characterizationofthehumancumulus cell transcriptome · pdf file ·...

ORIGINAL RESEARCH

Characterization of the human cumuluscell transcriptome during final follicularmaturation and ovulationG.M. Yerushalmi1,*, M.Salmon-Divon2,Y.Yung1,E.Maman1,A.Kedem1,L.Ophir1,O.Elemento3,G.Coticchio4,M.DalCanto4,M.MigniniRenzinu4,R. Fadini4, and A. Hourvitz1

1IVF Unit and Reproduction Lab, Department of Obstetrics and Gynecology, Sheba Medical Center, Tel Hashomer, Affiliated with the SacklerFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel 2Department of Molecular Biology, Ariel University, Ariel, Israel 3Institute forComputational Biomedicine, Weill Cornell Medical College, New York, NY, USA 4Biogenesi, Reproductive Medicine Centre, Istituti CliniciZucchi, Via Zucchi 24, 20052 Monza, Italy

*Correspondence address. E-mail: [email protected]

Submitted on November 2, 2013; resubmitted on April 1, 2014; accepted on April 17, 2014

abstract: Cumulus expansion and oocyte maturation are central processes in ovulation. Knowledge gained from rodent and other mam-malian models has revealed some of the molecular pathways associated with these processes. However, the equivalent pathways in humans havenot been thoroughly studied and remain unidentified. Compact cumulus cells (CCs) from germinal vesicle cumulus oocyte complexes (COCs)were obtained from patients undergoing in vitro maturation (IVM) procedures. Expanded CCs from metaphase 2 COC were obtained frompatients undergoing IVF/ICSI. Global transcriptome profiles of the samples were obtained using state-of-the-art RNA sequencing techniques.We identified 1746 differentially expressed (DE) genes between compact and expanded CCs. Most of these genes were involved in cellulargrowth and proliferation, cellular movement, cell cycle, cell-to-cell signaling and interaction, extracellular matrix and steroidogenesis. Out ofthe DE genes, we found 89 long noncoding RNAs, of which 12 are encoded within introns of genes known to be involved in granulosa cell pro-cesses. This suggests that unique noncoding RNA transcripts maycontribute to the regulation of cumulus expansion and oocyte maturation. Usingglobal transcriptome sequencing, we were able to generate a library of genes regulated during cumulus expansion and oocyte maturation pro-cesses. Analysis of these genes allowed us to identify important new genes and noncoding RNAs potentially involved in COC maturation andcumulus expansion. These results may increase our understanding of the process of oocyte maturation and could ultimately improve the efficacyof IVM treatment.

Key words: cumulus cells / follicle development / human / RNAseq / transcriptome

IntroductionLate folliculogenesis and final oocyte maturation is a complex and highlyregulatedprocess. This process involves the reactivation of final cytoplas-matic maturation and oocyte meiosis, rupture of the follicle wall, cumulusexpansion and tissue remodeling to form the corpus luteum. These pro-cesses are fundamental to the formation of an oocyte capable of fertiliza-tion and importantly also have an impact on the developmental potentialof resultant embryos and successful pregnancy.

In vitro maturation (IVM) of oocytes is a promising technique that hasthe potential advantages of reducing treatments costs and averting theside effects of gonadotrophins required by conventional ovarian stimula-tion approaches (Piquette, 2006). However, the efficiency of IVM ofoocytes as a technology to assist clinical infertility treatment remainspoor because of the reduced developmental potential of in vitro

matured oocytes. Although the first IVM pregnancy was described.20 years ago (Cha et al., 1991), pregnancy and live birth rates arelower than those of IVF. It is thought that the quality of the maturationprocess appears to be suboptimal since embryos resulting from in vivomatured oocytes have better developmental capacity when comparedwith their in vitro matured counterparts (Trounson et al., 2001; Piquette,2006). At present, during IVM, nuclear maturation occurs in 50–70%of oocytes, but cytoplasmic maturation is disturbed, leading to asyn-chrony between the cytoplasmic and nucleus compartments. Therehave been extensive efforts to elucidate the mechanisms that are respon-sible for the difference in the efficacy of in vitro and IVM (Sirard, 2011; Yer-ushalmi et al., 2011; Nogueira et al., 2012). The study of the cumulusgranulosa cell (GC) transcriptome has been proposed as a researchapproach with potential to lead to an improvement in the efficiencyof IVM.

& The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.For Permissions, please email: [email protected]

Molecular Human Reproduction, Vol.20, No.8 pp. 719–735, 2014

Advanced Access publication on April 25, 2014 doi:10.1093/molehr/gau031

Inside the follicle, the oocyte is surrounded by GC populations that,during folliculogenesis, differentiate into both mural GCs and cumuluscells (CCs) (Richards and Pangas, 2010). CCs are closer to the oocyteand maintain a close relationship via transzonal processes and gap junc-tions with the oocyte, providing nutrients, maturation-enabling factorsand an optimal microenvironment to ensure successful maturation andfurther developmental competence. This close relationship betweenthe oocyte and related CCs implies that transcriptome analysis of theCCs may serve as a marker for oocyte maturation and quality (Uyaret al., 2013). Therefore, CCs gene expression has been studied at themRNA and protein levels in different species to gain insight into the rela-tionship between CCs and oocytes (Assou et al., 2006; Assidi et al., 2011;Regassa et al., 2011; Sirard, 2011; Xu et al., 2011; Borgbo et al., 2013).

Several human and primate studies have indicated that the CC tran-scriptome is dysregulated during the IVM process (Lee et al., 2011;Ouandaogo et al., 2012; Guzman et al., 2013). In a rhesus monkey tran-scriptome study, a large number of genes were oppositely regulated inIVM compared with IVF (Lee et al., 2011). The human CC transcriptomeafter IVF differs significantly from the CC transcriptome after IVM (Ouan-daogo et al., 2012). It was suggested that such a difference is indicative of adelay in the acquisition of the mature CC phenotype following IVM.Several key factors related to meiotic maturation and oocyte compe-tence were quantified in small antral follicles before and after IVM orIVF (Guzman et al., 2013). It was found that genes encoding factorsreflecting oocyte competence were significantly altered in IVM-CC com-pared with in vivo matured oocytes.

Several papers have described the changes in CC gene expression fol-lowing the luteinizing hormone (LH) surge in primate, bovine and murinemodels (Hernandez-Gonzalez et al., 2006; Regassa et al., 2011; Xu et al.,2011). An increasing number of human studies have shown correlationsof CC gene expression with oocyte maturation, fertilization rate(McKenzie et al., 2004) and pregnancy outcome (Feuerstein et al.,2007; Hamel et al., 2008; Assou et al., 2010). However, all of thesestudies have used only CCs from pre-ovulatory follicles that were previ-ously exposed to human chorionic gonadotrophin (hCG) (Kenigsberget al., 2009; de los Santos et al., 2012; Grondahl et al., 2012) or CCderived from cumulus oocyte complexes (COCs) matured in vitro(Ouandaogo et al., 2012). We were unable to find any human studyaimed at discerning differences in CC gene expression between imma-ture COC derived from mid-antral follicles and COC obtained frommature follicles.

