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Mol. Hum. Reprod. Advance Access originally published online on January 13, 2009
Molecular Human Reproduction 2009 15(2):89-103; doi:10.1093/molehr/gan082
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© The Author 2009. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Gene expression microarray profiles of cumulus cells in lean and overweight-obese polycystic ovary syndrome patients

Shlomit Kenigsberg1,{dagger}, Yaakov Bentov2,{dagger},{ddagger}, Vered Chalifa-Caspi3, Gad Potashnik2, Rivka Ofir1 and Ohad S. Birk1,4,5

1The Morris Kahn Laboratory of Human Genetics, National Institute for Biotechnology in the Negev, Ben-Gurion University, Beer-Sheva 84105, Israel 2Fertility and IVF Unit, Soroka University Medical Center, Beer-Sheva 84101, Israel 3Bioinformatics Core Facility at the National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University, Beer-Sheva 84105, Israel 4Genetics Institute at Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University, PO Box 151, Beer-Sheva 84105, Israel

5 Corresponding author: Tel: +972 8-6403439; Fax: +972 8-6400042; Email: obirk{at}bgu.ac.il


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
The aim of this work was to study gene expression patterns of cultured cumulus cells from lean and overweight-obese polycystic ovary syndrome (PCOS) patients using genome-wide oligonucleotide microarray. The study included 25 patients undergoing in vitro fertilization and intra-cytoplasmic sperm injection: 12 diagnosed with PCOS and 13 matching controls. Each of the groups was subdivided into lean (body mass index (BMI) < 24) and overweight (BMI > 27) subgroups. The following comparisons of gene expression data were made: lean PCOS versus lean controls, lean PCOS versus overweight PCOS, all PCOS versus all controls, overweight PCOS versus overweight controls, overweight controls versus lean controls and all overweight versus all lean. The largest number of differentially expressed genes (DEGs), with fold change (FC) |FC| >= 1.5 and P-value < 0.01, was found in the lean PCOS versus lean controls comparison (487) with most of these genes being down-regulated in PCOS. The second largest group of DEGs originated from the comparison of lean PCOS versus overweight PCOS (305). The other comparisons resulted in a much smaller number of DEGs (174, 109, 125 and 12, respectively). In the comparison of lean PCOS with lean controls, most DEGs were transcription factors and components of the extracellular matrix and two pathways, Wnt/β-catenin and mitogen-activated protein kinase. When comparing overweight PCOS with overweight controls, most DEGs were of pathways related to insulin signaling, metabolism and energy production. The finding of unique gene expression patterns in cumulus cells from the two PCOS subtypes is in agreement with other studies that have found the two to be separate entities with potentially different pathophysiologies.

Key words: PCOS/cumulus/microarray/infertility/insulin resistance


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
Polycystic ovary syndrome (PCOS) is a common heterogeneous endocrinopathy in women of reproductive age, which is mostly associated with ovulation failure. The PCOS phenotype is variable and may include polycystic ovaries, hirsutism, hyperandrogenism and pre-diabetes (Franks, 1995). The phenotype of PCOS and its impact on reproductive function are profoundly affected by obesity which, in turn, has both genetic and environmental influences (Salehi et al., 2004). PCOS is considered by some authors to be a unique representation of the metabolic syndrome (Sam and Dunaif, 2003).

The etiology of PCOS remains unclear (Diamanti-Kandarakis and Piperi, 2005; Vink et al., 2006); however, the observation of familial segregation of PCOS is consistent with a genetic basis for this disorder. Studies in sisters have shown that hyperandrogenism is the component with the strongest concordance (Legro et al., 1998; Franks et al., 2006). More than 50 candidate genes have been studied for association with the syndrome, but none have been proven to play a definitive role (Escobar-Morreale et al., 2005). Association and linkage studies have provided evidence for linkage disequilibrium at D19S884, a dinucleotide-repeat marker, closely linked to the insulin receptor gene locus on chromosome 19p13.2 (Stewart et al., 2006). It has been suggested that X-linked genes and chromosome X inactivation are involved in the determination of the distribution and presentation of the syndrome (Hickey et al., 2006).

