a gene that has been implicated in the ubiquitin-mediated degradation of Kip1 (Koepp et al. 1999).

These data suggest that [E.sub.2] promotes cell proliferation by coordinately reducing Kip1 mRNA and protein levels. It is not clear whether KIP1 is a direct or indirect target of the activated ERs. However, KIP1 gene expression is controlled by ras-mediated PI3K signaling pathways (Olashaw and Pledger 2002), components of which are up-regulated rapidly in response to [E.sub.2] (e.g., DEXRAS1, RASSF1; Figure 4B).

Suppression of Apoptosis

[E.sub.2] protects against apoptosis in a number of tissues, including brain, testes, and uterus (Thompson 1994). Although the anti-apoptotic activity of estrogen in the uterus is thought to play a crucial role in the maintenance of uterine homeostasis, the mechanistic basis for this action has not been defined. Our data reveal that [E.sub.2] induces the expression of anti-apoptotic genes (BAG2, BAG3, DAD1) while simultaneously down-regulating the expression of pro-apoptotic genes (CASP2, NIX; Figure 6D). Thus, apoptosis appears to be suppressed through transcriptional mechanisms during [E.sub.2]-induced uterine growth. Consistent with these observations, [E.sub.2] also induces the apoptotic regulators BCL2 and BAG1 in cultured breast cancer cells (Perillo et al. 2000; Soulez and Parker 2001). It will be important to determine whether estrogens elicit similar changes in the expression of apoptosis-regulating genes in other tissues.

Phase 4: Induction of Genes Involved in Uterine Cell Differentiation and Defense Responses

The period from 24 to 72 hr after [E.sub.2] exposure is associated with remodeling of the luminal epithelial cell layer, including the formation of secretory epithelial cells and a glycocalyx layer consisting of glycoproteins (Paria et al. 2003; Weitlauf 1994). These changes result in the formation of a highly differentiated epithelial layer that is primed for cell recognition and adhesion events necessary for embryo attachment and implantation.

Changes in Cytoarchitecture

The final phase of the uterotrophic response coincides with the induction of a battery of genes involved in the cytoarchitectural remodeling of proliferating uterine cells, thus providing a further link between phenotypic and gene expression changes (Figure 7A). These genes encode components of desmosomes (DSG2), gap junctions (CX26), tight junctions (CLDN4, CLDN7), the cornified envelope (SPRRIA, 2A, 2B, 2E, 2F, 2G, 2I, 2J), intermediate filaments (KRT19), and a variety of cell-surface and extracellular-matrix glycoproteins (SPP1, BGP1, BGP2, MUC1, TROP2, CLU). The latter class of genes is likely to contribute to the formation of the glycocalyx layer present on differentiated uterine epithelium (Weitlauf 1994). The concomitant [E.sub.2]-dependent induction of a number of enzymes required for carbohydrate metabolism (MAN2B1, GALNT3) may provide the increase in sugar metabolism necessary for the production of these glycoprotcins. [E.sub.2] also induces genes encoding ion channels that regulate the balance of [Na.sup.+] absorption and [Cl.sup.-] secretion across the endometrial epithelium to maintain a luminal fluid microenvironment suitable for implantation (CFTR, CLCA3, MAT8; Figure 7A).

Defense Responses

A number of genes involved in host defense processes or detoxification are first regulated between 24 and 72 hr (Figure 7t3). We speculate that the products of these genes may provide an environment that is protective of, and facilitates, embryo implantation and development. These include genes encoding lysosomal enzymes (LYZP, LYZM, CTSH CTSL, CTSS, LGMN), genes involved in detoxification and clearance of xenobiotics (GSTO1, GSTT2, UGT1AI), and genes involved in immune and inflammatory responses (CD14, MX1, PIGR). The up-regulation of genes encoding chemoattractant cytokines (Figure 7C) for infiltrating eosinophils (EOTAXIN) and monocytes (MCP1/3) is consistent with previous observations of immune cell infiltration into the uterus (Gouon-Evans and Pollard 2001, and references therein). Another [E.sub.2]-regulated defense response may be provided by the induction of LTF (Liu and Teng 1992), an iron-binding protein with bacteriostatic activity (Singh et al. 2002). Our data reveal the induction of two additional iron metabolism genes at this time (CP, LCN2; Figure 7E; Kaplan 2002), suggesting a role for iron homeostasis in the uterotrophic response to [E.sub.2].

