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Molecular Phenotyping of Human Endometrium Distinguishes Menstrual Cycle Phases and Underlying Biological Processes in Normo-Ovulatory Women

Departments of Obstetrics and Gynecology (S.Ta., A.E.H., K.C.V., S.Tu., M.T.O., C.D., N.L.S., C.N.N., N.R.N., L.C.G.) and Pathology (R.K.), Stanford University, Stanford, California 94305; and Department of Obstetrics and Gynecology (B.A.L.), Greenville Hospital System, Greenville, South Carolina 29605

Address all correspondence and requests for reprints to: Linda C. Giudice, M.D., Ph.D., Professor and Chair, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, Parnassus, M1495, Box 0132, San Francisco, California 94143-0132. E-mail: giudice{at}obgyn.ucsf.edu.

	Abstract

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Histological evaluation of endometrium has been the gold standard for clinical diagnosis and management of women with endometrial disorders. However, several recent studies have questioned the accuracy and utility of such evaluation, mainly because of significant intra- and interobserver variations in histological interpretation. To examine the possibility that biochemical or molecular signatures of endometrium may prove to be more useful, we have investigated whole-genome molecular phenotyping (54,600 genes and expressed sequence tags) of this tissue sampled across the cycle in 28 normo-ovulatory women, using high-density oligonucleotide microarrays. Unsupervised principal component analysis of all samples revealed that samples self-cluster into four groups consistent with histological phenotypes of proliferative (PE), early-secretory (ESE), mid-secretory (MSE), and late-secretory (LSE) endometrium. Independent hierarchical clustering analysis revealed equivalent results, with two major dendrogram branches corresponding to PE/ESE and MSE/LSE and sub-branching into the four respective phases with heterogeneity among samples within each sub-branch. K-means clustering of genes revealed four major patterns of gene expression (high in PE, high in ESE, high in MSE, and high in LSE), and gene ontology analysis of these clusters demonstrated cycle-phase-specific biological processes and molecular functions. Six samples with ambiguous histology were identically assignable to a cycle phase by both principal component analysis and hierarchical clustering. Additionally, pairwise comparisons of relative gene expression across the cycle revealed genes/families that clearly distinguish the transitions of PEESE, ESEMSE, and MSELSE, including receptomes and signaling pathways. Select genes were validated by quantitative RT-PCR. Overall, the results demonstrate that endometrial samples obtained by two different sampling techniques (biopsy and curetting hysterectomy specimens) from subjects who are as normal as possible in a human study and including those with unknown histology, can be classified by their molecular signatures and correspond to known phases of the menstrual cycle with identical results using two independent analytical methods. Also, the results enable global identification of biological processes and molecular mechanisms that occur dynamically in the endometrium in the changing steroid hormone milieu across the menstrual cycle in normo-ovulatory women. The results underscore the potential of gene expression profiling for developing molecular diagnostics of endometrial normalcy and abnormalities and identifying molecular targets for therapeutic purposes in endometrial disorders.

	Introduction

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HUMAN ENDOMETRIUM, the anatomic prerequisite for establishing and sustaining pregnancy, undergoes remarkable histological and structural changes throughout the menstrual cycle, in preparation for embryonic implantation and subsequent shedding and regeneration in nonconception cycles (1). Different histological appearances of this tissue were first described in 1908 by Hitschmann and Adler (2), and since the classic manuscript by Noyes et al. (3) in 1950, it has been appreciated that endometrial histology directly correlates with changes in the circulating ovarian-derived hormones estradiol (E₂) and progesterone (P) (4, 5). Based on an ideal 28-d cycle, with cycle d 1 being the first day of menstrual flow and cycle d 14 the day of ovulation, Noyes and colleagues (3, 6) identified, in nearly 8000 endometrial biopsies, different histological appearances (phases), which now are appreciated as menstrual; postmenstrual repair; early-, mid-, and late-proliferative; interval; and early-, mid-, and late-secretory. Histological examination has been the gold standard for clinical evaluation of the endometrium (3, 6), including from which phase the tissue derives, whether ovulation has occurred, how many days postovulation the tissue is or is expected to be (dating the endometrium), the effects of exogenous estrogens and progestins, and detection of polyps and hyperplastic and/or neoplastic cells. In the proliferative phase, under the influence of E₂, there are numerous cellular mitoses and tissue growth (approximately 10 mm over a 2-wk period) (1, 7, 8). The secretory phase is heralded, under the influence of P, by epithelial (glandular) secretion, stromal edema, and stromal cell differentiation (decidualization). In addition, there is extensive angiogenesis in the proliferative phase, coiling of the spiral arteries in the secretory phase, and an influx of leukocytes in the latter part of the mid-secretory phase and during the late-secretory phase (1, 7). In the menstrual phase, there is cellular apoptosis and extensive tissue breakdown. Although many of the biological processes and molecular participants in these events have been elucidated, a complete understanding has yet to be achieved on a global scale and for transitioning from phase to phase.

Recently, the usefulness of histological dating of the endometrium for couples with infertility has been questioned because histological delay in endometrial maturation fails to discriminate between fertile and infertile couples (9). In another recent study (10), histological features fail to distinguish, reliably, specific menstrual cycle days or narrow intervals of days, leading to the conclusion that histological dating has neither the accuracy nor the precision to be useful in clinical management. Because histology has equivocal value in patient evaluation and management, the question arises as to whether molecular profiles of endometrium may distinguish among the phases of the cycle, define uterine receptivity to implantation, and identify a variety of endometrial disorders not apparent from histological evaluation. In the current study, we have investigated global gene expression profiling across the menstrual cycle in normo-ovulatory women with the goals of determining whether molecular profiles of human endometrium can distinguish among the phases of the menstrual cycle laying the foundation to identify endometrial disorders that may not be detectable with classical histological assessment. The data demonstrate specific patterns of gene expression that are characteristic of the different phases of the cycle, and they identify biological processes, molecular functions, and structural cellular participants in the dynamic histological events occurring across the cycle. Importantly, the gene expression patterns and molecular signatures that are pathognomonic of a particular cycle phase have enabled classification of samples of unknown, equivocal, or ambiguous histology, thus heralding a new era of molecular diagnostics and targeted therapeutics for endometrial disorders.

	Materials and Methods

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Sample collection and processing
Endometrial samples (n = 45) were obtained from normally cycling women undergoing hysterectomy or endometrial biopsy after written informed consent, under an approved protocol by the Stanford University Committee on the Use of Human Subjects in Medical Research. Samples were also obtained under an approved protocol, after written informed consent, at the University of North Carolina, Chapel Hill, and the Center for Women’s Medicine at Greenville Hospital. Of these 45 samples, 22 eventually were of adequate amount, yielded high-quality RNA, and had histological diagnoses agreed upon by two or more pathologists. These samples comprise the well-characterized samples used for microarray analysis and are listed in Table 1. All subjects of the well-characterized samples were normo-ovulatory with regular cycles (24–35 d), had not been on steroid hormone medications within 3 months of endometrial sampling, and were between the ages of 23 and 50 yr old (median age, 42 yr; mean ± SEM = 40 ± 6.6 yr old). Indications for the operative procedures (n = 14 hysterectomies; n = 8 endometrial biopsies) in which samples were obtained included uterine prolapse (n = 4), uterine leiomyomata (fibroids, diameters ranging from 0.5–13 cm, median of 3 cm, not submucosal) (n = 9), normal volunteers (n = 7), pelvic pain (n = 1), and ovarian cyst (n = 1). None of the subjects had endometriosis (laparoscopy proven), and pathology reports revealed no inflammation within the endometrium in any of the specimens. Samples were collected using a Pipelle catheter or curetting the endometrium from hysterectomy specimens. They were kept at room temperature and transported to the laboratory in 1x PBS. Portions of tissues were saved in 10% formalin for hematoxylin and eosin staining and histological evaluation, and portions were also snap frozen in liquid nitrogen for RNA isolation. Tissue specimens were examined blindly by up to four independent pathologists, and phases were assigned according to the criteria of Noyes et al. (3). Dating in the secretory phase was within a 2-d cycle window. Of the well-characterized samples, five were defined as mid-late proliferative phase, three as early secretory, eight as mid secretory, and six as late secretory, for a total of 22 sufficiently dated samples (agreement between at least two pathologists) (Table 1). Of the remaining 23 samples, one was appropriately assessed histologically and was used as part of the validation studies but not for microarray processing. Six samples had ambiguous interpretations (defined herein when two or more pathologists gave different histological interpretations of the same sample or when a sample was evaluated histologically by only one pathologist) (Table 2) and were processed for microarray analysis. Sixteen were excluded from further study on the basis of patient medical history or poor sample quality.

Data analysis
Microarray gene expression data analysis.
The intensity values of different probe sets (genes) generated by Affymetrix GeneChip Operating Software were imported into GeneSpring version 7.2 software (Silicon Genetics, Redwood City, CA) for data analysis. The data files (CEL files) containing the probe level intensities were processed using the robust multiarray analysis algorithm (GeneSpring) for background adjustment, normalization, and log₂-transformation of perfect match values (11). Subsequently, the data were subjected to per-chip and per-gene normalization using GeneSpring normalization algorithms. The normalized data were then subjected to a series of pairwise comparisons that included all 22 well-characterized specimens. Comparisons were made between successive cycle phases: early-secretory endometrium (ESE) vs. proliferative endometrium (PE); mid-secretory endometrium (MSE) vs. ESE; and late-secretory endometrium (LSE) vs. MSE. The resulting gene lists generated from each pairwise comparison included only the genes that had a fold change value of 1.5 or higher and a P value of less than 0.05 by a one-way ANOVA parametric test and a Benjamini-Hochberg multiple testing correction for false discovery rate. Individual pairwise comparison gene lists were combined to produce a list of all of the genes that are differentially expressed by microarray analysis. This combined gene list includes 7231 genes and ESTs (4135 genes and 3096 ESTs).

