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Endocrinology, doi:10.1210/en.2006-0916
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Endocrinology Vol. 147, No. 12 5662-5675
Copyright © 2006 by The Endocrine Society

Gene Expression Profiling of Human Endometrial-Trophoblast Interaction in a Coculture Model

Roxana M. Popovici, Nina K. Betzler, Miriam S. Krause, Man Luo, Julia Jauckus, Ariane Germeyer, Sandra Bloethner, Andreas Schlotterer, Rajiv Kumar, Thomas Strowitzki and Michael von Wolff

Department of Gynecological Endocrinology and Reproductive Medicine (R.M.P., N.K.B., M.S.K., M.L., J.J., A.G., T.S., M.v.W.), University of Heidelberg, 69115 Heidelberg, Germany; Division of Molecular Genetic Epidemiology (S.B., R.K.), German Cancer Research Center, and Division of Endocrinology and Metabolism (A.S.), Department of Medicine, University of Heidelberg, 69120 Heidelberg, Germany

Address all correspondence and requests for reprints to: Dr. R. M. Popovici, Department of Gynecological Endocrinology and Reproductive Medicine, University of Heidelberg, Voss Strasse 9, 69115 Heidelberg, Germany. E-mail: roxana.popovici{at}med.uni-heidelberg.de.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Investigating the interaction of human endometrium and trophoblast during implantation is difficult in vitro and impossible in vivo. This study was designed to analyze the effect of trophoblast on endometrial stromal cells during implantation by comprehensive gene profiling. An in vitro coculture system of endometrial stromal cells with first-trimester trophoblast explants was established. Trophoblast and endometrial stromal cells were separated after 24 h. Gene expression of endometrial stromal cells after coculture was compared with the gene expression of endometrial stromal cells cultured alone by microarray analysis. We confirmed the expression of distinct genes using real-time PCR. Genes up-regulated included those for inflammatory response, immune response, and chemotaxis (pentraxin-related gene 3, chemokine ligands, IL-8, IL-1 receptors, IL-18 receptor, IL-15, IL-15 receptor, TNF-{alpha}-induced protein 6, and IL-6 signal transducer), regulators of cell growth (IGF-binding proteins 1 and 2) and signal transduction. Also up-regulated were genes for growth and development, glucose metabolism, and lipid metabolism: DKK-1, WISP, IGF-II, hydroxysteroid 11ß-dehydrogenase 1, hydroxyprostaglandin dehydrogenase 15, prostaglandin E synthase, prostaglandin F receptor, aldehyde dehydrogenase 1 family, member A3 and phosphatidic acid phosphatase type 2B. Other genes included genes for cell-cell signaling (pre-B-cell colony-enhancing factor 1), proteolysis, calcium ion binding, regulation of transcription, and others. Down-regulated genes included genes for proteolysis (MMP-11 and mitochondrial intermediate peptidase), genes for cell death (caspase 6, death-associated protein kinase 1, and histone deacetylase 5), transcription factors (sex determining region Y-box 4, dachshund homolog 1, ets variant gene 1, and zinc finger protein 84 and 435), and genes for humoral immune response (CD24 antigen). Trophoblast has a significant impact on endometrial stromal cell gene expression. Some of the genes regulated by trophoblast in endometrial stromal cells are already known to be regulated by progesterone and show the endocrine function of trophoblast during pregnancy. Others are genes so far unknown to play a role in endometrial-trophoblast interaction and open a wide field of investigation.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
DESPITE THE POSSIBILITY of extracorporal fertilization and extensive new technology, the process of implantation and the interaction between maternal endometrium and invading trophoblast are even today difficult to explore. Hence, the search for better understanding of this process continues and is transferred into the in vitro setting (1, 2, 3). After coming into contact with the maternal decidua, cytotrophoblast cells proliferate and form columns of invasive cytotrophoblast cells, also known as the extravillous trophoblast (4). These cells invade into the decidualized endometrial stromal compartment and the inner third of the myometrium and thus anchor the pregnancy inside the uterus. They also provide the conceptus with nourishment by invading into the maternal vessels (5). Contrary to cancer cells, these invasive properties are well regulated and confined in time and space (6). Only first- and early second-trimester trophoblast has the capacity to invade; later on, the trophoblast only proliferates and increases in size (6). In the first trimester, cells migrate through the decidua; later on, the remodeling of the inner myometrial portion occurs at 14–18 wk gestation and is mediated by intravascular migration of extravillous trophoblast cells (4). Invasion is mediated by multiple factors such as matrix metalloproteinases (MMPs) and other collagenases (7, 8). In abnormalities such as preeclampsia, trophoblast invasion of the uterine spiral arteries does not proceed beyond the decidual portion of the spiral arteries (9, 10); on the other side, when trophoblast invasion becomes excessive, placenta accreta results (11).