Several components of the complement system are also induced 48-72 hr after exposure to [E.sub.2]. These include C1QA, C1QB, C1QC, C2, C3, C4, CFH, and CFI (Figure 7D). Although many complement components have been identified in female reproductive epithelium, only C3 has previously been established as an [E.sub.2]-responsive gene (Sundstrom et al. 1989). In addition to participating in immune and inflammatory responses and host resistance, there is increasing evidence that complement functions in tissue remodeling and organ regeneration (Mastellos and Lambris 2002). Intriguingly, complement also influences mammalian reproduction and particularly the integrity of maternofetal interfaces during pregnancy (Caucheteux et al. 2003; Mastellos and Lambris 2002). Therefore, it is possible that the complement system may play a noninflammatory role in the uterotrophic response.

Evidence for a Transcriptional Cascade in the Uterus

It is striking that many different induction profiles can be seen in the genes regulated by [E.sub.2]: some genes are induced within 1 hr of exposure, whereas others are not induced until 48 hr (Figure 3B). The induction of a large number of sequence-specific transcription factors during the first phase of the response suggests that a transcriptional cascade may operate in the uterus, with the products of genes induced at the beginning of the program regulating the transcription of those toward the end. The regulation of the SPRR genes provides evidence for the existence of such a cascade (Figure 8). The mouse SPRR genes are located in a tandem array at the same chromosomal locus, and their transcription is regulated by the AP-1 and Ets transcription factors (Patel et al. 2003; Figure 8A). Eight members of the SPRR gene family are induced between 4 and 72 hr, with maximal induction occurring between 24 and 48 hr (Figure 8B). Intriguingly, the mRNAs encoding Ets2 and components of AP-1 (c-Jun, JunB, c-Fos, FosB, and Atf3, Atf4, Atf5) are maximally induced during the first phase of the uterotrophic response, between 1 and 4 hr (Figure 8B). We speculate, therefore, that a transcriptional cascade operates, in which ER-[alpha] or ER-[beta] induces the expression of Ets2 and AP-1 components, which in turn regulate the transcription of the SPRR genes (Figure 8C). Alternatively, it is possible that ER-[alpha] or ER-[beta] cooperates with Ets2 and AP-1 to regulate the expression of the SPRR genes. In this way, transcription of the SPRR genes would not begin until sufficient levels of Ets2 and AP-1 were present. Consistent with this model, feed-forward loops (in which a transcriptional regulator controls a second transcription factor that then functions in concert with the initial regulator on a common downstream target gene) are emerging as common mechanisms in eukaryotes for transcriptional networks (Lee et al. 2002). It is likely that analysis of the regulatory regions of other [E.sub.2]-responsive genes during the uterotrophic response will suggest the existence of additional transcriptional networks.

Discussion

Our data describe at an unprecedented level of detail the molecular events that initiate and drive uterine physiologic changes upon exposure to the sex steroid hormone [E.sub.2] in the immature mouse uterus. Gene expression profiling reveals that [E.sub.2] induces a multistage and tightly coordinated transcriptional program that regulates successive and functionally interlinked cellular processes during the uterotrophic response (Figure 9). The temporal patterns of gene expression we have identified for [E.sub.2] are consistent with, and extend, those reported recently for the uterotrophic response of immature, ovariectomized mice after exposure to 17[alpha]-ethynylestradiol (Fertuck et al. 2003), in which concordant temporal responses were seen for genes involved in several functional categories in Figure 9. These include RNA and protein metabolism, cell cycle regulation, immune responses, and complement components. Furthermore, many of the genes regulated by exogenous [E.sub.2] in our study are also differentially regulated in response to endogenous hormones (Tan et al. 2003).

Comparison of gene expression changes with alterations in uterine weight and histologic alterations, and analysis of gene expression data according to gene function allowed us to implicate specific groups of genes in driving water imbibition in the stromal endothelium, synchronous cell proliferation, and cytoarchitectural changes associated with luminal epithelial cell differentiation. These data thus provide a detailed mechanistic view of the relationships between the uterotrophic response and the underlying transcriptional program. Furthermore, this work demonstrates that comparison of temporal changes in gene expression and conventional toxicology parameters (uterine weight and histologic changes) can provide an understanding of the relationships between gene expression patterns and phenotypic change.

