Analyse du transcriptome de l`ovocyte bovin en lien avec la

Transcription

Analyse du transcriptome de l’ovocyte bovin en lien
avec la compétence au développement
Thèse
Rémi Labrecque
Doctorat en sciences animales
Philosophiae doctor (Ph.D.)
Québec, Canada
© Rémi Labrecque, 2015
Résumé
Dans cette étude, nous nous sommes intéressés aux modulations du transcriptome de
l’ovocyte bovin en lien avec son potentiel à produire un embryon.
Nous avons tout d’abord utilisé un modèle où la qualité des ovocytes a pu être modulée in
vivo dans un contexte de stimulation ovarienne. Nous avons pu observer que la qualité des
ovocytes acquise au cours de cette période avait une durée limitée et qu’un possible
mécanisme de dégradation des ARN messagers pouvait être impliqué dans cette diminution
de la qualité.
Afin de mieux définir l’importance de la LH pour l’ovocyte lors de sa croissance finale
dans le follicule, un antagoniste de la GnRH a été utilisé durant la période cruciale où la
qualité des ovocytes est acquise. La suppression dela sécrétion de LH n’a pas eu d’impact
significatif sur le potentiel de l’ovocyte à produire un embryon, bien que des fonctions
cellulaires importantes, telles que la régulation de la traduction semble avoir été
affectéedans l’ovocyte par le traitement.
Le transcriptome des ovocytes récupérés à partir de follicules de taille spécifique nous a
permis de mieux définir les changements qui surviennent de façon séquentielle au cours de
la croissance normale du follicule. Une modulation importante et graduelle de plusieurs
transcrits impliqués dans le remodelage de la chromatine a d’ailleurs été observée,ce qui
illustre l’importance de cette modification au cours de l’acquisition graduelle de la
compétence au développement.
Compte tenu de l’importante des changements qui surviennent au cours de la croissance
finale de l’ovocyte au niveau de la configuration de la chromatine de la vésicule germinale,
nous avons analysé le transcriptome des ovocytes au cours des différents stades de
condensation de la chromatine. Malgré la diminution importante de la transcription qui se
produit en parallèle à la compaction de la chromatine, nous avons pu observer une
augmentation de plusieurs ARN messagers d’histones au cours de cette période, ce qui
suggère uneaccumulation en vue d’assurer les premiers cycles cellulaires.
III
Ce projet nous a donc permis de mieux définir les changements qui surviennent dans
l’ovocyte au cours de sa croissance finale, au moment où la qualité ovocytaire est
significativement modulée. Nous avons également proposé certaines hypothèses afin
d’expliquer cette diminution de la qualité.
IV
Abstract
In this study, we specifically looked at the mRNA modulations observed in the bovine
oocyte in regard of its potential to produce an embryo.
We first used a model where the oocyte quality could be modulated in vivo, using ovarian
stimulation. We were able to show that the oocyte quality acquired during that period will
not last and we proposed a possible mechanism of RNA degradation that could be
implicated in the decrease of oocyte quality.
In order to better understand the importance of the LH support for the oocyte at the end of
follicle growth, a GnRH antagonist was used during the period where the oocyte quality is
gained. Although the suppression of the LH secretion did not have a significant impact on
the potential of the oocyte to produce an embryo, important cellular functions such as
translation regulation were affected in the oocyte by the treatment.
The transcriptome analysis of the oocyte collected from follicles of gradually increasing
sizes has allowed us to better understand the sequential changes undergoing in the oocyte
during the follicular growth. An important and gradual modulation of several transcripts
involved in chromatin remodeling was also identified, illustrating the importance of this
process in the gradual developmental potential acquisition.
Considering the major chromatin remodeling of the germinal vesicle that is known to occur
towards the end of oocyte growth, we decided to analyse the transcriptome of these
oocytes, grouped according to their degree of chromatin compaction. Although an
important decrease of the transcriptional activity will occur in parallel with the chromatin
condensation, we were able to observe the accumulation of several histone mRNA over this
period, suggesting a potential storage to fulfill the first few cell cycles.
This project has allowed us to better define the molecular changes undergoing in the oocyte
during its growth, where the oocyte quality is significantly modulated. It was also possible
V
to propose different hypothesis in order to explain the underlying biological causes of this
decrease quality.
VI
Table des matières
Résumé................................................................................................................................. III
Abstract ................................................................................................................................. V
Table des matières ..............................................................................................................VII
Liste des tableaux................................................................................................................. XI
Table des figures ............................................................................................................... XIII
Liste des abréviations et des sigles .................................................................................... XV
Remerciements.................................................................................................................. XIX
Avant-propos ................................................................................................................. XXIII
1 Introduction ....................................................................................................................... 1
1.1 Le développement de l’ovocyte et du follicule ............................................................. 1
1.1.1 Ovogenèse ................................................................................................................. 1
1.1.2 Folliculogenèse ......................................................................................................... 2
1.1.2.1 Le follicule primordial et primaire ........................................................................ 2
1.1.2.2 Le follicule secondaire .......................................................................................... 2
1.1.2.3 Le follicule tertiaire............................................................................................... 3
1.1.3 Dynamique folliculaire ............................................................................................. 4
1.1.3.1 Vagues folliculaires .............................................................................................. 4
1.1.3.2 La croissance folliculaire ...................................................................................... 4
1.1.3.3 Ovulation............................................................................................................... 6
1.2 La maturation de l’ovocyte ........................................................................................... 7
1.2.1 La reprise de la méiose ............................................................................................. 7
1.2.1.1 L’AMPc ................................................................................................................ 8
1.2.1.2 GMPc .................................................................................................................... 9
1.2.2 Second arrêt méiotique ........................................................................................... 10
1.2.2.1 Facteur de promotion de la maturation (MPF).................................................... 10
1.2.2.2 Facteur cytostatique (CSF) ................................................................................. 11
1.3 La fécondation ............................................................................................................ 12
1.4 Qualité des ovocytes ................................................................................................... 13
1.4.1 Les différents niveaux de compétence .................................................................... 15
1.4.1.1 Compétence à reprendre la méiose ..................................................................... 15
1.4.1.2 Compétence de l’ovocyte à se diviser après la fécondation................................ 16
1.4.1.3 Capacité à supporter le développement jusqu’au stade de blastocyte ................ 17
1.5 Modèles d’études de la qualité ovocytaire .................................................................. 18
1.5.1 Aspect morphologique du complexe cumulus ovocytes ......................................... 18
1.5.2 Impact de la taille du follicule ................................................................................ 18
1.5.3 Impact de l’aspect de l’ovaire ................................................................................. 19
1.5.4 Impact du stade folliculaire ..................................................................................... 20
1.5.5 Impact de la cinétique du développement ............................................................... 21
1.5.5.1 Expulsion du premier globule polaire ................................................................. 21
1.5.5.2 Stade 2-cellules ................................................................................................... 21
1.5.5.3 Cinétique des cycles cellulaires suivants ............................................................ 22
1.5.6 BCB......................................................................................................................... 22
1.5.7 Migration dielectrophorétique ................................................................................ 23
1.5.8 Influence de l’âge maternel ..................................................................................... 23
1.5.9 Aneuploïdie ............................................................................................................. 24
VII
1.5.10 Configuration de la chromatine ............................................................................ 25
1.5.11 Impact du traitement hormonal ............................................................................ 27
1.6 Activité transcriptionnelle dans l’ovocyte.................................................................. 28
1.6.1 Synthèse d’ARN ..................................................................................................... 28
1.6.2 Diminution et arrêt de la transcription ................................................................... 29
1.6.2.1 Rôle de la chromatine dans la diminution de la transcription ............................ 30
1.6.3 Utilisation des ARNm ............................................................................................ 31
1.7 Régulation des ARNm dans l’ovocyte ....................................................................... 32
1.7.1 Longueur de la queue Poly-A ................................................................................. 32
1.7.2 Éléments de régulation dans la portion non traduite du transcrit ........................... 33
1.8 Approches méthodologiques pour l’étude de la qualité ovocytaire ........................... 33
1.8.1 Gène candidat ......................................................................................................... 33
1.8.2 Méthodes soustractives........................................................................................... 34
1.8.3 Microarray .............................................................................................................. 34
1.9 Hypothèse et objectifs ................................................................................................ 36
2 Gene expression analysis of bovine oocytes with high developmental competence
obtained from FSH-stimulated animals ...........................................................................39
2.1 Résumé ....................................................................................................................... 40
2.2 Abstract ...................................................................................................................... 41
2.3 Introduction ................................................................................................................ 42
2.4 Results ........................................................................................................................ 43
2.4.1 Microarray analysis ................................................................................................ 43
2.4.2 Functional analysis ................................................................................................. 44
2.4.3 Microarray validation by quantitative RT-PCR ..................................................... 45
2.5 Discussion .................................................................................................................. 46
2.5.1 RNA processing ..................................................................................................... 49
2.5.2 DNA stability – chromosome segregation ............................................................. 53
2.6 Materials and methods ............................................................................................... 56
2.6.1 Oocyte recovery ..................................................................................................... 56
2.6.2 RNA extraction and amplification ......................................................................... 57
2.6.3 Sample labeling and microarray hybridization ...................................................... 57
2.6.4 Microarray data analysis ........................................................................................ 58
2.6.5 cDNA preparation and quantitative RT-PCR ......................................................... 59
2.6.6 Statistical analysis of quantitative RT-PCR results ................................................ 59
2.6.7 Acknowledgements ................................................................................................ 60
2.7 References .................................................................................................................. 61
2.8 Tables ......................................................................................................................... 67
2.9 Figures ........................................................................................................................ 71
3 Gene expression analysis of bovine oocytes at optimal coasting time combined with
GnRH antagonist during the no-FSH period ...................................................................77
3.1 Résumé ....................................................................................................................... 78
3.2 Abstract ...................................................................................................................... 80
3.3 Introduction ................................................................................................................ 81
3.4 Results ........................................................................................................................ 82
3.4.1 Blastocyst rate ........................................................................................................ 82
3.4.2 Microarray analysis ................................................................................................ 83
3.4.3 Functional analysis ................................................................................................. 83
VIII
3.4.4 Microarray validation by quantitative RT-PCR ...................................................... 84
3.5 Discussion ................................................................................................................... 84
3.6 Conclusion .................................................................................................................. 90
3.7 Declaration of interest ................................................................................................. 91
3.8 Acknowledgements ..................................................................................................... 91
3.9 Materials and methods ................................................................................................ 91
3.9.1 Oocyte recovery ...................................................................................................... 91
3.9.2 RNA extraction and amplification .......................................................................... 92
3.9.3 Sample labeling and microarray hybridization ....................................................... 92
3.9.4 Microarray data analysis ......................................................................................... 93
3.9.4.1 Data processing ................................................................................................... 93
3.9.4.2 Functional analysis .............................................................................................. 94
3.9.5 cDNA preparation and quantitative RT-PCR ......................................................... 94
3.9.6 Statistical analysis of quantitative RT-PCR results ................................................ 95
3.10 References ................................................................................................................. 96
3.11 Figures .................................................................................................................... 102
3.12 Tables ...................................................................................................................... 105
4 Microarray analysis of bovine oocytes from distinct follicle sizes reveals gradual
mRNA modulations ...................................................................................................... 109
4.1 Résumé...................................................................................................................... 110
4.2 Abstract ..................................................................................................................... 111
4.3 Introduction ............................................................................................................... 112
4.4 Results ....................................................................................................................... 114
4.4.1 Microarray analysis ............................................................................................... 114
4.4.2 Functional analysis................................................................................................ 115
4.4.3 Quantitative RT-PCR validation of microarray results ........................................ 115
4.5 Discussion ................................................................................................................. 116
4.6 Conclusion ................................................................................................................ 123
4.7 Methods .................................................................................................................... 123
4.7.1 Oocyte recovery .................................................................................................... 124
4.7.2 RNA extraction and amplification ........................................................................ 124
4.7.3 Sample labelling and microarray hybridization .................................................... 125
4.7.4 Microarray data analysis ....................................................................................... 125
4.7.5 cDNA preparation and quantitative RT-PCR ....................................................... 126
4.7.6 Statistical analysis of quantitative RT-PCR results .............................................. 127
4.8 Acknowledgements ................................................................................................... 127
4.9 References ................................................................................................................. 128
4.10 Figures .................................................................................................................... 135
4.11 Tables ...................................................................................................................... 139
5 Chromatin remodeling and histones mRNA accumulation in bovine germinal vesicle
oocyte ............................................................................................................................ 143
5.1 Résumé...................................................................................................................... 144
5.2 Abstract ..................................................................................................................... 145
5.3 Introduction ............................................................................................................... 146
5.4 Results ....................................................................................................................... 148
5.4.1 Microarray results ................................................................................................. 148
5.4.2 Functional analysis................................................................................................ 149
IX
5.4.3 Quantitative RT-PCR validation of microarray results ........................................ 150
5.4.4 Protein-RNA interactions ..................................................................................... 150
5.5 Discussion ................................................................................................................ 151
5.6 Conclusion ................................................................................................................ 155
5.7 Materials and methods ............................................................................................. 155
5.7.1 Oocyte recovery ................................................................................................... 155
5.7.2 RNA extraction and amplification ....................................................................... 156
5.7.3 Sample labelling and microarray hybridization ................................................... 157
5.7.4 Microarray data analysis ...................................................................................... 157
5.7.5 cDNA preparation and quantitative RT-PCR ....................................................... 158
5.7.6 Statistical analysis of quantitative RT-PCR results .............................................. 159
5.7.7 Histones mRNA analysis...................................................................................... 159
5.8 Funding..................................................................................................................... 160
5.9 Acknowledgements .................................................................................................. 160
5.10 References .............................................................................................................. 161
5.11 Figures .................................................................................................................... 167
5.12 Tables ..................................................................................................................... 171
6 The study of mammalian oocyte competence by transcriptome analysis: progress and
challenges ...................................................................................................................... 177
6.1 Résumé ..................................................................................................................... 178
6.2 Abstract .................................................................................................................... 179
6.3 Introduction .............................................................................................................. 180
6.4 Markers of oocyte quality ........................................................................................ 182
6.5 Animal models for studying oocyte competence ..................................................... 183
6.6 Strategies for matching mRNA profiles with oocyte competence ........................... 184
6.6.1 The follicular phase of oocyte development ........................................................ 184
6.6.2 Follicle size........................................................................................................... 185
6.6.3 Follicular differentiation....................................................................................... 187
6.7 Ovarian stimulation .................................................................................................. 188
6.8 Chromatin configuration .......................................................................................... 190
6.9 In vivo / in vitro ........................................................................................................ 192
6.10 Maternal age ........................................................................................................... 193
6.11 Other methods for selecting competent oocytes .................................................... 197
6.12 Conclusion .............................................................................................................. 198
6.13 Acknowledgments .................................................................................................. 199
6.14 References .............................................................................................................. 200
6.15 Figures .................................................................................................................... 215
6.16 Tables ..................................................................................................................... 217
Conclusion générale ........................................................................................................... 219
Bibliographie ...................................................................................................................... 223
X
Liste des tableaux
Table 2-1 Number of differentially expressed genes between conditions ............................ 67
Table 2-2 Correlation results between microarray and q-RT-PCR data. .............................. 68
Table 2-3 RNA modulation in oocyte during the corresponding follicular phases .............. 69
Table 2-4 Supplemental data Primer sequences used for quantitative RT-PCR. ................. 70
Table 3-1 Primer sequences used for quantitative RT-PCR. .............................................. 105
Table 3-2 Number of differentially represented genes between control and treated
conditions (68-68Cet) in comparison with our previous results (Labrecque et al. 2013)
..................................................................................................................................... 106
Table 3-3 Functions affected and results from microarray and qRT-PCR experiment ...... 107
Table 3-4 Genes with high mRNA level in the Cetrotide treated group (FC > 2) and
information regarding their possible translation .......................................................... 108
Table 4-1 Number of genes presenting significant differences following LIMMA analysis
(P< 0.05) ...................................................................................................................... 139
Table 4-2 Supplemental data Primers used for quantitative RT-PCR validations ............. 140
Table 4-3 Supplemental data Number of genes present in each cluster generated by the
Mfuzz clustering analysis ............................................................................................ 141
Table 5-1 Number of genes presenting significant differences following LIMMA analysis
(P< 0.01) ...................................................................................................................... 171
Table 5-2 Number of histone-related probes. ..................................................................... 172
Table 5-3 List of possible Protein-RNA interactions calculated with catRAPID omics. ... 173
Table 5-4 Supplemental data Primers used for quantitative RT-PCR validations ............. 174
Mfuzz clustering analysis ............................................................................................ 175
Table 5-6 Supplemental data List of possible Protein-RNA interactions identified by
catRAPID omics. ......................................................................................................... 176
Table 6-1 Summary of the studies on the analysis of oocyte with different developmental
potential ....................................................................................................................... 217
XI
Table des figures
Figure 1-1 Le développement du follicule (Fair 2003)........................................................... 3
Figure 1-2 Dynamique folliculaire à deux ou trois vagues (Adams et al. 2008) .................... 6
Figure 1-3 Interactions entre les cellules folliculaires et l’ovocyte en lien avec la reprise de
la méiose (Gilchrist 2011) ............................................................................................... 9
Figure 1-4 Facteur de promotion de la maturation (MPF) (Thibault and Levasseur 2001) . 11
Figure 1-5 Potentiel de développement en lien avec l’origine de l’ovocyte (Merton et al.
2003) .............................................................................................................................. 14
Figure 1-6 Configuration de la chromatine chez la souris (Bellone et al. 2009) .................. 26
Figure 1-7 Configuration de la chromatine chez le bovin (Lodde et al. 2007)..................... 27
Figure 2-1 Microarray hybridization design used in the study ............................................. 71
Figure 2-2 Venn diagram showing the overlap between the probes considered as present in
the microarray analysis .................................................................................................. 72
Figure 2-3 Microarray profile and q-RT-PCR validations for genes in the cluster 1. .......... 73
Figure 2-4 Microarray profile and q-RT-PCR validations for genes in the cluster 3. .......... 74
Figure 2-5 Supplemental data Clusters of Genes generated by K-means clustering analysis.
....................................................................................................................................... 75
Figure 2-6 Supplemental data KEGG RNA degradation pathway enriched with genes from
cluster 3. ......................................................................................................................... 76
Figure 3-1 Experimental procedure. ................................................................................... 102
Figure 3-2 Quantitative RT-PCR validations of selected genes. ........................................ 103
Figure 3-3 Venn diagram illustrating the number of probes in common between the contrast
68 hrs vs. 68 hrs-Cetrotide (this study) and 68 hrs vs. 92 hrs (Labrecque et al. 2013).
..................................................................................................................................... 104
Figure 4-1 Between-Group Analysis (BGA) (Culhane et al. 2002) of the oocyte’s
microarray data coming from the four follicle size groups. ........................................ 135
Figure 4-2 Main clusters identified from Mfuzz clustering analysis (Kumar and Futschik
2007). ........................................................................................................................... 136
Figure 4-3 Quantitative RT-PCR validation of microarray results..................................... 137
Figure 4-4 Supplemental data 16 clusters generated by the mFuzz clustering analysis ..... 138
microarray data. ........................................................................................................... 167
Figure 5-2 Main clusters identified from Mfuzz clustering analysis (Kumar and Futschik
2007). ........................................................................................................................... 168
Figure 5-3 Quantitative RT-PCR validation of microarray results. mRNA quantifications
data are presented as mean ± standard error of the mean. ........................................... 169
Figure 5-4 Supplemental data 16 clusters generated by the mFuzz clustering analysis ..... 170
Figure 6-1 Follicular development representation, with RNA modulations occurring in the
oocyte at the end of follicular growth (and potentially beyond). ................................ 215
Figure 6-2 Schematic representation of the distribution of our multiple microarray datasets
in bovine oocyte. .......................................................................................................... 216
XIII
Liste des abréviations et des sigles
%
°C
6-DMAP
AC
ADN
AMPc
ARNm
ATAC
AURKA
AURKAIP1
BCB
BGA
CBX3
CBX8
CCNB1
CDC5L
CDK1
CENP-H
CENP-I
CENP-K
CGH
CNOT7
COC
CPE
CPEB1
CPG
cRNA
CSF
CSRP2BP
CTNNBL1
DAZL
DDRT
DDX39A
DP
EJC
ELP4
ENY2
ER
ERCC8
ESCO2
pourcent
degré Celsius
6-Dimethylaminopurine
adénylate cyclase
Acide désoxyribonucléique
adénosine monophosphate cyclique
acide ribonucléique messager
Ada two A containing
Aurora kinase A
Aurora kinase A interacting protein 1
Brilliant cresyl blue
Between-group analysis
Chromobox homolog 3
Chromobox homolog 8
Cyclin B1
CDC5 cell division cycle 5-like
Cyclin-dependant kinase 1
Centromere protein H
Centromere protein I
Centromere protein K
Comparative genomic hybridization
CCR4-NOT transcription complex subunit 7
Cumulus-oocyte complex
cytoplasmic polyadenylation element
Cytoplasmic polyadenylation element binding protein 1
cellules germinales primordiales
ARN complémentaire
facteur cytostatique
CSRP2 binding protein
Catenin beta like 1
Deleted in azoospermia-like
Differential display
DEAD (Asp-Glu-Ala-Asp) box polypeptide 39A
Discriminative Power
Exon Jonctions Complexes
Elongation protein 4 homolog
Enhancer of yellow 2 homolog
Endoplasmic reticulum
Excision repair cross-complementing rodent repair deficiency, complementation
group 8
Establishment of cohesion 1 homolog 2 (S. cerevisiae)
XV
FC
FDR
FISH
FQRNT
FSH
FSHr
FSK
G6DPH
GADD45A
GADD45B
GMPc
GV
GVBD
H2AFX
HAUS8
HICE1
hnRNP
HNRNPA2B1
HNRNPH3
HNRNPK
HNRNPM
HOXA9
HP1gamma
hSLBP
HSPD1
IBMX
InsP3
INTS3
IPA
IVF
IVM
L1G1
LAB
LAP2
LH
LIG4
LSM1
LSM10
LSM11
LSM3
LSM4
LSM5
LSM6
XVI
Fold change
false discovery rate
Fluorescence in-situ hybridization
Fonds Québécois de la Recherche sur la Nature et les Technologies
Follicle-stimulating hormone
récepteur de la FSH
Forskoline
glucose-6-phosphate déshydrogénase
Growth arrest and DNA-damage-inducible, alpha
Growth arrest and DNA-damage-inducible, Beta
guanosine monophosphate cyclique
Germinal vesicle
Germinal vesicle breakdown
H2A histone family member X
Augmin-like complex subunit 8
Hec1-interacting and centrosome-associated 1
Heterogeneous nuclear ribonucleoprotein
Heterogeneous nuclear ribonucleoprotein A2/B1
Heterogeneous nuclear ribonucleoprotein H3
Heterogeneous nuclear ribonucleoprotein K
Heterogeneous nuclear ribonucleoprotein M
Homeobox A9
Heterochromatin protein 1 homolog gamma
human Stem loop binding protein
Heat shock 60KDa protein 1
3-isobutyl-1-methylxanthine
inositol-1,4,5-triphosphate
Integrator complex subunit 3
Ingenuity Pathway Analysis
In vitro fertilization
In vitro maturation
Ligase 1
L'Alliance Boviteq
Lamina associated protein 2
hormone lutéinisante
Ligase IV, DNA, ATP-dependent
LSM1 homolog, U6 small nuclear RNA associated (S. cerevisiae)
MBD1
MBIP
MET
mg
MI
MII
mL
MLH3
mm
mM
MPF
MPG
MSH2
mSLBP
MUS81
ng
NLRP5
NO
NPM2
NSERC
NSN
NUDT21
NUF2
OA
OPU
PABP
PAIP2
PAPOLA
PDE
PDE3
PDE3A
pg
PKA
PLC
PMS1
PP2A
PPP1CB
PRC1
PRC2
PTV
q-RT-PCR
RBM42
RNF2
Methyl-CpG binding domain protein 1
MAP3K12 binding inhibitory protein 1
Maternal embryonic transition
milligramme
Metaphase I
Metaphase II
millilitre
MutL homolog 3 (E. coli)
millimètre
millimolaire
Maturation promoting factor
N-methylpurine-DNA glycosylase
MutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli)
mouse Stem loop binding protein
MUS81 endonuclease homolog
nanogramme
NLR Family, Pyrin Domain Containing 5
oxyde nitreux
Nucleopasmin 2
Natural Sciences and Engineering Research Council of Canada
Not-surrounded nucleolus
Nudix (nucleoside diphosphate linked moiety X)-type motif 21
NUF2, NDC80 kinetochore complex component, homolog (S. cerevisiae))
Okadaic acid
Ovum pick-up
Poly-A binding protein
Poly-A binding protein interacting protein 2
Poly(A) polymerase alpha
phosphodiestérase
phosphodiestérase 3
phosphodiestérase 3A
picogramme
protein kinase A
phospholipase C
Postmeiotic segregation increased 1 (S. cerevisiae)
protéine phosphatase 2A
Protein Phosphatase 1, catalytic subunit, beta isozyme)
Polycomb-repressive complex 1
Polycomb repressive complex 2
ponction transvaginale
Quantitative real-time reverse-transcriptase polymerase chain reaction
RNA binding motif protein 42
Ring finger protein 2
XVII
RPL28
RPLP1
RPS18
RPS19
RPS2
RPS7
RPSA
rRNA
RSRC1
SARNP
SCMC
SD
SFRS7
SIP1
SLBP
SLBP2
SN
SNP
Sp1
SPC25
SSH
SUV39H1
TACC3
TADA1
TAF1A
TBP
TFDP1
TREX
U7 snRNP
UI
UTR
VCP
WIBG
XRCC1
µL
µm
XVIII
Ribosomal protein L28
Ribosomal protein, large, P1
Ribosomal protein S18
Ribosomal protein SA
ARN ribosomaux
Arginine/serine-rich coiled-coil 1
SAP domain-containing ribonucleoprotein
Subcortical maternal complex
Standard Deviation
Serine/arginine-rich splicing factor 7
Survival of motor neuron protein interacting protein 1
Stem-loop binding protein
Stem loop binding protein 2
Surrounded nucleolus
Single nucleotide polymorphism
Sp1 transcription factor
SPC25, NDC80 kinetochore complex component, homolog (S. cerevisiae))
Suppressive subtractive hybridization
Suppressor of variegation 3-9 homolog 1
Transforming, acidic coiled-coil containing protein 3
Transcriptional adaptor 1
TATA box binding protein (TBP)-associated factor RNA polymerase 1, A
TATA-box binding protein
Transcription factor Dp-1
Transcription/Export complex
U7 small nuclear ribonucleoprotein
unité internationale
Untranslated region
Valosin-containing protein
Within bgcn homolog
X-ray repair complementing defective repair in Chinese hamster cells 1
microlitre
micromètre
Remerciements
Étant une personne peu bavarde, la section des remerciements constitue une bonne façon
pour moi de remercier toutes les personnes qui ont contribué de près ou loin à enrichir mon
parcours aux études graduées. À vous tous, je tiens donc à vous exprimer toute ma
gratitude.
J’aimerais tout d’abord remercier Marc-André, mon directeur de recherche, pour avoir
accepté de me prendre comme étudiant d’été il y a maintenant plus de six ans. Merci de
m’avoir fait confiance dès le début, de m’avoir soutenu, encouragé et motivé à toujours me
dépasser. Merci pour tes multiples conseils, mais surtout merci de m’avoir permis de vivre
une expérience des plus enrichissantes au cours de ces années.
Je remercie également Claude Robert, qui constitue une ressource indispensable pour un
laboratoire comme le notre. Ton expertise en biologie-moléculaire et ton sens critique sont
des atouts de taille afin d’exceller dans le domaine de la recherche. Merci d’avoir toujours
été disponible afin de répondre à mes questions techniques. Merci d’avoir partagé tes
connaissances et ton expérience de vie, ce qui a souvent contribué à mettre du vivant lors
de nos diners!
Je tiens également à remercier les membres de mon comité de thèse, François Richard,
François Castonguay et Svetlana Uzbekova pour leurs commentaires, discussions ainsi que
leurs conseils.
Je ne pourrais pas passer sous le silence la contribution importante des « Isabelle ». Tout
d’abord, un merci spécial à Isabelle Dufort, sans qui le laboratoire ne pourrait pas
fonctionner de façon aussi efficace. Merci à Isabelle Gilbert, avec qui j’ai eu le plaisir de
travailler, mais également de discuter au cours de ces années. Merci à Isabelle Laflamme,
notre experte en fécondation in vitro sur qui je pouvais toujours compter lorsque j’avais
besoin de quelques ovaires afin de récupérer des ovules. Merci à Julie Nieminem, notre
XIX
organisatrice hors-pair, à Dominic Gagné, avec qui j’ai eu le plaisir de discuter de ferme et
merci à Eric Fournier pour ton aide essentielle en bio-informatique.
Je tiens à remercier Christian Vigneault, pour m’avoir offert un premier contact avec la
recherche industrielle, ainsi que Patrick Blondin pour les nombreuses discussions lors du
projet à Boviteq.
Au cours de ces années passées dans le labo, j’ai eu la chance de côtoyer plusieurs étudiants
qui ont grandement contribué à enrichir mon parcours. Tout d’abord, mes acolytes du projet
coasting, Anne-Laure et Audrey, mes partenaires de party : Gaël, Nico et Gab, mon
complice de dégustation de Scotch Angus, Annie « bon matin » Girard, Ernesto, Florence,
Habib, Luis, Marie, Maella, Sara ainsi qu’Eve-Lyne et Catherine Gravel qui ont été
présentes lors de mes débuts dans le laboratoire.
Je voudrais également remercier les membres du laboratoire d’Alberto Luciano en Italie,
soit Valentina, Cecilia, Irene et Franca ainsi qu’Alberto lui-même pour m’avoir accueilli si
chaleureusement lors de mon séjour à Milan.
Je remercie également les personnes avec qui j’ai pu me changer les idées en dehors du
laboratoire. Merci à Etienne, Laurence, Julie, Geneviève et Dominique pour tous ces
soupers en si bonne compagnie!
Toutes ces années d’études n’auraient pas été possibles sans l’appui de la famille. Tout
d’abord, les membres de la famille Bernard, Lucie et Yvon, Claudia et Pascal, Mathieu,
Jérôme et Sophie, vous avez toujours été présents pour me soutenir et m’aider à votre façon
et je vous en suis très reconnaissant. Je remercie également tous les membres de ma
famille. Même si vous ne compreniez pas toujours tous les détails de mon projet de
recherche, j’étais toujours aussi heureux de vous expliquer les subtilités de mon projet lors
de nos discussions à la fin de la traite! Un merci spécial à mes parents qui m’ont toujours
encouragé dans mon choix de poursuivre aux études graduées, ma soeur Julie et son
conjoint Jean-Luc, la petite Cath pour les shows métal auxquels nous avons assistés
XX
ensemble, ma belle-sœur Cath B, sans oublier mes 2 brothers Ets et Dji avec qui j’ai pu
décrocher et me changer les idées plus d’une fois!
Je ne pourrais pas terminer mes remerciements sans parler d’une personne unique avec qui
j’ai le plaisir de partager ma vie, Marie-Eve. Tu as toujours été là pour moi dans les
moments heureux, comme dans les moments les plus difficiles ou stressants. Ta patience,
ton soutien constant et surtout ta compréhension ont grandement facilité les choses au cours
de ces années. Finalement, merci à mon petit homme Simon, à qui un jour je pourrai
expliquer ce que je faisais durant mon doc.
XXI
Avant-propos
Le chapitre 2, 3 et 6 sont des articles publiés. Le chapitre 4 et le chapitre 5 seront soumis
pour publication prochainement.
Chapitre 2 :
Labrecque R, Vigneault C, Blondin P, Sirard MA. 2013. Gene Expression Analysis of
Bovine Oocytes With High Developmental Competence Obtained From FSHStimulated Animals. Mol Reprod Dev 80:428-440.
Chapitre 3 :
Labrecque R, Vigneault C, Blondin P, Sirard MA. 2014. Gene expression analysis of
bovine oocytes at optimal coasting time combined with GnRH antagonist during the
no-FSH period. Theriogenology 81:1092-1100.
Dans ces deux chapitres, Rémi Labrecque est responsable de la réalisation de toutes les
expériences, de l’analyse et l’interprétation des données et de l’écriture du manuscrit.
Christian Vigneault a contribué à la conception du projet et a participé à la récupération des
échantillons biologiques. Patrick Blondin a participé à l’élaboration du projet. Marc-André
Sirard a contribué à l’élaboration du projet, l’analyse et l’interprétation des données ainsi
qu’à la révision du manuscrit. Tous les coauteurs ont lu et approuvé la version finale du
manuscrit.
Chapitre 4 :
Labrecque R, Sirard MA.Microarray analysis of bovine oocytes from distinct follicle sizes
reveals gradual mRNA modulations
Dans ce chapitre, Rémi Labrecque est responsable de la réalisation de toutes les
expériences, de l’analyse et l’interprétation des données, de l’écriture du manuscrit et a
participé à l’élaboration du projet. Marc-André Sirard a contribué à l’élaboration du projet,
l’analyse et l’interprétation des données ainsi qu’à la révision finale du manuscrit.
XXIII
Chapitre 5 :
Labrecque R, Lodde V, Dieci C, Tessaro I, Luciano AM, Sirard MA. Chromatin
remodeling and histones mRNA accumulation in bovine germinal vesicle oocyte.
Dans ce chapitre, Rémi Labrecque est responsable de la récupération des échantillons
biologiques, de la réalisation de toutes les expériences, de l’analyse et l’interprétation des
données, de l’écriture du manuscrit et a participé à l’élaboration du projet. Valentina Lodde,
Cecilia Dieci, Irene Tessaro et Alberto Maria Luciano ont contribué à la récupération des
échantillons biologiques et à la conception du projet. Marc-André Sirard a contribué à
l’élaboration du projet, l’analyse et l’interprétation des données ainsi qu’à la révision finale
du manuscrit. Tous les coauteurs ont lu et approuvé la version finale du manuscrit.
Chapitre 6 :
Labrecque R, Sirard MA. 2014. The study of mammalian oocyte competence by
transcriptome analysis: progress and challenges. Mol Hum Reprod 20:103-116.
Dans ce chapitre, Rémi Labrecque est responsable de l’acquisition des données, de
l’analyse et l’interprétation des données et de l’écriture du manuscrit. Marc-André Sirard a
contribué à l’analyse et l’interprétation des données ainsi qu’à la révision finale du
manuscrit.
XXIV
1 Introduction
1.1
1.1.1
Le développement de l’ovocyte et du follicule
Ovogenèse
C’est au cours d’un long processus, se déroulant en plusieurs étapes que la réserve
d’ovocytes sera d’abord constituée et ultimement utilisée plus tard lors de la vie
reproductive de la femelle.L’ovogenèse, qui correspond au processus de formation des
ovocytes à partir des cellules germinales primordiales (CPG), débute lors du
développement fœtal.
Chez le bovin, les crêtes génitales aussi appelées gonades primitives commencent à être
visibles à partir du jour 32 de la gestation. Au jour 35-36, il est possible de distinguer les
CPG et le sexe du fœtus peut être déterminé au jour 39 par la formation de la tunique
albuginée sur les testicules primaires (Erickson 1966). Les CPG sont alors entourées de
cellules somatiques, le tout inclus dans une membrane basale pour une dimension moyenne
de 30 µm (Rüsse 1983). Après environ 57 jours de gestation, les CPG vont se multiplier par
division mitotiquesuccessive, pour être appelées ovogonies.
Les premières divisions méiotiques débutent dans l’ovaire entre les jours 75-80 (Erickson
1966). Toutefois, cette première division ne sera pas complétée puisque les ovocytes
entreront en arrêt méiotique au stade diplotène de la prophase I, aussi appelé stade dictyé,
près du 170e jour de gestation chez la vache (Erickson 1966). C’est à ce moment que les
chromosomes homologues commencent à se séparer tout en demeurant liés à certains
endroits. Cet arrêt méiotique sera maintenu pendant une durée pouvant aller de quelques
mois à plusieurs années. À ce moment, l’ovocyte est entouré d’une seule couche de quatre à
huit cellules de pré-granulosa. Cela formera la première catégorie de follicules qui va
constituer la réserve d’ovocytes disponibles pour toute la vie de l’animal. Cependant, bien
que le nombre maximal de CPG chez le bovin soit estimé à 2 700 000 aujour 110 de la
gestation, ce nombre diminuera à environ 107 000 au jour 170 alors qu’à la naissance, c’est
environ 68 000 cellules germinales qui sont retrouvées dans l’ovaire à 270 jours (Erickson
1966). Une étude plus récente suggère que le nombre de cellules germinales retrouvées à la
naissance pourrait être encore plus faible, avec moins de 20 000 cellules après 285 jours de
1
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d’un follicule primordial jusqu’au stade antral est estimée à environ 180 jours (Lussier et al.
1987).
1.1.3
Dynamique folliculaire
1.1.3.1 Vagues folliculaires
Chez le bovin, chaque cycle œstral est caractérisé par la croissance de deux ou trois vagues
folliculaires(Savio et al. 1988; Sirois and Fortune 1988), bien que les vaches laitières aient
de façon prédominante des cycles à deux vagues (Townson et al. 2002). La durée moyenne
d’un cycle à deux vagues est d’environ 21 jours alors que le cycle à trois vagues sera plutôt
de 23 jours en raison d’une phase lutéale plus longue (Ginther et al. 1989). L’émergence de
la première vague débute le jour précédant l’ovulation (jour 0) et la deuxième vague
commence au jour 9-10 dans le cas d’un cycle à deux vagues. Alors que dans le cas d’un
cycle à trois vagues, c’est plutôt aux jours 8-9 et 15-16 que les vagues suivantes
commencent (Figure 1-2) (Ginther et al. 1996; Mapletoft et al. 2002). Certains suggèrent
que les ovocytes provenant des ovulations à trois vagues folliculaires seraient plus fertiles
que ceux provenant des ovulations à deux vagues chez les vaches en lactation. La raison
serait qu’en se développant quelques jours de moins, les ovocytes évitent les effets négatifs
d’une période de développement folliculaire prolongée, tel que suggéré par Revah and
Bulter (Revah and Butler 1996) dans le cas où les ovocytes sont récupérés à partir de
follicules maintenus dans un état de dominance prolongée.
1.1.3.2 La croissance folliculaire
La croissance folliculaire dépendante des gonadotrophines peut être divisée en trois phases
distinctes, soit le recrutement, la sélection et la dominance (Figure 1-2). Une cohorte
d’environ cinq à dix follicules, d’environ 4 à 5 mm de diamètre sera tout d’abord recrutée
suite à une augmentation de la concentration de FSH (Adams et al. 1992). À ce moment,
tous les follicules de la cohorte ont une chance équivalente d’atteindre l’ovulation (Gibbons
et al. 1997). Une fois le recrutement effectué grâce à la FSH, un processus de sélection va
se mettre en place. Les follicules vont alors supprimer la sécrétion de FSH via la sécrétion
d’inhibine et d’oestradiol (Gibbons et al. 1999) et ce, même si la FSH est toujours requise
pour permettre la croissance des follicules.
4
Lorsque le plus gros follicule de la cohorte aura atteint une taille d’environ 8,5 mm, soit
après environ trois jours de croissance, un phénomène de déviation va se produire et ce
folliculeva augmenter rapidement en taille par rapport aux autres pour éventuellement
devenir dominant (Ginther et al. 1997). Les cellules de granulosa des follicules de plus de
9mm vont alors exprimer l’ARNm des récepteurs de l’hormone lutéinisante(LH) alors que
tous les follicules en croissance de plus petite taille expriment seulement l’ARNm des
récepteurs à la FSH (Xu et al. 1995). Le follicule dominant continuera de croitre quelques
jours suivant cette sélection grâce à la sécrétion pulsatile de LH (Ginther et al. 1998).
Toutefois, avant qu’un follicule ne devienne dominant, tous les follicules en croissance
dans une même vague et de taille supérieure à 5 mm ont la capacité de devenir dominant.
Ainsi, lorsque le plus gros follicule de la vague est retiré peu de temps avant de devenir
dominant, le deuxième plus gros follicule de la vague va prendre sa place et deviendra ainsi
le nouveau follicule dominant (Gibbons et al. 1997). En absence du pic de LH essentiel à
l’ovulation, le follicule dominant commencera à régresser et une nouvelle cohorte de
follicules sera recrutée pour former la vague folliculaire suivante. Une périoded’environ 42
jours est requise pour passer d’un follicule antral à un follicule préovulatoire, soit
l’équivalent de deux cycles œstraux.
5
Figure 1-2 Dynamique folliculaire à deux ou trois vagues (Adams et al. 2008)
Les follicules dominants et subordonnés sont indiqués par des cercles clairs (viables) ou
ombragés (atrétiques). Une augmentation de la concentration de FSH en circulation (ligne
épaisse) précède l’émergence de chaque vague. L’augmentation de la concentration de LH
en circulation (ligne mince) précède l’ovulation. Figure reproduite avec la permission de
Elsevier © 2008.
