Genomic assessment of human cumulus cell marker genes as predictors of oocyte developmental competence: impact of various experimental factors.
ABSTRACT Single embryo transfer (SET) is the most successful way to reduce the frequency of multiple pregnancies following in vitro fertilisation. However, selecting the embryo for SET with the highest chances of pregnancy remains a difficult challenge since morphological and kinetics criteria provide poor prediction of both developmental and implantation ability. Partly through the expression of specific genes, the oocyte-cumulus interaction helps the oocyte to acquire its developmental competence. Our aim was therefore to identify at the level of cumulus cells (CCs) genes related to oocyte developmental competence.
197 individual CCs were collected from 106 patients undergoing an intra-cytoplasmic sperm injection procedure. Gene expression of CCs was studied using microarray according to the nuclear maturity of the oocyte (immature vs. mature oocyte) and to the developmental competence of the oocyte (ability to reach the blastocyst stage after fertilisation). Microarray study was followed by a meta-analysis of the behaviour of these genes in other datasets available in Gene Expression Omnibus which showed the consistency of this list of genes. Finally, 8 genes were selected according to oocyte developmental competence from the 308 differentially expressed genes (p<0.0001) for further validation by quantitative PCR (qPCR). Three of these 8 selected genes were validated as potential biomarkers (PLIN2, RGS2 and ANG). Experimental factors such as inter-patient and qPCR series variability were then assessed using the Generalised Linear Mixed Model procedure, and only the expression level of RGS2 was confirmed to be related to oocyte developmental competence. The link between biomarkers and pregnancy was finally evaluated and level of RGS2 expression was also correlated with clinical pregnancy.
RGS2, known as a regulator of G protein signalling, was the only gene among our 8 selected candidates biomarkers of oocyte competence to cover many factors of variability, including inter-patient factors and experimental conditions.
- SourceAvailable from: humrep.oxfordjournals.org[show abstract] [hide abstract]
ABSTRACT: Assisted reproduction technique (ART) is an efficacious treatment in subfertile couples. So far little attention has been paid to the safety of ART, i.e. to its adverse events and complications. The consensus meeting on Risks and Complications in ART held in Maastricht in May 2002 focused on four topics: multiple pregnancies, long-term effects of ART on women, effects of ART on offspring, and morbidity/mortality registries.Human Reproduction 03/2003; 18(2):455-7. · 4.67 Impact Factor
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ABSTRACT: Non-invasive and routine developmental markers are available to select the most viable embryo; however their respective values in terms of blastocyst development potential remain difficult to distinguish. During this prospective study, the sequential growth of 4042 embryos individually cultured from day 1 to day 5/6 was recorded. Pronuclear morphology on day 1, and early cleavage, cell number and fragmentation rate on day 2 were evaluated for each zygote. Additionally, blastocyst transfers were analysed with regard to their implantation ability and early embryo development parameters. Once adjusted to each other, each of the four parameters remained related to blastocyst development. Early cleavage and cell number on day 2 were the most powerful parameters to predict the development of a good morphology blastocyst at day 5. Moreover, whereas transfers of a good morphology blastocyst were associated with high implantation and live birth rates, parameters of early development were not helpful in predicting their implantation ability. The combination of all four parameters allowed the prediction of blastocyst development with an area under the receiver operating characteristics curve of 0.688, which represents a fairly low prediction of embryo viability. Such results indicate that it is necessary to search for additional criteria, including the ability of the blastocyst to develop.Human Reproduction 08/2007; 22(7):1973-81. · 4.67 Impact Factor