[E.sub.2] can regulate transcription through a combination of at least two distinct signaling pathways: a) via activation of the nuclear transcription factors ER-[alpha] and ER-[beta] (Hall et al. 2001; McKenna and O'Malley 2002; Moggs and Orphanides 2001; Tremblay and Giguere 2002) and b) via extranuclear or "nongenomic" signaling events (Falkenstein et al. 2000; Hammes 2003; Moggs et al. 2003). The transcriptional responses to [E.sub.2] that we have defined here are likely to involve a combination of direct gene regulation by nuclear ERs and indirect gene regulation via extranuclear signaling pathways. Although the uterus of the immature mouse expresses both ER subtypes ([alpha] and [beta]) at comparable levels (Weihua et al. 2000), recent transcript profiling studies using ovariectomized ER-knockout mice revealed a predominant role for ER-[alpha] in the regulation of estrogen-responsive genes in the uterus (Hewitt et al. 2003; Watanabe et al. 2003) consistent with the observation that only a partial uterotrophic response occurs in ER-[alpha] knockout mice (Lubahn et al. 1993). Therefore, it is likely that most [E.sub.2]-responsive genes we have identified are regulated by ER-[alpha]. However, identification of the direct gene targets for each ER subtype will ultimately require the development of methods for measuring the occupancy of receptor subtypes at promoters in vivo. Nevertheless, our temporal analysis of [E.sub.2]-responsive genes provides novel insights into the transcriptional cascades that are initiated through [E.sub.2]-responsive transcription factors.

The molecular events described here for the reference natural estrogen [E.sub.2] provide the basis for understanding how other estrogenic chemicals, including synthetic estrogens and phytoestrogens, induce their effects (Moggs et al. 2004). Increasing attention is being paid to the use of gene expression changes in the uterus for the identification of surrogate markers for short-term rodent estrogenicity assays (Naciff et al. 2002, 2003; Owens and Ashby 2002; Watanabe et al. 2002), and our data reveal a large number of novel candidate marker genes. The insights provided by these data, into how an ER ligand coordinates transcriptional regulatory networks that result in proliferation and differentiation in a complex organ, provide a paradigm for understanding the modes of action of other nuclear receptors.
Appendix. Gene nomenclature and Affymetrix probe sets for
Figures 4-8. (a)