Studies comparing gene expression arrays in tissues of PCOS and control patients have reported similar pathways for differentially expressed genes (DEGs) in theca cells (Wood et al., 2004), whole ovaries (Jansen et al., 2004; Oksjoki et al., 2005) and oocytes (Wood et al., 2007). Comparative analysis revealed genes involved in the Wnt/β-catenin and mitogen-activated protein kinase (MAPK)-signaling pathways, retinoic acid metabolism and apoptosis. However, to the best of our knowledge, gene expression in cumulus cells isolated from PCOS patients has never been studied.

The cumulus cells are a subset of granulosa cells which maintain an intimate connection with the oocyte and are responsible for providing several trophic and metabolic factors to the pre-ovulatory oocyte. Both cumulus and granulosa cells are the major source of estradiol. High levels of estradiol prevent the rise of follicle-stimulating hormone (FSH), an essential factor for follicular growth and ovulation induction, which in turn leads to anovulation, a major feature of PCOS. Indeed, the first line of treatment for PCOS, Clomiphen citrate or Letrozole, acts by reducing the effect of estrogen in order to treat anovulation (Speroff and Fritz, 2005).

Cultured luteinized granulosa cells from PCOS have been shown to maintain their unique functional qualities, despite being separated from the rest of the follicular structures, for as long as 12 days (Agrawal et al., 2002). However, unlike the granulosa cells which need to be isolated from the follicular fluid and might include theca cells and lymphocytes, cumulus cells are well-defined, homogeneous cells (Quinn et al., 2006). Therefore, in the current study, we used cultured cumulus cells isolated from patients undergoing in vitro fertilization (IVF) in order to minimize the effect of the hormonal stimulation. The patients, divided into PCOS and control groups, were further subdivided into four subgroups: lean PCOS, overweight-obese PCOS, lean controls and overweight-obese controls. We hypothesized that comparisons of gene expression in cumulus cells from PCOS patients versus controls, and between the lean PCOS and obese PCOS subgroups, would elucidate distinct gene expression patterns, allowing insights to molecular mechanisms of PCOS.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
Patient selection and tissue collection
This study was approved by the Institutional Ethical Review Board of Soroka Medical Center and the committee for genetic experiments of the Israeli Ministry of Health. All the patients signed an informed consent form. Twenty-five patients were enrolled in the study (Table I): 13 PCOS patients—6 lean (LP) with body mass index (BMI) 19–24 and 7 overweight (OP) with BMI 27–34; 12 non-PCOS controls—6 lean (LN) with BMI 19–24 and 6 overweight (ON) with BMI 27–34. After quality assurance, two arrays were excluded from the analysis, one from the ON group and the other from the LP group. The average age of the patients was 31 years (range 29–32). The lean PCOS had a higher ratio of LH/FSH.


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  		Table I Details of the subgroups of patients

 
All the participants included in the study were women undergoing in-vitro fertilization with intra-cytoplasmic sperm injection (IVF–ICSI) at the Soroka Medical Center IVF unit. All the IVF–ICSI cycles included in the study were conducted according to the long mid-luteal GnRH agonist (Diphereline 3.75 mg, PharmaBiotech, Paris, France) protocol. Controlled ovarian stimulation was conducted with recombinant FSH (Follitropin Alfa, Serono, London, UK). The indication for ICSI treatment was combined male factor, poor fertilization rate on standard IVF or as routine for the first IVF cycle on half of the oocytes. Oocyte injections and embryo cultures were performed as described elsewhere (Geary and Moon, 2006). PCOS patients were diagnosed according to the Rotterdam revised criteria after ruling out secondary causes.

Cumulus cell preparation and culture
Following oocyte retrieval, cumulus cells were mechanically stripped from the oocyte after brief exposure to 80 IU/ml hyaluronidase (Cook, Brisbane, Australia) in preparation for the ICSI procedure, by aspiration through a glass pipette with ~200 µm inner diameter (Geary and Moon, 2006). To reduce the effect of gonadotrophin on the cumulus cells, cells were cultured for 48 h in the original IVF plate, as described previously for granulosa cells (Rice et al., 2005). Briefly, the cumulus cells were washed three times with Dulbecco's modified Eagle's medium/Ham's F12 (1:1,) supplemented with penicillin, streptomycin and amphotericin, and resuspended in the same medium supplemented with 10% fetal calf serum. Cells were incubated for 48 h at 37°C with 5% CO2. Medium was replaced after 24 h.