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ABSTRACT: The transfer of blastocysts has been associated with a very high implantation rate. However, not all embryos achieve the blastocyst stage. Our study was set up to demonstrate whether embryo morphology on day 3 predicts subsequent blastocyst formation. A prospective study was carried out in 48 patients with a mean of 2.9 failed in-vitro fertilization (IVF) attempts. In this new cycle, the morphology of the embryos on day 3 was noted. After pre-selection of the embryos which would have been transferred on day 3, all embryos were cultured individually and allowed to develop further until transfer on day 5. The clinical pregnancy rate per transfer was 46%, and the overall implantation rate was 24%. When only blastocysts were transferred the pregnancy rate was 53% with an implantation rate of 30%. Thirty-nine per cent of all embryos reached the blastocyst stage on day 5; 47% of class 1 and 2 embryos (good quality) in contrast to 21% of class 3 and 4 embryos (poor quality). Respectively 45% of class 1 and 2 embryos and 69% of class 3 and 4 embryos arrested in development or degenerated. Only 51% of the embryos that were transferred on day 5 had been pre-selected for transfer on day 3. In conclusion, it appears that the predictive value of embryo morphology on day 3 for subsequent blastocyst formation is limited.Human Reproduction 11/1998; 13(1O):2869-73. · 4.67 Impact Factor
Genomic Assessment of Human Cumulus Cell Marker
Genes as Predictors of Oocyte Developmental
Competence: Impact of Various Experimental Factors
Prisca Feuerstein1,2,3., Vincent Puard1,2,3., Catherine Chevalier5, Raluca Teusan5, Veronique Cadoret1,3,4,
Fabrice Guerif1,2,3,4, Remi Houlgatte5, Dominique Royere1,2,3,4*
1INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France, 2Universite ´ de Tours, Tours, France, 3CNRS, UMR6175, Nouzilly, France, 4CHRU
de Tours, Laboratoire de Biologie de la Reproduction, Tours, France, 5Plateforme Puces a ` ADN de Nantes, Institut de Recherche The ´rapeutique de l’universite ´ de Nantes,
Background: Single embryo transfer (SET) is the most successful way to reduce the frequency of multiple pregnancies
following in vitro fertilisation. However, selecting the embryo for SET with the highest chances of pregnancy remains a
difficult challenge since morphological and kinetics criteria provide poor prediction of both developmental and
implantation ability. Partly through the expression of specific genes, the oocyte-cumulus interaction helps the oocyte to
acquire its developmental competence. Our aim was therefore to identify at the level of cumulus cells (CCs) genes related to
oocyte developmental competence.
Methodology/Principal Findings: 197 individual CCs were collected from 106 patients undergoing an intra-cytoplasmic
sperm injection procedure. Gene expression of CCs was studied using microarray according to the nuclear maturity of the
oocyte (immature vs. mature oocyte) and to the developmental competence of the oocyte (ability to reach the blastocyst
stage after fertilisation). Microarray study was followed by a meta-analysis of the behaviour of these genes in other datasets
available in Gene Expression Omnibus which showed the consistency of this list of genes. Finally, 8 genes were selected
according to oocyte developmental competence from the 308 differentially expressed genes (p,0.0001) for further
validation by quantitative PCR (qPCR). Three of these 8 selected genes were validated as potential biomarkers (PLIN2, RGS2
and ANG). Experimental factors such as inter-patient and qPCR series variability were then assessed using the Generalised
Linear Mixed Model procedure, and only the expression level of RGS2 was confirmed to be related to oocyte developmental
competence. The link between biomarkers and pregnancy was finally evaluated and level of RGS2 expression was also
correlated with clinical pregnancy.
Conclusion/Significance: RGS2, known as a regulator of G protein signalling, was the only gene among our 8 selected
candidates biomarkers of oocyte competence to cover many factors of variability, including inter-patient factors and
Citation: Feuerstein P, Puard V, Chevalier C, Teusan R, Cadoret V, et al. (2012) Genomic Assessment of Human Cumulus Cell Marker Genes as Predictors of Oocyte
Developmental Competence: Impact of Various Experimental Factors. PLoS ONE 7(7): e40449. doi:10.1371/journal.pone.0040449
Editor: Rodrigo Alexandre Panepucci, Hemocentro de Ribeira ˜o Preto, HC-FMRP-USP., Brazil
Received March 31, 2012; Accepted June 7, 2012; Published July 27, 2012
Copyright: ? 2012 Feuerstein et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by a grant from the Institut National de la Sante ´ et de la Recherche Me ´dicale (Re ´seau de Recherche ‘Reproduction Humaine-
AMP’, contrat No. 4REO3H) and the Institut National de la Recherche Agronomique (INRA). P.F. is supported by an INRA/Ferring SA fellowship. V.P. is supported by
an INRA/Merck Serono fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: P.F. was supported by an INRA/Ferring SA fellowship. V.P. was supported by an INRA/Merck Serono fellowship. These fellowships were
shared between public funding (Institut National de Recherche Agronomique) and private funding without any interference with research programm. This does
not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
* E-mail: email@example.com
. These authors contributed equally to this work.