To verify the GeneSpring analysis, significant changes in differentially expressed genes were evaluated using a second, independent method, namely, statistical analysis of microarrays (SAM) (12). This was performed for a select pairwise comparison group (LSE vs. MSE). Normalized and log-transformed gene expression data were analyzed using the GeneSpring implementation of the SAM algorithm. The SAM parameters were set as follows to analyze two classes of unpaired data (LSE vs. MSE) using cycle phases as the parameter; 1000 permutations were performed with a false discovery rate less than 0.05. The resulting gene list was filtered for a fold change of 1.5 or higher.

Principal component analysis (PCA).
PCA is an unsupervised pattern recognition and visualization tool used to analyze large amounts of data derived from array gene expression analysis (13). It displays a multidimensional data set in a reduced dimensionality (three dimensions) to capture as much of the variation in the data as possible, allowing its summarization and further analysis. Each dimension represents a component to which a certain percentage of variance in the data is attributed. We applied the unbiased PCA algorithm in GeneSpring to all 28 samples across the menstrual cycle, using all 54,600 genes and ESTs on the HG U133 Plus 2.0 chip to look for similar expression patterns and underlying cluster structures.

Hierarchical clustering.
Hierarchical clustering is an unsupervised way of grouping samples based only on their gene expression similarities (14). Herein, we conducted hierarchical cluster analysis of differentially expressed genes from all pairwise comparisons (the combined gene list) using the smooth correlation for distance measure algorithm (GeneSpring) to identify samples with similar patterns of gene expression. (This approach differs from that which we used for PCA in that for the latter, all of the genes on the array were used, whereas in the hierarchical clustering analysis, the pairwise comparisons-derived gene list was used.) In addition, the six ambiguous samples were included in the clustering analysis to interrogate in which cluster these samples would fall. The output data are displayed graphically as a Heatmap, based on the measured intensity values of the genes in the gene list (above) and are represented as a hierarchical tree with branches to indicate the relationships among different groups.

K-means analysis.
The differentially expressed genes from all pairwise comparisons (combined gene list) were also subjected to K-means analysis (15) using GeneSpring software. This analysis was used to detect trends of gene expression during different stages of the menstrual cycle. Kinetic patterns of gene expression were analyzed across the cycle in PE, ESE, MSE, and LSE. K-means was applied to the data using the smooth correlation of distance measure algorithm (GeneSpring), because this algorithm was developed specifically for time-dependant samples and it allows clear separation of gene expression profiles. Four cluster groups (A, B, C, and D) were the optimal number derived from the analysis, in that all genes were distributed among the four clusters, reflecting a mapping of gene expression profiles to the four endometrial cycle phases, PE, ESE, MSE, and LSE. This optimal clustering allowed all genes to be classified into these clusters, and no genes were unclassified (i.e. did not fit into any of the clusters). Gene expression values of members of each cluster group were averaged to show one profile for graphic representation of each cluster group (see Results).

Gene ontologies (GO) classification.
A web-based tool, GO tree machine (GOTM) (16), was used to interpret biological, molecular, and cellular functions of genes identified by both pairwise comparisons and K-means analyses in different phases of the menstrual cycle. GOTM uses GO hierarchies to discover significant biological processes, molecular functions, and cellular components in a gene list. GOTM also implements a statistical analysis of the GO categories for the input gene list and suggests biological areas that warrant further study (17). First, the differentially expressed genes are classified by their corresponding GO categories, and the observed number of genes in each of these GO categories is recorded. Genes represented on the HG U133 Plus 2.0 Affymetrix chip comprise the reference gene list. The expected number of genes in each GO category corresponds to the number of genes falling into that GO category in the reference gene list. A given GO category is considered enriched when the observed number of genes in that category is greater than the expected number.

Microarray validation by real-time PCR
Using RNA isolated as described above, cDNA was generated from 1 µg total RNA from each sample using the Omniscript reverse transcription kit (QIAGEN) with a 1:1 ratio of oligo-(dT)_16–18 (Invitrogen) and random hexamers (Invitrogen). Real-time PCR was performed in triplicate in 25-µl reactions using the QuantiTect SYBR Green PCR kit (QIAGEN), according to the manufacturer’s instructions. The template cDNA was diluted from the reverse-transcribed product by 4-fold for use in the PCR. The housekeeping gene ribosomal protein L19 was used as normalizer gene, because it displayed the lowest variation in expression levels as judged by microarray analysis and the smallest SD in threshold cycle (Ct) values when compared with other known normalizer genes (ß-actin, glyceraldehyde-3-phosphate dehydrogenase, ß₂-microglobulin, and ubiquitin) (Hamilton, A., S. Talbi, M. Nyegaard, M. Overgaard, and L. Giudice, unpublished data). Most PCR primers were designed to be intron spanning to serve as a control for contamination of genomic DNA and to have an optimal amplicon range of 150–260 bp (Table 3). For the primer pairs that were not intron spanning, control templates with no reverse transcriptase enzyme were run for each sample. PCR were run using the Mx4000 Q-PCR system (Stratagene, La Jolla, CA). The thermal cycling conditions included an initial activation step at 95 C for 15 min, followed by 40 cycles of denaturation, annealing, and elongation (94 C for 30 sec, 57–60 C for 30 sec, and 72 C for 30 sec, respectively). Fluorescence data collection was performed during the annealing and elongation steps. PCR products were analyzed by thermal dissociation (55–95 C) with a fluorescence measurement at every 1 C increment to ensure the production of a single PCR product. For the normalizer and each gene of interest, a standard curve was generated using serial dilutions of template DNA. The template for each gene of interest standard curve was chosen based on the highest levels of overall expression during the menstrual cycle. The dilutions of template used were 1:4, 1:16, 1:64, and 1:256. The efficiencies of amplification (EFF) for each gene were calculated by the following equation: EFF = 10[–1/slope] x –1. Genes with standard curves that showed approximately 100% amplification efficiency (± 5%) were used for subsequent sample analyses. The relative expression ratio (R) was calculated based on the corresponding efficiencies of amplification for each treatment compared with the control and the differences in Ct values (Ct) using the following mathematical model:

(1)

where R represents the ratio at which a given gene of interest (GOI) is expressed in the treatment group, relative to the control group, when normalized by ribosomal protein L19. Ct values were calculated by the Mx4000 software based on fluorescence intensity values after normalization with an internal reference dye and baseline correction. Pairwise comparisons were performed between treatment vs. control: ESE vs. PE, MSE vs. ESE, and LSE vs. MSE. All validation experiments were performed using n = 3 subject samples in each group. Statistical analysis of the PCR data were performed using the relative expression software tool (REST) that employs a pairwise fixed reallocation and randomization test to determine significance (18).

	Results

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View larger version (24K): [in this window] [in a new window]   FIG. 1. PCA of human endometrium across the menstrual cycle. PCA was applied to 28 endometrium samples (22 well-characterized and six ambiguous) that were characterized by the gene expression of all probes on the Affymetrix chip HG U133 Plus 2.0. Numbers refer to individual sample labels. Abbreviations designate PE (P), ESE (ES), MSE (MS), and LSE (LS). The ambiguous samples are labeled AMB. The x-axis refers to PCA component 1, with 22.37% variance. The y-axis refers to the PCA component 2, with 17.12% variance, and the z-axis refers to PCA component 3 with 10.28% variance.

View larger version (36K): [in this window] [in a new window]   FIG. 2. Gene expression profiling across the menstrual cycle. A, Heatmap representation of relative expression levels of genes in the endometrial samples across the menstrual cycle. Each horizontal line represents a single gene and each column represents a single sample. Samples cluster according to their cycle phase (bar at bottom of the Heatmap: proliferative (red), early-secretory (light blue), mid-secretory (olive), late secretory (purple), and ambiguous sample (dark blue). The relative expression of each gene is color-coded as high (red) or low (blue) (color legend on the right). B, The left panel represents Heatmap clusters of gene expression (A, B, C, and D), determined by K-means classification. Cluster A contains 1436 genes and 1034 ESTs. Cluster B contains 869 genes and 732 ESTs. Cluster C contains 1044 genes and 814 ESTs. Cluster D contains 786 genes and 516 ESTs. The right panel shows the average gene expression profile for each cluster.

K-means analysis, cluster groups, and gene expression patterns.
K-means clustering is a method that identifies a set of genes whose expression is regulated in a similar way across different experimental conditions. This is particularly useful in identifying genes with similar regulation across the menstrual cycle. K-means clustering applied to differentially expressed genes across the endometrial cycle phases revealed four major groups (A, B, C, and D) (Fig. 2B, left). These groups distinguish themselves by the following characteristics: cluster A, high expression of genes in samples that cluster as PE and low expression in the rest of the cycle; cluster B, high expression of genes in samples identified as ESE and low expression in PE, MSE, and LSE; cluster C, high expression of genes in MSE; and cluster D, high expression in LSE compared with the rest of the cycle. Figure 2B (right) illustrates the K-means analysis results (average gene expression profile for each cluster) that led to these clusters. These are described in more detail in the next section with regard to GO, biological processes, molecular functions, and cellular localization of events.

Cluster group GO
Cluster A.
GO analysis (Table 4) (complete GO tables are published as supplemental data on The Endocrine Society’s Journals Online web site at http://endo.endojournals.org) revealed high expression of genes in the proliferative phase that are involved in cell adhesion, cell-cell signaling, cell cycle regulation, and cell division (e.g. DNA replication, strand elongation, DNA metabolism, chromatin cycle and segregation, mitosis, G1/S and G2M transitions, and response to DNA damage). Other genes/families included in cluster A include collagen metabolism and extracellular matrix regulation, signal transduction, development, and regulation of enzyme and ion channels. Molecular functions highly represented in this cluster group are those related to cell cycle regulation and DNA synthesis, steroid binding, receptor-mediated events, and extracellular matrix structural constituents. The cellular components involved in the proliferative phase of the menstrual cycle include primarily the chromosomes, the replication fork, DNA polymerase/primer complexes, microtubule skeleton, the extracellular matrix, non-membrane-bound organelles, and collagen. These processes are highly consistent with the cell replication and growth of the tissue in the proliferative phase, extracellular matrix remodeling that occurs with this growth, and steroid hormone action and subsequent signaling to effect these processes.