RNA isolation
Total RNA was extracted using TRI-Reagent (MRC, Cincinnati, OH, USA) to obtain ~2 µg of RNA, which was eluted in a final volume of 20 µl. Concentration and quality were assessed using NanoDrop ND-100 spectrophotometer (NanoDrop Technologies Inc., Wilmington, Delaware, USA). RNA was stored at –80°C until further analysis.

Microarray hybridization
RNA samples were further processed at the DNA microarray laboratory core facility, using the ‘Small Sample Target Labeling Assay v.2’ protocol from Affymetrix (Santa Clara, CA, USA). Briefly, biotin-labeled cRNA prepared from 50 ng RNA from each patient was fragmented and hybridized to individual U133 + 2.0 arrays using the GeneChip Fluidics Station 400 protocol, and scanned using the Agilent GeneArray Scanner (Affymetrix). Overall, 25 microarray chips were analyzed in this study.

Gene expression analysis
Raw CEL microarray files were read into the Affy package of affylmGUI, a graphical user interface for the analysis of Affymetrix microarray data using the Linear Modes for MicroArray data (Limma) package (Smyth, 2004). Background adjustment, quantile normalization and probe summarization were achieved by robust multiarray averaging (Irizarry et al., 2003). Quality assessment was performed using R, SpotFire DecisionSite and Pertek® through distribution and box plots, scatter plots, principal component analysis and hierarchical clustering of the samples. A linear model was fitted to the expression data (the log-intensity values) for each gene. Empirical Bayes and other shrinkage methods were used to borrow information across genes, making the analyses stable even for experiments with small number of arrays. Batch information was treated using the block parameter in Limma's ‘lmfit’ function, with a fixed value of 0.06 for the correlation. Six binary comparisons were computed in Limma: (i) lean comparison: PCOS versus non-PCOS samples (LP/LN), (ii) overweight comparison: PCOS versus non-PCOS samples (OP/ON), (iii) disease comparison: PCOS versus non-PCOS samples (PS/NP), (iv) PCOS comparison: overweight-obese versus lean (OP/LP), (v) controls comparisons: overweight-obese non-PCOS versus lean non-PCOS (ON/LN) and (vi) weight comparison: overweight-obese versus lean (O/L). The probe sets which had a P-value <0.01 and fold change in linear scale >=|1.5| in at least one of the comparisons were considered as DEGs. The data were deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE10946 [NCBI GEO] (http://www.ncbi.nlm.nih.gov/geo/).

The Venn diagram was constructed using the Venny tool (Oliveros, 2007) and contains the four main comparison groups. Only probe sets with log2 signal intensity >5 in at least one array, and P < 0.01 and fold change |FC| >= 1.5 in at least one of the comparisons, were included.

Hierarchical clustering of the genes was conducted with SpotFire, using Pearson's correlation and complete linkage.

Gene annotation
Gene annotations were performed using the Netaffx server (www.affymetrix.com), and the Ovarian Kaleidoscope Database (http://ovary.stanford.edu) in order to find a potential role in ovarian physiology and pathogenesis of the disease.

Quantitative polymerase chain reaction (qPCR) validation
For validation of DEGs, 200 ng of RNA were reverse-transcribed into first-strand cDNA using the Reverse-iT 1st Strand Synthesis Kit (ABgene, Epsom, UK) in final volume of 20 µl. Duplicate samples of pooled cDNA from three patients were used for the PCR. Gene-specific primers were designed for the corresponding sequence of probe sets (see list of primers in Table II). The reaction was carried out using Absolute QPCR SYBR green (ABgene) in a final volume of 15 µl on the Corbett Rotorgene 3000 (Corbett Life Science, Australia). Cycling conditions were: 94°C for 15 min, followed by 35 cycles of 94°C for 30 s, 65°C for 15 s and 72°C for 30 s, and a final melting step (78–99°C). The fluorescence crossing threshold (Ct) value was calculated with Rotorgene 3000 system software. The calculation of relative change in mRNA was performed with the efficiency delta-delta Ct method (Pfaffl, 2001), with normalization for the housekeeping gene GAPDH as described elsewhere (Fleige et al., 2006).


