The Molecular Phenotype of Polycystic Ovary Syndrome (PCOS) Theca Cells and New Candidate PCOS Genes Defined by Microarray Analysis*,

Jennifer R. Wood {ddagger}, Velen L. Nelson §, Clement Ho {ddagger}, Erik Jansen ¶, Clare Y. Wang {ddagger}, Margrit Urbanek ||, Jan M. McAllister §, Sietse Mosselman ** and Jerome F. Strauss, III {ddagger} {ddagger}{ddagger}

From the {ddagger}Center for Research on Reproduction and Women's Health, University of Pennsylvania, Philadelphia, Pennsylvania 19104, the §Department of Cellular and Molecular Physiology, Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033, the ||Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine of Northwestern University, Chicago, Illinois 60611, and the Target Discovery Unit and **Department of Pharmacology, NV Organon, 5340 BH Oss, The Netherlands

Received for publication, January 21, 2003 , and in revised form, April 9, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Polycystic ovary syndrome (PCOS) affects 5% of reproductive aged women and is the leading cause of anovulatory infertility. A hallmark of PCOS is excessive theca cell androgen secretion, which is directly linked to the symptoms of PCOS. Our previous studies demonstrated that theca cells from PCOS ovaries maintained in long term culture persistently secrete significantly greater amounts of androgens than normal theca cells, suggesting an intrinsic abnormality. Furthermore, previous studies suggested that ovarian hyperandrogenemia is inherited as an autosomal dominant trait. However, the genes responsible for ovarian hyperandrogenemia of PCOS have not been identified. In this present study, we carried out microarray analysis to define the gene networks involved in excess androgen synthesis by the PCOS theca cells in order to identify candidate PCOS genes. Our analysis revealed that PCOS theca cells have a gene expression profile that is distinct from normal theca cells. Included in the cohort of genes with increased mRNA abundance in PCOS theca cells were aldehyde dehydrogenase 6 and retinol dehydrogenase 2, which play a role in all-trans-retinoic acid biosynthesis and the transcription factor GATA6. We demonstrated that retinoic acid and GATA6 increased the expression of 17{alpha}-hydroxylase, providing a functional link between altered gene expression and intrinsic abnormalities in PCOS theca cells. Thus, our analyses have 1) defined a stable molecular phenotype of PCOS theca cells, 2) suggested new mechanisms for excess androgen synthesis by PCOS theca cells, and 3) identified new candidate genes that may be involved in the genetic etiology of PCOS.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Polycystic ovary syndrome (PCOS)1 is characterized by failure of ovulation, excessive ovarian androgen production, and, consequently, infertility (1, 2). The PCOS ovaries are enlarged bilaterally and contain follicles arrested at a size no larger than 10 mm embedded in a dense stroma (3). The theca layers of these follicles are prominent and represent the major source of the increased circulating androgens in PCOS women (2). Previous studies have suggested that the ovarian hyperandrogenemia associated with PCOS clusters in families and appears to be inherited as an autosomal dominant trait (2). However, the genetic etiology of PCOS has not been defined.

Studies using freshly isolated and short and long term cultures of theca cells have demonstrated that androgen synthesis is increased in PCOS compared with normal theca cells, suggesting that this PCOS phenotype is an intrinsic property of the theca cell (1, 46). Increased theca cell steroidogenesis has been attributed to increased activity of the 17{alpha}-hydroxylase/17,20-lyase (CYP17) and 3{beta}-hydroxysteroid dehydrogenase type II enzymes (1, 5, 6) and increased expression of the P450 side chain cleavage (P450scc) and CYP17 mRNAs. Furthermore, CYP17 promoter activity is increased in PCOS compared with normal theca cells (6, 7). In contrast, the abundance of the mRNAs for steroidogenic acute regulatory protein (StAR), which controls the rate-limiting step in steroidogenesis (8), and 17{beta}-hydroxysteroid dehydrogenase type V, which is the theca cell enzyme that is thought to be responsible for the reduction of androsterone into testosterone (9) as well as the transcriptional activity of the StAR promoter, are not different between normal and PCOS theca cells (6, 7, 10). Thus, these collective studies have defined a stable steroidogenic phenotype for the PCOS theca cell that includes altered expression of a subset of proteins that are important for androgen synthesis.

Steroidogenesis in the ovarian theca cell is primarily regulated by luteinizing hormone (LH), which upon binding to the LH receptor promotes increased steroid production through activation of cAMP-dependent signal transduction cascades (11). Women with PCOS tend to have elevated levels of LH (3). Furthermore, there is evidence that PCOS theca cells may be hypersensitive to LH action (1). Interestingly, the differences in CYP17 and P450scc mRNA abundance and CYP17 promoter activity in PCOS theca cells are enhanced when cells are treated with the adenylate cyclase activator forskolin (6, 7), which mimics LH-dependent signal transduction in theca cells through production of the second messenger cAMP. However, the transcription of the StAR gene and other genes encoding steroidogenic proteins is also regulated by LH and cAMP (12, 13), suggesting that a downstream component of the cAMP-dependent signal transduction cascade that affects CYP17 and P450scc but not StAR gene transcription is affected in PCOS theca cells.

Although the studies described above have identified important correlations between increased steroidogenic enzyme gene expression and increased androgen biosynthesis in PCOS theca cells, they have not disclosed the upstream genes that are important for increased transcription. Furthermore, the global changes in theca cell gene expression or the alterations in gene networks or signal transduction cascades that may play an important role in the manifestation of other PCOS theca cell phenotypes, which may contribute to arrested follicular growth, have not been defined. In order to define the genes that are differentially expressed in PCOS theca cells and to identify new candidate genes that may contribute to the etiology of PCOS, we compared gene expression profiles of normal and PCOS theca cells using Affymetrix oligonucleotide microarray chips (Affymetrix, Santa Clara, CA).


    EXPERIMENTAL PROCEDURES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Theca Cell Culturing and RNA Isolation—Theca cells were isolated from 3–5-mm follicles from the ovaries of four normal women and five PCOS patients, and independent cultures were established using the cells that were isolated from each woman as previously described (6, 7). The diagnosis of PCOS and the steroidogenic capacity of each sample was determined as previously described (6, 7, 14). For microarray hybridizations and RT-PCR experiments, fourth passage cells from the four normal and the five PCOS theca cell samples were cultured for 48 h in serum-free medium, which contained no treatment (untreated), 20 µM forskolin (Sigma), and/or 5 µM all-trans-retinoic acid (Sigma). After treatment, the medium was removed, the cells were washed with phosphate-buffered saline, and RNA was isolated using TRIzol reagent (Invitrogen).

Dehydroepiandosterone (DHEA) Radioimmunoassay—Fourth passage normal and PCOS theca cells were grown to 80% confluence in six-well tissue culture plates. The cells were transferred to serum-free medium and were untreated, treated with 20 µM forskolin, treated with 5 µM atRA, or treated with 5 µM atRA and 20 µM forskolin. After 72 h, the medium was collected, and DHEA levels were detected with the Coat-A-Count DHEA Radioimmunoassay kit (Diagonistic Products Corp., Los Angeles, CA).

Microarray Hybridization—The Affymetrix GeneChip Human Genome U95A, U133A, and U133B microarray chips (Affymetrix) were hybridized at the University of Pennsylvania Microarray Core Facility. Briefly, biotin-labeled cRNA, which was generated from four different normal and five different PCOS theca cell samples that were untreated or forskolin-stimulated, was fragmented according to Affymetrix protocols. The fragmented cRNA from each sample was hybridized to individual Affymetrix U95A gene array chips using the GeneChip Fluidics Station 400 protocol (Affymetrix), the hybridized chips were scanned using the Agilent GeneArray Scanner (Affymetrix), and a scaling factor was applied to each chip using the Affymetrix Microarray Suite 5.0 software to normalize the mean raw fluorescence intensity for each chip to an average base-line fluorescence level. The same fragmented cRNA from each theca cell sample was subsequently hybridized to individual Affymetrix U133A and Affymetrix U133B gene array chips, and the hybridized chips were scanned and normalized as described.