1.1.3.3 Ovulation
C’est seulement au cours de la vague folliculaire ovulatoire, deuxième ou troisième vague
selon le cas, que le follicule dominant complète sa croissance pour atteindre plus de 14 mm
diamètre. La taille du follicule préovulatoire chez le bovin est parmi les plus élevées avec
6
une dimension de 16 à 22 mm (Hunter et al. 2004). L’ovocyte sera libéré du follicule
ovulatoire de 29 à 31 heures suite au pic de LH (Thibault and Levasseur 2001).
1.2
1.2.1
La maturation de l’ovocyte
La reprise de la méiose
La méiose, à la différence de la mitose, est constituée de deux divisions cellulaires
successives, mais d’une seule phase de réplication de l’ADN, ce qui mènera éventuellement
à l’obtention de cellules contenant seulement la moitié du bagage génétique. Les ovocytes
formés lors du développement fœtal sont bloqués au stade de vésicule germinale, en
prophase I de la première division méiotique, et sont en attente du signal qui leur permettra
de reprendre la méiose et ainsi débuter leur maturation. Ainsi, toute une mécanique
finement régulée doit donc être mise en place afind’assurer une série d’évènements qui
permettra la reprise de la méiose au moment opportun.
Le premier signe visible de la reprise de la méiose correspondà la rupture de la vésicule
germinale (GVBD, germinal vesicle breakdown). Chez le bovin, cette étape se produit
environ 4 et 8 heures après le pic de LH (Kruip et al. 1983), alors que dans un contexte de
superovulation, il faut plutôt compter de 6 à 9 heures (Hyttel et al. 1986). En utilisant un
système de maturation in vitro, il a été possible de déterminer avec précision le temps passé
à chacun des stades de la méiose. Ainsi, le stade GVBD peut être observé environ 6,5
heures après le début de la maturation in vitro (Sirard et al. 1989).
Bien que le pic de LH retrouvé plusieurs heures avant l’ovulation constitue le signal
universel permettant à l’ovocyte de reprendre sa méiose, les ovocytes de mammifères
peuvent reprendre leur méiose de façon spontanée lorsqu’ils sont retirés du follicule
(Edwards 1965; Pincus and Enzmann 1935). Il est donc évident que des facteurs
interagissent au niveau de l’ovocyte, à l’intérieur même du follicule pour maintenir ce
blocage.
7
1.2.1.1 L’AMPc
Parmi les joueurs importants dans la régulation de la reprise de la méiose, on retrouve un
second messager, l’adénosine monophosphate cyclique ou AMP cyclique (AMPc).
L’AMPc est synthétisé de façon continue dans l’ovocyte par l’adénylate cyclase (AC), en
plus d’être transféré des cellules du cumulus vers l’ovocyte via les jonctions
communicantes, ce qui permet de maintenir un niveau élevé d’AMPc dans
l’ovocyte(Bilodeau-Goeseels et al. 1993). C’est d’ailleurs ce niveau élevé d’AMPc
maintenu dans l’ovocyte qui est responsable de l’arrêt méiotique en prophase I. Des
expériences chez le bovin ont démontré que la diminution de l’AMPc est une première
étape obligatoire pour permettre la reprise de la méiose, ce qui a pour effet de diminuer
l’activité de la protéine kinase A (PKA) dépendante de l’AMPc. Ainsi, les inhibiteurs de
PKA peuvent renverser l’augmentation d’AMPc causée par l’inhibition de la
phosphodiestérase 3 (PDE3), une enzyme impliquée dans la dégradation de l’AMPc
(Bilodeau-Goeseels 2003) (Figure 1-3).
Bien que la mécanique de reprise de la méiose soit relativement bien comprise chez les
espèces modèles comme la souris, il semblerait que le contrôle de la reprise de la méiose
soit quelque peu différent chez le bovin comparativement à ce qui a été observé chez la
souris. Différentes expériences ont été réalisées pour tenter de bloquer la reprise de la
méiose des ovocytes bovins une fois retirés du follicule, soit en augmentant le niveau
d’AMPc dans l’ovocyte en activant l’adénylate cyclase avec la Forskoline (FSK) (Homa
1988), ou alors en inhibant l’activité des phosphodiestérases (PDE) avec l’IBMX (3isobutyl-1-methylxanthine, inhibiteur non spécifique des PDE) (Sirard and First 1988).
Toutefois, bien que ces produits bloquent complètement la reprise de la méiose chez la
souris, l’inhibition est seulement partielle ou transitoire chez le bovin. Une autre raison de
l’inefficacité de l’IBMX à bloquer la reprise de la méiose concerne le fait que la PDE8,
abondante dans les cellules folliculaires chez le bovin, y est insensible(Sasseville et al.
2009). Il a donc été proposé que d’autres mécanismes puissent agir en synergie avec
l’AMPc, par exemple la voie de signalisation de l’oxyde nitreux (NO)/GMPc, pour avoir un
impact dans le contrôle de la reprise de la méiose (Bilodeau-Goeseels 2011).
8
Figure 1-3 Interactions entre les cellules folliculaires et l’ovocyte en lien avec la
reprise de la méiose (Gilchrist 2011)
La reprise de la méiose est un processus finement régulé qui implique plusieurs acteurs qui
vont interagir entre l’ovocyte, les cellules de la granulosa et du cumulus. Copyright CSIRO
Publishing © 2011.
1.2.1.2 GMPc
La guanosine monophosphate cyclique (GMPc) a été identifiée chez la souris comme étant
impliquée dans le maintient du haut niveau d’AMPc dans l’ovocyte. La diffusion du GMPc,
des cellules de granulosajusqu’à l’ovocyte à travers les jonctions communicantes, permet
d’inhibiter la phosphodiestérase 3A (PDE3A) (Figure 1-3) et empêche ainsi la reprise de la
méiose(Norris et al. 2009). La GMPc est synthétisée par les guanylate cyclase solubles,
suite à leur activation par l’oxyde nitreux. Toutefois, bien que la guanylate cyclase peut être
activée dans les ovocytes bovins via la modulation de la voie de signalisation du NO,
l’accumulation de GMPc ne semble pas être directement impliquée dans le contrôle de la
reprise de la méiose chez le bovin (Bilodeau-Goeseels 2007).
9
Bien que toute la mécanique du contrôle de la reprise de la méiose ne soit pas encore
entièrement élucidée chez le bovin (Bilodeau-Goeseels 2011), les connaissances acquises
jusqu’à maintenant sont maintenant utilisées en vue d’améliorer le potentiel de
développement de l’ovocyte par un contrôle adéquat de la reprise de la méiose (BilodeauGoeseels 2012).
1.2.2
Second arrêt méiotique
1.2.2.1 Facteur de promotion de la maturation (MPF)
Chez le bovin comme chez plusieurs autres espèces, la maturation ovocytaire se produit en
réponse à l’activation du facteur de promotion de la maturation (MPF) suite à la diminution
de l’activité PKA (revu dans (Han and Conti 2006)). Le MPF, qui correspond également à
l’abréviation Facteur de Promotion de la phaseM, est une kinase de la division cellulaire
impliqué dans la transition G2/M lors du cycle cellulaire chez tous les eucaryotes.
L’activation du MPF va donc initier toute la cascade d’évènements, allant de la rupture de
la vésicule germinale, la condensation des chromosomes, la formation du fuseau méiotique,
l’entrée en méiose et l’arrêt en métaphase II.
L’activation du MPF est un processus en deux étapes. Tout d’abord, la formation du
complexe protéique qui est composé de deux sous-unités soit une sous-unité catalytique
(p34cdc2 ou CDK1 à l’état phosphorylé) et une sous-unité régulatrice (Cycline B1) pour
former ainsi le pré-MPF(Figure 1-4). Par la suite, le complexe doit être activé par la
déphosphorylation de certains résidus sur la sous-unité catalytique (CDK1). Cette étape est
contrôlée soit par la protéine cdc25 qui permet la déphosphorylation de CDK1 ou par wee1
qui joue le rôle inverse, en phosphorylant la protéine. Une fois le complexe activé, l’activité
du MPF peut être amplifié directement par l’inactivation de wee1 et l’activation de cdc25
ou indirectement via la protéine phosphatase 2A (PP2A) (Thibault and Levasseur 2001).
Dans l’ovocyte bovin, l’activation du MPF implique donc une phase de synthèse protéique
puisque la cycline B1 n’est pas présente dans les ovocytes immatures, ce qui veut donc dire
que la synthèse de la protéine doit prendre place durant la maturation (Levesque and Sirard
1996). D’ailleurs, l’utilisation d’un inhibiteur de synthèse protéique tel que la
10
cycloheximide peut bloquer la reprise de la méiose durant 24 heures et ce, de façon
réversible chez l’ovocyte bovin (Tatemoto et al. 1994).
Environ 24 heures après le début de la période de maturation, l’ovocyte aura atteint le stade
de métaphase II. L’activité élevée du MPF permettra le second arrêt méiotique (Hashimoto
and Kishimoto 1988), où l’ovocyte maturesera en attente du signal qui lui permettra
d’activer son développement.
Figure 1-4 Facteur de promotion de la maturation (MPF) (Thibault and Levasseur
2001)
L’activation du MPF requiert la formation complexe (p34cdc2 et Cycline B1) et la
déphosphorylation de p34cdc2. Cdc25 et Wee1 permettent de moduler l’activation du
complexe.Copyright Editions Quae © 2001.
1.2.2.2 Facteur cytostatique (CSF)
Le facteur cytostatique (CSF) est composé d’au moins trois protéines, soit c-mos (Sagata et
al. 1989), MAPk (Haccard et al. 1993) et p90Rsk(Bhatt and Ferrell 1999; Gross et al. 1999)
et joue un rôle essentiel dans la stabilisation du MPF. La protéine produite par le gène c-
11
mos occupe d’ailleurs un rôle central dans le processus de maturation, de la progression
jusqu’en métaphase II, mais également dans le deuxième arrêt méiotique et constitue une
composante essentielle du facteur cytostatique. Ce facteur a d’ailleurs été mis en évidence
lorsque des extraits de cytoplasme d’ovules matures de Xénope ont permis l’arrêt mitotique
une fois injectés dans des embryons en pleine division (Masui and Markert 1971). Chez les
souris où le gène c-mos a été supprimé, les ovocytes peuvent atteindre le stade de MII de
façon
normale,
mais
sont
incapables
de
s’arrêter
pour
le
deuxième
arrêt
méiotique.L’absence de l’une des composantes du CSF aura comme conséquence
l’activation des ovocytes matures, sans même qu’il y ait eu fécondation. L’activation
parthénogénétique des ovocytes à l’intérieur de l’ovaire provoquera la formation de
tumeurs ovariennes (Hashimoto et al. 1994). Chez le bovin, la protéine c-mos est présente
et synthétisée de façon active dans les ovules matures et il semblerait que les ovocytes qui
ne sont pas fécondés à temps perdent la capacité de synthétiser la protéine (Wu et al. 1997).
Ces résultats démontrent l’importance d’une régulation appropriée lors de la reprise de la
méiose, mais également lors de l’arrêt méiotique.
1.3
La fécondation
Le signal physiologique permettant la chute du MPF et la destruction du CSF correspond à
la fusion entre le spermatozoïde et l’ovocyte.Une augmentation de la concentration du
calcium intracellulaire sera initiée suite à l’entrée du spermatozoïde et sera suivi de
multiples phases d’oscillations du calciumà l’intérieur de l’ovocyte (Lawrence et al. 1997).
Il a été démontré que l’apport d’un facteur par le spermatozoïdedans l’ovule, soit la
phospholipase C (PLC zeta), va permettre la synthèse de l’inositol-1,4,5-triphosphate
(InsP3) pour ensuite provoquer le relâchement du calcium présent dans le réticulum
endoplasmique (Saunders et al. 2002). D’ailleurs, la micro-injection de l’ARN
complémentaire (cRNA) de PLCzeta dans les ovocytes bovins va provoquer l’activation du
développement, en plus d’induire les oscillations de calcium normalement retrouvées lors
de la fécondation(Ross et al. 2008). L’augmentation importante de la concentration de
calcium a été observée à partir du point d’entrée du spermatozoïde pour ensuite se propager
au reste de l’ovule. Cette « vague » constitue une étape importante pour empêcher la
12
polyspermie via l’exocytose des granules corticaux qui auront pour rôle de modifier la zone
pellucide pour ainsi la rendre imperméable aux autres spermatozoïdes (Ozil 1998).
Toutefois, PLCzeta ne serait pas le seul facteur responsable de l’activation délivré par le
spermatozoïde lors de la fécondation. En effet, une autre protéine spécifique au
spermatozoïde (sperm protein PAWP) a été identifiée comme étant un facteur obligatoire
pour permettre la reprise de la méiose ainsi que la formation des pro-noyaux (Wu et al.
2007).
Il existe cependant certaines exceptions concernant les signaux requis pour permettre
l’activation d’un ovocyte et ce, sans la contribution du spermatozoïde. Différents produits
chimiques (éthanol, ionophore de calcium, chlorure de strontium), la stimulation électrique
ou diverses manipulations mécaniques peuvent être utilisés pour provoquer le relâchement
du calcium intracellulaire en plus de permettre l’entrée du calcium extra-cellulaire, recréant
ainsi la vague de calcium dans l’ovocyte. Combinés à certains inhibiteurs de synthèse
protéique (cycloheximide) ou des inhibiteurs de phosphorylation (6-DMAP) dans le but
d’inhiber le MPF, ce type de protocole permet d’obtenir des taux élevés d’activation, mais
également le développement jusqu’au stade de blastocyste chez le bovin ainsi que chez
plusieurs autres espèces(Alberio et al. 2001). Toutefois, le développement de ces embryons
ne sera plus possible passé un certain stade, variable selon les espèces, en raison d’un
mécanisme qui contrôle l’expression de certains gènes en fonction de leur origine
(maternelle ou paternelle) appelé empreinte parentale (Kono 2006).
1.4
Qualité des ovocytes
La notion de qualité ovocytaire prend tout son sens chez l’humain, où environ 20% des
ovules fécondés in vivo vont permettre la production d’un embryon viable (Macklon et al.
2002). Chez le bovin, environ 30% des ovocytes récupérés sur des ovaires d’abattoir vont
pouvoir atteindre le stade de blastocyste une fois maturés et fécondés in vitro (Lonergan
and Fair 2008; Trounson et al. 1994) et une proportion encore plus faible va permettre
d’induire une gestation suite au transfert de l’embryon (Hasler 1998). Ces données
illustrent clairement la grande variabilité dans la qualité des ovules. La maturation in vitro a
13
longtemps été pointée du doigt comme étant un facteur impliqué dans le faible potentiel de
développement in vitro comparativement à la maturation in vivo(Greve et al. 1987;
Leibfried-Rutledge et al. 1987; Marquant-Le Guienne et al. 1989; Van de Leemput et al.
1999). Toutefois, il a été démontré que la qualité intrinsèque de l’ovocyte était le principal
élément responsable du potentiel de développement futur de l’embryon, alors que les
milieux de culture in vitro avaient plutôt un rôle dans la qualité de l’embryon produit
(Rizos et al. 2002). D’ailleurs, peu importe le système de production utilisé (in vivo/in
vitro) ou les méthodes de récupération des ovocytes (superstimulation, ovaires d’abattoir ou
ponction transvaginale), l’ovocyte apparait comme l’élément critique dans l’aptitude à
produire un embryon (Merton et al. 2003) (Figure 1-5).
Figure 1-5 Potentiel de développement en lien avec l’origine de l’ovocyte (Merton et
al. 2003)
L’ovocyte représente un facteur important en lien avec le potentiel à produire un embryon
et ce, peu importe le système de production (in vivo/in vitro). Figure reproduite avec la
permission de Elsevier © 2003.
Il a été démontré que la phase finale de croissance de l’ovocyte était cruciale dans
l’acquisition du potentiel au développement et ce, autant chez la souris (Sorensen and
Wassarman 1976), chez l’humain (Gougeon 1986) que chez le bovin (Sirard et al. 2006). Il
a donc été suggéré que l’absence du développement préovulatoire normal chez les ovocytes
maturés in vitro pourrait expliquer le potentiel de développement réduit de ces ovocytes
14
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Il a ensuite été suggéré que l’accumulation, au cours de la croissance de l’ovocyte, de
certains composants du MPF (CDK1 (p34cdc2) et Cycline B1) pouvait être responsable de
l’acquisition du potentiel à reprendre la méiose (Chesnel and Eppig 1995b).D’ailleurs,
l’accumulation de CDK1(Mitra and Schultz 1996)comparativement à la quantité de cycline
B1 (CCNB1) pourrait être importante puisque cette dernière est en excès par rapport à
CDK1 (Kanatsu-Shinohara et al. 2000). Toutefois, la compétence méiotique ne serait pas
seulement une question de quantité absolue de chacun des composants du MPF (CDK1,
CCNB1, cdc25, wee1, etc.), mais également une question d’interaction entre les membres
du complexe et ce, principalement au niveau post traductionnel (Mitra and Schultz 1996).
De plus, la quantité de transcrits associés à ces protéines à également été mesurée et il
semblerait que la différence de compétence serait également liée à une meilleure capacité
de synthèse protéique de CDK1 (3X plus élevé dans les ovocytes compétents par rapport
aux incompétents) puisque les niveaux des transcrits sont relativement similaires dans
chacun des cas (Mitra and Schultz 1996).
Chez le bovin, bien que de grandesquantités d’ARNm de cycline B1 soientdétectées dans
les ovocytes au stade GV, la protéine n’a pu être détectée(Robert et al. 2002). Cette
dernière apparait plutôt quelques heures après le début de la maturation (Levesque and
Sirard 1996). En ce qui concerne le deuxième membre du complexe MPF, CDK1, de
grandes quantités de protéine ont pu être détectées dans les ovocytes au stade GV provenant
de follicules de 3-5 mm(Robert et al. 2002).
1.4.1.2 Compétence de l’ovocyte à se diviser après la fécondation
Une fois que l’ovocyte a atteint le stade de métaphase II (MII), celui-ci doit être en mesure
de compléter sa dernière division méiotique en expulsant le deuxième globule polaire et
débuter le développement une fois qu’il aura reçu le signal d’activation provenant du
spermatozoïde. Il semblerait que cette compétence soit automatique et innée chez les
ovocytes ayant atteint le stade de métaphase II puisque ceux-ci ont la capacité de se
développer sans fécondation (parthénogenèse) suite à divers stimuli (électrique, chimique,
mécanique) (Ware et al. 1989). Toutefois, même en utilisant des protocoles d’activation
16
optimisés pour permettre le développement jusqu’au stade de blastocyste, les taux de
développement dépassent rarement ceux obtenus lors de la fécondation(Wang et al. 2008).
Suite à la fécondation, l’ovocyte doit ensuite avoir la capacité d’empêcher l’entrée de
spermatozoïde surnuméraire par l’exocytose des granules corticaux et la modification de sa
zone pellucide. Chez la souris, la capacité à bloquer la polyspermieest acquise au cours de
la maturation (Ducibella and Buetow 1994; Mehlmann and Kline 1994). Des résultats
similaires ont aussi été observés chez le bovin, où le relâchement incomplet du contenu des
granules corticaux provoque de la polyspermie chez les ovocytes immatures (Wang et al.
1997).
1.4.1.3 Capacité à supporter le développement jusqu’au stade de blastocyte
La capacité de l’ovocyte à supporter le développement jusqu’au stade de blastocyste
constitue un des points de référence les plus utilisés pour caractériser la compétence de
l’ovocyte, autant dans un système de culture in vitro (Lonergan and Fair 2008) que pour
définir l’efficacité des protocoles de stimulation in vivo(Blondin et al. 2012). Il faut
toutefois être prudent dans l’utilisation de ce terme, car ce dernier fait souvent référence au
potentiel des milieux de culture in vitro à supporter le développement, plutôt qu’à la qualité
intrinsèque de l’ovocyte (Duranthon and Renard 2001).
Puisque chez le bovin, les premiers cycles cellulaires se déroulent en absence de
transcription et que le génome du jeune embryon sera activé au stade 8-16 cellules(Barnes
and First 1991), le bon déroulement de cette période critique doit être assuré par
l’accumulation des transcrits au cours de l’ovogenèse. Pour pouvoir se développer au-delà
de ce stade, le jeune embryon devra nécessairement être en mesure d’activer son propre
génome. D’ailleurs, l’arrêt du développement fréquemment observé au stade 8-16 cellules
(Memili and First 2000) suggère que certains facteurs maternels (transcrits et protéines)
n’ont pu être accumulé à temps ou en quantité suffisante dans l’ovocyte (De Sousa et al.
1998a; Sirard 2012).
17
1.5
Modèles d’études de la qualité ovocytaire
Plusieurs modèles ont été utilisés afin de mieux comprendre ce qui caractérise les ovocytes
compétents. Ainsi, différentes observations permettent de sélectionner les ovocytes en
fonction de leur potentiel à se développer, avec une précision variable selon les cas.
1.5.1
Aspect morphologique du complexe cumulus ovocytes
Une fois retiré du follicule, les ovocytes sont généralement entourés de cellules du cumulus
et une grande variabilité dans l’aspect morphologique du complexe cumulus ovocyte peut
être observée. Ainsi, un système de classification basé sur la morphologie des cellules du
cumulus (nombre de couches de cellules et organisation) de même que l’aspect du
cytoplasme de l’ovocyte a été mis en place (Blondin and Sirard 1995).Suite à la récolte des
complexes cumulus ovocytes (COCs) bovins obtenus à partir d’ovaires d’abattoir, les
COCs étaient classés selon un système à six différentes classes. Les classes 1 et 2
représentent les COCs avec un cytoplasme homogène avec plusieurs couches de cellules du
cumulus compactes. Les classes 3 à 6 caractérisent les ovocytes ayant un cytoplasme
granuleux et un cumulus incomplet ou une expansion progressive. Dans un système de
culture individuel, les ovocytes de classe 3, c’est-à-dire ceux qui présententun début
d’expansion dans les couches externes de cumulus et la présence de quelques granulations
dans le cytoplasme, ont réussi à se développer au-delà du stade 16 cellules de façon
significativement plus élevéepar rapport aux autres classes(Blondin and Sirard 1995). Ces
travaux ont donc permis de démontrer qu’un début atrésie, tel qu’observé dans la classe 3,
pouvait simuler l’environnement préovulatoire et ainsi avoir un effet bénéfique sur la
compétence de l’ovocyte.
1.5.2
Impact de la taille du follicule
La taille du follicule d’où provient l’ovocyte a été identifiée relativement tôt comme étant
un facteur qui influence le potentiel à produire un embryon. Il a été démontré que les
ovocytes provenant de gros follicules avaient davantage de chances d’atteindre le stade de
blastocyste comparativement à ceux provenant de petits follicules. Ainsi, les ovocytes
provenant de follicules de taille inférieure à 2 mm ont un potentiel réduit à reprendre la
méiose et à être fécondé (Pavlok et al. 1992). Les ovocytes provenant de follicules de taille
18
inférieure à 3 mm ne se développent pas au-delà du stade de 16 cellules, alors que les
ovocytes provenant des follicules de taille supérieure à 3 mm ont pu se développer jusqu’au
stade de morula (Blondin and Sirard 1995). La corrélation positive entre la taille des
follicules et la qualité ovocytaire a également été confirmée par plusieurs autres groupes de
recherche (Hagemann et al. 1999; Lequarre et al. 2005; Lonergan et al. 1994; Machatkova
et al. 2004).
De façon générale, en utilisant des ovaires qui proviennent de l’abattoir, une augmentation
de l’ordre de 20% du taux de blastocyste peut être obtenue lorsque des ovocytes provenant
de petits follicules (<4 mm) sont utilisés comparativement à ceux de tailles plus élevées (>6
mm) (Lequarre et al. 2005). Néanmoins, peu importe les études, il devient difficile de
dépasser 40% de taux d’embryon, même en sélectionnant uniquement les gros follicules.
D’un point de vue physiologique, ces résultats ne sont pas surprenants considérant que des
follicules de 2 à 8 mm ont été utilisés dans ces différentes études, alors que le stade de
dominance est acquis à 8,5 mm (Ginther et al. 1997) et que la taille d’un follicule
préovulatoire chez le bovin varie entre 16 à 22 mm (Hunter et al. 2004). La variation
observée dans les taux de développement peut être expliquée par la grande hétérogénéité
retrouvée avec le matériel provenant de l’abattoir, que ce soit l’absence de contrôle quant
au stade du cycle oestral de l’animal en question, son âge ou de son statut gestationnel
(Vassena et al. 2003).
1.5.3
Impact de l’aspect de l’ovaire
Certains chercheurs ont également fait un lien entre l’aspect morphologique de l’ovaire et
la compétence au développement. Les ovaires étaient regroupés en fonction du nombre de
follicules présents sur l’ovaire, soit la présence ou non de plus de 10 follicules de 2 à 5 mm
de diamètre. Ainsi, un meilleur potentiel à reprendre la méiose et à supporter le
développement jusqu’au stade de blastocyste a été observé lorsque les ovaires avec plus de
10 follicules ont été utilisés pour la maturation et la fécondation in vitro (Gandolfi et al.
1997). Dans cette situation, à peine 5% des ovocytes provenant d’ovaires avec très peu de
follicules vont atteindre le stade de blastocyste. Ces résultats ont également été confirmés
dans d’autres études (Modina et al. 2007) et certains ont suggéré que le système de l’oxyde
19
nitreux, impliqué entre-autres dans la vascularisation du follicule, pouvait être impliqué
dans la faible qualité des ovocytes provenant d’ovaires avec un faible nombre de follicules
(Tessaro et al. 2011).
1.5.4
Impact du stade folliculaire
En considérant que des follicules de taille similaire pouvaient être à des stades
physiologiques très différents, le stade de développement folliculaire a également été étudié
en lien avec le potentiel de l’ovocyte à produire un embryon. Ainsi, les ovocytes récupérés
au début de la phase de croissance (jour 2 et jour 10 de la première et deuxième vague
folliculaire) présentent un meilleur potentiel de développement comparativement à ceux
récupérés en phase de dominance (jour 7 et jour 15), où un follicule dominant était visible
depuis au moins 3 jours. Ces résultats illustrent bien l’impact négatif que peut avoir le
follicule dominant sur les follicules subordonnés, autant en empêchant leur croissance
qu’en induisant un plus grand niveau d’atrésie dans ces follicules (Hagemann 1999).
En récupérant les ovocytes à trois moments différents au cours de la vague folliculaire, il a
été possible de définir avec davantage de précision le moment optimal de récupération des
ovocytes. Ainsi, une plus grande proportion d’ovocytes récupérés cinq jours suivant
l’émergence de la vague folliculaire, donc en phase de sélection, ont pu se développer
jusqu’au stade de blastocyste, comparativement à ceux récupérés en phase de croissance
(deux jours suivants l’émergence de la vague) et ceux en phase de régression (sept jours
après l’émergence)(Vassena et al. 2003). La phase de sélection correspond au moment où
les follicules sont soumis aux premiers signes d’atrésie, mais sans avoir l’effet néfaste
retrouvé, une fois la dominance établit. Dans ce genre de situation, l’amélioration du taux
de développement est généralement de l’ordre de 10% lorsque les ovocytes sont récupérés
au bon stade du développement folliculaire (Machatkova et al. 1996; Machatkova et al.
2004;Vassena et al. 2003).
20
1.5.5
Impact de la cinétique du développement
1.5.5.1 Expulsion du premier globule polaire
Parmi les facteurs cinétiques qui ont été observés en lien avec le potentiel à produire un
embryon, la vitesse d’extrusion du premier globule polaire au cours de la maturation in
vitro a été analysée. Sans surprise, les ovocytes qui avaient expulsé leur premier globule
polaire entre 16 et 20 heures après le début de la maturation ont démontré un meilleur
potentiel à atteindre le stade de blastocyste comparativement à ceux qui n’avaient pas
expulsé leur globule polaire (van der Westerlaken et al. 1994). En effet, les ovocytes
incapables de reprendre leur méiose n’ont certainement pas tout ce qu’il faut pour supporter
le développement du jeune embryon.
1.5.5.2 Stade 2-cellules
La grande variation dans la cinétique de développement des embryons produits in vitro a
également motivé plusieurs chercheurs à étudier le lien entre la vitesse du premier cycle
cellulaire suivant la fécondation et l’aptitude à atteindre le stade de blastocyste. En effet, il
semblerait que les embryons les plus rapides à se diviser aient davantage de chances
d’atteindre le stade de blastocyste (Grisart et al. 1994; Plante et al. 1994; Van Soom et al.
1992;Yadav et al. 1993). D’ailleurs, en sélectionnant les embryons les plus rapides (premier
clivage 27 heures après la fécondation), des taux de développement jusqu’au stade de
blastocyste aussi élevé que 60% ont pu être obtenus. Cette proportion diminue toutefois à
moins de 20% si plus de 36 heures sont requises avant la première division cellulaire
(Lonergan et al. 1999). D’ailleurs, il semblerait que les embryons produits in vitro qui se
divisent plus rapidement seraient plus similaires aux embryons produits in vivo(Holm et al.
2002). Bien que les mécanismes qui causent cette variation dans la cinétique de
développementne soient pas encore connus, il a été suggéré que le milieu de culture, la
présence d’aberrations chromosomiques, le sexe de l’embryon, des facteurs intrinsèques à
l’ovocyte, au spermatozoïde ou les deux pourraient être impliqués dans la vitesse à
atteindre le stade de 2-cellules (Lechniak et al. 2008).
21
1.5.5.3 Cinétique des cycles cellulaires suivants
En analysant le développement des embryons en time-lapse, la cinétique des cycles
cellulaires suivants a également été associée à un meilleur potentiel de développement. En
effet, plus rapidement l’embryon se divise pour atteindre le stade 2-cellules; 3-4-cellules et
5-8-cellules, plus élevées sont ses chances d’atteindre le stade de blastocyste (Grisart et al.
1994; Holm et al. 1998).
Ces données, combinées à la vitesse du premier clivage, suggèrent donc que les embryons
les plus rapides, donc les plus compétents, proviennent d’ovocytes qui ont été en mesure de
reprendre leur méiose rapidement, de décondenser l’ADN du spermatozoïde de façon plus
efficace et dont l’accumulation préalable d’ARNm maternels s’est produits de façon
optimale (revu dans (Lechniak et al. 2008)).
1.5.6
BCB
Il existe également des méthodes de sélection pouvant être utilisées avant la mise en
maturation des ovocytes bovins. L’une d’elles implique la détection de l’activité glucose-6phosphate déshydrogénase (G6DPH) via le marquage au bleu crésyl brillant (BCB). Il a été
démontré que cette enzyme était active dans les ovocytes en croissance, alors que l’activité
diminue une fois la croissance des ovocytes terminée. Le marquage au BCB est basé sur le
fait qu’en présence d’une activité enzymatique, le colorant initialement bleu deviendra
incolore (Opiela and Katska-Ksiazkiewicz 2013). Ainsi, il a été rapporté qu’après le
marquage au BCB, les ovocytes qui présentaient un marquage positif au BCB (BCB+ :
faible activité G6DPH) avaient un meilleur potentiel à atteindre le stade de blastocyste par
rapport aux BCB- (Alm et al. 2005; Pujol et al. 2004). Bien qu’une différence significative
dans les taux de développement a été observée entre les ovocytes BCB+ (30% de taux de
blastocyste) et BCB- (3% de taux de blastocyste), certains n’ont pu trouver de différences
en comparant des ovocytes BCB+ avec des ovocytes témoins non marqués (Opiela et al.
2008). Même si l’amélioration du taux de développement en chiffre absolu semble
importante, il faut garder en tête que les ovocytes BCB- correspondent à des ovocytes en
croissance. D’ailleurs, ceux-ci présentent un potentiel réduit pour reprendre leur méiose,
22
puisque moins de 60% des ovocytes BCB- peuvent atteindre le stade de MII (Alm et al.
2005).
1.5.7
Migration dielectrophorétique
La vitesse de migration dielectrophorétique des ovocytes matures a également permis
d’identifier des différences dans le potentiel de développement jusqu’au stade de
blastocyste (Dessie et al. 2007). Bien que le lien entre la vitesse de migration et le potentiel
de développement n’est pas encore bien compris, cette étude s’est basée sur le fait qu’il soit
possible de distinguer des cellules viables et non viables à l’aide d’un courant électrique
(Huang et al. 1992). Il a donc été démontré que les ovulesqui migrent très lentement ont
significativement moins de chance d’atteindre le stade de blastocyste comparativement à
ceux qui migrent rapidement. Il faut toutefois noter qu’aucune différence n’a été observée
entre ceux qui migrent très rapidement et des ovocytes témoins. Encore une fois,
l’amélioration potentielle du taux de développement est de l’ordre de 10% entre les
ovocytes qui migrent très lentement par rapport à ceux qui migrent rapidement. Bref, un
peu comme le marquage au BCB, ce type de méthode réussit plutôt à exclure les moins
bons ovocytes, mais sans vraiment pouvoir sélectionner les ovocytes au meilleur potentiel.
1.5.8
Influence de l’âge maternel
L’âge maternel a été reconnu comme un facteur qui affecte la fertilité, autant chez la femme
(Klein and Sauer 2001) que chez le bovin (Erickson et al. 1976). D’ailleurs, cette réduction
de la fertilité est vraisemblablement due à une diminution de la qualité des ovocytes plutôt
qu’au potentiel réduit de l’endomètre à recevoir l’embryon. Pour illustrer ce point, des
ovocytes fécondés et implantés dans des receveuses plus âgées ont plus de chance de
s’implanter si la donneuse est âgée de moins de 35 ans (Navot et al. 1991).
Chez le bovin, il a été démontré que l’âge maternel et le lien avec la fertilité avaient
plusieurs similarités avec ce que l’on retrouve chez l’humain à l’approche de la ménopause
(Malhi et al. 2005). Dans cette étude, la comparaison effectuée était entre des vaches de 1314 ans et leur fille âgée de 1 à 4 ans. Ainsi, les vaches plus âgées présentaient une plus
grande concentration de FSH en circulation et un plus petit nombre de follicules recrutés au
23
cours d’une vague folliculaire. Considérant que le bovin représente un bon modèle pour
étudier la reproduction chez l’humain en raison des similarités au niveau de la dynamique
de développement folliculaire (Adams and Pierson 1995), le modèle décrit par (Malhi et al.
2005) s’est avéré très utile dans l’étude de la qualité ovocytaire (Malhi et al. 2007). Dans
leur étude, des vaches âgées de 13 à 16 ans ainsi que leurs filles de 3 à 6 ans ont reçu des
traitements de superovulation et les embryons récupérés sept jours plus tard ont été
implantés dans un groupe de jeunes receveuses. Aucune différence n’a été observée dans le
nombre d’ovulations entre les deux groupes, alors qu’un nombre significativement réduit
d’embryons ont été récupérés chez les vaches plus âgées, en plus d’une plus grande
proportion d’ovocytes non fécondés. Toutefois, la survie des embryons suite au transfert
ainsi que la proportion de naissance à terme n’était pas différente entre les deux groupes
(Malhi et al. 2007).
Une étude rétrospective avait d’ailleurs démontré que chez la vache, l’âge maternel avancé
avait un effet négatif sur la production d’embryons lors de traitement de superovulation, où
une diminution des taux de fécondation et du nombre d’embryons transférables avait été
observée (Lerner et al. 1986). Des résultats similaires ont aussi été observés chez la souris,
où les ovocytes récupérés peu de temps après l’ovulation à partir de souris âgées avaient
des taux de fécondation in vitro plus faibles (Fujino et al. 1996). Toutefois, comme les
problèmes en lien avec la qualité ovocytaire et l’âge maternel semblent survenir dès la
fécondation, cela suggère l’implication de mécanismes différents comparativement à la
faible qualité observée avec les ovocytes provenant de petits follicules par exemple.
1.5.9
Aneuploïdie
Chez l’humain, la diminution de la qualité des ovocytes observée avec l’âge maternel
avancé semble être liée avec une plus grande proportion d’aneuploïdie dans les ovocytes
(revu dans (Fragouli et al. 2011b)). D’ailleurs, l’incidence d’un nombre anormal de
chromosomes dans les ovocytes est faible (moins de 5%)chez les femmes âgées de moins
de 25 ans, cette proportion va augmenter à 10-25% dans la trentaine pour dépasser les 50%
une fois passée 40 ans (Fragouli et al. 2011b). Les problèmes d’aneuploïdie surviennent
principalement lors d’erreurs de la séparation des chromosomes lors de la première division
24
méiotique, mais les mécanismes exacts qui expliquent ce phénomène ne sont pas encore
bien compris. La ségrégation d’un chromosome vers le même pôle que son homologue
(non-disjonction) a longtemps été considérée comme le principal mécanisme menant
l’aneuploïdie (Hassold and Hunt 2001). Toutefois, il semblerait que la séparation
prématurée des chromatides sœurs ait plutôt un rôle prédominant comme mécanisme
menant à l’aneuploïdie observée dans les ovules humains(Gabriel et al. 2011). Il a
également été suggéré que l’aneuploïdie pourrait être un mécanisme de défense de l’ovaire,
qui serait mis en place dans le but de diminuer la qualité de l’ovocyte dans un contexte non
favorable à la gestation (Sirard 2011c). Plusieurs liens ont également été faits entre les
gonadotrophines et l’aneuploïdie (Dursun et al. 2006). Par exemple, une grande quantité de
FSH utiliséelors de la maturation in vitro affecterait l’alignement des chromosomes en
métaphase I chez la souris (Roberts et al. 2005).
1.5.10 Configuration de la chromatine
Au cours de sa croissance, la vésicule germinale de l’ovocyte va subir un remodelage
important de sa chromatine et différentes configurations ont été rapportées selon les
espèces. Chez la souris, l’aspect de la chromatine sera tout d’abord diffus et caractérisé par
l’absence d’un anneau autour du nucléole suivant un marquage de l’ADN (Mattson and
Albertini 1990). Cette configuration est donc appelée non-surrounded nucleolus (NSN) et
est caractéristique des ovocytes qui n’ont pas complété leur croissance (Figure 1-6).
L’aspect de la chromatine va ensuite évoluer vers un état plus compacté une fois la
croissance de l’ovocyte complétée. La configuration généralement retrouvée à ce moment
est caractérisée par un anneau entourant le nucléole, ou surrounded nucleolus (SN). Ce type
de configuration a été observé chez plusieurs espèces, incluant l’homme, le porc et le singe
(revu dans (Luciano and Lodde 2013)). Toutefois, d’autres types de configurations ont
également été rapportés chez le cheval et le bovin et où la chromatine va plutôt se
condenser de façon graduelle pour éventuellement former un arrangement compact
(Franciosi et al. 2012; Hinrichs 2010;Lodde et al. 2007). Chez le bovin, quatre niveaux
graduels de compaction ont été observés (GV0; GV1; GV2 et GV3, Figure 1-7) (Lodde et
al. 2007).
25
Figure 1-6 Configuration de la chromatine chez la souris (Bellone et al. 2009)
Deux configurations de la chromatine peuvent être observées chez la souris. A : nonsurrounded nucleolus (NSN), B : surrounded nucleolus (SN). Copyright © 2009,
BioScientifica Ltd. Society for Reproduction and Fertility.
La configuration de la chromatine a également été corrélée avec le potentiel de l’ovocyte à
reprendre la méiose et à supporter le développement jusqu’au stade de blastocyste (Luciano
and Lodde 2013; Tan et al. 2009). Chez la souris, une proportion réduite d’ovocytes NSN
parvient à reprendre la méiose (Debey et al. 1993) et aucun d’entre eux ne pourra supporter
le développement au-delà du stade 2-cellules, alors que les ovocytes SN peuvent atteindre
le stade de blastocyste (Zuccotti et al. 2002). Des résultats similaires ont aussi été observés
chez le bovin où les ovocytes qui présentent une chromatine diffuse (GV0 et GV1, Figure
1-6) ont très peu de chance d’atteindre le stade de blastocyste comparativement aux stades
stades où la chromatine devient plus compactée (GV2 et GV3) (Lodde et al. 2007).
26
Figure 1-7 Configuration de la chromatine chez le bovin (Lodde et al. 2007)
Images en champ clair et après un marquage fluorescent au Hoechst 33342. GV0 (A, A1),
GV1 (B, B1), GV2 (C, C1) et GV3 (D, D1). La flèche sur l’image en champ clair indique
l’enveloppe nucléaire. Barre d’échelle : 50 µm. Figure reproduite avec la permission de
Wiley-Liss © 2006.
1.5.11 Impact du traitement hormonal
Bien que dans la plupart des modèles discutés précédemment, des améliorations notables
dans la qualité des ovocytes en terme d’augmentation du taux de développement ont pu être
obtenues, les résultats sont encore loin de ce qui peut être obtenu dans un cycle naturel.