Long noncoding RNAs (lncRNAs) are currently defined as transcriptsof .200 nucleotides without evident protein coding function (Rinn andChang, 2012). They are generally characterized by nuclear localization,low level of expression (Ravasi et al., 2006; Djebali et al., 2012; Kumaret al., 2013) and rapid transcriptional turnover (Kutter et al., 2012).Their expression is strikingly tissue-specific compared with codinggenes (Cabili et al., 2011; Djebali et al., 2012), and they participate in mul-tiple networks regulating gene expression and function (Gribnau et al.,2000; Martens et al., 2004; Ponting et al., 2009). lncRNAs are emergingas key regulators of diverse cellular processes, and they have a significantimpact on the development of human diseases (Calin et al., 2007; Yuet al., 2008; Huarte and Rinn, 2010; Kumar et al., 2013). AntisenselncRNAs can affect expression of their host genes in several ways, forexample by hybridization to their overlapping sense transcripts, whichresults in the formation of dsRNAs that are cleaved by Dicer to

endogenous siRNAs. A role for endogenous siRNAs in mammalianoocytes has been demonstrated (Tam et al., 2008; Watanabe et al.,2008).

Here, we used global transcriptome sequencing to characterize thefinal stages of follicular maturation and ovulation pathways in humans.The aim of this study was to identify genes and pathways that are differ-entially expressed (DE) between the cumulus of compact COC andexpanded/stimulated COC.

We envisage that the elucidation of the control of gene expressioninvolved in the final stages of follicular and oocyte maturation mighthelp us to better understand the process of IVM, and consequentlyimprove the results of this attractive procedure.

Materials and MethodsThe study was approved by the local Institutional Review Board committee(ethical approval number 8707-11-SMC), and written informed consentwas obtained from each patient. Patient demographics and baseline charac-teristic are presented in Table I.

IVF protocolNormo-ovulatory patients aged 28–39 undergoing IVF/ICSI due to male orpreimplantation genetic-diagnosis were selected. Treatment protocolsincluded the long agonist protocol or short antagonist protocol are describedelsewhere (Elizuret al., 2005; Hourvitz et al., 2006). When the leading folliclesreached 18 mm in diameter, the patients received hCG (Ovitrelle 250 mg,Merck Serono). Oocyte retrieval was performed 36 h later by transvaginalultrasound-guided needle aspiration.

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Table I Patient demographics and baselinecharacteristics.

CCGV CCM2

Number of patients 9a 7b

Age (years) 34.9+1.0 33.1+0.5

Reason for treatment

Unexplained 1

Tubal 1

Male factor 4 3

Preimplantation genetic diagnosis 4

Ovulatory defect 3

Long protocol n/a 5

Antagonist protocol n/a 2

FSH total (IU) 450 2112+382

FSH days 3 12.0+1.0

E2 on day of hCG (pmol/l) n/a 8095+571

Number of oocytes retrieved n/a 16.0+2.5

Data are the mean+ SE. aTwo patients for the RNAseq analysis (both due to malefactor) and an additional seven for the qPCR analysis. bThree patients for the RNAseqanalysis (all undergoing preimplantation genetic-diagnosis) and an additional four forthe qPCR analysis. CCGV: compact cumulus cells from germinal vesicles; CCM2:expanded cumulus cells from metaphase 2 oocytes.

720 Yerushalmi et al.

IVM protocolPatients aged 31–40 underwent an IVM cycle according to accepted IVMprotocols (Fadini et al., 2009). On Day 3 of a spontaneous menstrual cycle,women underwent a baseline transvaginal ultrasound assessment to deter-mine ovarian morphology, antral follicle count and endometrial thicknessand basal blood concentrations of estradiol and progesterone were assessed.Following that, 150 IU/day rFSH were administered to the patients for 3days. A second evaluation was performed on Day 6. An injection of10 000 IU hCG (Pregnyl; Organon, Oss, Holland) was administered subcuta-neously when the endometrial thickness was ≥5 mm and the leading folliclewas at least 12 mm. Oocyte retrieval was performed 36 h later by transvagi-nal ultrasound-guided needle aspiration.

Isolation of CCsExpanded CCs that surround metaphase 2 (M2) oocytes were obtainedduring the IVF/ICSI protocol (CCM2 sample). Oocytes were denudedmechanically using needles before the ICSI procedure. Compact CCs wereobtained from surplus germinal vesicle (GV) oocytes that were acquiredduring IVM treatment (CCGV sample). Cells were centrifuged at 200 g for5 min at room temperature and the resulting pellets were subjected toRNA purification.

For the RNA sequencing (RNAseq) experiment, we used CCs obtainedfrom single oocyte (GV or M2). For the qPCR experiments, purified CCsof two to three patients were separately pooled to form the CCGV andCCM2 groups. Pooled cells were subjected to total RNA purification. Wegrouped cumulus samples in order to reduce variation of samples due tothe heterogeneity of patients and the low RNA amount retrieved from thecumulus of single COC.

Isolation of RNATotal RNA was extracted from CCs by a Mini RNA Isolation I Kit (Zymo Re-search Corp., CA, USA), according to the manufacturer’s instructions. Fol-lowing isolation, total RNA integrity was tested with a Bioanalyzer (AgilentTechnologies 2100). RNA was used for RNAseq and qPCR. The RNA con-centrations for the CCGV samples and CCM2 samples were 3–6 and 16–50 ng/ml, respectively.

Generation of cDNA libraries and sequencingGlobal transcriptome sequencing was performed on CCM2 samplesobtained from three women and CCGV samples obtained from two women.

We used cDNA library preparation procedures recommended for high-throughput DNA sequencing on the Illumina Cluster Station and GenomeAnalyzer. mRNA was purified from total RNA using pre-preparedSera-mag Magnetic Oligo(dT) Beads (Thermo Fisher Scientific, MA, USA).

The extracted mRNA was fragmented by incubating the samples at 708Cfor 5 min in the presence of fragmentation buffer (Ambion; catalog no.AM8740). The cDNA was synthesized using a SuperScript II Reverse Tran-scriptase Kit, 1 mg random primers and 10 U RNaseOUT (Invitrogen),10 mM dNTP and 10 ng T4 Gene 32 (New England Biolabs; catalog no.M0300S) in a final reaction volume of 20 ml during the first-strand cDNA syn-thesis. Library preparation was performed following the Illumina recommen-dations (preparing samples for mRNA sequencing; Illumina). Quality andsizes of the products were checked using an Agilent DNA 1000 kit of theBioanalyzer 2100 (Agilent Technologies), and each library had an insertsize of �200 base pairs. Cluster generation and single-end sequencing wascarried out using the standard Illumina procedures for the HiSeq 2000sequencer (Illumina).