Gene Expression Analysis—Each transcript on the U95A, U133A, and U133B chip was determined to be present or absent in each theca cell sample using the statistical expression algorithm of the Affymetrix Microarray Suite 5.0 software package (15) and was identified as expressed in the normal or PCOS theca cell samples if it was called present in at least three samples in each group. The average normalized fluorescence intensity for each expressed transcript in the four normal or the five PCOS samples was determined using GeneSpring 4.2 (Silicon Genetics, Redwood City, CA) and expressed as a ratio of the mean normalized fluorescence intensity for the transcript in PCOS theca cells to the mean normalized fluorescence intensity for the transcript in normal theca cells (PCOS-to-normal ratio). The S.E. associated with each transcript's average normalized fluorescence intensity was determined using a cross-gene error model generated by the GeneSpring software program (16). Statistically significant differences (p < 0.05) in the average normalized fluorescence intensity of each transcript between the normal and PCOS samples were determined by parametric testing, which used the cross-gene error model (16). The gene associated with each differentially expressed transcript on the U95A, U133A, and U133B chips was identified, and the function of each of gene was determined.

Reverse Transcription, PCR, and Quantitative RT-PCR—Total RNA (5 µg) which was isolated from the same theca cell samples that were used for the microarray hybridization was treated with DNase I (Promega, Madison, WI) and reverse transcribed with Moloney murine leukemia virus (Promega) as previously described (10). The resulting cDNA was used to carry out PCR amplification of prostate short-chain dehydrogenase reductase (5'-CCACCTCTACTAAAAAATTGTGTATATCTTTG and 5'-TGTGGCTGTTTTGAACTTTGTGA), retinol dehydrogenase 4 (5'-GGCACCAATCCCACTCCTT and 5'-CCCGTTTTTCAGCTGCGTAA), cellular retinoic acid-binding protein 1 (5'-TGGCCTTGGTGCCTCTTG and 5'-TGACTTCGAAACCGTGCAAA), and cellular retinoic acid-binding protein 2 (5'-GGTCACTGGGATGCCTCTTG and 5'-GCTCTTGCAGCCATTCCTCTT). The cDNA was also subjected to quantitative PCR amplification for 28 different transcripts (Supplemental Table 3) that were identified as present in theca cell samples by the Affymetrix U133A or U133B chip. For each of the 28 transcripts, primers were designed using the Primer Express 1.5 software (PerkinElmer Life Sciences). Each primer set was tested empirically to determine the maximal concentration of primers that could be used to produce specific amplification of the target sequence in the absence of primer dimer amplification. For each of the 28 targets, quantitative PCRs were carried out using equivalent dilutions of each cDNA sample, the fluorescent indicator SYBR green, the empirically determined concentration of each primer, and the Applied Biosystems model 7700 sequence detector PCR machine (PerkinElmer Life Sciences) as previously described (10). To verify that only a single PCR product was generated for each amplified transcript, the multicomponent data for each sample was subsequently analyzed using the Dissociation Curves 1.0 program (PerkinElmer Life Sciences). To account for differences in starting material, quantitative PCR was also carried out for each cDNA sample using the Applied Biosystems human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) 20x primer and probe reagent (PerkinElmer Life Sciences). These quantitative PCRs defined a threshold cycle (Ct) of detection for the target or the GAPDH in each cDNA sample. In order to convert this Ct value into a relative abundance of target and GAPDH in each cDNA sample, quantitative PCR for the target and for the GAPDH was also carried out using serial dilutions of theca cell cDNA. An arbitrary value of template was assigned to the highest standard, and corresponding values were assigned to the subsequent dilutions, and these relative values were plotted against the Ct value determined for each dilution to generate a standard curve. The relative amount of target and GAPDH in each sample was then determined using the equation,

(Eq. 1)
where b represents the y intercept, and m is the slope. The relative abundance of the target was divided by the relative abundance of GAPDH in each sample to generate a normalized abundance for each of the 28 transcripts tested. Analysis of variance was then used to determine the mean and S.E. of the normalized abundance of each target in normal and PCOS theca cells. The nonparametric, Wilcoxon (rank sums) test was carried out to determine whether differences in the normalized abundance for each target between normal and PCOS samples were statistically significant (p < 0.05).

Enzyme Assay—Whole cell extracts from two independent normal and two independent PCOS theca cell samples were assayed for retinol dehydrogenase activity. Briefly, 100 µg of each protein sample was combined with 178 pmol of [11,12-3H]all-trans-vitamin A alcohol (PerkinElmer Life Sciences) and 100 µM NAD+ (Sigma) in buffer (100 mM Tris, pH 7.5, 150 mM NaCl, 1% Nonidet P-40, and 0.02% sodium azide) containing protease inhibitors (50 µg/ml leupeptin, 5 µg/ml phenylmethylsulfonyl fluoride, 1 µg/ml pepstatin, and 5 µg/ml aprotinin). Samples were incubated for 1 h at 37 °C. Reactions were stopped by the addition of 200 µl of chloroform/methanol (2:1). The retinoids were collected in the organic phase, which was evaporated using a stream of liquid nitrogen. Each sample was resuspended in 50 µl of ethanol. Each sample was applied to a Silica-Rapid-Platten Woelm F 254 thin layer chromatography plate. In addition, 2 µmols of retinaldehyde (Sigma) was also applied to the plate. The retinol and retinaldehyde were resolved on the plate using a petroleum ether/acetone (82:18) solvent, the plate was sprayed with EN3HANCE spray surface autoradiography enhancer (PerkinElmer Life Sciences), and the retinaldehyde was detected by autoradiography. The radioactive retinaldehyde in each sample was counted, the background levels were subtracted, and the mean fmol of retinaldehyde/mg of protein/h of incubation was determined.

Western Blot Analysis—Nuclear extracts from two independent normal and three independent PCOS theca cells samples, which were untreated or treated with 20 µM forskolin, were probed for the presence of GATA-6 protein. The GATA-6 antibody (Santa Cruz Biotechnology, Inc., Santa Cruz, CA) was used as described by the manufacturer for immunoblot assays. The 64- and 52-kDa immunoreactive bands were detected using SuperSignal West Pico Sensitivity Substrate (Pierce).

Plasmids—The promoter region of CYP17 was amplified from genomic DNA using the primers 5'-GAACGAGCAAGCCTTCATCG-3' (–1876 to –1857) and 5'-GACAGCAGTGGAGTAGAAGAGC-3' (+12 to +33). Likewise, the promoter region of P450scc was amplified from genomic DNA using the primers 5'-GGAATGTGGGGCTGCGTAGA-3' (–1843 to –1824) and 5'-CAGCTGTGACTGTACCTGCT-3' (+12 to +31). Each amplified promoter product was cloned into pCR2.1-TOPO (Invitrogen), excised using the restriction endonucleases KpnI and XhoI, and ligated into the pGL3-basic luciferase reporter vector (Promega) to generate pGL3.CYP17–1876 and pGL3.CYP11A-1843. Sequence integrity and insert orientation were confirmed by DNA sequencing. The pGL2.StAR-885 reporter plasmid has been previously described (17). pcDNAG6, encoding the full-length murine GATA-6 was a generous gift from Dr. Edward Morrisey (18). pcDNA3 (Invitrogen) and pRL-TK (Promega) were purchased.

