Meta-analysis of gene expression profiles in granulosa cells during folliculogenesis

in Reproduction
Authors:
Daulat Raheem KhanCentre de Recherche en Biologie de la Reproduction, Département des Sciences Animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Quebec City, Québec, Canada

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Éric FournierCentre de Recherche en Biologie de la Reproduction, Département des Sciences Animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Quebec City, Québec, Canada

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Isabelle DufortCentre de Recherche en Biologie de la Reproduction, Département des Sciences Animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Quebec City, Québec, Canada

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François J RichardCentre de Recherche en Biologie de la Reproduction, Département des Sciences Animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Quebec City, Québec, Canada

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Jaswant SinghDepartment of Veterinary Biomedical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

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Marc-André SirardCentre de Recherche en Biologie de la Reproduction, Département des Sciences Animales, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Quebec City, Québec, Canada

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Correspondence should be addressed to M-A Sirard; Email: marc-andre.sirard@fsaa.ulaval.ca
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Abstract

Folliculogenesis involves coordinated profound changes in different follicular compartments and significant modifications of their gene expression patterns, particularly in granulosa cells. Huge datasets have accumulated from the analyses of granulosa cell transcriptomic signatures in predefined physiological contexts using different technological platforms. However, no comprehensive overview of folliculogenesis is available. This would require integration of datasets from numerous individual studies. A prerequisite for such integration would be the use of comparable platforms and experimental conditions. The EmbryoGENE program was created to study bovine granulosa cell transcriptomics under different physiological conditions using the same platform. Based on the data thus generated so far, we present here an interactive web interface called GranulosaIMAGE (Integrative Meta-Analysis of Gene Expression), which provides dynamic expression profiles of any gene of interest and all isoforms thereof in granulosa cells at different stages of folliculogenesis. GranulosaIMAGE features two kinds of expression profiles: gene expression kinetics during bovine folliculogenesis from small (6 mm) to pre-ovulatory follicles under different hormonal and physiological conditions and expression profiles of granulosa cells of dominant follicles from post-partum cows in different metabolic states. This article provides selected examples of expression patterns along with suggestions for users to access and generate their own patterns using GranulosaIMAGE. The possibility of analysing gene expression dynamics during the late stages of folliculogenesis in a mono-ovulatory species such as bovine should provide a new and enriched perspective on ovarian physiology.

Abstract

Folliculogenesis involves coordinated profound changes in different follicular compartments and significant modifications of their gene expression patterns, particularly in granulosa cells. Huge datasets have accumulated from the analyses of granulosa cell transcriptomic signatures in predefined physiological contexts using different technological platforms. However, no comprehensive overview of folliculogenesis is available. This would require integration of datasets from numerous individual studies. A prerequisite for such integration would be the use of comparable platforms and experimental conditions. The EmbryoGENE program was created to study bovine granulosa cell transcriptomics under different physiological conditions using the same platform. Based on the data thus generated so far, we present here an interactive web interface called GranulosaIMAGE (Integrative Meta-Analysis of Gene Expression), which provides dynamic expression profiles of any gene of interest and all isoforms thereof in granulosa cells at different stages of folliculogenesis. GranulosaIMAGE features two kinds of expression profiles: gene expression kinetics during bovine folliculogenesis from small (6 mm) to pre-ovulatory follicles under different hormonal and physiological conditions and expression profiles of granulosa cells of dominant follicles from post-partum cows in different metabolic states. This article provides selected examples of expression patterns along with suggestions for users to access and generate their own patterns using GranulosaIMAGE. The possibility of analysing gene expression dynamics during the late stages of folliculogenesis in a mono-ovulatory species such as bovine should provide a new and enriched perspective on ovarian physiology.

Introduction

The ovary is a highly dynamic structure, of which the principal functional unit is the follicle. In foetal ovaries, primordial germ cells proliferate during the first trimester of gestation and develop into primordial follicles by mid-gestation. A primordial follicle is typically 30–40µm in diameter and each is composed of a partially differentiated oocyte (arrested in prophase-1 of meiosis) enclosed by one layer of specialized somatic cells called follicular or granulosa cells. Further follicle development begins before birth as small cohorts of primordial follicles undergo progressive growth and atresia until puberty. Folliculogenesis progresses in the adult ovary, leading to the formation of a fluid-filled cavity called the antrum and the emergence of a highly specialized type of granulosa cell called cumulus cells, which are in direct contact with the oocyte (Gougeon 1996). In mono-ovulatory species such as cattle, one ovule per reproductive cycle is released from a single dominant follicle, whereas the remaining follicles undergo atresia (Lussier et al. 1987).