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  		Table II qPCR primers

 

   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
Cumulus cells from each of the participants were cultured for 48 h under the same conditions, followed by RNA extraction. RNA from each of the participants was analyzed on a separate microarray chip. Six comparisons were made: lean PCOS versus lean controls (LP/LN), overweight PCOS versus overweight controls (OP/ON), all PCOS versus all controls (PS/NP), overweight PCOS versus lean PCOS (OP/LP), overweight non-PCOS versus lean non-PCOS (ON/LN) and overweight versus lean (O/L).The Venn diagram (Fig 1) describes the number of DEGs with a P-value <0.01 and |FC| >= 1.5 found with each of the comparisons. The numbers in the overlapping parts of the circles represent the number of genes that were differentially expressed in respective comparisons. Tables A1 (Appendix) contain the probe sets that were differentially expressed in each of the comparisons, the genes they represent, their relative FC, P-value and annotation. Due to the length of the gene list, only genes with a fold change greater than two are shown. The full list of DEGs is available upon request.


Figure 1
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  		Figure 1 Venn diagram representing the number of DEGs in each comparison and the overlaps between the four main comparison groups. Probe sets with P < 0.01 and |FC| >= 1.5 in at least one of the comparisons are included.

 
The LP/LN comparison included 487 probe sets, 78% (375/487) of which showed decreased expression in lean PCOS cumulus cells. Note that 96% of the genes that were annotated as transcription factors were down-regulated in PCOS (44/46). The most prominent pathways in this group were the Wnt/β-catenin and MAPK-signaling pathways, with 27 related genes. These included genes such as TCF7L2, DACT1 and WNT5a. Interestingly, a group of genes encoding extracellular matrix constituents and proteins of the innate immune system showed increased expression in the LP cells.

The OP/ON comparison yielded 174 DEGs with 60% of them (105/174) demonstrating decreased expression in the overweight PCOS group. Up-regulation of genes associated with various insulin-signaling pathways, diabetes and obesity was observed, including the insulin receptor (INSR), the leptin receptor (LEPR) and genes participating in the Dentatorubropallidoluysian atrophy (DRPLA) pathway, which mediates insulin's effect on cells (Okamura-Oho et al., 1999).

The comparison of all PCOS with all non-PCOS (PS/NP), disregarding the BMI criterion, yielded only 109 probe sets, 55% of which (60/109) demonstrated decreased expression in the PCOS group. The comparison of obese with lean PCOS samples (OP/LP) revealed 305 DEG probe sets, 68% of which (206/305) demonstrated increased expression in the overweight PCOS group compared with the lean PCOS group.

Eighty-five of the DEGs overlapped in the LP/LN and LP/OP comparisons, representing 17 (85/487) and 28% (85/305) of the genes, respectively. Only six genes overlapped between the lean comparison (LP/LN) and the overweight comparison (OP/ON); five of these exhibited inverse expression patterns in the two groups (MTRF1L, HNRPD, ROBO3, FTO and SQSTM1). Hundred and twenty-five DEGs were found in the comparison of overweight non-PCOS with lean non-PCOS (ON/LN), 51 of those genes overlapped with the group of DEGs that resulted from the comparison of lean PCOS with the lean non-PCOS. The comparison of all overweight with all lean patients yielded only 12 genes, all of which had a fold change lower than two.

Quantitative PCR
To validate the microarray data, five of the DEGs were chosen for validation by qPCR: MRO, HAPLN1, OGT, CALR and FLNA (Fig. 2). The criteria for their selection were a high FC value in one of the groups and the relevance of the gene to future study of PCOS pathogenesis. For all five genes, the qPCR results were in line with the microarray data. Interestingly, the differential expression of MRO demonstrated by qPCR was not as prominent as in the microarray. PCR analysis using primers for exons 4–9 (data not shown), following sequencing, revealed multiple splice variants. The full transcript exhibited significantly higher expression in the LP group. The significance of this finding and its potential role in PCOS pathology are currently under investigation.