Transient Transfections and Reporter Gene Assays—HeLa cells, which do not express GATA-6 mRNA (19), were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum. For transient co-transfection experiments, 5.5 x 104 cells were seeded into each well of a 12-well plate. After 24 h, 500 ng of pGL3.CYP17–1876, CYP11A-1843, or pGL2.StAR-885; 25 ng of pcDNAG6 or pcDNA3; and 20 ng of pRL-TK were transiently transfected into cells using FuGENE6 transfection reagent (Roche Applied Science) per the manufacturer's protocol. After 48 h, the transfected cells were lysed, and the firefly and Renilla luciferase activities were determined using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's protocol. The firefly luciferase activity for each sample was normalized using the Renilla luciferase activity. Analysis of variance was used to calculate the mean and S.D. of the normalized luciferase activity for each experimental group in three independent experiments. The unpaired Student's t test was used to detect statistically significant differences (p value < 0.05) between pcDNAG6 and pcDNAG3-transfected cells.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
PCOS Theca Cells Exhibit a Distinct Gene Expression Profile Compared with Normal Theca Cells—Previous studies indicated that excess androgen synthesis is an intrinsic property of PCOS theca cells (6, 7). In agreement with these previous findings, the PCOS theca cell samples used in this study also consistently produced higher levels of DHEA than the normal theca cell samples (Fig. 1), consistent with the possibility that PCOS and normal theca cells have distinct molecular phenotypes. To assess the molecular phenotype of these normal and PCOS theca cells, we carried out gene expression profiling using Affymetrix GeneChip arrays. RNA was collected from four independent normal and five independent PCOS theca cell samples, was hybridized to individual Affymetrix U133 gene chips, and the gene expression profile of each sample was determined using the GeneSpring 4.2 data-mining software program. Of the 45,000 transcripts interrogated on the U133 chips, 15,267 (~34%) transcripts were identified as present in either normal or PCOS theca cells, which is consistent with gene expression profiles of other tissues (2022). When the gene expression profiles from the normal and PCOS theca cell samples were compared, 346 genes had a statistically significant difference in mRNA abundance between normal and PCOS theca cells (Fig. 2A). These 346 transcripts represented only a small percentage (2.3%) of genes expressed in theca cells. Furthermore, only 106 of the genes had greater than a 2-fold difference in gene expression, and only four of the genes had greater than a 5-fold difference in mRNA abundance between normal and PCOS theca cells, demonstrating that the magnitude of differential gene expression between normal and PCOS theca cells is modest. When the 346 genes were organized into functional categories, signal transduction molecules, genes associated with cellular metabolism, transcription factors, cell adhesion molecules, cell surface antigens, ion channels, and expressed sequence tags were among the groups of genes with altered mRNA abundance in PCOS theca cells (Supplemental Table 4). One of the transcription factors that exhibited a highly significant increase in mRNA abundance in the PCOS theca cells was GATA-6, which has been shown to regulate StAR expression in the porcine ovary (23), suggesting a role for this transcription factor in increased PCOS theca cell steroidogenesis. Several of the differentially expressed genes could also be classified into specific signaling cascades or gene networks. For example, the mRNA levels of retinol dehydrogenase 2 (RoDH2) and aldehyde dehydrogenase 6 (ALDH6), which are involved in the conversion of retinol to atRA (2426), and tazarotene-induced gene 1 (TIG1), which is a target of atRA action (27), were increased in PCOS theca cells. In addition, inhibin {beta}A, which is a member of the TGF-{beta} superfamily of growth factors (28, 29) and Gremlin and PACE4, which modulate the biological activity of TGF-{beta} growth factors (30, 31), were also identified as differentially expressed in PCOS theca cells.



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FIG. 1.
PCOS theca cells secrete increased levels of DHEA compared with normal theca cells. Conditioned medium from the four normal (open boxes) and five PCOS (closed boxes) samples that were untreated or treated with 20 µM forskolin was assayed for DHEA. The mean levels of DHEA ± S.E. were plotted.

 


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FIG. 2.
Untreated and forskolin-stimulated PCOS theca cells exhibit altered gene expression compared with normal theca cells. The normalized fluorescence intensity for each gene in normal theca cells was plotted against the normalized fluorescence intensity for each gene in the PCOS theca cells for the 346 and 445 genes that were defined, respectively, as differentially expressed in untreated (A) and forskolin-treated (B) PCOS theca cells. The solid diagonal lines indicate 2-fold increased or decreased fluorescence intensity.

 

LH plays an important role in the regulation of theca cell differentiation and function. Therefore, we were also interested in the affect of LH-induced signal transduction on gene expression in normal compared with PCOS theca cells. However, PCOS theca cells tend to exhibit increased sensitivity to LH (1). Since it is unclear whether increased expression of the LH receptor is a mechanism for the increased sensitivity of PCOS theca cells to LH, we mimicked the cAMP-dependent actions of LH in the theca cells with forskolin, which stimulates adenylate cyclase activity and thereby increases cAMP levels in the theca cell. In order to induce a maximal steroidogenic response, normal and PCOS theca cells were treated for 48 h with 20 µM forskolin (6). Following forskolin treatment, RNA was collected from the four different normal and five different PCOS theca cell samples and hybridized to the Affymetrix U133 chips. Analysis of global gene expression in these forskolin-stimulated cells demonstrated that 15,606 of the 45,000 interrogated transcripts (~35%) were expressed in forskolin-stimulated normal or PCOS theca cells. Statistical analysis of the microarray hybridization of the forskolin-stimulated cells revealed 445 genes that had increased or decreased mRNA abundance in PCOS theca cells (Fig. 2B). Thus, similar to the data obtained from the untreated theca cell hybridizations, only a small percentage of expressed genes showed a modest difference in mRNA abundance in the forskolin-stimulated PCOS compared with normal theca cells. The 445 differentially expressed genes were organized into the categories of signal transduction factors, genes involved in cellular metabolism, transcription factors, cell adhesion molecules, cell surface antigens, ion channels, and expressed sequence tags (Supplemental Table 5). Interestingly, a number of genes involved in de novo cholesterol synthesis including hydroxymethylglutaryl-CoA reductase, hydroxymethylglutaryl-CoA synthase, squalene epoxidase, and lanosterol 14{alpha}-demethylase as well as CYP17 and ferredoxin had increased expression in the forskolin-stimulated PCOS compared with normal theca cells, which is consistent with the increased steroidogenic activity of the PCOS theca cell. The up-regulation of genes involved in de novo cholesterol synthesis is probably a secondary response to the depletion of cellular cholesterol stores for hormone synthesis and the compensatory increase in cholesterol biosynthesis. In addition, the gene encoding cytochrome b5, which serves as an allosteric effector activating the lyase activity of CYP17 (32), was increased, whereas the expression of the gene encoding phosphoprotein phosphatase 2A, which reverses the serine phosphorylation of CYP17 that is associated with increased lyase activity (33), was decreased. Furthermore, genes involved in specific signaling pathways or gene networks including Wnt 5A, Dickkopf homolog 1, and Sox17 (3436), which modulate the Wnt signal transduction cascade, had altered abundance in forskolin-stimulated PCOS theca cells compared with forskolin-stimulated normal theca cells. In addition, RoDH2, ALDH6, and TIG1 had increased expression in the forskolin-treated PCOS theca cells, suggesting that altered atRA metabolism/signaling is maintained in the PCOS theca cell after forskolin stimulation.

Microarray Analysis Accurately Predicts Genes with Altered mRNA Abundance in PCOS Theca Cells—Given the modest differences in gene expression between normal and PCOS theca cells, it was important to validate the ability of these microarray analyses to accurately predict genes with altered mRNA abundance in PCOS theca cells. In order to determine whether our results could be replicated on two different chip platforms, each RNA sample was sequentially hybridized onto the Affymetrix U133 and U95A chips. The gene expression data for 142 and 175 genes that had statistically significant differences in mRNA abundance on the U133 chips in untreated and forskolin-stimulated PCOS theca cells, respectively, were also detected on the U95A chip and therefore the PCDS-to-normal ratio for each gene on each chip format was compared. Of the 142 genes, which were differentially expressed on the U133 chips in the untreated PCOS theca cells, 84 (59%) were also identified as having a statistically significant difference in mRNA abundance by the U95A chip (Supplemental Table 1). Likewise, of the 175 genes, which were differentially expressed on the U133 chips in the forskolin-stimulated PCOS theca cells, 96 (55%) were identified as having a statistically significant difference in mRNA abundance by the U95A chip (Supplemental Table 2). When the PCOS-to-normal ratios for the 142 and 175 differentially expressed targets on the U133 chip were plotted against the PCOS-to-normal ratios for these same targets on the U95A chip (Fig. 3A), there was a statistically significant correlation between the PCOS-to-normal ratios generated by the two chip platforms despite the fact that the probe set sequences for each target on the U133 and U95A arrays were not identical and in some cases were generated to completely different sequences of each target. These data demonstrated that similar trends of gene expression were obtained using two different chip platforms and from two different hybridization experiments.



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FIG. 3.
Microarray analysis accurately predicts altered gene expression in PCOS theca cells. A, the PCOS-to-normal ratios of 133 and 175 genes, which were differentially expressed in untreated (left plot) and forskolin-treated (right plot) PCOS theca cells, respectively, were compared using the U133 or U95A chip formats. The ratios were plotted, a trend line was drawn, and regression analysis was carried out. The equation of the trend line, the R2 value of the trend line, and the F value from the regression analysis are indicated. B, the PCOS-to-normal ratio of mRNA abundance of 28 genes from untreated (left panel) and forskolin-stimulated (right panel) theca cells was determined using the U133 gene array chips or by quantitative RT-PCR. These ratios were plotted and analyzed as described for A.

