Differences in the transcriptional profiles of human cumulus cells isolated from MI and MII oocytes of patients with polycystic ovary syndrome

in Reproduction

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in women. The abnormalities of endocrine and intra-ovarian paracrine interactions may change the microenvironment for oocyte development during the folliculogenesis process and reduce the developmental competence of oocytes in PCOS patients who are suffering from anovulatory infertility and pregnancy loss. In this microenvironment, the cross talk between an oocyte and the surrounding cumulus cells (CCs) is critical for achieving oocyte competence. The aim of our study was to investigate the gene expression profiles of CCs obtained from PCOS patients undergoing IVF cycles in terms of oocyte maturation by using human Genome U133 Plus 2.0 microarrays. A total of 59 genes were differentially expressed in two CC groups. Most of these genes were identified to be involved in one or more of the following pathways: receptor interactions, calcium signaling, metabolism and biosynthesis, focal adhesion, melanogenesis, leukocyte transendothelial migration, Wnt signaling, and type 2 diabetes mellitus. According to the different expression levels in the microarrays and their putative functions, six differentially expressed genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) were selected and analyzed by quantitative RT-PCR (qRT-PCR). The qRT-PCR results were consistent with the microarray data. Moreover, the molecular signatures (LHCGR, TNIK, and SOCS3) were associated with developmental potential from embryo to blastocyst stage and were proposed as biomarkers of embryo viability in PCOS patients. Our results may be clinically important as they offer a new potential strategy for competent oocyte/embryo selection in PCOS patients.

Abstract

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in women. The abnormalities of endocrine and intra-ovarian paracrine interactions may change the microenvironment for oocyte development during the folliculogenesis process and reduce the developmental competence of oocytes in PCOS patients who are suffering from anovulatory infertility and pregnancy loss. In this microenvironment, the cross talk between an oocyte and the surrounding cumulus cells (CCs) is critical for achieving oocyte competence. The aim of our study was to investigate the gene expression profiles of CCs obtained from PCOS patients undergoing IVF cycles in terms of oocyte maturation by using human Genome U133 Plus 2.0 microarrays. A total of 59 genes were differentially expressed in two CC groups. Most of these genes were identified to be involved in one or more of the following pathways: receptor interactions, calcium signaling, metabolism and biosynthesis, focal adhesion, melanogenesis, leukocyte transendothelial migration, Wnt signaling, and type 2 diabetes mellitus. According to the different expression levels in the microarrays and their putative functions, six differentially expressed genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) were selected and analyzed by quantitative RT-PCR (qRT-PCR). The qRT-PCR results were consistent with the microarray data. Moreover, the molecular signatures (LHCGR, TNIK, and SOCS3) were associated with developmental potential from embryo to blastocyst stage and were proposed as biomarkers of embryo viability in PCOS patients. Our results may be clinically important as they offer a new potential strategy for competent oocyte/embryo selection in PCOS patients.

Introduction

Polycystic ovary syndrome (PCOS), which is characterized by increased circulating androgen levels, anovulatory infertility, and, frequently, insulin resistance and hyperinsulinemia, is a common endocrine and metabolic disorder (Franks 1995, Legro 2001, Ehrmann 2005). Although anovulation can be overcome via the use of pharmacological agents or lifestyle intervention, a number of women with PCOS are at an increased risk of pregnancy loss (Sagle et al. 1988, Carmina & Lobo 1999), which is possibly accounted for by prolonged folliculogenesis or a suboptimal intrauterine environment due to endocrinopathy or induction of ovulation itself (Glueck et al. 2002, Arredondo & Noble 2006). Previous microarray analyses have demonstrated that normal and PCOS oocytes exhibit different gene expression profiles, and annotation of the differentially expressed genes (DEGs) indicates that the reduced developmental competence of PCOS oocytes is associated with the defects in meiosis (Wood et al. 2007). Oocyte maturation, which is promoted by the resumption of meiosis, can be divided into nuclear and cytoplasmic maturation. According to the different stages of meiosis, oocytes are at three different phases of nuclear maturation, which are as follows: germinal vesicle (GV), metaphase I (MI), and MII (Cha & Chian 1998, Marteil et al. 2009). At the end of the nuclear maturation process, the oocyte that is arrested at MII following the extrusion of the first polar body (PB) is considered mature and able to be fertilized by the sperm. However, in PCOS patients, the main problems that hinder IVF-aided pregnancy are the abnormality of the folliculogenesis process and the incompetence of the oocytes with regard to embryonic development and implantation.

The cross talk between an oocyte and the surrounding cumulus cells (CCs) is critical for achieving oocyte competence, early embryonic development and CC expansion (Salustri et al. 1989, Cha & Chian 1998, Goud et al. 1998). Previous researches have proved that different gene expressions of CCs could indicate oocyte competence or predict the efficiency of embryo development and pregnancy outcome (McKenzie et al. 2004, Zhang et al. 2005, Feuerstein et al. 2007, Assou et al. 2008, Hamel et al. 2008, van Montfoort et al. 2008, Kenigsberg et al. 2009, Adriaenssens et al. 2010, Assou et al. 2010). Nevertheless, among all these previous studies on the gene expression profiles of human CCs, only one study (Ouandaogo et al. 2011) has highlighted the distinct gene expression of human CCs isolated from oocytes at the GV, MI, and MII stages, while others have mainly focused on CCs surrounding the oocytes at MII stage. Furthermore, in studies on PCOS, differential gene expression patterns in CCs were analyzed by comparing CCs isolated from the oocytes of PCOS patients with those obtained from the oocytes of normal patients.

The aim of this study was to establish the gene expression profiles of human CCs isolated from oocytes at different maturation stages (MI and MII) in PCOS patients who were under controlled ovarian stimulation (COS) cycles by using a cDNA microarray technology. The results would help us to identify candidate genes involved in oocyte nuclear maturation in PCOS. By comparing our results with those of Ouandaogo's research on the gene expression profiles of human CCs at different nuclear maturation stages of non-PCOS patients, the understanding of different molecular mechanisms governing the process of oocyte nuclear maturation between PCOS and non-PCOS patients might be improved.