Figure 2
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  		Figure 2 A histogram showing qPCR and microarray results for selected genes: MRO (maestro), HAPLN1 (hyaluronan and proteoglycan link protein1), CALR (calreticulin), OGT (GlcNAc transferase) and FLNA (filamin A) compared with the GAPDH expression. The Y-axis represents relative linear fold change.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
This is the first report of differential gene expression profiles in cultured cumulus cells taken from PCOS and non-PCOS patients. The comparison involved a homogeneous group of PCOS patients and controls, testing of specific and unique synchronized cells under identical culture conditions. We chose to analyze cumulus cells despite their relatively smaller number in comparison with other follicular cells for several reasons: cumulus cells are a unique subset of granulosa cells that are in direct contact with the oocyte throughout its development. These cells play a major role in the control of oocyte metabolism, and therefore it is likely that malfunction of these cells might play a role in PCOS. Moreover, due to the method of separation, the cumulus cells are more homogeneous with almost no contamination with other cells. Granulosa cells on the other hand, despite meticulous efforts to isolate them, would generally contain theca and blood cells. Homogeneity of the examined tissue is crucial for a valid comparison of gene expression.

The cells were retrieved with the oocytes following intense ovarian stimulation, as part of an IVF procedure. The separation of the cumulus cells from the oocyte was done with a combination of a short exposure to hyaluronidase and mechanical stripping. We decided to culture the cumulus cells for 48 h under identical conditions in order to achieve homogeneous extracellular environment, attenuating the effects of the hormonal stimulation and stripping. We hypothesized that differences in gene expression under controlled and identical conditions will most probably be weaker but would best reflect differences that arise from the cells’ unique innate function. The manipulations to which the cumulus cells were exposed most probably affected their gene expression; however, as cells of all experimental groups underwent the same identical procedures, it is unlikely that these manipulations are the cause of the differential gene expression patterns.

The following discussion relates to the list of DEGs with a |FC| >= 1.5, which provides a detailed comparison of the groups. Despite the relatively large number of DEGs, choosing a higher FC would have resulted in a loss of a significant amount of information mainly in overlapping genes and gene pathways. We included DEGs with |FC| higher than two in Table A1 only due to the length of the original list of genes. The two comparisons that showed the largest number of DEGs were the lean PCOS versus lean controls (487) and the overweight PCOS versus lean PCOS (305). The comparison of overweight PCOS with overweight controls resulted in a smaller number of DEGs (174). Comparison of the entire group of PCOS patients with the entire group of controls, irrespective of weight, yielded the smallest group (109) of DEGs. These findings suggest that the most unique group in these comparisons was the lean PCOS. The expression of the genes in this group's cumulus cells was very different from both the overweight PCOS and the same-weight controls. The smaller number of differential genes in the more general comparison of all PCOS with all controls suggests that when weight is disregarded, the difference between PCOS and non-PCOS cumulus cells becomes less pronounced. Furthermore, the cumulus cells from the overweight PCOS group were most similar in terms of gene expression pattern to the cumulus cells of overweight controls. Taken together, it may be suggested that the lean PCOS group is the source of the unique characteristics of ‘authentic’ PCOS, while there is a similarity in cumulus cell function in the overweight patients of both groups. Moreover, the list of DEGs from the LP/LN and OP/LP comparisons contained 85 overlapping genes. This may suggest that the groups being compared with the lean PCOS group are not as different from one another.

In recent years, there have been a number of studies suggesting similarities in pathogenesis between obesity and PCOS (Morales et al., 1996; San Millan et al., 2004; Magnotti and Futterweit, 2007). Other studies have highlighted the differences that exist between lean and obese PCOS patients, demonstrating higher levels of basal LH, as was shown in this study, and a higher LH response to GnRH (Dale et al., 1992) as well as a higher growth hormone pulse amplitude in lean PCOS. In contrast, obese PCOS individuals demonstrated a higher rate of insulin resistance and the presence of β-cell dysfunction, lower levels of sex hormone binding globulin (SHBG), lower concentration of the insulin-like growth factor binding protein 1 (IGFBP1), and higher ratio of estradiol and testosterone to SHBG (Morales et al., 1996). The recent increase in the incidence of obesity worldwide has been accompanied by a parallel increase in the incidence of anovulatory infertility due to PCOS (Alvarez-Blasco et al., 2006). Furthermore, the first line of treatment for overweight PCOS is weight reduction, which has proven to be an effective treatment for resumption of ovulation (Guzick, 2007).