Despite its increasing use to alleviate human infertility, assisted
reproductive technology (ART) continues to face two major
challenges, the first being that it is relatively ineffective. The
second challenge is that multiple embryo transfer has often been
proposed in order to increase pregnancy rates and thus multiple
pregnancies remain a common and serious complication of in vitro
fertilisation (IVF) procedures. Moreover, the adverse outcomes
associated with high-order gestations include the increased
incidence of maternal, perinatal and neonatal morbidity and
mortality . Single embryo transfer (SET) is the most successful
way to reduce the frequency of multiple pregnancies in IVF 
but it may reduce the chance of getting pregnant. Defining the
developmental competence of one oocyte after fertilisation (its
ability to reach the blastocyst stage after 5/6 days of extended
culture after fertilisation) and the development ability of an
embryo and its implantation potential during IVF remain major
goals in order to select the most suitable embryo for transfer.
Morphological criteria are the most frequently used to evaluate the
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development potential and implantation ability of embryos in
human ART. However such morphological criteria (oocyte
morphology, zygote scoring, early cleavage and embryo morphol-
ogy at day 2 or 3) remain poorly predictive of development or
implantation ability [3–6]. Both genomic and proteomic analysis
are difficult in human embryos, since such an approach is invasive
and might affect embryo integrity . Several indirect and non-
invasive selection criteria focusing on oocyte or embryo quality
have been proposed in the last few years.
Various studies have focused on molecules inside the follicle or
the embryo microenvironment (see  for review). Proteomic
analysis of individual human embryos [9,10], metabolomic
analysis of oocytes and embryos [11,12] and oxygen consumption
at the oocyte level  have all been proposed as potential
biomarkers of oocyte or embryo quality.
Other studies have focused on the somatic cells (cumulus and/
or granulosa cells) surrounding the oocyte since their interactions
are involved in the acquisition of oocyte meiotic and develop-
mental competence [14,15]. Indeed specific oocyte factors are
involved in the differentiation and expansion of cumulus cells
(CCs) and prevent the apoptosis and luteinisation of the cumulus-
oocyte complex (COC) (see  for review). Via such interactions,
oocytes may promote specific patterns of gene expression and
protein synthesis in these somatic cells [17,18]. Several studies
have therefore focused on specific gene expression in CCs
according to oocyte quality in humans and animals (see  for
Developments in microarray technology have more recently
allowed a global transcriptomic approach to identify differentially
expressed genes according to the oocyte maturity. Studies showed
different expression profiles in follicular cells according to oocyte
nuclear maturity  or to oocyte developmental competence
(early cleavage of the embryo , embryo quality 3 days after
fertilisation  and implantation potential ).
Microarray analyses have to date focused on early embryo
development (early cleavage or embryo development at day 3) or
implantation ability. Early embryo development is highly depen-
dent on oocyte quality, but embryo genome activation takes place
beyond the 4 cell stage in the human . Moreover, implantation
involves both the development ability of the embryo and the
In an initial study, we evaluated the level of expression of 6
genes in human cumulus cells according to nuclear maturity and
the developmental competence of the oocyte . In this study, we
undertook a global assessment of gene expression in cumulus cells.
Our aim was thus to relate the transcriptome of individual human
CCs to the full competence of the oocyte for pre-implantation
development of the embryo as assessed by blastocyst stage
development by comparing in one hand CCs from mature oocyte
to immature oocyte and CCs from mature fertilised oocyte
yielding a blastocyst after 5/6 days of in vitro culture to CCs from
mature fertilised oocyte arresting development in other hand. We
then analysed the behaviour of the genes related to the oocyte
competence in a dataset of transcriptome of cumulus cells
available in the Gene Expression Omnibus (GEO) to determine
their consistency. Following this analysis, 8 genes were selected to
be validated by qPCR according to their differential expression.
To evaluate fully the validity of the genes as markers of oocyte
developmental competence, we investigated the impact of
technical and biological variability such as qPCR series and
patients on the level of gene expression. Finally, the gene selected
according to such criteria was investigated as a marker of
pregnancy outcome. All these requirements are needed before
any potential use of biomarkers to predict embryo developmental
ability and finally choose the embryo for transfer.