 Affymetrix
Gene symbol Probe Set Gene description

Figure 4B--Signaling components

IL17R 99992_at interleukin 17 receptor
RAP1 160822_at Rap1, GTPase-activating protein 1
DEXRAS1 99032_at RAS, dexamethasone-induced 1
MKP1 104598_at dual specificity phosphatase 1
WNT4 103238_at wingless-related MMTV integration site 4
IGFBP10 92777_at cysteine rich protein 61
PIP92 99109_at immediate early response 2
PIM3 96841_at similar to serine/threonine-protein kinase
 pim-3
ARHU 96747_at ras homolog gene family, member U
CISH3 162206_f_at cytokine inducible SH2-containing
 protein 3
NAB2 100962_at Ngfi-A binding protein 2
SOCS3 92232_at cytokine inducible SH2-containing protein 3
EPLG2 98407_at ligand for receptor tyrosine kinase ELK
IL17R 99991_at interleukin 17 receptor
CDKN1A 98067_at cyclin-dependent kinase inhibitor 1A(P21)
CDKN1A 94881_at cyclin-dependent kinase inhibitor 1A (P21)
WSBI 98946_at WD-40-repeat-containing protein with a SOCS
 box
VEGF 103520_at vascular endothelial growth factor A
GADD45 102292_at growth arrest and DNA-damage-inducible 45
SYT 99610_at synovial sarcoma translocation,
 chromosome 18
SOCS1 92832_at cytokine inducible SH2-containing protein 1
GADD45g 101979_at growth arrest and DNA-damage-inducible 45
 gamma
GLY96 94384_at immediate early response 3
MAPKAP2 160353_i_at MAP kinase-activated protein kinase 2
KLK22 101289_f_at epidermal growth factor binding protein
 type 1
TROB 99532_at tob family
RGS3 160747_at regulator of G-protein signaling 3
GNA13 100514_at guanine nucleotide binding protein,
 alpha 13
RAB11A 96238_at RAB11a, member RAS oncogene family
PLGF 92909_at placental growth factor
BDKRB1 101748_at bradykinin B1 subtype receptor
CF3 97689_at coagulation factor III
PDK4 102049_at pyruvate dehydrogenase kinase, isoenzyme 4
HERPUD1 95057_at homocysteine-inducible, endoplasmic
 reticulum stress-inducible,
 ubiquitin-like domain member 1
MYD116 160463_at myeloid differentiation primary response
 gene 116
NORE1 102028_at Ras association (RaIGDS/AF-6) domain
 family 5
NET1A 94223_at neuroepithelial cell transforming gene 1
GEM 92534_at GTP binding protein (gene overexpressed in
 skeletal muscle)
SNRK 97429_at SNF related kinase
ALASH 93500_at aminolevulinic acid synthase 1
NTTP1 161171_at dual specificity phosphatase 8
MAPKAP2 95721_at MAP kinase-activated protein kinase 2
MEK1 92585_at mitogen activated protein kinase kinase 1
RGSr 94378_at regulator of G-protein signaling 16
RASSF1 102379_at Ras association (RaIGDS/AF-6) domain
 family 1
NGEF 93178_at neuronal guanine nucleotide exchange factor
C-KIT 99956_at kit oncogene
NOTCH1 97497_at Notch gene homolog 1
BTG3 96146 _t B-cell translocation gene 3
PC4 160092_at interferon-related developmental
 regulator 1
SGK 97890_at serum/glucocorticoid regulated kinase
ADM 102798_at adrenomedullin
ANGPT2 92210_at angiopoietin 2
UBQLN1 95601_at ubiquilin 1
THBS1 160469_at thrombospondin
ROCK2 98504_at rho-associated coiled-coil forming kinase 2
SNK 92310_at serum-inducible kinase
MAP2K3 93315_at mitogen activated protein kinase kinase 3
ENG 100134_at endoglin
PTDSR 95486_at phosphatidylserine receptor
SWIP2 160296_at WD-40-repeat-containing protein with a SOCS
 box
AKT 100970_at thymoma viral proto-oncogene 1
RHOC 96056_at ras homolog gene family, member C
TGFB2 93300_at transforming growth factor, beta 2
EPCR 98018_at protein C receptor, endothelial
KLK6 100061_f_at kallikrein 6
GALN 100407_at galanin
NEDD4B 103907_at neural precursor cell expressed,
 developmentally down-regulated gene
 4-like
KLK22 95775_f_at kallikrein 22
KLK9 94716_f_at kallikrein 9
MCP1 102736_at platelet-derived growth factor-inducible
 protein JE
TIE1 99936_at tyrosine kinase receptor 1
RAMP1 104680_at receptor (calcitonin) activity modifying
 protein 1
PGF 97769_at prostaglandin F receptor
PDGF 95079_at platelet derived growth factor
[alpha]RA receptor, alpha polypeptide
OB-RGRP 93600_at leptin receptor
ERK1 101834_at mitogen activated protein kinase 3
GRB7 103095_at growth factor receptor bound protein 7
ADCY6 102321_at adenylate cyclase 6
TIE1 161184_f_at tyrosine kinase receptor 1
GNAI1 104412_at guanine nucleotide binding protein, alpha
 inhibiting 1
ADCY7 103392_at adenylate cyclase 7
TIE2 102720_at endothelial-specific receptor tyrosine
 kinase
GPCR26 100435_at endothelial differentiation,
 lysophosphatidic acid G-protein-coupled
 receptor, 2
Figure 4C--Transcription factors

GIF 99603_g_at TGFB inducible early growth response
GIF 99602_at TGFB inducible early growth response
ETS2 94246_at E26 avian leukemia oncogene 2, 3' domain
ID1 100050_at inhibitor of DNA binding 1
SMAD7 92216_at MAD homolog 7
C-JUN 100130_at Jun oncogene
BRF2 160273_at zinc finger protein 36, C3H type-like 2
IRF8 98002_at interferon concensus sequence binding
 protein
AGP/EBP 92925_at CCAAT/enhancer binding protein (C/EBP),
 beta
C-FOS 160901_at c-fos oncogene
KROX24 98579_at zinc finger protein Krox-24
FOSB 103990_at FBJ osteosarcoma oncogene B
NR4A1 102371_at N10 nuclear hormonal binding receptor
SOX18 161025_f_at SRY-box containing gene 18
SOX18 104408_s_at SRY-box containing gene 18
KROX20 102661_at Early growth response 2
ESG 104623_at transducin-like enhancer of split 3,
 homolog of Drosophila E(spl)
FOG 97974_at zinc finger protein, multitype 1
NCOR2 95129_at nuclear receptor co-repressor 2Gene symbol
SOX11 101631_at SRY-box containing gene 11
C/EBP 94466_f_at CCAAT/enhancer binding protein alpha
 (C/EBP), related sequence 1
PRX2 103327_at paired related homeobox 2
ATF4 100599_at activating transcription factor 4
STAT5B 100422_i_at signal transducer and activation of
 transcription 5A
HEY1 95671_at hairy/enhancer-of-split related with YRPW
 motif 1
ATF5 103006_at activating transcription factor 5
C/EBP 98447_at CCAAT/enhancer binding protein
RIP140 103288_at nuclear receptor interacting protein 1
CRTR1 103761_at Tcfcp2-related transcriptional repressor 1
MEF2A 93852_at myocyte enhancer factor 2A
TIS11 92830_s_at zinc finger protein 36
STAT5B 100423_f_at signal transducer and activation of
 transcription 5A
ATF3 104155_f_at activating transcription factor 3
CART1 100005_at TNF receptor associated factor 4
JUNB 102362_i_at transcription factor junB