In contrast to other somatic tissues, granulosa cells during folliculogenesis undergo very dynamic and highly coordinated changes. During the late stages of folliculogenesis, the changes accelerate in all compartments of the follicle wall (granulosa, cumulus, and theca cells, vascular and inter-cellular stromal components), culminating in the release of a competent oocyte and the formation of a new tissue called the corpus luteum. In the developing follicle, acquisition of oocyte competence involves interplay between a multitude of intrinsic and extrinsic factors, which all act to bring about rapid development of distinct gene expression profiles in different follicular cells (Wigglesworth et al. 2014, Khan et al. 2015). This is particularly apparent in granulosa cells (Sirard 2014), in which gene expression patterns are important not only for the ovulation and luteinization processes but also for the developmental competence of the oocyte contained therein (Assidi et al. 2008, Hamel et al. 2010). Interestingly, FSH has been implicated in acquisition of oocyte developmental competence, both in vivo and in vitro (Sirard et al. 2007). In bovine, in vivo experiments have shown that ovarian super-stimulation with a FSH support for 5 days (endogenous FSH following removal of dominant follicle (for 2 days) followed by 3 days of FSH injections twice a day) followed by no FSH period (called coasting) for 44–68h yields the best oocyte quality for subsequent development of embryos (Nivet et al. 2012). The study of granulosa cell transcriptome dynamics in different physiological contexts therefore remains crucial to understand the physiology of ovarian tissue as a whole.

Conventional experimental designs do not provide the overall perspective that is essential in order to understand follicular dynamics. The huge amounts of data that have accumulated remains scattered in database repositories and require integration and meta-analysis in order to chart overall gene dynamics in this tissue. The regular manuscript format allows sharing of 1–2% of the data analysed (i.e. highlighted genes), and access to supplemental data, although possible, is difficult to re-analyse. Other than a recent comparison of cumulus and mural granulosa cell transcriptomes in mice (Wigglesworth et al. 2015), meta-analysis of different ovarian transcriptomic studies remains scarce. An online public collection called the ovarian kaleidoscope database or OKdb (http://okdb.appliedbioinfo.net/) provides information on gene expression in different ovarian cell types and their association with various ovarian functions (Hsueh & Rauch 2012). However, a chronological/dynamic interface of folliculogenesis based on integrated ovarian cell gene expression profiles has yet to be constructed. The principal obstacles to achieving this are incomparable technological platforms and experimental conditions in the different studies (Tseng et al. 2012). The integration of such studies requires vast knowledge of ovarian physiology combined with highly specialized bioinformatics skills.

Based on the availability of several publicly available transcriptomic analyses generated on a single technological platform called “EmbryoGENE”, an online interface called GranulosaIMAGE (Granulosa Integrative Meta-Analysis of Gene Expression) has been developed. GranulosaIMAGE provides easy consultation of the temporal kinetics of gene expression during follicular development from small-diameter (>5mm) follicles to pre-ovulatory in different physiological contexts, along with dominant follicle gene expression profiles for various post-partum time intervals and metabolic states.

Programme description and methods

GranulosaIMAGE provides a view of the dynamics of bovine genes and their isoforms by integrating 74 microarray datasets generated using the EmbryoGENE platform (Robert et al. 2011) and a uniform analysis pipeline. Although these studies were conducted independently, they cover collectively most stages of ovarian follicle development (Fig. 1). GranulosaIMAGE (http://emb-bioinfo.fsaa.ulaval.ca/granulosaIMAGE/) was thus generated as a web resource for easy consultation of the relative dynamics of practically any gene in bovine granulosa cells.

Figure 1
Figure 1

Summary of GranulosaIMAGE workflow. Data from transcriptome studies conducted by EmbryoGENE network scientists on granulosa cells have been deposited in the ELMA database. Granulosa cells were obtained from follicles at various stages of folliculogenesis from cows in different physiological and metabolic states (top panel). This flow diagram provides the working model of data retrieval from ELMA, its normalization, statistical tests and graphical representation on the GranulosaIMAGE web-based resource.

Citation: Reproduction 151, 6; 10.1530/REP-15-0594

Studies included

The studies included in GranulosaIMAGE database are summarized in Table 1, which provides principally two types of granulosa gene expression pattern for each gene.

  1. Time-course gene kinetics during follicular development from the small (6mm) to pre-ovulatory stage under different hormonal or physiological conditions.

  2. Gene expression in granulosa cells recovered post-partum from dominant follicles of cows synchronized using prostaglandins (52h after synchronization, for uniformity across samples and to precede the endogenous LH surge).

Table 1

The studies included in GranulosaIMAGE.