L’utilisation de multiples injections de FSH a été démontrée comme étant un moyen
efficace pour obtenir une grande population de follicules en croissance (Blondin et al. 1996;
Goodhand et al. 2000), mais sans pour autant obtenir davantage d’embryons en raison du
statut inapproprié de différenciation du follicule. Ce n’est que suite à l’amélioration des
traitements de stimulation ovarienne que la qualité des ovocytes récupérés, par ponction
27
transvaginale (PTV) en vue de réaliser la maturation et la fécondation in vitro, a pu être
améliorée pour atteindre un niveau pratiquement équivalent à ce qui peut être obtenu dans
un cycle naturel.
Ce n’est qu’en mimant la phase de dominance retrouvée normalement avant l’ovulation via
l’utilisation d’une période de « coasting » que les taux de succès ont pu être améliorés de
façon significative (Blondin et al. 2002). En effet, suivant les multiples injections de FSH
dans le but de permettre la croissance d’un grand nombre de follicules, une période sans
FSH appelé « coasting » permet la différenciation des follicules de façon similaire à ce qui
est retrouvée durant la phase de dominance. Ainsi, une période de 48 heures entre la
dernière injection de FSH et la récupération des ovocytes par PTV a donc permis d’obtenir
jusqu’à 80% d’embryons suivant la maturation et la fécondation in vitro(Blondin et al.
2002). Récemment, l’optimisation de ce protocole a permis d’identifier le moment idéal
pour récupérer les ovocytes de façon à maximiser la production d’embryons. Il a été
démontré qu’une période de coasting d’environ 54 heures représentait la période idéale afin
d’obtenir un niveau optimal de différenciation folliculaire (Nivet et al. 2012). Même si les
follicules continuent leur croissance lorsqu’une période de coasting plus longue est utilisée
(92 heures), passé un certain stade la qualité de l’ovocyte contenu dans le follicule va être
affectée. Cette étude a donc permis d’observer que la taille des follicules n’était pas
associée de façon linéaire avec le potentiel de développement de l’ovocyte (Nivet et al.
2012).
1.6
1.6.1
Activité transcriptionnelle dans l’ovocyte
Synthèse d’ARN
Puisque le développement du jeune embryon va dépendre principalement des ARNm et des
protéines synthétisées puis accumulées au cours de l’ovogenèse, la synthèse d’ARN au
cours de l’ovogenèse possède une importance cruciale.
L’activité transcriptionnelle a été étudiée au cours des différents stades de développement
de l’ovocyte. Chez la souris, cette activité est tout d’abord très intense dans les ovocytes en
croissance et va diminuer de façon importante lors de la formation du follicule antral
28
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importante (Jahn et al. 1976; Kaplan et al. 1982). Malgré cette diminution importante, la
synthèse d’ARN peut encore être détectée entre la fin de la croissance de l’ovocyte et la
reprise de la méiose (Brower et al. 1981; Rodman and Bachvarova 1976). En effet, l’arrêt
complet de la transcription va avoir lieu au stade GVDB (Wassarman and Letourneau
1976). Chez le bovin, la synthèse d’ARN peut être détectée jusqu’à une heure avant la
rupture de la vésicule germinale (Hunter and Moor 1987). Contrairement aux cellules
somatiques où l’arrêt de la transcription se produit lors de l’entrée en mitose (Gottesfeld
and Forbes 1997), la diminution de la transcription observée à la fin de la croissance des
ovocytes au stade GVsuggère que des mécanismes uniques aux ovocytes doivent donc être
mis en place pour permettre cette répression de la transcription (De La Fuente 2006).
1.6.2.1 Rôle de la chromatine dans la diminution de la transcription
Le changement de configuration de la chromatineobservé dans les ovocytes au cours de
leur croissance a longtemps été considéré comme le facteur responsable de la diminution de
la transcription. Toutefois, cela semble peu probable puisque des études chez la souris
déficiente pour le gène Nucleopasmin 2 (NPM2), bien que les ovocytes soientincapables de
faire la transition d’une chromatine diffuse (NSN) à un stade plus compact(SN), une
répression de l’activité transcriptionnelle est perceptible dans les ovocytes au stade
préovulatoire (De La Fuente et al. 2004). Ces résultats démontrent que malgré la présence
simultanée de ces deux processus (remodelage de la chromatine et arrêt de la transcription),
ceux-ci sont sous le contrôle de mécanismes distincts. La présence des cellules folliculaires
a déjà été démontrée comme étant requise pour permettre la répression de la transcription
(De La Fuente and Eppig 2001). Il a également été suggéré que des changements dans les
composantes de la machinerie transcriptionnellepourraient avoir un rôle important dans la
diminution de la transcription (Pan et al. 2005). D’ailleurs, des changements dans
l’expression de certains facteurs de transcription tels que Sp1 (Sp1 transcription factor) et
TBP (TATA-box binding protein) présentent une diminution importante durant la
croissance de l’ovocyte (Worrad et al. 1994).
Les facteurs responsables d’une meilleure compétence au développement chez les ovocytes
qui présentent une chromatine compactée (SN) comparativement aux ovocytes ayant une
30
chromatine diffuse (NSN) ne sont pas encore connus. Toutefois, diverses expériences ont
été réalisées pour tenter de déterminer si des facteurs présents dans le cytoplasme ou le
noyau pouvaient être responsables du meilleur potentiel de développement chez les
ovocytes SN (Inoue et al. 2008). Des transferts de noyau ont donc été réalisés entre ces
différents types d’ovocytes. Ainsi, les vésicules germinales des ovocytes SN et NSN ont été
interchangées en utilisant chacune des combinaisons possibles (noyau/cytoplasme : SN/SN,
NSN/NS, NS/NSN et NSN/NSN). La maturation et la fécondation in vitro ont ensuite été
réalisées. Bien que la majorité des NSN/SN aient pu atteindre le stade de MII, très peu se
sont rendus au stade de blastocyste, alors que les SN/SN ont démontré des taux élevés de
développement. Toutefois, en transférant les plaques métaphasiques des ovocytes NSN/SN
maturés in vitro dans un cytoplasmed’ovocytes SN et ayant atteint le stade de métaphase II,
ceux-ci ont pu se développer à terme. Ces résultats suggèrent donc que la différence dans
lepotentiel de développement des ovocytes SN serait reliée à des facteurs présents dans le
cytoplasme et qui seraient transférés à partir du noyau au cours de la maturation. Ces
résultats démontrent également que la chromatine des ovocytes SN n’est pas essentielle au
développement à terme, mais plutôt ce qui est contenu dans son cytoplasme (Inoue et al.
2008).
1.6.3
Utilisation des ARNm
Les ARNm accumulés au cours de la croissance folliculaire sont très stables avec une demivie d’environ 12 jours (Bachvarova and De Leon 1980). Toutefois, une grande partie des
ARNm maternels sera dégradéetrès tôt lors du développement de sorte que près de 40% de
l’ARN maternel sera dégradé peu de temps après la fécondation (Bachvarova and De Leon
1980). Seulement au cours de la maturation, une diminution d’environ 20% de l’ARN total
a été observée (Bachvarova et al. 1985). Ainsi, c’est plus de 90% de l’ARN maternel qui
sera dégradé une fois le stade de 2-cellules atteint chez la souris (revu dans (Schultz 1993)).
Chez le bovin, une diminution graduelle de la quantité d’ARN a également été observée
jusqu’au stade 8-cellules (Gilbert et al. 2009).
31
1.7
Régulation des ARNm dans l’ovocyte
Contrairement à la plupart des cellules somatiques, l’ovocyte doit entreposer des ARNm
maternels qui devront demeurer dans un état stable durant une période pouvant
s’échelonner sur plusieurs jours, soit jusqu’à l’activation du génome embryonnaire. Durant
cette période de silence transcriptionnel, une régulation adéquate est donc requise afin de
maintenir les fonctions cellulaires durant la maturation et les premiers cycles cellulaires.
Les mécanismes de régulation post-transcriptionnelle des ARNm maternels sont donc
d’une grande importance.
1.7.1
Longueur de la queue Poly-A
De façon générale, une grande partie des ARNm contenus dans l’ovocyte sont dans un état
dormant et seront traduits seulement plus tard, soit à la reprise de la méiose ou lors des
étapes subséquentes de développement. Suite à la transcription, les ARNm maternels vont
subir une étape de déadénylation, où la queue poly-A normalement retrouvée à l’extrémité
3’ de la majorité des ARNm sera raccourcie pour permettre leur stabilisation. Ainsi, les
ARNm stabilisés auront une queue poly-A courte d’environ 15 à 90 résidus adénosines.
L’activation de la traduction des ARNm maternels sera accompagnée d’un allongement de
la queue poly-A pour atteindre jusqu’à 250 résidus adénosines (revu dans (Bachvarova
1992)). Ainsi, plusieurs transcrits tels que la Cycline B1 (Tay et al. 2000; Tremblay et al.
2005) et c-mos(Gebauer et al. 1994) qui sont retrouvés à l’état stabilisé dans l’ovocyte
immature, vont subir un allongement de la queue poly-A pour ensuite être traduits en
protéine au cours de la maturation.
Considérant que la longueur de la queue peut donner une indication sur le potentiel de
traduction des transcrits, plusieurs efforts ont été faits pour caractériser la longueur de la
queue poly-A dans l’ovocyte au cours de la maturation. Certains ont utilisé une approche
par gène candidat chez la souris (Sakurai et al. 2005), mais également chez le bovin
(Brevini-Gandolfi et al. 1999; Tremblay et al. 2005), ou la longueur de la queue poly-A a
été mesurée. Plus récemment, une étude a été réalisée pour d’identifier un grand nombre
d’ARNm qui présentent soit une longue (>200 A) ou une courte (20-50A) queue poly-A au
cours de la maturation dans l’ovocyte bovin (Gohin et al. 2014). Ces études permettent
32
donc de mieux comprendre la dynamique de traduction des transcrits présents dans
l’ovocyte.
1.7.2
Éléments de régulation dans la portion non traduite du transcrit
Le processus de déadénylation et d’entreposage des ARNm dans l’ovocyte implique
différents mécanismes de régulation en lien avec des éléments (séquences) localisés dans la
région 3’ non traduite (3’UTR) du transcrit. Bien que ces mécanismes aient été
majoritairement caractérisés chez des organismes modèles tels que le Xenope, certaines
particularités sont également retrouvées chez la souris(Richter 1999). Le contrôle de la
polyadénylation cytoplasmique implique entre autres la présence de la séquence de
polyadénylation cytoplasmique (CPE, cytoplasmic polyadenylation element, UUUUUA12U)
ainsi que sa protéine de liaison (CPEB1) (Radford et al. 2008). Un code basé sur les
propriétés du 3’UTR en lien avec la présence d’un ou des CPEs pourrait expliquer la
dynamique de polyadénylation et de traduction lors de la reprise de la méiose (Pique et al.
2008). D’autres éléments de reconnaissance présents dans le 3’UTR ont également été
identifiés comme ayant un rôle dans la stabilisation et le contrôle de la traduction des
ARNm maternels, tel que DAZL (deleted in azoospermia-like) (Chen et al. 2011; Venables
et al. 2001). Bien que ces données ne s’appliquent pas toutes à l’ovocyte bovin, les récentes
découvertes permettent de suggérer que plusieurs mécanismes fonctionnent de façon
similaire à ce qui a été démontré chez les espèces modèles.
1.8
1.8.1
Approches méthodologiques pour l’étude de la qualité ovocytaire
Gène candidat
Dans le but d’identifier des ARNm potentiellement importants pour la qualité de l’ovocyte,
différentes approches ont été utilisées au cours des deux dernières décennies. Les premières
études qui ont tenté d’établir un lien entre la qualité de l’ovocyte et l’abondance de certains
ARNm ont été réalisées à la fin des années 90 en utilisant une approche par gène candidat
(De Sousa et al. 1998b). Cette étude avait alors démontré qu’il était possible de caractériser
l’hétérogénéité retrouvée dans la morphologie des ovocytes individuels en fonction de
l’abondance d’un transcrit spécifique. En utilisant la méthode de PCR quantitative, où le
produit d’amplification est mesuré à chaque cycle, il est alors possible de quantifier
33
l’abondance de certains ARNm. En comparant l’abondance d’un transcrit entre deux
groupes d’ovocytes dont la qualité diffère, il devient alors possible de spéculer sur la cause
physiologique du potentiel de développement réduit. Ainsi, plusieurs autres études ont été
réalisées pour identifier ces gènes candidats impliqués la qualité de l’ovocyte en utilisant
les différents modèles décrits dans la section 1.7 (revu dans (Wrenzycki et al. 2007)).
1.8.2
Méthodes soustractives
Toutefois, l’approche par gène candidat limite grandement le potentiel à détecter les
variations qui pourraient expliquer la différence observée dans la qualité ovocytaire. C’est
pourquoi des techniques ayant le potentiel de comparer de façon globale le contenu en
ARNm de l’ovocyte ont été utilisées par la suite. La technique de « Differential Display
DD-RT » a tout d’abord été utilisée chez la souris (Davis et al. 1996) et chez le lapin
(Henrion et al. 1997), puis chez le bovin (Robert et al. 2000). La technique d’hybridation
suppressive soustractive (SSH) a également démontré son potentiel à détecter des
différences dans l’abondance de certains transcrits entre deux populations d’ovocytes
provenant de différentes tailles de follicules (Donnison and Pfeffer 2004; Pfeffer et al.
2007; Robert et al. 2000) ou entre des embryons présentant différents potentiels de
développement (Fair et al. 2004). Toutefois, déjà à ce moment certains avaient soulevé
l’importance des précautions à prendre afin d’éviter l’introduction de biais introduit lors de
l’amplification de l’ARN (Donnison and Pfeffer 2004).
1.8.3
Microarray
Le raffinement des techniques d’amplification linéaire de l’ARN (Phillips and Eberwine
1996) à partir d’un groupe de quelques ovocytes ou embryons (Zeng and Schultz 2003) a
ensuite permis le développement des puces à ADN, aussi appelé microarray. Cette
technique présente un avantage de taille par rapport à l’approche par gènes candidats
puisqu’elle permet de mesurer simultanément l’abondance d’un grand nombre de transcrits.
Malgré la disponibilité de certaines plateformes commerciales développées pour des
espèces telles que la souris et l’humain, ce n’est qu’en 2005 que ce genre d’outil a été
développé pour le bovin (Sirard et al. 2005). Pour construire la première version de cette
puce à ADN, la technique de SSH a été mise à contribution afin d’identifier les transcrits
34
présents de façon spécifique dans certains tissus. La « Blue chip » contient plus de 2000
clones spécifiquement enrichis dans les ovocytes et les jeunes embryons par rapport aux
cellules somatiques (Sirard et al. 2005). Cette plateforme a d’ailleurs démontré son
potentiel à détecter des différences chez les ovocytes de différentes qualités(Dessie et al.
2007; Ghanem et al. 2007;Torner et al. 2008).
L’annotation complète du génome bovin a ensuite ouvert de nouvelles possibilités dans le
développement des microarrays avec la possibilité d’inclure tous les gènes connus, sans
devoir se limiter à certains gènes identifiés préalablement. Ainsi, l’une des plateformes de
microarrays les plus complètes a récemment été développée pour permettre l’étude du
transcriptome de l’ovocyte et du jeune embryon bovin (Robert et al. 2011). En plus de
contenir tous les gènes de référence, cette plateforme inclut également un grand nombre de
sondes associées à des transcrits qui présentent des 3’UTR alternatifs, à des variants
d’épissage en plus de certaines régions non traduites. Toutes ces caractéristiques rendent
cet outil extrêmement puissant pour l’étude du transcriptome
35
1.9
Hypothèse et objectifs
Compte tenu de la dynamique d’accumulation des ARNm au cours de la croissance de
l’ovocyte, de l’arrêt graduel de la transcription en plus de l’importance cruciale du
développement final de l’ovocyte dans le follicule, il apparait évident que la qualité de
l’ovocyte réside dans l’accumulation de molécules importantes pour le développement.
Notre hypothèse de recherche est que le contexte de différenciation optimal de l’ovocyte va
permettre l’accumulation de transcrits importants pour soutenir le développement jusqu’à
l’activation du génome embryonnaire.
L’objectif général de cette thèse était d’identifier les variations du transcriptome des
ovocytes ayant différents potentiels à supporter le développement. Pour ce faire, nous
avons tout d’abord utilisé un protocole de stimulation ovarienne (coasting) qui permet
d’obtenir des ovocytes ayant un très grand potentiel de développement (Nivet et al. 2012).
Des ovocytes ont été récupérés plus tôt ou plus tard par rapport à la période identifiée
comme étant optimale. Le transcriptome des ovocytes a ensuite été comparé à l’aide des
microarrays.
Ensuite, pour étudier l’importance de la pulsatilité du LH normalement retrouvée avant
l’ovulation, nous avons utilisé un antagoniste de la GnRH (Cetrotide) de façon à supprimer
la sécrétion de LH durant la phase de différenciation folliculaire. Le transcriptome des
ovocytes traités avec l’antagoniste a ensuite été comparé avec celui des ovocytes ayant reçu
une période optimale de différenciation (coasting).
Puisque les deux modèles précédents avaient utilisé des ovocytes provenant de différentes
tailles de follicules, nous avons voulu identifier les modulations du transcriptome de
l’ovocyte associées spécifiquement à la croissance normale du follicule. Pour ce faire, nous
avons récupéré des ovocytes provenant de 4 tailles de follicules spécifiques, soit < 3 mm;
3-5 mm; >5-8 mm et > 8 mm. Le transcriptome de ces ovocytes a ensuite été comparé
suivant la même approche méthodologique.
36
Finalement, afin de mieux comprendre la dynamique d’accumulation des ARNm dans
l’ovocyte au cours de sa croissance, nous avons utilisé le modèle du remodelage de la
chromatine décrit par (Lodde et al. 2007). Des ovocytes regroupés en fonction de la
configuration de leur chromatine (GV0; GV1; GV2 et GV3) ont donc été récupérés et
comparés avec les microarrays.
37
2 Gene expression analysis of bovine oocytes with high
developmental competence obtained from FSH-stimulated
animals
Rémi Labrecque1, Christian Vigneault2, Patrick Blondin2, and Marc-André Sirard1
1.
Centre de Recherche en Biologie de la Reproduction, Faculté des sciences de
l’Agriculture et de l’Alimentation, Département des Sciences Animales, Pavillon INAF,
Université Laval, Québec, Canada
2.
L’Alliance Boviteq, Saint-Hyacinthe, Québec, Canada.
Cet article a été publié dans la revue « Molecular Reproduction and Development » sous la
référence suivante :
Bovine Oocytes With High Developmental Competence Obtained From FSH-Stimulated
Animals. Mol Reprod Dev 80:428-440.
39
2.1
Résumé
Les récents progrès réalisés dans les protocoles de stimulation ovarienne utilisés en vue de
réaliser la maturation et la fécondation in vitro, principalement par l’optimisation de la
période sans hormone folliculo-stimulante (FSH) communément appelé le « coasting »
suite au prétraitement ovarien avec la FSH, ont permis d’améliorer de façon significative
les taux d’embryons produits. Malgré ce grand succès, les facteurs qui vont mener à
l’amélioration de la qualité ovocytaire n’ont pas encore été identifiés. Le but de ce projet
était de comparer le transcriptome des ovocytes récupérés au stade vésicule germinale à
partir de vaches ayant reçu une stimulation à la FSH, suivi d’une période de coasting de
longueur variée (20, 44, 68 et 92 heures) afin de déterminer quels transcrits étaient
accumulés ou diminués lors de cette augmentation et diminution de la compétence. Les
ovocytes issus de chacune des périodes de coasting ont été comparés aux trois autres
groupes (conditions optimales : 44 et 68 heures, sous-différencié : 20 heures et surdifférencié : 92 heures) et ce pour chaque animal, permettant ainsi à chaque vache d’être
son propre témoin (pour un total de 24 échantillons). L’analyse avec les microarrays a
démontré qu’entre 5 et 338 transcrits étaient significativement différents parmi les six
comparaisons effectuées, avec une importante modulation dans le temps en termes de profil
d’expression des gènes. Sans surprise, avec la diminution de l’activité transcriptionnelle
dans ces ovocytes, plusieurs transcrits significativement modulés sont reliés à des fonctions
de régulation des ARN tels que démontré par l’analyse fonctionnelle. Ingenuity Pathway
Analysis a également mis en lumière une autre fonction importante, soit le contrôle de la
séparation des chromosomes. Ces résultats indiquent que la qualité acquise au cours de la
période optimale de coasting ne durera pas longtemps et suggère également la possibilité
d’un mécanisme de contrôle de la qualité, qui pourrait agir par la dégradation de certains
transcrits si l’ovocyte n’est pas ovulé au bon moment.
40
2.2
Abstract
Recent progress in the ovarian stimulation protocol used to realize IVM-IVF in the bovine,
especially through optimization of the FSH withdrawal period length (coasting) after
ovarian pre-treatment with FSH, has significantly improved the blastocyst outcome.
Despite this important clinical success, the underlying factors leading to improved oocyte
quality have not yet been identified. The aim of this project was to compare the
transcriptome of GV stage oocytes collected from FSH-stimulated cows after various
coasting periods (20, 44, 68, and 92 hrs) to determine which transcripts were accumulated
or depleted during the rise and fall of competence. Oocytes from each coasting period were
compared to the three other times (optimal conditions: 44 and 68 hours, under-matured: 20
hours, and over-matured: 92 hours) for each animal individually allowing each cow to be
its own control (24 collections). Microarray analysis revealed that between 5 and 338
transcripts were significantly different across the sixcontrasts, with an important
modulation across time in terms of gene expression profile. Not surprisingly, as the
transcriptional activity decreased in these oocytes, several transcripts that are significantly
modulated during coasting are related to RNA processing functions as shown by the
functional analysis. Ingenuity pathway analysis also highlighted another important
function:the chromosome segregation control. The results presented here indicate that the
quality gained with the optimal coasting time does not last and also suggest a possible
mechanism of control by transcript degradation that could be implicated if the oocyte is not
ovulated at the right time.
41
2.3
Introduction
The production of bovine embryos by in vitro fertilization (IVF) relies almost exclusively
on the use of immature oocytes processed by in vitro maturation (IVM). The success rate of
IVM-IVF-IVC in terms of blastocyst production has remained relatively low (<40%)
especially when oocytes are obtained from untreated, slaughterhouse-derived ovaries(Ali et
al. 2004; Blondin et al. 1996). In the last decade, super-stimulation protocols were
improved and IVM-IVF became much more popular to treat infertile cows. The harvest of
oocytes from such animals by ovum pick-up is often preceded by an ovarian pretreatment
with FSH followed by a FSH withdrawal period (coasting) of 44 hours after the last FSH
injection. The concept of coasting was proposed ten years ago(Blondin et al. 2002) and has
resulted in a significant improvement in blastocyst rates and pregnancies. In a recent study
from our lab, the above-mentioned protocol was optimized in an industrial in vitro embryo
production context (Nivet et al. 2012). The oocyte competence acquisition window during
the coasting period was optimized by the harvest of oocytes at the right moment of
follicular differentiation. As a result, one of the highest blastocyst rate (up to 75% at the
best coasting time and 100% in some animals) was obtained by using a coasting duration
between 44 and 68 hours, with pregnancy rates above 50%(Nivet et al. 2012). Despite this
important clinical success, the underlying factors in the improvement of oocyte quality
have not been identified. The challenge associated with the very limited amount of material
(oocytes) available for chemical or molecular characterization is likely the major
explanation for this difficult situation. Oocytes contain only picograms of RNA or
nanograms of proteins (Gilbert et al. 2009), the vast majority of which are acquired during
growth and change minimally during competence acquisition or maturation (Coenen et al.
2004).
In the bovine as in many other species, the oocyte becomes transcriptionally inactive in a
progressive manner and by the end of the oogenesis aclear decrease of RNA synthesis
activity is noted when the oocyte reaches a diameter of more than 110 µm (Fair et al. 1995).
This transcriptionally inactive state is fully established at the GVBD stage during
maturation (Kastrop et al. 1991)and lasts until the maternal embryonic transition (MET) at
the 8-16–cell stage in the cow(Memili et al. 1998). Chromatin configuration coupled to
42
uridine incorporation in growing bovine oocytes has also shown that the final growth phase
implies a gradual transition from transcriptionally active to an almost inactive state during
the last stage of follicular growth (Lodde et al. 2008). The early embryo must therefore rely
on transcripts accumulated in the oocyte prior the silencing and stabilized by a number of
different chemical modifications such as polyadenylation regulation. These modifications
are well characterized in other model species as the frog (Richter 1999). In this context, the
GV stage oocyte represents a relatively stable picture to study the differences in
accumulated mRNA level in regard of its competence to form a viable embryo.
Few studies have looked at mRNA abundance in different competence contexts using a
microarray approach with bovine oocytes. Patel et al. (2007) compared prepubertal versus
adult GV stage oocytes with microarray and found that approximately 200 transcripts were
different in each category. Ghanem et al. (2007) studied the impact of follicular status
using cDNA microarray, and identified 51 genes that show differences in their mRNA level
between oocytes recovered at the growth phase compared to the dominance phase. In a
previous study, different coasting times have led to oocytes of distinct quality, namely
lower at early [20 hrs] and late [92 hrs] coasting periods compared to the middle times of
44 and 68 hrs where higher quality was observed(Nivet et al. 2012). The optimization of
the coasting protocol represents an invaluable model to study transcript abundance in GV
stage oocyte that results in blastocyst rates above 75%. Therefore, in this study, gene
expression of bovine oocytes at the GV stage after various coasting periods (appropriate
conditions: 44 and 68 hrs; under-matured: 20 hrs; and over-matured: 92 hrs) was performed
with the EmbryoGENE bovine microarray slide (Robert et al. 2011).
2.4
2.4.1
Results
Microarray analysis
In this study, multiple microarray comparisons were performed in order to get a better
picture of the differences found at the transcript level in oocytes with distinct
developmental potential. A total of six microarray comparisons were performed between
the four coasting times (Figure 2-1) corresponding to the different oocyte recovery timepoints used in our previous study (Nivet et al. 2012). A total of 42,242 probes composedthe
43
EmbryoGENE bovine microarray slide and after control spots are removed, 31,138 probes
represent reference genes, novel transcribed regions, alternatively spliced exons and 3’
UTR variants. Considering the threshold used to detect the presence/absence of the spots on
the microarray slide, a total of 14,826, 14,919, 14,649, and 14,793 probes were considered
as present specifically in each of the four conditions (20, 44, 68, and 92 hrs respectively)
with an average of 14,797 probes. Taking in consideration that 13,445 probes are common
between the four coasting conditions studied, this represent 90.8% of the probes being
constitutively present in the oocyte in the conditions studied (Figure 2-2).
Statistical analysis of the microarray data revealed that 7,490 probes showed a statistically
significant difference between times with an adjusted P value less than 0.01. The 7,490
significant probes were then grouped in six different clusters generated by K-means
analysis (available in Supplemental data Figure 2-5). For the number of transcripts
differentially represented between each condition, using the genes with a fold change > 2,
there were between 5 and 338 transcripts significantly different across the 6 comparisons
(Table 2-1). The number of transcripts over- and under-expressed in each contrast indicates
a general tendency where the under-expressed group (FC < 1) was more important along
the coasting than the over-expressed one (FC > 1), except for the 68 vs. 92 hrs, where the
numbers were similar (Table 2-1).
2.4.2
Functional analysis
The Gene Ontology analyses identified the statistically overrepresented GO terms (adj. P
value < 0.05) in the different clusters of genes. Although some clusters did not give any
statistically significant terms, 3 out of 6clusters of genes gave between 3 and 10 significant
terms. For example, genes from cluster 1 with an important increase at the beginning of
coasting (44 hrs) and a decrease thereafter (Figure 2-3 A), gave terms related to translation
(translational elongation, translation) and cellular process (cellular metabolic process and
cellular process). On the other hand, genes from cluster 3 with an increase at 68 hrs and a
decrease at 92 hrs (Figure 2-4 A) gave two significant terms related to mRNA processing
(RNA splicing, mRNA processing) and several terms related to cell cycle process (mitotic
44
cell cycle, mitosis, cell cycle process). It is also important to note that no fold change cutoff have been used for the lists used in Gene Ontology analyses.
DAVID bioinformatics resources were used to look more in details through the function
related to specific gene lists in each cluster. A fold change cut-off (FC > 1.25) was used to
select an average of 300 genes closely related to each cluster in order to look more
precisely in the functions enriched in these gene lists. Results were in accordance with the
Gene Ontology analyses where statistically significant GO-terms were previously found.
High enrichment score in the functional annotation clustering performed by DAVID
indicates that members in the group (genes) are playing a more important role in the
dataset. Cluster 1 with an up-regulation in transcript level at the beginning of the coasting
(Figure 2-3 A) gave similar terms globally related to translation, such as ribosome,
ribonucleoprotein complex, ribosomal protein and structural component of ribosome.
Furthermore, the cluster 3 which represented genes with an up-regulation at 68hrs followed
by a decreased at 92hrs (Figure 2-4 A) showed the highest enrichment score for the
functional annotation clustering with an important emphasis on the RNA processing
function. A list of terms such as mRNA metabolic process, RNA splicing, RNA
degradation and RNA binding as well as cell cycle related terms were highly enriched in
the components of this cluster.
Multiple analyses were generated through the use of IPA with the gene list from every
comparison. A fold change cutoff of 1.5 and adjusted Pvalue <0.01 for the six comparisons
individually were set to identify molecules or functions significantly and differently
affected between each coasting time. IPA identified post-transcriptional RNA modification
and cell cycle as the most significant molecular and cellular functions in most of the
comparisons analyzed. Moreover, IPA also identified DNA replication recombination and
repair as a function significantly affected in the comparison 68hrs vs 92hrs.
2.4.3
Microarray validation by quantitative RT-PCR
With four successive time-point conditions in the microarray data, candidate genes that
followed a specific pattern across the coasting period were selected for q-RT-PCR (Figure
45
2-3B and Figure 2-4B). To validate the microarray results, q-RT-PCR experiments were
conducted on a total of 23 genes. As these time-related measures are more interesting in
patterns, a correlation analysis (Pearson) was performed between the mean intensity level
(microarray data) of each condition and the mean expression level of the q-RT-PCR data.
As a result, a validation rate of 62.5% was obtained for a list of selected genes (R2> 0.8, P
value < 0.05, Table 2-2).
2.5
Discussion
The GV stage oocyte was chosen because it represents the initial stockpile of maternal
transcripts accumulated before the transcriptional shutdown to ensure development until the
MET. Therefore, the comparison of GV stage oocytes in different physiological contexts
linked to distinct developmental competence gives very important information on what
must be accumulated. To put this into perspective, oocytes originating from small follicles
(<4 mm for example) from slaughterhouse-derived ovaries can generate around 20% of
blastocysts once matured, fertilized and cultured in vitro, while oocytes from large follicles
(>6 mm) may reach the blastocyst stage at a rate of up to 45% (Lequarre et al. 2005). In a
natural cycle, the FSH levels are low for several days before ovulation and this period is
associated with oocytes of increasing competence, as shown by the higher blastocyst rate
obtained with oocytes from dominant compared to growing follicles (Vassena et al. 2003).
The biological model used in this study clearly represents a subsequent step in the process
of developmental competence acquisition, as the blastocyst rate reaches an average of 70%,
with some animals achieving 100% at the optimal coasting time.
Among the 42,242 probes composing the most complete microarray covering the
transcriptome of bovine oocyte and early embryo (Robert et al. 2011),oocyte expresses or
stores at least 14,797 different mRNAs, of which 13,445 (90.8%)are constitutively present
in the four coasting conditions studied. This means that less than 10% of probes are
changing (absent/present) between the different conditions. Moreover, the majority of these
changing probes showed an intensity level on the microarray close to the detection cutoff
used. The overall grouping of the probes common or specific to each condition illustrated
by the Venn diagram (Figure 2-2) showed no sign of distortion between each group. Taken
46
together, these results indicate that no large absolute increase in mRNA amount occurs in a
context where developmental competence, as measured by blastocyst rate, increases
significantly (Nivet et al. 2012). This observation makes physiological sense since the RNA
content of the oocyte should not change much in those last few days before ovulation, when
transcription is minimal (Table 2-3).
The grouping of all the probes (7,490) with a statistically significant difference between
times (adj. P value< 0.01) in different clusters with distinct pattern allowed to focus on
genes that follow specific trend. Even though the general profile of each cluster appears
relatively clear (red line, Figure 2-3 A and Figure 2-4 A), the variation within the same
cluster is caused by the fact that every significant probe without any fold-change cut-off has
been plotted in one of the 6 clusters. This grouping coupled to the GO-terms and DAVID
analysis has really illustrated the major changes at the mRNA level occurring in the oocyte
at that time.
The fact that clusters with significant GO-terms followed a global profile of transcripts
accumulation at the optimal time of the coasting could suggests two main hypotheses. The
foremost, even in a context of reduced transcription, is that the coasting allows the
transcription of new or existing transcripts that need to be accumulated to ensure the high
developmental competence observed. Thereafter, the decrease at the mRNA level observed
at the end of the coasting could be due to either translation or transcript degradation or
more likely both. The gene products could be translatedimmediately to change the
physiological context and ensure the proper chain of action leading to maturationfertilization and maternal to embryo transition (MET). Another mechanism of transcript
degradation could also control the quality of the oocyte when a decrease in the transcript
level is observed. Accumulated mRNA could be destroyed in a prolonged FSH starved
environment to decrease the capacity of an un-ovulated oocyte to self-activate.
Furthermore, if the oocyte stays too long in the ovary instead of being ovulated, maybe the
protein produced will not be available to perform its genuine role due to protein turnover
(Table 2-3).
47
The fact that oocytes with high developmental potential across all the coasting period
(blastocyst rate of 50% vs. 70% approximately) were compared together supports the
observation of small changesin term of transcripts difference between good and excellent
quality oocyte. It is interesting to note a tendency for the number of differentially expressed
genes all along the coasting time in favor of a higher number of genes with a negative fold
change, which reflects the gradual decrease in transcript abundance. This period would
reflect the end of the building process and beginning of the silencing of the genome. The
number of differentially expressed genes between the four conditions is relatively low,
except for the contrast 44hrs vs 68hrs and 68hrs vs 92hrs (339 and 191 genes, Table 2-1).
This could be the result of a threshold point reached by the oocyte at 68hrs, where beyond
this point; many transcriptional changes probably linked to RNA processing and RNA
degradation (Table 2-3) observed between 68hrs vs 92 hrs could be associated with the
decrease of developmental competence while the same differences could not be apparent in
the others contrasts. However, maybe that before reaching that point, the oocyte is already
engaged in an early response mainly seen between 44hrs vs 68hrs and not really apparent
elsewhere while a relatively high competence is observed all along the coasting.
In fact, very few have looked at the differences between such close conditions in term of
phenotypic response (blastocyst rate between 50 and 70%) and also with such a dynamic
modulation of the response like a rise and fall of the competence instead of a simple
increase or decrease. However, the overall number of genes is approximately of the same
magnitude as in other microarray studies where comparison of different categories of GV
stage bovine oocytes was performed (Ghanem et al. 2007; Patel et al. 2007; Torner et al.
2008), MII bovine oocytes (Dessie et al. 2007; Katz-Jaffe et al. 2009) or MII human
oocytes (Fragouli et al. 2010; Grondahl et al. 2010). Even though the design and platform
used in those microarray studies were completely different, the proportion of genes that
differ between the same stages (i.e., GV from small follicles vs. GV from large follicles,
GV recovered during growth phase vs. GV recovered during dominance phase of follicular
development) remains similar in term of quantity (number of genes), significant oocyte
quality changes reflects a distinct final differentiation process. Globally, all these studies
reported less than 350 differentially represented genes between the different conditions.
48
The quantitative RT-PCR validation of the microarray data demonstrates that microarray
data are reliable, but also shows the limit of the microarray approach with very close
experimental conditions. Genes with fold change >1.4 andgood intensity level on the
microarray were validated with strong correlation between microarray and q-RT-PCR data
(Table 2-2). On the other hand, compared to somatic tissues, the storage potential of the
oocyte requires a completely different perspective when measuring RNA levels. In somatic
tissues, RNA turnover is quite important and differences rapidly become visible, while in
oocytes, RNA is stored. Furthermore, a careful attention must be given when interpreting
an increase or decrease of a specific transcript in the oocyte especially in regard of its
storage potential.
As mentioned before, the two main functional changes observed in oocyte RNA during the
critical period where competence goes up or down are mRNA processing and DNA
stability - chromosome segregation. Those twofunctions will be addressed in order.
2.5.1
RNA processing
As the oocyte must accumulate all the transcripts needed to ensure the developmental
process until the maternal embryonic transition (MET), the coasting period may represents
the last chance to proceed before transcriptional arrest. Therefore, it appears logical that
oocytes accumulate transcripts related to optimal translational machinery at the beginning
of the coasting period. For example, many ribosomal protein genes such as RPLP1, RPL28,
RPS2, RPS7, RPS18, RPS19 and RPSA followed specifically the cluster 1 (Figure 2-3 A)
with a high level at the beginning of the coasting followed by a decrease thereafter. When
the coasting period is extended, transcription becomes less and less important until a
complete arrest at the GVBD stage (Kastrop et al. 1991), the transcript differences during
the coasting become predominantly related to post-transcriptional RNA modifications
(Table 2-3). Moreover, among the RNA processing terms identified by DAVID and
enriched in the cluster 3 (Figure 2-4 A), many genes were part of the KEGG RNA
degradation pathway (Supplemental Figure 2-6). Many members of the Lsm family of
RNA-binding proteins were identified, such as LSM1-3-4-5 and 6, CCR4-NOT
49
transcription complex subunit 7 (CNOT7), Heat shock 60KDa protein 1 (HSPD1), Poly(A)
polymerase alpha (PAPOLA) and are all components of the RNA degradation pathway.
Even though many members of this pathway are better characterized in other model
organisms such as yeast in regard of their role in mRNA decapping, deadenylation and
mRNA decay in general (He and Parker 2000), their underlying role in gene expression
control are interesting in the search for a mechanism related to quality control in the context
of rise and fall of oocyte competence. Recent finding in the maternal mRNA degradation
process during the oocyte maturation in the mouse (Ma et al. 2012) clearly demonstrated
the importance of RNA degradation machinery in the oocyte to express competence. Taken
together, the over-representation of these genes with a significant increase at the optimal
time of coasting could support the idea of RNA degradation as a quality control mechanism
to 1) shift from the storage to the usage mode for mRNA, 2) to ensure that maternal
inhibitory components essential to stabilize the GV stage are destroyed or 3) to reduced the
quality of the oocyte if not ovulated at the right time (Table 2-3). Further studies will
therefore be required to identify target mRNAs in this process.
Ingenuity Pathway Analysis also identified mRNA processing as an important function in
the contrasts analyzed. For example, SIP1 (survival of motor neuron protein interacting
protein 1, also known as GEMIN2), HNRNPH3 (heterogeneous nuclear ribonucleoprotein
H3), HNRNPA2B1, HNRNPM, NUDT21 (nudix (nucleoside diphosphate linked moiety
X)-type motif 21), SLBP (stem-loop binding protein), RSRC1 (arginine/serine-rich coiledcoil 1), and SFRS7 (serine/arginine-rich splicing factor 7), TAF1A (TATA box binding
protein (TBP)-associated factor RNA polymerase 1, A) and TFDP1 (transcription factor
Dp-1) are all genes implicated in diverse aspects of post-transcriptional RNA
modifications.Allthese genes followed the pattern of the cluster 3 (Figure 2-4 A) with an
increase at the beginning of the coasting until 68 hrs and decreased at 92hrs with a strong
correlation between microarray and q-RT-PCR for SFRS7, TAF1A and TFDP1 (Figure 2-4
B).
Cyclin-dependant kinase 1 (CDK1; catalytic subunit) along with Cyclin B1 (CCNB1;
regulatory subunit) together form the maturation promoting factor (MPF), which plays a
50
crucial role in cell cycle regulation during the resumption of meiosis. A study from our
laboratory has shown that the GV stage bovine oocyte does not accumulate CCNB1 protein
in follicles from <2 mm to >6 mm (Robert et al. 2002) but contains mRNA ready to use
since CCNB1 protein synthesis is immediately necessary for meiotic resumption in bovine
(Levesque and Sirard 1996). At the mRNA level, CDK1 is also detected throughout follicle
growth (Robert et al. 2002). CDK1 showed an increase at 68 hrs and then a decrease at 92
hrs with microarray and q-RT-PCR validation (Figure 2-4). This decrease in CDK1 RNAis
probably not due to recruitment for translation at the end of coasting as it was shown that
the protein is already present at an earlier stage of folliculogenesis (follicle diameter of 3 to
5 mm) (Robert et al. 2002). The polyadenylated form of the transcript showed a significant
decrease during maturation, which is not the case with the total transcript amount measured
with random primers (Thelie et al. 2007). However, the opposite situation was observed
with an increase of the CDK1 polyadenylated form during in vivo maturation (Vigneron et
al. 2004).The higher level of CDK1 mRNA could represent a last minute/optimal
accumulation of cell cycle components that are required for early cleavage divisions. The
decrease of the mRNA level at the end of the coasting period (92 hrs) could be the result of
RNA degradation in the stored form since a lower blastocyst rate is obtained with a
coasting duration of 92 hrs.These results support the importance of post-transcriptional
RNA modification in the oocyte, in this case polyadenylation/deadenylation. Clearly, the
polyadenylation status of these keys cell cycle components seems to play an important role
in the developmental potential of oocyte and need to be accumulated appropriately as is the
case for CCNB2 during follicular growth (Mourot et al. 2006).