Cluster C.
Genes in cluster C have high expression in the mid-secretory phase. In MSE (Table 4), these include genes for cell communication, intracellular signaling cascades, negative regulation of cell proliferation, cell ion homeostasis, metabolism and synthesis of amino acids, organic acids, and polysaccharides, and the first appearance of genes involved in the immune response and in response to chemicals, ions, wounding, and stress. Consistent with these biological processes are molecular functions of glutathione and metallothionein activities, a variety of protein and enzyme binding, glycoprotein biosynthesis (acetyl-glucosaminyl transferase activity), inhibition of protease activities, and a variety of transporters. These processes occur in the plasma membrane and other cellular membranes, in the cytoplasm, cell-cell adherens junctions, the basement membrane, and the extracellular region.

Cluster D.
In cluster D, the following biological processes are represented in the late-secretory phase (Table 4): cell motility and communication, cell adhesion and interactions with the matrix, signal transduction and intracellular signaling cascades including the MAPK cascade, regulation of the cell cycle with the primary process of cell cycle arrest and negative regulation of cell proliferation, cellular physiological processes, and cell activation as well as apoptosis, cleavage of cytoskeletal and extracellular matrix proteins, and icosanoid biosynthesis. In addition, genes are highly expressed that regulate endocytosis, phagocytosis, blood coagulation/hemostasis, and the immune response [including regulation of lymphocyte and T-cell differentiation and activation, cellular and humoral immune responses, antimicrobial humoral response, and natural killer (NK) cell activity] as well as responses to inflammation, pathogens, chemicals and xenobiotics, and wounding. In addition, chemotaxis and wound healing are represented in this cluster. Molecular functions include carbohydrate, protein, cytokine, and Ig binding, IL-1 receptor and TGFß receptor binding, protease and protease inhibitor activity regulation, cyclin-dependent protein kinase inhibitory activity, signal transduction activity, icosanoid and prostanoid receptor activity, thromboxane receptor activity, and hematopoietic/interferon-class cytokine receptor activity (for details, see the supplemental data published on The Endocrine Society’s Journals Online web site at http://endo.endojournals.org). These processes occur primarily at the plasma membrane, vacuoles, lysosomes, and the extracellular matrix, and are consistent with this phase having known influx of leukocytes and, if no conception occurs, preparation for tissue desquamation with concomitant vasoconstriction.

Relative gene expression across the menstrual cycle
In this section we present the results of pairwise comparisons of genes expressed in distinct phases of the menstrual cycle relative to each other, specifically, ESE vs. PE, MSE vs. ESE, and LSE vs. MSE. This is in contrast to the K-means clustering in the previous section that is based on profiles throughout all four phases of the cycle. Only genes that are statistically significantly different by one-way ANOVA and have at least a 1.5-fold -change were considered. Their gene ontology categories, fold change, and gene families are presented below and in accompanying tables.

Early-secretory vs. proliferative endometrium
In comparing ESE vs. PE, 1589 genes and ESTs were significantly up-regulated and 1470 were significantly down-regulated. The GO classifications for up-regulated genes in ESE vs. PE are shown in Table 5. During the transition to the early-secretory phase there is an up-regulation of metabolism of alcohols, amino acids, lipids, fatty acids, and icosanoids, a large representation of transporters for biological molecules participating in these metabolic processes, and negative regulation of cell proliferation, among others. The most highly up-regulated genes in the early-secretory phase, transitioning from the proliferative phase, are shown in Table 6. (Select gene lists for select GO categories are in Supplement A on The Endocrine Society’s Journals Online web site at http://endo.endojournals.org). Interestingly, these genes govern specifically estradiol availability within the endometrium [17ß-hydroxysteroid dehydrogenase (17ß-HSD) type 2, which converts E₂ to estrone (E1)] and secretory proteins [including members of the secretoglobin family and osteopontin (secreted phosphoprotein)]. In addition, transporters for amino acids, peptides, and ions are highly up-regulated, as are enzymes involved in collagen metabolism and other processes [matrix metalloproteinase 26 (MMP-26)], a variety of cytochrome P₄₅₀ proteins (important in electron transport and energy pathways), and regulators of blood coagulation (IL-20R1 and tissue factor pathway inhibitor 2). Inhibitors of Wnt signaling are tightly regulated in this part of the cycle, with Dkk-1 being up-regulated 6-fold, and secreted frizzled-related protein 1 (SFRP-1) down-regulated (see below). Aquaporin 3, important in water transport, is up-regulated, and mucin 1 (MUC-1), important as an antibacterial agent and surface lubricant (19), is first noted to be up-regulated in this part of the menstrual cycle. Lipid metabolism, phospholipase activity, and icosanoid/prostaglandin metabolism are highly up-regulated. The data further demonstrate that metallothioneins are first noted to increase in the cycle in the early-secretory phase, as are arginase 2, phospholipase C, keratin 8, IL-1R type I, the GABA-R -subunit, IL-15, and monoamine oxidase, which are commonly attributed to being mid-secretory endometrial products (20). Evidence of epidermal growth factor signaling is apparent, as is an antiapoptotic marker (FOXOA1A).

Among the down-regulated processes in LSE vs. MSE (Table 17) are cell motility, migration, and adhesion; negative regulation of monocyte, osteoclast, and myeloid blood cell differentiation; ion homeostasis; cellular proliferation; and metabolism of sugars, amino acids, steroid hormones, lipids, fatty acids, and molecules needed for DNA synthesis. Genes that are most highly down-regulated (Table 18 and Supplement F) are MCP-2 (SCYA8), CXCL13 (SCYB13), secretoglobins, S100P, metallothioneins, MMP-26 (collagen metabolism), Dkk-1, IGF-I, PTGDS, MME, MAO-A, adipsin, 17ßHSD2, LIF, gastrin, and members of the complement family. Most of these down-regulated genes in LSE vs. MSE are up-regulated in MSE vs. ESE and thus peak in MSE.

Validation of microarray data by real-time PCR
For validation by real-time PCR, we tested 29 comparisons from the microarray data analysis. Some gene changes have been previously identified as being regulated during various phases of the cycle, whereas others have not. This approach resulted in 29 comparisons regulated as in the microarray analysis for a concordance rate of 100%. Of these 29 validated comparisons, 24 were statistically significant, yielding a concordance of 83% (Fig. 3). Also, 21 of the 29 comparisons had a fold change of 2.0 or higher by the microarray analysis, of which 18 were statistically significant by real-time PCR and REST analysis. The remaining eight comparisons fell into the 1.5- to 1.99-fold change range by microarray analysis, of which six were statistically significantly regulated in the real-time PCR analysis. In the comparison of ESE vs. PE, there is consistent up-regulation of metallothionein 1H (MT1H), Dkk-1, and TIMP-3. We also observed down-regulation of thrombospondin 1 (THBS1), MMP-11, and sex-determining region Y-box 4 (SOX4, a transcription factor that may be involved in apoptotic pathways) (22) (Fig. 3A). In the comparison of MSE vs. ESE, numerous genes are consistent with up-regulation observed with the microarray data. IGFBP-1 shows a higher increase by real-time PCR in the MSE vs. ESE comparison than observed with the microarray analysis. Among other significantly up-regulated genes are CXCL14 (the most highly regulated gene by microarray analysis), decay-accelerated factor (DAF), THBS1 (which continues to rise in the mid-secretory phase), IL-6 signal transducer (IL6ST), Dkk-1, leptin receptor (LEPR), solute carrier family 16, member 3 (SLC16A3), and STAR. TIMP-3 was up-regulated but not significantly. HOXA10 and IGF-I are down-regulated, consistent with the microarray data, although the IGF-I data were not statistically significant (Fig. 3B). In LSE vs. MSE, significant up-regulation of endometrial-associated bleeding factor (EBAF), IGFBP-1, MMP-11, SOX4, THBS1, IGF1R, and TIMP-3 is observed, consistent with the microarray data. Among the down-regulated genes, significant down-regulation of MT1H, HOXA10, and HOXA11 was observed, and (nonsignificant) down-regulation of IGF-I was also observed (Fig. 3C).

View larger version (19K): [in this window] [in a new window]   FIG. 3. Validation of microarray data by real-time PCR. Real-time PCR was performed in pairwise comparisons of ESE vs. PE (A), MSE vs. ESE (B), and LSE vs. MSE (C). Each phase group contains an n = 3 subject samples. Fold-change values are displayed above each gene and are plotted on the y-axis on a log10 scale. Bars represent SEM. Statistical significance, as determined by REST analysis (see Materials and Methods), is represented as follows: *, P < 0.05; **, P < 0.01.

	Discussion

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Clustering and identification of histologically ambiguous samples
In the current study, we used two independent clustering algorithms to analyze data generated from the microarray experiments and to analyze how samples cluster together based on similarities in their gene expression profiles. All 54,600 probe sets on the Affymetrix chip were used in the PCA, whereas the hierarchical clustering analysis used a more limited gene set (7231) derived from the pairwise comparison combined gene list. The finding of equivalent clustering patterns generated by these two methods using different gene lists and different analytical approaches supports segregation of phases of the menstrual cycle based on their unique gene expression profiles (molecular signatures). Furthermore, assignment of menstrual cycle stage of ambiguous samples, based on their gene expression profiles and their cluster grouping, is a powerful adjunct to the historical histological gold standard of endometrial assessment. That ambiguous samples were classified in the same cycle phase using both PCA and hierarchical clustering, despite the fact that all genes and ESTs were used in the former and only the differentially expressed genes from the pairwise comparisons were used in the latter analysis, further underscores the potential for these approaches in endometrial diagnostics. It is important to note that although obtaining specimens in the secretory phase would be ideally timed to the LH surge for appropriate histological assignment and having circulating E₂ and P levels would give insight into steroid hormone effects on tissues obtained, this study demonstrates that samples obtained at any time in the cycle have unique molecular signatures that preclude the need to assign a histological phase to the sample a priori. Although unique gene signatures lead to clustering in groups (phases), it is also important to recognize that the molecular signatures are not identical and may reflect subject-to-subject variability, complement of cell types in a specimen, and other factors, described in more detail at the end of the Discussion. These results set the stage for larger-scale studies to investigate the utility of identifying biologically similar samples of endometrial tissue through their gene expression profiling, used adjunctively with histological assessment. It remains to be seen whether abnormal endometria will have gene expression profiles that cluster according to cycle phase or whether new cluster patterns will evolve.