 

In addition to verifying the reproducibility of the microarray data on two different chip formats, we also compared the gene expression results from the microarray analyses with gene expression in normal and PCOS theca cells using the independent methodology of quantitative real time RT-PCR. For these experiments, total RNA was collected from the same theca cell samples used for the microarray hybridizations and was reverse transcribed. Quantitative real time PCR was subsequently carried out in each theca cell sample using gene-specific primers designed against 28 different targets that were identified as expressed in normal or PCOS theca cells by microarray analysis, and the PCOS-to-normal ratio for these 28 transcripts was determined (Supplemental Table 3). When the PCOS-to-normal ratio that was determined by real time PCR was plotted against the PCOS-to-normal ratio that was determined by microarray analysis for each target in the untreated or forskolin-stimulated theca cells (Fig. 3B), there was a statistically significant correlation between the ratios calculated by the two methodologies. Furthermore, if a gene was predicted to be up-regulated in PCOS theca cells by microarray analysis, the gene had a PCOS-to-normal ratio greater than one obtained by real time PCR. Likewise, if a gene was predicted to be down-regulated in PCOS theca cells by microarray analysis, the gene had a PCOS-to-normal ratio less than one obtained by real-time PCR, providing additional evidence that the microarray analysis was a good predictor of differential gene expression in the PCOS theca cells. Given this extensive scrutiny of the microarray data, it is our conclusion that these analyses were reasonable predictors of genes with altered mRNA abundance in PCOS theca cells and indicated that microarray analysis is a valid method for defining the gene expression profile of PCOS theca cells.

Forskolin-dependent Regulation of Gene Expression Is Different in Normal and PCOS Theca Cells—Once the microarray analyses were validated, we began to assess the functional significance of the gene expression data with regard to the PCOS phenotype of increased androgen synthesis. Since cAMP signal transduction plays an important role in normal theca cell steroidogenesis, we first determined whether there was an overlap of genes that were differentially expressed in untreated and forskolin-stimulated PCOS theca cells. Interestingly, there were only 50 genes that were differentially expressed in both untreated and forskolin-treated PCOS theca cells (Table I). Among these genes was cAMP-GEFII, which is an intracellular signaling molecule that is activated by cAMP (37). cAMP-GEFII had increased expression in both untreated and forskolin-stimulated PCOS theca cells and thus provided a plausible mechanism for altered cAMP-mediated signal transduction in PCOS theca cells. Therefore, we determined whether the 296 and 391 genes that exhibited altered mRNA abundance in either untreated or forskolin-stimulated PCOS theca cells, respectively, were differentially regulated by forskolin in normal and PCOS theca cells. Of these 687 genes, 84 genes were regulated by forskolin in normal but not PCOS theca cells, and 67 genes were regulated by forskolin in PCOS but not normal theca cells (Supplemental Table 6). Furthermore, these differences in forskolin-regulated gene expression in the PCOS and normal theca cells could predict that the gene would have altered mRNA abundance in only the untreated or the forskolin-treated normal compared with PCOS theca cells. For example, forskolin-treated PCOS theca cells had a 3-fold higher abundance of insulin-like growth factor-binding protein 3 (IGFBP3) compared with untreated PCOS theca cells. However, IGFBP3 mRNA levels were not different in forskolin-stimulated compared with untreated normal theca cells (Supplemental Table 6). Therefore, under basal conditions, there was no difference in IGFBP3 expression between normal and PCOS theca cells, but under forskolin-stimulated conditions, IGFBP3 expression was 3-fold greater in PCOS compared with normal theca cells. Taken together, these analyses indicated that cAMP-mediated signal transduction is altered in PCOS theca cells and suggested that increased expression of cAMP-GEFII is one mechanism for the differential effects of forskolin on normal and PCOS theca cell gene expression.


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TABLE I
Subset of genes with altered expression in untreated and forskolin-treated PCOS theca cells

The PCOS-to-normal ratios (PCOS/NL) for the 50 genes, which were identified as differentially expressed in both untreated (supplemental Table 4) and forskolin-stimulated (supplemental Table 5) PCOS theca cells are shown.

 

Retinoic Acid Modulates Androgen Biosynthesis in Normal Theca Cells—In addition to cAMP-GEFII, several other genes showed highly significant differences in mRNA abundance between normal and PCOS theca cells. Among these genes were RoDH2, which converts retinol to retinaldehyde in the rate-limiting step of atRA biosynthesis (25, 26, 38), and ALDH6, which converts retinaldehyde to atRA (24). Quantitative real time PCR verified that these two genes have significantly increased expression in untreated and forskolin-stimulated PCOS theca cells (Fig. 4). Furthermore, RT-PCR demonstrated that PCOS theca cells express cellular retinoic acid-binding protein II, RoDH4, and prostate short-chain dehydrogenase/reductase mRNAs, which play a role in atRA synthesis (39) as well as the atRA-responsive gene TIG1 (Fig. 4), suggesting that 1) the theca cell is a site of atRA synthesis and 2) PCOS theca cells synthesize increased amounts of atRA compared with normal theca cells.



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FIG. 4.
RT-PCR demonstrates that genes that are involved in retinoic acid synthesis are expressed in theca cells. Quantitative, real time RT-PCR was carried out using cDNA from untreated and forskolin-stimulated normal (n = 4; open bars) and PCOS (n = 5; closed bars) theca cells and gene-specific primers for ALDH6, RoDH2, and TIG1. The mean ± S.E. normalized arbitrary value for each target was plotted, and statistically significant differences in mRNA abundance between normal and PCOS theca cells were determined by Wilcoxon rank sums test (*, p < 0.05). RT-PCR was carried out for cellular retinoic acid-binding protein II (CRABP2), cellular retinoic acid binding protein I (CRABP1), RoDH4, and prostate short-chain dehydrogenase/reductase (PSDR1) using cDNA from normal theca cells (N), PCOS theca cells (P), a positive control cDNA (+), or a no-template control (–).

 

To determine whether PCOS theca cells metabolize retinol more efficiently than normal theca cells, the conversion of retinol to retinaldehyde by normal and PCOS theca cell extracts was determined. Briefly, whole cell extracts from two independent normal and two independent PCOS theca cell samples were combined with [3H]retinol and NAD+, and the retinol and retinaldehyde were resolved using thin layer chromatography. The PCOS theca cells synthesized 4-fold greater levels of retinaldehyde compared with the normal theca cells (Fig. 5A), which is in agreement with the increased mRNA levels of RoDH2 in PCOS theca cells and therefore suggests that the activity of RoDH2 is increased in PCOS theca cells. Since we had evidence that atRA synthesis was enhanced in PCOS theca cells, the effect of atRA on normal theca cell steroidogenesis was examined. Specifically, the effect of atRA on DHEA levels was measured. When normal theca cells were treated with 5 µM atRA for 48 h, there was a significant increase in DHEA production (Fig. 5B). When cells were treated with both atRA and forskolin for 48 h, there was a synergistic affect on DHEA production by the normal theca cells. In order to define the mechanism of increased DHEA production by atRA-treated normal theca cells, the abundance of the mRNAs for the steroidogenic proteins CYP17, P450scc, and StAR was determined using quantitative RT-PCR. Both CYP17 and P450scc mRNA levels were increased by atRA treatment of normal theca cells (Fig. 5C). Taken together, these data suggested that atRA contributes to the increased androgen production by PCOS theca cells and demonstrated a functional link between altered PCOS gene expression and a PCOS theca cell phenotype.



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FIG. 5.
PCOS theca cells synthesize increased levels of retinaldehyde and atRA-stimulated DHEA secretion and steroidogenic enzyme expression in normal theca cells. A, thin-layer chromatography was carried out to detect retinaldehyde synthesis from all-trans-retinol by whole cell extracts from normal (open bar) and PCOS (mottled bar) theca cells. The mean levels of retinaldehyde that was synthesized by the normal (75.03 fmol/mg of protein/h) and the PCOS (310.97 fmol/mg of protein/h) theca cells were determined. The range of retinaldehyde synthesis by the two normal and two PCOS theca cell samples is indicated (*). B, DHEA levels were measured using conditioned media from normal theca cells that were cultured in the presence (closed bars) or absence (open bars) of 20 µM forskolin and the absence (–) or presence of 5 µM atRA. The DHEA levels were normalized for cell number, and the mean ± S.D. from a representative normal theca cell culture grown in triplicate is shown. C, quantitative, real time RT-PCR was carried out using cDNA from untreated (n = 4; open bars) and atRA-stimulated (n = 4; hatched bars) normal theca cells and gene-specific primers for CYP17, P450scc, and StAR. The mean ± S.E. normalized arbitrary value for each target was plotted.