It is well know that the regulation of polyadenylation is important during oocyte maturation
in Xenopusbut also in mammals. Poly-A binding protein interacting protein 2 (PAIP2) is
known to inhibit translation of poly-A containing mRNA by promoting the dissociation of
Poly-A Binding protein (PABP) from the poly-A tail (Karim et al. 2006; Khaleghpour et al.
2001). Interestingly, PAIP2 showed an increase at 68 hrs and a decrease immediately after
at 92 hrsboth in microarray and q-RT-PCR data (Figure 2-4). Moreover, the protein has
been detected in the bovine oocyte only at the GV and MII stages but was almost
undetectable in embryos at later stages of early development (Siemer et al. 2009). The
51
presence of the protein restricted to these two stages of oocyte maturation illustrates the
importance of polyadenylation control in the oocyte at that time. Therefore, this could mean
that optimal level of PAIP2 is required to control translation of polyadenylated mRNA
mainly before fertilization.
Among the genes that showed an important increase at 68hrs, enhancer of yellow 2
homolog (ENY2) showed strong correlation between microarray and q-RT-PCR (Figure 24). This gene was first identified in Drosophila studies, and it was shown that the protein
enhances transcriptional activation in an in vitro system and seems to influence the
expression of many other genes (Georgieva et al. 2001). ENY2, also known as SUS1 in the
yeast, has a known role in transcription and mRNA export (Galan and Rodriguez-Navarro
2012). A link between cytoplasmic mRNA decay machinery members, such as LSM1 and
LSM6, and SUS1 was found, which suggest a nuclear function (mRNA export) but also a
role in the cytoplasmic mRNA metabolism (Cuenca-Bono et al. 2010). The fact that ENY2
and many LSM family members all showed an increase level at 68hrs followed by a
decrease at 92hrs suggest a similar link in the oocyte with a possible role in RNA
degradation as quality control mechanism described earlier. Recently, a paper where
mRNA level in bovine MII oocyte were correlated with developmental potential to the
blastocyst stage identified ENY2 mRNA as less abundant in good vs bad quality oocytes
(Biase et al. 2012). Even though the role of ENY2 in the bovine oocyte is still not known,
these results could suggest degradation or translation during the maturation period.
Therefore, this could mean that appropriate mRNA processing as well as RNA degradation
machinery must be accumulated at the optimal coasting time and will be use shortly after.
This could potentially explain why good quality MII oocyte presented a lower amount for
this mRNA compare to the bad quality oocyte.
Heterogeneous nuclear ribonucleoprotein (hnRNP) is a complex made of RNA and protein
involved in post-transcriptional modification of a pre-mRNA before its export to the
cytoplasm. One member of hnRNP family, hnRNP K, is involved in various processes of
gene expression such as chromatin remodeling, transcription, RNA splicing and mRNA
stability. It was demonstrated that this member interacts with RNA binding motif protein
52
42 (RBM42) and the latter acts as hnRNP K-binding protein in mammalian cells. It was
proposed that RBM42 might be involved in the nucleus-cytoplasmic shuttling of hnRNP K,
since exogenous expression of RBM42 is able to recruit endogenous hnRNP K to the
cytoplasm (Fukuda et al. 2009). Microarray and q-RT-PCR data showed a higher level of
mRNA for RBM42 in oocytes at 44 hrs (Figure 2-3). Although hnRNP K did not display
any statistically significant difference in microarray data, if the pairing of these two
components is essential, the increase in one of them may result is better stability in stored
mRNA, which is crucial during embryonic development.
Histone mRNAs are among the most abundant mRNAs accumulated during oogenesis. The
proper histone mRNA biosynthesis involves U7 snRNP, which contains two proteins,
LSM10 and LSM11. In Drosophila, mutations in either LSM10 or LSM11 disrupt normal
histone pre-mRNA processing (Godfrey et al. 2009). A similar phenotype was observed
with the mutation of SLBP, which encodes the protein that binds the stem-loop of histone
mRNAs (Godfrey et al. 2006). In microarray and q-RT-PCR data, LSM10 showed an
increase at 44 hrs followed by a decrease thereafter at 68 hrs (Figure 2-3), while LSM11
did not show any statistically significant difference. Furthermore, SLBP showed a
significant increase at 68 hrs (cluster 3) in the microarray data. It is interesting to note the
increased level of those two mRNAs (LSM10 and SLBP) at the optimal time of the
coasting where the oocyte gains a higher level of competence. These two mRNAs are
potentially important for the appropriate accumulation of histone mRNAs in a similar way
as in Drosophila and in mouse (Arnold et al. 2008).
2.5.2
DNA stability – chromosome segregation
Ingenuity Pathway Analysis highlighted another important function that was not identified
by the other bio-informatics tools used in this study, namely DNA replication,
recombination and repair with many genes significantly modulated during coasting. Several
genes such as ERCC8 (excision repair cross-complementing rodent repair deficiency,
complementationgroup 8), GADD45A (growth arrest and DNA-damage-inducible, alpha),
LIG4 (ligase IV, DNA, ATP-dependent), and also several genes implicated in the DNA
mismatches repair pathway such as MSH2 (mutS homolog 2, colon cancer, nonpolyposis
53
type 1 (E. coli)), ELP4 (elongation protein 4 homolog), ESCO2 (establishment of cohesion
1 homolog 2 (S. cerevisiae)) and PMS1 (postmeiotic segregation increased 1 (S.
cerevisiae)) all presented a significant modulation during the coasting. All these transcripts
showed a particular profile with an important increase at 68 hrs followed by a decrease at
92 hrs with a strong correlation between microarray and q-RT-PCR data (ELP4, ESCO2
and PMS1, Figure 2-4).
A recent paper characterized the localization of ESCO2 protein during female meiosis in
the mouse (Evans et al. 2012). ESCO2 was co-localized with gamma H2A histone family
member X (H2AFX) in pachytene oocyte. The authors then suggest a possible role for
ESCO2 in DNA double-stranded repair. Among the other genes implicated in DNA
mismatch repair pathway, composed of two different protein complexes MutS-related and
MutL-related proteins, MSH2 and PMS1 are both members of this pathway. Human oocyte
also possessed high level of MSH2 mRNA in GV and MI stage oocyte followed by a
decrease at the MII stage (Assou et al. 2006). This decrease during the maturation period
could suggest degradation or translation of the mRNA during the maturation period.
Furthermore, another member of the MutL-related protein (MLH3) has important role in
meiotic arrest and aneuploidy as shown by the phenotype observed in MLH3 deficientmice (Lipkin et al. 2002). Taken together, the significant modulation of PMS1 and ESCO2
mRNA at the end of the coasting could suggest an important role played by this pathway in
the oocyte related to competence acquisition.
In the bovine oocyte, a higher level of mRNA for AURKAIP1 is present at the beginning of
coasting (20 hrs) and this level decreases until the end of coasting (92 hrs) as demonstrated
by the microarray and q-RT-PCR data (Figure 2-3). Although much less is known about
aurora kinase A interacting protein 1 (AURKAIP1) compared to its counterpart aurora
kinase A (AURKA), the former has been identified as a negative regulator of AURKA in
yeast studies (Kiat et al. 2002). In human cell lines, AURKAIP1 (also known as AIP)
regulates spindle organization through regulation of the level of AURKA (Fumoto et al.
2008). Furthermore, AURKA silencing by siRNA results in a significant increase in
abnormal chromosome segregation in MII mouse oocytes (Ding et al. 2011). It was also
54
demonstrated that a continuous treatment of mouse oocyte with an Aurora kinase inhibitor
blocked meiotic maturation while a short treatment with the inhibitor rather speeds the
extrusion of the first polar body, resulting in an increase in aneuploidy rate (Lane et al.
2010). It was also shown that among the three aurora kinases (A, B and C), AURKA is the
most abundant in the bovine oocyte from follicles of 3 to 6 mm at the mRNA level, and that
the protein is detected at the GV stage and increases during maturation (Uzbekova et al.
2008). These results suggest that a precise regulation of these key regulators must be in
place before the meiosis resumption. Therefore, this could mean that the decrease of
AURKAIP1 observed during coasting is required to ensure appropriate AURKA function
with a possible link with aneuploidy.
Another important gene in spindle integrity and chromosome stability in human cells is
HAUS8 (Augmin-like complex subunit 8) also known as HICE1 (Hec1-interacting and
centrosome-associated 1)(Wu et al. 2008). This gene is one of the eight components of the
protein complex Augmin, which plays an important role in centrosome regulation and
spindle integrity (Lawo et al. 2009). Interestingly, Aurora A is implicated in the regulation
of one Augmin component (HICE1) with the modulation of its microtubule binding activity
during spindle assembly (Tsai et al. 2011). HAUS8 showed a relatively constant mRNA
level during coasting and then an important decrease at 92 hrs with strong correlation
between microarray and q-RT-PCR data (Figure 2-3). Human oocyteshowed a high level of
HAUS8 mRNA at the GV stage, but the level decrease at MI and MII stage (Assou et al.
2006). Therefore, if the decrease at the mRNA level between GV and MII stage is due to
degradation or translation, an early translation at 92 hrs of coasting is unlikely in bovine
oocytes in regard of the mRNA profile found in human. This observation would support an
active mechanism to reduce spindle functionality in oocytes staying too long in a follicle
that does not progress toward ovulation. A recent review and an editorial comment are
indeed suggesting that aged follicles may work to decrease oocyte competence by raising
the risk of aneuploidy in mono-ovulating species (Sirard 2011a; Sirard 2011c).
The results presented in this study suggest that even in a context of reduced transcription in
oocytes from large follicles, there are still significant differences in transcript abundance
55
between oocytes of distinct developmental competence. Coasting allows the accumulation
of specific transcripts with important functions related to RNA processing but also
chromosome segregation control. The new candidates discovered in this experiment will
potentially reveal some of the mechanisms involved in the mystery of oocyte competence
and certainly pave the way for a range of new experiments.
2.6
Materials and methods
All chemicals were obtained from Sigma-Aldrich (St. Louis, MO), unless otherwise stated.
2.6.1
Oocyte recovery
Ovum pick-up (OPU) was performed after super-stimulation protocol as described by Nivet
et al. (2012). Briefly, each animal (commercial Holstein cycling cows) was treated during
the luteal phase to prevent spontaneous ovulation. The dominant follicle was aspirated 36
hours before administration of hormones. Animals were stimulated for three days with FSH
(6 x 40 mg NIH Folltropin-V, Bioniche Animal Health, Belleville, ON, Canada), given at
12-hr intervals, followed by a coasting period (no FSH) of 20, 44, 68 or 92 hrs. Each
animal was exposed to one of the 4 conditions (in a randomized design) so each cow served
as its own control. Using transvaginal ultrasonography, follicular diameters were measured
and cumulus-oocyte complexes (COCs) were collected by transvaginal puncture, under
epidural, with a 19G needle and COOK aspiration unit (COOK Medical, Bloomington, IN).
COCs were collected in warm Hepes-buffered Tyrode’s media (TLH) containing Hepalean
(10 UI/mL) and transferred to the laboratory. The COCs were then placed in TLH solution
supplemented with 10% bovine serum, 0.2 mM pyruvate and 50 µg/mL gentamicin and
washed three times to remove follicular fluid. Healthy COCs were randomly divided into
two groups; the first half was used for in vitro maturation, fertilization and development.
These steps were described in details by Nivet et al. (2012). Blastocyst development was
assessed using industrial criteria for embryo transfer at Day 7 and Day 8 post-fertilization.
The other half of COCs was mechanically denuded by vortexing for 2-3 minutes in PBSPVA to remove all the cumulus cells. Oocytes were then washed two times in PBS-PVA
solution and one more time in PBS solution, collected in minimal volumes of PBS, snapfrozen in liquid nitrogen and stored at -80°C until RNA extraction.
56
2.6.2
RNA extraction and amplification
Total RNA was extracted from pools of five to ten oocytes with Pico-Pure RNA Isolation
Kit (Applied Biosystems, Carlsbad, CA) following the manufacturer’s protocol and
including DNase treatment on the purification column. Total RNA integrity and
concentration were evaluated on a 2100-Bioanalyzer (Agilent Technologies, Palo Alto,
CA) with the RNA PicoLab Chip (Agilent Technologies). To generate enough material for
hybridization, the samples were linearly amplified. Antisense RNA (aRNA) was produced
using the RiboAmp HS RNA amplification kit (Applied Biosystems). After two
amplification rounds of 6 hrs each, the aRNA output was quantified using the NanoDrop
ND-1000 (NanoDrop Technologies, Wilmington, DE).
2.6.3
Sample labeling and microarray hybridization
For each sample, 2 µg of aRNA were labelled using the ULS Fluorescent Labelling Kit for
Agilent arrays (Cy3-Cy5) (Kreatech Diagnostics, Amsterdam, Netherlands). The labelled
product was then purified with the Pico-Pure RNA Isolation Kit but without DNase
treatment. Labelling efficiency was measured using the Nano-Drop ND-1000. Samples
from the 3 biological replicates (cow) were hybridized on EmbryoGENE’s bovine 44K
microarray (Robert et al. 2011). The hybridizations were performed in the following
design: for each biological replicate individually, each coasting time was compared to
others (i.e., 20 hrs vs. 44 hrs; 20 hrs vs. 68 hrs; 20 hrs vs. 92 hrs; 44 hrs vs. 68 hrs; 44 hrs
vs. 92 hrs; and 68 hrs vs. 92 hrs) for a total of six comparisons (Figure 2-1). Overall, 36
hybridizations, corresponding to the three biological replicates and six comparisons, were
performed using a dye-swap set-up. A total of 825 ng of each labelled sample (Cy3 and
Cy5) were incubated in a solution containing 2x blocking agent and 5x fragmentation
buffer in a volume of 55 µL at 60°C for 15 min and were put on ice immediately after.
Then, 55 µL of 2x GEx Hybridization Buffer HI-RPM were added for a total volume of
110 µL. The hybridization mix (100 µL) was added onto the array and hybridization was
performed at 65°C for 17 hrs using an Agilent Hybridization chamber in a rotating oven.
Slides were then washed with Gene Expression Wash Buffer 1 containing 0.005% Triton
X-102 for 3 min at room temperature and then transferred to Gene Expression Wash Buffer
2 containing 0.005% Triton X-102 for 3 min at 42°C. Final washes with acetonitrile for 10
57
sec at room temperature and with Stabilization and Drying Solution (Agilent) for 30 sec at
room temperature were performed before air-drying of the slides. The slides were scanned
using the Tecan PowerScanner microarray scanner (Tecan Group ltd, Männerdorf,
Switzerland) and features were extracted using ArrayPro 6.4 (Media Cybernetics, Bethesda,
MD).
2.6.4
Microarray data analysis
Microarray data were subjected to a simple background subtraction, Loess within array
normalization and statistically analyzed using Limma package in a reference design (20
hrs). Foreground mean intensities and median background intensities were used as well as
the average of technical replicates. Differences were considered statistically significant
with an adjusted P value less than 0.01. Microarray data have been deposited in NCBI’s
Gene Expression Omnibus and are accessible through GEO series accession number
GSE38345 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38345).
To detect the presence or absence of the signal for each spot, an arbitrary cut-off level of
7.6 (log2) was used, which corresponds to the mean intensity of the background level + 2
times the Standard Deviation (SD) of the background. A Venn diagram was created using
the online tool VENNY (Oliveros 2007) to show the probes present and common between
each conditions. Genes were then grouped in clusters with K-means analysis using the
package fpc in R. First, the number of clusters was selected using the pamk function using
the average silhouette width partition method. Genes were then assigned to the
corresponding clusters and plot using a homemade R script.Gene Ontology analyses were
performed with the Bioconductor package GOstats (Falcon and Gentleman 2007) based on
the cluster grouping to observe the enrichment terms in each profile. DAVID
bioinformatics resources were also used to perform functional enrichment analysis of
specific gene-list (Huang et al. 2009a; Huang et al. 2009b). The gene-lists from individual
comparison were then analyzed through the use of Ingenuity Pathway Analysis, IPA
(Ingenuity Systems, Mountain View, CA). All statistically significant genes (adjusted
Pvalue < 0.01; fold change > 1.5) were uploaded to the application and each identifier was
58
mapped to its corresponding object in the Ingenuity database. The functional analysis
identified the biological functions that were most significant to the molecules in the dataset.
2.6.5
cDNA preparation and quantitative RT-PCR
To confirm the results from microarray analysis, quantitative RT-PCR was performed with
aRNA. Fifty ng of aRNA were reverse transcribed using q-Script Flex cDNA Synthesis Kit
(Quanta Biosciences, Gaithersburg, MD) with random primers following manufacturer’s
recommendations. The primers used for q-RT-PCR are listed in Supplemental Table 2-4
and
were
designed
using
the
IDT
(http://www.idtdna.com/Scitools/Applications/Primerquest/)
PrimerQuest
from
sequences
tool
obtained
using the UMD3.1/bosTau5 assemble version of the bovine genome and results from our
microarray analysis. To confirm the specificity of each pairs of primers, electrophoresis on
a standard 1.2% agarose gel was performed for each amplified fragment. The PCR product
was then purified with the QIAquick Gel Extraction kit (Qiagen), quantified using the
NanoDrop ND-1000 and sequenced. The products were then used to create the standard
curve for quantification experiment, with dilutions ranging from 2 x 10-4 to 2 x 10-8 ng/µL.
Real-time PCR was performed on a LightCycler 480 (Roche Diagnostics, Laval, QC,
Canada) using SYBR incorporation. Each q-RT-PCR reaction, in a final volume of 20 µL,
contained the cDNA corresponding to 1/40 of the total volume of the reverse transcription
reaction described previously, 0.25 mM of each primer and 1x SYBR mix (LightCycler 480
SYBR Green I Master, Roche Diagnostics). The PCR conditions used for all genes were as
follows: denaturing cycle for 10 min at 95°C; 50 PCR cycles (denaturing, 95°C for 1 sec;
annealing, [Table 2-4] for 5 sec; extension, 72°C for 5 sec), a melting curve (94°C for 5
sec, 72°C for 30 sec and a step cycle starting at 72°C up to 94°C at 0.2°C/sec) and a final
cooling step at 40°C. Complementary DNA quantification was performed with the
LightCycler 480 Software Version 1.5 (Roche Diagnostics) by comparison with the
standard curve. PCR specificity was confirmed by melting-curve analysis.
2.6.6
Statistical analysis of quantitative RT-PCR results
For each gene tested, three biological replicates were used. Analysis of gene expression
stability over the four coasting durations was performed using the GeNorm VBA applet
59
software as described by Vandesompele et al. (2002). The most stable reference genes were
identified by the stepwise exclusion of the least stable gene and recalculating the M values.
Following GeNorm analysis, ACTB, GAPDH and H2A.1 were the most stable genes with
M values < 1.5 as recommended by the software (M value = 0.650). Data are presented as
mean ± s.e.m. Thereafter, a correlation analysis (Pearson) was performed between the mean
expressionintensities (20, 44, 68, and 92 hrs) of the microarray data and the mean
expression data of the quantitative RT-PCR results. Differences were considered to be
statistically significant at the 95% confidence level (p<0.05) with correlation R2> 0.8. Data
were analyzed with Graph Pad Prism version 5.0.
2.6.7
Acknowledgements
We thank Dr Isabelle Dufort for her technical assistance in all the molecular biology
experiments, all the staff at LAB for the help in the collection of biological samples and Dr
Marc-André Laniel for revising the manuscript.
60
2.7
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66
2.8
Tables
Table 2-1 Number of differentially expressed genes between conditions
20-44
44-68
20-68
68-92
44-92
20-92
FC > 2
5
339
31
191
28
10
Down-regulated genes
1
237
21
93
28
9
Up-regulated genes
4
102
10
98
0
1
67
Table 2-2 Correlation results between microarray and q-RT-PCR data.
Gene
R2
PMS1
0.99
Correlation
P value
<0.05
ENY2
0.98
<0.05
PAIP2
0.98
<0.05
CDK1
0.97
<0.05
RBM42
0.97
<0.05
TFDP1
0.96
<0.05
HAUS8
0.94
<0.05
ELP4
0.94
<0.05
LSM10
0.93
<0.05
TAF1A
0.92
<0.05
MED29
0.89
<0.05
SFRS7
0.89
<0.05
NFYB
0.88
<0.05
ESCO2
0.86
<0.05
AURKAIP1
0.82
<0.05
CDC26
0.80
0.0515
TCEA1
0.78
0.0567
MAD2
0.74
0.0688
INTS3
0.54
0.13
FZR1
0.52
0.13
MAD2L2
0.36
0.3964
PDE6C
0.32
0.21
ARRB2
0.23
0.25
68
Table 2-3 RNA modulation in oocyte during the corresponding follicular phases
Making RNA
(capacity)
Making RNA
(new forms)
Processing of
RNA
Translation
of RNA
Degradation of
RNA
Growth
Early dominant
(20hrs)
Differentiation
(44-68hrs)
Atresia
(92hrs)
++
-
-
-
-
++
++
+
+
++
+++
+
+++
+
+
++
+
+
++
+++
69
Table 2-4 Supplemental dataPrimer sequences used for quantitative RT-PCR.
Gene
symbol
Forward primer (5' - 3')
Reverse primer (5' - 3')
Annealing
temp.(°C)
Product
size
(bp)
ENY2
GGAGAAAGAGAACGCCTCAAAGAG
GCCACCAAGTCATCAACAGTAACG
57
137
MAD2L2
GCTGAACCAGTATATCCAGGACAC
GCATCACACACACTGATCTTCAGG
56
228
CDK1
GGCACTCCCAATAATGAAGTGTGG
CGAGAGCAGATCCAAGCCATTT
57
135
AURKAIP1
GGGTGGTAAGGATGTCTTTGATGC
TTAGACGTGCTTCCCTGACTTTCC
57
146
HAUS8
GAGAACTTGGTATTGGCAGCTCAG
GCCTCCTTGGGATTAAAGTACCAC
57
229
TCEA1
GCTGGTGCTGAAGTCTTTCCTTAG
GACAGAGGAGTGTGTGGGCTATAA
57
204
PDE6C
CAGAGTGGAATGGAAATCCCTAGC
TATGCTAGTCACAGGGTAGCCTTC
57
297
MAD2
AGTGGTCAGACAGATCAGCTACAG
CAACCCTCTGGAAAGTCAGATAGG
57
411
FZR1
GACGAGACACTGAGGTTCTGGAAT
TGCAATCTTCAGGTTCTCTCCAGG
57
205
CDC26
GACCTGGAGACCCGTAAGAAACAA
CTGGCACGCAAGATAATCCACTTC
57
209
ARRB2
ATTGAGGTGTGAACACAGCTTGCC
CAGCTCTTGTGGAACATGGGCATT
59
91
TAF1A
GAGAGAAGGCACCAAGAGAGAAAC
CTCTTCTTGAACCCAGGCAAGATG
57
260
TFDP1
GTCCAGCGGGTTTACACTTTGGTT
CTGCACCAGCATGGCAGTAAACTT
59
107
LSM10
GTCATCCATCGGGTGCGTTACTTT
AAGAAGTCTGCAGGAACCTGAGTG
59
115
NFYB
GGGCAAACATCATGAATTAACCCAGC
AGCTGTAATGTAGCTTCCCAATGC
57
276
RBM42
AGGGAAACCAGAGAAACTGAAGCG
AAAGCCATAACCCTTGGTCTTGCC
59
229
PAIP2
TTCCAGCTCGAGATCTCCCACAAA
TGGTGCTTCTTTCCTTGCTGTCAC
59
400
MED29
CAGTGCCAAGCACTCTCCAACATT
ACAGGCAATCTGGGCTTTGATGAC
60
115
PMS1
TCACAAACTTCCTGCAGAGCCACT
GCTGTACCTTTGGTCATCTGCTGT
58
118
INTS3
ATTCCTTCCTGAACACCTGTGGCA
AGTTGCCCTCTGTACATCAGTGTG
59
105
ELP4
CCTCCTTAGTCATTGCCTGCAGAA
TCAAGGATTGGAGGGTCACATCGT
58
101
ESCO2
CCTAGAGTCTTACTGCCAGAGTGA
AATATGTCTAGGCTGATGGGCCAC
57
168
SFRS7
GGCGATCAAGATCTGTGTCTCTTC
CACTTCAGTCCACTCTTTCAGGAC
57
231
GAPDH
CCAACGTGTCTGTTGTGGATCTGA
GAGCTTGACAAAGTGGTCGTTGAG
58
217
ACTB
ATCGTCCACCGCAAATGCTTCT
GCCATGCCAATCTCATCTCGTT
59
101
H2A.1
GTCGTGGCAAGCAAGGAG
GATCTCGGCCGTTAGGTACTC
57
182
70
2.9
Figures
Figure 2-1 Microarray hybridization design used in the study
71
Figure 2-2 Venn diagram showing the overlap between the probes considered as
present in the microarray analysis
72
Figure 2-3 Microarray profile and q-RT-PCR validations for genes in the cluster 1.
A) Cluster 1 representing genes with an up-regulation at the beginning of the coasting
followed by a decrease thereafter from microarray data. B) q-RT-PCR validations of the
genes specific to the cluster 1.mRNA quantifications data are presented as mean ± s.e.m.
73
Figure 2-4 Microarray profile and q-RT-PCR validations for genes in the cluster 3.
A) Cluster 3 representing genes with an up-regulation at 68hrs followed by a decrease
thereafter from microarray data. B) q-RT-PCR validations of the genes specific to the
cluster 3. mRNA quantifications data are presented as mean ± s.e.m.
74
Figure 2-5 Supplemental dataClusters of Genes generated by K-means clustering
analysis.
Every significant probes (Adjusted P value < 0.01) without any fold-change cut-off have
been used to generate these 6 clusters.
75
Figure 2-6 Supplemental dataKEGG RNA degradation pathway enriched with genes
from cluster 3.
Genes with red stars were detected and enriched in the cluster 3 using DAVID.
76
3 Gene expression analysis of bovine oocytes at optimal
coasting time combined with GnRH antagonist during the
no-FSH period
Rémi Labrecque1, Christian Vigneault2, Patrick Blondin2, and Marc-André Sirard1
1.
Centre de Recherche en Biologie de la Reproduction, Faculté des sciences de
l’Agriculture et de l’Alimentation, Département des Sciences Animales, Pavillon INAF,
Université Laval, Québec, Canada
2.
L’Alliance Boviteq, Saint-Hyacinthe, Québec, Canada.
Cet article a été publié dans la revue « Theriogenology » sous la référence suivante :
bovine oocytes at optimal coasting time combined with GnRH antagonist during the noFSH period. Theriogenology 81:1092-1100.
77
3.1
Résumé
La stimulation ovarienne avec la FSH combinée à une période sans FSH (coasting) de
longueur appropriée avant la récupération des ovocytes apparait maintenant comme une
façon efficace d’obtenir des ovocytes ayant une grande compétence au développement chez
le bovin. Des résultats récents ont démontré qu’un allongement de la période de croissance
folliculaire de seulement 24 heures avait un effet néfaste sur la qualité des ovocytes, tel que
démontré par la diminution des taux d’embryons. Bien que les traitements de stimulation
soient initiés durant la phase lutéale en présence d’une quantité minimale de LH, la
sécrétion pulsatile de LH retrouvée à ce moment pourrait potentiellement avoir un impact
important sur le développement folliculaire ainsi que sur la qualité ovocytaire. Dans cette
étude, un antagoniste de la GnRH (Cetrotide) a été utilisé pour supprimer la sécrétion de
LH durant la période de différenciation folliculaire de façon à mieux comprendre
l’importance physiologique de la LH durant cette période. Les ovocytes ont été récupérés
par ponction transvaginale et la qualité ovocytaire a été mesurée par le taux de
développement des embryons suite à la maturation et la fécondation in vitro. Le
transcriptome des ovocytes provenant des animaux ayant reçu le traitement a été comparé
avec celui provenant d’un groupe témoin (coasting d’une durée de 68 heures) afin de
détecter des possibles altérations dans le transcriptome des ovocytes. La qualité des
ovocytes n’a pas été affectée de façon significative par le traitement tel que démontré par le
taux de développement des embryons. Toutefois, l’analyse des données de microarrays a
démontré qu’un total de 226 gènes présentait une différence significative dans leur niveau
d’ARNm (ratio de changement > 2; P < 0.05), où la majorité d’entre eux présentaient une
surabondance dans le groupe traité. Plusieurs gènes ayant des fonctions en lien avec la
modification post-transcriptionnelle des ARN présentaient une abondance différentielle
dans leur niveau d’ARNm dans le groupe témoin (68 heures), alors que la fonction de
traduction a plutôt été affectée dans le groupe traité avec l’antagoniste de la GnRH, où
plusieurs gènes reliés aux composantes structurelles du ribosome présentaient une
surabondance de leur ARNm. Des ARN messagers ayant des fonctions importantes dans le
contrôle de la séparation des chromosomes ont également démontré des différences
significatives suite au traitement avec le Cetrotide. Ces résultats démontrent que la
suppression de la sécrétion de LH dans un contexte de stimulation optimal va avoir un
78
impact sur l’ovocyte, avec une altération possible des fonctions critiques reliées à la
capacité de traduction et le contrôle de la séparation des chromosomes.
79
3.2
Abstract
Ovarian stimulation with FSH combined with an appropriate period of FSH withdrawal
(coasting) before ovum pick-up (OPU) now appears to be a successful way to obtain
oocytes with high developmental competence in bovine. Recent results showed that
extending follicular growth by only 24 hours has a detrimental effect on oocyte quality as
shown by the reduced blastocyst formation rate. Even though these treatments are initiated
during the luteal phase with low LH level, the small LH pulsatility present at that time
could potentially impact follicular development as well as oocyte quality. In this study, a
GnRH antagonist (Cetrotide) was used to suppress LH secretion during follicular
differentiationto get a better insight into the physiological importance of the LH support
during that period. Oocytes were collected by OPU and quality was assessed by measuring
the blastocyst formation rate obtained after IVM-IVF. The oocyte transcriptome from
GnRH antagonist-treated animals was also compared with that from a control group
(coasting duration of 68 hours) to detect possible alterations at the mRNA level. The oocyte
quality was not statistically affected by the treatment as shown by the blastocyst formation
rate obtained. However, microarrayanalysis showed that a total of 226 genes had a
significant difference (fold change >2, P <0.05)at the mRNA level, with the majority being
in over-abundance in the treated group. Many genes related to RNA post-transcriptional
modifications presented different abundance at the mRNA level significant differences in
the control group (68 hrs), while translation function appeared to be affected, with many
genes related to structural constituents of the ribosome presenting an over-abundance in the
GnRH antagonist-treated group. Specific mRNAs with crucial roles in chromosome
segregation control also showed significant difference at the mRNA level following
Cetrotide treatment. The results presented here indicated that the suppression of the LH
secretion in an optimal stimulated context would have an impact on the oocyte, with the
possible alteration of critical functions related to translation capacity and chromosome
segregation control.
80
3.3
Introduction
The popularity of in vitro maturation (IVM) and in vitro fertilization (IVF) has increased in
recent years, with a particular interest in the bovine species. Previously,this technique was
mainly used with infertile cows, but is becoming more and more common with fertile cows.
The rapid genetic progress gained with the higher number of embryos that could be
produced through repeated oocyte collection followed by IVM-IVF may explain this
growing use. Even though the introduction of coasting after FSH stimulation in 2002
(Blondin et al. 2002) greatly improved the blastocyst formation rate that could be obtained
in an in vitro system, the recent optimization of a similar protocol (Nivet et al. 2012) has
really showed the potential of this procedure. These recent findings demonstrate the
importance of adequate follicular differentiation in a stimulated cycle to provide an optimal
environment to the oocyte, which will ensure a high developmental potential. However, it
appears that even though follicular size is still increasingbecause of the longer coasting
period that results inprolonged follicular growth, it negatively affects oocyte quality as seen
by the lower blastocyst formation rate obtained with a coasting duration of 92 hrs (Nivet et
al. 2012).
During the course of follicular development in a regular cycle, once the FSH starts to
decrease, the dominant follicle will continue its growth through the support of endogenous
LH (Ginther et al. 1998). Therefore, various GnRH analogs have been used to better
understand the physiological role of LH during follicular development. Few differences
exist between GnRH agonist and antagonist in terms of their potential to suppress LH
secretion. The GnRH agonist Deslorelin is able to suppress pulsatile secretion while the
basal concentration is still maintained, whereas the GnRH antagonist Cetrorelix suppresses
both the pulsatile secretion and the basal level (Maclellan et al. 1998). Furthermore, the
suppressive activity of the antagonist Cetrorelix is effective only on the days of injection
(Ulker et al. 2001) while a 28-day agonist treatment (Deslorelin) can induce pituitary
desensitization for up to 12 days after the last injection in the bovine (Bergfeld et al. 1996).
Considering the crucial role of LH during follicular development, the effects of suppressing
LH pulses through the use of GnRH antagonist on the developmental competence were
studied in sheep and bovine. While a decrease in embryo development was observed both
81
in vivo and in vitro in the sheep (Oussaid et al. 1999), no such effects were found in the
bovine after GnRH antagonist treatment (Oussaid et al. 2000). When our coasting
treatments are initiated, during the luteal phase (Nivet et al. 2012), we assume that LH
pulse frequency will not increase due to the high progesterone level (Cupp et al. 1995).
However, the basal LH level present during the luteal phase could potentially have a
beneficial impact on follicular development as demonstrated by the continuing follicular
growth.
Very few studies have looked at the consequences of follicular growth extension in the
absence of LH pulse, especially on oocyte quality (Dias et al. 2013). In our coasting
protocol, a difference of only 24 hours has a visible effect on the resulting blastocyst
formation rate (Nivet et al. 2012). Furthermore, important changes at the transcriptome
level are also observed in the oocyte collected by ovum pick-up (OPU) between the
different coasting regimen (Labrecque et al. 2013). We hypothesized that the suppression of
the LH secretion using an in vivo treatment of GnRH antagonist (Cetrotide) during the
optimal coasting duration would have a negative impact on oocyte quality.Therefore, the
aim of this project was to study the physiological impact of the follicular development
without the LH support and to compare the developmental potential, up to the blastocyst
stage, of oocytes recovered at the optimal coasting time (68 hrs) with or without GnRH
antagonist. This study also aimed at comparing the transcriptome of GV-stage oocyte to
determine which transcripts were affected by the treatment.
3.4
3.4.1
Results
Blastocyst rate
In this study, a super-stimulation protocol using a coasting duration of 68hrs with or
without GnRH antagonist treatment (Cetrotide) was used to look at the effects of the
suppression of LH secretion in an optimal context of follicular differentiation. Half of the
COCs collected in the control and treated groups were used in IVM-IVF-IVC. Blastocyst
rate (67.1% ±13.9%; 52.4% ±5.6% for the control and the treated group respectively,
data are presented as mean ± s.e.m.) showed no statistically significant difference (P value
= 0.15).
82
3.4.2
Microarray analysis
Transcriptomic analysis was performed to compare oocytes from the control (68hrs) and
the treated (68hrs-Cetrotide) groups. Considering the threshold used to detect the
presence/absence of a transcript on the microarray slide (background level + 2 times the SD
of the background), a total of 14,806 probes were found to be present in both the control
and treated conditions. Statistical analysis of the microarray comparison (68hrs vs. 68hrsCetrotide) showed that 226 probes were significantly different with a fold change > 2 and P
value < 0.05. Of these, 218 genes/probes were more abundant in the treated group and only
8 genes/probes were less abundant in the treated group.
3.4.3
Functional analysis
To get a better overview of the differences found between oocytes from the control and the
treated groups, DAVID bioinformatics resources were used to group overrepresented
functions of statistically significant genes into clusters of molecular functions. A fold
change cut-off of 1.5 was used to select approximately 450 and 250 genes respectively
showing higher or lower abundance of their transcript level. DAVID analysis showed an
important role for genes related to translation, chromatin assembly and chromosome
organization among the list of genes presenting an overabundance at the mRNA level in the
Cetrotide-treated group. Genes related to functions such as mRNA transport, RNA
localization and transcription were decreased at the mRNA level in the treated group.Using
the same lists of genes as for DAVID analysis, IPA was used to identify molecules or
functions significantly and differentially affected between the two conditions. Among the
most significant molecular and cellular functions in the list of overabundant mRNA, IPA
identified RNA post-transcriptional modification and DNA replication, recombination and
repair and protein synthesis as the most affected. Among the list of genes showing a
decrease at the mRNA level, IPA also highlighted an important role for genes related to
molecular transport and RNA trafficking.
83
3.4.4
Microarray validation by quantitative RT-PCR
In order to validate the microarray results, quantitative RT-PCR experiments were
conducted on seven genes mainly involved in chromosome segregation control, posttranscriptional RNA modifications and chromatin organization. Three of these genes
(TACC3, SARNP and CTNNBL1) showed a statistically significant difference with a P
value < 0.05, while three genes (NUF2, CENPK and MBIP) showed a tendency (P value <
0.1) and one gene showed no significant difference (GADD45B: P = 0.1892) (Figure 3-2).
3.5
Discussion
The results presented herein are the first description of the oocyte transcriptome in twovery
close follicular conditions where basal LH influences the oocyte through its mRNA
content.Endogenous LH secretion was suppressed through the use of a GnRH antagonist
(Cetrotide) to look at the possible effects on oocyte quality. The IVF results demonstrated
that oocyte competence was not affected by the GnRH antagonist treatment as shown by
the blastocyst formation rate (P value = 0.15). In a different study where another GnRH
antagonist (Antarelix) was used to suppress endogenous LH secretion, a high proportion of
the follicles showed signs of advanced atresia, although the rates of blastocyst formation
were similar in the two groups(Oussaid et al. 2000). However, the same antagonist
(Antarelix) administrated the day before ovulation in the sheep reduced the blastocyst
formation rate obtained in vivowith more unfertilized or degenerated sheep oocytes
collected eight days after insemination (Oussaid et al. 1999). Moreover, a significantly
higher number of embryos were blocked before the 16-cell stage in the treated group seven
days after IVF (Oussaid et al. 1999). Taken together, the suppression of LH pulses is not
without impact either on follicle health or on the oocyte quality. These data confirm that the
alteration of follicular growth through the suppression of LH secretion will likely cause a
faster alteration of oocyte quality, even though the parameter observed to assess the quality,
in this case blastocyst formation rate, did not show statistically significant difference.
GnRH antagonist is widely used in human IVF protocol to prevent premature LH surge and
avoid spontaneous ovulation (Al-Inany et al. 2007). However, very few studies looked at
the possible quality deterioration when the oocyte is not ovulated at the right time. A recent
84
study in the mouse reported a decrease in the number of developing embryos with an
increase in the number of resorption sites when the ovulation was delayed through the use
of Cetrorelix compared to a control without delayed ovulation. These authors suggested
that preovulatory oocyte ageing due to delayed ovulationmight affect fertility and embryo
development (Bittner et al. 2011).
Few teams have analyzed gene expression following treatment with a GnRH antagonist to
better understand the possible alteration in the follicular environment following this kind of
treatment. Human granulosa cells in culture (Winkler et al. 2010), human cumulus cells
(Devjak et al. 2012) or bovine granulosa cells (Luo et al. 2011) were investigated with
regard to possible changes in gene expression after GnRH antagonist treatment. However,
to our knowledge, this is the first time that gene expression analysis is performed
comparing GV stage bovine oocytestreated with a GnRH antagonist widely used in human
IVF.
Microarray analysis showed significant differences in terms of genes differentially
represented between oocytes from the control and the treated groups. Statistical analysis
showed a higher proportion of transcripts increased in the treated group compared to the
control (Table 3-2). This is particularly intriguing, as it was already known that
transcription becomes less and less active at the end of oogenesis (Fair et al. 1995) and the
fully inactive transcriptional state is established at the GVBD stage following the initiation
of maturation (Kastrop et al. 1991). Results from a previous experiment where the
transcriptome of oocytes from different coasting durations were compared could lead to the
supposition that changes in terms of transcripts differences should be similar to what was
observed between 68hrs and 92 hrs (Labrecque et al. 2013) considering the reduced
blastocyst formation rate observed in both cases. Comparative analysis of the microarray
results from this study and those where the transcriptome of oocytes from different coasting
durations was analyzed, revealed widely different transcriptional dynamics between the two
groups of oocytes (Cetrotide and 92 hrs, Figure 3-3) in term of number of significantly
affected probes. The different physiological context in each case could explain the lack of
similitude between the number of genes presenting significant difference.In fact, by looking
85
at the number of probes in common between the contrast 68 hrs vs. 68 hrs-Cetrotide (this
study) and 68hrs vs. 92hrs(Labrecque et al. 2013), a relatively low number of probes (11)
exhibited a similar profile with a fold change cut-off of two(Figure 3-3). A clear
overabundance of genes presenting a higher mRNA level was observed in the Cetrotide
contrast compared to the 68 hrs vs 92 hrs contrast.