Of interest is a recent cDNA microarray study of gene expression across the menstrual cycle (23). Initial hierarchical clustering analysis of 43 endometrial samples revealed two main branches: one containing menstrual, early PE (EPE)/mid-PE (MPE), late PE (LPE), and ESE/MSE, and the second containing menstrual, menstrual/EPE, MSE, LSE, and LSE/menstrual. After removal of six outliers (for disagreement of histological stage between two pathologists or for poor hybridization), the two main branches for 37 samples contained two branches: one branch with menstrual, EPE/MPE, LPE/ESE, and ESE/MSE and the other having menstrual, MSE/LSE, and LSE/menstrual. In our study, before we performed pairwise comparisons, we removed samples with poor hybridization and ambiguous histology, and our data fell into branches that correspond with phases of the cycle with no overlap from branch to branch. In the study by Ponnampalam et al. (23), some cycle phases had too few samples for the analysis and thus were merged (e.g. MSE and LSE), in contrast to the current study where numerous samples were in these phases of the cycle, and this may account for some of the differences observed (discussed below).

K-means analysis
K-means analysis of gene expression profiles provides an unbiased approach to investigate patterns of gene expression in tissues and cells and to investigate biological similarities among specimens. In the current study, four cluster groups were identified that reveal biological processes in PE, ESE, MSE, and LSE, some of which have already been identified and some of which are new. It is well known, e.g. that in LPE, under the influence of E₂, cellular constituents of the endometrium undergo proliferation, as evidenced by high mitotic indices, extensive DNA synthesis (24), and increasing height of the tissue (20). The genes, biological processes, molecular functions, and cellular components in cluster A are consistent with mitotic activity and tissue remodeling necessary for growth in PE. However, of particular interest are ion channels in PE, which, to date, have received little attention in terms of the physiology of the endometrium, their dependence on E₂, and as potential therapeutic targets (e.g. clinically thin endometrium with concomitant fertility compromise or thickened endometrium and hyperplasia/cancer). In contrast, the large number of genes related to cellular metabolism in cluster B underscore the unique metabolic capacity of the ESE phase. In this phase, the cluster analysis is consistent with energy generation for glycogen synthesis, which occurs in the absence of excessive glycogen intake (7). Major transporters and some secretions as well as genes for germ cell migration may facilitate sperm transport and assure an aseptic environment. Clusters C and D further reveal processes related to the immune response with regard to the innate immune system and the cellular and humoral immune responses that are even more clearly revealed in the pairwise analyses (see below). We are unable to compare this GO analysis with the data of Ponnampalam et al. (23), because the latter were not analyzed by GO.

In the current study, we observed four major kinetic patterns of gene expression across the menstrual cycle (PE, ESE, MSE, and LSE). In the cDNA microarray analysis of gene expression in samples obtained from across the cycle (23), more cycle phases were evaluated (EPE/MPE, MPE, LPE/ESE, ESE/MSE, MSE/LSE, LSE/menstrual, and menstrual), and thus more K-means patterns were obtained. Because the K-means categories were not equivalent in the two studies, it is difficult to compare genes that are regulated in similar ways across the cycle. Nonetheless, some genes do show similar regulation, including high expression in ESE (keratin-8), high expression in MSE [glutathione peroxidase 3 (GPX3), annexin 4, SCYB14 (CXCL14), S100P, and aquaporin-3], high expression in MSE and LSE [Apo-E and stanniocalcin 1 (STC1)], and high expression in LSE (Sox-4, ADAMTS5, GNG-4, integrin ₂, and the prolactin receptor).

Pairwise comparisons
ESE vs. PE.
ESE in situ is stimulated by high levels of circulating E₂ during the proliferative phase and then by low, but rising, levels of P (and E₂). Pairwise comparison of genes differentially expressed in ESE vs. PE has not heretofore been reported. Of interest in the gene list for the comparison of ESE vs. PE is a mixture of potentially E₂- and P-regulated genes, although for most genes regulated in this transition, it is not known with certainty which steroid hormone (E₂ or P) regulates their expression. A recent study on human endometrium, using the same Affymetrix chip as in the current study, may shed some light into E₂-regulated genes in this tissue (25). In this study, gene expression in LPE (high E₂) vs. menstrual endometrium (very low E₂ levels) was investigated, as were genes regulated in explant cultures of tissues from both phases and treated with E₂. Genes up-regulated in LPE vs. menstrual endometrium included, e.g. oviductal glycoprotein-1, connexin-37, olfactomedin-1, and SFRP4, and down-regulated genes included several MMPs (-1, -3, and -10), IL-1ß, IL-8, IL-11, inhibin ßA, SOX4, and CC-ligand 20, among others (25). In our data, olfactomedin-1 was down-regulated in ESE, suggesting that P inhibits its expression in this phase of the cycle. SOX4 was down-regulated in LPE vs. menstrual endometrium (25), and it is also down-regulated in ESE vs. PE (Table 8), suggesting that E₂ down-regulates this gene. In a recent study on global gene expression (12,000 genes/ESTs) in response to E₂ treatment of cultured human endometrial cells, N-cadherin was up-regulated by E₂ (26). However, in the current study, N-cadherin was down-regulated in ESE vs. PE, suggesting that N-cadherin expression is inhibited by P. Of interest, also, is the up-regulation of FOXO1A (2.1-fold) in ESE vs. PE, especially in view of recent data in breast cancer cells that demonstrate the importance of FOXO1A in E₂ action (27). Whether there is dysregulation of this transcription factor in endometrial abnormalities awaits further investigation.

In a recent study by Tan et al. (28), global gene profiling of mouse uterus during the estrous cycle was investigated. Of interest among regulated genes in estrus vs. diestrus is the up-regulation of 17ßHSD-2; cathepsins L, H, and S; 1 protease inhibitors 1 and 5; N-myc downstream regulator (Ndr1); regulation of G protein signaling 2 (RGS2); and complement C3 and H. The up-regulation of 17ßHSD-2 in ESE vs. PE in the current study would suggest, based on analogy of the data from estrus vs. diestrus study, that 17ßHSD-2 is E₂ regulated in ESE vs. PE (Table 6). However, the data are convincing that 17ßHSD-2 in human endometrium is regulated via PR in the stroma, with paracrine factors up-regulating it in the epithelium (29). Up-regulation of 17ßHSD-2 in human ESE vs. PE is consistent with results from Mustonen et al. (30). The other regulated genes in the study by Tan et al. (28) were not regulated in human endometrium (up- or down-regulated in ESE vs. PE or MSE vs. ESE). Furthermore, no down-regulated genes in estrus vs. diestrus (28) were regulated in any pairwise comparison of cycle stages in human endometrium (data not shown). Interestingly, in contrast to many P-regulated genes in mouse uterus (31), many E₂-regulated genes are not similarly regulated in human endometrium.

With regard to P-regulated genes, MUC-1, which lubricates and maintains hydration of cell surfaces and protects from microorganisms and degradative enzymes, is stimulated in mouse uterus by E₂ but is highly up-regulated in the secretory phase in human endometrium and is presumably P regulated (19). In the current study, we found that Dkk-1 is up-regulated more than 6-fold in ESE vs. PE, which likely represents P action on the tissue, a conclusion supported by our recent finding of Dkk-1 mRNA and protein being up-regulated in human endometrial stromal cells within 3 h of treatment with P (32). IL-15 has also been demonstrated to be P regulated (33, 34), and inhibition of MMP-11 and MMP-7 expression is known to be inhibited by P (35). Full analysis of steroid hormone response elements of genes regulated across the cycle is anticipated to give insight into their regulation by E₂ and P and is underway in our laboratory. However, the absence of such response elements does not preclude E₂ or P regulation (36, 37, 38).

Other aspects of ESE vs. PE that are of interest are the regulation of the Wnt family members and genes involved in suppressing cellular mitosis and in accelerating cellular metabolism (Tables 6 and 8). Wnt-5a ligand is down-regulated, as are three inhibitors of Wnt action (SFRP-1, WIF, and WISP), whereas another inhibitor, Dkk-1, is highly up-regulated. Some Wnt ligands do not change in their expression across the cycle (e.g. Wnt-7a) (39), and thus, although Wnt-5a is down-regulated, the balance of inhibitors of Wnt action may or may not curtail the actions of other Wnt ligands in the endometrium during this (and other) phases of the menstrual cycle. With regard to inhibition of cellular mitosis, this is in marked contrast to the mitotic activity that occurs in PE, and the down-regulation of numerous growth factors in this phase underscores this process. Furthermore, the shift to cellular metabolism underscores that ESE is biosynthetically active, likely in preparation for embryonic implantation.