Expression analysisAfter applying quality control check on the raw sequenced reads using theFastQC tool (Babraham Bioinformatics, Babraham Institute,Cambridge,UK), all sequenced reads were mapped and aligned to the human genome(hg19) using Tophat v1.3.3 software (Trapnell et al., 2009) with ‘–g 1’ param-eter to allow one alignment to the reference for a given read. A counting tablewas generated for all Ensembl genes (release 64). The summary of the totalnumber of reads aligned to the genome and the transcriptome is listed in Sup-plementary Table SI.

The number of reads overlapping each of the annotated genes wascounted using the HTSeq-count script from the HTSeq python package,using the ‘union’ overlap resolution mode. The count was done for genesand not for transcripts such that if a read overlaps with one or more exonsof a gene, it is counted for this gene. DESeq v.1.8.3 within the Bioconductorframework, which was designed specifically to deal with small number of rep-licate experiments, was used for normalization, differential expression ana-lysis and clustering (Anders and Huber, 2010). Briefly, for normalization, ascaling factor was calculated for each sample. This factor was computed asthe median of the ratio, for each gene, of its read count over its geometricmean across all samples. Raw read counts were then divided by the factorassociated with their sequencing sample.

Cluster analysis was performed prior to the differential expression test forquality control. The Euclidean distance between samples was calculated fromthe variance stabilizing transformation of the count data, and hierarchical clus-tering was performed using the complete linkage method. Pearson correl-ation calculation was done after applying variance stabilizing transformationof the count data.

In order to increase detection power, independent filtering was done firstby dropping 40% of genes whose counts overall were so small (an averagecount ,6) that they would have a negligible chance of being detected asDE (Bourgon et al., 2010).

Next, a statistical test was applied on the 18 842 genes left after filteringusing the DESeq methodology. Briefly, in order to compensate for thesmall number of replicates, the DESeq package assumes that genes withsimilar expression strength have similar variance, and so pools informationfrom these in order to get a reasonable estimate of biological variability,which is then used for the test. P-values were calculated through a methodthat is analogous to a Fisher’s exact test, but instead of assuming that theprobabilities follow a hypergeometric distribution, they follow a negative bi-nomial distribution parameterized from the mean and the estimated disper-sion. The resulting P-values were corrected for multiple testing using theBenjamini and Hochberg approach, and genes having corrected P-values,0.05 and fold change .2 were called as being DE. A detailed analysis pipe-line is given in Supplementary Fig. S1.

Sequencing data were deposited in GEO as: GSE50174.

Creation of genome browser imagesThe BEDTools (Quinlan and Hall, 2010) utilities were used to create ‘bed-graph’ files (see http://genome.ucsc.edu/goldenPath/help/bedgraph.htmlfor definition) from the mapped data. First, the mapped ‘bam’ files outputby Tophat were converted to bed format using ‘bamTobed’ utility withthe—split parameter on. Next, the ‘genomeCoverageBed’ utility withthe—bg parameter on was used in order to compute the read coverageamong the genome and to create ‘bedgraph’ files. Next, we normalizedthe coverage data in the bedgraph files for the read density to be comparablebetween different samples. The normalization is done such that we got thepileup per 10 million reads in each sample. Finally, we used the UCSCutility, ‘bedGraphToBigWig’ (http://genome.ucsc.edu/goldenPath/help/bigWig.html), in order to convert the bedgraph files to the bigwig format,which we have downloaded to the UCSC genome browser.

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Geneontology,pathways andnetworkanalysisThe GOstats package (Falcon and Gentleman, 2007) from the Bioconductorframework was used for testing the over-and-under representation of GOterms in the list of DE genes (False Discovery Rate , 0.05). This was doneusing a conditional Hypergeometric test that uses the relationships amongthe GO terms for conditioning. As the universe group, we used allEnsembl genes that passed independent filtering (as described above) andwere tested for differential expression.

Integration of gene expression data with molecular and chemical interac-tions and cellular phenotypes was performed using the ingenuity IPA platform(www.ingenuity.com). The list of DE genes was imported into Ingenuity, andeach gene identifier was overlaid onto a global molecular network developedfrom information contained in the Ingenuity Pathways Knowledge Base. TheIPAdownstreameffects analysis featurewasused in order to identify whethersignificant biological process are increased or decreased based on the geneexpression results. The upstream regulator analysis feature was used topredict upstream molecules, which may be causing the observed geneexpression changes.

Validation of RNAseq results by quantitativeRT–PCRExpression level of 24 transcripts was measured by qPCR in CCGV andCCM2 samples. These transcripts were chosen for the validation of theRNAseq expression data. Samples of 1 ml (100 ng) of RNA solution wasused for RT with a high-capacity cDNA RT kit (Applied Biosystems, FosterCity, CA, USA) according to the manufacturer’s instructions. A powerSYBR Green PCR mix (Applied Biosystems) was used for the PCR step. Amp-lification and detection were performed using the StepOnePlus real-timePCR system (Applied Biosystems) with the following profile: one cycle at958C for 20 s and 40 cycles each at 958C for 3 s and 608C for 30 s.Samples of 1 ml of cDNA was used per reaction in a 10 ml reactionvolume. All samples were done in duplicates. Actin, beta (ACTB) mRNAwas chosen as a housekeeping gene. mRNA quantification were obtainedby the comparative Ct method, using DDCt. The average Ct values forACTB from the GV and M2 groups were 19.58 and 20.60, respectively.Results are expressed as fold change with respect to the experimentalcontrol that was set to 1. Student’s t-test with a two-tailed distribution andtwo samples equal variance testing was used to compare samples for the nor-malization of data. For all statistical analysis of qPCR, SPSS 20 software (IBM,Armonk, NY, USA) was used. Difference in measurable gene expression.2-fold was considered biologically significant and P-values ,0.05 was con-sidered statistically significant. The primers that were used in this work arelisted in Supplementary Table SII.

Results

Sequencing of short expressed reads fromcompact and expanded CCsIllumina-based RNAseq was carried out on RNA extracted from twosamples of compact (CCGV) and three samples of expanded (CCM2)CCs. A total of 499.5 million reads were generated on an IlluminaHiSeq2000 instrument, including 129.6 million reads from CCGV and369.9 million reads from CCM2. At least 60.8 million reads were gener-ated from each sample, with an average of 99.9 million reads per library.Reads were mapped to the human genome (hg19) using Tophat soft-ware. On average, 70% of reads in each sample were mapped to thehuman genome. After genomic locations were obtained for eachmapping read, we next used HTSeq-count script in order to count the

number of reads aggregated over each annotated gene (Ensemblversion 64), where each gene is considered as the union of all itsexons. Between 84 and 91% of mapping reads were aggregated within31 445 annotated genes and on average, 85% uniquely overlapped togenes.

General characteristics of gene expressionWith the RNAseq data inspected, all quality control parameters werewithin the acceptable ranges (see Materials and Methods), theseinclude unsupervised hierarchical cluster analysis, which shows that thebiological replicates cluster to their corresponding groups (Fig. 1A).

Prior to differential expression analysis, and in order to enable accur-ate comparisons of expression level between samples, the digital readcount of every gene was normalized. Next, in order to increase detectionpower, an independent filtering was applied by dropping genes with verylow count overall (see Materials and Methods).