The functional analysis performed with DAVID bioinformatics resources highlighted the
general tendencies in terms of significantly affected functions among the gene lists.
Therefore, four main functions seem to be predominantly affected by the Cetrotide
treatment: chromosome segregation control, translation, RNA post-transcriptional
modifications and chromatin organization (Table 3-3). The overrepresentation of functions
related to transcription such as mRNA transport and RNA processing among the transcripts
presenting a decrease in the treated group, and thereby an increase in the control group (68
hrs), is consistent with our previous analysis (Labrecque et al. 2013). Optimal coasting
duration allows the accumulation of transcripts to adequately regulate RNA posttranscriptional modifications even with minimal transcriptional activity.However, this does
not seem to be the case in the Cetrotide group. Indeed, many genes that showed an increase
in the treated group are related to translation function such as structural constituent of the
ribosome. Considering the decreased transcriptional activity in the oocyte at that time, it is
unlikely that Cetrotide treatment will result in such an important transcriptional response
from the oocyte. Therefore, these results could suggest a reduced translation capacity in the
oocyte following Cetrotide treatment as these mRNA are accumulating instead of being
translated. To support this hypothesis, mRNAs for many of these genes were either
identified as decreasing between GV and MII stage bovine oocyte (Mamo et al. 2011) or
were identified among a list of bovine GV stage oocyte mRNAs harboring a long poly A
tail(Gohin et al. 2014), while the corresponding protein for some of these genes was also
identified in MII oocyte from mouse proteomic studies (Pfeiffer et al. 2011; Wang et al.
2010; Zhang et al. 2009)(Table 3-4). Therefore, the higher mRNA level for these genes in
the treated group could explain the reduced translation capacity in the oocyte, which would
affect the developmental potential of these oocytes. However, validation of this hypothesis
in the bovine requires the protein measurement in control and Cetrotide-treated oocytes,
86
which is simply not possible given the financial and technical implications to collect all
those samples.
Genes related to chromatin organization function, such as suppressor of variegation 3-9
homolog 1 (Drosophila) (SUV39H1), chromobox homolog 8 (CBX8), and methyl-CpG
binding domain protein 1 (MBD1), also presented high levels of mRNA in the treated
group. Furthermore, MAP3K12 binding inhibitory protein 1 (MBIP) showed a decrease in
the amount of mRNA in the treated group. MBIP is a member of the human ATAC
complex (Ada two A containing), along with CSRP2 binding protein (CSRP2BP, also
known as ATAC2). This complex is involved in histone acetyltransferase activity. Taken
together, these data suggest that the suppression of the LH secretion could have an
important impact on chromatin organization machinery.
Data analysis with IPA gave similar results as DAVID with the list of decreased transcripts
by highlighting functions related to molecule transport and RNA trafficking. The IPA
analysis performed with the list of genes with an increased mRNA level yielded an
interesting function that was not highlighted by DAVID, namely DNA replication,
recombination and repair. This last finding is in close relation with our recent findings in
the oocyte following a prolonged coasting period (92 hrs), where many genes related to
chromosome segregation control function were affected (Labrecque et al. 2013). Indeed,
genes such as Integrator complex subunit 3 (INTS3), Ligase 1, DNA, ATP-dependent
(LIG1), N-methylpurine-DNA glycosylase (MPG), MUS81 endonuclease homolog
(MUS81), Valosin-containing protein (VCP), X-ray repair complementing defective repair
in Chinese hamster cells 1(XRCC1) and Growth arrest and DNA-damage-inducible, Beta
(GADD45B) all showed an increase at the mRNA level in the oocyte after Cetrotide
treatment and are all implicated in DNA replication, recombination and repair function.
Multiple centromeric proteins involved in the structure and the function of the kinetochore
(Okada et al. 2006), such as Centromere protein H, I and K (CENP-H, -I and -K), were all
affected at the mRNA level by the GnRH antagonist treatment. Furthermore, members of
the Ndc80 complex like NUF2 (NUF2, NDC80 kinetochore complex component, homolog
(S. cerevisiae)) and SPC25 (SPC25, NDC80 kinetochore complex component, homolog (S.
87
cerevisiae)) (Ciferri et al. 2007) were also affected at the mRNA level. The perturbation of
all these factors in chromosome segregation control suggests a deregulation in Cetrotidetreated oocyte.
Among the genes that showed an increasedmRNA level following Cetrotide treatment,
Transforming, acidic coiled-coil containing protein (TACC3) plays a specific role in
microtubule organization (Peset and Vernos 2008)and is regulated by Aurora A (Kinoshita
et al. 2005). While being implicated in the control of cell growth, high expression of
TACC3 has been reported in many cancer cell lines, suggesting an oncogenic potential for
this gene (Still et al. 1999). In situ hybridization of mouse ovarian tissue revealed a high
abundance of TACC3 mRNA in the cytoplasm of growing oocytes, but not in those from
primordial or atretic follicles (Hao et al. 2002). Proteomic analysis during mouse oocyte
maturation showed a decreased amount of TACC3 protein between the GV and MII stages,
suggesting a tight regulation of this gene during oocyte maturation (Vitale et al. 2007). In
human oocyte, TACC3 mRNA was also detected at high levels in GV oocyte, but decreased
at the MII stage (Assou et al. 2006). Another interesting fact is the similarity between the
Xenopus translational regulator of CPE-containing mRNAs maskin and human and mouse
TACC3 protein (Stebbins-Boaz et al. 1999). Therefore, it has been suggested that
TACC3could play a similar role as maskin in terms of translational regulation in
mammalian oocytes (Evsikov et al. 2004; Hao et al. 2002). Taken together, these results
suggest that the higher level of TACC3 mRNA found in the Cetrotide-treated oocytes could
result from inefficient translation, especially in light of the proteomic results in mouse
(Vitale et al. 2007)and combined with the fact that the transcript was identified as bearing a
long polyA tail in bovine GV stage oocyte(Gohin et al. 2014). As a consequence, this could
lead to aneuploidy in the oocytes in a similar fashion as in TACC3-depleted cells where
aberrant spindle morphology and misaligned chromosomes were observed (Gergely et al.
2003; Schneider et al. 2007). Even though the validation of this hypothesis would require
measurement of the protein level, further analysis will be carried out along these lines given
the fact that aneuploidy has already been suggested as a possible mechanism of quality
control in the oocyte (Labrecque et al. 2013).
88
SAP domain-containing ribonucleoprotein (SARNP), also known as CIP29, showed a
significant decrease at the mRNA level in the treated group, both with microarray and qRTPCR data. SARNP was identified as a nuclear matrix protein with cell growth function, and
expression of this protein in human cell line causes apoptosis resulting in cells arrested at
the G2/M stage (Leaw et al. 2004). Furthermore, SARNP is also part of the TREX
(Transcription/Export) complex, which couples transcription and splicing to mRNA export
(Dufu et al. 2010). However, SARNP can also be associated with RNA helicase DDX39A
(DEAD (Asp-Glu-Ala-Asp) box polypeptide 39A) to form an alternative mRNA export
complex named AREX. Together with the mRNA export function, this complex is also
implicated in mitotic progression. It has been suggested that there could be a hierarchy
within the AREX complex and its function may depend more on the presence of
DDX39A(Yamazaki et al. 2010). In our microarray data, no significant difference was
observed for the DDX39A, but an important mRNA decrease was observed following
bovine oocyte maturation (Mamo et al. 2011), in addition to a possible role in maternal
embryonic transition (MET) (Vigneault et al. 2009). SARNP was also identified as bearing
a long polyA tail in GV stage bovine oocyte (Gohin et al. 2014) while a decrease was
observed between GV and MII stage in human oocyte (Assou et al. 2006). Therefore, an
early translation would be unlikely at the GV stage, even though protein validation would
be required to confirm this hypothesis. RNA degradation could explain the decrease for this
transcript in a similar fashion as what we proposed to explain the decrease for many
transcripts when the oocyte stays too long in the ovary during coasting (Labrecque et al.
2013).Nevertheless, it could be hypothesized that Cetrotide treatment will cause early
degradation of SARNP mRNA. This could result in similar mitotic defects to those
observed in human cells due to a possible interaction with DDX39A.
Catenin beta like 1 (CTNNBL1) showed a higher level of mRNA, both in microarray and
qRT-PCR data, after Cetrotide treatment. CTNNBL1 has been identified as a nuclear
protein with possible apoptosis-inducing role when over-expressed in cultured cells
(Jabbour et al. 2003). Apart from this role, this gene has also been linked to RNA splicing
function through its association with the spliceosome complex member CDC5L (CDC5 cell
division cycle 5-like) (Ganesh et al. 2011). CDC5L mRNA has already been identified as
89
being accumulated in the oocyte during follicular growth (Mourot et al. 2006), but our
microarray data revealed no significant differences between control and Cetrotide-treated
oocytes for CDC5L. A recent study demonstrated that although CTNNBL1 was not essential
for RNA splicing activity in B cell lines, it was shown to ultimately result in embryonic
lethality in knockout mouse. The authors suggested that this gene may function as a link
between RNA splicing and cell cycle progression (Chandra et al. 2013). CTNNBL1
transcript was also identified in a group of transcript bearing a long polyA tail in GV stage
oocyte (Gohin et al. 2014) and a decrease was observed between GV and MII stage both in
the bovine (Mamo et al. 2011) and human (Assou et al. 2006). Furthermore, a reduced
amount of CTNNBL1 mRNA was detected in mouse GV stage oocyte presenting a
surrounded nucleolus (SN) configuration of the chromatin compared to their notsurrounded nucleolus (NSN) counterpart, where the latter are known to be more active
transcriptionally (Ma et al. 2013). Therefore, the increased mRNA level for CTNNBL1
could also be the result of inefficient translation in the Cetrotide-treated oocytes as
previously described. Altogether, this could lead to further defects related to RNA posttranscriptional modifications.
3.6
Conclusion
The exact mechanism by which the absence of the LH pulses causes all these disturbances
in the oocyte at the transcript level is still unknown, but GnRH antagonist treatment affects
the abundance of specific mRNAs with crucial role in chromosome segregation control as
well as RNA post-transcriptional modifications and chromatin organization.The increased
mRNA level of many genes in the treated group could therefore be the result of the
inefficient translation of these transcripts. Even though this hypothesis will require protein
measurement, it seems plausible due to the over-abundance of functions related to
translation in the list of higher-abundance mRNAs identified by the functional analysis. A
possible consequence on the oocyte could therefore be a predisposition to aneuploidy. The
genes identified in this study will certainly be the subjectof further experiments to better
understand the process of oocyte quality control.
90
3.7
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as
prejudicing the impartiality of the research reported.
3.8
Acknowledgements
We thank Serono MERCK for the GnRH antagonist, Dr. Isabelle Dufort for her technical
assistance in all the molecular biology experiments, Dr. Marc-André Laniel for revising the
text and all the staff at LAB for their help in the collection of biological samples. This work
was supported by the Natural Sciences and Engineering Research Council of Canada
(NSERC-CRSNG), L'Alliance Boviteq (LAB) and the Fonds Québécois de la Recherche
sur la Nature et les Technologies (FQRNT).
3.9
3.9.1
Oocyte recovery
Bovine cumulus oocyte complexes (COCs) were obtained by OPU after super-stimulation
protocol as described by Nivet et al. (2012). Briefly, five commercial Holstein cycling
cowswere treated during the luteal phase to prevent spontaneous ovulationand no hormonal
synchronization was used. The dominant follicle was aspirated 36 hours before
administration of hormones.The cows were stimulated for three days with FSH (6 x 40 mg
NIH Folltropin-V, Bioniche Animal Health, Belleville, ON, Canada), given at 12-hr
intervals, followed by a coasting period (no FSH) of 68 hrs (control) before OPU. This
coasting duration was identified as optimal (between 44 and 68hrs) in our previousstudy
(Nivet et al. 2012). For the treated group, the same five animals were usedwith at least one
complete sexual cycle between twointerventions, so each cow served as its own controlfor a
total of five biological replicates. The overall length of the collection procedures takes part
over a period of 13 months.Therefore, the animals received the FSH stimulation over three
days exactly as in the control group, in addition to GnRH antagonist injections (cetrorelix,
Cetrotide, Merck Serono) (20 µg/kg) given intramuscularat 12-hr intervals starting from
Day 2 of the FSH stimulation until OPU (Figure 3-1).Using transvaginal ultrasonography,
91
follicular diameters were measured and an average of 16COCs were collected by OPU
from each animal on a total of ten COCs collections, under epidural, with a 19G needle and
COOK aspiration unit (COOK Medical, Bloomington, IN). COCs were collected in warm
Hepes-buffered Tyrode’s media (TLH) containing Hepalean (10 UI/mL) and transferred to
the laboratory. The COCs were then placed in TLH solution supplemented with 10%
bovine serum, 0.2 mM pyruvate and 50 µg/mL gentamicin and washed three times to
remove follicular fluid. Healthy COCs were randomly divided into two groups; the first
half representing an average of eight COCs per animal was used for in vitro maturation,
fertilization and development. These steps were described in details by Nivet et al. (2012).
Blastocyst development was assessed at Day 7 and Day 8 post-fertilization using industrial
criteria for embryo transfer as recommended by the IETS.The other half of COCs were
mechanically denuded by vortexing for 2-3 minutes in PBS-PVA to remove all the cumulus
cells. Oocytes were then washed two times in PBS-PVA solution and one more time in
PBS solution, collected in small volumes of PBS, snap-frozen in liquid nitrogen and stored
at -80°C until RNA extraction.
3.9.2
Total RNA was extracted from six pools of oocytes (five to ten oocytes per pools),
corresponding to three different biological replicates (cows) and the two conditions (control
and treated)with Pico-Pure RNA Isolation Kit (Applied Biosystems, Carlsbad, CA)
following the manufacturer’s protocol and including DNase treatment on the purification
column. Total RNA integrity and concentration were evaluated on a 2100-Bioanalyzer
(Agilent Technologies, Palo Alto, CA) with the RNA PicoLab Chip (Agilent
Technologies). To generate enough material for hybridization, the samples were amplified.
Antisense RNA (aRNA)was produced using the RiboAmp HS RNA amplification kit
(Applied Biosystems). After two amplification rounds of 6 hrs each, the aRNA output was
quantified using the NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, DE).
3.9.3
Sample labeling and microarray hybridization
For each sample, 2 µg of aRNA were labeled using the ULS Fluorescent Labeling Kit for
Agilent arrays (Cy3-Cy5) (Kreatech Diagnostics, Amsterdam, Netherlands). The labeled
92
product was then purified with the Pico-Pure RNA Isolation Kit but without DNase
treatment. Labeling efficiency was measured using the Nano-Drop ND-1000. Samples from
the 3 biological replicates (cow) were hybridized on EmbryoGENE’s bovine 44K
microarray (Robert et al. 2011)with the following design: 68 hrs (control) vs 68 hrs Cetrotide (treatment) in a dye-swap set-up. A total of 825 ng of each labeled sample (Cy3
and Cy5) were incubated in a solution containing 2x blocking agent and 5x fragmentation
buffer in a volume of 55 µL at 60°C for 15 min and were put on ice immediately after.
Then, 55 µL of 2x GEx Hybridization Buffer HI-RPM were added for a total volume of
110 µL. The hybridization mix (100 µL) was added onto the array and hybridization was
performed at 65°C for 17 hrs using an Agilent Hybridization chamber in a rotating oven.
Slides were then washed with Gene Expression Wash Buffer 1 containing 0.005% Triton
X-102 for 3 min at room temperature and then transferred to Gene Expression Wash Buffer
2 containing 0.005% Triton X-102 for 3 min at 42°C. Final washes with acetonitrile for 10
sec at room temperature and with drying and stabilization solution for 30 sec at room
temperature were performed before air-drying of the slides. The slides were scanned using
the Tecan PowerScanner microarray scanner (Tecan Group ltd, Männerdorf, Switzerland)
and features were extracted using ArrayPro 6.4 (Media Cybernetics, Bethesda, MD).
3.9.4
3.9.4.1 Data processing
Microarray data were subjected to a simple background subtraction, normalized within
array (Loess) and between array (Quantile) and statistically analyzed with Limma package
using FlexArray version 1.6.1 (Blazejczyk et al. 2007). Differences were considered
statistically significant with a P value less than 0.05. To detect the presence or absence of
the signal for each spot, we used an arbitrary cut-off level of 7.8 (log2), which corresponds
to the mean intensity of the background level + 2 times the standard deviation (SD) of the
background. Microarray data have been deposited in NCBI’s Gene Expression Omnibus
and are accessible through GEO series accession number GSE44664.
(http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44664).
93
3.9.4.2 Functional analysis
DAVID bioinformatics resources were used to perform functional enrichment analysis
(Huang et al. 2009a; Huang et al. 2009b)of specific genelist based on fold change cut-off.
The gene lists were then analyzed through the use of IPA (Ingenuity Systems,
www.ingenuity.com). All statistically significant genes (P value < 0.05; fold change > 1.5)
were uploaded to the application and each identifier was mapped to its corresponding
object in the Ingenuity database. The functional analysis identified the biological functions
that were most significant to the molecules in the dataset.
3.9.5
recommendations. The primers used for qRT-PCR are listed in Table 4-1 and were
designed
using
the
IDT
PrimerQuest
tool(http://www.idtdna.com/Scitools/Applications/Primerquest/) from sequences obtained
microarray analysis. To confirm the specificity of each pair of primers, electrophoresis on a
standard 1.2% agarose gel was performed for each amplified fragment. The PCR product
was then purified with the QIAquick PCR purification kit (Qiagen), quantified using the
curve for quantification experiment, with dilutions ranging from 2 x 10-4 to 2 x 10-8 ng/µL.
Real-time PCR was performed on a LightCycler 480 (Roche Diagnostics, Laval, QC,
Canada) using SYBR incorporation. Each q-RT-PCR reaction, in a final volume of 20 µL,
contained the cDNA corresponding to 1/40 of the total volume of the reverse transcription
reaction described previously, 0.25 mM of each primer and 1x SYBR mix (LightCycler 480
SYBR Green I Master, Roche Diagnostics). The PCR conditions used for all genes were as
follows: denaturing cycle for 10 min at 95°C; 50 PCR cycles (denaturing, 95°C for 1 sec;
annealing, [Table 3-1] for 5 sec; extension, 72°C for 5 sec), a melting curve (94°C for 5
sec, 72°C for 30 sec and a step cycle starting at 72°C up to 94°C at 0.2°C/sec) and a final
cooling step at 40°C. Complementary DNA quantification was performed with the
94
LightCycler 480 Software Version 1.5 (Roche Diagnostics) by comparison with the
standard curve. PCR specificity was confirmed by melting-curve analysis.
3.9.6
For each gene tested, three biological replicates corresponding to oocytes pools collected
from three different cows were used. Analysis of gene expression stability over the two
groups of oocytes (control and treated) from the different cows was performed using the
GeNorm VBA applet software as described by Vandesompele et al. (2002). The most
stable reference genes were identified by the stepwise exclusion of the least stable gene and
recalculating the M values. Following GeNorm analysis, ACTB and H2A.1were the most
stable genes with M values < 1.5 as recommended by the software (M value = 0.504).
Graph Pad Prism (version 5.0) was used to perform the evaluation of the mRNA
differences by one-tailed unpaired t-test. Follicle size and blastocyst formation rate were
analyzed with an ANOVA using Graph Pad Prism. Differences were considered to be
statistically significant at the 95% confidence level (p<0.05). Data are presented as mean ±
s.e.m.
95
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101
3.11 Figures
Figure 3-1 Experimental procedure.
Each animal received the same FSH stimulation over three days, followed by a coasting
period (no-FSH) of 68 hrs (control) or by GnRH antagonist treatment (Cetrotide) during the
whole coasting period. Ovum pick-up was performed shortly after the last injection of
Cetrotide for the treated group.
102
Figure 3-2 Quantitative RT-PCR validations of selected genes.
mRNA quantifications are presented as mean ± s.e.m.and represent the result of three
biological replicates.
* Significantly different from control with P value < 0.05
103
Figure 3-3 Venn diagram illustrating the number of probes in common between the
contrast 68 hrs vs. 68 hrs-Cetrotide (this study) and 68 hrs vs. 92 hrs (Labrecque et al.
2013).
The upper and lower part of the diagram shows the number of probes with a positive or
negative fold change (FC > 2 or FC < -2).
104
3.12 Tables
Table 3-1 Primer sequences used for quantitative RT-PCR.
Gene
symbol
Annealing
temp. (°C)
Product
size (bp)
TACC3
CGGTTTGAGAAACAGAAGGAGGTG
TCCACCCTCTCTATGTAATCCTCG
57
92
NUF2
GAGGTGCTGTCTATGAGCGAGTAA
TTAGCATGGCAGTCCTCTGTTG
57
190
CENPK
TGGTGTTCCACATGACCCAT
TGGATCTTCTGGATGTCTCAAGGC
57
106
SARNP
TTCAGCTGGAACAGGAACCACAGA
TGCAAGGCAGGACCTAAGCACATA
57
184
MBIP
ACAGAATCCATGGCAACACGTCAC
CTTGCTGCAAAGTGGACTCACA
57
180
CTNNBL1
CGGCGTGGAGAGATCATAGACAAT
CTTCCACGCATATTCAGGATCTGG
57
170
GADD45B
GCTCATGTTGCTCTGTACCCAT
CCCTCTGTCTTTCAGTGGTTCAAG
57
105
ACTB
59
101
H2A.1
GTCGTGGCAAGCAAGGAG
GATCTCGGCCGTTAGGTACTC
57
182
105
Table 3-2Number of differentially represented genes between control and treated
conditions (68-68Cet) in comparison with our previous results (Labrecque et al. 2013)
68-68Cet
68-92
Fold Change> 2
226
191
Genes up
218
98
Genes down
8
93
106
Table 3-3 Functions affected and results from microarray and qRT-PCR experiment
Profile
Function
Genes
Chromosome segregation
TACC3
Microarray
Fold change
2.3
control
CENPH
-1.8
CENPI
-1.32
CENPK
-1.5
P value = 0.089
NUF2
-1.41
P value = 0.09
SPC25
-1.35
INTS3
2.47
LIG1
8.26
MUS81
1.66
MPG
2.14
VCP
2.6
XRCC1
1.85
EIF4A1
3.4
MRPS7
2.0
RPL18
3.2
RPS14
2.7
TUFM
2.0
RNA post-transcriptional
SARNP
-1.6
P value < 0.05
modifications
CTNNBL1
2.3
P value < 0.05
SNRPC
2.9
SNRPD1
2.3
MBIP
-2.1
CBX8
2.2
MBD1
2.6
SUV39H1
1.7
Translation
Chormatin organization
q-RT-PCR
(P value)
P value < 0.05
P value = 0.097
107
Table 3-4Genes with high mRNA level in the Cetrotide treated group (FC > 2) and information regarding their possible
translation
Bovine GV stage oocyte
mRNAs harboring a long poly
A tail (Gohin et al. 2014)
mRNA profile between GV
and MII stage bovine oocyte
Protein detected in mouse MII oocyte
EIF4A1
-
-
(Wang et al. 2010; Zhang et al. 2009)
MRPL14
-
Decrease (Mamo et al. 2011)
-
MRPS11
-
-
MRPS7
+
(Pfeiffer et al. 2011)
RPL18
+
-
RPL18A
-
-
(Pfeiffer et al. 2011; Zhang et al. 2009)
RPL28
+
-
RPL31
+
-
(Pfeiffer et al. 2011; Wang et al. 2010;
Zhang et al. 2009)
RPLP2
-
-
RPS14
-
RPS15
-
-
(Wang et al. 2010)
TUFM
-
(Pfeiffer et al. 2011; Zhang et al. 2009)
Gene Symbol
108
4 Microarray analysis of bovine oocytes from distinct follicle
sizes reveals gradual mRNA modulations
Rémi Labrecque and Marc-André Sirard
Centre de Recherche en Biologie de la Reproduction, Faculté des sciences de l’Agriculture
et de l’Alimentation, Département des Sciences Animales, Pavillon INAF, Université
Laval, Québec, Canada
109
4.1
Résumé
La taille du follicule est reconnue depuis un long moment comme un facteur qui affecte la
probabilité de l’ovocyte contenu dans le follicule à produire un embryon suivant la
maturation et la fécondation in vitro. Les ovocytes provenant de follicules plus gros ont
davantage de chance d’atteindre le stade de blastocyste que ceux provenant de follicules
plus petits. Durant sa croissance, l’ovocyte doit accumuler tous les transcrits
nécessairesafind’assurer le développement jusqu’à l’activation du génome embryonnaire et
cette accumulation doit être complété avant l’arrêt de la transcription. Le but de cette étude
était d’analyser le transcriptome des ovocytes bovin récupérés à partir de follicules de taille
croissante (<3 mm; 3-5 mm; >5-8 mm et>8 mm) afin de mieux comprendre les
modulations du transcriptome qui surviennent dans l’ovocyte au fur et à mesure que le
follicule grossit. Pour accomplir cet objectif, la plateforme bovine d’analyse
transcriptomique d’EmbryoGENE (custom Agilent 44K), spécifiquement conçue pour
l’étude de l’ovocyte et du jeune embryon a été utilisée. L’analyse des données de
microarray a révélé très peu de différences entre les ovocytes provenant de petits follicules
(<3 mm vs 3-5 mm), alors qu’un nombre croissant de différences a été détecté dans
l’abondance des ARN entre les follicules de plus grande taille. Deux principaux profils de
modulations des transcrits ont été identifiés : plusieurs ARN subissent une diminution
progressive alors que plusieurs autres sont accumulés de façon constante. De plus, les
follicules de taille croissante présentent une accumulation des transcrits tels que RNF2,
PPP1CB et MBIP, en lien avec l’assemblage de la chromatine, l’organisation des
chromosomes et l’empaquetage de l’ADN, mais également une modulation de plusieurs
autres transcrits impliqués dans la régulation des ARN tels que ENY2, DAZL, WIBG et
HOXA9 dans les ovocytes. Ces résultats renforcent l’hypothèse que l’acquisition de la
compétence au développement est un processus graduel, particulièrement en ce qui
concerne la modulation des ARN messagers. Cela illustre également l’importance du
remodelage adéquat de la chromatine à la fin de la croissance de l’ovocyte.
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4.2
Abstract
The size of the follicle has been recognized for a long time as a factor affecting the
probability of the enclosed oocyte to yield an embryo following in vitro maturation and in
vitro fertilization. Oocytes from larger follicles are more likely to reach the blastocyst stage
than those from smaller follicles. During its growth, the oocyte accumulates all the
transcripts needed to ensure development until the maternal embryonic transition and this
accumulation must be completed before the transcriptional arrest. The aim of this study was
to analyze the transcriptome of bovine GV stage oocytes collected from follicles of
increasing sizes (<3 mm; 3-5 mm; >5-8 mm and >8 mm) in order to better understand the
transcriptional modulation in the oocyte as the follicle becomes larger. To achieve our
objectives, the EmbryoGENE bovine transcriptomic platform was used, which includes a
custom Agilent 44K microarray slide specifically designed for the study of oocyte and early
embryo. Microarray analyses have revealed very few differences between oocytes from
small follicles (<3 mm vs 3-5 mm), while an important number of differences were
detected at the mRNA level between oocytes from larger follicles. Two main patterns of
transcript modulation were identified: several mRNAs underwent progressive depletion
while several others underwent constant accumulation. Interestingly, follicles of increasing
size present an accumulation of transcripts such as RNF2, PPP1CB, and MBIP related to
chromatin assembly, chromosome organization and DNA packaging, but also the
modulation of several other transcripts involved in RNA processing like ENY2, DAZL,
WIBG and HOXA9 in oocytes. The results presented here reinforce the hypothesis that
developmental competence acquisition is a gradual process especially in regard of the
mRNA modulations. It also illustrates the importance of adequate chromatin remodeling at
the end of oocyte growth.
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4.3
Introduction
In vitro maturation (IVM) and in vitro fertilization (IVF) of bovine oocytes obtained from
slaughterhouse-derived ovaries is a routine procedure in many laboratoriesused to study
various questions related to reproductive biology. However, even after more than 25 years
of improvements of this system, from optimization of culture media to morphological
selection of the best cumulus-oocyte complexes, success rates remain quite low after IVF
with an average blastocyst rate of 30%(Lonergan and Fair 2008).
In general, bovine oocytes are collected from slaughterhouse-derived ovaries by aspiration
of follicles 2 to 8 mm in diameter (Blondin and Sirard 1995; Lonergan et al. 1994; Pavlok
et al. 1992; Rizos et al. 2002). However, in the bovine, the dominance status of the follicle
is acquired once the follicle reaches a diameter of 8.5 mm (Ginther et al. 1997), while the
pre-ovulatory follicle ranges from about 16 to 22 mm in diameter (Hunter et al. 2004).
Nevertheless, for practical reasons and to obtain sufficient material (oocytes), follicles of
smaller sizes, which are present in higher numbers on slaughterhouse-derived bovine
ovaries, are generally used to collect oocytes for IVM-IVF. One factor known for a long
time regarding the probability of an oocyte developing into an embryo following IVM-IVF
is related to the size of the follicle of origin. Effectively, higher blastocyst rates are
obtained with oocytes from larger follicles compared to oocytes collected from smaller
follicles (reviewedin (Blondin et al. 2012)). Therefore, the follicle size model represents a
relevant and relatively easy approach to obtain oocytes of distinct developmental
competence.
The important transcriptional activity occurring in the oocyte during folliculogenesis allows
the accumulation of maternal transcripts needed to ensure development until the maternal
embryonic transition at the 8- to 16-cell stage in the bovine (Memili et al. 1998). It is also
generally accepted that the end of oocyte growth represents a crucial moment as regards the
acquisition of developmental competence. Once the oocyte reaches 110 µm, transcriptional
activity becomes less important (Fair et al. 1995); however, changes at the mRNA level
continue to occur until the complete arrest of transcription following meiosis resumption at
the germinal vesicle breakdown (GVBD) stage (Kastrop et al. 1991).
112
As the accumulation of potentially important mRNAs in the oocyte is suspected to occur at
the end of folliculogenesis, many have attempted to identify those factors by using the
bovine follicle size model. The first attempts to identify these factors involved techniques
such as differential display (DDRT) or suppressive subtractive hybridization (SSH) to
compare the whole population of mRNA found in oocytes from distinct follicle sizes
(Donnison and Pfeffer 2004; Robert et al. 2000). Even though these techniques are suited to
compare the entire transcriptome, the low number of transcripts identified in these studies
reflects the major issue when working with scarce tissue such as oocytes. Fortunately,
important progress in molecular biology techniques now allows the amplification of RNA
(Phillips and Eberwine 1996) from pools of a few oocytes or embryos (Zeng and Schultz
2003).
The candidate gene approach has been extensively used to detect transcript differences
between oocytes from different sources. The major findings obtained in the bovine using
this molecular approach were recently summarized (Wrenzycki et al. 2007), while few
other studies have been published using the same model since (Caixeta et al. 2009; Racedo
et al. 2008). However, the major drawback from the candidate gene approach is related to
the low number of transcripts analyzed in these different studies, which did not allow to get
the global picture of all the changes possibly impacting on oocyte quality that a genomewide approach such as microarray could provide.
Lequarre et al.(Lequarre et al. 2005) collected oocytes from small (<4mm) and large
follicles (>6mm) to perform a transcriptome comparison between these two groups by
using a human cDNA slide representing 1176 genes. Their analysis showed that no gene
showed a relative variation over three-fold between oocytes from small and large follicles.
Mourot et al.(Mourot et al. 2006) used a SSH approach to identify transcripts associated
with high quality oocytes and then built a custom microarray containing 1075 clones.
Microarray comparisons were then performed in order to identify transcripts modulations
during follicular growth. In their study, four distinct groups of follicle sizes were used: < 3
mm of diameter; 3 to 5 mm; >5 to 8 mm and > 8 mm. They were able to identify gradual
113
accumulation for specific transcripts related to cell cycle components, cell metabolism and
chromatin support between oocytes from different follicle sizes (Mourot et al. 2006).
By taking advantage of the complete bovine genome annotation combined with the
development of one of the most advanced bovine microarray platform suited to analyze
oocytes and early embryos (Robert et al. 2011), the changes at the mRNA level in oocytes
were revisited using the bovine follicle size model. Therefore, the aim of this project was to
compare the transcriptome of bovine germinal vesicle (GV) stage oocytes originating from
follicles of specific sizes. These results will help us better understand the developmental
competence acquisition process of the oocyte during follicular growth.
4.4
4.4.1
Results
Microarray analysis
To decipher the oocyte mRNA modulations occurring during the follicular growth, the
transcriptome of bovine GV stage oocytes collected from four specific and gradually
increasing follicle size groups were compared with the EmbryoGENE bovine
transcriptomic platform (Robert et al. 2011). With the threshold used to detect the
presence/absence of the probe on the microarray slide, an average of 14 645 probes were
considered to be present among the four groups. The between-group analysis (BGA, Figure
4-1) showedthat biological replicates (dots) for each group are clustered together. The first
two groups (<3 mm and 3-5 mm) are relatively close, while the other two groups (>5-8 mm
and >8 mm) showed increasing distance.
Statistical analysis of microarray data with FlexArray using the MAANOVAalgorithm
allowed the global analysis of all the comparisons performed. By using a P value <0.05, a
total of 6035 probes showed statistically significant differences between follicle size
groups. The Limma analysis then showed that between 7 and 857 genes presented
significant difference in the abundance of their transcripts for each contrast individually
(P< 0.05, Table 4-1).
114
The clustering analysis showed that between 38 and 1 705 probes considered statistically
significant were grouped in each of the 16 clusters representing the major trends associated
with gene expression profiles (Additional files: Figure 4-4, Table 4-2). Moreover,
threeclusters (# 1, # 4 and # 16) regrouped significantly more probes out of the 16 clusters
generated (Figure 4-2). Cluster 4 corresponds to transcripts undergoing progressive
accumulation while cluster 16 refers to transcripts undergoing a decrease in the oocyte
during follicle growth. Cluster 1 is composed of transcripts with a constant increase until
>5-8 mm, followed by a relatively stable profile in the larger follicle size (Figure 4-2).
4.4.2
Functional analysis
DAVID bioinformatics analyses usinggene list from the first comparison (<3mm vs 35mm) returned few biological processes affected,althoughprocesses such as cell cycle,
protein localization and transcription appear significantly modulated. The second
comparison (<3mm vs >5-8mm) resulted in more biological processes affected with terms
like DNA repair, response to DNA damage stimulus and DNA metabolic process while the
last comparison (<3mm vs >8mm) returned regulation of translation and posttranscriptional regulation of gene expression as the most affected biological processes, as
well as RNA localization and RNA transport.
DAVID analyses were also performed with gene lists from the different clusters (Figure 42). Cluster 4, which includes transcripts with a global accumulation profile showed an
enrichment of terms related to chromatin assembly,chromosome organization and DNA
packaging among the biological processes represented. Cluster 16, which includes
transcripts with a global depletion profile showed enrichment in terms related to generation
of precursor metabolites and energy, glycolysis protein transport as well as RNA
processing. Finally, cluster 1 showed enrichment of terms related to protein transport,
regulation of signal transduction as well as DNA metabolic process.
4.4.3
Quantitative RT-PCR validation of microarray results
Microarray results were validated through quantitative RT-PCR of ten selected genes,
presenting the major profiles observed with cluster analysis. Six of these genes (RNF2,
115
PPP1CB, MBIP, TADA1, ENY2 and NLRP5) showed a statistically significant difference
between the different follicle sizes (P value < 0.05), while four genes did not reach the
statistically significant level (SUV39H1: P = 0.16; WIBG: P = 0.21; DAZL: P = 0.128 and
HOXA9: P = 0.23)(Figure 4-3).
4.5
Discussion
This study provides, to our knowledge, the first comprehensive and sequential
transcriptome analysis of oocytes collected from follicles of increasing sizes with four
distinct groups. The microarray platform used in this study has previously shown its
potential to detect differences between bovine oocytes of closely related conditions
(Labrecque et al. 2013). Several teams have attempted to detect accumulation of specific
mRNA that could eventually be associated with oocyte quality (reviewed in (Wrenzycki et
al. 2007)). However, without a genome-wide approach to interrogate the oocyte
transcriptome, the factors that modulate oocyte quality during that period could hardly be
detected as oocyte competence most likely depends on many small modulationsrather than
absolute quantities (Donnison and Pfeffer 2004).
The relation between the size of the follicle and the quality of the enclosed oocyte has been
clearly demonstrated (reviewedin (Blondin et al. 2012)). However, it was recently shown
that in an in vivostimulated context, follicular size is not always linearly associated with
developmental competence. Following hormonal stimulation, an extended period of FSH
withdrawal (92 hours of coasting) decreases oocyte quality resulting in a lower blastocyst
rate obtained after IVM-IVF, even if the follicles were still growing (Nivet et al. 2012).
Furthermore, follicles of a similar size may be at very different physiological status,
meaning that the enclosed oocytes could possess different developmental competence
(Vassena et al. 2003). Still, the careful selection of follicles of specific sizes provides an
appropriate model to study mRNA abundance in oocytes to better understand the
competence acquisition process.
The transcriptomic analysis showed that the number of probes detected above the
background level is relatively constant regardless of the source of oocytes. In our previous
116
study where bovine oocytes from FSH-stimulated animals were used (Labrecque et al.
2013), an average of 14 797 probes were detected while in the present study 14 645 probes
were detected in the different groups. These results are also consistent with a mouse study
where oocytes from various stages of folliculogenesis were compared (Pan et al. 2005).
Overall, a total of 6 035 probes showed statistically significant difference, which closely
match the number of differences detected between oocytes from different FSH withdrawal
durations (Labrecque et al. 2013). However, the comparison between the differences
observed in these two studies revealed a widely different dynamic with less than 50% of the
probes in common. Furthermore, the amplitude in the differences detected between oocytes
of distinct groups is also relatively low, as very few genes showed more than a three-fold
difference. These results are also consistent with others oocyte microarray studies in the
bovine and in the mouse (reviewed in (Labrecque and Sirard 2014)). Taken together, it
reinforces the hypothesis that the oocyte quality improvement is dependant of subtle
mRNA modulations rather than absolute mRNA accumulation.
The between-group analysis provides a visual indication of the degree of separation
between groups (Culhane et al. 2002) and suggests a gradual progression of the oocyte
transcriptome. The first two groups (<3 mm and 3-5 mm) are somehow similar considering
their very close positioning in the plot. Even though the >5-8 mm group is distinctively
separated from the others, the first three groups seem to follow a linear trend of progression
while the >8 mm group rather diverges from this linear trend with a distinct positioning
(Figure 4-1). One possible explanation for the divergence of the >8 mm group could refer
to the modulation of transcripts specific to the acquisition of dominance status by the
follicle at this stage (8.5mm) (Ginther et al. 1997). Alternatively, it could be the result of an
increased proportion of follicles undergoing atresia within this group. Effectively,a large
proportion of the follicles found in the ovary is not progressing toward the pre-ovulatory
stage but is rather undergoing atresia. Even with the intrinsic resistance of the oocyte to
death stimuli (Reynaud and Driancourt 2000), this is not without consequences as shown
by the alterations in transcript abundance of many members of the apoptotic machinery in
human mature oocytes (Santonocito et al. 2013).
117
Sequential analysis with four distinct groups presents an important advantage over previous
studies that have used the follicle size model and really puts in perspective the changes
observed in the oocyte as the follicle becomes larger. The progressive increase in the rate of
blastocyst formation associated with the follicle sizes used in this study demonstrates an
interesting parallel considering the main transcript profiles identified (global mRNA
accumulation, clusters # 4 and # 1). However, an important proportion of the modulated
transcripts followed the opposite patternwiththe global decrease in mRNA abundance
(cluster # 16, Figure 4-2). The functional analysis of the genes presenting a constant
increase uncovers important functions related to chromatin assembly,chromosome
organization as well as DNA packaging. In several mammalian species,the chromatin of the
germinal vesicle will reach a more condensed state at the end of oocyte growth (Tan et al.
2009). In the bovine, a progressive chromatin compaction could be observed in oocytes
collected from follicles of 2 to 6 mm in diameter (Lodde et al. 2007). Therefore, the
modulation of several transcripts involved in chromatin modification and DNA packaging
such as RNF2, SUV39H1, PPP1CB, MBIP and TADA1 in the oocyte as the follicle increase
in size could indicate important steps in order to ensure adequate chromatin remodeling.