MSE vs. ESE.
The plethora of biological processes and molecular participants in transitioning from ESE to MSE underscores the multiplicity of events in preparing for embryonic implantation. Cell adhesion, suppression of cell proliferation, regulation of proteolysis, up-regulation of metabolism, growth factor and cytokine binding and signaling, the immune and inflammatory responses, and the responses to wounding and stress, to name a few, are all relevant to the cellular differentiation and cell communications that underlie receptivity to embryonic implantation in this phase of the cycle. Gene expression differences in these phases of the cycle have been investigated herein and by others (40, 41, 42), and discussion is limited to comparing results and commenting on particularly important and/or novel observations in the current data set. In the study of Ponnampalam et al. (23), pairwise comparison of genes expressed in MSE vs. ESE was not reported, and thus we cannot compare our data with theirs. Genes that are in common (up- and down-regulated) among the four studies are marked with footnote symbols in Tables 10 and 13. Of the 75 genes up-regulated in MSE vs. ESE in the study by Riesewijk et al. (42), 41 are identical to up-regulated genes in this study. Of the 56 down-regulated genes, 11 were the same as in the current study. Of the 74 genes encoding cell surface components, extracellular matrix components, growth factors, and cytokines in MSE vs. ESE in the study by Carson et al. (40), 11 were in common with the current study, and of the 76 down-regulated genes, only one was in common with the current study. Of the 49 up-regulated genes in MSE vs. ESE in the study by Mirkin et al. (41), 14 are similarly regulated as in the current study, and of the 58 that were down-regulated, three are common to the current study. Why these differences may occur among different laboratory investigations is discussed at the end of the Discussion.

In the current study, the most-highly up-regulated gene (61-fold) in MSE vs. ESE is CXCL14, and the only other report on its expression in human endometrium derives from Ponnampalam et al. (23). However, its fold-change expression in MSE vs. ESE has not previously been reported, and interestingly, regulation of CXCL14 was not detected in previous studies comparing MSE vs. ESE gene expression (40, 41, 42), despite probe sets for this cytokine on the current and previous versions of the Affymetrix arrays. This chemokine, also known as breast and kidney expressed chemokine (BRAK), recruits monocytes in the setting of inflammation and without inflammation (43). Loss of this chemokine in tumor tissues is associated with low infiltration by dendritic cells (DC), whereas its restoration results in attraction of DC in vitro as well as in vivo (44). This chemokine is selectively chemotactic for monocytes activated by prostaglandin E₂ (PGE₂), which become significantly more responsive to CXCL14 while losing their chemotactic responsiveness to the traditional monocyte chemokines, including monocyte chemotactic protein-1 (MCP-1, CCL2), regulated on activation, normal T-cell expressed and secreted (RANTES, CCL5), and stromal cell-derived factor 1 (CXCL12) (45). MCP-1 and RANTES increase in secretory endometrium (46), whereas CXCL12 decreases (in the stroma) in the secretory phase (47). Interestingly, activation of DC with CXCL14 is accompanied by up-regulation of NF-B activity in tumor cells (44). Although the cell type of CXCL14 and its targets cells in endometrium are not known, the observation of up-regulation of the NF-B cascade in this phase of the cycle is consistent with a possible role for this chemokine in the receptive phase (MSE). In addition, CXCL14 may have other roles, e.g. as a chemoattractant for the trophectoderm. Overall, the marked up-regulation of CXCL14 in MSE vs. ESE and validated herein by quantitative RT-PCR, suggests that CXCL14 may be a major recruiter of monocytes and perhaps other cell types to endometrium during the implantation window. Studies on the cellular localization and role(s) of this chemokine in human endometrium are currently underway in our laboratory.

Cysteine-rich secretory protein (CRISP) 3 was markedly up-regulated in MSE vs. ESE, as was secretoglobin family 2A member 2. However, these were not observed in earlier array studies, for reasons that are not completely understood, although secretoglobins have been reported to be cyclically expressed in human endometrium (48). In the current study, glutathione peroxidase-3 (GPX-3) and metallothioneins 1G, 1H, 1E, 1F, 1L, 1X, and 2A were highly up-regulated in MSE vs. ESE. The former is consistent with previous observations (41, 42) (and in MSE vs. PE) (49, 50, 51), although the finding of multiple metallothionein family members being up-regulated in MSE vs. ESE has not previously been observed. GPXs (antioxidants) and metallothioneins protect cells from unstable reactive radicals and heavy metals (52). Up-regulation of these genes (and their gene products) may be important at this time of the cycle for protecting an embryo from free radicals and heavy metals. GPXs are selenium dependent, and interestingly, selenium deficiency in women is associated with infertility and miscarriage (53). Whether endometrial GPX dysfunction accompanies such deficiency resulting in a clinical phenotype remains to be determined.

In the current study, we found that LIF is significantly highly up-regulated in MSE vs. ESE, in contrast to other studies (40, 41, 42), although it is known to be up-regulated in the MSE in women (54, 55). Recently, Horcajadas et al. (56) reported up-regulation of LIF in MSE vs. ESE in an extension of their earlier study (41). LIF plays a central role in endometrial receptivity in the mouse (57, 58), and increasing evidence suggests that it is important in human endometrial receptivity (59). Importantly, some women with infertility and repetitive miscarriage have either low levels of endometrial LIF in MSE (60, 61) or have point mutations in the coding region of the LIF gene (59).

Among down-regulated genes, SFRP is highly down-regulated in MSE vs. ESE, as are olfactomedin 1, PR, PR membrane component 1, ER-, MUC-1, 17ßHSD-2, and MMP-11. Most of these were not found to be regulated in earlier MSE vs. ESE microarray studies, and the reasons for differences in down-regulated genes among the various studies may be similar to those for the up-regulated genes (discussed below).

The complement family is regulated in MSE vs. ESE (Tables 9–13), as found in all studies investigating this comparison (40, 41, 42, 62). However, the current study has investigated whole-genome profiling across the entire cycle, which has the advantage of observing dynamic gene expression. For example, decay-accelerating factor and other complement family members largely peak in MSE and then drop in LSE, as do LIF and the metallothioneins. The innate immune system is discussed more below.

Of major interest in MSE vs. ESE is the immune response. In our data set, there is a clear up-regulation of genes involved in activation of the innate immune response (complement, antimicrobial peptides, Toll-like receptor expression). There is also enhancement of chemotaxis of monocytes, T cells, and NK cells (CXCL14, granulysin, IL-15, carbohydrate sulfotransferase 2, and suppression of NK and T-cell activation). IL-15 is regulated by P in endometrial stromal cells (33, 34); however, its regulation is complex and involves interferon- and PGE₂ (33). Recent evidence suggests a central role for IL-15 in secretory-phase endometrium in the recruitment of peripheral blood CD16-NK cells into the tissue in this phase of the cycle (63). Many of the genes observed in our data set were also observed in the data from Carson et al. (40), Riesewijk et al. (42), and Mirkin et al. (41). However, Riesewijk et al. (42) also found up-regulation of Fc receptors and HLA class I molecules, which in our data set clearly segregate with the late secretory phase (see below). The gene expression profile observed herein is consistent with the marked increase in lymphocyte infiltration (3, 64). In the section on LSE vs. MSE pairwise comparisons below, we discuss in more detail the immune responses in this phase of the cycle compared with those in MSE vs. ESE.

Genes that are candidates for regulation by P can be approximated by their relative expression in different phases of the cycle. For example, genes regulated in MSE vs. PE are likely to be regulated by P, as reported by our group (51) and others (49) and in the nonhuman primate by Ace and Okulicz (47). Also, reports using antiprogestins and PR knockout models have given further insight into P-regulated genes in mouse uterus/endometrium (65). Of interest is the regulation of genes in MSE vs. PE from all of the published studies in humans and nonhuman primates (47, 49, 51) that are in common with those regulated in ESE vs. PE (this study) and MSE vs. ESE [this study and Carson et al. (40) and Riesewijk et al. (42)] (Table 19). This table provides a list of genes that likely are regulated by P, either directly or indirectly, and their validation of such regulatory mechanisms awaits further investigation. Analysis of P-responsive elements (PREs) and estrogen-responsive elements (EREs) by Borthwick et al. (49) and Mirkin et al. (41) support some of these conclusions.

LSE vs. MSE.
Comparison of LSE vs. MSE by genome-wide microarray analysis, to our knowledge, has not heretofore been reported. The transition from the window of implantation to LSE is heralded, from a gene expression perspective, by preparations for alterations in the extracellular matrix, the cytoskeleton, cell viability, vasoconstriction, smooth muscle contraction, hemostasis, and transition in the immune response to include an inflammatory response (69, 70). P is known to inhibit stromal cell-derived proteases, including uPA, MMP-1, and MMP-3 (71). In the microarray data set for LSE vs. MSE, the striking up-regulation of the metalloproteinases (MMPs and ADAMs), serine proteases [uPA (PLAU) and tPA (PLAT)], and associated inhibitors is consistent with declining P levels within the tissue at this time of the cycle and concomitant disinhibition of expression of these genes. P also inhibits leukocyte transit into the endometrium (72), a process that is controlled in the secretory phase by the stromal cell, the main cell type that retains PR in this phase of the cycle (73, 74, 75, 76). It should be noted that uPA (PLAU) is up-regulated in LSE compared with MSE, and thus it is able to activate TGFß1 (77), and it is regulated by a tissue factor that is itself P dependent. Also, it activates plasminogen, which can activate MMPs. Thus, uPA likely plays a major role in preparing the tissue for desquamation. TGFß family members also play a role in tissue breakdown, and EBAF, which is one of the most highly up-regulated genes in LSE vs. MSE, has been shown to stimulate production of pro-MMP-3 and -7 in proliferative-phase explants, a process that is inhibited by P, which inhibits EBAF expression and itself inhibits MMP expression (78).