Hence, invasion of the trophoblast is a very complex process, and multiple factors must necessarily regulate differentiation, proliferation, and invasion of trophoblast cells. The maternal endometrium is equipped with different cell populations that help to regulate these processes. Endometrial epithelial cells, endometrial stromal cells, immune cells in the endometrium as well as vascular endothelial cells, and, later on in pregnancy, myometrial cells all come into contact with trophoblast. Endometrial stromal cells play a major role during the interaction with the invading trophoblast and change their morphology and secretory pattern during the secretory phase of the cycle in preparation for the invasion. This study was therefore designed to evaluate in vitro the interaction between endometrial stromal cells and trophoblast explants.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patients and samples
Endometrial biopsies were obtained from women undergoing diagnostic hysteroscopy at the Women’s University Hospital, Heidelberg (n = 10), during the proliferative phase (d 8–12) for reasons of uterine myoma (60%) or septal uterus (40%). Patients had a regular cycle without hormonal treatment and no suspect of malignancy. Average age was 33 ± 4 yr. Trophoblast tissue was obtained from legal early pregnancy terminations of uncomplicated, unwanted pregnancies (n = 10). Median age was 24 ± 5 yr. All samples were obtained after informed consent, and the protocol was approved by the University of Heidelberg Ethics Committee.

Establishment of cocultures
Endometrial stromal cells were isolated by a protocol previously published (12) from 10 different patients. Trophoblast tissue (n = 10) was isolated by mechanical dissection and washed thoroughly with warm PBS, and approximately 25 trophoblast explants of an average of 3 mm were carefully set on confluent stromal cells in a 4 cm diameter culture dish. Most trophoblast explants attached to stromal cells after approximately 2 to 3 h and coculture was incubated for a total of 24 h. Stromal cell monocultures from the same patient were used as controls. After 24 h, trophoblast explants were carefully removed from stromal cells with sterile tweezers under the microscope. All stromal cells used were from proliferative-phase endometrium and had been passaged once in culture. Trophoblast explants were taken from placentas at 6–8 wk of pregnancy.

Immunofluorescence
To ascertain that the trophoblast was close to totally removed from stromal cells, immunofluorescent staining of stromal cells before and after separation was performed in preliminary experiments (see Fig. 1Go). Briefly, cocultures (n = 4) were visualized under a microscope and photographed using a digital camera. After three washings with PBS, cocultures before and after separation with tweezers were fixed with ice-cold 80% methanol for 10 min, air dried, and stored at –80 C. For immunofluorescent staining, nonspecific background was blocked with solution A of the Histostain-Plus Kit (Zymed Laboratories, San Francisco, CA), and samples were then incubated with primary antibodies, monoclonal mouse-antihuman vimentin for stromal cells (1:50; Dako, Copenhagen, Denmark) and monoclonal mouse-antihuman cytokeratin-7 for cytotrophoblast cells (1:50; Dako) for 1 h at room temperature (21 C), respectively. After three careful washings with PBS and incubation with a goat-antimouse fluorescein-isothiocyanate-conjugated secondary antibody (1:100; Dako) for 1 h at 4 C, slides were washed in distilled water and covered with Vectashield plus 4',6-diamidino-2-phenylindole (DAPI) mounting medium (Vector, Burlingame, CA).


Figure 1
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  		FIG. 1. Endometrial stromal cells cocultured with trophoblast before and after removal of trophoblast explant. Trophoblast explants were cocultured with endometrial stromal cells for 24 h and then removed with sterile tweezers under the microscope. Cell cultures were stained with cytokeratin-7 and DAPI before and after removal of trophoblast to check for cytotrophoblast cells. A, Trophoblast explant, budding cytotrophoblast, and stromal cells in low magnification (x200); B, higher magnification (x400) from A; C and D, cytotrophoblast stained with specific antibody for cytokeratin-7 (green) and counterstaining for all nuclei with DAPI (blue) before trophoblast removal; E and F, cytokeratin-7-specific immunofluorescent staining of the few remaining cytotrophoblast cells after careful removal of trophoblast branches. The remaining counterstained nuclei are from stromal cells.