Materials and Methods
Patient Selection and IVF Treatment
One hundred and six patients were included in this study, all
undergoing an intracytoplasmic sperm injection (ICSI) procedure
for male infertility. The mean number of oocytes retrieved per
patient was 7 (range 3–15 oocytes). Average patient age was 33
years (range 21–42 years), 49 patients were included in the
microarray analysis and 36 patients in the qPCR analysis. To
further analyse variability between patients, 29 patients (21 new
patients and 8 patients from qPCR analysis) were selected on the
basis that at least one embryo had reached the blastocyst stage and
that there was at least one arrested embryo after 6 days of
extended culture. The patient groups are presented in Figure 1.
The ovarian stimulation protocol, the ICSI and the embryo
culture procedures have been described by Guerif et al. 2003 .
Cumulus Cell Recovery and Assessment of Oocyte and
Shortly before ICSI, individual COC were subjected to
dissociation, as already described by Feuerstein et al. 2007 .
CCs were washed in cold phosphate buffer saline (80 IU/ml,
SynVitro Hyadase, Medicult, Jyllinge, Denmark) then centrifuged
at 300 g for 5 minutes. The supernatant was removed and the
pellet was resuspended in 50 ml of RLT buffer of the RNeasyH
Micro Kit (Qiagen, Courtaboeuf, France) before storage at -80uC
until RNA extraction. Labelling allowed individual follow-up of
the whole process.
Follow-up of the morphological characteristics of the oocyte and
embryo were recorded on an individual basis. Assessment of
oocyte nuclear maturity and embryo quality has been described by
Feuerstein et al. 2007 . At the time of ICSI, the oocytes were
first classified into two categories on the basis of nuclear status:
mature oocyte with first polar body (metaphase II, MII) or
immature oocyte at the germinal vesicle (GV) stage. CCs from a
mature oocyte were denominated CCMII and CCs from an
immature oocyte CCGV. For mature and fertilised oocytes, we
evaluated the developmental competence of each oocyte according
to its ability to reach the blastocyst stage after extended culture (5
or 6 days after ICSI). As described by Feuerstein et al. 2007 ,
the blastocyst assessment score was based on the expansion of the
blastocoel cavity and the number and cohesiveness of the inner cell
mass and trophectodermal cells . MII COC were divided
retrospectively into two groups following the ICSI procedure on
this basis, CCs from an oocyte yielding a blastocyst after 5/6 days
of in vitro culture being denominated CCB+and CCs from an
oocyte arresting development at the embryo stage after 5/6 days of
in vitro culture being denominated CCB-.
Clinical pregnancy was defined as described by Guerif et al.
2007 , i.e. presence of a gestational sac with a foetal heartbeat
on ultrasound examination at 7 weeks of pregnancy, and the
implantation rate was defined as the number of gestational sacs per
number of embryos transferred. CCs from an oocyte yielding a
blastocyst after 5/6 days of in vitro culture resulting in a clinical
pregnancy were denominated P+ and CCs from an oocyte yielding
a blastocyst after 5/6 days of in vitro culture which did not lead to a
clinical pregnancy were denominated P-.
genomic DNA were performed using the RNeasyHMicro Kit
Total RNA extraction and removal of
Cumulus Cell Biomarkers of Oocyte Competence
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(Qiagen, Courtaboeuf, France) according to the manufacturer’s
recommendations. The quality and integrity of RNA samples used
for microarray analysis were assessed using the 2100 Bioanalyser
and RNA 6000 Nano LabChip kit series II (Agilent Technologies).
Total RNA was quantified using a NanodropH
spectrophotometer (Nyxor Biotech, Paris, France). The mean
quantity of RNA per cumulus was 217 ng (6 134 ng).
Ninety-six hybridisations were per-
formed with 10 CCs from immature oocytes (GV) and 60 CCs
from mature oocytes (MII), including 30 CCB+and 30 CCB-. As
far as nuclear maturity was concerned, hybridisations from 10 CCs
(GV) were compared to 60 CCs (MII). Regarding developmental
competence, hybridisations from 30 CCB+were compared to 30
CCB-, all issued from the 60 previous ones. Complementary RNA
samples were prepared according to the manufacturer’s protocol
(Two-Color Microarray-Based Gene Expression Analysis) and
hybridised on Whole Human Genome Oligo Microarray 4x44K
(Agilent Technologies). Each array contained 45,220 probes,
corresponding to 41,000 single human transcripts. Briefly, an
average of 72.6 ng of extracted RNA for each sample (range 65.5–
89.9 ng) was amplified with one round of amplification. Each
sample was labelled with cyanine 3 or cyanine 5. After purification
using the RNeasyHMicro Kit (Qiagen, Courtaboeuf, France), the
quantity of cRNA and the specific activity of the cyanine were
assessed using a NanodropHND-1000 spectrophotometer. Two
samples (825 ng of cRNA for each) were hybridised on each slot of
the 4x44K array, one sample labelled with cyanine 3 and one
sample labelled with cyanine 5. In order to validate the
microarray, some samples were labelled alternatively by cyanine
3 or 5, some samples were repeatedly introduced in each
microarray experiment (3 experiments). After 17 hours of
hybridisation, arrays were washed and scanned using the Agilent
Microarray Scanner. Finally results were extracted using Feature
Extraction software 9.5.1 (Agilent Technologies).