Ring finger protein 2 (RNF2) is a component of the Polycomb-repressive complex 1
(PRC1), which is an evolutionary conserved group of proteins involved in transcriptional
repression that was first identified in Drosophila (Jürgens 1985). The mechanisms by which
the PRC1 complex represses transcription probably involve chromatin compaction or the
blocking of the elongation phase of transcription (Simon and Kingston 2009). A recent
study showed that the deletion of both members of the PRC1 complex (Rnf2 and Ring1) in
growing mouse oocytes resulted in a developmental arrest at the 2-cell stage (Posfai et al.
2012). These authors also demonstrated that Ring1 compensated for the Rnf2 deficiency. In
our microarray data, no differences were found for RING1, while RNF2 transcript is
progressively accumulated in the oocyte collected from follicle of increasing size. The
absolute requirement of these two members is also clearly illustrated by the massive
transcriptional deregulation observed during the growth of oocytes from double knockout
mice. These authors concluded that Rnf2 and Ring1 are essential components during oocyte
growth to ensure an optimal chromatin remodeling in order to silence a wide array of genes
118
required only later in the development (Posfai et al. 2012). These results could suggest that
bovine oocytes need to accumulate sufficient amount of RNF2 mRNA to ensure adequate
chromatin remodeling in the GV stage oocyte. However, the accumulation of RNF2
transcript could also have an implication later in the development and act as an epigenetic
modifier in the definition of parental asymmetry in early embryo as shown in the mouse
(Puschendorf et al. 2008).
SUV39H1 (suppressor of variegation 3-9 homolog 1 (Drosophila)) is a histone
methyltransferase that specifically tri-methylates H3K9 to establish the heterochromatin
state and contributes to heterochromatin gene silencing (Schotta et al. 2003). The
microarray data showed a reduced amount of mRNA for SUV39H1 in oocyte collected
from follicles larger than 5 mm. Even if another study reported a reduced amount of
SUV39H1 transcript in bovine oocytes from follicles < 2 mm compared to those from
follicles of 2-8 mm, these results can hardly be compared due to the different follicle sizes
used (Racedo et al. 2009). Nevertheless, the modulation of SUV39H1 mRNA level in
oocyte from different follicle sizes could be implicated in the chromatin remodeling since
the SUV39H1 protein was co-localized with DNA of surrounded nucleolus (SN) sheep
oocytes (Russo et al. 2013). These results could suggest the importance of this gene in
oocyte chromatin remodeling during follicular growth.
Protein phosphatase owns an important role during oocyte maturation. Okadaic acid (OA),
a specific inhibitor of protein phosphatase 1 and 2A (Bialojan and Takai 1988) was shown
to induce premature meiotic resumption (Rime and Ozon 1990) in addition to an increased
proportion of aneuploid oocytes following maturation (Mailhes et al. 2003). Recently, a
team aimed at identifying the specific protein phosphatase responsible for chromatin
condensation regulation by determining the spatial and temporal localization of OAsensitive protein phosphatase (Swain et al. 2007). They found that PPP1CB (Protein
Phosphatase 1, catalytic subunit, beta isozyme) was associated with condensed chromatin
in mouse oocytes at the GVBD, MI and MII stages, suggesting that this specific
phosphatase is involved in the regulation of chromatin condensation. The accumulation of
PPP1CB mRNA in the oocyte from follicle of increasing size could ensure optimal
119
chromatin remodeling at the end of oocyte growth. It was also demonstrated that mouse
not-surrounded nucleolus (NSN) oocytes contained a reduced amount of Ppp1cb transcript
following maturation (Zuccotti et al. 2008), which could reflect their inability to condense
chromatin. Taken together, these results suggest that the progressive accumulation of
PPP1CB transcript in the oocyte could be required for adequate chromatin condensation.
The MAP3K12 binding inhibitory protein 1 (MBIP), along with other members such as
GCN5, is part of the ATAC histone acetyltransferase complex, which was first identified in
Drosophila (Guelman et al. 2006). In mouse knockout studies, deletion of Atac2 (also
known as Csrp2bp) caused embryonic lethality, disrupted MBIP levels and affected the
pattern of histone acetylation. Even though these authors observed embryonic lethality after
8.5 days post-conception (Guelman et al. 2009), the modified pattern of histone acetylation
found in their study is particularly interesting as regards the chromatin compaction
normally found during oocyte growth. Furthermore, inadequate histone acetylation during
meiosis could lead to aneuploidy as shown in mouse (Akiyama et al. 2006) and human
oocytes (van den Berg et al. 2011). As normal chromatin compaction involves
reorganization of many histone residues, the accumulation of MBIP mRNA in oocyte from
follicle of increasing sizes could ensure appropriate chromatin remodeling through histone
acetylation.
Microarray data also showed a progressive accumulation of Transcriptional adaptor 1
(TADA1) mRNA in the oocyte. This gene is a subunit of the STAGA histone
acetyltransferase complex (Martinez et al. 2001), which is composed of SPT3, TAF9 and
GCN5 (Martinez et al. 1998) and other subunits (reviewed in (Nagy and Tora 2007)). This
complex performs various roles in the regulation of gene expression, from chromatin
remodeling through post-transcriptional modifications (Martinez et al. 2001). Taken
together, the modulation of several transcripts involved in chromatin remodeling in the
oocyte as the follicle increases in size clearly illustrated the importance of chromatin
remodeling at the end of oocyte growth.
120
One of the main biological processes represented among the transcripts showing the global
depletion is RNA processing. This could indicate that these mRNAs were produced during
the transcriptionally active period in small follicles and were then slowly translated or
degraded in follicle of larger size. Even though this hypothesis would require protein
validation, the important transcript storage that must be established in the oocyte to ensure
development until the maternal embryonic transition effectively confirms the relevance of
this function.
Within bgcn homolog (Drosophila) (WIBG), also known as PYM, performs various
functions in post-transcriptional modifications such as splicing, mRNA export and
translation regulation. It was recently shown that WIBG, which is part of the Exon
Jonctions Complexes (EJC) along with others members such as EIF4A3, MAGOH, Y14 and
BTZ, could serve as a bridge between EJC and ribosomes to enhance translation (Diem et
al. 2007). The decrease observed for WIBG mRNA in oocytes from follicles larger than 5
mm could simply mean its translation to ensure protein supply later in follicle development.
Even though the confirmation of this hypothesis will require protein validation, EIF4A3,
which is another member of the EJC complex also showed a decreased mRNA level in our
microarray data when the follicle increases in size. Furthermore, the EIF4A3 protein was
detected in mouse MII oocytes (Wang et al. 2010), which reinforces this hypothesis.
NLR Family, Pyrin Domain Containing 5 (NLRP5) is a member of the NALP protein
family (also known as NALP5 or MATER). This gene is the first to have been identified as
a maternal effect gene, where it is required to ensure development beyond the 2-cell stage
in the mouse (Tong et al. 2000). However, the mechanism involved in NLRP5 regulation of
early development is still unclear. Endoplasmic reticulum (ER) distribution and Ca2+
homeostasis (Kim et al. 2013) in addition to mitochondrial function (Fernandes et al. 2012)
have been proposed as targets of this gene in mouse knockout studies. The decrease of
NLRP5 mRNA observed in oocyte from follicles > 5 mm is intriguing since the mRNA is
already detected in oocytes from secondary to antral follicles (Pennetier et al. 2004), while
the protein is present in oocytes from primary follicles until the blastocyst stage (Pennetier
et al. 2006). NLRP5, along with other proteins, namely OOEP, Filia and TLE6 will form
121
the subcortical maternal complex (SCMC) essential for early development (Li et al. 2008a).
Furthermore, the absence of NLRP5 and OOEP in the oocyte will impede the formation of
the SCMC. Considering that OOEP is a RNA-binding protein (Herr et al. 2008), the
decrease observed for NLRP5 mRNA could indicate an optimal supply of protein in order
to ensure adequate formation of the SCMC and potentially play an important role in RNA
localisation as suggested in mouse study (Li et al. 2008a).
Deleted in azoospermia-like (DAZL) is a RNA-Binding protein presenting a specific
expression in male and female germ cells. This gene is essential for gametogenesis, as
gamete production was completely abolished in knockout mice (Ruggiu et al. 1997). DAZL
was later identified as a translational regulator in germ cells in Xenopus oocytes (Collier et
al. 2005) and similar findings were also reported in the rat (Reynolds et al. 2005). DAZL
also appears to be regulated post-transcriptionally through a specific member of the
cytoplasmic polyadenylation element binding protein family, namely CPEB1. DAZL is
then required during maturation and early development in the mouse since its downregulation caused failure of the meiotic spindle assembly in MI and MII stage oocytes
(Chen et al. 2011). Furthermore, DAZL displays a very specific pattern of expression in
oocytes as the protein accumulates at the beginning of oocyte maturation but becomes
undetectable after the 1-cell stage (Chen et al. 2011). Even if the information regarding the
protein is lacking in the bovine, the modulation of its mRNA level in the oocyte as the
follicle increases in size could suggest an important role in the translation of specific
mRNA targets in a similar fashion as in the mouse (Chen et al. 2011).
Enhancer of yellow 2 homolog (ENY2), first identified in a Drosophila study, plays
specific roles in transcription and mRNA export (Galan and Rodriguez-Navarro 2012). A
recent study from our laboratory identified this gene as specifically modulated in oocyte
during FSH withdrawal (coasting) (Labrecque et al. 2013). Bovine oocytes contained
increasing level of mRNA for this gene until 68 hours of coasting, where the oocyte
developmental potential was considered as optimal (Nivet et al. 2012). Thereafter, a
decrease of the mRNA level was observed (92 hours), corresponding with a decrease in
oocyte quality. A RNA-degradation quality-control mechanism was then proposed to
122
explain the important decrease for several transcripts observed when oocyte quality
decreased. This mechanism could then serve to reduce oocyte quality if the oocyte is not
ovulated at the right time (Labrecque et al. 2013). Interestingly, ENY2 mRNA level also
present a significant modulation in the oocyte as the follicle becomes larger. An important
increase is observed until the follicle reaches 5-8 mm followed by a decrease in the follicle
>8 mm. Even if it is tempting to speculate that a similar mechanism could take place at the
end of follicular growth to possibly reduce oocyte quality in follicles undergoing atresia,
further studies will be required to confirm this hypothesis.
Homeobox A9 (HOXA9) is part of an important family of transcription factors
characterized by a specific DNA binding domain (homeodomain). It represents
approximately 260 homeobox genes in mammalian genomes, with various functions such
as developmental patterning, differentiation and cell proliferation (Berger et al. 2008). In a
study aimed at identifying transcription factors involved in the regulation of senescence,
HOXA9 was able to repress transcription of the key mediator of senescence CDKN2A
through recruitment of Polycomb repressive complex and HDAC1 (Martin et al. 2013). The
modulation of HOXA9 mRNA in bovine oocytes as the follicles increase in size combined
with the fact that some members of the Polycomb repressive complex (PRC1) such as
RNF2 also showed an increase of its mRNA level at the same time, could suggest a
possible link with the capacity of the oocyte to retain its quality for a certain period of time
before presenting advanced signs of atresia.
4.6
Conclusion
The transcriptome analysis of bovine oocyte from various follicle sizes has allowed us to
identify an important number of transcripts presenting gradual modulations during
follicular growth. Furthermore, the importance of adequate chromatin remodeling during
that period was clearly demonstrated by the transcriptomic data provided in this study.
4.7
Methods
123
4.7.1
Oocyte recovery
Dairy cow ovaries (Bos Taurus)were obtained from a slaughterhouse, recovered and
transported to the laboratory in saline solution (0.9% NaCl) containing an antibioticantimycotic agent and maintained at a temperature of 30-35°C. In the laboratory, ovaries
were washed twice in warm physiological saline solution and follicles were punctured with
an 18-gauge needle attached to a 10-mL syringe to collect cumulus - oocyte complexes
(COCs). Four distinct groups of follicle sizes were used: < 3 mm of diameter; 3 to 5 mm;
>5 to 8 mm and > 8 mm, which correspond to the same groups used in a previous paper
published by our laboratory(Mourot et al. 2006). Three different weeks of oocyte collection
were required to collect the oocytes from the first three groups of follicle sizes, while the
oocytes from follicles larger than 8 mm were collected over a period of eight weeks.For
each group individually, COCs with a homogenous cytoplasm surrounded by at least five
layers of cumulus cells were mechanically denuded by vortexing for 2-3 minutes in PBS to
remove all the cumulus cells. Oocytes were then washed three times in PBS solution,
collected in minimal volumes of PBS, snap-frozen in liquid nitrogen and stored at -80°C
until RNA extraction.
4.7.2
Total RNA was extracted from pools of seven to ten oocytes with the Pico-Pure RNA
Isolation Kit (Applied Biosystems, Carlsbad, CA) following the manufacturer’s protocol
and including DNase treatment (Qiagen, Mississauga, ON, Canada) on the purification
column. Total RNA integrity and concentration were evaluated using a 2100-Bioanalyzer
(Agilent Technologies, Palo Alto, CA) with the RNA PicoLab Chip (Agilent
Technologies), which yielded an average of 361 ±66 pg of total RNA per oocyte (mean
±SEM).To generate enough material for hybridization, the RNA samples were linearly
amplified. Antisense RNA (aRNA) was produced using the RiboAmp HS RNA
amplification kit (Applied Biosystems). After two amplification rounds of six hours each,
the aRNA output was quantified using the NanoDrop ND-1000 (NanoDrop Technologies,
Wilmington, DE).
124
4.7.3
Sample labelling and microarray hybridization
product was then purified with the Pico-Pure RNA Isolation Kit without DNase treatment.
Labelling efficiency was measured using the Nano-Drop ND-1000. Samples from the three
biological replicates, were hybridized on a custom bovine Agilent 44K microarray
slide(Robert et al. 2011). The hybridizations were performed using a reference design
where oocytes from follicles < 3 mm were compared with the three other groups (<3 mm vs
3-5 mm; <3 mm vs >5-8 mm and <3 mm vs >8 mm). Overall, eighteen hybridizations,
corresponding to the three biological replicates and three comparisons, were performed
using a dye-swap set-up.A total of 825 ng of each labelled sample (Cy3 and Cy5) were
incubated in a solution containing 2x blocking agent and 5x fragmentation buffer in a
volume of 55 µL at 60°C for 15 min and were put on ice immediately after. Then, 55 µL of
2x GEx Hybridization Buffer HI-RPM were added for a total volume of 110 µL. The
hybridization mix (100 µL) was added onto the array and hybridization was performed at
65°C for 17 hours using an Agilent Hybridization chamber in a rotating oven. Slides were
then washed with Gene Expression Wash Buffer 1 containing 0.005% Triton X-102 for 3
min at room temperature and then transferred to Gene Expression Wash Buffer 2
containing 0.005% Triton X-102 for 3 min at 42°C. Final washes with acetonitrile for 10
sec at room temperature and with Stabilization and Drying Solution (Agilent) for 30 sec at
room temperature were performed before air-drying of the slides. The slides were scanned
using the Tecan PowerScanner microarray scanner (Tecan Group Ltd, Männerdorf,
Switzerland) and features were extracted using ArrayPro 6.4 (Media Cybernetics, Bethesda,
MD).
4.7.4
Intensities files were first uploaded to ELMA (EmbryoGENE LIMS Microarray Analysis,
http://elma.embryogene.ca/), which is part of the EmbryoGENE pipeline analysis tools
(Robert et al. 2011). Microarray datasets were then analysed with the FlexArray microarray
analysis software, Version 1.6.1 (Blazejczyk et al. 2007). Briefly, raw data were subjected
to a simple background subtraction, Loess within array and Quantile between array
125
normalizations. MAANOVA algorithm was applied considering the four follicle size
groups, with <3 mm as a reference, followed by a Limma simple analysis to obtain the fold
change for each contrast individually. Differences were considered statistically significant
with aP value less than 0.05.To detect the presence or absence of the signal for each spot
present on the slide, an arbitrary cut-off level of 7.4 (log2) was used, which corresponds to
the mean intensity of the background level + twice the Standard Deviation (SD) of the
background. Genes were then grouped in clusters according to their profile with the mFuzz
Bioconductor package (Kumar and Futschik 2007). A between-group analysis (BGA) was
performed to classify the samples according to their transcriptomic profile (Culhane et al.
2002). Microarray data have been deposited in NCBI’s Gene Expression Omnibus and are
accessible through GEO series accession number GSE48283.DAVID bioinformatics
resources were also used to perform functional enrichment analysis of specific gene-list
(Huang et al. 2009a; Huang et al. 2009b).
4.7.5
recommendations. The primers used for q-RT-PCR are listed in Table 4-3 and were
designed
using
the
IDT
(http://www.idtdna.com/Scitools/Applications/Primerquest/)
PrimerQuest
from
sequences
tool
obtained
microarray analysis. To confirm the specificity of each pair of primers, electrophoresis on a
standard 1.2% agarose gel was performed for each amplified fragment. The PCR product
was purified with the QIAquick PCR Purification kit (Qiagen), quantified using the
curve for quantification experiments, with dilutions ranging from 2 x 10-4 to 2 x 10-8 ng/µL.
Quantitative real-time PCR was performed on a LightCycler 480 (Roche Diagnostics,
Laval, QC, Canada) using SYBR incorporation. Each q-RT-PCR reaction, in a final volume
of 20 µL, contained the cDNA corresponding to 1/40 of the total volume of the reverse
transcription reaction described above, 0.25 mM of each primer and 1x SYBR mix
126
(LightCycler 480 SYBR Green I Master, Roche Diagnostics). The PCR conditions used for
all genes were as follows: a denaturing step of 10 min at 95°C followed by 50 PCR cycles
(denaturing, 95°C for 1 sec; annealing, [Table 4-3] for 5 sec; extension, 72°C for 5 sec), a
melting curve (94°C for 5 sec, 72°C for 30 sec and a step cycle starting at 72°C, up to 94°C
at 0.2°C/sec) and a final cooling step at 40°C. Complementary DNA quantification was
performed with the LightCycler 480 Software Version 1.5 (Roche Diagnostics) by
comparison with the standard curve. PCR specificity was confirmed by melting-curve
analysis.
4.7.6
For each gene tested, three biological replicates were used. Analysis of gene expression
stability over the four follicle size groups was performed using the GeNorm VBA applet
software as described by Vandesompele et al. (2002). The most stable reference genes were
identified by the stepwise exclusion of the least stable gene and recalculating the M values.
Following GeNorm analysis, ACTB and GAPDH were the most stable genes with M values
< 1.5 as recommended by the software (M value = 0.557). One-way ANOVA with
Newman-Keuls post-test was performed using Graph Pad Prism version 5.0. Differences
were considered to be statistically significant at the 95% confidence level (P value <0.05).
Data are presented as mean ± s.e.m.
4.8
Acknowledgements
This work was supported by the Natural Sciences and Engineering Research Council of
Canada (NSERC-CRSNG). The authors thank Dr Isabelle Dufort for her technical
assistance in all the molecular biology experiments, Eric Fournier for his help with
bioinformatics analysis and Dr Sylvie Bilodeau-Goeseels for revising the manuscript.
127
4.9
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4.10 Figures
Figure 4-1Between-Group Analysis (BGA) (Culhane et al. 2002)of the oocyte’s
microarray data coming from the four follicle size groups.
Blue: oocyte from follicles < 3 mm; Red: oocyte from follicles of 3-5 mm; Green: oocyte
from follicles >5-8 mm; Brown: oocyte from follicles > 8 mm.The BGAillustrates the
global distribution of transcriptomic data from the biological samples (four groups of
follicle size) and the biological replicates (dot in each group).
135
Figure 4-2Main clusters identified from Mfuzz clustering analysis (Kumar and
Futschik 2007).
The clusters represent genes with an increase mRNA level until 5-8 mm (#1) or a relatively
constant increase along the follicle size studied (#4). The cluster #16 represents the global
mRNA decrease in the oocyte until 5-8 mm. The number of statistically significant probes
specific to these profiles is display for each cluster.
136
Figure 4-3Quantitative RT-PCR validation of microarray results.
mRNA quantifications data are presented as mean ± standard error of the mean. Different
superscripts represent significant differences (P< 0.05) after one-way ANOVA followed by
Newman-Keuls post-test.
137
Figure 4-4 Supplemental data16
data16 clusters generated by the mFuzz clustering analysis
138
4.11 Tables
Table 4-1Number of genes presenting significant differences following LIMMA
analysis (P< 0.05)
<3mm vs 3-5mm
<3mm vs 5-8mm
<3mm vs >8mm
Total
7
687
225
Fold change > 2
4
287
160
Fold change < -2
3
400
65
139
Table 4-2 Supplemental data Primers used for quantitative RT-PCR validations
Gene
symbol
GenBank
accession number
Annealing
temp. (°C)
Product size
(bp)
RNF2
NM_001101203
GGCCAGTGAGAAGCAGTATACCAT
GTTCCATGGGTTTGTTCACCTTCC
58
125
PPP1CB
NM_001034653
ACTTTCTGGTAACACCCTCACCTG
CAAATGTCCCACTGACCAGCATTC
58
186
MBIP
NM_001077518
ACAGAATCCATGGCAACACGTCAC
CTTGCTGCAAAGTGGACTCACA
57
180
TADA1
NM_001101106
CAGTTATGGGACTAGGCACTGAGA
GTCACCTACCTCACATGAAGACCA
57
100
NLRP5
NM_001007814
CCTTCATGCATCCCACCTCTAATC
CAGGTTCTTCAGATTCTCCAGCAG
57
178
ENY2
NM_001034747
GGAGAAAGAGAACGCCTCAAAGAG
GCCACCAAGTCATCAACAGTAACG
57
137
WIBG
NM_001102344
CAAGATCTCAGTGACTATCCCAGG
GAACAGGAACCAAGACTCCAAGAC
56
146
SUV39H1
NM_001046264
AGGCAAAGCACAGACCAAGAATGG
ACATTTGTGGGCAGGGAACTGAGA
59
146
DAZL
NM_001081725
GTAAATCGTGACAAGCACCTGGAG
GATTATCCCACTGCTACCCTTTCC
57
177
HOXA9
NM_001105617
TACACTATGAAACCGCCATTGGGC
ACGATTTGGTCAGTAGGCCTTGAG
58
125
GAPDH
NM_001034034
CCAACGTGTCTGTTGTGGATCTGA
GAGCTTGACAAAGTGGTCGTTGAG
58
218
ACTB
NM_173979
59
102
140
Mfuzz clustering analysis
Cluster number
Number of probes P < 0.05 (MAANOVA)
1
1282
2
287
3
24
4
719
5
290
6
46
7
178
8
68
9
38
10
482
11
99
12
152
13
72
14
34
15
559
16
1705
141
5 Chromatin remodeling and histones mRNA accumulation in
bovine germinal vesicle oocyte
Labrecque R1; Lodde V2; Dieci C2; Tessaro I2; Luciano AM2; Sirard MA1
1.
Centre de Recherche en Biologie de la Reproduction, Faculté des sciences de l’Agriculture
et de l’Alimentation, Département des Sciences Animales, Pavillon INAF, Université Laval,
Québec, Canada
2.
Reproductive and Developmental Biology Laboratory, Department of Health, Animal
sciences and Food Safety, University of Milan, Italy.
143
5.1
Résumé
Chez plusieurs mammifères, un important remodelage de la chromatine de la vésicule
germinale va se produire vers la fin de la croissance de l’ovocyte. Différentes configurations
de la chromatine ont été identifiées, allant de deux niveaux distincts de compactions, à
plusieurs stades progressifs de condensation. Toutefois, les mécanismes impliqués dans ce
processus de remodelage ne sont pas encore bien compris. Chez le bovin, quatre stades
distincts et graduels de compaction de la chromatine ont été caractérisés et sont liés à une
acquisition graduelle du potentiel de développement. Des ovocytes bovins au stade de
vésicule germinale ont été récupérés et regroupés en fonction de leur niveau de condensation,
allant d’une chromatine à l’état diffus jusqu’à une configuration entièrement compactée. Pour
mieux comprendre les changements qui surviennent dans l’ovocyte durant cette période, une
analyse transcriptomique a été réalisée avec la plateforme EmbryoGENE de façon à identifier
les modulations qui surviennent durant ce processus de remodelage. Une proportion
importante des gènes présentent un niveau réduit de leur ARNm à mesure que la chromatine
devient plus compactée, ce qui est d’ailleurs bien corrélé avec la diminution de l’activité
transcriptionnelle retrouvée à la fin de la croissance de l’ovocyte. Toutefois, parmi les
transcrits qui présentent une augmentation, plusieurs sont associés à des gènes d’histones.
Tout dépendant de la famille d’histones (H2A, H2B, H3, H4 ou H1), une accumulation
importante d’ARNm se produit pour plusieurs de ces gènes avant l’ovulation. Des
interactions prédites (Protéine-ARN) entre certains ARNm d’histones et la protéine bovine
Stem-loop binding protein 2 (SLBP2) ont été identifiées, ce qui suggère une accumulation
possible de ces histones afin de soutenir les besoins important du jeune embryon jusqu’à
l’activation du génome embryonnaire. Cet ensemble de données offre donc une opportunité
unique afin de mieux définir parmi tous les ARNm d’histones, lesquels seront utilisés pour
compléter la compaction de la chromatine, mais également pour assurer le remplacement des
protamines paternelles par des histones maternelles suivant la fécondation et finalement pour
assurer un approvisionnement suffisant lors des premiers cycles cellulaires.
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5.2
Abstract
In several mammalian species, a major remodeling of the germinal vesicle chromatin is
known to occur towards the end of the oocyte growth. Various chromatin configurations have
been identified, ranging from two distinctive compaction states, to several progressive
chromatin condensation statuses. However, the mechanisms involved in this remodeling
process are yet not completely understood. In the bovine species, four distinct and gradual
states of chromatin compaction have been characterized and are linked to a gradual
acquisition of the developmental potential.
Bovine germinal vesicle oocytes were collected and separated in four groups according to
their degree of chromatin condensation, ranging from a diffused state to a fully compacted
configuration. To better understand the molecular changes undergoing in the oocyte during
that period, transcriptomic analysis was performed with the EmbryoGENE microarray
platform (custom Agilent 44K) in order to identify mRNA modulations occurring during this
remodeling process.
An important proportion of genes showed a reduced mRNA level as the chromatin becomes
more compacted, which greatly correlates with the decreased transcriptional activity found at
the end of oocyte growth. However, among the transcripts presenting an increased mRNA
level, many were associated with the histone genes. Depending on the specific histone family
(H2A, H2B, H3, H4 or linker H1), an important mRNA accumulation occurs for several
histone genes in the oocyte before ovulation. Predicted interactions (RNA-protein pair)
between specifics histone transcripts and the bovine Stem loop binding protein 2 (SLBP2)
were identified, suggesting the potential storage of these histones in order to sustain the
important need of the early embryo before embryonic genome activation. This dataset then
offers a unique opportunity to picture the stock of accumulated histone mRNAs either to
complete the build-up of a compacted chromatin, but also to ensure the protamine-histone
replacement following fertilization and the completion of the first three cell cycles.
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5.3
Introduction
Germinal vesicle oocyte can harbour various pattern of chromatin compaction during its
growth.Following various DNA staining and microscopy techniques,a diffuse chromatin
pattern frequently termed nonsurrounded nucleolus (NSN) can be observed in the mouse, due
to the absence of a compacted ring of chromatin around the nucleolus. The presence of this
distinctive ring will then characterize the surrounded nucleolus (SN) configuration(Mattson
and Albertini 1990). This important chromatin remodeling has been observed in the oocyte of
all mammalian species studied up to now, although the configuration varies between species.
The chromatin configuration has also been greatly correlated with the ability of the immature
oocyte to reach the metaphase II stage or to develop into an embryo in several species (for
review see (Luciano and Lodde 2013; Tan et al. 2009)). Even though the formation of a ring
of chromatin around the nucleolus is a common feature in several species, other
configurations have been reported in the bovine and equine species, where chromatin will
condense into a single clump instead of forming the usual ring around the nucleolus
(Franciosi et al. 2012; Hinrichs 2010; Lodde et al. 2007).
In
the
bovine,
different
teamshave
identified
progressive
states
of
chromatin
compaction(Chohan and Hunter 2003; Fuhrer et al. 1989; Liu et al. 2006). Then, in 2007,
Lodde and colleagues proposed a unified classification composed of four discrete and
progressive stages of chromatin condensation found in growing and fully-grown bovine
oocytes. They also correlated the gradual chromatin compaction with the sequential
acquisition of meiotic and developmental potential (Lodde et al. 2007).
During oocyte growth, an intense transcriptional activity will be followed by an important
decrease of the RNA synthesis rate, once the oocyte reaches 110 um as shown by
autoradiographic labelling (Fair et al. 1995). However, the complete arrest of transcription
will occur only at the meiosis resumption since RNA synthesis, although at a low level, could
be detected 1 hour before the germinal vesicle breakdown (GVBD) (Hunter and Moor 1987).
Similar results were reported in the mouse where transcription is still detectable between the
end of oocyte growth and GVBD (Rodman and Bachvarova 1976) while transcriptional
activity will completely cease only at the time of GVBD (Wassarman and Letourneau 1976).
146
The chromatin configuration of the germinal vesicle is also closely related to the
transcriptional activity of the oocyte. Oocytes presenting a diffuse configuration (NSN) are
still transcriptionally active, while oocytes with a compacted chromatin (SN) completely lack
any transcriptional activity from polymerase I and II(Bouniol-Baly et al. 1999; De La Fuente
and Eppig 2001). In the bovine, autoradiographic labelling showed intense RNA synthesis
activity in GV0 oocyte, while GV1 and GV2 display an important decrease of transcription
and GV3 is associated with a global repression of transcriptional activity (Lodde et al. 2008).
However, although the two processes (chromatin remodeling and transcriptional repression)
occur simultaneously, it was demonstrated that they are controlled by different mechanisms
(De La Fuente et al. 2004). By using knockout mouse for the gene Nucleopasmin 2 (Npm2),
it was demonstrated that these oocytes were unable to do the transition from NSN to the SN
configuration, although the repression of transcriptional activity was observed in preovulatory oocytes (De La Fuente et al. 2004). It was then suggested that changes in
transcriptional machinery components could instead be responsible for the transcription
decrease (Pan et al. 2005). In fact, a decrease is observed in the expression of transcription
factors such as Sp1 (Sp1 transcription factor) and TBP (TATA-box binding protein) towards
the end of mouse oocyte growth(Worrad et al. 1994).
Considering that the last phases of oocyte growth will allow the accumulation of transcripts
potentially important for the development of the early embryo, many efforts have been made
in order to identify these developmentally important genes. Therefore, various transcriptome
analysis tools have been used to study this question either in the bovine but in mouse and
human oocytes as well (for review see (Labrecque and Sirard 2014)). Few teams used the
mouse chromatin configuration model in order to better understand the molecular changes
undergoing in the oocyte during the transition from a de-condensed state (NSN) to a more
compacted state (SN). Therefore, the transcriptome of NSN and SN immature (Monti et al.
2013) or mature oocytes (Zuccotti et al. 2008) was compared with microarray technology.
Recently, the entire transcriptome was interrogated through an RNAseq experiment where
NSN and SN immature mouse oocytes were compared(Ma et al. 2013). Even though all these
studies were able to detect an important number of differences at the mRNA level for several
genes potentially important for the developmental competence, no study has looked at the
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transcriptome between oocyte harbouring a gradual chromatin condensation occurring in
species such as the bovine.
The aim of our study was to take advantage of the gradual chromatin remodeling process
characterized in the bovine (Lodde et al. 2007) and compare the transcriptome of these
oocytes, grouped according to their chromatin configuration. These results will help us to
better understand the molecular changes undergoing in the oocyte during the chromatin
remodeling process in addition to the possible implication for the acquisition of the
developmental potential.
5.4
5.4.1
Results
Microarray results
The transcriptome of bovine germinal vesicle stage oocyte, grouped according to their
chromatin configuration, was interrogated with microarray in order to better understand the
molecular changes leading to the chromatin remodeling. Considering the threshold used to
detect the presence/absence of the probes on the microarray slide, a total of 14733 probes
were detected among the four groups. The BGA plot (Figure 5-1) showed that every
biological replicates (dot) within each group are clustered together. However, the GV1 and
GV2 groups displayed more variation (increased distance between the dots) compared to
GV0 and GV3. However, the relative distance between each group effectively confirmed the
global transcriptional differences among the four chromatin configuration.
A total of 2792 probes showed statistically significant (P< 0.01) different between chromatin
configuration stages when the MAANOVA analysis was performed. By looking at each
contrast individually, between 264 and 325 probes showed a fold change difference of more
than two (Table 5-1). The number of probes presenting a negative fold change across the
different comparisons was more important than those presenting a positive fold change,
where less than 20% of the probes showed a positive fold change (FC > 2). To group the
probes according to their profile across the different chromatin configurations, a clustering
analysis was performed using all the probes from our microarray analysis. Sixteen different
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clusters showing the main profiles of transcript modulation were generated (Figure 5-4 and
Table 5-5). Thereafter, by considering only the 2792 probes presenting a statistically
significant difference between groups, fiveclusters out of the 16 regrouped significantly more
probes (Figure 5-2). Furthermore, one cluster alone(#12) regrouped more than 45% of the
statistically significant probes. The profile of the cluster # 12 displayed an important decrease
between GV0 and GV1 and then stays relatively stable in the successive chromatin
configuration states. The other main profiles identified were clusters #11, #8, #5 and #6.
Interestingly, among the probes that were presenting an important increase as the chromatin
becomes more compacted (cluster # 11, 8 and 5), an important proportion of them were
related to histone genes. The microarray platform used in this study includes a total of 160
histone-related probes, from which 104 are considered as present in the oocyte. By
considering only those showing a statistically significant difference (MAANOVA, P<0.01),
30 probes presented an increase mRNA level as the chromatin becomes more condensed
(Table 5-2). Interestingly, the majority of these probes are related to the core histone (29/30:
H2A, H2B, H3, H4 and linker H1), while only one variant was identified (H3.3, also known
as H3F3C), although the fold changes observed across the various comparisons were small
for this probes.
5.4.2
Functional analysis
Functional enrichment analyses were performed with DAVID Bioinformatics resources to
get a better overview of the differences found in the oocyte during the chromatin
condensation in term of molecular functions affected. Gene lists from each of the main
cluster identified or from individual comparison (fold change cut-off > 1.5) were submitted
to DAVID. The genes included in the cluster 12 (important decrease between GV0 and GV1)
were enriched in GO terms related to various biological processes related to catabolic
process, transcription, apoptosis regulation and cell proliferation. Genes included in the
cluster 11 (increase at GV1-GV2 followed by a decrease) or those from the cluster 8 both
showed an enrichment of terms related to protein transport and protein localization in
addition to transcription. The cluster 5 (increase at the beginning of chromatin condensation)
was enriched in genes related to nucleosome assembly, chromatin organization and DNA
packaging. Similar results were obtained when gene list from individual contrast were used.
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For the contrast GV0 vs GV1 and GV0 vs GV2, the main molecular function affected was
transcription while in GV0 vs GV2, another biological process appears as significantly
affected, namely protein-DNA complex assembly. Chromosome organization, protein-DNA
complex assembly, DNA packaging and transcription were the main functions affected in the
contrast GV0-GV3.
5.4.3
Quantitative RT-PCR validation of microarray results
The microarray results were validated through quantitative RT-PCR on a subset of ten genes
representing the main gene expression profiles identified. Considering the general
particularities of histone transcripts (absence of poly-A tail), reverse transcription was
performed either with random primers or with an oligo(dT). For all the histone transcripts
assessed except one (HIST1H3A), higher mRNA abundance was observed in random-primed
cDNA preparations compared to the oligo(dT)-primed reaction, as shown by an lower cycle
threshold value obtained during qPCR experiment (data not shown). Therefore, the
quantifications were performed with random-primed cDNA for these histone genes, while the
rest of the quantifications are based on the oligo(dT)-primed cDNA. Moreover, eight genes
(HIST1H1A, HIST1H1E, H2B, HIST1H3A, PIWIL3, AEBP2, PTCH1, CBX3) showed a
statistically significant difference between chromatin configuration states (P< 0.05), one of
them showed a tendency (HIST1H4I, P = 0.10) and one of them showed no significant
difference (TMPO, P = 0.17) (Figure 5-3).
5.4.4
Protein-RNA interactions
The catRAPID omics web server was used to calculate in silico the interaction propensity
between bovine the Stem-loop binding protein2 (SLBP2) and histone transcripts (Table 5-3).
The results showed that the protein could effectively bind histone mRNA as shown by high
interaction score for several Protein-RNA pairs. For example, high interaction scores were
obtained for core histone such as H3 and H2B while some histone variants (H2B testisspecific, H1FOO and H1FX) showed low interaction score. To validate this approach, the
prediction analysis was run using the SLBP protein sequencefrom other species (human and
150
mouse) against their respective transcriptome and high interaction propensities for histone
mRNA were observed (Table 5-6, supplemental data).
5.5
Discussion
This study provides, to our knowledge, the first comprehensive transcriptome analysis of the
bovine oocyte based on the gradual chromatin condensation. The four distinct and
progressive states of chromatin compaction described by (Lodde et al. 2007) offers a unique
opportunity to characterize the molecular changes undergoing in the oocyte during this major
remodeling process. Compared to the mouse studies where similar experiment were
performed (Ma et al. 2013; Monti et al. 2013; Zuccotti et al. 2008), the comparison described
in this study has an important advantage in regard of gradual changes captured by the
microarray experiment instead of comparing only two distinct phenotypes (NSN vs SN
oocytes).
Even if there is a significant increase of the diameter of the oocyte between GV0, GV1-GV2
and GV3 (Lodde et al. 2007), the amount of RNA extracted from each oocytes pool showed
no significant difference between the four groups (data not shown). The number of probes
detected on the microarray slide is constant across the different chromatin configurations and
similar to our previous studies with bovine oocyte (Labrecque et al. 2013; Labrecque et al.
2014). This effectively shows no global increase in the amount of RNA present either in
oocytes from early antral follicles (GV0) than those from larger follicles. Similar results were
reported in the mouse, where oocytes from primordial follicles were compared to those from
large antral follicles (Pan et al. 2005).
Considering that GV0 oocytes are more transcriptionally active than their GV3 counterpart,
as shown by the absence of uridine incorporation labelling (Lodde et al. 2008), the important
number of genes presenting a decrease mRNA level as the chromatin becomes more
compacted is not surprising. However, even with the global repression of transcription
specific to the GV3 oocyte, a low level of transcription is still possible in the oocytes
harbouring a compacted chromatin. To confirm this hypothesis, an additional microarray
comparison was performed between GV2 and GV3 oocyte. Even with such a compacted
151
chromatin, 51 transcripts were presenting an increased abundance at the GV3with a fold
change > 2, which strongly suggest that some transcriptional activity is still ongoing in these
oocytes, while only 10 transcripts where showing a decrease abundance. Interestingly, by
using the list of transcripts presenting an increase or decrease abundance, the functional
analysis highlighted respectively important biological processes, namely translation and
transcriptional regulation. Many members of the eukaryotic translation initiation factor
(EIF3G, EIF3I, EIF5A) and several ribosomal proteins (RPL18, RPL28, RPL29) effectively
showed an increased mRNA level in GV3 oocyte. Moreover, genes involved in
transcriptional regulation rather showed a decrease at the mRNA level following chromatin
compaction. As previously suggested, the decrease in the expression of specific members of
the transcriptional machinery could explain the transcriptional arrest observed towards at the
end of oocyte growth (Pan et al. 2005; Worrad et al. 1994). Therefore, the important decrease
observed for several genes involved in the transcriptional regulation could suggest a similar
mechanism in the bovine oocyte. For example, Chromobox homolog 3 (CBX3), also known
as HP1gamma (Heterochromatin protein 1 homolog gamma), possess important roles from
transcription to nuclear organization (Lomberk et al. 2006) and AEBP2, a member of the
Polycomb repressive complex 2 (PRC2) involved in transcriptional repression (Cao and
Zhang 2004; Kim et al. 2009), both showed an important decrease at the mRNA level once
the chromatin has started to condense. In this case, it is possible to speculate that the decrease
could be associated with translation as CBX3 protein was detected in mouse GV-stage
oocyte (Pfeiffer et al. 2011; Wang et al. 2010) and AEBP2 was detected in the polysomal
mRNA population of mouse GV-stage oocyte (Potireddy et al. 2006). In the other hand,
genes involved in the organization of nuclear envelope such as Thymopoietin (TMPO), also
known as Lamina associated protein 2 (LAP2) (Gerace and Huber 2012) presented an
increase at the mRNA level when the chromatin compaction is nearly completed. Taken
together, these data illustrate the dynamic modulation of several transcripts involved in
crucial functions during this remodeling process.