 
RNA isolation
RNA (n = 6; three experiments for microchip analysis and another three experiments for real-time PCR) was isolated from stromal cells after trophoblast coculture as well as from controls using Trizol (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Total RNA was then subjected to a cleanup using RNeasy Mini Kit (QIAGEN, Hilden, Germany). RNA concentration was measured by UV spectrophotometry, and OD 260/280-nm ratios between 1.9 and 2.1 were obtained for all RNA samples. The integrity of total RNA isolated from stromal cells was determined on the Bioanalyzer 2100 System (Agilent Technologies, Palo Alto, CA) using 400 ng RNA from each sample.

Gene expression profiling
A total of three experimental sets made up of stromal cells after coculture and stromal cells without previous coculture were used for microarray analysis. Two micrograms of total RNA from each sample were converted into double-stranded cDNA using SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen) according to the manufacturer’s protocol. Double-stranded cDNA was cleaned up with the GeneChip Sample Cleanup Module (Affymetrix, Sunnyvale, CA).

Twelve microliters of the purified cDNA were used for the synthesis of biotin-labeled cRNA using ENZO Labeling Kit (ENZO Diagnostics, Farmingdale, NY). Each cRNA was fragmented according to the protocol in the Affymetrix GeneChip Expression Analysis manual, with quality assessed by hybridization of a 5-µg aliquot to a test chip (TestChip3; Affymetrix). Only cRNA samples that showed at least 27% present calls and 3' to 5' signal ratios of 0.9–1.5 for the housekeeping genes ß-actin and RPL on the test arrays were hybridized to Human HG-U133A 2.0 microarray chips (Affymetrix) with 22,277 probe sets, representing 14,500 human genes (the list of genes is available at www.affymetrix.com). Ten micrograms (0.05 µg/µl) of fragment-labeled cRNA was hybridized onto the array at 45 C for 16 h. Hybridization, washing (on GeneChip Fluidics Station 400; Affymetrix) and scanning (with GeneArray Scanner; Affymetrix) were performed at the German Cancer Research Centre, Heidelberg, Germany, according to the manufacturer’s (Affymetrix) instructions. Complete transcription and hybridization were validated using bacterial sequences as external control as well as several housekeeping genes as internal controls.

Data analysis
Image analysis.
Image analysis was performed with Affymetrix GeneChip operating software (GCOS) to analyze scanned images, to convert intensities to a numerical format, and to obtain a detection call. Target intensities of microarray images were scaled to an average hybridization intensity of 100 to normalize signals between individual chips. Detection call indicated whether a transcript was reliably detected (present) or below background levels (absent). Probe sets whose hybridization signals were below background level, i.e. called absent, were not included. A detection P value, which is calculated using the one-sided Wilcoxon‘s signed rank test, reflects the confidence of the detection call. Additionally, a signal value was calculated for each probe set on the array using the one-step Tukey’s biweight estimate, which assigns a relative measure of abundance to the transcript (signals).

Pairwise comparison.
GCOS was used for pairwise comparisons of expression profiles between stromal cells after coculture with trophoblast (designated experimental arrays) and stromal cells that had not been in coculture (designated as baseline arrays). During comparison analysis, each probe set on the experimental array was compared with its counterpart on the baseline array, and a change in P value was calculated indicating the change call: increase, marginal increase, decrease, marginal decrease, or no change in gene expression. A second algorithm was used to calculate a quantitative estimate of the gene expression change in the form of signal log ratio. A signal log ratio of 1 or –1 corresponded to an increase or decrease, respectively, in transcript level by 2-fold. Thus, from the three experiments, a total of nine comparisons (stroma only vs. stroma after coculture) were made, and the obtained signal log ratios were averaged and then translated into a fold change number. Additional analysis that included sorting of data and identification of overlaps between changed probes was done by using Data Mining Tool software (Affymetrix). Comparisons with a no change call were removed. Gene expression data were sorted according to the relative change and fold change values, and for identification of differentially expressed genes and data interpretation, probe sets with a fold change greater than or equal to 2 were used. Because the data were not normally distributed, nonparametric testing was conducted using Mann-Whitney U test to calculate the P values, applying P ≤ 0.05 to assign statistical significance between the two groups.

Identification of genes.
For gene identification and annotation, we applied our results to Netaffx Analysis Center (http://www.affymetrix.com/analysis/index.affx), which maps the Affymetrix probe identifiers to gene identities including links to Gene Ontology and Pathway software.

Gene ontology (GO) classification.
GO tree machine (GOTM) is a web-based tool that was used to generate groups of genes from the genes identified by pairwise comparisons to be up- or down-regulated. GOTM uses GO hierarchies to discover significant biological processes, molecular functions, and cellular components in a gene list.