 

GATA-6 Stimulated CYP17 and P450scc but Not StAR Promoter Activity—Another gene that exhibited a highly significant increase in mRNA abundance in PCOS theca cells was the transcription factor GATA-6 (Fig. 6A). To determine whether the increased mRNA abundance was correlated with increased protein expression of GATA-6, Western blot analysis was carried out using nuclear extracts from normal and PCOS theca cells. GATA-6-specific bands were detected on the blots at 52 and 64 kDa in agreement with previous reports of a long and short form of this protein (40). Furthermore, the levels of the short form of GATA-6 were consistently higher (~3-fold) in PCOS compared with normal theca cells, whereas the levels of the long form were higher in one of the PCOS theca cell samples compared with the normal theca cell samples (Fig. 6B). This increase in GATA-6 protein was consistent with the increased abundance of GATA-6 mRNA in PCOS compared with normal theca cells. Previous studies had demonstrated that GATA-4 regulates StAR gene expression (41). Thus, increased mRNA and protein expression of GATA-6 may directly impact expression of the steroidogenic proteins in the PCOS theca cell. To address this hypothesis, HeLa cells, which do not express GATA-6, were transfected with pcDNAG6, which is an expression vector that contains the coding region for the long form of the mouse GATA-6 gene and a luciferase reporter vector that contained the promoter region of CYP17 or P450scc. GATA-6 increased transcription from the CYP17 and P450scc promoter construct by 143% (p < 0.01) and 334% (p < 0.01), respectively, compared with control cells, which were transfected with the empty expression vector pcDNA3 (Fig. 6C). In contrast, when cells were transfected with the StAR promoter construct, no difference in luciferase activity was observed between pcDNAG6- and pcDNA3-cotransfected groups. Similarly, when HeLa cells were transfected with an expression vector that contained the coding region for the short form of the mouse GATA-6 gene, the activities of the CYP17 and the P450scc promoters were increased (data not shown). These data indicate that increased GATA-6 expression in PCOS theca cells may be correlated to the increased steroidogenesis in the PCOS theca cell. Furthermore, these data suggest that multiple factors modulate theca cell androgen synthesis, and it is the combination of these factors that results in the manifestation of the PCOS phenotype.



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FIG. 6.
GATA-6 increased CYP17 and P450scc promoter activities but not StAR promoter activity. A, quantitative, real time RT-PCR was carried out using cDNA from untreated and forskolin-stimulated normal (n = 4; open bars) and PCOS (n = 5; closed bars) theca cells and primers against GATA-6. The mean ± S.E. normalized arbitrary value for GATA-6 was plotted, and statistically significant differences in mRNA abundance between normal and PCOS theca cells were determined by Wilcoxon rank sums test (*, p < 0.05). B, Western blot analysis was carried out to determine the levels of GATA-6 protein expression in nuclear extracts from two normal and three PCOS theca cell samples that were either untreated (C) or treated with 20 µM forskolin (F). The 64- and 52-kDa GATA-6-specific bands are indicated. C, HeLa cells were transfected with the firefly luciferase reporter vectors pGL2-StAR, pGL3-CYP17, or pGL3-CYP11A and either the empty vector pcDNA3 (open bars) or pcDNAG6 (closed bars), which contains the coding sequence for GATA-6. The firefly luciferase activity in each sample was normalized using Renilla luciferase activity (normalized luciferase activity). The mean ± S.D. from three independent experiments was plotted for each experimental group. Statistically significant differences in luciferase activity between pcDNA3- and pcDNAG6-transfected cells was determined by Student's t test (*, p < 0.005).

 


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
We previously demonstrated that excess androgen production is a stable characteristic of PCOS theca cells in long term culture (6). Furthermore, these studies correlated excess androgen production with increased expression of several steroidogenic enzymes and thereby defined a stable molecular and biochemical signature for PCOS theca cells (7). However, the underlying cause of this pattern of gene expression has not been elucidated. By analyzing global changes in gene expression between normal and PCOS theca cells, we sought metabolic pathways and candidate genes that might account for the theca cell steroidogenic phenotype. Although outside the scope of this paper, the genes identified in this study can subsequently be compared with the information in the ovarian kaleidoscope data base in order to more completely define the interplay of these affected pathways in PCOS theca cells (42).

Normal and PCOS Theca Cells Exhibited Modest Differences in Gene Expression—Although the morphological features of the PCOS ovary are impressive, these structural changes as well as the associated steroidogenic abnormalities can be corrected at least temporarily by relatively modest elevations of follicle-stimulating hormone, which push follicle development past maturation arrest to a state at which ovulation is possible (43). The resulting ovulation is generally followed by a decline in androgen levels. Consequently, it is not unexpected that the gene expression profiles of PCOS and normal theca cells are relatively similar such that only a small subset of expressed genes were significantly altered, and the differences in the expression levels of these genes were relatively small. In fact, given the similarities of the two cell types and the complexity of the disease, our microarray results are reminiscent of the alterations in gene expression reported in the comparison of muscle biopsies from control subjects and patients with type II diabetes (22).

Validity of the Microarray Analysis Data—Given the significant but modest differences in gene expression between normal and PCOS theca cells, several steps were taken including appropriate experimental design and normalization to ensure the accuracy and reproducibility of the microarray hybridizations and the subsequent gene expression profile comparisons so that meaningful conclusions could be drawn from these analyses. First, cRNA from four different normal and five different PCOS samples were hybridized to individual chips, allowing for differences in gene expression between normal and PCOS theca cells to be tested for statistical significance and thereby eliminating genes that have differential expression due to differences in biological variation between individual samples. Second, we compared the gene expression profiles of the normal and PCOS theca cells from sequential hybridizations on the Affymetrix U133 and U95A chip platforms. We observed that 55–60% of the differentially expressed genes in PCOS theca cells were identified by both chip platforms, which was consistent with a study carried out by Brown et al. (20) that demonstrated an ~64% duplication of differential gene expression using the Affymetrix MG-U74 and Mu19K chips. Furthermore, the PCOS-to-normal ratios for each gene on each chip had a significant correlation, demonstrating that the array data were reproducible. Finally, we used real time RT-PCR to determine whether the microarray data could be replicated using an independent methodology. Although several other investigators have validated microarray data using either semiquantitative or quantitative RT-PCR or Northern blot analysis, the number of targets that were validated in these studies was low (20, 22, 44, 45). Conversely, we carried out real time RT-PCR for 28 targets, which were identified as expressed in the normal or PCOS theca cells. From this intense scrutiny of the data, we determined that there was a significant correlation between the data generated by the microarray analysis and by RT-PCR. These complementary experiments confirmed that our experimental design, normalizations, and analysis were appropriate and demonstrated that the microarray analyses were good predictors of differential gene expression in PCOS theca cells.

Genes Involved in Theca Cell Steroidogenesis Had Altered Expression in PCOS Theca Cells—The expression profile of the forskolin-stimulated PCOS theca cells as determined by microarray analysis is consistent with the molecular phenotype of excess androgen synthesis, which was anticipated from our previous studies. Specifically, CYP17 expression, which is the key enzyme regulating theca cell androgen biosynthesis, was elevated in forskolin-stimulated PCOS theca cells. The P450c17 enzyme encoded by the CYP17 gene has both 17{alpha}-hydroxylase and 17,20-lyase activities, which are carried out at a single catalytic site. However, it is the lyase activity that promotes androgen production. P450c17 lyase activity is enhanced directly by the allosteric effector, cytochrome b5 (46). Furthermore, phosphorylation of P450c17 by a yet to be identified kinase results in increased P450c17 lyase activity, possibly by enhancing the interaction of cytochrome b5 with P450c17 (47, 48). It is notable that the microarray analysis of forskolin-treated PCOS theca cells demonstrated increased cytochrome b5 expression as well as reduced expression of protein phosphatase 2A, the phosphatase recently suggested to be responsible for the removal of the phosphate groups that promote lyase activity (33). Thus, the microarray analysis disclosed previously unknown alterations in gene expression, which collectively would enhance theca cell androgen production (6). Furthermore, given the recently reported affect of androgens on cell cycle regulation in the granulosa cell (49), these findings support the hypothesis that an intrinsic abnormality in PCOS theca steroidogenesis can result in follicular maturation arrest. However, the microarray analysis did not indicate that P450scc and type II 3{beta}-hydroxysteroid dehydrogenase gene expression was increased in PCOS theca cells as was expected from our prior work. These conflicting results are probably due to the insufficient hybridization of our theca cell RNA samples to the 3{beta}-hydroxysteroid dehydrogenase oligonucleotide probe set on the U133 chips and represent an important limitation of the microarray analysis.