In LSE vs. MSE, there is much immune activity, and the data set is consistent with the known histological observations of a marked influx of extravasated polymorphonuclear leukocytes (3, 64). Most impressive is the up-regulation of Fc receptors, MHC molecules, NK molecules, and T cell molecules. It appears that the system is preparing for immune action involving innate and adaptive immunity (T cell specific and antibody-mediated). By up-regulating Fc receptors, monocytes/granulocytes are ready to respond to antibodies, and by expressing MHC II molecules, antigen-presenting cells are becoming more effective. IL-1 is a proinflammatory cytokine that induces T cell activation. In addition, IL-ß and TNF- are secreted by leukocytes in the stromal compartment at the end of the cycle, and these stimulate release of matrix-degrading enzymes that contribute to the breakdown of the vascular basement membrane and connective tissue integrity in the functionalis layer (79, 80). Also, numerous molecules in T cell signaling/activation are up-regulated. Withdrawal of P is known to up-regulate key inflammatory mediators, including IL-1ß, CXCL8 (IL-8), and CCL2 (MCP-1) (70, 81), and the synthesis of prostaglandins, by induction of COX-2 (82). The microarray data are consistent with a major shift from an innate immune response in MSE to an inflammatory response in the LSE (83, 84). This inflammatory milieu, along with up-regulation of matrix-degrading enzymes and promoters of cellular apoptosis, appear to be most inhospitable to an embryo should implantation occur outside of the normal receptive period. Indeed, the gene expression profile in LSE vs. MSE may serve to define closure of the receptive period and the onset of the subsequent nonreceptive period of endometrial development in normal, nonconception cycles. It is of interest to determine how the pattern of immune gene regulation proceeds in LSE in a conception cycle, where one would expect more immune suppression and less activation of immune cells.

The activities in LSE vs. MSE are most consistent with the preparation of the tissue for desquamation and menstruation. This is facilitated by molecular mechanisms described above and uterine smooth muscle contraction and vasospasm (85, 86, 87, 88), promoted by elevated prostaglandin concentrations (PGE₂ and PGF₂) (82, 89) whose synthases are elevated in this phase of the cycle. It should be noted, however, that gene regulation in LSE vs. MSE, although preparing the endometrium for the menstrual phase, does not result in the actual sloughing of the tissue. Thus, in clinical disorders associated with endometrial bleeding at unexpected times (90), e.g. it is possible that the endometrium has already undergone significant biochemical changes by the time clinical symptoms of spotting or bleeding occur.

Comment on use of human tissue specimens and different results in different studies
Obtaining normal human tissue, and in particular, normal hormone-dependent tissues from cycling women, for biochemical analysis involves several levels of complexity that warrant further comment, underscoring challenges associated with this type of translational research. We collected samples serially from subjects undergoing operative procedures for benign gynecological conditions and normal volunteers after informed consent and under protocols approved by the human subjects’ review boards at our respective institutions. We began with 45 subjects’ endometrial samples, and after rigorous evaluation of subjects’ medical and surgical history, specimen quality, adequacy of specimens, and histological evaluation, 22 specimens were sufficiently well characterized to be used for array analysis. This approximately 50% drop in the number of samples eventually used from those obtained originally is similar to our previous experience for microarray analysis of the endometrium (51). It is important to note that the samples that we have used in this study are as normal as we can ascertain, with the caveat that the presence of uterine fibroids or uterine prolapse may have an impact on the final gene expression in the endometrium. The two independent clustering analyses, however, suggest these conditions do not significantly affect sample clustering, although proximity of a fibroid, e.g. to the endometrium, may confound normal endometrial gene expression. These issues are virtually unavoidable when analyzing normal human samples, and a long-term goal is to increase the number of normal samples analyzed for a more accurate and complete data set.

The question naturally arises why different results have been obtained by different groups using similar, if not identical, microarrays and histologically equivalent endometrial samples. Differences in results may be a result of 1) the newer chips having sequences not present in the earlier versions; 2) different types of chips used (oligonucleotides vs. cDNA); 3) different hybridization conditions for samples, scanners used, and software for analyses; 4) subject-to-subject variability [e.g. in our study different subjects were sampled in MSE and ESE, whereas Riesewijk et al. (42) had the same subject sampled twice in the cycle, once in the ESE and then again in the MSE]; 5) precise timing in the cycle when samples were obtained; 6) where in the endometrium the sample was obtained (fundus, lower uterine segment, or periostium); 7) the proportion of epithelium, stroma, immune cells, and blood vessels in individual specimens; 8) the method to obtain the tissues (endometrial biopsy generally samples the upper functionalis layer, whereas curetting the endometrium in a hysterectomy specimen may sample the lower functionalis and even some of the basalis layer), although the cluster analyses used herein suggest that tissue sampling methods are not major contributors to sample characterization by cycle phase in this study, although researchers studying human endometrium should take precaution regarding potentially sampling the basalis layer in hysterectomy specimens; and 9) different extents of medical annotation of subjects’ medical and surgical histories (e.g. endometriosis) and concurrent medications (e.g. contraceptive steroids, hormonal replacement therapy, nonsteroidal anti-inflammatory inhibitors, among others) that may affect gene expression.

Summary
This study contains a voluminous amount of data and information involving events that occur on molecular and structural levels in human endometrium throughout the cycle in response to ovarian-derived steroid hormones and their downstream effectors. There is still much to be done with the current data set, including cellular localization and validation of specific genes and gene families, mining of the ESTs, and determining whether the transcriptome is translated into the proteome. We propose that the gene list derived herein provides a molecular signature of normal endometrial tissue that can be confirmed further through cluster analyses of an expanding number of histologically well-defined specimens. The approaches used herein also offer the opportunity to identify abnormalities in endometrium (e.g. endometrial hyperplasia, cancer, and endometriosis and in hyperandrogenic and hyperinsulinemic states) and the molecular bases of these and morphometric changes in the endometrium accompanying, e.g. different types of ovarian stimulation for infertility treatment (91). Furthermore, they also provide the opportunity to define molecular mechanisms predisposing to abnormal implantation and placentation resulting in, e.g. infertility, recurrent miscarriage, and intrauterine fetal growth restriction. The possibilities are profound with regard to developing diagnostics and targeted therapeutics for these and other disorders of the endometrium. It is our goal that other investigators will use the data set posted in the NCBI GEO database to investigate these and other research questions in endometrial biology and medicine.

	Acknowledgments

 
We acknowledge acquisition of some endometrial samples from the National Institutes of Health (NIH) Specialized Cooperative Centers Program in Reproduction Research Tissue and DNA Bank at Stanford University and at the University of North Carolina, Chapel Hill. We also thank the funding agencies that supported this work, the subjects who gave of their endometrium for scientific investigation, and the clinical staff who facilitated the process of procuring the tissue specimens used in this study.

We dedicate this manuscript to the memory of Dr. Georgeanna Seegars-Jones and Dr. Gary Hodgen, two pioneers in primate endometrial biology and endocrinology, who passed away this past year and whose investigations contributed significantly to our understanding of the endometrium in health and disease.

	Footnotes

 
Current address for S.T., A.E.H., K.C.V., and L.C.G.: Department of Obstetrics, Gynecology, and Reproductive Sciences, Health Science West Building 1671, University of California, San Francisco, San Francisco, California 94143.

This work was supported by NIH Specialized Cooperative Centers Program in Reproduction Research HD no. 31398-09 (to L.C.G., B.A.L.), NIH Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) HD no. 043452-03 (to N.R.N.), and, in part, by the Nova Cara Foundation, University of North Carolina (to B.A.L.).

First Published Online November 23, 2005

¹ S.T. and A.E.H. contributed equally to this work.

Abbreviations: Ct, Threshold cycle; DC, dendritic cells; E₂, estradiol; EPE, early proliferative endometrium; ESE, early-secretory endometrium; ER, estrogen receptor-; EST, expressed sequence tag; GO, gene ontology; GOTM, GO tree machine; HG, human genome; 17ß-HSD, 17ß-hydroxysteroid dehydrogenase; LIF, leukemia inhibitory factor; LPE, late proliferative endometrium; LSE, late-secretory endometrium; MHC, major histocompatibility complex; MMP, matrix metalloproteinase; MPE, mid-proliferative endometrium; MSE, mid-secretory endometrium; MUC-1, mucin 1; NF, nuclear factor; NK, natural killer; P, progesterone; PCA, principal component analysis; PE, proliferative endometrium; PGE₂, prostaglandin E₂; PR, progesterone receptor; REST, relative expression software tool; SAM, statistical analysis of microarrays; SFRP-1, secreted frizzled-related protein 1; tPA, tissue plasminogen activator.

Received August 23, 2005.

Accepted for publication November 11, 2005.

	References

Top Abstract Introduction Materials and Methods Results Discussion References

 