Ingenuity Pathways Analysis.
A data set containing gene identifiers and corresponding expression values was uploaded into the Ingenuity Pathways application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. A 2-fold change was set to identify genes whose expression was significantly differentially regulated. These genes, called focus genes, were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Networks of these focus genes were then algorithmically generated based on their connectivity.

The molecular relationship between genes is shown in a graphical representation. Genes or gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. Human, mouse, and rat orthologs of a gene are stored as separate objects in the Ingenuity Pathways Knowledge Base but are represented as a single node in the network. The intensity of the node color indicates the degree of up-regulation (red) or down-regulation (green). Nodes are displayed using various shapes that represent the functional class of the gene product.

Validation of gene expression data
Real-time PCR.
Gene expression of certain genes that were regulated in the array experiment was determined by quantitative real-time PCR in a fluorescent temperature cycler (LightCycler; Roche Diagnostics, Berkeley, CA) according to the manufacturer’s instructions, using the oligonucleotide primer pairs shown in Table 1Go. Total RNA that had been used for the chip hybridization (n = 3) and additional total RNA from three new experiments (total n = 6) was isolated using the RNeasy Mini Kit (QIAGEN), and 1 µg total RNA was reverse transcribed using the First-Strand cDNA Synthesis Kit for RT-PCR (Roche Diagnostics). One tenth of each reverse transcriptase reaction was amplified in a PCR tube containing 3 mM (IL-8, MMP-11, and RPL-19), 4 mM (TIMP-3, FGF-1, PTX-3, and IGFBP-1), or 5 mM (SOX4 and DKK-1) MgCl2, 0.5 µM of each primer and 1x LightCycler FastStart DNA Master SYBR Green I mix (Roche Diagnostics). After an initial preincubation step of 10 min at 95 C, the amplification process, consisting of 40 PCR cycles, was performed. Each cycle consisted of 95 C for 10 sec as denaturation phase, followed by primer annealing and extension (Table 2Go).


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  		TABLE 1. Primers used for microarray gene expression validation

 

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  		TABLE 2. Annealing and extension temperature and time for real-time PCR

 
SYBR Green I fluorescence was monitored after each cycle. Levels of mRNA expression were quantified by using the second-derivative maximum method of the LightCycler Software (Roche Diagnostics) and were normalized to RPL19. Amplification of specific transcripts was confirmed by melting curve profiles at the end of each PCR.

Statistical analysis of PCR data.
All variables were tested in six culture experiments (stroma cells only vs. stromal cells after coculture; n = 6). Data were analyzed by ANOVA using the Stat-View software (Abacus Concepts, Berkeley, CA). Significance between groups was determined using Fisher’s protected least significant difference post hoc test, with P < 0.05 considered significant, P < 0.01 highly significant, and P < 0.001 extremely highly significant. Results are shown as fold change ± SEM on a log10 scale.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Cell culture and immunofluorescence
Cell cultures were established as described in Materials and Methods. Stromal cells that had been cultured together with trophoblast explants for 24 h (and from which trophoblast explants were then removed) were compared with stromal cells from the same patient that were cultured without trophoblast. Trophoblast explants were removed carefully without leaving significant cytotrophoblast cells in contact with stromal cells before RNA isolation. This was ascertained by immunofluorescent staining of cocultures before and after trophoblast removal in preliminary experiments (Fig. 1Go). Exemplary light microscopy of a trophoblast explant on stromal cells is shown in panels A and B. C and D show immunofluorescence of cytokeratin-7-stained trophoblast in coculture. Panels E and F show trophoblast staining by cytokeratin-7 (green staining) after removal of trophoblast. The DAPI-stained (blue stain) cells in panel F are stromal cells (with exception of the two cytokeratin-7-positive cytotrophoblast cells). For the microarray experiments, three experimental groups were selected.

Data analysis
Expression analysis using the GCOS module showed an average of 58.93% (13,130 ± 411) of transcripts were scored as being present in stromal cells without coculture and an average of 57.63% (12,833 ± 383) of transcripts were present in stromal cells after coculture. Pairwise comparison was performed, and we thus received nine data sets from the three experimental setups. Two criteria were applied for sorting the data in Data Mining Tool. First, change of expression level of 2-fold or more (which is equal to a signal log ratio of at least 1 or –1, respectively) and, second, 88–100% concordance in increase or decrease of expression in each single comparison (from three different experiments from which a total of nine comparisons were made, a change had to be seen in at least eight of these nine comparisons, i.e. 88%). Gene expression changes identified by these criteria were then tested further by Mann-Whitney U test applying P < 0.05 to show statistical significance between the two groups. Based on these criteria, expression of 165 genes was increased (Table 3GoGoGo) in stromal cells after coculture with trophoblast vs. stromal cells that had not been cultured with trophoblast, and expression of 119 genes was decreased (Table 4GoGo).