All quality controls were performed
according to the manufacturer’s recommendations.
Lowess fitness regression was applied for global normalisation
of raw expression ratios . Gene expression profiles were used
to classify genes, and biological samples were classified by a
hierarchical analysis method using Cluster software , and the
results of hierarchical clustering analysis were visualised using the
TreeView programme. A Student t-test was applied to determine
the differentially expressed genes, with a statistical significance
threshold of p,0.0001. Annotations of genes and functions were
performed using GoMiner software (http://discover.nci.nih.gov/
gominer). Following the functional annotation of the genes, we
calculated the enrichment of differentially expressed genes for
each function . Functions with .1.6 fold enrichment and p-
value,0.001 were considered as statistically regulated according
to the situation studied. The findings are accessible on the Gene
Expression Omnibus (GEO) through the series accession number
Figure 1. Distribution of patients included in study. Patients were separated into two main groups: microarray and qPCR. The variability group
was composed of patients who had one CCB+and at least one CCB-. The pregnancy group was composed of CCB+transferred from patients included
in the variability group. CCB+, cumulus cells from a mature oocyte yielding a blastocyst at day 5/6 of in vitro culture once fertilised; CCB-, cumulus cells
from mature oocyte which stopped developing at the embryo stage at day 5/6 of in vitro culture once fertilised; CCGV, cumulus cells from immature
oocyte at germinal vesicle stage; P+, pregnancy; P-, no pregnancy.
Cumulus Cell Biomarkers of Oocyte Competence
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Datasets were obtained from the GEO (http://www.ncbi.nlm.
nih.gov/geo/) and are presented in Table 1. In each dataset,
probes for the 308 genes differentially expressed between CCB-
and CCB+ were investigated using MADGene . Findings
corresponding to these probes were extracted from each dataset.
They were subjected to hierarchical clustering after log transfor-
mation and median centering of probes. The measurement used
was the distance of correlation, and the aggregation method was
the average linkage. The ability of these genes to discriminate
samples was measured by analyzing the composition of the main
separation on the sample dendrogram. Significance was calculated
by Fisher’s exact test.
Quantitative PCR Experiments
The following procedures were used in order to comply as far as
possible with the Minimum Information for Publication of
Quantitative PCR experiments MIQE guidelines.
RNA extraction and cDNA synthesis.
tion and genomic DNA removal were performed as already
described in microarray procedure. The quality and integrity of
RNA samples were further evaluated using the RNA 6000 Pico
LabChip kit series II (Agilent Technologies, Massy, France). Only
RNA samples that displayed a RIN (RNA integrity number)
greater than or equal to 7 were reverse transcribed to cDNA. The
mean quantity of RNA per cumulus was 99 ng (range 21–205 ng).
Total RNA from each sample was reverse transcribed into cDNA
using the iScriptTMcDNA Synthesis kit (Bio-Rad Laboratories,
Marnes-la-Coquette, France) with a blend of oligo(dT) and
random hexamer primers to provide complete RNA sequence
Quantitative PCR design.
validation stage were independent of samples used for microarray
hybridisations. CCs from a total of 56 mature oocytes were
analysed for the qPCR validation stage, including 28 oocytes
yielding a blastocyst at day 5/6 once fertilised (CCB+) and 28
oocytes arresting development at the embryo stage at day 5/6
once fertilised (CCB-).
To study the impact of patient variability and qPCR series on
the level of gene expression, CCs from 102 mature oocytes were
analysed, including 54 CCB+and 48 CCB-, of which 9 CCB+and 8
CCB-were from the cohort of the qPCR validation stage.