Results

Identification of the sets of genes differentially expressed in PCOS patient-derived CCs at different stages (MII or MI) of oocyte nuclear maturation

For the gene expression analysis, three CCMII groups and three CCMI groups (derived from nine PCOS patients) were analyzed using six microarrays. The raw microarray data have been deposited in NCBI's Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and can be accessed through the GEO series accession number GSE40400. Of the 47 000 probe sets on the arrays, 24 360 had a present call. Most of these probe sets showed a similar expression between the CCMII and CCMI groups, except for 70 probe sets that were differentially expressed (P<0.05). Annotations of the molecular functions of the DEGs by Gene Ontology are listed in Supplementary Table 2, see section on supplementary data given at the end of this article. For five of these probe sets, the corresponding gene is not yet known. Of the 65 remaining probe sets, which correspond to 59 different genes, 48 (74%) were upregulated and 17 (26%) were downregulated in the CCMII groups compared with the CCMI groups. Clustering analysis of the arrays based on the 59 DEGs perfectly clustered the CCMII and CCMI groups (Fig. 1).

Figure 1
Figure 1

Cluster of genes overexpressed in cumulus cells (CCs) obtained from PCOS patients. The supervised hierarchical clustering of genes overexpressed in CCs obtained from PCOS patients according to the oocyte nuclear maturation stages (MII vs MI) is shown. Distinct signatures were observed in the CCMII and CCMI groups. The value of each gene was adjusted by a median-centering algorithm in log scale, and the colors indicate the relative gene expression in the red–green heat map. 0 indicated by pure black represents no change from the median gene expression level in all samples. −3 indicated by pure green represents relatively lower expression. +3 indicated by pure red represents relatively higher expression. CCMII and CCMI CCs were isolated from oocytes at MII and MI stages respectively.

Citation: REPRODUCTION 145, 6; 10.1530/REP-13-0005

Of the 59 genes that were differentially expressed between the CCMII and CCMI groups (P<0.05), 39 were categorized based on their involvement in one or more of the biological processes. Of these processes, inflammatory response, amino acid biosynthesis, synaptic transmission, signal transduction, epithelial-to-mesenchymal transition, anti-apoptosis, regulation of heart contraction, G-protein signaling, and regulation of protein amino acid phosphorylation were significantly overrepresented. In Table 1, the significant DEGs (n=59) are shown categorized based on their most prominent role.

Table 1

Genes differentially expressed (P<0.05) in the CCMII groups vs the CCMI groups categorized based on biological processes.

Gene IDGene descriptionProbe setFolda
Inflammatory response
 CXCL3Chemokine (C-X-C motif) ligand 3207850_at0.3893
 CXCL1Chemokine (C-X-C motif) ligand 1204470_at0.3057
 CXCL2Chemokine (C-X-C motif) ligand 2209774_x_at0.2847
 ANXA1Annexin A1201012_at2.1445
 HRH1Histamine receptor H1205579_at2.0289
Amino acid biosynthesis
 ALDH18A1Aldehyde dehydrogenase 18 family, member A1217791_s_at0.499
 CTHCystathionase (cystathionine γ-lyase)217127_at2.1085
 PSAT1Phosphoserine aminotransferase 1223062_s_at3.9287
Synaptic transmission
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 DLGAP1Discs, large (Drosophila) homolog-associated protein 1 /// hypothetical protein LOC284214235527_at3.188
 PDE7BPhosphodiesterase 7B230109_at2.2716
Signal transduction
 NPY2RNeuropeptide Y receptor Y2210729_at2.1905
 ANGPTL1Angiopoietin-like 1231773_at2.1411
 LHCGRLH/choriogonadotropin receptor207240_s_at3.4612
 GABRA5γ-Aminobutyric acid (GABA) A receptor, α5206456_at0.3149
 SFRP4Secreted frizzled-related protein 4204052_s_at0.2612
Epithelial-to-mesenchymal transition
 HGFHepatocyte growth factor (hepapoietin A; scatter factor)210997_at2.0589
Anti-apoptosis
 SOCS3Suppressor of cytokine signaling 3227697_at0.3546
Regulation of heart contraction
 CELF2 (CUGBP2)CUG triplet repeat, RNA binding protein 2202158_s_at2.0959
 HOPXHOP homeobox211597_s_at0.4748
G-protein signaling, coupled to IP3 second messenger (phospholipase C activating)
 EDNRBEndothelin receptor type B206701_x_at0.4955
Regulation of protein amino acid phosphorylation
 RAPGEF4Rap guanine nucleotide exchange factor (GEF) 4205651_x_at2.2647
Cell–matrix adhesion
 ITGB5Integrin, β5201125_s_at0.4905
Directional locomotion
 GRIN2AGlutamate receptor, ionotropic, N-methyl d-aspartate 2A242286_at0.4388
Protein amino acid phosphorylation
 STYK1Serine, threonine, and tyrosine kinase 1220030_at2.0848
 TNIKTraf2- and Nck-interacting kinase213109_at2.0698
 ROR1Receptor tyrosine kinase-like orphan receptor 1232060_at2.0609
Protein transport
 RAB8BRAB8B, member RAS oncogene family222846_at2.0987
 TMED2Transmembrane emp24 domain trafficking protein 2204426_at2.3534
 RAB2ARAB2A, member RAS oncogene family208733_at2.2728
10-Formyltetrahydrofolate catabolism
 ALDH1L2Aldehyde dehydrogenase 1 family, member L2231202_at2.1561
 Cell cycle
 11-SepSeptin 11201308_s_at2.2763
 STAG2Stromal antigen 2209023_s_at2.3415
Development
 ENC1Ectodermal–neural cortex (with BTB-like domain)201340_s_at0.3252
 ADAMTS9ADAM metallopeptidase with thrombospondin type 1 motif, 91554697_at2.3627
 EMP1Epithelial membrane protein 1201324_at2.1585
Proteolysis
 C1SComplement component 1, s subcomponent1555229_a_at2.351
Actin cytoskeleton organization and biogenesis
 TMSB15A (TMSL8)Thymosin β15a205347_s_at0.4478
Regulation of small GTPase-mediated signal transduction
 RASGEF1ARasGEF domain family, member 1A230563_at2.1574
Other or unknown functions
 ITM2AIntegral membrane protein 2A202746_at4.2735
 RUNX2Runt-related transcription factor 2236859_at2.6116
 SLC7A11Solute carrier family 7 (cationic amino acid transporter, y+ system) member 11217678_at2.5892
 LRRN3Leucine-rich repeat neuronal 3209841_s_at2.4714
 PTERPhosphotriesterase related218967_s_at2.4129
 SPDYE1Speedy homolog E1 (Xenopus laevis)232964_at2.351
 CREB5cAMP-responsive element binding protein 5229228_at2.2827
 TTMATwo transmembrane domain family member A229523_at2.2462
 TUBE1Tubulin, ϵ1226181_at2.165
 OSBPL10Oxysterol binding protein-like 10219073_s_at2.1568
 C5ORF24Chromosome 5 open reading frame 24224875_at2.1151
 PBRM1Polybromo 1223400_s_at2.0873
 GCNT1Glucosaminyl (N-acetyl) transferase 1239761_at2.0839
 LOC401074Hypothetical LOC4010741559827_at2.0796
 GRAMD1CGRAM domain containing 1C219313_at2.0499
 MICU3 (EFHA2)EF-hand domain family, member A2238458_at2.0193
 CENPFCentromere protein F207828_s_at0.4872
 LOC129293Hypothetical protein LOC129293227867_at0.4871
 ANK2Ankyrin 2, neuronal202920_at0.4863
 SYNPO2Synaptopodin 2227662_at0.4124