Figure 5A--RNA synthesis

SFPQ 99621_s_at splicing factor proline/glutamate rich
 (polypyrimidine tract binding protein
 associated)
U2AF1 97486_at U2 small nuclear ribonucleoprotein
 auxiliary factor (U2AF), 35 kDa
RBMXP1 160192_at RNA binding motif protein, X chromosome
 retrogene
DDX21 94361_at DEAD/H (Asp-Glu-Ala-Asp/His) box
 polypeptide 21 (RNA helicase II/Gu)
DDX3 101542_f_at DEAD (aspartate-glutamate-alanine-
 aspartate) box polypeptide 3
NSAP1 94985_at NS1-associated protein 1
MKI67 bp 93342_at Mki67 (FHA domain) interacting nucleolar
 phosphoprotein
ELAVL1 94001_at ELAV (embryonic lethal, abnormal vision,
 Drosophila)-like 1 (Hu antigen R)
PSP1 103393_at paraspeckle protein 1
SRP20 101003_at splicing factor, arginine/serine-rich 3
 (SRp20)
JKTBP 96084_at heterogeneous nuclear ribonucleoprotein
 D-like
RPA2 92225_f_at RNA polymerase 1-2 (128 kDa subunit)
RALY 98511_at hnRNP-associated with lethal yellow
SFRS10 95791_s_t splicing factor, arginine/serine-rich 10
FBL 160503_at fibrillarin
SNRPA1 101506_at small nuclear ribonucleoprotein
 polypeptide A'
TASR 98048_at neural-salient serine/arginine-rich
RPB10 93551_at RNA polymerase II subunit 10
AUF1 94303_at heterogeneous nuclear ribonucleoprotein D
HRMT1L2 96696_at heterogeneous nuclear ribonucleoproteins
 methyltransferase-like 2
CGI-110 95714_at pre-mRNA branch site protein p14
SMN 103620_s_at survival motor neuron
RPB8 97254_at RNA binding motif protein
RNPSI 93518_at ribonucleic acid binding protein S1
NCL 160521_at nucleolin
RPA1 93620_at RNA polymerase 1-4 (194 kDa subunit)
HNRPA2B1 93118_at heterogeneous nuclear ribonucleoprotein
 A2/B1
SNRPD1 100577_at small nuclear ribonucleoprotein D1
H/ALAsnRNP 97824_at nucleolar protein family A, member 2
TAF10 103910_at TAFII30
DDX24 99096_at DEAD/H (Asp-Glu-Ala-Asp/His) box
 polypeptide 13 (RNA helicase A)
Figure 5B
MAD4 99024_at Max dimerization protein 4
EZH1 100486_at enhancer of zeste homolog 1 (Drosophila)
HDA1 104376_at histone deacetylase 5
AUH 96650_at AU RNA binding protein/enoyl-coenzyme A
 hydratase
TGIF 101502_at TG interacting factor

Figure 5C--Nuclear import/export

POM121 96174_at nuclear pore membrane protein 121
NXF1 101079_at nuclear RNA export factor 1 homolog (S.
 cerevisiae)
IMPORTINa3 96010_at karyopherin (importin) alpha 3
RAE1 160466_at RNA export 1 homolog (S. pombe)
IMPORTINa2 92790_at karyopherin (importin) alpha 2
G38P2 94913_at Ras-GTPase-activating protein (GAP120)
 SH3-domain binding protein 2
Figure 5D--Protein translation