Signal Transduction Pathways with Altered Gene Expression in PCOS Theca Cells—Although several genes that directly impact theca cell steroidogenesis were identified by the microarray analysis, we were also interested in defining the genes that act upstream of the steroidogenic phenotype in order to determine possible mechanisms for excess androgen production by the PCOS theca cell. In this process, we identified several genes with altered mRNA abundance in PCOS theca cells that are associated with distinct signal transduction cascades, including genes involved in atRA biosynthesis and signaling. The genes that regulate atRA synthesis consist of a short chain dehydrogenase/reductase, which converts retinol to retinaldehyde, and an aldehyde dehydrogenase, which converts retinaldehyde to atRA (50, 51). Although both the short chain dehydrogenase/reductase and ALDH family of proteins are large and several members of each family have been associated with retinoic acid synthesis, there is little evidence linking any of these family members to atRA synthesis in the theca cell. However, RoDH2 uses retinol as a substrate in in vitro enzymatic assays (25, 26, 38), and ALDH6 converts retinaldehyde to retinoic acid in breast epithelial cells (24). Interestingly, RoDH2 acts on several androgen precursors, such as 3{alpha}-androstanediol, androsterone, and DHEA, and therefore may serve a dual role in the theca cells (25, 26, 38). Additional evidence that the theca cell is a site of atRA synthesis includes the expression of the retinoic acid-binding protein cellular retinoic acid-binding protein II, which is intimately linked to retinoic acid synthesis (39), and expression of the atRA-responsive gene TIG1 (27).

In the adult testis, atRA increases Leydig cell testosterone secretion and CYP17 gene expression (52). We have demonstrated that atRA also regulates DHEA synthesis and CYP17 and P450scc gene expression in normal theca cells. Since this pattern of altered gene expression has been previously documented in PCOS theca cells (6), these data provide evidence that retinoic acid contributes to the phenotype of hyperandrogenemia in PCOS theca cells. However, exposure of normal theca cells to atRA did not result in as dramatic an increase in androgen synthesis or augmented expression of CYP17 as found in PCOS theca cells. Thus, increased atRA synthesis and action on theca cells cannot account for the full PCOS theca steroidogenesis phenotype. In addition to modulating theca cell steroidogenesis, atRA inhibits follicle-stimulating hormone (FSH)-dependent FSH and LH-receptor expression in the granulosa cell (5355), suggesting a mechanism for regulating granulosa cell differentiation. atRA action is mediated through the retinoic acid receptor, which upon ligand binding heterodimerizes with the related receptor retinoid X receptor and activates or represses transcription of target genes (56). Indeed, the inhibition of FSH receptor gene expression in the porcine ovary is mediated through the retinoic acid receptor, which binds to a putative response element in the promoter region of the follicle-stimulating hormone receptor gene (55). However, it is not clear if CYP17 or P450scc gene expression is directly regulated by retinoic acid receptor and/or if the promoter regions of these two genes contain a retinoic acid receptor response element.

In addition to altered expression of genes related to atRA metabolism and signaling in the PCOS theca cells, we also observed that cAMP-dependent signal transduction is different between normal and PCOS theca cells. Our previous studies demonstrated that CYP17 but not StAR gene expression is increased in PCOS theca cells (7). Furthermore, this increased expression of CYP17 is enhanced when cells are treated with the adenylate cyclase activator forskolin. Since both CYP17 and StAR promoter activity is regulated by cAMP, these observations suggested that cAMP-dependent signal transduction is affected in PCOS theca cells (7, 57). When we compared forskolin-mediated regulation of gene expression in normal and PCOS theca cells by microarray analysis, we identified several genes that are regulated by forskolin in normal or PCOS theca cells but not both, again suggesting that there is a disruption in the cAMP signal transduction cascade in PCOS theca cells. In addition, cAMP-GEFII, which has homology to the Ras superfamily of small G-proteins (37), exhibits increased expression in PCOS theca cells. The downstream target of cAMP-GEFII is Rap1, which subsequently activates and/or inhibits the Raf/mitogen-activated protein kinase signaling pathway in a protein kinase A-independent and cell-specific manner (58), suggesting that cAMP regulation of gene expression occurs by two diverging pathways in the theca cell. It will be of interest to determine whether StAR and CYP17 mRNA expression are regulated by these diverging pathways, which could potentially explain their differential expression patterns in PCOS compared with normal theca cells.

Two additional signal transduction pathways were represented among the genes with altered mRNA abundance in PCOS theca cells. Three of these genes (inhibin {beta}A subunit, PACE4, and Gremlin) modulate TGF-{beta} signaling. Decreased expression of the inhibin {beta}A subunit could decrease the amount of inhibin A, activin A, or activin AB synthesized by PCOS theca cells and directly influence theca and/or granulosa cell steroidogenesis (59). Gremlin is a bone morphogenetic protein antagonist (30) and therefore could also negatively influence TGF-{beta} signaling pathways in the PCOS theca cell. Conversely, PACE 4 is a proprotein convertase, which increases the bioactivity of bone morphogenetic protein 4 as a result of protein processing (31) and may enhance TGF-{beta} signaling in PCOS theca cells. Although these results may seem contradictory, the TGF-{beta} signaling family is complex, and opposing signals may act together to bring about changes in theca cell function in PCOS. Likewise, two genes (Wnt5A and Dickkopf 1) with altered expression in PCOS theca cells affect Wnt signaling. This is particularly interesting, since Hsieh et al. (60) demonstrated that Wnt ligands and receptors are expressed and hormonally regulated in the adult rat ovary. Our microarray results indicated that the ligand, Wnt5A, is down-regulated in the PCOS theca cells, whereas the antagonist Dickkopf 1 is up-regulated. We have also shown by subtractive suppressive hybridization that another Wnt antagonist, sFRP4, is also up-regulated in PCOS theca cells.2 Collectively, these data suggest that Wnt signaling is repressed in the PCOS theca cells. Furthermore, since these three genes all encode secreted factors, their down-regulation in PCOS theca cells could impact granulosa cell function through a paracrine mechanism.

Although the four signal transduction pathways discussed here could affect theca cell function individually, there is also the potential for cross-talk between these signaling cascades. Specifically, studies have demonstrated interactions between the Wnt and mitogen-activated protein kinase, Wnt and retinoic acid, TGF-{beta} and Wnt, and TGF-{beta} and retinoic acid signaling pathways (6164). Future studies will be required to assess the interaction of these pathways in the theca and/or granulosa cell and what impact altered signaling by these pathways has on the pathophysiology of the ovary.

Given the importance of cAMP-dependent and -independent signal transduction in theca cell steroidogenesis and follicle growth, it is appealing to speculate that altered expression of genes important for theca cell signal transduction is a mechanism for the theca cell PCOS phenotype. Several transcription factors that could alter regulation of downstream targets and therefore contribute to the phenotype of the PCOS theca cell were also identified by the microarray analysis. Specifically, GATA6 had increased expression in untreated and forskolin-stimulated PCOS theca cells. GATA6 and GATA4, which are zinc finger transcription factors, are expressed in the mammalian ovary (23, 65, 66). In the porcine ovary, the expression of these two transcription factors is spatially and temporally regulated with increasing amounts of GATA-6 expressed in the theca cell layer as the follicle grows from a primary to an antral follicle (23). Furthermore, the ratio of GATA6 and GATA4 expression in the follicle may be important for determining the fate of the follicle, since increased GATA6 expression and decreased GATA4 expression is a marker for apoptosis. These data suggest that increased expression of GATA6 in the PCOS theca cell may contribute to the phenotype of arrested follicle growth. Gillio-Meina et al. (23) also demonstrated that GATA6 regulates StAR expression in the porcine granulosa cell. Whereas our data did not show that human StAR promoter activity is regulated by GATA6, human CYP17 and P450scc promoter activities were increased by GATA6. These data are reminiscent of our previous promoter studies (7) and suggest that this transcription factor plays a role in excess androgen production by the PCOS theca cell. Another transcription factor with decreased expression in the forskolin-stimulated PCOS theca cell is SOX17, which is a high mobility group box transcription factor. Interestingly, SOX17 interacts with {beta}-catenin and TCF/LEF, which are the nuclear targets of the Wnt signal transduction pathway, and represses TCF/LEF-dependent transcription (35). Therefore, altered expression of this transcription factor may add a layer of complexity to altered Wnt signaling in the PCOS theca cell.