aCCMII:CCMI expression ratio.

The thresholds of P value and false discovered rate (FDR) derived from the hypergenomic test were set <0.05 to obtain the significantly represented pathway (P<0.05) as indicated in Table 2. P value and FDR could reflect the significance of the correlation between the enriched DEGs and the respective pathways. As shown in Fig. 2, the pathway having the maximum enriched genes is ‘neuroactive ligand–receptor interaction (n=6)’, and in descending order follow the ‘calcium signaling pathway (n=4), cytokine–cytokine receptor interaction (n=4), focal adhesion (n=3), glycine, serine and threonine metabolism (n=2), prostate cancer (n=2), melanogenesis (n=2), leukocyte transendothelial migration (n=2), systemic lupus erythematosus (n=2), Wnt signaling pathway (n=2), vitamin B6 metabolism (n=1), cysteine, methionine, nitrogen, selenoamino acid metabolism (n=1), urea cycle and metabolism of amino groups (n=1), thyroid cancer (n=1), O-glycan biosynthesis (n=1), and type 2 diabetes mellitus (n=1)’.

Table 2

Genes differentially expressed (P<0.05) in the CCMII groups vs the CCMI groups per significantly overrepresented (P<0.05) pathway.

Gene IDGene descriptionProbe setFolda
Neuroactive ligand–receptor interaction (n=6)*
 NPY2RNeuropeptide Y receptor Y2210729_at2.1905
 GRIN2AGlutamate receptor, ionotropic, N-methyl d-aspartate 2A242286_at0.4388
 LHCGRLH/choriogonadotropin receptor207240_s_at3.4612
 GABRA5γ-Aminobutyric acid (GABA) 206456_at0.3149
 HRH1Histamine receptor H1205579_at2.0289
 EDNRBEndothelin receptor type B206701_x_at0.4955
Calcium signaling pathway (n=4)*
 GRIN2AGlutamate receptor, ionotropic, N-methyl d-aspartate 2A242286_at0.4388
 LHCGRLH/choriogonadotropin receptor207240_s_at3.4612
 HRH1Histamine receptor H1205579_at2.0289
 EDNRBEndothelin receptor type B206701_x_at0.4955
Cytokine–cytokine receptor interaction (n=4)*
 CXCL3Chemokine (C-X-C motif) ligand 3207850_at0.3893
 CXCL1Chemokine (C-X-C motif) ligand 1204470_at0.3057
 HGFHepatocyte growth factor (hepapoietin A; scatter factor)210997_at2.0589
 CXCL2Chemokine (C-X-C motif) ligand 2209774_x_at0.2847
Focal adhesion (n=3)*
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 ITGB5Integrin, β5201125_s_at0.4905
 HGFHepatocyte growth factor (hepapoietin A; scatter factor)210997_at2.5519
Glycine, serine, and threonine metabolism (n=2)*
 CTHCystathionase (cystathionine γ-lyase)206085_s_at2.0034
 CTHCystathionase (cystathionine γ-lyase)217127_at2.1085
 PSAT1Phosphoserine aminotransferase 1223062_s_at3.9287
Prostate cancer (n=2)*
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 CREB5cAMP-responsive element binding protein 5229228_at2.2827
Vitamin B6 metabolism (n=1)*
 PSAT1Phosphoserine aminotransferase 1223062_s_at3.9287
Melanogenesis (n=2)*
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 EDNRBEndothelin receptor type B206701_x_at0.4955
Leukocyte transendothelial migration (n=2)*
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 RAPGEF4Rap guanine nucleotide exchange factor (GEF) 4205651_x_at2.2647
Systemic lupus erythematosus (n=2)*
 GRIN2AGlutamate receptor, ionotropic, N-methyl d-aspartate 2A242286_at0.4388
 C1SComplement component 1, s subcomponent1555229_a_at2.3510
Wnt signaling pathway (n=2)
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
 SFRP4Secreted frizzled-related protein 4204052_s_at0.2612
Cysteine, methionine, nitrogen, and selenoamino acid metabolism (n=1)
 CTHCystathionase (cystathionine γ-lyase)206085_s_at2.0034
 CTHCystathionase (cystathionine γ-lyase)217127_at2.1085
Urea cycle and metabolism of amino groups (n=1)
 ALDH18A1Aldehyde dehydrogenase 18 family, member A1217791_s_at0.4990
Thyroid cancer (n=1)
 CTNNB1Catenin (cadherin-associated protein), β11554411_at2.3465
O-glycan biosynthesis (n=1)
 GCNT1Glucosaminyl (N-acetyl) transferase 1, core 2 239761_at2.0839
Type 2 diabetes mellitus (n=1)
 SOCS3Suppressor of cytokine signaling 3227697_at0.3546

*P<0.01; P<0.05.