A total of 18 842 annotated Ensembl genes passed the independentfiltering and were included in subsequent analysis.

The follicle stimulating hormone receptor (FSHR) gene was chosen asa representative of genes not expressed in CCM2 cells (Grondahl et al.,2011). Since its normalized expression value in our dataset was 75, thisvalue was chosen as an expression cutoff. All genes with expressionvalue above this cutoff were considered as expressed.

A total of 14 010 genes were expressed in the compact CCs (CCGV),and 14 202 genes were expressed in the expanded CCs (CCM2). AVenndiagram of these two gene lists is shown in Fig. 1B, illustrating the numbersof selective and co-expressed genes in the two conditions. The genescomprising the compartments of the Venn diagram are given in Supple-mentary Table SIII.

Differential expression analysis (see Materials and Methods) revealeddramatic changes in the CCs transcriptome during the CC expansion/maturation process. A total of 1746 genes were DE (10.2% of annotatedEnsembl genes identified) between compact and expanded CCs with afold change .2, and adjusted P-value ,0.05 (Fig. 1C). Of these, 1145genes changed .10-fold, 451 changed between 5- and 10-fold,whereas the rest (150 genes) changed between 2- and 5-fold.

Our results indicated that a total of 1100 genes (6.4% of annotatedgenes) were up-regulated in the expanded CC cells, including genesinvolved in LH signaling, oocytes maturation and cumulus expansion,while 646 genes (3.8% of annotated genes) were down-regulated. Theten most abundant genes in each sample are listed in Table II. None ofthe lists of expressed genes represented the common leukocyteantigen CD45 Protein tyrosine phosphatase, receptor type, C indicatingthat there is no or negligible blood contamination to the samples.

Validation of selected mRNA levels asestimated by qPCR and RNAseqWe used qPCR analysis to validate the results obtained by RNAseq. Weselected genes that were identified as DE by RNAseq and are known tobe involved in various pathways and processes of cumulus expansion andmaturation. The genes selected included matrix and gap junction mole-cules (ADAMTS1, GJA1), signaling cascades molecules (FSHR, LHCGR,BMPR2, PTGS2, SFRP4 and NOS2), steroidogenesis (CYP19, STAR),putative oocyte quality markers (GREM1, GPX3) and genes ofunknown ovarian function (transmembrane 4 L6 family member 1





(TM4SF1), angiomotin-like protein 1 (AMOTL1) and tolloid-like 2(TLL2)).

qPCR was performed in three different experiments as shown inFig. 2 and demonstrated agreement with the RNAseq relativeexpression data.

Gene ontology analysisGene ontology (GO) analysis of DE genes revealed a number of cellularprocesses regulated during the periovulatory interval. The most

significant GO terms overrepresented in DE genes are listed in Supple-mentary Table SIV. The most highly represented processes werephospholipid efflux (83% of genes), positive regulation of macrophageactivation (83%), nitric oxide-mediated signal transduction (78%),mitotic chromosome condensation (54%), hydrogen peroxide catabolicprocess (50%), regulation of transcription involved in G1/S phase ofmitotic cell cycle (48%) and eicosanoid transport (47%).

We used Ingenuity IPA downstream effect analysis to identifywhether significant downstream biological processes were increasedor decreased based on the direction of change of the genes in our

Figure 1 Cluster analysis. The cluster dendrogram (A) shows how the CCGV and CCM2 samples cluster in separate clusters. The Venn diagram (B) ofgenes expressed in compact CCs from GV oocytes (CCGV) and expanded CC from M2 oocytes (CCM2) divided to represent either specific or overlappinggenes between the cell populations. Hierarchical cluster of genes (C) selected as being differentially expressed (fold change 2, P , 0.05, Benjamini–Hoch-berg correction) between the compact CCs (two women) and the expanded CCs (three women). The intensity of the pseudocolors reflects relative geneexpression level.




Table II The most abundant genes in CCGV (A) and CCM2 (B).

A.

Gene name Description Mean expression level across replicatesCCGV

Log2 foldchange

CYP19A1 Cytochrome P450, family 19, subfamily A, polypeptide 1 795721 23.89

SERPINE2 Serpin peptidase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1),member 2

438912 21.96

VCAN Versican 357233 21.54

MT-CYB Mitochondrially encoded cytochrome b 277533 0.76

MT-ND4 Mitochondrially encoded NADH dehydrogenase 4 273671 0.75

THBS1 Thrombospondin 1 261254 22.72

HSPG2 Heparan sulfate proteoglycan 2 249306 21.53

VIM Vimentin 240707 21.73

MCL1 Myeloid cell leukemia sequence 1 (BCL2-related) 227620 21.22

ACTG1 Actin, gamma 1 217647 0.21

B.

Gene name Description Mean expression level across replicatesCCM2

Log2 foldchange

DHCR24 24-Dehydrocholesterol reductase 971510 5.67

MT-CYB Mitochondrially encoded cytochrome b 468915 0.76


FN1 Fibronectin 1 439439 2.37

TIMP1 TIMP metallopeptidase inhibitor 1 370010 4.45

DPYSL3 Dihydropyrimidinase-like 3 323613 1.91



ACTG1 Actin, gamma 1 [Source:HGNC Symbol;Acc:144] 252153 0.21

PSAP Prosaposin 211125 1.51

Mean expression levels across replicates were calculated based on normalized counts. Common genes in the two lists are printed in bold.CCGV: compact cumulus cells from germinal vesicles; CCM2: expanded cumulus cells from metaphase 2 oocytes.The fold change is the difference in expression levels between CCGV and CCM2.

Figure 2 Validation of RNAseq results by qPCR in seven women in three separate experiments. Total mRNA was purified from CCs denuded from GVCOC aspirated during IVM procedures (CCGV) and CCs denuded from M2 COC (CCM2) aspirated during IVF procedures. The mRNAs were subjectedto qPCR in duplicates with examined genes andb-actin primers. Gene expression was calculated relative tob-actin level in the same sample and expressionlevels were compared using Student’s t-test. The difference reacheda level of significance (P , 0.05) forall testedgenes. Resultsof RNAseq (blue) and qPCR(red) are presented as log2 fold change between CCM2 and CCGV samples.


dataset. The results, visualized as a heat map (Fig. 3), indicate therelative expression was increased in five and decreased in threemajor biological processes. The processes include cellular movement,inflammatory response, immune cell trafficking, tissue developmentand lipid metabolism (increased) and tissue morphology, DNA replica-tion and cell cycle (decreased). A comprehensive list of genes asso-ciated with each GO theme was generated (see Suppmentary TableSV). These results are in agreement with previous studies performedin animal models.

Correlation between DE genes and CCmarkers of oocyte quality/embryocompetence

As CCs can be obtained easily before the ICSI procedure or before in-semination for standard IVF, several groups have used microarray tech-nologies and quantitative RT–PCR analyses to link the CC geneexpression profile with oocyte quality and/or embryo competenceand/or pregnancy outcome.