Among the genes that are presenting an increase at the mRNA level as the chromatin
becomes more compacted, many of them are related to histone genes. In comparison to
previous studies where oocytes bearing different chromatin configurations were compared,
152
the number of histone transcripts detected in our study is significantly higher than what was
previously reported. Monti et al. (2013) found a higher abundance for Hist3h2bb, Hist1h2ab
and Hist1h4j at the mRNA level in SN mouse oocyte during their microarray analysis. Ma et
al. (2013) have observed an increase mRNA level in SN oocyte for Hist1h2bc and Hist1h4h
through their RNAseq experiment. The observation of such a high proportion of histone
genes in our data is both surprising and interesting due to the nature of the procedure used to
amplify the RNA (T7-based technique). It is generally assumed that the T7 RNA
amplification procedure will mainly target transcripts bearing a poly-A tail, since the T7
RNA polymerase promoter is coupled to an oligo(dT). Histones mRNA represent the
principal class of transcripts without a poly-A tail (Marzluff et al. 2008). Even though the
length of the oligo(dT) used in most commercial T7-based RNA amplification kit is
generally between 15 to 24 Ts(Kerkhoven et al. 2008), the length of the poly-A tail at the 3’
end of the targeted transcripts is currently unknown. However, it was demonstrated that the
oligo(dT) coupled to the T7 promoter is able to target transcripts with 10 adenine residues,
while those with 5 A’s were not targeted (Graindorge et al. 2006). Furthermore, the presence
of a short poly-A tail after the stem loop has been known for a long time in Xenopus oocyte
(Ballantine and Woodland 1985), where its length is approximately 6-10 adenine residues,
attached after the stem-loop sequence at the 3’ end of histone mRNA in frog oocyte (Sanchez
and Marzluff 2004). Taken together, these data suggest that the priming of histone transcripts
is possible despite the short or absent poly-A tail.
Results from our laboratory have shown that some histones mRNA could effectively be
polyadenylated, such as H1Foo (McGraw et al. 2006), H3 variants (H3F3A, H3F3B) and
even some replication-dependant histones genes such as HIST1H2BN(Gohin et al. 2014).
Similar results were also reported in the literature for the H2B histone family (Kari et al.
2013). A recent study where the poly-A tail length was assessed genome-wide through a
novel approach (PAL-seq, PolyA tail length profiling by sequencing) in various cell lines has
also shown the polyadenylation status of several core histones(Subtelny et al. 2014).
Interestingly, the production of polyadenylated transcripts from core histone genes was also
reported during spermatogenesis, concurrent with an important chromatin remodeling. In
fact, all the histone mRNA produced during the chicken spermatogenesis are polyadenylated
153
and it has been suggested that this is caused by the absence of some specifics components of
the histone pre-mRNA processing machinery, such as U7 snRNP (Challoner et al. 1989).
However, two member of the u7 snRNP complex, namely LSM10 and LSM11 (Schumperli
and Pillai 2004), which are both important essential for histone pre-mRNA processing
(Godfrey et al. 2009), are detected in the mRNA polysomal fraction of bovine GV stage
oocyte (Scantland et al. 2014). Therefore, a different mechanism from what was observed in
avian sperm cells would explain the polyadenylation of histone mRNA in bovine oocyte.
Although the histone pre-mRNA processing and translation is controlled by protein such as
Stem loop binding protein (SLBP) (reviewed by (Dominski and Marzluff 1999)), finding in
Xenopus Laevis have also shown the existence of a different SLBP protein, namely xSLBP2
present exclusively in the oocyte. This protein completely lacks any histone pre-mRNA
processing function and was shown to bind histone mRNA in the immature oocyte in order to
inhibit the translation. Throughout the frog oocyte maturation process, SLBP2 will be
degraded rendering histone mRNAs available to be processed by SLBP1 and then translated
(Wang et al. 1999). It was also demonstrated that in addition to SLBP2, the short poly-A tail
found at the 3’ end of the histone mRNA of frog oocyte would play a role in their
translational repression (Sanchez and Marzluff 2004). Considering that a mammalian SLBP2
protein ortholog was recently identified in bovine oocyte (Thelie et al. 2012), this could
suggest that histone mRNA may be stored in the bovine oocyte/early embryo in a similar
fashion as in Xenopus. In the bovine, SLBP2 protein is detected in the oocyte and will be
gradually degraded (RNA and protein) towards the morula stage (Thelie et al. 2012). The
constant presence of SLBP2 during the transcriptionally silent period could ensure an optimal
supply of histone mRNA ready to use and protected from an early translation or degradation.
Protein-RNA interactions calculated by catRAPID omics strongly suggest that some histone
mRNA could be stored in the oocyte through their associations with SLBP2. This in-silico
approach was successfully validated through the adequate predicted associations between
various histone transcripts and the human and mouse SLBP protein, as previously
demonstrated by immunoprecipitation experiments in both species (Townley-Tilson et al.
2006; Whitfield et al. 2004). The high interaction scores obtained for specific core histones
154
(H3 and H2B) could ensure an optimal supply for the first three cell cycles. Furthermore, low
scores were obtained for various histones variants, such as H2B testis variant, H1FX, H1F0
and H1FNT demonstrating that their accumulation in the oocyte is unlikely, especially
considering that these specifics transcripts were not detected in the oocyte. Interestingly, out
of the three possible H1FOO transcripts, two of them showed a low interaction score, while
the other one seems to be accumulated for a later use. Considering that H1FOO is specific to
the oocyte and will be progressively degraded (RNA and protein) until embryonic genome
activation (McGraw et al. 2006), it could be hypothesized that a specific variant (H1FOO_X1
bound to SLBP2) would be used to ensure a constant protein supply during that period.
However, further experiments will be required to confirm this hypothesis.
5.6
Conclusion
The gradual chromatin remodeling process characterized in the bovine germinal vesicle
oocyte has allowed us to identified dynamic mRNA modulations through this sequential
transcriptome analysis. By comparing four distinct and progressive states of chromatin
compaction, the establishment of the transcriptional repressive context was clearly observed
by the important proportion of transcripts presenting a reduced abundance as the chromatin
condenses. Among the genes presenting an important increase, histones genes were
identified. Through the possible association with the Stem-loop binding protein 2, several
transcripts could be store in the oocyte in a similar process as in Xenopus.
5.7
5.7.1
Oocyte recovery
Bovine ovaries were obtained from a local abattoir and transported to the laboratory within 2
hours in a saline solution (9 g/LNaCl) maintained at 32-34°C. Cumulus oocytes complexes
(COCs) were aspirated from follicles of 2 to 6 mm with a 16G needle mounted on an
aspiration pump (COOK-IVF, Brisbane, QLD, Australia) with a vacuum pressure of -28
mm/Hg. Following aspiration, small pieces of ovarian cortex were removed and examined
under a dissecting microscope. COCs were isolated from early antral follicles of 0.5 to <2
155
mm through the rupture of the follicle wall with a scalpel. All the COCs collected were
washed in medium 199 (M199) supplemented with HEPES (20 mM), Heparin (1790
units/L), and 0.4% of bovine serum albumin (HM199). Only COCs with uniform cytoplasm
and five or more layers of cumulus cells were used. The chromatin configuration assessment
was performed as described by (Lodde et al. 2007). Briefly, COCs were mechanically
denuded by vortexing (2 minutes, 35 Hz) in HM199 supplemented with 5% of calf serum
(Gibco, Invitrogen srl, Milan, Italy). Denuded oocytes (DOs) were washed twice in HM199,
stained in HM199 containing 1 ug/mL Hoechst 33342 for five minutes in the dark, and then
individually transferred into a 5 uL drop of the same medium, overlaid with mineral oil.
Chromatin configuration was evaluated under an inverted fluorescence microscope (Olympus
IX, magnification 40x). Oocytes were exposed to UV light for less than 3 seconds and
classified according to the degree of chromatin compaction. The same evaluator did the
entire chromatin configuration assessments performed over the course of this study in order
to reduce the variation. Following chromatin evaluation, oocytes were washed twice in PBSPVA solution and one more time in PBS solution, collected in group of 10 in a minimal
volume of PBS, snap-frozen in liquid nitrogen and stored at -80°C until RNA extraction.
5.7.2
Total RNA was extracted from four pools of ten oocytes with the Pico-Pure RNA Isolation
Kit (Applied Biosystems, Carlsbad, CA) following the manufacturer’s protocol and including
DNase treatment (Qiagen, Mississauga, ON, Canada) on the purification column. Total RNA
integrity and concentration were evaluated using a 2100-Bioanalyzer (Agilent Technologies,
Palo Alto, CA) with the RNA PicoLab Chip (Agilent Technologies). To generate enough
material for hybridization, the RNA samples were linearly amplified. Antisense RNA
(aRNA) was produced using the RiboAmp HS RNA amplification kit (Applied Biosystems).
After two amplification rounds of six hours each, the aRNA output was quantified using the
NanoDrop ND-1000 (NanoDrop Technologies, Wilmington, DE).
156
5.7.3
Sample labelling and microarray hybridization
product was then purified with the Pico-Pure RNA Isolation Kit. Labelling efficiency was
measured using the Nano-Drop ND-1000. Samples from the four biological replicates,
representing six different weeks of oocyte collection were hybridized on a custom bovine
Agilent 44K microarray slide (Robert et al. 2011). The hybridizations were performed using
a reference design, where oocytes bearing the GV0 chromatin configuration were compared
with the others three groups (GV0 vs GV1; GV0 vs GV2; GV0 vs GV3). Overall, 12
hybridizations corresponding to the four biological replicates and three comparisons, were
performed using a dye-swap set-up. A total of 825 ng of each labelled sample (Cy3 and Cy5)
were incubated in a solution containing 2x blocking agent and 5x fragmentation buffer in a
volume of 55 µL at 60°C for 15 min and were put on ice immediately after. Then, 55 µL of
2x GEx Hybridization Buffer HI-RPM were added for a total volume of 110 µL. The
hybridization mix (100 µL) was added onto the array and hybridization was performed at
65°C for 17 hours using an Agilent Hybridization chamber in a rotating oven. Following
hybridization, chambers were disassembled and slides were washed with Gene Expression
Wash Buffer 1 containing 0.005% Triton X-102 for 3 min at room temperature and then
transferred to Gene Expression Wash Buffer 2 containing 0.005% Triton X-102 for 3 min at
42°C. Final washes with acetonitrile for 10 sec at room temperature and with Stabilization
and Drying Solution (Agilent) for 30 sec at room temperature were performed before airdrying of the slides. The slides were scanned using the Tecan PowerScanner microarray
scanner (Tecan Group Ltd, Männerdorf, Switzerland) and features were extracted using
ArrayPro 6.4 (Media Cybernetics, Bethesda, MD).
5.7.4
Intensities files were uploaded to ELMA (EmbryoGENE LIMS Microarray Analysis,
http://elma.embryogene.ca/), to run the quality control module. Microarray datasets were
then analysed with the FlexArray microarray analysis software, Version 1.6.1 (Blazejczyk et
al. 2007). Briefly, raw data were subjected to a simple background subtraction, Loess within
array and Quantile between array normalizations. MAANOVA algorithm was applied
157
considering the four chromatin configurations and by using the GV0 group as a reference. A
Limma simple analysis was performed to obtain the fold change for each contrast
individually. Differences were considered statistically significant with a P value less than
0.01. To detect the presence or absence of the signal for each spot present on the slide, an
arbitrary cut-off level of 7.4 (log2) was used, which corresponds to the mean intensity of the
background level + twice the Standard Deviation (SD) of the background. Genes were then
grouped in clusters according to their profile with the mFuzz Bioconductor package (Kumar
and Futschik 2007). A between-group analysis (BGA) was performed to classify the samples
according to their transcriptomic profile (Culhane et al. 2002). Microarray data have been
deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO series
accession number GSE48376. DAVID bioinformatics resources were also used to perform
functional enrichment analysis of specific gene-list (Huang et al. 2009a; Huang et al. 2009b).
5.7.5
four independent biological replicates (pools of 10 oocytes). Total RNA was extracted and
reverse transcribed in duplicate using q-Script Flex cDNA Synthesis Kit (Quanta
Biosciences, Gaithersburg, MD), either with random primers or oligo(dT) following
manufacturer’s recommendations. The primers used for q-RT-PCR are listed in Table 5-4
and
were
designed
using
the
IDT
PrimerQuest
tool
(http://www.idtdna.com/Scitools/Applications/Primerquest/) from sequences obtained using
the UMD3.1 assembly of the bovine genome and results from our microarray analysis. To
confirm the specificity of each pair of primers, electrophoresis on a standard 1.2% agarose
gel was performed for each amplified fragment. The PCR product was purified with the
QIAquick PCR Purification kit (Qiagen), quantified using the NanoDrop ND-1000 and
sequenced. The products were then used to create the standard curve for quantification
experiments, with dilutions ranging from 2 x 10-4 to 2 x 10-8 ng/µL. Quantitative real-time
PCR was performed on a LightCycler 480 (Roche Diagnostics, Laval, QC, Canada) using
SYBR incorporation. Each q-RT-PCR reaction, in a final volume of 20 µL, contained the
cDNA corresponding to 1/40 of oocyte, 0.25 mM of each primer and 1x SYBR mix
(LightCycler 480 SYBR Green I Master, Roche Diagnostics). The PCR conditions used for
158
all genes were as follows: a denaturing step of 10 min at 95°C followed by 50 PCR cycles
(denaturing, 95°C for 1 sec; annealing, [Table 5-4] for 5 sec; extension, 72°C for 5 sec), a
melting curve (94°C for 5 sec, 72°C for 30 sec and a step cycle starting at 72°C, up to 94°C
at 0.2°C/sec) and a final cooling step at 40°C. Complementary DNA quantification was
performed with the LightCycler 480 Software Version 1.5 (Roche Diagnostics) by
comparison with the standard curve. PCR specificity was confirmed by melting-curve
analysis.
5.7.6
For each gene tested, four independent biological replicates were used. Analysis of gene
expression stability over the four follicle size groups was performed using the geNorm
algorithm (Vandesompele et al. 2002) through the Biogazelle’s qBase+ software (Biogazelle,
Zwijnaarde, Belgium). ACTB, B2M and YWHAZ were identified as the most stable
references genes among the different oocyte groups. One-way ANOVA with Newman-Keuls
post-test was performed using Graph Pad Prism version 5.0. Differences were considered to
be statistically significant at the 95% confidence level (P value < 0.05). Data are presented as
mean ± s.e.m.
5.7.7
Histones mRNA analysis
To identify histones mRNA potentially bound to the Stem-loop binding protein 2 (SLBP2),
the catRAPID-omics web server (http://s.tartaglialab.com/page/catrapid_omics_group) was
used to estimate the binding propensity between a pair of protein-RNA (Agostini et al. 2013).
The interaction is calculated for each RNA-protein pair, based on the physicochemical
properties of nucleotide and amino acids sequences. The interaction strength is computed
using a reference list of 100 random proteins and 100 random RNA sequences having the
same length as those under investigation. The Discriminative Power is a statistical measure to
evaluate the Interaction Propensity and represents the confidence of the prediction. The
Discriminative Power (DP) ranges from 0% (unpredictability) to 100% (predictability).
Discriminative power values above 50% indicate that the interaction is likely to take place,
whereas DPs above 75% represent high-confidence predictions. The global score is then
159
calculated based on three individual values, the catRAPID normalized propensity, the
presence of RNA binding domain and the presence of known RNA-binding motifs. A custom
library was generated using the bovine histone mRNA sequences retrieved from NCBI
(http://www.ncbi.nlm.nih.gov/nuccore/). The Stem-loop binding protein sequences were
retrieved from Uniprot (http://www.uniprot.org/). The catRAPID algorithm calculates an
interaction score and a discriminative power for each RNA-protein pair. Additional analyses
were performed with the protein sequences of human Stem loop binding protein (hSLBP)
and mouse SLBP (mSLBP), where each protein sequence was analyzed against its respective
transcriptome.
5.8
Funding
This work was supported by the Natural Sciences and Engineering Research Council of
Canada (NSERC-CRSNG).
5.9
Acknowledgements
We thank Federica Marchese, Valentina Baruffini and Franca Raucci for the help in the
collection of oocyte samples, Dr Isabelle Dufort for her technical assistance in all the
molecular biology experimentsand Eric Fournier for his help in bioinformatics analyses.
160
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165
166
5.11 Figures
microarray data.
Red: GV0; Blue: GV1; Green: GV2 and Brown: GV3. The BGA illustrates the global
distribution of transcriptomic data from the biological samples (4 chromatin configuration)
and the four biological replicates (dot in each group).
167
[1266]
[509]
Chromatin con guration
[270]
[195]
[192]
Figure 5-2 Main clusters identified from Mfuzz clustering analysis (Kumar and
Futschik 2007).
The number of statistically significant probes specific to these profiles is display for each
cluster. The cluster 12 represents more than 45% of the statistically significant genes with a
decrease mRNA level between GV0 and GV1 and then stays relatively stable. The other
clusters illustrate the transcripts with increase and decrease profile (# 11), a constant increase
until GV2 (# 8), a decrease followed by an increase at GV2 (# 6) and an increase at GV1
(#5).
168
Figure
5-3
Quantitative
RT-PCR
validation of
microarray
results.
mRNA
quantifications data are presented as mean ± standard error of the mean.
Different superscripts represent significant differences (P< 0.05) after one-way ANOVA
followed by Newman-Keuls post-test.
169
data16 clusters generated by the mFuzz clustering analysis
Figure 5-4 Supplemental data16
170
5.12 Tables
Table 5-1 Number of genes presenting significant differences following LIMMA
analysis (P< 0.01)
GV0 vs GV1
GV0 vs GV2
GV0 vs GV3
FC > 2 total
FC>2
FC<-2
264
325
302
48
62
44
216
263
258
171
Table 5-2 Number of histone-related probes.
Number of probes on the
EmbryoGENE microarray
platform
Number of probes
detected in the
oocyte
H1
9
6
Number of probes
presenting a statistically
significant differences
(MAANOVA, P < 0.01)
5
H2A
15
10
4
H2B
68
33
7
H3
14
13
9
H4
16
12
4
H2A variant
12
10
0
H2B variant
3
0
0
H3 variant
12
10
1
H1 variant
11
10
0
Histone
family
The number of probes detected in the oocyte corresponds to the probes with signal intensity
above the cut-off level of 7.4. The complete list of histone variants is described by
(Kamakaka and Biggins 2005). H2A variants include H2AFB2, H2AFJ, H2AFV, H2AFX,
H2AFY, H2AFY2, H2AFZ, H2A testis specific, H2B variants include H2B testis variant, H3
variant include H3.3 and CENPA and H1 variants include H1F0, H1FNT, H1FX and H1foo.
172
Table 5-3 List of possible Protein-RNA interactions calculated with catRAPID omics.
Protein ID/Transcript ID
bSLBP2/HIST1H3a-like_XM_005223798.1
bSLBP2/HIST1H3a-like_XM_005196649.1
bSLBP2/H2AFJ_NM_001078089.2
bSLBP2/H2AFV_NM_001038197.1
bSLBP2/H2AFZ_X1_XM_005207694.1
bSLBP2/H2AFZ_X2_XM_005207695.1
bSLBP2/H2AFY2_NM_001076086.1
bSLBP2/H4-like_germinal_NM_001145871.2
bSLBP2/H1FOO_X1_XM_005223064.1
bSLBP2/H2AFZ_NM_174809.2
bSLBP2/H2AFY_X4_XM_005209379.1
bSLBP2/H2B_1/2-like_XM_002699769.2
bSLBP2/H2B_1/2-like_XM_003585165.2
bSLBP2/H2B_type_1-like_XM_582268.5
bSLBP2/H2AFJ_X1_XM_005206990.1
bSLBP2/H1FNT_XM_869593.6
bSLBP2/H2AFY_NM_001046340.1
bSLBP2/H1F0_NM_001076487.1
bSLBP2/H2B_5-like_XM_002684861.1
bSLBP2/H1FX_XM_002697143.3
bSLBP2/H2bd-like_XM_002693561.1
bSLBP2/H2A-Bbd_NM_001075905.2
bSLBP2/H1FOO_X2_XM_005223065.1
bSLBP2/H2ai-like_XM_005223728.1
bSLBP2/H2A-like_XM_002683953.1
bSLBP2/H2AFX_NM_001079780.2
bSLBP2/H1FOO_NM_001035372.1
bSLBP2/H2B_testis-specific-like_XM_002699853.1
Interaction
Strength
0,92
0,87
1,00
0,99
0,99
0,99
0,98
0,97
0,95
0,95
0,94
0,93
0,93
0,95
0,89
0,95
0,81
0,81
0,77
0,72
0,73
0,73
0,64
0,69
0,41
0,34
0,57
0,34
0,31
0,20
0,12
0,34
0,07
0,08
0,17
0,17
0
Discriminative
Power
0,87
0,66
1,00
0,99
0,98
0,98
0,97
0,95
0,93
0,92
0,90
0,90
0,90
0,89
0,81
0,80
0,79
0,76
0,71
0,71
0,69
0,69
0,57
0,50
0,42
0,35
0,35
0,33
0,28
0,22
0,20
0,17
0,14
0,14
0,10
0,10
0
Score
1,90
1,85
1,37
1,05
1,03
1,02
0,98
0,94
0,93
0,92
0,91
0,91
0,91
0,91
0,89
0,89
0,88
0,87
0,86
0,86
0,86
0,86
0,84
0,83
0,82
0,81
0,81
0,81
0,80
0,78
0,78
0,77
0,76
0,74
0,68
0,68
0,64
bSLPB2 : Bovine Stem-loop binding protein 2. Interactions with a discriminative power (DP)
above 75% represent high-confidence predictions, while DP above 50% are likely to take
place.
173
Table 5-4 Supplemental data Primers used for quantitative RT-PCR validations
Genesymbol
GenBankaccessionnumber
Forwardprimer(5'-3')
Reverseprimer(5’ -3')
Annealing
temp.(°C)
Productsize
(bp)
HIST1H1A
XM_005196651.1
GTTGACAAAGAAGTCCTCTAAG
CTTGGTTACCTTCACTTTGG
59
124
HIST1H1E
XM_870179.4
CAGCTAAGAAGCCGAAGAAG
TTTGGCGGTCTTTGGTTTAG
60
215
H2B
XM_870344.3
AGAAGAAGGATGGGAAGAAG
ATATCGTTGACGAAGGAGTT
59
142
HIST1H3A
XM_005196648.1
TCAAGACCGACTTGCGTTTC
ACAGGTTGGTGTCCTCAAAG
62
96
HIST1H4I
XM_005223796.1
AACATCCAGGGTATTACCAA
CATAGATGAGGCCAGAAATAC
58
82
PIWIL3
XM_867297.4
GATAGCTGTACCATCATCAG
CCACTCCATTTAGAAGTATAGG
57
156
AEBP2
NM_001083485.2
AATTACGGGAGGCTTTATTC
ACCATTCCAGAATGTCTTAC
57
121
PTCH1
XM_005210407.1
GGAATGGGTTTGTGTCATTTC
CACTCATGTCTCAGCAAAGT
60
178
CBX3
NM_001101198.1
AGTCTCTGTGCTGTATTTAG
GTAGGTCAAGCGTAGATTAG
57
152
TMPO
NM_001098005.1
GGCTAGCTATAAGGCTATAA
CAAATGGAATCTAAGACTGG
56
152
ACTB
NM_173979
59
102
B2M
NM_173893.3
AGACACCCACCAGAAGATGG
GGGGTTGTTCCAAAGTAACG
54
234
YWHAZ
NM_174814.2
CCAGTCACAGCAAGCATACC
CTTCAGCTTCGTCTCCTTGG
55
286
174
Mfuzz clustering analysis
Cluster
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Number of probes
(P < 0.01)
102
10
88
13
195
192
31
270
3
28
509
1266
2
10
24
49
175
Table 5-6 Supplemental data List of possible Protein-RNA interactions identifiedby
catRAPID omics.
Protein
Transcript
Discriminative
Score
Interaction Strength
ID
ID
Power
hSLBP
HIST1H4J
0,87
0,97
1,97
hSLBP
HIST1H1B
0,76
0,86
1,95
0,5
0,63
1,92
hSLBP HIST1H2AE
hSLBP HIST3H2BB
0,32
0,54
1,91
hSLBP
HIST1H3B
0,17
0,25
1,89
hSLBP HIST1H3H
0,17
0,21
1,89
hSLBP
HIST1H1E
0,1
0,22
1,86
mSLBP
Hist1h1e
0,84
0,88
1,6
mSLBP
Hist1h4c
0,47
0,85
1,6
mSLBP
Hist1h3b
0,71
0,64
1,6
mSLBP Hist2h2aa1
0,37
0,49
1,59
mSLBP
Hist1h2bg
0,17
0,14
1,59
hSLPB : human Stem-loop binding protein, mSLBP: mouse Stem-loop binding protein.
176
6 The study of mammalian oocyte competence by transcriptome
analysis: progress and challenges
Rémi Labrecque and Marc-André Sirard
Centre de Recherche en Biologie de la Reproduction, Faculté des sciences de l’Agriculture et
de l’Alimentation, Département des Sciences Animales, Pavillon INAF, Université Laval,
Québec, Canada
Cet article a été publié dans la revue « Molecular Human Reproduction » sous la référence
suivante :
177
6.1
Résumé
Plusieurs caractéristiques morphologiques et cytologiques des ovocytes ainsi que de leurs
cellules du cumulus peuvent être utilisées pour sélectionner les ovocytes dans un contexte de
reproduction assistée. Toutefois, même après une sélection minutieuse, les chances de succès
suite à la fécondation in vitro et le développement de l’embryon demeurent incertaines. Les
facteurs qui assurent la compétence de l’ovocyte ne sont pas clairs et d’autres approches
visant à déterminer le potentiel de développement doivent être explorées. Avec le
développement constant des outils moléculaires, l’analyse génomique/transcriptomique est
devenue une approche de plus en plus intéressante afin de mieux comprendre la qualité
ovocytaire en fonction de la composition en ARN. En utilisant des modèles bovins, souris et
humains où le potentiel de développement des ovocytes est connu, plusieurs efforts sont en
cours afin de caractériser le profil en ARNm de l’ovocyte compétent en utilisant la
technologie des microarrays. La prolifération des ensembles de données d’expression des
gènes offre également de nouvelles opportunités dans le but d’identifier les mécanismes
impliqués dans ce phénomène complexe, ce qui devrait mener à l’amélioration des
techniques de reproduction assistées. Bien que plusieurs marqueurs moléculaires de la qualité
ovocytaire soient connus, la transposition de ceux-ci en fonctions cellulaires demeure
complexe, en raison de la faible corrélation entre le niveau d’ARNm et la synthèse protéique.
Contrairement à la plupart des cellules somatiques, l’ovocyte peut entreposer des ARNm
durant plusieurs jours, et ce, conjointement à un arrêt de la transcription pour une durée de 45 jours, débutant peu de temps avant l’ovulation et se terminant au moment de l’activation du
génome embryonnaire. Cette revue donne un aperçu des données transcriptomiques obtenues
à partir d’ovocytes de différentes qualités en plus d’offrir des pistes d’analyses à explorer
afin d’améliorer notre compréhension de la compétence ovocytaire.
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6.2
Abstract
Various morphological and cytological traits of oocytes and their surrounding cumulus cells
may be used to select oocytes for assisted reproduction. However, even with careful
selection, successful in vitro fertilization and subsequent embryo development remain
uncertain. The factors that ensure oocyte competence are unclear and other approaches to
assessing developmental potential must be explored. With the constant development of the
molecular toolbox, genomic/transcriptomic analysis is becoming a more and more interesting
approach to understand oocyte quality on the basis of RNA composition. Using bovine and
mouse models as well as human oocytes of known developmental potential, various efforts
are underway to characterize the mRNA profile of the competent oocyte using microarray
technology. The proliferation of gene expression datasets raises new opportunities to identify
the mechanisms involved in this complex phenotype, which should lead to improved
techniques of assisted reproduction. Although several molecular markers of oocyte quality
are known, translating these into cellular functions remains challenging, due largely to the
poor correlation between mRNA level and protein synthesis. Unlike most somatic cells, the
oocyte can store mRNA for days, with transcriptional activity remaining at a halt during the
4–5 days beginning before ovulation and ending with embryonic genomeactivation. This
review provides an overview of the transcriptomic data obtained from oocytes of different
quality as well as interesting avenues to explore in order to improve our understanding of
oocyte competence.
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6.3
Introduction
Ovulation or the release of an oocyte (one or a few, depending on the species) is the result of
a long and well-orchestrated building process designed to culminate in fertilization, followed
by the development of an embryo and eventually the birth of healthy offspring. Even though
the underlying mechanism of developmental competence has not yet been identified, a
crucial part of this process is certainly the latter phase of oocyte growth, in which
developmental potential is gained. This is known to be the case in mice (Sorensen and
Wassarman 1976), humans (Gougeon 1986)and a recent review in cattlesuggested the same
thing (Sirard et al. 2006). While the building process is now better understood, we are still far
from a clear blueprint of what composes a competent oocyte. In humans, at best the chance to
conceive a clinically recognized pregnancy during one cycle is around 30% (Macklon et al.
2002), which approximately reflect the blastocyst rate that had been reported in few studies
(Van Landuyt et al. 2005; Walls et al. 2012). However, even under optimized conditions of
assisted reproduction techniques using IVF, the success in term of pregnancy or implantation
rates are not significantly higher, even considering the fact that more than one embryo is
generally transferred (de Mouzon et al. 2012). Nonetheless, these data did not take in account
the pregnancy loss that couldhave occurred. This could explain the low live birth rate per
mature oocyte retrieved for assisted reproduction techniques, which is approximately 4.5%
(Stoop et al. 2012). This result is not surprising if we consider that almost half of the oocytes
tested are aneuploid according to array comparative genomic hybridization (array CGH)
(Gabriel et al. 2011; Handyside et al. 2012), orfollowing preimplantation screening (Kuliev
et al. 2011).In humans, only one out of five fully grown, ovulated and in vivo fertilized
oocytes will give rise to a viable embryo(Macklon et al. 2002). The success rate obtained
using in vitro fertilization (IVF) techniques is not much better, based on the total number of
mature oocytes obtained for the procedure (de Mouzon et al. 2012). This limited oocyte
competence is particularly apparent in other mono-ovulatory species such as cattle. Only
30 % of oocytes obtained from slaughterhouse ovaries are able to reach the blastocyst stage
in conventional IVM-IVF (Trounson et al. 1994) and an even lower proportion will result in
pregnancy following embryo transfer (Hasler 1998). These observations indicate the need for
a more practical definition of oocyte quality.
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Among the many factors that could be used to evaluate oocyte quality, an important criterion
is the rate of full-term development (i.e. live birth) of healthy offspring. However, for
technical, practical and ethical reasons, not all embryos produced in the research context are
singly transferred to a recipient, making it difficult to associate a given condition with an
outcome. Depending on the goal of the study, several measures may be used to define the
developmental potential of the oocyte. A widely used criterion in routine laboratory
experiments with animals is the rate of blastocyst formation. However, this criterion should
be interpreted with caution since it often refers more to the ability of the in vitro culture
system to support development than to intrinsic oocyte quality (Duranthon and Renard 2001).
Quality expressed in terms of the developmental stage that the oocyte may be expected to
reach (meiosis resumption, fertilization, blastocyst, pregnancy, etc.) is well recognized and
has been described in detail in a review by Sirard et al. (2006).
Many criteria used to assess the quality of oocytes from large mammals are morphological.
Several of these, such as oocyte cytoplasm or polar body shape, zona pellucida thickness and
meiotic spindle position, have been used to select the best mature oocytes for human IVF
(Coticchio et al. 2004; Wang and Sun 2007). However, a more recent review reached the
conclusion that no combination of these criteria have improved the prediction of IVF
outcome (Rienzi et al. 2011). The major determinant of the quality of an oocyte seems to be
the follicle from which it comes, and several papers have examined the direct interaction
between follicular size or differentiation level and developmental outcome (reviewed by
Blondin et al. (2012)).
In order to avoid destroying human oocytes, other non-invasive approaches to the indirect
evaluation of quality have been explored. Much effort has been devoted to developing
molecular methods of selecting the most competent oocyte (reviewed by Patrizio et al.
(2007)). Fluorescence in-situ hybridization (FISH) and comparative genomic hybridization
(CGH) have been used on the polar body in attempt to select the ideal oocyte (Wells et al.
2008; Fragouli et al. 2011a). More recently, microarray-based CGH has been proposed as a
faster alternative for detecting chromosomal anomalies in early embryos (Gutierrez-Mateo et
al. 2011)and to predict oocyte ploidy status by analyzing the polar body(Geraedts et al.
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2011). Comprehensive chromosome screening using single nucleotide polymorphism (SNP)
microarray (Treff et al. 2010)or array CGH (Gabriel et al. 2011; Handyside et al. 2012)could
also be used to successfully select euploid embryos before transfer (Scott et al. 2012;
Capalbo et al. 2013). However, the transfer of a euploid embryo is not a guarantee of success,
although it improves the chances to predict a pregnancy (Scott et al. 2013).
Gene expression analysis is emerging as a promising tool for improving understanding of
what determines the quality of the oocyte (Patrizio et al. 2007). Although this approach is
destructive and therefore not directly applicable to humans, it is an interesting option for
studying the variability of oocyte quality in animal models. The focus of this review is
transcriptomic analysis of oocytes of different quality based on follicular status or on
conditions known to lead to higher blastocyst yields.
6.4
Markers of oocyte quality
The concept of biomarkers of quality is not applicable to specific oocytes, since the analysis
requires extraction of mRNA and hence destruction of the cell. This explains why so much
effort has been devoted to identifying proxiesamong follicular cells such as granulosa cells,
reviewed by Li et al. (2008) andUyar et al. (2013) and studied by others (Hamel et al. 2008;
Hamel et al. 2010b; Hamel et al. 2010a), and cumulus cells, reviewed by Assou et al. (2010)
and studied by others (Assidi et al. 2011; Feuerstein et al. 2012; Fragouli et al. 2012; Iager et
al. 2013). Although potentially useful in the clinical context, the information gained from
surrounding follicular cells does not yet explain the molecular or structural characteristics of
a competent oocyte.
It is now possible to use a microarray to analyze gene expression in a polar body separated
from a mature oocyte. Such analyses have revealed a relatively high level of similarity
between transcriptome of the polar body and the corresponding metaphase II oocyte in
human(Reich et al. 2011). This technological advance may provide the missing knowledge
that will make it possible to select oocytes based on markers. Possible markers of the
competence of mouse oocytes are being studied using this approach (Jiao et al. 2012).
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However, without reliable knowledge of the transcriptome of competent oocytes, analysis of
polar body gene expression will not by itself lead to the identification of relevant markers.
6.5
Animal models for studying oocyte competence
Ovaries obtained from livestock processing have been used extensively to study the oocyte
transcriptome. The main limitation associated with this source is the heterogeneity of the
oocytes due to lack of control over the stage of the estrous cycle, gestational status or animal
age (Vassena et al. 2003). Furthermore, follicles of all sizes are present in an ovary, and in
theory half of these are undergoing atresia (Lussier et al. 1987). As a result, only one of three
oocytes has the capability of developing into an embryo after IVM-IVF, even with the most
careful morphological selection.
Different co-variables may be used to classify the potential competence of bovine oocytes:
the size of the follicle from which the oocyte is collected (Blondin and Sirard 1995),
follicular wave (growth/dominance) dynamics (Vassena et al. 2003), stimulatory context
such as hormonal pre-treatment (Blondin et al. 2002; Nivet et al. 2012), timing of polar body
extrusion (van der Westerlaken et al. 1994) or time between fertilization and first cleavage
(Lonergan et al. 1999)or the kinetics of the first cell cycles (Grisart et al. 1994; Holm et al.
1998). In human, much more efforts and resources have been spent on morpho-kinetics as a
selection method of the best embryo but still, half of the selected embryos based on the best
parameters are still aneuploid (Scott et al. 2012) and time-lapse monitoring does not seem to
have reached yet the precision level of pre-implantation genetic screening to detect
embryonic aneuploidy (Swain 2013). Using these approaches, a population of oocytes with
contrasting developmental competence may be obtained, which offer the possibility of
correlating RNA content with probability of competence. This is of particular interest
considering the importance of oocyte mRNA composition in regard of the embryonic
genome activation (Sirard 2010).
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6.6
Strategies for matching mRNA profiles with oocyte competence
With the means now available to identify oocytes from different species with a given
developmental capability, it should be possible to correlate this capability with differences in
the transcriptome at each distinct stage of follicular growth and progressing towards
ovulation (Table 6-1).
6.6.1
The follicular phase of oocyte development
Using a genome-wide approach (Affymetrix murine genome array), Pan et al. (2005)
performed paired comparisons of the transcriptome of mouse oocytes collected from follicles
at the primordial and primary, primary and secondary, secondary and small antral, and small
antral and large antral stages. The latter two comparisons are of particular interest in the
present review, since oocytes at these stages are able to resume meiosis (Sorensen and
Wassarman 1976)and support early development(Eppig et al. 1994). The number of genes
differentially expressed when comparing the least to the most advanced stage of follicular
development was relatively small. This should be expected, in view of the gradual decrease
in transcriptional activity that is known to occur towards the end of oocyte growth (De La
Fuente and Eppig 2001). However, RNA synthesis is still detectable between the period
following oocyte growth arrest and GVBD (Rodman and Bachvarova 1976) and transcription
will cease only at the time of germinal vesicle breakdown (Wassarman and Letourneau 1976)
while polyadenylated RNA synthesis is still detected in fully-grown mouse oocyte (Brower et
al. 1981).Among the genes presenting significant differences in oocyte from different stages
of follicle development, 60–65 % decreased in abundance as the follicle grew. The
proportion that waspresent at increased levels was nevertheless considerable, given the
overall repression of transcriptional activity. Among them, there was an over-representation
of genes involved in DNA repair and the response to DNA damage in the oocyte(Pan et al.
2005), which was also noted in a previous comparison of mouse fully grown oocytes and 1cell embryos (Zeng et al. 2004). This shift in gene expression might play an important role in
ensuring the genomic integrity of the female germ cell line.
Similar analyses have been performed on human oocytes collected at the primordial,
intermediate and primary stages of follicular development (Markholt et al. 2012), and on
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bovine oocytes collected from the primordial to large antral stages (Bessa et al. 2013). Using
a candidate genes approach, the authors of the bovine study found very few differences
between oocytes from small and large antral follicles(Bessa et al. 2013), which are the stage
where competence is acquired while authors of the human study found many differences but
not directly related to competence acquisition(Markholt et al. 2012). Based on these studies,
the early stages of follicular growth do not appear to contain direct indicators of oocyte
quality, at least not in terms of mRNA abundance.
6.6.2
Follicle size
The size of the follicle from which the oocyte originates was identified relatively early as a
factor contributing to the likelihood of yielding an embryo after in vitro maturation and
fertilization. Oocytes from larger follicles are more likely than those from small follicles to
reach the blastocyst stage. Blondin and Sirard (1995)observed that oocytes collected from
small follicles (< 3 mm) rarely develop beyond the 16-cell stage, an observation corroborated
by numerous researchers (Pavlok et al. 1992; Lonergan et al. 1994; Hagemann et al. 1999;
Machatkova et al. 2004; Lequarre et al. 2005). Relationships between follicle size and oocyte
mRNA content should therefore be deciphered and correlated with variations in
developmental competence.
One of the first studies using follicle size as a model of oocyte competence was that of
Robert et al. (2000). These authors were the first to use suppressive subtractive hybridization
(SSH) to compare the entire mRNA contents of immature oocytes.Examining small (< 2
mm) and medium-sized (3–5 mm) follicles collected from cattle, they found a relatively low
number of transcriptsdifferentially represented, although the methods described have since
proven their value for studying minute quantities of cells such as oocytes samples. Donnison
and Pfeffer (2004) conducted a similar study with immature oocytes using follicles divided
into two size groups (< 2 mm and > 5 mm) but used linear amplification of the starting
material to obtain sufficient amounts of RNA before performing SSH. They were thus able to
identify more than 20 genes, of which 90 % were found more abundant in competent oocytes
(from follicles > 5 mm) even though the majority of the differences observed were less than
three-fold. The authors concluded that developmental competence could depend on small
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quantitative changes in the mRNA levels associated with a wide range of genes(Donnison
and Pfeffer 2004).
Among the few other studies of the relationship between follicle size and the expression of
specific genes potentially involved in oocyte competence are those of Racedo et al. (2008),
who tested two groups (< 2 mm and 2–8 mm) of follicles in cattle, and Caixeta et al. (2009)
and Mourot et al. (2006), who both tested four size categories (< 3 mm; 3–5 mm; 5–8 mm
and > 8 mm), also in cattle. While Mourot et al. (2006)havefirst used SSH to build a library
of genes functionally important for oocyte competence to then identified gradual
accumulation of specific mRNAs with increasing follicle size category, Caixeta et al. (2009)
found very few differences in transcript abundance among a shortened list of seven genes.
Many of the genes identified by Mourot et al. (2006)were related to cell cycle, which
suggested to the authors that the purpose of accumulating their mRNA in oocytes at the
germinal vesicle stage during follicular growth could be to ensure the presence of enough
cyclin messengers to drive the four subsequent cell divisions until transcription resumes.