1. Hess AP, Nayak NR, Giudice LC 2006 Oviduct and endometrium: cyclic changes in the primate oviduct and endometrium. In: Neill JD, ed. The physiology of reproduction. 3rd ed. St. Louis: Elsevier; 359–403
2. Hitschmann F, Adler L 1908 er Bau der Uterus-schleimhaut des geschlectsreifen Weibes mit besonderer Berucksightigung der Menstruation. Monatschr Geburtsch Gynakol 27:1–23
3. Noyes RW, Hertig AT, Rock J 1950 Dating the endometrial biopsy. Fertil Steril 1:3–17
4. Good RG, Moyer DL 1968 Estrogen-progesterone relationships in the development of secretory endometrium. Fertil Steril 19:37–49[Medline]
5. Hodgen GD 1983 Surrogate embryo transfer combined with estrogen-progesterone therapy in monkeys. Implantation, gestation, and delivery without ovaries. JAMA 250:2167–2171[Abstract/Free Full Text]
6. Noyes RW, Hertig AT, Rock J 1975 Dating the endometrial biopsy. Am J Obstet Gynecol 122:262–263[Medline]
7. Giudice LC, Ferenczy A 1995 The endometrial cycle: morphologic and biochemical events. In: Adashi RJ, Rosenwaks Z, eds. Reproductive endocrinology, surgery, and technology. New York: Raven Press; 171–194
8. Giudice LC, Ferenczy A 1996 The endometrial cycle: morphologic and biochemical events. In: Adashi EY, Rosenwaks Z, eds. Reproductive endocrinology, surgery, and technology. New York: Raven Press; 271–300
9. Coutifaris C, Myers ER, Guzick DS, Diamond MP, Carson SA, Legro RS, McGovern PG, Schlaff WD, Carr BR, Steinkampf MP, Silva S, Vogel DL, Leppert PC 2004 Histological dating of timed endometrial biopsy tissue is not related to fertility status. Fertil Steril 82:1264–1272[CrossRef][Medline]
10. Murray MJ, Meyer WR, Zaino RJ, Lessey BA, Novotny DB, Ireland K, Zeng D, Fritz MA 2004 A critical analysis of the accuracy, reproducibility, and clinical utility of histologic endometrial dating in fertile women. Fertil Steril 81:1333–1343[CrossRef][Medline]
11. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP 2003 Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264[Abstract]
12. Tusher VG, Tibshirani R, Chu G 2001 Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98:5116–5121[Abstract/Free Full Text]
13. Joliffe IT, Morgan BJ 1992 Principal component analysis and exploratory factor analysis. Stat Methods Med Res 1:69–95[Medline]
14. Eisen MB, Spellman PT, Brown PO, Botstein D 1998 Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868[Abstract/Free Full Text]
15. Herwig R, Poustka AJ, Muller C, Bull C, Lehrach H, O’Brien J 1999 Large-scale clustering of cDNA-fingerprinting data. Genome Res 9:1093–1105[Abstract/Free Full Text]
16. Zhang B, Schmoyer D, Kirov S, Snoddy J 2004 GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 5:16[CrossRef][Medline]
17. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G 2000 Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29[CrossRef][Medline]
18. Pfaffl MW, Horgan GW, Dempfle L 2002 Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30:e36
19. Brayman M, Thathiah A, Carson DD 2004 MUC1: a multifunctional cell surface component of reproductive tissue epithelia. Reprod Biol Endocrinol 2:4[CrossRef][Medline]
20. Giudice LC 2003 Elucidating endometrial function in the post-genomic era. Hum Reprod Update 9:223–235[Abstract/Free Full Text]
21. Ben-Shlomo I, Yu Hsu S, Rauch R, Kowalski HW, Hsueh AJ 2003 Signaling receptome: a genomic and evolutionary perspective of plasma membrane receptors involved in signal transduction. Sci STKE 2003:RE9
22. Hur EH, Hur W, Choi JY, Kim IK, Kim HY, Yoon SK, Rhim H 2004 Functional identification of the pro-apoptotic effector domain in human Sox4. Biochem Biophys Res Commun 325:59–67[CrossRef][Medline]
23. Ponnampalam AP, Weston GC, Trajstman AC, Susil B, Rogers PA 2004 Molecular classification of human endometrial cycle stages by transcriptional profiling. Mol Hum Reprod 10:879–893[Abstract/Free Full Text]
24. Ferenczy A, Bertrand G, Gelfand MM 1979 Proliferation kinetics of human endometrium during the normal menstrual cycle. Am J Obstet Gynecol 133:859–867[Medline]
25. Punyadeera C, Dassen H, Klomp J, Dunselman G, Kamps R, Dijcks F, Ederveen A, de Goeij A, Groothuis P 2005 Oestrogen-modulated gene expression in the human endometrium. Cell Mol Life Sci 62:239–250[CrossRef][Medline]
26. Pole JC, Gold LI, Orton T, Huby R, Carmichael PL 2005 Gene expression changes induced by estrogen and selective estrogen receptor modulators in primary-cultured human endometrial cells: signals that distinguish the human carcinogen tamoxifen. Toxicology 206:91–109[CrossRef][Medline]
27. Carroll JS, Liu XS, Brodsky AS, Li W, Meyer CA, Szary AJ, Eeckhoute J, Shao W, Hestermann EV, Geistlinger TR, Fox EA, Silver PA, Brown M 2005 Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 122:33–43[CrossRef][Medline]
28. Tan YF, Li FX, Piao YS, Sun XY, Wang YL 2003 Global gene profiling analysis of mouse uterus during the oestrous cycle. Reproduction 126:171–182[Abstract]
29. Yang S, Fang Z, Gurates B, Tamura M, Miller J, Ferrer K, Bulun SE 2001 Stromal PRs mediate induction of 17ß-hydroxysteroid dehydrogenase type 2 expression in human endometrial epithelium: a paracrine mechanism for inactivation of E2. Mol Endocrinol 15:2093–2105[Abstract/Free Full Text]
30. Mustonen MV, Isomaa VV, Vaskivuo T, Tapanainen J, Poutanen MH, Stenback F, Vihko RK, Vihko PT 1998 Human 17ß-hydroxysteroid dehydrogenase type 2 messenger ribonucleic acid expression and localization in term placenta and in endometrium during the menstrual cycle. J Clin Endocrinol Metab 83:1319–1324[Abstract/Free Full Text]
31. Cheon YP, Li Q, Xu X, DeMayo FJ, Bagchi IC, Bagchi MK 2002 A genomic approach to identify novel progesterone receptor regulated pathways in the uterus during implantation. Mol Endocrinol 16:2853–2871[Abstract/Free Full Text]
32. Tulac S, Overgaard MT, Hamilton AE, Jumbe NL, Suchanek E, Giudice LC, Dickkopf-1, an inhibitor of Wnt signaling, is regulated by progesterone in human endometrial stromal cells. J Clin Endocrinol Metab, in press
33. Dunn CL, Critchley HO, Kelly RW 2002 IL-15 regulation in human endometrial stromal cells. J Clin Endocrinol Metab 87:1898–1901[Abstract/Free Full Text]
34. Okada H, Nakajima T, Sanezumi M, Ikuta A, Yasuda K, Kanzaki H 2000 Progesterone enhances interleukin-15 production in human endometrial stromal cells in vitro. J Clin Endocrinol Metab 85:4765–4770[Abstract/Free Full Text]
35. Bruner KL, Rodgers WH, Gold LI, Korc M, Hargrove JT, Matrisian LM, Osteen KG 1995 Transforming growth factor-ß mediates the progesterone suppression of an epithelial metalloproteinase by adjacent stroma in the human endometrium. Proc Natl Acad Sci USA 92:7362–7366[Abstract/Free Full Text]
36. Brosens JJ, Hayashi N, White JO 1999 Progesterone receptor regulates decidual prolactin expression in differentiating human endometrial stromal cells. Endocrinology 140:4809–4820[Abstract/Free Full Text]
37. Gellersen B, Brosens J 2003 Cyclic AMP and progesterone receptor cross-talk in human endometrium: a decidualizing affair. J Endocrinol 178:357–372[Abstract]
38. Lessey BA 2003 Two pathways of progesterone action in the human endometrium: implications for implantation and contraception. Steroids 68:809–815[CrossRef][Medline]
39. Tulac S, Nayak NR, Kao LC, Van Waes M, Huang J, Lobo S, Germeyer A, Lessey BA, Taylor RN, Suchanek E, Giudice LC 2003 Identification, characterization, and regulation of the canonical Wnt signaling pathway in human endometrium. J Clin Endocrinol Metab 88:3860–3866[Abstract/Free Full Text]
40. Carson DD, Lagow E, Thathiah A, Al-Shami R, Farach-Carson MC, Vernon M, Yuan L, Fritz MA, Lessey B 2002 Changes in gene expression during the early to mid-luteal (receptive phase) transition in human endometrium detected by high-density microarray screening. Mol Hum Reprod 8:871–879[Abstract/Free Full Text]
41. Mirkin S, Arslan M, Churikov D, Corica A, Diaz JI, Williams S, Bocca S, Oehninger S 2005 In search of candidate genes critically expressed in the human endometrium during the window of implantation. Hum Reprod 20:2104–2117[Abstract/Free Full Text]
42. Riesewijk A, Martin J, van Os R, Horcajadas JA, Polman J, Pellicer A, Mosselman S, Simon C 2003 Gene expression profiling of human endometrial receptivity on days LH+2 versus LH+7 by microarray technology. Mol Hum Reprod 9:253–264[Abstract/Free Full Text]
43. Muller WA 2001 New mechanisms and pathways for monocyte recruitment. J Exp Med 194:F47–51
44. Shurin GV, Ferris R, Tourkova IL, Perez L, Lokshin A, Balkir L, Collins B, Chatta GS, Shurin MR 2005 Loss of new chemokine CXCL14 in tumor tissue is associated with low infiltration by dendritic cells (DC), while restoration of human CXCL14 expression in tumor cells causes attraction of DC both in vitro and in vivo. J Immunol 174:5490–5498[Abstract/Free Full Text]
45. Kurth I, Willimann K, Schaerli P, Hunziker T, Clark-Lewis I, Moser B 2001 Monocyte selectivity and tissue localization suggests a role for breast and kidney-expressed chemokine (BRAK) in macrophage development. J Exp Med 194:855–861[Abstract/Free Full Text]
46. Caballero-Campo P, Dominguez F, Coloma J, Meseguer M, Remohi J, Pellicer A, Simon C 2002 Hormonal and embryonic regulation of chemokines IL-8, MCP-1 and RANTES in the human endometrium during the window of implantation. Mol Hum Reprod 8:375–384[Abstract/Free Full Text]