Approximately 35% of the mRNAs with altered abundance in untreated and forskolin-stimulated PCOS theca cells were classified as expressed sequence tags. Since these mRNAs represent hypothetical proteins, it is difficult to assess what role these genes may play in PCOS. However, they represent a large pool of candidate genes, which need to be investigated in order to fully understand the mechanisms underlying the manifestation of the PCOS phenotype in the theca cell.

Genetic Etiology of PCOS—Although PCOS is a prevalent disease among reproductive aged women, the etiology of the disorder remains elusive. Several studies suggest that there is a genetic component to PCOS (see Ref. 2 and references therein). To test this hypothesis, Urbanek et al. (67, 68) carried out a comprehensive study using affected sib-pair and transmission/disequilibrium tests for 37 candidate genes and demonstrated a strong association between PCOS and an allele on 19p13.3 centromeric to the insulin receptor gene. Although our microarray analysis did not identify any genes near this locus that were differentially expressed in PCOS theca cells, the mRNAs of several genes that map to 19p13.3, including the insulin receptor, p114-Rho-GEF, and several expressed sequence tags, were detected in normal and PCOS theca cells. The lack of evidence of altered expression of genes in this region suggests that the genes in the 19p13.3 locus could affect the expression of downstream targets. Furthermore, PCOS may be an oligogenetic disease, and therefore the microarray results provide a new pool of candidate genes, which can be tested for genetic association and linkage with PCOS. Alternatively, PCOS may arise due to an epigenetic event. However, we did not detect altered expression of any imprinted or X-linked genes, which could account for an epigenetic mechanism.

Taken together, our results have for the first time defined a stable molecular signature of PCOS theca cells that includes altered expression of steroidogenic proteins as well as genes that directly affect steroid synthesis in the theca cell. More importantly, we have identified new factors, including cAMP-GEFII, genes involved in atRA synthesis/signaling, genes that participate in the Wnt signal transduction pathway, and GATA6, which could impact theca cell steroidogenesis and function. These genes not only provide a mechanism for the manifestation of the PCOS phenotype in the theca cell but also represent new candidate genes that may contribute to the etiology of this disorder.


    FOOTNOTES
 
* The experiments in this paper were supported by National Institutes of Health Grants T32-HD07305 and U54-HD34449 and the Mellon Foundation. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

The on-line version of this article (available at http://www.jbc.org) contains six additional tables. Back

{ddagger}{ddagger} To whom correspondence should be addressed: Center for Research on Reproduction and Women's Health, 1349 BRB II/III, 421 Curie Blvd., Philadelphia, PA 19104. Tel.: 215-898-0147; Fax: 215-573-5408; E-mail: jfs3{at}mail.med.upenn.edu.

1 The abbreviations used are: PCOS, polycystic ovary syndrome;P450scc, P450 side chain cleavage; StAR, steroidogenic acute regulatory protein; LH, luteinizing hormone; CYP17, 17{alpha}-hydroxylase/17,20-lyase; RT, reverse transcriptase; atRA, all-trans-retinoic acid; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; RoDH2 and -4, retinol dehydrogenase 2 and 4, respectively; ALDH6, aldehyde dehydrogenase 6; TGF, transforming growth factor; TIG1, tazarotene-induced gene 1; IGFBP3, insulin-like growth factor-binding protein 3; FSH, follicle stimulating hormone; DHEA, dehydroepiandosterone. Back

2 J. R. Wood, L. K. Christenson, V. L. Nelson, J. M. McAllister, and J. F. Strauss III, unpublished data. Back


    ACKNOWLEDGMENTS
 
We thank Dr. Richard Spielman for helpful comments during preparation of the manuscript.