Figure3 Analysis of significantly represented gene ontology terms. Results were generated by running the ingenuity IPA function analysis for differentiallyexpressed genes. Colors of heat map corresponds to relative expression of genes represented in the subgroups of the major annotations terms found (i.e.most ‘cell cycle’ genes were down-regulated whereas most ‘cellular movement’ were up-regulated).

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Table III Potential follicular cell biomarkers, correlated to oocyte quality, embryo competence or pregnancy outcome thatwere significantly regulated >2-fold between CCGV and CCM2.

Genename

Description Log2 foldchange

Reference

ALCAM Activated leukocyte cell adhesion molecule 2.28 Wathlet et al. (2011)

CAMK1D Calcium/calmodulin-dependent protein kinase ID 2.25 Wathlet et al. (2012)

EFNB2 Ephrin-B2 2.61 Wathlet et al. (2012)

EREG Epiregulin 5.35 Hamel et al. (2008)

GPX3 Glutathione peroxidase 3 (plasma) 25.03 van Montfoort et al. (2008)

GREM1 Gremlin 1 25.62 McKenzieet al. (2004); Cillo et al. (2007); Andersonet al. (2009); Wathletet al. (2011)

HSPB1 Heat shock 27 kDa protein 1 2.76 van Montfoort et al. (2008)

HTRA1 HtrA serine peptidase 1 22.53 van Montfoort et al. (2008)

ITPKA Inositol-trisphosphate 3-kinase A 3.94 Wathlet et al. (2011); Wathlet et al. (2012)

PTX3 Pentraxin 3, long 8.51 McKenzie et al. (2004); Zhang et al. (2005); Cillo et al. (2007)

SCD5 Stearoyl-CoA desaturase 5 4.68 Feuerstein et al. (2007)

STAR Steroidogenic acute regulatory protein 1.98 Feuerstein et al. (2007)

UGP2 UTP—glucose-1-phosphate uridylyltransferaseisoform b

+4.14 Hamel et al. (2010)

VEGFC Vascular endothelial growth factor C 2.38 van Montfoort et al. (2008)




In the present study, we used RNAseq to characterize genes involvedin the final stages of follicular maturation and ovulation pathways. Basedon the assumption that in vivo COC maturation is related to healthycompetent oocytes, we chose to compare the cumulus genes wefound to be regulated during oocyte maturation to CC genes relatedto oocyte quality.

We conducted an extensive literature search to assemble andcompare a list of 56 human genes to the gene expression profiles thatwe generated (see Supplementary Table SVI). From the generated list,13 genes (22%) were highly regulated (.2-fold change, adj. P , 0.06)in our experiment and are listed in Table III (hypergeometric test,P , 0.001).

Transcription regulators of cumulusexpansion and maturation genesUsing Ingenuity’s IPA upstream regulator analytic feature, we identified acascade of transcriptional regulators in the promoter regions of the DEgenes, which may control the gene expression involved in cumulus ex-pansion and maturation. These transcription regulators must play an im-portant role in this process. A total of 140 upstream regulators wereidentified. In 28 of these (Table IV), the predicted activation state wassuggested (bias-corrected z-score .+2).

Among the transcription regulators that were significantly repre-sented in this analysis was cyclin-dependent kinase inhibitor 2A(CDKN2A, p16INK4A). The CDKN2A gene was also up-regulated inour analysis (16-fold, P ¼ 0.018).

Other putative transcription factors and regulators identified includedadditional CDK complex members such as TP53, Rb and E2F. Severalgenes with estrogen receptor response elements in their promoterregion predict that estrogen receptor signaling has an inhibiting role,affecting CC genes in the maturation/expansion process (Table IV).

Identification of DE lncRNAsIn order to test if lncRNAs might have functional roles in follicular devel-opment and maturation, we searched the DE gene list for knownlncRNAs. We found that of 1746 DE genes, 89 (�5%) are annotatedas lncRNAs (44 long intergenic RNAs (lincRNAs) and 45 antisense tran-scripts of genes). Of the DE lncRNAs, 28 were up-regulated and 61 weredown-regulated in CCM2 samples.

Of the genes with DE antisense lncRNAs, 17 were also DE betweenthe CCGV and CCM2 samples (one up-regulated and 16 down-regulated in CCM2). Interestingly, 12 of the antisense transcripts arelocated within the introns of genes known to be involved in GC physi-ology, including ADAMTS9, AQP2, AQP5, CACNA1C, CHRM3, DPP4,FABP6, GPC5, HAS2, HSD11B1, ITGA6 and WNT5A (Table V). Wehave validated the differential expression of seven lncRNAs (four antisenseand three lincRNAs, Fig. 4 and Supplementary Fig. S2).

DiscussionLate folliculogenesis and final oocyte maturation is a complex and highlyregulated process. The oocyte and surrounding CCs undergo extensivechanges in structure and function in preparation for ovulation and fertil-ization. While this process has been extensively studied in murine, bovineand primate models, data in humans are limited.

Therefore, the aim of our study was to carry out an extensive study ofthe dynamics of the CC transcriptome during late folliculogenesis inorder to identify the genes and pathways involved in the final stages of fol-licular maturation and ovulation. Elucidating these molecular pathwaysmight help to improve the IVM process and achieve better results inART by helping to further develop the IVM approach.

Recently,Ouandaogo et al. (2012) studied the CC transcriptome fromCC surrounding GV, MI and M2 stage oocytes matured in vivo and in vitro.All samples were luteinized CC and included samples from polycysticovary syndrome (PCOS) patients, which could affect the cumulus tran-scriptome. There have been other published works that have examinedthe human CC transcriptome from IVM or IVF procedures (Zhang et al.,2005; Assou et al., 2006; van Montfoort et al., 2008; Kenigsberg et al.,2009; Assidi et al., 2011; Ouandaogo et al., 2012). However, all ofthese investigations compared CC obtained from in vitro maturedCOC with CC of controls matured in vivo, thus limiting our ability to

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Table IV List of putative transcription regulatorsidentified by Ingenuity IPA analysis of promoter regionsin differentially expressed genes.

Transcriptionregulator

Predictedactivationstate

Regulationz-score

P-value ofoverlap

TBX2 Inhibited 24.685 6.82E209

MYC Inhibited 23.827 1.95E202

E2F1 Inhibited 23.643 4.66E206

FOXM1 Inhibited 23.595 1.90E206



E2f Inhibited 22.832 3.84E206

MAX Inhibited 22.308 6.85E202

MYCN Inhibited 22.237 1.80E201

SPDEF Inhibited 22.135 1.54E202

KLF5 Inhibited 22.034 6.45E202

Estrogen receptor Inhibited 22.007 6.34E205

VDR Activated 2.007 4.64E202

STAT4 Activated 2.044 8.66E202

FOXO3 Activated 2.092 3.34E202

NR1I2 Activated 2.124 3.82E201

PAX6 Activated 2.435 4.50E202

TCF3 Activated 2.486 9.78E202

STAT1 Activated 2.549 1.23E201

TP73 Activated 2.624 5.76E203

KDM5B Activated 2.885 1.10E203

RB1 Activated 2.896 2.95E213

CEBPA Activated 3.063 2.81E204

Rb Activated 3.16 4.92E207

SMARCB1 Activated 3.196 5.87E205

TOB1 Activated 3.219 2.40E203

TP53 Activated 3.799 3.56E219

CDKN2A Activated 5.144 1.53E210

Elements with z-score .+2 and P , 0.05 are shown.