CCMII:CCMI expression ratio.

Figure 2
Figure 2

Number of genes enriched in the significantly represented pathway (P<0.05). The thresholds of P value and FDR derived from the hypergenomic test were set at <0.05 to find the significantly represented pathway. The significantly represented pathway is shown on the y-axis and the number of genes is shown on the x-axis.

Citation: REPRODUCTION 145, 6; 10.1530/REP-13-0005

Validation of the microarray results by quantitative RT-PCR

According to the relevant functional annotations and their high fold-change (FC) values, two genes involved in the neuroactive ligand–receptor interaction pathway (LHCGR and GRIN2A), two genes involved in the Wnt signaling pathway (SFRP4 and TNIK), one gene involved in the type 2 diabetes mellitus pathway (suppressors of cytokine signaling (SOCS)), and one gene mediating angiogenesis (ANGPTL1) were chosen for quantitative RT-PCR (qRT-PCR) validation. For all the six genes, the qRT-PCR results were in accordance with the microarray data obtained using the original samples for the microarray analysis (Fig. 3).

Figure 3
Figure 3

Differentially expressed genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) studied through microarray experiments and validated by qRT-PCR. The qRT-PCR results were in line with the microarray data set. The gray bars show the relative gene expression measured using qRT-PCR. The white bars show the relative gene expression measured using the Affymetrix microarrays. The same cumulus cells from PCOS COCs at MII and MI stages were used for qRT-PCR and microarray analysis. The y-axis represents the fold change CCMII/CCMI and the selected genes are shown on the x-axis.

Citation: REPRODUCTION 145, 6; 10.1530/REP-13-0005

In addition, the expression levels of the six selected genes were also tested in a cohort of CC samples isolated from the additional nine PCOS patients by qRT-PCR. On comparing the CCMII groups with the CCMI groups, it was observed that the mean transcript levels of three genes were significantly higher, with 4.55-, 3.76-, and 2.61-fold increases being observed for LHCGR (4.6±0.13 vs 1.0±0.03), ANGPTL1 (3.6±0.26 vs 0.95±0.04), and TNIK (2.5±0.67 vs 0.95±0.16), respectively, while the expression levels of the other three genes were significantly decreased in the CCMII samples, with 2.06-, 3.69-, and 2.52-fold decreases being observed for GRIN2A (0.49±0.05 vs 1.1±0.02), SFRP4 (0.29±0.03 vs 1.1±0.06), and SOCS3 (0.62±0.17 vs 1.56±0.48) respectively (Fig. 4). The results of qRT-PCR for different samples further suggested the reasonable certainty of the fold change observed during the microarray analysis.

Figure 4
Figure 4

mRNA expression of candidate genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) in human CCs, organized according to the oocyte nuclear maturation stage (MI vs MII stages). The signal intensity for each gene is shown on the y-axis in arbitrary units determined by qRT-PCR with GAPDH as an endogenous reference. Asterisk (*) indicates a significant difference in gene expression between CC categories (**P<0.01 and *P<0.05). The results are presented as the means±s.e.m. CCMI, cumulus cells from oocytes at MI stage; CCMII, CCs from oocytes at MII stage.

Citation: REPRODUCTION 145, 6; 10.1530/REP-13-0005

Differences in the transcript levels of target genes analyzed according to oocyte development outcome

The transcript levels of the target genes were evaluated as the follow-up of the oocytes with 2PN visualization after IVF (Fig. 5). CCs from MII oocytes were divided into two groups: CCB+ and CCB−. The mean expression levels of LHCGR and TNIK were significantly higher in the CCB+ groups than in the CCB− groups (LHCGR (2.19±0.12 vs 1.02±0.03) and TNIK (1.87±0.08 vs 0.89±0.04)). The expression level of SOCS3 was significantly lower in the CCB+ groups than in the CCB− groups (SOCS3 (0.65±0.08 vs 1.42±0.03)). The levels of the other three genes (ANGPTL1, SFRP4, and GRIN2A) were not significantly different between the CCB+ and CCB− groups.

Figure 5
Figure 5

mRNA expression of candidate genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) in human CCs, organized according to oocyte development capability after normal fertilization. The signal intensity for each gene is shown on the y-axis in arbitrary units determined by qRT-PCR with GAPDH as an endogenous reference. *Significant difference in gene expression between CC categories (**P<0.01 and *P<0.05). The results are presented as the means±s.e.m. CCB+, cumulus cells from oocytes yielding blastocysts after 5–6 days of invitro culture; CCB−, CCs from oocytes that had not developed into blastocysts on days 5–6.