47. Ace CI, Okulicz WC 2004 Microarray profiling of progesterone-regulated endometrial genes during the rhesus monkey secretory phase. Reprod Biol Endocrinol 2:54[CrossRef][Medline]
48. Herrler A, von Rango U, Beier HM 2003 Embryo-maternal signalling: how the embryo starts talking to its mother to accomplish implantation. Reprod Biomed Online 6:244–256[Medline]
49. Borthwick JM, Charnock-Jones DS, Tom BD, Hull ML, Teirney R, Phillips SC, Smith SK 2003 Determination of the transcript profile of human endometrium. Mol Hum Reprod 9:19–33[Abstract/Free Full Text]
50. Ioachim EE, Kitsiou E, Carassavoglou C, Stefanaki S, Agnantis NJ 2000 Immunohistochemical localization of metallothionein in endometrial lesions. J Pathol 191:269–273[CrossRef][Medline]
51. Kao LC, Tulac S, Lobo S, Imani B, Yang JP, Germeyer A, Osteen K, Taylor RN, Lessey BA, Giudice LC 2002 Global gene profiling in human endometrium during the window of implantation. Endocrinology 143:2119–2138[Abstract/Free Full Text]
52. Sies H 1993 Strategies of antioxidant defense. Eur J Biochem 215:213–219[Medline]
53. Kingsley PD, Whitin JC, Cohen HJ, Palis J 1998 Developmental expression of extracellular glutathione peroxidase suggests antioxidant roles in deciduum, visceral yolk sac, and skin. Mol Reprod Dev 49:343–355[CrossRef][Medline]
54. Charnock-Jones DS, Sharkey AM, Fenwick P, Smith SK 1994 Leukaemia inhibitory factor mRNA concentration peaks in human endometrium at the time of implantation and the blastocyst contains mRNA for the receptor at this time. J Reprod Fertil 101:421–426[Abstract/Free Full Text]
55. Cullinan EB, Abbondanzo SJ, Anderson PS, Pollard JW, Lessey BA, Stewart CL 1996 Leukemia inhibitory factor (LIF) and LIF receptor expression in human endometrium suggests a potential autocrine/paracrine function in regulating embryo implantation. Proc Natl Acad Sci USA 93:3115–3120[Abstract/Free Full Text]
56. Horcajadas JA, Riesewijk A, Polman J, van Os R, Pellicer A, Mosselman S, Simon C 2005 Effect of controlled ovarian hyperstimulation in IVF on endometrial gene expression profiles. Mol Hum Reprod 11:195–205[Abstract/Free Full Text]
57. Bhatt H, Brunet LJ, Stewart CL 1991 Uterine expression of leukemia inhibitory factor coincides with the onset of blastocyst implantation. Proc Natl Acad Sci USA 88:11408–11412[Abstract/Free Full Text]
58. Stewart CL, Kaspar P, Brunet LJ, Bhatt H, Gadi I, Kontgen F, Abbondanzo SJ 1992 Blastocyst implantation depends on maternal expression of leukaemia inhibitory factor. Nature 359:76–79[CrossRef][Medline]
59. Kondera-Anasz Z, Sikora J, Mielczarek-Palacz A 2004 Leukemia inhibitory factor: an important regulator of endometrial function. Am J Reprod Immunol 52:97–105
60. Hambartsoumian E 1998 Endometrial leukemia inhibitory factor (LIF) as a possible cause of unexplained infertility and multiple failures of implantation. Am J Reprod Immunol 39:137–143
61. Tsai HD, Chang CC, Hsieh YY, Lo HY 2000 Leukemia inhibitory factor expression in different endometrial locations between fertile and infertile women throughout different menstrual phases. J Assist Reprod Genet 17:415–418[CrossRef][Medline]
62. Hasty LA, Brockman WW, Lambris JD, Lyttle CR 1993 Hormonal regulation of complement factor B in human endometrium. Am J Reprod Immunol 30:63–67
63. Kitaya K, Yamaguchi T, Honjo H 2005 Central role of interleukin-15 in postovulatory recruitment of peripheral blood CD16^– natural killer cells into human endometrium. J Clin Endocrinol Metab 90:2932–2940[Abstract/Free Full Text]
64. Daly DC, Tohan N, Doney TJ, Maslar IA, Riddick DH 1982 The significance of lymphocytic-leukocytic infiltrates in interpreting late luteal phase endometrial biopsies. Fertil Steril 37:786–791[Medline]
65. Lydon JP, DeMayo FJ, Conneely OM, O’Malley BW 1996 Reproductive phenotypes of the progesterone receptor null mutant mouse. J Steroid Biochem Mol Biol 56:67–77[CrossRef][Medline]
66. Pahl PM, Horwitz MA, Horwitz KB, Horwitz LD 2001 Desferri-exochelin induces death by apoptosis in human breast cancer cells but does not kill normal breast cells. Breast Cancer Res Treat 69:69–79[CrossRef][Medline]
67. Kao LC, Germeyer A, Tulac S, Lobo S, Yang JP, Taylor RN, Osteen K, Lessey BA, Giudice LC 2003 Expression profiling of endometrium from women with endometriosis reveals candidate genes for disease-based implantation failure and infertility. Endocrinology 144:2870–2881[Abstract/Free Full Text]
68. Ogawa S, Inoue S, Watanabe T, Orimo A, Hosoi T, Ouchi Y, Muramatsu M 1998 Molecular cloning and characterization of human estrogen receptor betacx: a potential inhibitor of estrogen action in human. Nucleic Acids Res 26:3505–3512[Abstract/Free Full Text]
69. Critchley HO, Jones RL, Lea RG, Drudy TA, Kelly RW, Williams AR, Baird DT 1999 Role of inflammatory mediators in human endometrium during progesterone withdrawal and early pregnancy. J Clin Endocrinol Metab 84:240–248[Abstract/Free Full Text]
70. Critchley HO, Kelly RW, Brenner RM, Baird DT 2001 The endocrinology of menstruation: a role for the immune system. Clin Endocrinol (Oxf) 55:701–710[CrossRef][Medline]
71. Schatz F, Krikun G, Runic R, Wang EY, Hausknecht V, Lockwood CJ 1999 Implications of decidualization-associated protease expression in implantation and menstruation. Semin Reprod Endocrinol 17:3–12[Medline]
72. Booker SS, Jayanetti C, Karalak S, Hsiu JG, Archer DF 1994 The effect of progesterone on the accumulation of leukocytes in the human endometrium. Am J Obstet Gynecol 171:139–142[Medline]
73. Bergqvist A, Ferno M 1993 Oestrogen and progesterone receptors in endometriotic tissue and endometrium: comparison of different cycle phases and ages. Hum Reprod 8:2211–2217[Abstract/Free Full Text]
74. Lessey BA, Killam AP, Metzger DA, Haney AF, Greene GL, McCarty Jr KS 1988 Immunohistochemical analysis of human uterine estrogen and progesterone receptors throughout the menstrual cycle. J Clin Endocrinol Metab 67:334–340[Abstract/Free Full Text]
75. Mote PA, Balleine RL, McGowan EM, Clarke CL 2000 Heterogeneity of progesterone receptors A and B expression in human endometrial glands and stroma. Hum Reprod 15(Suppl 3):48–56
76. Mylonas I, Jeschke U, Shabani N, Kuhn C, Balle A, Kriegel S, Kupka MS, Friese K 2004 Immunohistochemical analysis of estrogen receptor-, estrogen receptor-ß and progesterone receptor in normal human endometrium. Acta Histochem 106:245–252[CrossRef][Medline]
77. Casslen B, Sandberg T, Gustavsson B, Willen R, Nilbert M 1998 Transforming growth factor-ß1 in the human endometrium. Cyclic variation, increased expression by estradiol and progesterone, and regulation of plasminogen activators and plasminogen activator inhibitor-1. Biol Reprod 58:1343–1350[Abstract/Free Full Text]
78. Cornet PB, Picquet C, Lemoine P, Osteen KG, Bruner-Tran KL, Tabibzadeh S, Courtoy PJ, Eeckhout Y, Marbaix E, Henriet P 2002 Regulation and function of LEFTY-A/EBAF in the human endometrium. mRNA expression during the menstrual cycle, control by progesterone, and effect on matrix metalloproteinases. J Biol Chem 277:42496–42504[Abstract/Free Full Text]
79. Salamonsen LA 1996 Matrix metalloproteinases and their tissue inhibitors in endocrinology. Trends Endocrinol Metab 7:28–34
80. Salamonsen LA, Woolley DE 1999 Menstruation: induction by matrix metalloproteinases and inflammatory cells. J Reprod Immunol 44:1–27[CrossRef][Medline]
81. Jones RL, Kelly RW, Critchley HO 1997 Chemokine and cyclooxygenase-2 expression in human endometrium coincides with leukocyte accumulation. Hum Reprod 12:1300–1306
82. Pickles VR 1967 Prostaglandins in the human endometrium. Int J Fertil 12:335–338[Medline]
83. Dosiou C, Giudice LC 2005 Natural killer cells in pregnancy and recurrent pregnancy loss: endocrine and immunologic perspectives. Endocr Rev 26:44–62[Abstract/Free Full Text]
84. King AE, Critchley HO, Kelly RW 2001 The NF-B pathway in human endometrium and first trimester decidua. Mol Hum Reprod 7:175–183[Abstract/Free Full Text]
85. Fitzpatrick RL, Liggins GC 1980 Effects of prostaglandins on the cervix of pregnant women and sheep. In: Naftolin F, Stubblefield PF, eds. Dilatation of the uterine cervix. New York: Raven Press; 287–300
86. Ylikorkala O, Makila UM 1985 Prostacyclin and thromboxane in gynecology and obstetrics. Am J Obstet Gynecol 152:318–329[Medline]
87. Ferenczy A 2003 Pathophysiology of endometrial bleeding. Maturitas 45:1–14[CrossRef][Medline]
88. Fraser IS 1999 Regulating menstrual bleeding. A prime function of progesterone. J Reprod Med 44:158–164[Medline]
89. Downie J, Poyser NL, Wundeerlich M 1974 Levels of prostaglandins in human endometrium during the normal menstrual cycle. J Physiol 236:465–472[Medline]
90. Livingstone M, Fraser IS 2002 Mechanisms of abnormal uterine bleeding. Hum Reprod Update 8:60–67[Abstract/Free Full Text]
91. Bonhoff A, Naether O, Johannisson E, Bohnet HG 1993 Morphometric characteristics of endometrial biopsies after different types of ovarian stimulation for infertility treatment. Fertil Steril 59:560–566[Medline]

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