elF3S7 99101_at eukaryotic translation initiation factor 3,
 subunit 7 (zeta, 66/67kDa)
elF2B 160365_at eukaryotic translation initiation factor 2,
 subunit 2 (beta, 38kDa)
elF3S4 96883_at eukaryotic translation initiation factor 3,
 subunit 4 (delta, 44kDa)
EBNA1-bp2 96297_at EBNA1 binding protein 2
GLNRS 96628_at glutamyl-prolyl-tRNA synthetase
NAT1 100535_at eukaryotic translation initiation factor 4,
 gamma 2
elF3S9 93973_at eukaryotic translation initiation factor 3,
 subunit 9
RPS18b 95159_at ribosomal protein S18b
VALRS 97894_at valyl-tRNA synthetase 2
RPL12 160431_at mitochondrial ribosomal protein L12
eIF1A 93058_at eukaryotic translation initiation factor 1A
eRF1 160451_at translation releasing factor eRFI
eIF1A 103708_at eukaryotic translation initiation factor 1A
eIF6 94826_at integrin beta 4 binding protein
eRF1 98608_at translation releasing factor eRFI
RPL20 94875_at mitochondrial ribosomal protein L20
PHERS 94494_at phenylalanine-tRNA synthetase-like
ASNS 95133_at asparagine synthetase
eIF3S10 94250_at eukaryotic translation initiation factor 3
NOP56 95109_at nucleolar protein 5A
eIF2AS1 94253_at eukaryotic translation initiation factor 2A
RRS1 96778_at regulator for ribosome resistance homolog
 (S. cerevisiae)
eRF1 96755_at translation releasing factor eRFI
eRF1 96754_s_at translation releasing factor eRFI
SUI1 92855_at suppressor of initiator codon mutations,
 related sequence 1 (S. cerevisiae)
RPL11 98876_at mitochondrial ribosomal protein L11
RPL52 97443_at mitochondrial ribosomal protein L52

Figure 5E--Protein folding

CCT3 98153_at chaperonin subunit 3 (gamma)
FKBP4 92808_f_at FK506 binding protein 4 (59 kDa)
CCT7 160562_at chaperonin subunit 7 (eta)
PPID 97445_at peptidylprolyl isomerase D (cyclophilin D)
CCT10 92829_at heat shock 10 kDa protein 1 (chaperonin 10)
CCT8 160102_at chaperonin subunit 8 (theta)
CCT6A 162279_f_at chaperonin subunit 6a (zeta)
CCT3 161238_f_at chaperonin subunit 3 (gamma)

Figure 5F--Protein degradation

PAD1 97274_at 26S proteasome-associated pad1 homolog
PSMB5 101558_s_at proteasome (prosome, macropain) subunit,
 beta type 5
PSMD4 94302_at proteasome (prosome, macropain) 26S
 subunit, non-ATPase, 4
PSMB3 94025_at proteasome (prosome, macropain)
 subunit, beta type 3
SUG1 160534_at protease (prosome, macropain) 26S subunit,
 ATPase 5
PSMB6 101992_at proteasome (prosome, macropain) subunit,
 beta type 6
PSMB2 94219_at proteasome (prosome, macropain) subunit,
 beta type 2
Figure 6B--DNA replication and cell division

SAKB 98996_at serine/threonine kinase 18
RRM2 102001_at ribonucleotide reductase M2
CAF1 p60 100890_at chromatin assembly factor, p60 subunit
ORC6 95712_at origin recognition complex, subunit 6-like
 (S. cerevisiae)
PCNA 101065_at proliferating cell nuclear antigen
MCM2 93112_at mini chromosome maintenance deficient 2
CDC6 103821_at cell division cycle 6 homolog (S.
 cerevisiae)
MCM4 93041_at mini chromosome maintenance deficient 4
 homolog
MCM3 160496_s_at mini chromosome maintenance deficient (S.
 cerevisiae)
MCM3 100062_at mini chromosome maintenance deficient (S.
 cerevisiae)
TOPB1 103071_at topoisomerase (DNA) II binding protein
CHK1 103064_at checkpoint kinase 1 homolog (S. pombe)
MCM5 100156_at mini chromosome maintenance deficient 5
CKSI 97468_at CDC28 protein kinase 1
ORC1 92458_at origin recognition complex, subunit 1-like
 (S. cerevisiae)
RRM1 100612_at ribonucleotide reductase M1
FEN1 97327_at flap structure specific endonuclease 1
GEMININ 160069_at geminin
E2F1 102963_at E2F transcription factor 1
PLK1 93099_f_at polo-like kinase homolog (Drosophila)
CCNB1 160159_at cyclin B1, related sequence 1

Figure 6C--Cell-cycle regulators

CCND1 94232_at cyclin D1
CDC34 94048_at cell division cycle 34 homolog
KIP2 95471_at cyclin-dependent kinase inhibitor 1C (P57)
CCNG2 98478_at cyclin G2
KIP1 161010_r_at cyclin-dependent kinase inhibitor (p27)
CCNI 94819_f_at cyclin I