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  		TABLE 3. Genes up-regulated in human endometrial stromal cells after trophoblast coculture

 

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  		TABLE 3A. Continued

 

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  		TABLE 3B. Continued

 

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  		TABLE 4. Genes down-regulated in human endometrial stromal cells after trophoblast coculture

 

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  		TABLE 4A. Continued

 
The data received by this stringent filtering and analysis was then imported into GOTM, which allowed grouping of genes into different categories (Tables 3GoGoGo and 4GoGo). In stromal cells that had been in contact with trophoblast, genes involved in inflammatory response and signal transduction were most markedly up-regulated. In order of maximal fold change (Table 3GoGoGo), these include IL-8, chemokine ligand 1 and 2 (CXCL1 and 2), pentraxin-related gene (PTX3), and IL-1 receptor type 1 and type 2 (IL1R1 and IL1R2) as well as glycoprotein hormone {alpha}-polypeptide (CGA), G protein-coupled receptor 126 (GPR126), and IL-15 receptor {alpha} (IL-15RA). Further up-regulated were genes involved in proteolysis such as MMP-12, ADAM metallopeptidase type 1 motif 1 and 3 (ADAMTS1 and ADAMTS3), and the proteolysis inhibitor TIMP3. Additional genes were up-regulated, including apoptotic genes like TNF-{alpha}-induced protein 3 and 6 (TNFAIP3 and TNFAIP6) as well as cell-growth-related genes like IGFBP-1, IGFBP-2, IGFBP-4, osteopontin, and IGF-2. Genes in response to reactive oxygen species and oxidative stress, e.g. superoxide dismutase 2 (SOD2) or glutathione peroxidase 3 (GPX3) were also up-regulated. Genes responsible for transport, be it metal ion (metallothionein 2A, MT2A, and solute carrier family 39 zinc transporter, SLC39), sodium ion (solute carrier family 22, SLC22), or electron (monoamine oxidase A and B, MAO A and MAO B) as well as peptide transport (transporter 1, TAP1) were significantly up-regulated. Genes for lipid metabolism (hydroxysteroid 11ß-dehydrogenase, HSD11B1), prostaglandin metabolism (hydroxyprostaglandin dehydrogenase 15, HPGD15, and prostaglandin E synthase, PTGES) and carbohydrate metabolism (carbonic anhydrase 2 and 12, CA2 and CA12) were also increased in stromal cells. Genes involved in regulation of progression through the cell cycle and regulation of transcription (v-myc myelocytomatosis viral oncogene homolog, MYC, and signal transducer and activator of transcription 1, STAT1) were up-regulated.

Many genes that have hitherto been unknown to be expressed in endometrial stromal cells were down-regulated in stromal cells after coculture with trophoblast compared with controls (Table 4GoGo). GO grouping of these genes includes many genes for regulation of transcription that is DNA dependent, such as dachshund homolog 1 (DACH1) and sex-determining region Y-box 4 (SOX4) as well as genes responsible for regulation of progression through the cell cycle such as fibroblast growth factor-1 and -9 (FGF-1 and FGF-9) or phospholipase Cß1 (PLCB1). Furthermore, genes for protein amino acid phosphorylation (dual-specificity tyrosine phosphorylase, DYRK2) and glycosylation (UDP-galactosidase, B3GALT3) were down-regulated between 2- and 6-fold. Many genes for cell adhesion (integrin-{alpha}6, ITGA6), signaling transduction (G protein-coupled receptor 153, GPR153), and intracellular signaling cascade (plexin B1, PLXNB1) as well as intracellular protein transport (ADP-ribosylation factor-like 7, ARL7) were also decreased in our experimental setup. Genes with the highest fold change in the decreased group were genes for cytoskeletal anchoring (ankyrin 3, ANK3), for proteolysis (MMP-11; platelet derived growth factor D, PDGFD; mitochondrial intermediate peptidase, MIPEP; and tripeptidyl peptidase 1, TPP-1), and for humoral immune response (CD24). The most highly down-regulated gene is chondrolectin (CHODL), which is known to be involved in muscle development but has also been shown to be expressed in human testis and was decreased 16.8-fold in our experiment.