To study the relationship between the level of gene expression
and pregnancy, 22 patients were selected from the previous cohort
from variability study (Fig. 1). Only the CCB+ samples were
included, corresponding to 9 clinical pregnancies after transfer of a
single embryo, 18 pregnancy failures represented by 7 failures after
single embryo transfer and 11 after double embryo transfer.
Quantitative PCR was performed using a
Light Cycler apparatus with the iQ detection system and the iQTM
SYBRHGreen Supermix kit (Bio-Rad Laboratories). Each reaction
Total RNA extrac-
Samples used for the qPCR
mixture contained 10 ml 2x of iQ SYBR Green Supermix (dNTPs,
iTaq DNA polymerase, 6 mM MgCl2, SYBR Green I, fluorescein,
and stabilizers), 5 ml cDNA (25-fold, 125-fold or 250-fold dilution),
300 nM of each primer and 4.5 ml of RNase free water to a final
volume of 20 ml. Amplification was performed in triplicate in 96
well plates (ABgene Ltd, Epson, UK) with the following thermal
cycling conditions: initial activation at 95uC for 3 minutes,
followed by 40 cycles of 30 s at 95uC, 30 s at 60uC and 30 s at
72uC. A no template control (NTC) was included in all plates.
Dissociation analysis of PCR products was performed by using a
melting curve to confirm the absence of contaminants or primer
dimers. Four-fold serial dilutions of cDNA derived from pooled
human cumulus cells were used to establish the standard curve and
repeated for each run as described by Feuerstein et al. 2007 .
Primers were designed using the Beacon
Designer version 2.0 software (Bio-Rad Laboratories) to have a
melting temperature of 60uC, and if possible to cross an exon-exon
junction to avoid amplification of genomic DNA. Primers used for
qPCR experiments and qPCR parameters are listed in Table 2.
Data were normalized to RPL19 selected by
the GeNorm algorithm  as the most stable gene. The qPCR
data were recorded with iCycler IQ software version 3.1 (Bio-Rad
laboratories). Melting temperatures, mean efficiency values and
mean r2values for standard curves are presented in Table 2.
Outlier replicates of the triplicates with a variation greater than 1
quantification cycle (Cq) were excluded from the data analysis. For
each sample, detection was normalized for the mean of each
triplicate to RPL19. Each gene amplification for the qPCR
validation step was performed with 3 series and 7 series for the
Statistical analysis of qPCR results was performed on 26 data
points (after the deletion of outliers corresponding to the
maximum and the minimum values in each group) using variance
analysis followed by post-hoc comparison using the Scheffe ´ test
(Statview 4.1H, Abacus Concept, Berkeley, USA) with statistical
significance defined as p,0.05.
In order to evaluate the impact of developmental competence,
the patient variability and the qPCR series respectively on the level
of gene expression, an analysis of variance (Anova) with the
Generalised Linear Mixed Model (GLMM) procedure was
performed for each gene with Statistical Analysis System (SASH)
software. The model chosen was fitted for each gene indepen-
dently yijkn=m+Pi+Aj+Qk+PiAj+eijkn, where yijknis the gene expres-
sion level in the nth CC for a gene according to the ith phenotype
from the jth patient after the kth qPCR series, m is the gene
expression level mean, Pi the fixed effect of the ith phenotype (i=
CCB+or CCB-), Aj the random effect of the jth patient (j=1 to 29),
Qk the random effect of the kth qPCR series (k=1 to 7), (PA)ij the
first-order interaction between variables phenotype and patient,
and eijknthe residual random effect. The levels of significance of
Table 1. Dataset of transcriptome of cumulus cells used for meta-analysis.
StudySpecies GEO Accession Number
Influence of hCG on the transcriptome of CCs Mouse GSE4260 
Comparison of transcriptome of mural granulosa cells with CCs transcriptomeHuman GSE18559 
Comparison of transcriptome of CCs from immature oocyte to CCs from mature oocyte BovineGSE21005 
CCs transcriptome according to embryo cleavage HumanGSE9526 
CCs, cumulus cells; GV, germinal vesicle stage; MII, metaphase II.
Cumulus Cell Biomarkers of Oocyte Competence
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the model and the different effects were set at p,0.01 and p,0.05,
The relationship between the level of expression of candidate
genes and pregnancy was assessed using one way analysis of
variance, Bartlett’s test to compare variances, followed by post-hoc
comparison using the Scheffe ´ test (p,0.05).