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Table V List of genes, in which antisense lncRNA is present.

Genename

Description Differentiallyexpressed

Granulosa cell function(reference)

ADAMTS9 ADAM metallopeptidase withthrombospondin type 1 motif, 9 + + (Fortune et al., 2009)

AQP2 Aquaporin 2 (collecting duct) + (Skowronski et al., 2009)

AQP5 Aquaporin 5 + (Skowronski et al., 2009)

ATP1B3 ATPase, Na+/K+ transporting, beta 3 polypeptide

BRD3 Bromodomain containing 3

C11orf35 Chromosome 11 open reading frame 35

CACNA1C Calcium channel, voltage-dependent, L type, alpha 1C subunit + + (Grondahl et al., 2011)

CHRM3 Cholinergic receptor, muscarinic 3 + + (Fritz et al., 2001)

CTTN Cortactin

DGUOK Deoxyguanosine kinase

DLEU7 Deleted in lymphocytic leukemia 2-like

DPP4 Dipeptidyl-peptidase 4 + + (Fujiwara et al., 1994)

FABP6 Fatty acid binding protein 6, ileal + + (Park et al., 2010)

FAM83A Family with sequence similarity 83, member A +G0G2 G0/G1switch 2

GK Glycerol kinase

GPC5 Glypican 5 + (Watson et al., 2012)

HAS2 Hyaluronan synthase 2 + (Stock et al., 2002)

HSD11B1 Hydroxysteroid (11-beta) dehydrogenase 1 + + (Xu et al., 2011)

ITGA6 Integrin, alpha 6 + + (Le Bellego et al., 2002)

KCNJ10 Potassium inwardly-rectifying channel, subfamily J, member 10

KIAA1191 KIAA1191

L3MBTL2 l(3)mbt-like 2 (Drosophila)

LCT Lactase +MID1IP1 MID1 interacting protein 1 (gastrulation specific G12 homolog (zebrafish))

MYLK MYLK myosin light chain kinase

NPAS2 Neuronal PAS domain protein 2 +NPY2R Neuropeptide Y receptor Y2

PLXDC2 Plexin domain containing 2

PPFIA1 Protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF),interacting protein (liprin), alpha 1

PTPN1 Protein tyrosine phosphatase, nonreceptor type 1 +RASSF8 Ras association (RalGDS/AF-6) domain family (N-terminal) member 8

RHOQ ras homolog gene family, member Q

ROR1 Receptor tyrosine kinase-like orphan receptor 1 +RPL31 Ribosomal protein L31

SCTR Secretin receptor +SNX10 Sorting nexin 10

SVIL Supervillin

THSD7A Thrombospondin, type I, domain containing 7A +TXNRD1 Thioredoxinreductase 1

UBXN10 UBX domain protein 10 +WNT5A wingless-type MMTV integration site family, member 5A + + (Harwood et al., 2008)

Of these genes, 16 were differentially expressed between CCGV and CCM2 (+), and 12 are linked to granulosa cell physiology (see references).


Figure 4 Graphical representations of genomic locations and relative expression for lncRNA and corresponding genes and validation of results by qPCR.(A) ITGA6 expression is decreased in CCM2 compared with CCGV. (B) ITGA6-AS (AC078883.3) expression in increased CCM2 compared with CCGV.(C) FAM83A is expressed only in CCM2. (D) FAM83A-AS (RP11-539E17.4) is expressed only in CCM2. (E) mIR202-lnc (RP13-49115.5) lncRNA andmIR-202 expression is decreased in CCM2 compared with CCGV. (F) CHRM3 expression in increased CCM2 compared with CCGV. (G)CHRM3-AS2 is expressed only in CCM2. Validation by qPCR of ITGA6 (H), ITGA6-AS (I), FAM83A (J), FAM83A-AS (K), mIR202-lnc (L), mIR-202(M), CHRM3-AS2 (N).


explore the actual changes in the CC transcriptome during CC expansionand maturation.

Our model enabled us to evaluate the effect of final in vivo oocyte mat-uration. Our data indicate that indeed CC undergo significant changesduring this relatively short period of time: over 1700 of the expressedgenes (10.2%) are significantly regulated, most of them by .10-foldchange.

In this study, we chose to explore two critical time points relevant toIVM: compact CCs obtained from 12 mm follicles containing GV oocytes(the starting point in the IVM process) and expanded CCs obtained from18 mm follicles containing M2 oocytes matured in vivo (the ideal IVM end-point). During the interval between these two maturation stages, severalimportant processes take place: the processes of late folliculogenesiswith the final stages of follicular maturation from mid-antral to pre-ovulatory follicle, and the LH/hCG effect over 36 h until just beforethe rupture of the follicle and ovulation.

We assume that genes which are DE in our experiment are thoseinvolved in late folliculogenesis and ovulation and that, therefore, therelevant gene pathways are also crucial to the IVM process.

The CCGV cells used in this study were exposed to hCG. However,they were chosen due to the limited availabilityof human material from im-mature GV COC. Nevertheless, from our broad experience and others(Maman et al., 2012; Guzman et al., 2013) with these cells, we haveobserved that the small follicles do not respond to the LH surge andremain as compact cumulus (both visually and at the molecular level ofcumulus expansion gene expression) and that the oocyte remains at theGV stage. Taken together, we believe that these specific follicles, inwhich the oocyte remained at the GV stage and cumulus expansion didnot occur, the GCs indeed do not express the LHCGR and do notrespond to it; thus, hCG administration had no effect on these CCs.

Another limitation to our study is the small sample size used forRNAseq. To overcome this, we employed stringent inclusion and exclu-sion criteria; we specifically attempted to minimize the potential forRNAseq to be confounded by inclusion of conditions, for which priorgene expression signature profiles have been suggested, such as endo-metriosis, PCOS, and the presence of hydrosalpinges. We also validatedthe RNAseq results using qPCR in over 25 different genes of known andunknown ovarian function.

Figure 4 Continued.


Lee et al. (2011) performed a microarray analysis of the cumulus tran-scriptome during in vivo maturation and IVM of rhesus monkey oocytes.They described 71 genes that significantly changed by .10-fold during invivo maturation of oocytes. Of these genes, 43% were also represented inthe list of genes that were significantly changed (2-fold) in our humanCCM2 transcriptome.