Citation: REPRODUCTION 145, 6; 10.1530/REP-13-0005

Discussion

PCOS is the most common endocrine abnormality in women of reproductive age. Although sufficient oocytes are usually retrieved from PCOS patients who are under COS, high-quality mature oocytes are limited in number. Oocyte competence depends on the quality of the follicular microenvironment. The cross talk between CCs and oocytes plays a pivotal role in oocyte maturation and metabolism. At present, oocyte competence is evaluated on the basis of morphological features including the PB, zona pellucida, meiotic spindle, and cytoplasm. But a lot of evidence has proved that the morphological evaluation is not a reliable predictor of oocyte competence and embryo viability. Until now, the studies of gene expression profiles of CCs have reported a noninvasive method to predict oocyte and embryo competence (Assou et al. 2010). Together with morphological evaluation, CC genes may serve as biomarkers of oocyte and embryo selection during the IVF process or might result in an oocyte selection tool for those who are obliged to select a limited number of oocytes for fertilization in countries where embryo cryopreservation is legally restricted (Ludwig et al. 2000). Herein, for the first time, we have reported a significant alteration of gene expression in CCs isolated from mature oocytes and immature oocytes of PCOS patients under COS to identify molecular signatures in CCs that are associated with oocyte maturation or embryo development. This study introduces a new perspective that helps understanding oocyte nuclear maturation in PCOS patients and has an important clinical significance for increasing the oocyte competence of PCOS patients undergoing IVF.

It is worth mentioning that we have focused on a subtle biological question involving CCs that are fairly homogeneous and with almost no contamination from other cells. All the patients involved in our study suffered from PCOS and used the same GNRH agonist protocol in the IVF process. In contrast to other publications that have reported a large number of DEGs originating from the comparison of PCOS and non-PCOS patients (Wood et al. 2007, Kenigsberg et al. 2009), we report that there are only few genes (n=59) that are differentially expressed in CCs isolated from the mature or immature oocytes of PCOS patients. To the best of our knowledge, this is the first time that the gene expression profiles of CCs in PCOS have been studied according to oocyte nuclear maturation stages.

All the PCOS patients were administered exogenous hormones (GNRH agonist and recombinant FSH) to synchronize follicular development and ovarian hyperstimulation, respectively, and human chorionic gonadotrophin (hCG) was used to induce ovulation. The previous results from studies conducted on rats show that hormones used for estrus synchronization and ovarian hyperstimulation have minimal effects on gene expression and that the induction of ovulation causes major changes in the gene expression of cumulus–oocyte complexes (COCs; Agca et al. 2013). Compared with the different gene expression profiles of COCs in rats induced by hCG, only one gene, runt-related transcription factor (Runx2), was upregulated in the CCMII groups of PCOS patients in our study. Moreover, all the PCOS patients involved in our study used the same ovarian stimulation and oocyte retrieval protocols. So, we thought that the 59 genes that were differentially expressed between the CCMII and CCMI groups of PCOS patients in our study were mainly involved in oocyte maturation and not induced by hCG.

Among the DEGs identified in our study, there were several previously described genes, including an antagonist of the Wnt signaling pathway (SFRP4), a neurotransmitter receptor (GABRA5), a gene involved in cytoskeleton formation (ANK2) (Devjak et al. 2012), a growth factor (HGF; Haouzi et al. 2012), and a hormone receptor (LHCGR) (Kenigsberg et al. 2009). In addition, by comparing the list of DEGs (n=59) between the CCMI and CCMII groups of PCOS patients in our study with that of DEGs (n=25) of non-PCOS patients reported by Ouandaogo (Ouandaogo et al. 2011), we found that all genes were not exactly the same, while only two genes (SLC7A11 in our study and SLC38A2 in Ouandaogo's study) belonged to the same gene family. This may indicate that there are different molecular mechanisms governing the process of oocyte nuclear maturation in PCOS and non-PCOS patients.

The analysis of gene annotations in this study implied that the different expressed genes mainly play an important role in signal transduction and are involved in the Wnt signaling pathway, neuroactive ligand–receptor interaction, cytoskeleton and extracellular matrix formation, and angiogenesis process. These pathways or related genes have been previously proved to have a key role in folliculogenesis and oocyte maturation (Devjak et al. 2012).

Wnt signaling is involved in development, cell proliferation, cell migration, and angiogenesis (Richards et al. 2002). Three genes (CTNNB1, SFRP4, and TNIK) associated with the Wnt signaling pathway were identified in our study. It has recently been reported that the WNT/β-catenin signaling pathway plays a complicated role in ovulation, and as a Wnt antagonist, SFRP4 is an important marker of ovulation and luteinization (Fan et al. 2010). Moreover, SFRP4 is expressed in human GCs and its expression, which is inhibited by LH/HCG, declines during late antral follicular growth (Maman et al. 2011). Traf2- and Nck-interacting kinase (TNIK) is another gene that is a novel activator of Wnt signaling. It is an activating kinase for T-cell factor-4 (TCF4) and is essential for β-catenin–TCF4 transactivation (Satow et al. 2010). Because the aberrant activation of Wnt signaling is also associated with several types of cancers, most reports on TNIK concern its role in cancer growth (Shitashige et al. 2010). In our study, the upregulated expression of TNIK (2.61-fold) and the downregulated expression of SFRP4 (3.69-fold) in the CCMII groups compared with the CCMI groups may further verify that the Wnt signaling pathway is involved in oocyte maturation in PCOS patients. Moreover, as the expression level of TNIK is higher (2.1-fold) in the CCB+ groups than in the CCB− groups, we propose that the activation of TNIK on Wnt signaling is important for embryo development and TNIK might serve as a biomarker of embryo viability in the CCs of PCOS patients.

Besides the signal transduction genes in Wnt signaling, there are several genes that are involved in neuroactive ligand–receptor interaction (GABRA5, GRIN2A, and LHCGR) or mediate angiogenesis (ANGPTL1). It has been supposed that neurotransmitters play an important role in folliculogenesis and oocyte maturation and the gene (GABRA5) responsive to γ-aminobutyric acid, GABA, might serve as a biomarker of oocyte maturation in CCs (Devjak et al. 2012). In our study, the expression levels of two genes (GABRA5 and GRIN2A) that encoded receptors for two neurotransmitters, GABA and glutamate, respectively, were downregulated in the CCMII groups compared with the CCMI groups. So far, little is known about the function of GRIN2A in folliculogenesis and oocyte maturation. In view of the fact that ovarian steroids increased GRIN2A expression in serotonin neurons of macaques (Bethea & Reddy 2012), we guessed that the downregulated expression of GRIN2A in the CCMII groups perhaps was related to steroid levels in the microenvironment for oocyte development. In agreement with Devjak's opinion, we also supposed that GABRA and GRIN2A might be used as biomarkers to predict oocyte maturation in CCs.