Figure 6D--Apoptosis

CASP2 99049_at caspase 2
NIX 96255_at BCL2/adenovirus E1B 19 kDa-interacting
 protein 3-like
APR3 160271_at apoptosis related protein APR3
TNFSF12 93917_at tumor necrosis factor (ligand) superfamily,
 member 12
PDCD4 103029_at programmed cell death 4
MIAP2 102734_at baculoviral IAP repeat-containing 3
MTD 98031_at Bcl-2-related ovarian killer protein
SDNSF 97451_at neural stem cell derived neuronal survival
 protein
DAD1 96008_at defender against Apoptotic Death 1
AAC11 101035_at apoptosis inhibitor 5
BAG3 96157_at Bcl2-associated athanogene 3
BAG2 161129_r_at similar to BAG-family molecular chaperone
 regulator-2
Figure 7A--Cytoarchitecture

MDEG2 99910_at amiloride-sensitive cation channel 1,
 neuronal (degenerin)
MAT8 103059_at FXYD domain-containing ion transport
 regulator 3
CLCA3 162287_r_at chloride channel calcium activated 3
CD133 93389_at prominin
CD133 93390_g_at prominin
PIGF 104725_at ras-like protein
DSG2 104480_at desmoglein 2
MAN2B1 99562_at mannosidase 2, alpha B1
CLDN4 101410_at claudin 4
CLDN7 99561_f_at claudin 7
SPRR2E 100723_f_at small proline-rich protein 2E
SPRR2J 101755_f_at small proline-rich protein 2J
SPRR2A 101025_f_at small proline-rich protein 2A
TROP2 103648_at tumor-associated calcium signal
 transducer 2
SPRR21 95794_f_at small proline-rich protein 21
SPRR2C 101761_f_at small proline-rich protein 2C
SPRR2A 101024_i_at small proline-rich protein 2A
LRG 97420_at leucine-rich alpha-2-glycoprotein
TROP2 160651_at tumor-associated calcium signal
 transducer 2
SPRR2G 101754_f_at small proline-rich protein 2G
SPRR2F 94120_s_at small proline-rich protein 2F
BGP1 102805_at CEA-related cell adhesion molecule 1
BGP1 102804_at CEA-related cell adhesion molecule 1
BGP1 102806_g_at CEA-related cell adhesion molecule 1
BGP2 101908_s_at CEA-related cell adhesion molecule 2
CX26 98423_at connexin 26
MUC1 102918_at mucin 1, transmembrane
SPP1 97519_at secreted phosphoprotein 1
CLU 161294_f_at clusterin
CLU 95286_at clusterin
CFTR 94757_at cystic fibrosis transmembrane conductance
 regulator homolog
KRT19 92550_at keratin complex 1, acidic, gene 19
KRT19 102121_f_at keratin complex 1, acidic, gene 19
SPRR1A 160909_at small proline-rich protein 1A
GALNT3 162313_f_at UDP-N-acetyl-alpha-D-
 galactosamine:polypeptide
 N-acetylgalactosaminyltransferase 3
Figure 7B--Defense responses

PLGR 99926_at polyimmunoglobulin receptor
CTSL 101963_at cathepsin L
LAMPI 100136_at lysosomal membrane glycoprotein 2
CTSS 98543_at cathepsin S
GST01 97819_at glutathione S-transferase omega 1
GSTT2 104603_at glutathione S-transferase, theta 2
CTSH 94834_at cathepsin H
UGT1A1 99580_s_at UDP glycosyltransferase 1 family,
 polypeptide A6
CD14 98088_at CD14 antigen
LGALS3 95706_at lectin, galactose binding, soluble 3
PGLYRP 104099_at peptidoglycan recognition protein
LGMN 93261_at legumain
GARG16 100981_at interferon-induced protein with
 tetratricopeptide repeats
H2D1 99378_f_at MHC beta-2-microglobulin
ISGFG3 103634_at interferon dependent positive acting
 transcription factor 3 gamma
H2Q1 101886_f_at histocompatibility 2, D region locus 1
LYZP 101753_s_at P lysozyme structural
LYZM 100611_at lysozyme M
MLGP85 101389_at scavenger receptor class B, member 2
H2D1 97540_f_at histocompatibility 2, D region locus 1
CD68 103016_s_at CD68 antigen
LY6A 93078_at lymphocyte antigen 6 complex, locus A
MX1 98417_at myxovirus (influenza virus) resistance 1