The current work has adopted a whole transcriptome sequencing ap-proach to characterize the CC transcriptome. This method allowed us to

identify and quantify more genes than do microarray methods. Addition-ally, it gave us the ability to estimate the actual copy number of each genecompared with the relative fluorescence results acquired by microarraytechnique. In addition, our experimental design included selective verifi-cation of mRNA levels within the same tissue samples using qPCR. Thegene products chosen for comparison of RNAseq results and qPCRresults encompassed a number of follicular activities. Our goal was notonly to validate the RNAseq data, but also to extrapolate examples

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Table VI Potential targets of transcription regulator CDKN2A identified by Ingenuity’s IPA upstream regulator analytictool.

Gene name Description Log2 (fold change)

PTX3 Pentraxin-related protein PTX3 precursor 28.09

NR3C1 Glucocorticoid receptor isoform beta 23.48

CCNA1 Cyclin-A1 isoform b 23.20

APPL2 Adaptor protein, phosphotyrosine interaction, PH domain and leucine zipper containing 2 22.95

AK1 Adenylate kinase isoenzyme 1 22.80

PEG3 Paternally expressed 3 isoform 3 1.59

HUNK Hormonally up-regulated neu tumor-associated 1.83

CDK2 Cyclin-dependent kinase 2 isoform 1 1.87

FANCA Fanconi anemia group A protein isoform a 1.90

GAS7 Growth arrest-specific protein 7 isoform b 1.97

RECQL4 ATP-dependent DNA helicase Q4 2.06

FBXO5 F-box only protein 5 isoform b 2.09

MCM8 DNA replication licensing factor MCM8 isoform 2 2.17

RFC4 Replication factor C subunit 4 2.19

CDCA4 Cell division cycle-associated protein 4 2.20

FHL2 Four and a half LIM domains protein 2 2.42

CHAF1A Chromatin assembly factor 1 subunit A 2.44

PPFIBP1 Liprin-beta-1 isoform 4 2.50

POLD3 DNA polymerase delta subunit 3 2.60

ITGB5 Integrin beta-5 precursor 2.65

CCNA2 Cyclin-A2 2.67

MCM7 DNA replication licensing factor MCM7 isoform 1 2.67

EZH2 Histone-lysine N-methyltransferase EZH2 isoform d 2.76

MCM4 DNA replication licensing factor MCM4 2.83

MELK Maternal embryonic leucine zipper kinase 2.94

PDGFA Platelet-derived growth factor subunit A isoform 2 preproprotein 3.00

E2F1 Transcription factor E2F1 3.02

HMGB2 High mobility group protein B2 3.08

ASF1B Histone chaperone ASF1B 3.11

CDC25A M-phase inducer phosphatase 1 isoform b 3.12

BIRC5 Baculoviral IAP repeat-containing protein 5 isoform 3 3.42

CDCA5 Sororin 3.87

RAD51AP1 RAD51-associated protein 1 isoform b 3.93

CENPK Centromere protein K 4.04

AURKB Serine/threonine-protein kinase 12 4.16

CDK1 Cyclin-dependent kinase 1 isoform 4 4.36

MYBL2 myb-related protein B 4.64

KIFC1 Kinesin-like protein KIFC1 4.87

Between CCGV and CCM2, five genes were up-regulated by .2-fold and 33 genes were down-regulated by .2-fold.


based on the current literature from animal models to gene activities andfunctions anticipated in humans.

We validated the RNAseq results using qPCR on a number of geneswith known ovarian function in several aspects of follicular development.We also examined several unknown ovarian genes including TM4SF1,AMOTL1 and TLL2.

TM4SF1 was up-regulated in CCM2 cells. It has been found to be an-drogen receptor target in human prostate cancer cells and involved incancer cell migration and invasion (Allioli et al., 2011) as well as in endo-thelial cell migration (Zukauskas et al., 2011). It functions a molecular or-ganizer that interacts with membrane and cytoskeleton-associatedproteins and uniquely initiates the formation of nanopodia and facilitatescell polarization and migration (Zukauskas et al., 2011), which suggest aputative important role in cumulus expansion and ovulation.

AMOTL1 was up-regulated in CCM2 cells. It is a paralog of amotilin,which was originally identified as a binding partner of angiostatin, andwas found to regulate endothelial cell migration through angiostatinbinding (Bratt et al., 2005). In addition, AMOTL1 was shown to be apart of the actin cytoskeleton and cell-to-cell junction complexes. It isknown to interact and regulate the Yes-associated protein 1 oncogeneof the hippo pathway (Wang et al., 2011), which was recently shownto be involved in regulation of ovarian reserve (Kawamura et al., 2013).

TLL2 was down-regulated in CCM2 cells. It is a protease highly similarto bone morphogenetic protein 1 (BMP1) metalloproteinase of theastacin family. TLL2 and BMP1 play a key role in regulating the formationof the extracellular matrix, particularly by processing the C-propeptide offibrillar procollagens. BMP1 has been found in ovine GCs and in follicularfluid (Canty-Laird et al., 2010).

GO analysis allows us to further characterize the different molecularand cellular functions, processes, compartments and domains that areinvolved in cumulus expansion and maturation (Harris et al., 2004).Our results further corroborate the extensive changes that occurduring cumulus expansion and maturation. Processes that are positivelyregulated include cellular movement, lipid metabolism, inflammatoryresponse and immune cell trafficking. Akison et al. (2012) has de-monstrated that CCs in the expanded COC transition to an adhesive,motile and invasive phenotype in the periovulatory period similar toMB-231 invasive breast cancer cell line, which is in agreement with theincreased cellular movement observed in CCM2 samples.

Processes that are negatively regulated final follicular maturationinclude the cell cycle and DNA replication. These changes demonstratethe shift of CCs from the immature proliferating stage toward a morespecialized mature condition. Our results are similar to results obtainedin animal models including mouse, bovine and primates (Hernandez-Gonzalez et al., 2006; Lee et al., 2011; Regassa et al., 2011; Xu et al.,2011).

It has been suggested that CCs continue to proliferate for a limitedperiod after the ovulatory stimulus, thereby leading to cumulus expan-sion followed by cell cycle arrest. In the mouse, CCs are highly mitoticfor a few hours after hCG administration and then stop dividing after16–24 h concomitantly with maturation (Hernandez-Gonzalez et al.,2006; Fru et al., 2007). The CC samples we analyzed were derivedfrom periovulatory follicles 36 h after the hCG maturation signal andtherefore these cells are expected to be in cell cycle arrest.

Cell cycle regulation is complex and involves positive regulators suchas cyclins and CDKs and also negative regulators such as CDK inhibitors.There are two groups of inhibitors: CDKN2A also known as INK4

(p16INK4a, p15INK4b, p18INK4c, p19INK4d) and proteins p21WAF1/Cip1,p27Kip1 and p57Kip2 (Canepa et al., 2007). CDKN2A is an importantnegative regulator of cell cycle progression. CDKN2A has direct aswell as indirect effect on E2Fs through inhibition of Cdk4, which inhibitsE2F inhibitor Rb (Eymin et al., 2001; Canepa et al., 2007). Indeed, ourresults demonstrate the inhibition of E2Fs function, as well as activationof Rb activity (see Table IV).