Interestingly, there are many GO groups that are both increased and decreased. Instead of just enumerating a list of genes, we also wanted to know how they interact as parts of complex pathways and biological networks. For this purpose, the differentially expressed genes were analyzed using Ingenuity Pathways Analysis software (Ingenuity Systems), and networks as well as individual signaling pathways were generated. The networks describe functional relationship between genes based on known interactions in the literature. Biological functions are assigned to each network, and these networks are then associated with individual canonical pathways. Fourteen highly significant networks with a score of at least 12 were identified from the 284 genes differentially regulated in stromal cells after coculture with trophoblast. First we looked at the network in which our highest regulated gene IL-8 is involved, which was network two. The top functions for this network are labeled inflammatory, immune, hematological, and development and is shown in Fig. 2Go. IL-15 has direct impact on the expression of genes like IL-8, IL-18 receptor type 1 (IL-18R1), STAT1, and LYN. IL-8, which is most significantly up-regulated in our experiments, is regulated by genes like CXCL1, IL-1 receptor type 1 (IL-1R1), NF-{kappa}B inhibitor {alpha} (NFKBIA), and MAP3K8. Current literature shows that IL-15 elicits synthesis and release of IL-8 in neutrophils (13) and monocytes (36). Furthermore, IL-15 has the ability to induce NFKB in neutrophils (13). All three of these genes are up-regulated in endometrial stromal cells after coculture with trophoblast. Also IL-1R1, which is the essential receptor for IL-1 signaling (15), induces IL-8 by its activation (see also Fig. 3Go). In contrast, both CD24 and KIT were down-regulated in our experiments, with CD24 known to be involved in LYN-mediated apoptosis (16) and KIT known to increase proliferation of cells (17).


Figure 2
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  		FIG. 2. Functional relationship of factors involved in inflammatory, immune, hematological, and developmental processes during trophoblast interaction with endometrial stromal cells. The intensity of color indicates the degree of up-regulation (red) or down-regulation (green). Uncommon gene symbols: MT1G, metallothionein 1G; MT1H, metallothionein 1H; EDG2, endothelial differentiation, lysophosphatidic acid G protein-coupled receptor 2; OSMR, oncostation M receptor. IL-15 has direct impact on the expression of genes such as IL-8, IL-18 receptor type 1 (IL-18R1), STAT1, and LYN. IL-8, which is most significantly up-regulated in our experiments, is regulated by genes such as CXCL1, IL-1 receptor type 1 (IL-1R1), NF-{kappa}B inhibitor {alpha} (NFKBIA), and MAP3K8.

 

Figure 3
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  		FIG. 3. IL-8 pathway in stromal cells activated by trophoblast. Gene symbols are equivalent to those in Fig. 2Go. Uncommon gene symbols: I{kappa}B, nuclear factor of {kappa}-light polypeptide gene enhancer in B-cells inhibitor {alpha}; TSG6, TNF-{alpha}-induced protein 6; GP130, IL-6 inducer. The effect of IL-1 produced by the trophoblast acting on endometrial stromal cells through the IL-1R and the NFKB pathway on the induction of IL-8 transcription in the nucleus of endometrial stromal cells is shown.

 
Figure 3Go shows the pathway for IL-6 and IL-8 as it is known to date. Seven of the differentially expressed genes from our data are increased and are shown in red where the symbol IL-1R includes both IL-1 receptor type 1 and IL-1 receptor type 2 (5- and 28-fold increased, respectively). The transcription factor nuclear factor-{kappa}B (NF-{kappa}B) is increased 2-fold in our data, and NF-{kappa}B inhibitor {alpha} (I{kappa}B) is increased 6-fold. IL-6 signal transducer (IL-6ST, labeled GP130 in Fig. 3Go) is 2.5-fold increased. IL-8 is the most highly increased gene in our data set (over 300-fold) and TNFAIP6 (TNF {alpha}-induced protein 6, labeled TSG6) is increased 4-fold. The figure shows the possible effect of IL-1 produced by the trophoblast acting on endometrial stromal cells through the IL-1R and the NFKB pathway on the induction of IL-8 transcription in the nucleus of endometrial stromal cells.

Validation of gene expression
Real-time PCR was used to validate a selected group of up-regulated and down-regulated genes. The primer sets used are shown in Table 1Go. IGFBP-1 is already known to play a role in endometrial-trophoblast interaction (18); DKK-1, TIMP-3, MMP-11, and SOX4 have been shown to be regulated in the endometrium during the cycle (19) as well as IL-8 (20, 21), whereas PTX-3 to our knowledge has not been shown in the endometrium to date. All eight genes selected for validation were regulated in PCR as in the microarrays. IL-8, PTX-3, IGFBP-1, DKK-1, and TIMP-3 were up-regulated in a similar fold change both in the microarrays and in real-time PCR experiments (Fig. 4Go). MMP-11, SOX4, and FGF-1 were all decreased in the same order in the PCR experiments as in the microarray experiments. All but FGF-1 expression changes were statistically significantly regulated in the real-time PCR analysis.