The analysis of gene annotations in this study resulted in the identification of several important pathways and groups of genes that shed light on molecular processes in PCOS. Genes of the Wnt/β-catenin- and MAPK-signaling pathways were generally down-regulated in the lean PCOS group when compared with PCOS controls. Wnts are secreted extracellular signaling molecules that exert local control over diverse developmental processes, cell-fate specification, differentiation and regulation of cell-to-cell interactions through β-catenin. Together with the MAPK-signaling pathway, they are known to function in gender differentiation, folliculogenesis and ovulation (Richards et al., 2002). For example, TCF7L2, a transcription factor in the Wnt pathway whose expression was lower in lean PCOS cells compared with lean controls, forms a complex with the androgen receptor (AR) and β-catenin, providing a mechanism for cooperative and selective gene regulation by AR and the Wnt/β-catenin-Tcf pathway (Amir et al., 2003). Several studies described altered expression of a few members of the Wnt and MAPK pathways in other cell types of PCOS patients (Jansen et al., 2004; Wood et al., 2004; Corton et al., 2006). It should be noted that each of those studies examined different tissues at different stages of the menstrual cycle. Another group of DEGs that were prominent in the comparison of lean PCOS with lean controls were genes encoding proteins of the extracellular matrix and proteins with significant roles in O- and N-glycosylation (a key step in extracellular-matrix assembly). The extracellular matrix plays a major role in folliculogenesis (reviewed in Irving-Rodgers, 2005). For example, transcript levels of the hyaluronan and proteoglycan link protein 1 (HAPLN1) were significantly up-regulated in the lean PCOS group. HAPLN1 is induced by gonadotrophins and plays an important role in cumulus expansion; this finding might explain in part the increased responsiveness of PCOS cumulus cells to gonadotrophins (Kobayashi et al., 1999).

The comparison of overweight PCOS versus overweight controls yielded a group of genes related to the insulin-signaling system whose transcript levels were up-regulated in the OP group. Among them are INSR, IRS1 and the fat-mass and obesity-associated gene (FTO), whose variant rs9939609 has been shown to be associated with PCOS (Barber et al., 2008). In the same comparison, down-regulation of transcript levels was demonstrated for a group of genes encoding components of the cells energy system signaling, such as proteins of the mitochondrion, the oxidative phosphorylation system and biosynthesis of purines and pyrimidines.


   Concluding remarks
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
Funding
 Appendix
 Acknowledgements
 References
 
Our results support the largely accepted tenet that PCOS patients who do not suffer from the co-morbidity of obesity represent the authentic syndrome with its unique characteristics. Obesity may be regarded as modifier of the syndrome or as a separate pathological mechanism that results in similar consequences. Analyses of individual gene expression levels at the mRNA level are always prone to personal variation. The fact that comparison of the entire PCOS group with healthy controls (without taking BMI into consideration) revealed significantly less DEGs, implies that grouping the individuals according to BMI reduces variability. Thus, our data support previous notions that PCOS in lean and obese individuals should be regarded as separate subentities. Further large-scale molecular studies and subgrouping PCOS patients into more homogeneous groups may provide a better understanding of the molecular pathophysiology of this syndrome.


   Funding
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
Appendix
 Acknowledgements
 References
 
This work was supported by the Ben-Gurion University Faculty of Health Sciences research fund (to Y.B., S.K., G.P. and O.S.B), NIBN research grant (S.K. and O.S.B) and the Morris Kahn Family Foundation for Humanitarian Support.


   Appendix
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
 Appendix
Acknowledgements
 References
 


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  		Table A1 The DEGs with FC >= |2| and P-value <0.01 in six comparisons: (LP/LN) lean PCOS versus lean controls; (OP/ON) overweight PCOS versus overweight controls; (PS/NP) all PCOS versus all controls; (OP/LP) overweight PCOS versus lean PCOS; (ON/LN) overweight non-PCOS versus lean non-PCOS; (O/L) overweight versus lean

 


   Acknowledgements
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
References
 
The authors thank Yael Sunin, Sarit Albuteino, Iris Har-Vardi and Tatiana Rabinski for their excellent technical assistance.


   Footnotes
 
{dagger} The first two authors contributed equally to the study. Back

{ddagger} Recipient of the American Physician Fellowship for Medicine in Israel. Back


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Concluding remarks
 Funding
 Appendix
 Acknowledgements
 References
 
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Submitted on October 6, 2008; resubmitted on December 6, 2008; accepted on December 19, 2008.


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