In addition, another gene that is involved in the neuroactive ligand–receptor interaction pathway is LHCGR, which encodes a G-protein-coupled receptor for LH and HCG. In the ovary, the induction of LHCGR during granulosa cell differentiation allows the preovulatory follicle to respond to the mid-cycle surge of LH, leading to the ovulation and release of the mature oocytes. In women, inactivating mutations of LHCGR are associated with increased LH levels, enlarged ovaries, oligomenorrhea, resistance to LH or hCG, and infertility (Toledo et al. 1996, Latronico et al. 1998). In our study, the expression level of LHCGR in the CCMII samples was 4.55-fold higher than that in the CCMI samples. This indicates that LHCGR expression in CCs may contribute to oocyte maturation in PCOS patients. It should be kept in mind that the regulation of LHCGR expression in CCs has previously been reported to depend on oocyte-secreted factors and also on the FSH dose (Kawashima et al. 2008, Romero et al. 2011). Moreover, a genome-wide association study aimed at identifying susceptibility loci for PCOS indicated that LHCGR may influence the expression of FSH receptor (FSHR) even though the genes are located in different LD blocks (Chen et al. 2011). FSHR has been identified as a biomarker in bovine CCs that helps to predict oocyte competence and select higher embryo quality (Assidi et al. 2008). We found that the mRNA level of LHCGR was significantly higher (2.15-fold) in the CCB+ groups than in the CCB− groups. Maybe LHCGR could also serve as an early marker of embryo viability in PCOS patients.

Another gene involved in signal transduction is ANGPTL1, which is a member of the angiopoietin-related protein family and known to mediate angiogenesis. Previous studies have suggested that ANGPTL1 and ANGPTL2 interact with unidentified receptors on endothelial cells to modulate angiogenesis in a context-dependent manner (Dhanabal et al. 2002). Both ANGPTL1 and ANGPTL2 exert anti-apoptotic effects via the phosphatidylinositol 3-kinase/Akt pathway, and their cooperative activity is likely required for vascular development during zebrafish embryogenesis (Kubota et al. 2005a, 2005b). Recently, it has been suggested that the change in ANGPT1 and ANGPT2 levels may be associated with follicular growth and angiogenesis during the preovulatory period (Nishigaki et al. 2011). We observed a higher expression level of ANGPTL1 in the CCMII groups in our study and considered that the results further testified the relationship between angiogenesis and folliculogenesis.

It has been proved that the extracellular matrix of CCs plays a major role in folliculogenesis and is crucial for ovulation, oviduct passage, and fertilization (Irving-Rodgers & Rodgers 2005). We found that ADAMTS9, a gene involved in extracellular matrix binding, was upregulated in the CCMII groups compared with the CCMI groups. Moreover, it has been reported that Adamts9 is widely expressed during mouse embryo development (Jungers et al. 2005). ANK2, another gene involved in cytoskeleton formation, was downregulated in the CCMII samples. It has been suggested that ANK2 contains SOWAHC (ANKRD57) protein, which has already been recognized to be influenced by oocyte maturation in CCs (Ouandaogo et al. 2011). Changes in the expression levels of ADAMTS9 and ANK2 during oocyte maturation further confirmed that cytoskeleton and extracellular matrix formation was important for folliculogenesis and oocyte maturation in PCOS.

One interesting gene that we identified was SOCS3, a member of the SOCS family, which is involved in the type 2 diabetes mellitus pathway. SOCS family members are regulatory proteins that are rapidly induced in response to intracellular JAK–STAT signaling, a cascade controlling biological functions including cytokine-induced immunological responses and reproductive processes. It has been reported that SOCS3 plays a vital role in reproduction by regulating trophoblast differentiation (Fitzgerald et al. 2009). In PCOS, SOCS3 affects adipogenesis and insulin resistance (Chazenbalk et al. 2012). The results of our study showed decreased SOCS3 expression (2.52-fold) in the CCMII groups compared with the CCMI groups and a downregulated (2.18-fold) expression level in the CCB+ groups compared with the CCB− groups. We propose that the expression of SOCS3 is not only associated with the increased prevalence of obesity in PCOS patients but also related to oocyte or embryo competence and might serve as a biomarker of oocyte maturation or embryo viability in CCs.

In conclusion, the comparison of the gene expression profiles of CCs isolated from oocytes at different nuclear maturation stages (MII and MI) of PCOS patients suggested that the gene expression of CCs was altered according to the oocyte maturation process. Specifically, major signaling pathways (particularly the Wnt signaling pathway, neuroactive ligand–receptor interaction, and cytoskeleton and extracellular matrix formation) play a key role in the oocyte nuclear maturation of PCOS patients. Moreover, several genes (LHCGR, TNIK, and SOCS3) were suggested to serve as biomarkers of oocyte or embryo competence in CCs of PCOS patients.

Materials and Methods

Processing of CCs

PCOS patients referred to our center for IVF were included in this study after obtaining written informed consent. This study was approved by the Institutional Ethical Review Board of Yuhuangding Hospital of Yantai. All the PCOS patients were diagnosed by the presence of two or more of the following features: chronic oligo-ovulation or anovulation, androgen excess, and polycystic ovaries. We excluded patients with Cushing's syndrome, congenital adrenal hyperplasia, and androgen-secreting tumors. The inclusion criteria of the recruited PCOS patients in this study were as follows: age <36 years, BMI ranging between 20 and 26 kg/m2, number of obtained oocytes ranging between 12 and 28 per cycle, number of obtained blastocysts ranging between 4 and 12 per cycle, basal serum LH/FSH more than 2.0, serum androgen more than 0.5 ng/ml, and normal spermiogram of the partner according to the WHO criteria.