Figure 4
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  		FIG. 4. Validation of microarray data by real-time PCR. Real-time PCR was performed in six experimental samples. Fold change values are displayed above each gene and are blotted on a log10 scale, with P values as follows: *, P < 0.05 considered significant; **, P < 0.01 highly significant; and ***, P < 0.001 extremely highly significant. Error bars represent SEM.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
High-density DNA microarrays are a powerful tool for the snapshot analysis of the complete gene expression profile of cells and tissues involved in biological processes. In the present study, we used this technology for the analysis of human endometrial stromal cells that have interacted with trophoblast explants in culture mimicking human invasion of trophoblast. Even though we used stromal cells and trophoblast isolated from different individuals, we do not expect an effect on the immune reaction because we know from in vitro fertilization cycles with oocyte donations that a similar implantation and pregnancy rate is seen as in blastocysts transferred to their biological mothers. We identified genes that are regulated by this interaction. Our data show tellingly how many different biological processes are regulated concomitantly within stromal cells by contact with trophoblast. The genes identified can be used to further analyze the process of implantation and highlight gene groups involved in stromal cell response to trophoblast invasion.

Of the GOTM categories for up-regulated genes, there is a striking increase of genes involved in immune response and regulation and modulation of inflammatory reaction. Based on published data, all but four of the genes in this GO group have been previously described in endometrium but have not necessarily been associated with trophoblast-stromal interaction. IL-8 has been described to play a role in parturition (22) as well as in the pathogenesis of endometriosis (23). Furthermore, it has been described to be important for the leukocyte recruitment in the endometrium during the secretory phase (24). IL-15 has been demonstrated in first-trimester decidua (25) as well as in nonpregnant endometrium (26) in secretory-phase stromal cells. It is interesting to note that both IL-8 and IL-15 as well as the IL-1 receptor type 1 are up-regulated in the trophoblast coculture as well as in stromal cells treated with progesterone or cAMP (12, 27, 28); however, other cytokines known to be increased in endometrial stromal cells after progesterone or cAMP treatment are not regulated after trophoblast contact, even though progesterone levels in the coculture conditioned medium after 24 h were an average of 35 ng/ml (data not shown). This may reflect inhibitory effects of the trophoblast on cytokine expression. An alternative explanation would be that cytokines or cytokine receptors inhibit production of other cytokines, which would also explain the high redundancy of the cytokine networks. Notably, IL-11 production is regulated by progesterone, estradiol, and relaxin in in vitro cultured endometrial stromal cells in both directions (29), showing a complex process of regulation. Our data do not show IL-11 regulation; however, gp130 (IL6ST), which is dimerized by binding of IL-11 to its receptor and leads to the activation of the janus kinase/signal transducer and activator of transcription (JAK/STAT) signal transduction pathway (30), is increased 2-fold in our data. Gp130 has hitherto been described in endometrial epithelial cells and is reduced in infertile women (31). Accordingly, STAT1 is increased 2.4-fold and STAT3 1.9-fold. IL-15 is known to be stimulated by progesterone and at the same time is inhibited by IL-1 (32). Both factors are present in our coculture conditioned medium (data not shown). The total effect shown by the data presented herein is an increase of IL-15 expression in endometrial stromal cells because of trophoblast action. Other factors such as IGFBP-1 are even more increased in stromal cells with trophoblast contact than in stromal cells with just progesterone and estrogen treatment (33), which is in accordance with published data showing hCG to further increase IGFBP-1 production in decidualized endometrial stromal cells (34). Our data indicate that a fingerprint gene expression pattern can be shown for trophoblast action on endometrial stromal cells that can be discriminated from progesterone and estradiol action on these cells. Furthermore, our data provide insight on genes that are distinctly changed in their expression by trophoblast action despite both inhibitory and enhancing factors of known origin playing a role. We conclude that the trophoblast-induced reaction in stromal cells is a result of multiple factors that have very complex interactions.