The effects of p15 INK4B and p16INK4A of the INK family and p21Cip1

and p27Kip1 of the Cip/Kip family on rodent folliculogenesis havebeen studied (Bayrak and Oktay, 2003). In rats, p27kip1 and p21cip1

up-regulation and cyclin D2 down-regulation lead to proliferationarrest following hCG triggering (Robker and Richards, 1998). It was pre-viously shown that growth differentiation factor 9 (GDF9) treatment ofhuman luteinized GCs stimulates proliferation and cell cycle progressionthrough inhibition of CDKN2A (Huang et al., 2009). Our results furthershow that CDKN2A expression in cumulus GC is increased during ex-pansion and maturation and that a large number of DE genes have poten-tial promoter CDKN2A binding sites (Table VI). Among CDKN2Atargets were genes positively associated with cell cycle progressionand proliferation (ASF1B,URKB, CDC25A, CDK1, CDK2, CENPK,FANCA, FBXO5, FBXO5, FHL2, HUNK, MCM4, MCM7, MCM8,MELK, RAD51AP1) that were all down-regulated suggesting CDKN2Ais a major player in the differentiation and growth arrest of human CCsin the periovulatory period.

Other potential CDKN2A targets that were up-regulated during CCmaturation/expansion includes PTX3, NR3C1 (glucocorticoid recep-tor) and APPL2 (adaptor protein, phosphotyrosine interaction, PHdomain and leucine zipper containing two).

PTX3 is an important component of the cumulus expansion process(Salustri et al., 2004), NR3C1 has been shown to play a role in suppres-sion of apoptosis in bovine corpus luteum (Komiyama et al., 2008) andAPPL2 has been identified as a binding partner of FSHR in complexwith akt2 and FOXO1a (Nechamen et al., 2007) and has a possiblerole in FSH signaling. Taken together, these results suggest thatCDKN2A plays a major role in the arrest of proliferation and maturationof human CC during final folliculogenesis.

Based on the assumption that normal COC maturation is related tohealthy competent oocytes, we chose to compare the list of genes regu-lated during CC maturation/expansion to genes expressed in CC thatconsidered markers of oocyte quality. We found that many of thegenes regulated in this process are indeed also related to oocytequality and embryo developmental potential. Thus, we believe that thislibrary of genes can be used to identify novel markers of oocyte quality.

Recently, several studies have shown that miRNA may play an import-ant role in female fertility and in the regulation of oocyte and CC cross-talk (Mishima et al., 2008; Assou et al., 2013; Donadeu and Schauer,2013; Velthut-Meikas et al., 2013). However, to the best of our knowl-edge, no study has tested the involvement of other noncoding RNAsin folliculogenesis and ovulation.

We have found a large number of DE lncRNAs between CCGV andCCM2 cells. One of the lncRNAs that were found to be up-regulatedin CCM2 cells is AC078883.3 (ITGA6-AS), antisense to integrinalpha-6 (ITGA6). Interestingly, its host gene (ITGA6) was found to be sig-nificantly down-regulated (3-fold) in CCM2.

Integrins are integral cell-surface proteins, composed of an alpha chainand a beta chain that known to participate in cell adhesion as well ascell-surface-mediated signaling. Integrin a6/b1 is the receptor for


laminin on GCs and it has been shown that the interaction between themin GCs enhances survival and proliferation and modulates steroidogen-esis (Le Bellego et al., 2002). Whether the ITGA6 antisense lncRNA dir-ectly regulates the expression of its ITGA6 host gene requires furtherinvestigation.

Another antisense lncRNA that was found to be expressed only inCCM2 cells is FAM83A-AS. Additionally, FAM83A itself is expressedonly in CCM2. Recently, FAM83A was shown to be involved in modulat-ing EGFR signaling in cancer in murine models in vivo and in vitro. FAM83Ainteracted with and caused phosphorylation of c-RAF and PI3K p85, up-stream of MAPK and downstream of EGFR and was shown to confer re-sistance to EGFR–tyrosine kinase inhibitors (EGFR-TKIs) (Lee et al.,2012). EGF and EGF-like molecules play a major role in CCs physiology(Fru et al., 2007; Richards and Pangas, 2010) and FAM83A and FAM83A-ASmight be involved in their regulation in GCs.

We identified that RP13-49115.3 lncRNA is down-regulated in CCM2compared with CCGV. Interestingly, miR202, which is expressed as anintron of this lncRNA, is also down-regulated. This miRNA is highlyenriched in the mammalian gonad (Mishima et al., 2008; Bannisteret al., 2011). In early development, it is a regulator of SOX9 in gonadaldifferentiation and its expression is influenced by estrogen (Bannisteret al., 2011). Increased miR-202-5p expression in chicken gonads corre-lates with down-regulation of FOXL2 and aromatase and up-regulationof DMRT1 and SOX9 (Bannister et al., 2011). miR202 expression in fol-licular fluid is higher in an ovulatory equine follicles compared with ovu-latory follicles (Donadeu and Schauer, 2013). In human GCs, it isup-regulated in cumulus GC compared with mural GC (Velthut-Meikaset al., 2013). It was recently reported to function as a tumor suppressorgene in gastric cancer and is down-regulated in cancer tissue. Overex-pression of miR-202-3p in gastric cancer cells caused suppressed cellproliferation and induced cell apoptosis (Zhao et al., 2013).

We also found that CHRM3-AS2 lncRNA, antisense-to-cholinergicreceptor muscarinic 3 (CHRM3), is up-regulated in CCM2 comparedwith CCGV, as well as its host gene. It has been shown that primateovaries produce acetylcholine (ACh) and express CHRM3 (Fritz et al.,2001), and it has been further suggested that ACh can modulate gonado-trophin induced estrogen and progesterone secretion from culturedGCs (Kornya et al., 2001).

In conclusion, using global transcriptome sequencing, we were able togenerate a library of genes regulated during cumulus expansion andoocyte maturation processes. Analysis of these results allowed us toidentify new important genes, transcription factors and noncodingRNAs potentially involved in COC maturation and cumulus expansion,and may help us better understand the in vivo and IVM process. Thiswork has the potential to contribute to improve the process of IVM ofimmature oocytes utilized in IVM cycles.

Supplementary materialSupplementary material is available at http://molehr.oxfordjournals.org/online.

Authors’ rolesG.M.Y., Y.Y., E.M. and A.H. performed the study design, analysis andinterpretation of the data, and writing and finalizing of the manuscript.

M.S.D. performed the bioinformatics analysis. G.M.Y and M.S.D. pre-pared the figures, tables and supplementary tables. O.E., A.K. andL.O. contributed to the interpretation of data and the manuscript prep-aration. L.O. and Y.Y. were involved in the sample preparation and val-idation of data. A.K., G.C., M.D.C., M.M.R. and R.F. were involved inpatient recruitment. All of the authors contributed to the data analysisand finalizing of the manuscript.

FundingThis work was supported by the Legacy Heritage Fund of the IsraelScience Foundation (ISF) [1727/10] to E.M. and by grant from the Min-istry of Health, Israel [3-00000-7410] to A.H.

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