Figure 7C--Chemoattractant cytokines

MCP3 94761_at monocyte chemoattractant protein 3
MCP1 102736_at platelet-derived growth factor-inducible
 protein JE
EOTAXIN 92742_at small inducible cytokine al l
Figure 7D--Complement
CF1 99927_at complement component factor i
C3 93497_at complement component 3
CFH-related 92291_f_at complement component factor-related
C2 103673_at complement component 2 (within H-2S)
CFH-related 101853_f_at complement component factor h
C1QA 98562_at complement component 1, q subcomponent,
 alpha polypeptide
C1QB 96020_at complement component 1, q subcomponent,
 beta polypeptide
C4 103033_at complement component 4 (within H-2S)
C1QC 92223_at complement component 1, q subcomponent, c
 polypeptide
CFH-related 94743_f_at complement component factor-related

Figure 7E--Iron homeostasis

CP 92851_at ceruloplasmin
LTF 101115_at lactotransferrin
LCN2 160564_at lipocalin 2/24p3 gene.

Figure 8B

ETS2 94246_at E26 avian leukemia oncogene 2, 3' domain
ATF3 104155_f_at activating transcription factor 3
JUN 100130_at Jun oncogene
JUNB 102362_i_at transcription factor junB
FOS 160901_at c-fos oncogene
FOSB 103990_at FBJ osteosarcoma oncogene B
ATF5 103006_at activating transcription factor 5
ATF4 100599_at activating transcription factor 4
SPRR21 95794_f_at small proline-rich protein 2I
SPRR2C 101761_f_at small proline-rich protein 2C
SPRR2G 101754_f_at small proline-rich protein 2G
SPRR2J 101755_f_at small proline-rich protein 2J
SPRR2A 101025_f_at small proline-rich protein 2A
SPRR2F 94120_s_at small proline-rich protein 2F
SPRR2E 100723_f_at small proline-rich protein 2E
SPRR1A 160909_at small proline-rich protein 1A

(a) Gene annotations were derived by interrogation of the NetAffx (Liu
et al. 2003) database; http://www.affymetrix.com/analysis/index.affx
and by homology searching of nucleotide sequence databases (BLASTn;
http://www.ncbi.nih.gov/BLAST/) using Affymetrix probe target
sequences.

Table 1. Quantitative histologic analysis of mitotic
figures in uterine cells after exposure to [E.sub.2] for 8,
24, 48, and 72 hr. (a)

 Mitosis/m[m.sup.2] (mean [+ or -] SD)

Time (hr) AO (5 mL) [E.sub.2] (400 [micro]g)

8 1.36 [+ or -] 1.81 0.51 [+ or -] 0.41
24 3.86 [+ or -] 5.05 25.15 [+ or -] 6.37 **
48 3.81 [+ or -] 0.83 3.46 [+ or -] 3.26
72 3.88 [+ or -] 2.28 1.67 [+ or -] 1.77

Quantitative mitotic index data were derived from four
animals per group.

(a) Data were assessed for statistical significance using
ANOVA and a two-sided Student t-test (see "Materials
and Methods"I. **p < 0.01.


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Jonathan G. Moggs, (1) Helen Tinwell, (1) Tracey Spurway, (1) Hur-Song Chang, (2) * Ian Pate, (1) Fei Ling Lim, (1) David J. Moore, (1) Anthony Soames, (1) Ruth Stuckey, (1) Richard Currie, (1) Tong Zhu, (2) Ian Kimber, (1) John Ashby, (1) and George Orphanides (1)

(1) Syngenta Central Toxicology Laboratory, Alderley Park, Cheshire, United Kingdom; (2) Syngenta Biotechnology Inc., Research Triangle Park, North Carolina, USA

Address correspondence to G. Orphanides, Syngenta CTL, Alderley Park, Cheshire, SK10 4TJ, UK. Telephone: 44-1625-510803. Fax: 44-1625-585715. E-mail: george.orphanides@syngenta.com

* Present address: Diversa Corporation, 4955 Directors Place, San Diego, CA 92121 USA.

Supplemental data is available online (http:// ehp.niehs.nih.gov/txg/members/2004/7345/ supplemental.pdf

We thank M.G. Parker, D.G. Deavall, N. Wallis, and T. Barlow for critical comments on the manuscript; P. Lefevre and J. Odum for technical assistance; and I. Kupershmidt and E. Hunter (Silicon Genetics) for advice on statistical analysis of microarray data.

This work was partially supported by the UK Food Standards Agency.

The authors declare they have no competing financial interests.

Received 22 June 2004; accepted 7 October 2004.
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Title Annotation:Toxicogenomics
Author:Orphanides, George
Publication:Environmental Health Perspectives
Geographic Code:1USA
Date:Nov 15, 2004
Words:11045
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