Ovarian stimulation and oocyte retrieval protocols were carried out as described previously (Wood et al. 2007, Kenigsberg et al. 2009). All the selected PCOS patients were administered GNRH agonist triptorelin acetate (Diphereline, Ipsen Pharma Biotech, Paris, France) from the mid-luteal phase at a daily dose of 0.05 mg subcutaneously. Once adequate pituitary downregulation was confirmed (serum LH levels <3.0 ng/ml and serum estradiol (E2) levels <30 pg/ml), the patients were s.c. administered 150–187.5 IU recombinant FSH (Gonal-f, Follitropin Alfa, Serono) for COS. When two or more follicles were at least 18 mm in diameter and the serum E2 levels were at least 300 pg/ml per dominant follicle, all the patients were administered 250 μg hCG (Profasi, Serono). COCs were recovered under ultrasound guidance 36 h after hCG administration. After COC retrieval, a proportion of the CCs surrounding a single oocyte were removed using a sharp needle, lysed in 80 μl RLT buffer (RNeasy Mini Kit, Qiagen), snap-frozen in liquid nitrogen, and stored at −80 °C (CCs from one oocyte per vial). A total of 162 CC samples obtained from 30 patients were used in this study.

Oocytes were further inseminated and cultured individually in 20 μl droplets covered by mineral oil. Oocytes were denudated to assess the maturation stage 3 h after insemination. Immature MI oocytes had no polar bodies or GVs, while mature MII oocytes extruded a clearly visible PB. Fertilization status was evaluated 16–18 h after insemination by observing the appearance of two pronuclei. Only fertilized oocytes (2PN) were further cultured to the blastocyst stage for 5 or 6 days, and unfertilized oocytes were discarded. On day 5/6, one or two embryos at the blastocyst or morula stage were transplanted and the remaining blastocysts were cryopreserved. CCs obtained from MI and MII oocytes were considered for a transcriptome analysis.

Experimental design

As the total RNA extracted from a single CC is too limited, CCs separated from COCs at the same stage were pooled together for this study. Because very few GV-stage COCs were retrieved from the PCOS patients under controlled ovarian hyperstimulation, we focused on COCs at MII and MI stages. For microarray analysis, 54 individual CC samples obtained from nine patients were isolated from COCs at MI and MII stages and divided into six groups (CCMII-1, CCMII-2, CCMII-3, CCMI-1, CCMI-2, and CCMI-3). That is, each group had nine CC samples isolated from oocytes at MI or MII stage. The original samples were also analyzed using qRT-PCR to validate the microarray results.

To further confirm the differences in the expression of candidate genes in CCs isolated from oocytes at different nuclear maturation stages, 54 CC samples obtained from an additional nine PCOS patients were used. All the CCs were classified into two groups (CCMI and CCMII) and tested using qRT-PCR.

Furthermore, to evaluate whether the specific MII CC molecular signatures observed correlate with embryo viability, 54 CC samples from the other 12 PCOS patients were also tested using qRT-PCR. The 54 CC samples derived from the normally fertilized (2PN) oocytes were classified into the following two groups as described previously (Gardner & Schoolcraft 1999, Guerif et al. 2007): the ‘blastocyst’ group (CCB+), defined as CCs from COCs that yielded top-quality embryos on day 2 and developed into blastocysts on day 5/6, and the ‘unable to develop into blastocyst’ group (CCB−), defined as CCs from COCs that formed weak- or low-quality embryos on day 2 and failed to develop into blastocysts on day 5/6.

Each group (CCMI, CCMII, CCB+, or CCB−) had three biological replicates.

Complementary RNA preparation and microarray hybridization

RNA extraction and microarray hybridization were performed at CapitalBio Corporation (Beijing, China). Total RNA isolation was performed using the Qiagen RNeasy Mini Kit (Qiagen) according to the manufacturer's instructions. This RNA isolation kit significantly reduces contamination from both genomic DNA and proteins. Only the extracted RNA that had a rRNA 28S:18S intensity ratio of 1.0–1.5:1 was used in the microarray and qRT-PCR assays.

Aliquots (2 μg) of total RNA from three CCMI (CCMI-1, CCMI-2, and CCMI-3) and three CCMII (CCMII-1, CCMII-2, and CCMII-3) groups were used to synthesize a double-stranded cDNA, which was subsequently transcribed into a biotin-tagged cDNA using the MessageAmp II aRNA Amplification Kit (Ambion, Austin, TX, USA). The cDNA was then fragmented to produce strands that were 35–200 bases in length in accordance with the published protocols (Affymetrix, Santa Clara, CA, USA). The fragmented cDNA was hybridized to the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array, which contains 47 000 transcripts. Microarray hybridization was performed at 45 °C with rotation for 16 h using an Affymetrix GeneChip Hybridization Oven 640. The arrays were washed and stained (streptavidin–phycoerythrin) at an Affymetrix GeneChip Fluidics Station 450 and later scanned on an Affymetrix GeneChip Scanner 3000 to analyze the hybridization data. Overall, six microarray chips were analyzed in this study.

Data processing and microarray data analysis

The scanned images that were obtained were first assessed by visual inspection and then analyzed using the Affymetrix GeneChip Operating Software (GCOS 1.4). The detection algorithm uses probe pair intensities to generate a detection P value and assign a present, a marginal or an absent call, which represents whether the measured transcript is detected (present) or not detected (absent). P value was derived from one-sided Wilcoxon's signed rank test with the default value 0.015 defined by the Affymetrix Corporation. Any P value that falls below 0.04 is assigned a present call and above 0.06 is assigned an absent call. Marginal calls are given to probe sets that have P values between 0.04 and 0.06. The lesser the P value correlates, the more likely the given transcript is truly present in the sample. The raw data were filtered to mask genes with signal intensities <100, which is at the background threshold, and to retain only genes that were called present by the GCOS in at least two replicates. To normalize the different arrays, dChip Software was used in a global scaling procedure.