    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Ehrmann, D. A., Barnes, R. B., and Rosenfield, R. L. (1995) Endocr. Rev. 16, 322–353[Medline] [Order article via Infotrieve]
  2. Legro, R. S., Spielman, R., Urbanek, M., Driscoll, D., Strauss, J. F., III, and Dunaif, A. (1998) Recent Prog. Horm. Res. 53, 217–256[Medline] [Order article via Infotrieve]
  3. Franks, S. (1995) N. Engl. J. Med. 333, 853–861[Free Full Text]
  4. Axelrod, L., and Goldzieher, J. (1962) J. Clin. Endocrinol. Metab. 22, 431–440
  5. Gilling-Smith, C., Willis, D. S., Beard, R. W., and Franks, S. (1994) J. Clin. Endocrinol. Metab. 79, 1158–1165[Abstract]
  6. Nelson, V. L., Legro, R. S., Strauss, J. F., III, and McAllister, J. M. (1999) Mol. Endocrinol. 13, 946–957[Abstract/Free Full Text]
  7. Wickenheisser, J. K., Quinn, P. G., Nelson, V. L., Legro, R. S., Strauss, J. F., III, and McAllister, J. M. (2000) J. Clin. Endocrinol. Metab. 85, 2304–2311[Abstract/Free Full Text]
  8. Christenson, L. K., and Strauss, J. F., III (2000) Biochim. Biophys. Acta 1529, 175–187[Medline] [Order article via Infotrieve]
  9. Luu-The, V., Dufort, I., Pelletier, G., and Labrie, F. (2001) Mol. Cell. Endocrinol. 171, 77–82[CrossRef][Medline] [Order article via Infotrieve]
  10. Nelson, V. L., Qin Kn, K. N., Rosenfield, R. L., Wood, J. R., Penning, T. M., Legro, R. S., Strauss, J. F., III, and McAllister, J. M. (2001) J. Clin. Endocrinol. Metab. 86, 5925–5933[Abstract/Free Full Text]
  11. Leung, P. C., and Steele, G. L. (1992) Endocr. Rev. 13, 476–498[Abstract]
  12. Christenson, L. K., Johnson, P. F., McAllister, J. M., and Strauss, J. F., III (1999) J. Biol. Chem. 274, 26591–26598[Abstract/Free Full Text]
  13. Strauss, J. F., III, Kallen, C. B., Christenson, L. K., Watari, H., Devoto, L., Arakane, F., Kiriakidou, M., and Sugawara, T. (1999) Recent Prog. Horm. Res. 54, 369–394[Medline] [Order article via Infotrieve]
  14. Zawadski, J., and Dunaif, A. (1992) in Current Issues in Endocrinology and Metabolism (Dunaif, A., Givens, J., Haseltine, F., and Merriam, G., eds) pp. 377–384, Blackwell Scientific Publications, Boston
  15. Affymetrix, Inc. (2001) Affymetrix Microarray Suite, Version 5.0, Affymetrix, Santa Clara, CA
  16. Silicon Genetics, Inc. (2002) GeneSpring, Version 4.2, Silicon Genetics, Redwood City, CA
  17. Sugawara, T., Holt, J. A., Kiriakidou, M., and Strauss, J. F., III (1996) Biochemistry 35, 9052–9059[CrossRef][Medline] [Order article via Infotrieve]
  18. Morrisey, E. E., Ip, H. S., Lu, M. M., and Parmacek, M. S. (1996) Dev. Biol. 177, 309–322[CrossRef][Medline] [Order article via Infotrieve]
  19. Bruno, M. D., Korfhagen, T. R., Liu, C., Morrisey, E. E., and Whitsett, J. A. (2000) J. Biol. Chem. 275, 1043–1049[Abstract/Free Full Text]
  20. Brown, V., Jin, P., Ceman, S., Darnell, J. C., O'Donnell, W. T., Tenenbaum, S. A., Jin, X., Feng, Y., Wilkinson, K. D., Keene, J. D., Darnell, R. B., and Warren, S. T. (2001) Cell 107, 477–487[Medline] [Order article via Infotrieve]
  21. Rus, V., Atamas, S. P., Shustova, V., Luzina, I. G., Selaru, F., Magder, L. S., and Via, C. S. (2002) Clin. Immunol. 102, 283–290[CrossRef][Medline] [Order article via Infotrieve]
  22. Sreekumar, R., Halvatsiotis, P., Schimke, J. C., and Nair, K. S. (2002) Diabetes 51, 1913–1920[Abstract/Free Full Text]
  23. Gillio-Meina, C., Hui, Y. Y., and LaVoie, H. A. (2003) Biol. Reprod. 68, 412–422[Abstract/Free Full Text]
  24. Rexer, B. N., Zheng, W. L., and Ong, D. E. (2001) Cancer Res. 61, 7065–7070[Abstract/Free Full Text]
  25. Chetyrkin, S. V., Hu, J., Gough, W. H., Dumaual, N., and Kedishvili, N. Y. (2001) Arch. Biochem. Biophys. 386, 1–10[CrossRef][Medline] [Order article via Infotrieve]
  26. Napoli, J. L. (2001) Mol. Cell. Endocrinol. 171, 103–109[CrossRef][Medline] [Order article via Infotrieve]
  27. Nagpal, S., Patel, S., Asano, A. T., Johnson, A. T., Duvic, M., and Chandraratna, R. A. (1996) J. Invest. Dermatol. 106, 269–274[Abstract]
  28. Gaddy-Kurten, D., Tsuchida, K., and Vale, W. (1995) Recent Prog. Horm. Res. 50, 109–129[Medline] [Order article via Infotrieve]
  29. Knight, P. G. (1996) Front. Neuroendocrinol. 17, 476–509[CrossRef][Medline] [Order article via Infotrieve]
  30. Hsu, D. R., Economides, A. N., Wang, X., Eimon, P. M., and Harland, R. M. (1998) Mol. Cell 1, 673–683[Medline] [Order article via Infotrieve]
  31. Constam, D. B., and Robertson, E. J. (1999) J. Cell Biol. 144, 139–149[Abstract/Free Full Text]
  32. Geller, D. H., Auchus, R. J., and Miller, W. L. (1999) Mol. Endocrinol. 13, 167–175[Abstract/Free Full Text]
  33. Pandey, A. V., Mellon, S. H., and Miller, W. L. (2002) J. Biol. Chem. 278, 2837–2844
  34. Wu, W., Glinka, A., Delius, H., and Niehrs, C. (2000) Curr. Biol. 10, 1611–1614[CrossRef][Medline] [Order article via Infotrieve]
  35. Zorn, A. M., Barish, G. D., Williams, B. O., Lavender, P., Klymkowsky, M. W., and Varmus, H. E. (1999) Mol. Cell 4, 487–498[Medline] [Order article via Infotrieve]
  36. Dale, T. C. (1998) Biochem. J. 329, 209–223[Medline] [Order article via Infotrieve]
  37. Kawasaki, H., Springett, G. M., Mochizuki, N., Toki, S., Nakaya, M., Matsuda, M., Housman, D. E., and Graybiel, A. M. (1998) Science 282, 2275–2279[Abstract/Free Full Text]
  38. Chetyrkin, S. V., Belyaeva, O. V., Gough, W. H., and Kedishvili, N. Y. (2001) J. Biol. Chem. 276, 22278–22286[Abstract/Free Full Text]
  39. Zheng, W. L., Bucco, R. A., Sierra-Rievera, E., Osteen, K. G., Melner, M. H., and Ong, D. E. (1999) Biol. Reprod. 60, 110–114[Abstract/Free Full Text]
  40. Brewer, A., Gove, C., Davies, A., McNulty, C., Barrow, D., Koutsourakis, M., Farzaneh, F., Pizzey, J., Bomford, A., and Patient, R. (1999) J. Biol. Chem. 274, 38004–38016[Abstract/Free Full Text]
  41. Silverman, E., Eimerl, S., and Orly, J. (1999) J. Biol. Chem. 274, 17987–17996[Abstract/Free Full Text]
  42. Ben-Shlomo, I., Vitt, U. A., and Hsueh, A. J. (2002) Endocrinology 143, 2041–2044[Abstract/Free Full Text]
  43. Strauss, J. F., III, and Dunaif, A. (1999) Mol. Endocrinol. 13, 800–805[Free Full Text]
  44. Richer, J. K., Jacobsen, B. M., Manning, N. G., Abel, M. G., Wolf, D. M., and Horwitz, K. B. (2002) J. Biol. Chem. 277, 5209–5218[Abstract/Free Full Text]
  45. Bell, S. E., Mavila, A., Salazar, R., Bayless, K. J., Kanagala, S., Maxwell, S. A., and Davis, G. E. (2001) J. Cell Sci. 114, 2755–2773[Medline] [Order article via Infotrieve]
  46. Auchus, R. J., Lee, T. C., and Miller, W. L. (1998) J. Biol. Chem. 273, 3158–3165[Abstract/Free Full Text]
  47. Dufau, M. L., Miyagawa, Y., Takada, S., Khanum, A., Miyagawa, H., and Buczko, E. (1997) Steroids 62, 128–132[CrossRef][Medline] [Order article via Infotrieve]
  48. Zhang, L. H., Rodriguez, H., Ohno, S., and Miller, W. L. (1995) Proc. Natl. Acad. Sci. U. S. A. 92, 10619–10623[Abstract]
  49. Pradeep, P. K., Li, X., Peegel, H., and Menon, K. M. (2002) Endocrinology 143, 2930–2935[Abstract/Free Full Text]
  50. Gottesman, M. E., Quadro, L., and Blaner, W. S. (2001) Bioessays 23, 409–419[CrossRef][Medline] [Order article via Infotrieve]
  51. Napoli, J. L. (1996) Clin. Immunol. Immunopathol. 80, S52–S62[CrossRef][Medline] [Order article via Infotrieve]
  52. Livera, G., Rouiller-Fabre, V., Pairault, C., Levacher, C., and Habert, R. (2002) Reproduction 124, 173–180[Abstract/Free Full Text]
  53. Minegishi, T., Hirakawa, T., Kishi, H., Abe, K., Ibuki, Y., and Miyamoto, K. (2000) Arch. Biochem. Biophys. 373, 203–210[CrossRef][Medline] [Order article via Infotrieve]
  54. Minegishi, T., Hirakawa, T., Kishi, H., Abe, K., Tano, M., Abe, Y., and Miyamoto, K. (2000) Biochim. Biophys. Acta 1495, 203–211[CrossRef][Medline] [Order article via Infotrieve]
  55. Xing, W., and Sairam, M. R. (2002) Biol. Reprod. 67, 204–211[Abstract/Free Full Text]
  56. Giguere, V. (1994) Endocr. Rev. 15, 61–79[Medline] [Order article via Infotrieve]
  57. Christenson, L. K., Stouffer, R. L., and Strauss, J. F., III (2001) J. Biol. Chem. 276, 27392–27399[Abstract/Free Full Text]
  58. Lee, J. H., Cho, K. S., Lee, J., Kim, D., Lee, S. B., Yoo, J., Cha, G. H., and Chung, J. (2002) Mol. Cell. Biol. 22, 7658–7666[Abstract/Free Full Text]
  59. Knight, P. G., and Glister, C. (2001) Reproduction 121, 503–512[Abstract/Free Full Text]
  60. Hsieh, M., Johnson, M. A., Greenberg, N. M., and Richards, J. S. (2002) Endocrinology 143, 898–908[Abstract/Free Full Text]
  61. Behrens, J. (2000) J. Cell Sci. 113, 911–919[Abstract/Free Full Text]
  62. Tice, D. A., Szeto, W., Soloviev, I., Rubinfeld, B., Fong, S. E., Dugger, D. L., Winer, J., Williams, P. M., Wieand, D., Smith, V., Schwall, R. H., Pennica, D., and Polakis, P. (2002) J. Biol. Chem. 277, 14329–14335[Abstract/Free Full Text]
  63. Nishita, M., Hashimoto, M. K., Ogata, S., Laurent, M. N., Ueno, N., Shibuya, H., and Cho, K. W. (2000) Nature 403, 781–785[CrossRef][Medline] [Order article via Infotrieve]
  64. Skillington, J., Choy, L., and Derynck, R. (2002) J. Cell Biol. 159, 135–146[Abstract/Free Full Text]
  65. Heikinheimo, M., Ermolaeva, M., Bielinska, M., Rahman, N. A., Narita, N., Huhtaniemi, I. T., Tapanainen, J. S., and Wilson, D. B. (1997) Endocrinology 138, 3505–3514[Abstract/Free Full Text]
  66. Laitinen, M. P., Anttonen, M., Ketola, I., Wilson, D. B., Ritvos, O., Butzow, R., and Heikinheimo, M. (2000) J Clin. Endocrinol. Metab. 85, 3476–3483[Abstract/Free Full Text]
  67. Urbanek, M., Legro, R. S., Driscoll, D. A., Azziz, R., Ehrmann, D. A., Norman, R. J., Strauss, J. F., III, Spielman, R. S., and Dunaif, A. (1999) Proc. Natl. Acad. Sci. U. S. A. 96, 8573–8578[Abstract/Free Full Text]
  68. Urbanek, M., Legro, R. S., Driscoll, D., Strauss, J. F., III, Dunaif, A., and Spielman, R. S. (2000) J. Pediatr. Endocrinol. Metab. 13, 1311–1313[Medline] [Order article via Infotrieve]