Initially, differentially expressed genes were analysed according
to the nuclear maturity of the oocyte. From the 45,220 probe sets
in the array, 15,531 unique genes were expressed in cumulus cells,
among which 724 unique genes (854 probes) were differentially
expressed between CCGVand CCMII. Six hundred and thirty-four
genes were upregulated and 90 genes were downregulated in
CCMIIcompared to CCGV. Hierarchical clustering based on the
132 most differentially expressed genes allowed separation of all
CCGV from other samples, corresponding to CCMII (Fig. 2).
Sixteen functions were upregulated in CCMII as compared to
CCGV,according to the calculated enrichment and p-value of each
function (Table 3). Among them the following annotations should
be emphasized: activation of MAPKK activity, positive regulation
of lipid biosynthesis process, caspase activator activity, caspase
regulator activity and apoptotic protease activator activity.
Following similar criteria, 37 functions were downregulated in
CCMIIcompared to CCGV(Table 4), among which the following
annotated functions should be emphasized: tRNA processing,
induction of apoptosis, induction of programmed cell death, tRNA
Differentially expressed genes were then analysed according to
the ability of the oocyte to yield a blastocyst. From the 45,220
probes set on the array, 354 were differentially expressed between
CCB+and CCB-. These 354 probes referred to 308 single genes,
with 133 genes downregulated and 175 genes upregulated in CCs
enclosing a mature oocyte yielding a blastocyst, compared to those
unable to reach this stage. The hierarchical clustering based on the
354 differentially expressed probes allowed separation of almost all
CCB+ from CCB- (Fig. 3). Upregulation of 23 functions was
observed in CCB+ compared to CCB-, including negative
regulation of cell differentiation, fatty acid biosynthesis, organic
acid biosynthesis, carboxylic acid biosynthesis and transcription
factor binding (Table 5). On the other hand, 31 functions such as
cell redox homeostasis, cyclin-dependent protein kinase regulator
activity, respiratory gaseous exchange and transporter activity
were downregulated in CCB+(Table 6).
We further analysed the behavior of our 308 genes discrimi-
nating CCB-and CCB+in other datasets available in the GEO
[17,21,33,34]. All the probes corresponding to the 308 genes using
MADGene  were extracted in each study. Data from these
probes were log-transformed and median centered and subjected
to hierarchical classification. The ability of these probes to
discriminate sample types was measured by Fisher’s exact test on
sample composition of the main separation on the sample
The results are shown in Figure 4. The 308 genes allowing
separation of almost all CCB+from CCB-(Fig. 4A) were under the
influence of hCG (Fig. 4C). These genes allowed discrimination of
mural granulosa cells from CCs, as expected of cumulus genes
(Fig. 4D). Moreover, these genes seemed to discriminate the
degree of nuclear maturity of the oocytes although without
statistical significance (Fig. 4E) while they allowed separation of
almost all CCB-from CCGVin our study (Fig 4B). However, they
did not discriminate the developmental stage of the embryo (early
Table 2. qPCR primer sequences, PCR efficiency, correlation coefficient of standard curves, Cq range of standard curves, amplicon
size and melting temperature.
GeneIDForward and Reverse primer (59 -39)E (%)r2
Cq rangeTm (6C)
size (bp)GeneBank No.
103.1611.8 0.99460.00327.8–34.4 *87 125NM_001097577
ANKRD22 F: GTGTATGTGTGTGGGCTTAGAGATTC
101.1616.2 0.99160.006 28.5–35.2 * 81.5187 NM_144590
C10orf10 F: GCAGCAAGAAGGTGAGGCATC
96.066 0.99360.00224.1–35.3 88142NM_007021
IMPA2 F: AGCAGGCGGCATCGTGATAG
108.962.10.99560.003 27.7–33.8 *91.5 256NM_014214
RGS2 F: CAGAACGCAAGAAGGGAATAGGTG
100.1610.30.99160.00624.3–31 * 83 392NM_002923
100.365.90.99060.005 30.6–37.1 *88 225NM_024787
RPL19 F: TGAGACCAATGAAATCGCCAATGC
E, qPCR Efficiencies (mean 6 SEM); r2, correlation coefficient of standard curves (mean 6 SEM); Cq, quantification cycle; Tm, melting temperature; *, Standard curve
calculated with 3 points.
Cumulus Cell Biomarkers of Oocyte Competence
PLoS ONE | www.plosone.org5 July 2012 | Volume 7 | Issue 7 | e40449