In a comparison analysis, a two-class, unpaired method from the Significance Analysis of Microarrays (SAM version 3.02, Stanford University, Stanford, CA, USA) software was used to compare significantly DEGs in the CCMII and CCMI groups. The algorithm used to sort the statistically significant DEGs was a modified t-test, and the criteria for DEGs were FDR <0.05 and fold change >2.0 or <0.5. FDR was a corrected P value by post hoc test. The SAM-M results were used to perform a supervised hierarchical clustering based on the expression level of the probe sets (multi-class gene set), and the cluster was visualized using the Tree View Software (Stanford University, Stanford, CA, USA) (Eisen et al. 1998). All the DEGs were analyzed using a free web-based Molecular Annotation System 3.0 (MAS 3.0, www.capitalbio.com), which integrates three different open-source pathway resources: KEGG, BioCarta, and GenMAPP. The significantly represented pathway was chosen by the threshold of P value and FDR (corrected P value) <0.05 derived from the hypergenomic test.

Quantitative RT-PCR

qRT-PCR was conducted on candidate genes that were found to be differentially expressed in the microarrays and whose functions were found, upon a biological function analysis, to be closely related to oocyte maturation and/or embryo development. The first-strand complementary synthesis reaction was performed using a PrimeScript RT reagent kit (Perfect Real Time; TaKaRa Biotechnology (Dalian) Co. Ltd., Dalian, China). Amplification reactions were conducted using SYBR Premix Ex Taq (Perfect Real Time; TaKaRa Biotechnology (Dalian) Co. Ltd.) and an ABI PRISM 7300 system. Gene-specific qRT-PCR primers that were used are listed in Supplementary Table 1, see section on supplementary data given at the end of this article. GAPDH served as an internal control to normalize the loading of the template cDNA. Each set of qRT-PCR reactions was repeated three times, and the fold change in the expression of each gene of interest was analyzed using the ΔΔCt method (Livak & Schmittgen 2001). Student's t-test of independent data was used for statistical analysis. The differences among groups were considered significant when the P value was <0.05.

Supplementary data

This is linked to the online version of the paper at http://dx.doi.org/10.1530/REP-13-0005.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This study was supported by the National Basic Research Program (grant 81170622), Science and Technology Development Project of Shangdong Population and Family Planning Commission (grant 2011-15), and Science and Technology Development Project of Yantai (grant 2010-148-26).

Acknowledgements

We gratefully acknowledge the CapitalBio Corporation for conducting the RNA extractions and microarray analysis.

References

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  • ToledoSPBrunnerHGKraaijRPostMDahiaPLHayashidaCYKremerHTAP1996An inactivating mutation of the luteinizing hormone receptor causes amenorrhea in a 46,XX female. Journal of Clinical Endocrinology and Metabolism8138503854. (doi:10.1210/jc.81.11.3850)

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  • WoodJRDumesicDAAbbottDHStraussJFIII2007Molecular abnormalities in oocytes from women with polycystic ovary syndrome revealed by microarray analysis. Journal of Clinical Endocrinology and Metabolism92705713. (doi:10.1210/jc.2006-2123)

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  • ZhangXJafariNBarnesRBConfinoEMiladMKazerRR2005Studies of gene expression in human cumulus cells indicate pentraxin 3 as a possible marker for oocyte quality. Fertility and Sterility83 (Suppl 1) 11691179. (doi:10.1016/j.fertnstert.2004.11.030)

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    Cluster of genes overexpressed in cumulus cells (CCs) obtained from PCOS patients. The supervised hierarchical clustering of genes overexpressed in CCs obtained from PCOS patients according to the oocyte nuclear maturation stages (MII vs MI) is shown. Distinct signatures were observed in the CCMII and CCMI groups. The value of each gene was adjusted by a median-centering algorithm in log scale, and the colors indicate the relative gene expression in the red–green heat map. 0 indicated by pure black represents no change from the median gene expression level in all samples. −3 indicated by pure green represents relatively lower expression. +3 indicated by pure red represents relatively higher expression. CCMII and CCMI CCs were isolated from oocytes at MII and MI stages respectively.

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    Number of genes enriched in the significantly represented pathway (P<0.05). The thresholds of P value and FDR derived from the hypergenomic test were set at <0.05 to find the significantly represented pathway. The significantly represented pathway is shown on the y-axis and the number of genes is shown on the x-axis.

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    Differentially expressed genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) studied through microarray experiments and validated by qRT-PCR. The qRT-PCR results were in line with the microarray data set. The gray bars show the relative gene expression measured using qRT-PCR. The white bars show the relative gene expression measured using the Affymetrix microarrays. The same cumulus cells from PCOS COCs at MII and MI stages were used for qRT-PCR and microarray analysis. The y-axis represents the fold change CCMII/CCMI and the selected genes are shown on the x-axis.

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    mRNA expression of candidate genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) in human CCs, organized according to the oocyte nuclear maturation stage (MI vs MII stages). The signal intensity for each gene is shown on the y-axis in arbitrary units determined by qRT-PCR with GAPDH as an endogenous reference. Asterisk (*) indicates a significant difference in gene expression between CC categories (**P<0.01 and *P<0.05). The results are presented as the means±s.e.m. CCMI, cumulus cells from oocytes at MI stage; CCMII, CCs from oocytes at MII stage.

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    mRNA expression of candidate genes (LHCGR, ANGPTL1, TNIK, GRIN2A, SFRP4, and SOCS3) in human CCs, organized according to oocyte development capability after normal fertilization. The signal intensity for each gene is shown on the y-axis in arbitrary units determined by qRT-PCR with GAPDH as an endogenous reference. *Significant difference in gene expression between CC categories (**P<0.01 and *P<0.05). The results are presented as the means±s.e.m. CCB+, cumulus cells from oocytes yielding blastocysts after 5–6 days of invitro culture; CCB−, CCs from oocytes that had not developed into blastocysts on days 5–6.

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