This interrelationship can be elucidated on one hand by using both molecular and proteomic systems and on the other hand by using computerized pathway and network building systems, which can help immensely at indicating possible relevant processes, especially for genes not yet described in the endometrium. It is known that IL-8 increases expression of CXCL1 (Gro {alpha}) and CXCL2 (Gro ß) (35), and simultaneously CXCL1 decreases binding of IL-8 protein (Fig. 3Go) in the sense of negative regulation. Also, IL-15 induces IL-8 production in human monocytes (36). Pentraxin (PTX3), a gene inducible by TNF-{alpha}, IL-ß, and lipopolysaccharides, increases CXCL2 production in inflammatory reactions of mice (37). It is very interesting that PTX3 has recently been shown to be elevated in maternal serum of women with preeclampsia and intrauterine growth restriction (38), which are both pathological conditions caused by faulty implantation. Interestingly, in a previous study, human endometrial epithelial cells expressed chemokine receptors CXCR1, CXCR4, and CCR5 when cultured in the presence of a blastocyst in an apposition model for human implantation (39). This shows the importance of the chemokine system for apposition, adhesion, and invasion.

Next to the cytokine system (Fig. 3Go), additional relevant pathways derived from our data include IGF signaling, PDGF signaling, PPAR signaling and GM-CSF signaling. Common denominators in our study are NF{kappa}B signaling, JAK/STAT signaling, and G-protein signaling pathways.

Of further note is the group of genes related to cell death. Many differentially expressed genes belonging to this group are down-regulated in our experiment. Death-associated protein kinase 1 (DAPK1) functions as a positive mediator of apoptosis (40) and is increased by p53 (41) and by TGF-ß signal transduction (42). It is reduced 2.2-fold by trophoblast invasion. Histone deacetylase 5 (HDAC 5) is not a p53 target gene, but it also induces apoptosis in multiple tumor cell lines (43). Furthermore, histone deacetylase inhibitors such as suberoylanilide hydroxamic acid (SAHA) or trichostatin A (TSA) induce cell differentiation and are currently tested as anticancer drugs (44). Other genes related to apoptosis are SOX4 and caspase 6. SOX4, a member of the SOX [Sry-related high-mobility group (HMG) box 4] family transcription factors and involved in the determination of cell fate is repressed in stromal cells by trophoblast contact. It may function in the apoptosis pathway leading to cell death (45, 46). In our experiment, a distinct down-regulation of 3.9-fold is notable after trophoblast attachment, which shows an antiapoptotic effect of the trophoblast on endometrial stromal cells. Caspase 6 protein is known to increase apoptosis of different cell types (47). It is processed by caspase 7, 8, and 10 and is thought to function as a downstream enzyme in the caspase activation cascade (48). In rat endometrium, it has been shown to play a key role in apoptosis at estrus, when no embryo has implanted (14), which underscores the importance of its down-regulation when implantation has taken place. In summary, our data suggest that when implantation occurs and trophoblast comes into contact with endometrial stromal cells, a decrease of apoptotic stimulus is required to assure that shedding and destruction of endometrial stroma do not occur. Additionally, antiapoptotic signaling in endometrial stromal cells ensures that stromal cells remain in place and provide a barrier against excessive trophoblast invasion, which could clinically result in placenta accreta. This also supports the finding of increased gene expression of proliferation-related genes such as IGF-II and IGFBP-1, -2, and -4, as well as hepatocyte growth factor (HGF). In summary, we conclude that trophoblast action is inducing proliferation and inhibiting apoptosis in endometrial stromal cells, which is concordant with the implantation-related growth of all uterine structures. At the same time, the down-regulation of cell adhesion molecules between stromal cells (i.e. ankyrin) possibly facilitates trophoblast invasion during implantation.

The whole extent of gene expression and interaction cannot entirely be discussed herein; however, additional investigations can be based on the data recruited by this analysis. The extent to which our results can be reproduced in vivo remains to be seen, but we hope that they will help future investigation of molecular mechanisms in human implantation and to elucidate predisposing mechanisms to abnormal implantation that result in infertility, recurrent miscarriages, and intrauterine growth restriction as well as preeclampsia.


   Acknowledgments
 
We gratefully thank Dr. Brian Dron and Dr. Adam Corner from Ingenuity Systems, Inc., for their friendly help with the network and pathwork system development. Thanks to all colleagues from the Department of Obstetrics and Gynaecology for support in the collection of tissue samples. Special thanks to Dr. S. Kolay for her constant support during manuscript writing.


   Footnotes
 
Disclosure statement: R.M.P., N.K.B., M.S.K., M.L., J.J., A.G., S.B., A.S., R.K., and M.v.W. have nothing to declare. T.S. received lecture fees from Serono.

First Published Online August 31, 2006

Abbreviations: DAPI, 4',6-Diamidino-2-phenylindole; GCOS, GeneChip operating software; GO, gene ontology; GOTM, GO tree machine.

Received July 11, 2006.

Accepted for publication August 24, 2006.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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