Although simplistic, this hypothesis would explain the phenotype observed.
Taking advantage of the recent development of microarray technology, which specifically
included all the isoforms identified in cattle oocyte and embryos by RNAseq in addition to
all the bovine reference genes(Robert et al. 2011), a complete transcriptomic analysis of the
follicle size categories was performed in our laboratory. Multiple comparisons were made
between GV stage oocytes collected from follicles in the size ranges of < 3 mm, 3–5 mm, 5–
8 mm, and > 8 mm in ovaries obtained from livestock processing (Labrecque and Sirard,
unpublished). Differences between the < 3 mm and 3–5 mm categories were minimal, while
those between < 3 mm and the 5–8 mm category were more apparent. Furthermore, there
appeared to be a linear progression in the expression of many genes among these first three
categories, from which the > 8 mm category diverged. This might be due to the proportion of
oocytes undergoing atresia, mixed with those still acquiring competence in this follicle size
category, or perhaps due to the acquisition of dominant status by the follicle once it reaches
8.5 mm in diameter (Ginther et al. 1997). A major proportion of the genes differentially
represented as a function of follicular growth could be clustered using statistical methods,
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one cluster showing a relatively constant depletion of mRNA and the other showing a
relatively constant accumulation of mRNA. This is both surprising and interesting since it is
known that transcriptional activity in oocytes gradually ceases towards the end of their
growth. In fact, the complete arrest of the transcription will take place only at the meiosis
resumption and transcription inhibition in presence of alpha-amanitin effectively blocks
meiosis resumption as demonstrated in the bovine by Hunter and Moor (1987) and by
Kastrop et al.(1991). This low level of transcription could explainthe increase observed for
several mRNA during oocyte maturation (between GV and MII stage), regardless of RNA
isolation procedure used (Mamo et al. 2011).Nevertheless, the cluster presentingmRNA
accumulation is composed of genes involved in chromatin assembly, chromosome
organization as well as DNA packaging and might reflect cellular needs to ensure oocyte
competence. Even with a complete survey of the oocyte transcriptome during follicular
growth, the biological interpretation of all the differences observed remains a challenge.
However, such characterization of the oocyte transcriptome during follicular growth, in
association with recognized potential, opens the door to comparison with other oocyte
transcriptome datasets, and hence better interpretation of the differences in mRNA
expression level and potentially identification of the key actors involved in producing the
complex phenotype that is developmental competence.
6.6.3
Follicular differentiation
It was demonstrated that oocytes collected during the growth phase of follicular development
are of better quality than those collected during the dominance phase, due to negative effects
exerted by the dominant follicle on the subordinate follicle (Hagemann 1999). The
transcriptomes of immature oocytes recovered from small follicles during the growth phase
(day 3 post estrus) and the dominance phase (day 7 post estrus) were compared using a
bovine cDNA custom microarray (Ghanem et al. 2007). Of the > 2000 clones included on the
slide (Blue Chip) specifically enriched in bovine oocyte and early embryo mRNAs(Sirard et
al. 2005), 51 genes were represented with differentials ranging between 1.5 and 5.3. In spite
of the limited genomic coverage provided by the microarray platform used, and the fact that
30 % of the differentially regulated genes were of unknown function, the results nevertheless
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demonstrated that subtle differences could be detected in the transcriptional activities of
oocytes close to ovulation.
It was also demonstrated that the persistence of the dominant follicle is associated with
reduced oocyte quality, based on the proportion of embryos that fail to develop beyond the
16-cell stage (Ahmad et al. 1995). It was later observed in a study using GV stage oocytes
focused on 10 genes believed to be important for early embryogenesis that the presence of a
persistent dominant follicle affected the abundance of specific transcripts involved in the
regulation of transcription and translation (Lingenfelter et al. 2007). This reflects the reduced
potential of the oocyte to retain its quality in terms of mRNA abundance if ovulation does not
occur at the right time. However, the problem persists regarding the biological interpretation
of transcript differentials for a few selected genes versus querying the entire transcriptome.
Bearing in mind that accumulating mRNA for later use is a characteristic peculiar to oocytes,
one must ask whether a drop in the level of a specific mRNA in an oocyte from a persistent
follicle is the result of degradation or of engagement in translation. It remains difficult to
associate a change in mRNA level with a specific and complex phenotype without measuring
protein levels.
6.7
Ovarian stimulation
Normal ovarian physiology has proven robust enough to tolerate a certain degree of
modulation for the purpose of stimulating the development of many follicles instead of only
one per estrus cycle. In the bovine, improvement of these protocols, combined with a
coasting period (i.e. FSH withdrawal after stimulation), has resulted in a significant increase
in the quality of the oocytes collected, approaching a proportion of 100 % capable of yielding
blastocysts (Blondin et al. 2002). Recent optimization of such a protocol has made it possible
to pinpoint the perfect time to harvest the immature oocyte in order to maximize the
blastocyst rate (Nivet et al. 2012). A recent large-scale study has demonstrated the efficiency
of coasting following FSH stimulation combined with hCG to retrieve competent oocyte in in
vitro maturation procedure. It resulted in a higher number of clinical pregnancy compared to
no stimulation (Fadini et al. 2009), which was also confirmed in a latter study (Dal Canto et
al. 2012). A recent review (Fadini et al. 2013) on human in vitro maturation had underlined
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the fact that further attention should be given to ovarian priming followed by a coasting
period (FSH deprivation) in regard of the success rate obtained in the bovine (Nivet et al.
2012). However, investigations on different coasting durations in human ART have not been
performed since the last ten years (Suikkari et al. 2000; Mikkelsen et al. 2003). The
collection of such high quality oocytes offers a new perspective on the study of associations
between mRNA and high developmental competence.
Studies of mice alsohave shown that ovarian stimulation without ovulation induction may
enhance oocyte quality. Gonadotropin-stimulated oocytes collected from large antral follicles
were not only more likely to yield blastocysts, but also to develop to term following embryo
transfer (Pan et al. 2005). Using a microarray, 117 genes were found differentially regulated
between GV stage oocyte from eCG-primed and unprimed animals. Genes involved in
oocyte activation or in cell cycle regulation were identified among those for which subtle
increases in mRNA level were noted. However, almost 80 % of the differences detected have
shown a decreased mRNA level, many of these associated with genes involved in the basal
transcription machinery. The authors suggest that transcriptional machinery components
could be an important factor in the reduction of transcriptional activity and that decreased
levels of the associated mRNA could be an important aspect of competence acquisition(Pan
et al. 2005).
Differences in mRNA levels in GV stage oocytes obtained from natural and stimulated estrus
cycles have been studied in cattle (Chu et al. 2012) using a custom microarray composed of
1153 transcripts identified in a previous SSH study where in vivo bovine oocyte and embryo
were used(Vallee et al. 2009). Even without the complete coverage offered on more up-todate microarray platforms, this study showed a considerable number of differences between
the two cycles, with genes related to cell cycle regulation as well as transcription-modulating
functions appearing significantly affected(Chu et al. 2012).
We have recently studied the effects of different coasting conditions on the bovine oocyte
transcriptome (Labrecque et al. 2013). The GV stage oocytes were obtained from our
previous study in which increased blastocyst formation was observed followed by a decrease
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if the coasting duration was extended (Nivet et al. 2012). The microarray revealed that
among the genes differentially regulated between the conditions that increased or decreased
oocyte competence, more were represented at decreased levels. These results are consistent
with the those of the mouse study (Pan et al. 2005). With reduced transcriptional activity at
the end of oocyte growth, it appears that post-transcriptional modification of RNA plays an
important role in the development of oocyte competence (reviewed by Clarke (2012)).
Another important finding is the over-representation of genes related to DNA replication,
recombination and repair among those that are significantly modulated during coasting. This
finding also confirms those of mouse studies (Zeng et al. 2004; Pan et al. 2005), in which it
was concluded that accumulation of these transcripts could protect genome integrity and also
help ensure successful DNA replication following fertilization. A mechanism was also
proposed to explain the reduced developmental competence of the oocyte following an
extended period of coasting, namely RNA degradation (Labrecque et al. 2013). The quality
gained by the oocyte is apparently not very durable, since prolonging the coasting period by
only 24 h decreases blastocyst yield (Nivet et al. 2012). Oocyte quality might therefore
decrease due to RNA degradation if ovulation does not occur at the right time (Figure 6-1).
This hypothesis has already been proposed (Sirard 2011a; Sirard 2011b)and our microarray
results point in this direction. One of the targets of this RNA degradation mechanism could
be spindle function, which would result in an increased proportion of aneuploid oocytes or
early embryos. Nevertheless, further investigations will be required to confirm this
hypothesis.
6.8
Chromatin configuration
Germinal vesicle oocytes can possess different and discrete patterns of chromatin
condensation, which may be observed following Hoechst staining (Mattson and Albertini
1990). The absence of the peri-nucleolar rim is called the non-surrounded nucleolus (NSN)
pattern, while its presence is called the surrounded nucleolus (SN) pattern (Debey et al. 1993;
Zuccotti et al. 1995). These chromatin configurations have been correlated with
developmental competence in many species while the chromatin condensation is associated
with a repressed transcriptional state(reviewed by Tan et al. (2009)). Zuccotti et al. (2008)
compared the transcriptomes of NSN and SN mouse oocytes at the metaphase II stage.
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Among the mRNA transcripts that were differentially regulated, 303 were more abundant in
NSN oocytes while 77 were less abundant in this group. The main functions involved were
related to transcription regulation, protein biosynthesis and cellular function. Analysis of the
up-regulated group showed that pathways such as oxidative phosphorylation and
mitochondrial dysfunction were significantly affected. The transcripts identified by these
researchers could represent an interesting list of candidate genes associated with oocyte
competence, since it is known that NSN oocytes do not yield embryos beyond the two-cell
stage (Zuccotti et al. 1998).
A more recent comparison of mouse NSN and SN oocytes at the germinal vesicle stage led to
the identification of 478 transcripts presenting significant differences, of which 459 were
more abundant in the SN group and 19 more abundant in the NSN group (Monti et al. 2013).
Messenger RNA encoding ribosomal proteins were identified as more abundant in the SN
group. However, these results are surprising in view of the results obtained at the metaphase
II stage (Zuccotti et al. 2008) as well as the reduced transcriptional activity normally found in
SN oocytes. The list of up-regulated genes could nevertheless represent important mRNA
molecules that need to be accumulated in order to ensure high developmental competence.
The same comparison was also made using RNAseq to survey the entire transcriptome of
mouse GV stage oocyte(Ma et al. 2013). The number of up-regulated genes thus identified in
the SN group was 627 and the number of down-regulated genes was 332. A higher
proportion of up-regulated genes was still found in the SN group, even though the technique
used was fundamentally different from that used by Monti et al. (2013). Among these genes,
many were associated with translation, cell division and oxidative phosphorylation, while the
list of down-regulated genes indicated that transcription was the main function affected. This
latter finding is consistent with previous reports of reduced levels of mRNA associated with
transcriptional machinery at the end of oocyte growth (Pan et al. 2005).
It should be kept in mind that intermediate states of chromatin condensation (partly-SN and
partly-NSN) are possible (Bouniol-Baly et al. 1999) and could be mixed during oocyte
collection. NSN oocytes are generally found in primordial to pre-antral follicles, although
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they are also found in antral follicles in decreasing proportions as the follicle grows (Luciano
and Lodde 2013). In order to obtain pure groups for comparison purposes, a strict
characterization of chromatin remodeling states is needed. Cattle oocytes are particular in
presenting four distinct and progressive states (GV0, GV1, GV2 and GV3) of chromatin
condensation at the germinal vesicle stage (Lodde et al. 2007) rather than two as generally
found in mice. Microarray comparisons of bovine GV stage oocytes displaying these four
chromatin configurations were performed and analyses are underway (Labrecque et al.,
unpublished). The results of these analyses will provide more detailed understanding of
transcriptome dynamics during chromatin remodeling. Comparison with our follicle size
dataset will allow us to distinguish mRNA differences associated with chromatin
condensation from those associated with follicle growth. Furthermore, global integration with
our coasting durations dataset (Labrecque et al. 2013) could also allow to decipher the
differences associated with the increase competence in natural and stimulated cycles. With a
clearer picture of the context in which the changes in mRNA level are occurring, the
identification of important transcripts will be more meaningful (Figure 6-2).
6.9
In vivo / in vitro
It is generally accepted that oocytes matured in vivo are of better quality than those matured
in vitro. Even with the constant improvement, culture media still do not provide results
comparable to those obtained in the maternal environment, especially not in the case of large
animals. Immature human oocytes can be matured and fertilized in vitro, but the percentage
of successful implantation is generally low (Loutradis et al. 2006). Cattle oocytes have
performed slightly better(Rizos et al. 2002) and fairly well when an ovarian pre-treatment has
been used (Nivet et al. 2012). The different phenotypes of oocytes matured in vivo or in vitro
offer the opportunity to compare their mRNA contents. In the case of cattle, researchers have
used a candidate gene approach (Lonergan et al. 2003) or a microarray approach (Katz-Jaffe
et al. 2009), while two studies of human oocytes using microarrays have been published
(Jones et al. 2008; Wells and Patrizio 2008). Although these studies have provided further
insight into alterations that occur during maturation, suboptimal quality material (oocyte that
failed to fertilized in most cases) and uncontrolled variables make it difficult to interpret the
data. Neither the hormonal stimulation protocols used to obtain the in vivo matured oocytes
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nor the culture media used for in vitro maturation have been standardized, making it difficult
to pinpoint the factor or factors responsible for the differences in mRNA observed.
6.10 Maternal age
Maternal age was recognized quite early as an important factor affecting the performance of
artificial reproductive technologies. It is generally accepted that fertility declines in women
past the age of 35 (Klein and Sauer 2001). This reduced fertility appears due to decreased
oocyte quality and not related to diminishing endometrium function. Oocytes fertilized in
vitro and then transferred to older recipients are more likely to implant successfully when the
donor is younger than 35 (Navot et al. 1991). This could be due to aneuploidy, since the
incidence of this defect is low (2–3 %) in oocytes from young women, and increases
significantly towards the end of reproductive life (Hassold and Hunt 2001). This subject has
been reviewed recently byFragouli et al. (2011b).
Studies of oocyte quality and mRNA composition as a function of maternal aging are
numerous.One of the first human studies compared oocytes (unsuitable for assisted
fertilization procedures) obtained from two groups of women, aged less than or more than 36.
Examining the abundance of transcripts for only two genes related to spindle function at the
germinal vesicle stage and metaphases I and II, these researchers observed a significant
negative correlation between mRNA abundance and age, suggesting that mRNA degradation
accelerates as women age (Steuerwald et al. 2001). Not long after this study, another group
(Hamatani et al. 2004) took advantage of the development of a mouse microarray platform
developed for analyzing scarce samples such as oocytes and early embryos (Carter et al.
2003) in order to look closely at the differences in mRNA abundance in metaphase II oocytes
from mice aged 5–6 weeks and 42–45 weeks. The microarray revealed relatively few
differences between oocytes from young and old mice, the majority of transcripts presenting
less than two-fold differences, with a higher proportion of up-regulatedmRNA in the oocytes
of young animals. In addition to general functions relating to mitochondrial activity and
oxidative stress management, the abundance of genes involved in chromatin structure, DNA
methylation, genome stability and RNA helicase activity were also depressed in oocytes from
older animals. In a similar study of mice aged 6–12 weeks and 60–70 weeks, a major
193
increase (six-fold) in the incidence of hyperploidy after chromosome spreading was noted in
oocytes collected from the older animals (Pan et al. 2008). The microarray revealed relatively
few differences (5 % of all the transcripts assayed) among oocytes collected at the germinal
vesicle stage, while the proportion of genes differentially regulated rose to 33 % in oocytes at
the metaphase II stage. Comparing their results with a previous analysis of selective
transcript degradation during oocyte maturation (Su et al. 2007), they noted a high level of
similarity of degradation in the case of oocytes from young mice, while only one third of the
detected transcripts in common were degraded in the case of older mice. A possible
deregulation in the transcript degradation process was proposed, which could affect the
competence of oocytes obtained from older mice. Numerous transcripts related to spindle
assembly checkpoint, chromosome congression and kinetochore-microtubule attachment
showed deregulation in relation to maternal age(Pan et al. 2008).These authors pushed
further their analysis by successfully demonstrating the importance of a specific transcript
significantly modulated in oocyte from aged mouse and involved in spindle formation
through siRNA experiment. Similar validation experiments have also been carried in bovine
(Paradis et al. 2005; Tesfaye et al. 2010).
Transcriptional differences were queried between bovine oocytes from pre-pubertal (24week-old) and adult animals(Patel et al. 2007; Dorji et al. 2012). It was already known that
oocytes from young animals are of lower quality, less likely to yield embryos (Armstrong
2001). Patel et al. (2007)used a bovine-specific cDNA microarray (Suchyta et al. 2003) while
Dorji et al. (2012) used the Affymetrix Bovine genome array to compared oocytes collected
either at the germinal vesicle stage or germinal vesicle and metaphase II stage respectively.
Although they detected many differences in mRNA levels, for example genes involved in
hormone secretion (more abundant in oocytes from adult animals)(Patel et al. 2007), the very
low developmental potential of pre-pubertal oocytes was reminiscent of that of oocytes
obtained at the very early stages of follicular genesis as described above.
Using a genome-wide strategy, (Steuerwald et al. 2007) compared oocytes at the metaphase
II stage discarded following IVF procedures from women aged less than 32 and more than
40. They thus identified 181 genes presenting a differential greater than two, and noted
194
patterns similar to those previously observed in a study of mice (Hamatani et al. 2004).
Genes related to chromosome stability, cell cycle regulation, oxidative damage, stress
response, protein trafficking and transcriptional regulation all appeared to be affected by
maternal age.
In a more recent human study (Grondahl et al. 2010), mature oocytes donated by women
younger that 36 were compared to those from women aged 37–39 using an Affymetrix
transcriptome analysis platform. The particularity of this study was that 15 individual oocytes
at the metaphase II stage were obtained from both groups of women, representing a
significant improvement over previous human studies, in which discarded material unsuitable
for in vitro fertilization was generally used. The researchers found 342 genes of interest, of
which 125 were represented at higher levels in oocytes from older women and 217 were
represented at higher levels in oocytes from younger women. Contrary to the findings of
(Hamatani et al. 2004), mitochondrial function and stress responses did not appear to be
among the genes significantly affected. However, DNA repair and response to DNA damage
were still represented as significantly affected functions in this dataset, along with
deregulation of genes involved in spindle formation, thus confirming findings of mouse
studies.
Confocal microscopy observations in human oocyte also provide evidences regarding the
possible modulation of molecules involved in spindle organisation affected by maternal age.
Battaglia et al. (1996) compared human oocyte from younger and older woman with no
history of infertility. These authors observed a proportion close to 80% of oocytes from older
woman with abnormal meiotic spindle, while this proportion decrease to 17% in their
younger counterpart, suggesting an alteration of the regulatory mechanism involved in
meiotic spindle assembly due to advanced maternal age (Battaglia et al. 1996). Furthermore,
among the genes identified by Grondahl et al. (2010), several are specifically involved in
spindle organization and appeared significantly modulated in oocyte from aged oocyte. As
stated by the authors, even if these data did not add new information on the origin of the
problem, namely the fertility decrease with advanced age, it presents the effects of age on the
195
oocyte machinery (transcriptome) and suggest a molecular explanation for the increase
proportion of aneuploid oocyte in human (Grondahl et al. 2010).
Cows aged 13–16 years compared to their daughters aged 3–6 years also displayed signs of
reduced fertility, such as increased numbers of unfertilized oocytes and un-cleaved zygotes,
and reduced numbers of embryos recovered after superovulation treatment, even though the
number of corpora lutea did not differ (Malhi et al. 2007). RNAseq studies subsequently
revealed differences between oocytes (germinal vesicle and metaphase II stages) collected
from cows aged 25–35 months and > 120 months (Takeo et al. 2013). Numerous genes
related to oxidative phosphorylation and mitochondrial dysfunction were found expressed at
higher levels, especially in metaphase II oocytes from the older cows. This closely matches
the findings of the mouse study by Hamatani et al. (2004) and the human study by
Steuerwald et al. (2007), as well as confirming the lack of differencesat the germinal vesicle
stage (Pan et al. 2008). However, these authors propose an interpretation that diverges from
other microarray analyses of the effect of maternal age, asserting that aging allows a large
number of aberrant transcripts to accumulate in oocytes(Takeo et al. 2013), without citing the
possible deterioration of the transcript degradation process, as suggested previously in the
case of mice(Pan et al. 2008).
Microarray analysis of the transcriptome of human mature oocytes previously tested for
euploidy or aneuploidy by comparative genomic hybridization (CGH) on the removed polar
body revealed significant differences.Among 327 genes, 181 decreased and 146 increased in
aneuploid oocytes, mainly associated with functions like spindle assembly, chromosome
alignment, chromatin packaging, DNA replication, and apoptosis (Fragouli et al. 2010).
Interestingly, these authors used a relatively new technique for high-throughput real-time
PCR analysis called TLDA. This technique allowed the interrogation of a higher number of
genes compared to standard q-RT-PCR (94 genes assessed out of the 327 statistically
significant with microarray). The analysis of TLDA data showed subtle differences when the
types of chromosomal abnormality were considered separately. Aneuploid samples due to
whole chromosome non-disjunction grouped together and abnormal samples due to
chromatid errors were clustered in a different group, suggesting different regulatory
196
mechanisms based on real-time PCR results. Furthermore, a significant interaction between
meiotic aneuploidy and maternal age was also observed, which confirms previous findings
with human (Steuerwald et al. 2007; Grondahl et al. 2010) and mouse oocyte (Hamatani et al.
2004; Pan et al. 2008). Moreover, an important number of biological processes affected in
oocyte from older woman (Grondahl et al. 2010) are similarly affected in aneuploid oocyte,
which suggests a close relationship between these two conditions (Fragouli et al. 2010).As
these authors noted, although the link between transcript abundance and aneuploidy is now
becoming clearer, the direct impact of differences in mRNA levels on the specific pathways
in which the corresponding genes are involved remains to be confirmed (Fragouli et al.
2010).
6.11 Other methods for selecting competent oocytes
Other techniques using brilliant cresyl blue (BCB) staining to measure glucose-6-phosphate
dehydrogenase activity (reviewed byOpiela and Katska-Ksiazkiewicz (2013)) or dielectric
migration speed of the oocyte (Dessie et al. 2007) have been used to select competent
oocytes. Some have then used these techniques in combination with gene expression analysis
to identify mRNA modulations in competent cattle oocytes (Dessie et al. 2007; Torner et al.
2008). However, these molecular/cellular predictors are difficult to link to intrinsic oocyte
quality, since they provide only minor improvement in the blastocyst yield. Another
comparative approach that has been used to explore competence is mRNA profiling of
mature oocytes obtained from healthy women and from women with polycystic ovary
syndrome (Wood et al. 2007). These data are even more difficult to interpret, since the
mechanisms responsible for this endocrine and metabolic disorder are still unclear (Goodarzi
et al. 2011).
Few teams have also attempted to analyze either bovine metaphase II oocyte cytoplasm
biopsy (Biase et al. 2012) or splitted-2-cell embryo (Held et al. 2012) with microarray in
relation with the ability to reach the blastocyst stage. Even if many genes appeared
significantly affected in both cases where embryo development was achieved, we are still
dealing with the same problem as with polar body transcriptome analysis described
previously. Even if similar cellular functions affected in incompetent oocytes were also
197
identified in splitted-2-cell embryo that did not developed beyond the 2-cell or 8-cell stage
compared to those who reach the blastocyst stage, namely response to oxidative stress and
oxidative phosphorylation (Held et al. 2012), a careful interpretation of these transcriptomic
data is required in regard of the underlying causes of the developmental arrest.
6.12 Conclusion
In spite the wide array of models that have been used to perform mRNA profiling in attempts
to decipher the factors that determine oocyte competence, no clear set of key genes has yet
emerged, although the number of cellular functions that appear to be involved has been
narrowed down. The first explanation for this slow progress is a factor frequently
underestimated in many microarray studies, namely the weak correlation between mRNA
and protein expression in oocytes compared to other somatic cells. This poor correlation has
been observed in many cell types(reviewed by Ne et al. (2007))but is particularly apparent in
oocytes probably because of the storage component of the mRNA pool. Besides, even if
criteria highly correlated with competence were identified, intrinsic variance among
individual oocytes would still exist, between genotypes but also within a single genotype, as
shown in mice(Reich et al. 2012) and human transcriptome studies (Shaw et al. 2013). This
certainly contributes to the blurred picture still obtained during the characterization of the
competent oocyte.
There is also a great need for global integration of the data. Few analyses combining two or
more datasets have been attempted (Hamatani et al. 2008; O'Shea et al. 2012), but
comprehensive meta-analyses involving multiples datasets are still lacking.Extra care should
also be taken when performing such analysis especially in regard of the wide variety of
models used to study oocyte competence, where only comparable elements should be
included with respect to oocyte stage for example. The constant refinement of in-silico
analysis tools will eventually help to improve our comprehension of the biological
complexity(Andreu-Vieyra et al. 2006; Mulas et al. 2012), in spite of the challenges that
studying the oocyte will continue to represent. Our laboratory is currently investigating
oocyte transcriptomes using combinations of models based on follicle size, coasting duration
and chromatin configuration in an attempt to identify the exact mechanism involved in the
198
acquisition of competence. The eventual integration of other regulatory mechanisms or
datasets, such as transcription factors (Kageyama et al. 2007), regulatory elements present in
the 3’UTR of maternal transcripts such as CPE (Tremblay et al. 2005; Pique et al. 2008),
ARE (Thelie et al. 2007), DAZL (Chen et al. 2011), long non-coding RNA (Mercer and
Mattick 2013), microRNAs(Xu et al. 2011; Zuccotti et al. 2011; Mondou et al. 2012; Abd El
Naby et al. 2013; Assou et al. 2013), polysomal RNA (Potireddy et al. 2006; Chen et al.
2011; Scantland et al. 2011) and poly-A tail length management (Sakurai et al. 2005) will all
contribute to answering our fundamental question:What is the mRNA composition of the
competent oocyte?
6.13 Acknowledgments
We thank Mélanie Tremblay for the conception of the figures and Stephen Davids for
revising the text.
199
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6.15 Figures
Figure 6-1 Follicular development representation, with RNA modulations occurring in
the oocyte at the end of follicular growth (and potentially beyond).
A gradual decrease in transcriptional activity is known to occur towards the end of oocyte
growth (A). Messenger RNAs potentially important to embryo development could also be
accumulated close to the ovulation (B). A mechanism was proposed to explain the reduced
developmental competence of the oocyte if ovulation does not occur at the right time, namely
RNA degradation (C) (see text for more details).
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Figure 6-2 Schematic representation of the distribution of our multiple microarray
datasets in bovine oocyte.
Based on the Between Group Analysis (BGA) graph that could be obtained during
microarray analysis, it shows the transcriptome data distribution an
andd variance (by the size of
the ovals) across all the groups analyzed. The position of each group is also correlated with
the relative competence (Y axis) while the X axis refers to the global follicle size. The color
scale used refers to the oocyte “ripeness” where the middle state (5) could represent the
optimal competence and the darker color (10) would be related to the loss of quality of the
oocyte not ovulated at the right time.
time. The coasting part is based on the results from
(Labrecque et al. 2013), while the follicle size and chromatin configuration refers to the
preliminary analysis of our unpublished results.
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6.16 Tables
Table 6-1 Summary of the studies on the analysis of oocyte with different developmental potential
Reference
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Lonergan et al., 2003
Donnison and Pfeffer, 2004
Mourot et al., 2006
Ghanem et al., 2007
Patel et al., 2007
Dessie et al., 2007
Lingenfelter et al., 2007
Racedo et al., 2008
Torner et al., 2008
Katz-Jaffe et al., 2009
Opiela et al., 2010
Biase et al., 2012
Chu et al., 2012
Dorji et al., 2012
Labrecque et al., 2013
Takeo et al. , 2013
Caixeta et al., 2009
Bessa et al., 2013
Steuerwald et al., 2001
Steuerwald et al., 2007
Wood et al., 2007
Wells and Patrizio, 2008
Jones et al., 2008
Fragouli et al., 2010
Grondahl et al., 2010
Markholt et al. ,2012
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Pan et al., 2005
Kageyama et al., 2007
Pan et al., 2008
Zuccotti et al., 2008
Monti et al., 2013
Ma et al., 2013
Okamoto et al., 2013
Specie
s
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Bovine
Human
Human
Human
Human
Human
Human
Human
Human
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Criteria
Groups
Follicle size
In vivo / in vitro
Follicle size
Follicle size
Follicular development
Maternal age
Migration speed
Follicle size
BCB staining
In vivo / in vitro
BCB staining
Development (oocyte biopsy)
Ovarian stimulation
Maternal age
Coasting duration
Maternal age
Follicle size
Oocyte growth
Maternal age
Maternal age
PCOS
In vivo / in vitro
In vivo / in vitro
Aneuploidy
Maternal age
Maternal age
Oocyte growth
Maternal age
Maternal age
<2mm; 3-5mm
OPU oocyte matured in vitro;in vivo
<2mm; >5mm
1-3mm; 3-5mm; 5-8mm; >8mm
Growth; dominance
Prebubertal; adult cow
Very fast dielectrophoretic migration speed; very slow
Growing; persistent follicle
<2mm; 2-8mm
BCB+; BCBOPU oocyte matured in vitro;in vivo
BCB+; BCBBlasotcyst stage; arrest at the 8-16-cell stage
Natural; stimulated cycle
Prepubertal; adult cow
20hrs; 44hrs, 68hrs and 92hrs
25-35 months; >120 months
1-3mm; 3-6mm; 6-8mm; >8mm
<20um; 20-35; 40-60; 65-85; 100-120; >128um
<36 years; >36 years old
<32 years; >40 years old
PCOS; healty woman
Oocyte matured in vitro;in vivo
Oocyte matured in vitro;in vivo
normal; aneuploid (CGH screening of the PB)
<36 years; 37-39 years old
Primordial; intermediate; primary follicles
5-6 weeks old; 42-45 weeks old mouse
Primordial to large antral, eCG primed
Growing; fully grown; 1cell; 2cell and blastocyst
6-12 weeks; 60-70 weeks old
NSN; SN
NSN; SN
NSN; SN
7 weeks old; 44 weeks old
Oocyte
stage
GV
MII
GV
GV
GV
GV
MII
GV
GV
GV
MII
GV
MII
GV
GV-MII
GV
GV-MII
GV
GV
GV
MII
MII
MII
MII
MII
MII
GV
MII
GV
GV
GV-MII
MII
GV
GV
MII
Technique used
SSH; DDRT
Candidate genes (Q-PCR)
SSH
Candidate genes (Q-PCR) from SSH library
Microarray, Blue Chip (Sirard et al., 2005)
Bovine cDNA microarray (Suchyta et al.,2003)
Q-PCR
Affymetrix GeneChip Bovine Genome Array
13,257-element bovine oligoarray (GPL2853)
Microarray, Blue Chip v1.2 (Vallée et al.,2009)
Affymetrix GeneChip Bovine Genome Array
Microarray (EmbryoGene)
RNA-seq (Illumina Genome Analyzer II)
Affymetrix GeneChip Human Genome Focus Arrays
Affymetrix chip HG-U133 Plus 2.0
Applied Biosystems Human Genome Survey Microarray
Codelink Whole Human Genome microarray
Applied Biosystems Human Genome Survey Microarray
Affymetrix HG-U133 Plus 2.0
Affymetrix chip HG-U133 Plus 2.0
NIA 22K 60mers Oligo microarray
Affymetrix Murine Genome Array MOE430 A and B
Oligo DNA for 898 TF genes spotted on a microarray
Affymetrix Murine Genome Array MOE430 v2 chip
Illumina mouse-6 BeadChips
NIA Mouse 44K Microarray v3.0
RNA-seq (Applied Biosystems SOLiD RNA-Seq System)
Candidate genes (Q-PCR), sirtuin genes
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Conclusion générale
L’utilisation de plus en plus répandue des techniques de reproduction assistée, autant chez
le bovin que chez l’humain, a permis une optimisation importante des méthodes utilisées.
Les progrès réalisés ont permis d’améliorer considérablement les taux de succès suite à la
fécondation in vitro en terme d’embryons produits, mais également du nombre de
naissances à terme. Néanmoins, la grande variabilité qui persiste dans les taux de succès
suggère que les mécanismes cellulaires sous-jacents ne sont pas encore totalement compris
et qu’il demeure possible d’améliorer le système de production. La qualité de l’ovocyte
produit par le follicule est donc au cœur de ce phénomène. L’acquisition du potentiel de
l’ovocyte à produire un embryon repose sur une suite de processus finement orchestrés, se
déroulant au cours de la croissance finale du follicule. Parmi ces processus, on retrouve
l’accumulation importante d’ARN messagers au cours d’une période transcriptionnelle très
active, suivi par une diminution graduelle de la transcription. Ce sont ces transcrits qui
vont permettre de soutenir le développement du jeune embryon jusqu’à l’activation du
génome embryonnaire. Afin de mieux comprendre ce qui définit la qualité ovocytaire, nous
avons entrepris d’analyser le transcriptome des ovocytes dans différents contextes où la
qualité de l’ovocyte pouvait être modulée.
La première partie du projet nous a permis de constater que dans un contexte de
stimulation ovarienne, malgré la présence d’une différenciation optimale du follicule, la
qualité de l’ovocyte était de durée limitée. Une différence d’à peine 24 heures est suffisante
pour affecter significativement la qualité de l’ovocyte, alors que celui-ci est encore dans le
follicule. Malgré la diminution importante de l’activité transcriptionnelle peu de temps
avant l’ovulation, la modulation de plusieurs transcrits liés à des fonctions telles que la
modification post-transcriptionnelle des ARNm illustre l’importance de la régulation
adéquate des ARNm pour l’ovocyte. Comme il a été suggéré précédemment, le contrôle de
la séparation des chromosomes pourrait être un mécanisme utilisé par l’ovaire de façon à
réduire la qualité de l’ovocyte si l’ovulation n’a pas lieu à temps. Puisque la différenciation
folliculaire permet une acquisition graduelle de la compétence de l’ovule, suivie d’une
diminution, cette première étude nous a également permis de proposer la présence d’un
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mécanisme de dégradation des ARNm, qui pourrait également contribuer à réduire la
qualité des ovocytes dans un contexte de différenciation trop avancé.
La deuxième partie du projet nous a permis de mettre en lumière l’importance de la LH
lors de la différenciation finale du follicule en lien avec la qualité ovocytaire. Bien que la
suppression de la sécrétion pulsatile de LH par l’utilisation d’un antagoniste de la GnRH
ne semble pas avoir d’effet direct sur le potentiel de l’ovocyte à produire un embryon, nous
avons pu démontrer que le transcriptome des ovocytes ayant subi ce type traitement était
significativement affecté. Encore une fois, plusieurs transcrits impliqués dans la
modification post-transcriptionnelle des ARNm et le contrôle de la séparation des
chromosomes ont été affectés par l’absence de la sécrétion de LH. De plus, une
augmentation de l’abondance de plusieurs ARNm suite au traitement semble suggérer une
capacité de traduction réduite dans ces ovocytes. Toutefois, des expériences
supplémentaires seront requises afin de confirmer cette hypothèse.
La troisième partie du projet nous a permis de caractériser le profil transcriptomique des
ovocytes au cours de la croissance folliculaire dans un contexte non stimulé. L’analyse
séquentielle des ovocytes récupérés à partir de follicules de taille croissante (< 3 mm
jusqu’à > 8 mm), au cours duquel la compétence au développement augmente
graduellement, a démontré une modulation progressive des ARNm. Durant cette période,
plusieurs transcrits impliqués dans l’organisation de la chromatine vont d’ailleurs être
graduellement accumulés. Cet ensemble de données, combinés aux modulations
transcriptionnelles identifiées dans les deux premières parties, permet de mieux distinguer
les changements attribuables à la croissance normale du follicule, de ceux induits par la
stimulation ovarienne.
La quatrième partie du projet avait pour but d’identifier les modulations du transcriptome
de l’ovocyte qui surviennent au cours des différentes phases du remodelage progressif de la
chromatine, retrouvées dans les ovocytes au cours de leur croissance finale. Puisque les
différents stades de compaction de la chromatine sont associés à une diminution importante
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de la transcription, très peu de transcrits présentent une augmentation de leur abondance
dans les stades plus compactés. Néanmoins, malgré le contexte répressif de la transcription
retrouvé à la fin de la croissance de l’ovocyte, certains ARNm continuent d’être
accumulés. Parmi ces transcrits, plusieurs d’entres eux correspondent à des gènes
d’histones. Nous avons donc proposé que certains de ces ARNm d’histones soient
accumulés grâce à un mécanisme similaire à ce qui a été proposé chez le Xénope (Wang et
al. 1999). Ainsi, ces ARNm d’histones seraient protégés d’une traduction précoce par leur
association avec la protéine SLBP2, récemment identifiée chez le bovin (Thelie et al.
2012). L’entreposage adéquat des ARNm d’histones permettra ainsi de soutenir la grande
demande en histones nécessaire lors des premiers cycles cellulaires.
Le dernier chapitre de cette thèse a finalement permis de mettre en relief l’ensemble des
études réalisées autant chez l’humain, la souris et le bovin, où le transcriptome des
ovocytes de différentes qualités a été étudié. Malgré les multiples efforts réalisés au cours
des 15 dernières années afin de mieux définir le transcriptome de l’ovocyte compétent, la
qualité ne semble pas se résumer à une simple liste de transcrits qui doivent être
accumulés, mais est plutôt la somme d’une multitude de petits changements (Donnison and
Pfeffer 2004). Bien que certains marqueurs de la qualité ovocytaire aient pu être identifiés
au cours des années, l’identificationprécise des mécanismes cellulaires impliqués dans
l’acquisition de ce meilleur potentiel de développement demeure complexe. Contrairement
à la majorité des cellules somatiques, un des principaux défis dans l’étude du transcriptome
de l’ovocyte est sans contredit la relation indirecte entre l’abondance d’un ARNm et la
synthèse protéique. Néanmoins, différents efforts ont été réalisés récemment afin d’obtenir
une image plus claire des changements moléculaires qui surviennent dans l’ovocyte. Ainsi,
certains ont réussi à isoler une sous-population d’ARNm présents dans l’ovocyte bovin, en
fonction de la longueur de la queue poly-A (Gohin et al. 2014). Ces données permettent
ainsi de donner une première indication sur le potentiel de traduction des ARNm. Une
autre approche visant à isoler les ARNm présents sur les polyribosomes a également
permis d’identifier les transcrits en cours de traduction dans l’ovocyte bovin (Scantland et
al. 2011). Finalement, l’importance d’intégrer les différentes bases de données disponibles
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a été démontrée comme étant le prochain grand défi à réaliser afin de réussir à comprendre
ce phénotype complexe qu’est la qualité ovocytaire.
Les travaux réalisés au cours de cette thèse ont permis de démontrer que les conditions
folliculaires qui précèdent la récupération de l’ovocyte sont essentielles à l’acquisition de
la compétence. Les données accumulées au cours de ces différents projets constituent donc
une source d’informations très riche en termes de modulations du transcriptome lorsque la
compétence au développement est acquise. Ainsi, il est maintenant possible d’observer le
profil d’un transcrit spécifique au cours des différentes étapes de différenciation du
follicule. Avec une connaissance précise de cette information, il devient alors possible de
proposer de nouvelles hypothèses concernant la qualité de l’ovocyte, principalement
lorsque l’information sur la protéine n’est pas disponible.
L’augmentation progressive et une diminution rapide de la qualité illustre clairement la
complexité de ce mécanisme, mais offre des possibilités afin de mieux cibler les
interventions possibles dans le but de moduler la qualité. Il y aurait potentiellement des
efforts à réaliser afin d’empêcher la dégradation de la qualité en sus de viser à l’augmenter
davantage. Ainsi, une meilleure compréhension des mécanismes qui pourraient affecter la
stabilité du fuseau méiotique et mener à une ségrégation inadéquate des chromosomes dans
un contexte de différenciation sous-optimale du follicule apportera des indices
supplémentaires afin d’expliquer cette dégradation de la qualité et éventuellement pour la
contrôler.
Le fait que des ovocytes provenant de follicules de petites tailles et ayant une conformation
de la chromatine variable soient capables de se développer jusqu’au stade de blastocyste
indique une combinaison complexe mais définie donnant accès à la totipotence.
Maintenant que nous avons une carte relativement complète des ARNm associés à ces
stades et niveaux de compétences, l’arrimage de ces données avec les informations sur le
potentiel de traduction et les protéines permettront de mieux définir la cascade moléculaire
requise en vue d’obtenir une nouvelle génération.
222
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Analyse du transcriptome de l`ovocyte bovin en lien avec la

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