Reference Group Title of article
Douville & Sirard (2014) 6–9mm follicles (follicular state effect) Changes in granulosa cells gene expression associated with growth, plateau and atretic phases in medium bovine follicles
Girard et al. (2015) >9mm follicles (follicular state effect) Global gene expression in granulosa cells of growing, plateau and atretic dominant follicles in cattle
In vitro culture (FSH effect) Unpublished
Guillemin et al. (2015) Aromatase expression level A genetical genomics methodology to identify genetic markers of a bovine fertility phenotype based on CYP19A1 gene expression
Dominant follicle (age effect) Unpublished
Nivet et al. (2012) Dominant follicle (coasting effect) FSH withdrawal improves developmental competence of oocytes in the bovine model
Gilbert et al. (2012) Dominant follicle (time to LH surge) Impact of the LH surge on granulosa cell transcript levels as markers of oocyte developmental competence in cattle GSE69247
Pre-ovulatory follicle (24h post-LH Age effect) Unpublished
Dias et al. (2013a,b) Super-stimulated pre-ovulatory follicle (24h post-LH) Effect of duration of the growing phase of ovulatory follicles on oocyte competence in super-stimulated cattle
Differential gene expression of granulosa cells after ovarian super-stimulation in beef cattle
Golini et al. (2014) Dominant follicle (post-partum period effect) Transcriptome analysis of bovine granulosa cells of pre-ovulatory follicles harvested 30, 60, 90 and 120 days post-partum
Girard et al. (2015) Dominant follicle (energy balance effect at 60±5 days post-partum) The effect of energy balance on the transcriptome of bovine granulosa cells at 60 days post-partum
Dominant follicle (pre-LH surge effect of vitamin B9 & B12) Unpublished
Gagnon et al. (2015) Dominant follicle (vitamin B9 and B12 effect irrespective of LH surge) Effects of intramuscular administration of folic acid and vitamin B12 on granulosa cells gene expression in post-partum dairy cows

Statistical methods

Data including intensity files and sample annotation (metadata) from previous studies of granulosa cells using the EmbryoGENE microarray platform (Robert et al. 2011) have been filed in the EmbryoGENE LIMS and Microarray Analysis (ELMA) database (Robert et al. 2011). These data are used here to perform meta-analysis and to generate expression profiles. The working model of GranulosaIMAGE is summarized in Fig. 1. Intensity values for any probe (along with their associated metadata) are retrieved from the ELMA database and after logarithmic transformation (base 2) of the raw measured intensity; normalization of the intensity values is performed by subtraction of the background threshold. The background threshold is defined as the mean of the intensities of the negative control spots on an array plus twice the standard deviation of these intensities. The resulting relative intensities are then quantile-normalized using the limma bioconductor package (Ritchie et al. 2015) and plotted on the y-axis for each condition. These normalized intensity values are used to produce the colour scale applied to the different points (the higher the intensity, the darker the point appears) showing the expression level under each condition.

The levels of significant difference for each probe were determined using ad hoc statistical tests comparing specific conditions within each experiment. We used the t-test for experiments with two conditions and a fixed-effect ANOVA for experiments with three or more conditions. All P-values were then corrected to a unified FDR (false discovery rate). In the resulting graphs, the comparisons with corrected P-values <0.05 are highlighted in red. The statistical tests do not take into consideration array effects, dye effects, technical replicates, or secondary experimental factors such as individual animals. For further details, the reference articles should be consulted where the above effects have been considered for each study separately.

Data visualization

GranulosaIMAGE charts the intensity profiles of a given gene in granulosa cells under different conditions. The GranulosaIMAGE homepage (Fig. 2) provides a brief description of the use of the programme along with an introduction to the gene search results panel, links to a help section, a description of the statistical methods used and a list of published results included in the programme, and the search bar on which the sought gene probe ID or gene symbol is entered. A link to the EmbryoGENE microarray annotation file (Robert et al. 2011) is provided for consultation of probe IDs with the corresponding gene names, isoforms and symbols.

Figure 2
Figure 2

GranulosaIMAGE homepage. This screenshot illustrates the organization of the homepage containing a brief introduction to the database along with the gene search bar into which the sought probe ID or gene symbol is entered. This page also shows the manner in which a gene search result is displayed and lists the statistical methods used and the publications from which results are included in the database. External links to EmbryoGENE microarray annotation files and the ELMA database are also provided.

Citation: Reproduction 151, 6; 10.1530/REP-15-0594

GranulosaIMAGE displays the results page in response to submission of a gene symbol. The results page contains several sets of information, which we present along with examples later in this article.

The example of ADAMTS1 further illustrates this notion (Fig. 3B). This gene is related closely to the cleavage of extra-membranous domains and activation of AREG in granulosa cells in response to the LH surge (Sayasith et al. 2013). GranulosaIMAGE shows that the dynamics of its response to FSH stimulation and LH surge are very similar to those of AREG. GranulosaIMAGE provides the additional information that expression of this gene is increased significantly during follicular growth and atresia in small (6mm) but not large follicles (>9mm), and more in pre-ovulatory follicles of older than younger cows. This observation of the effect of age on the transcript abundance of a gene that is usually studied in association with ovulatory response and post-LH surge cumulus expansion is very interesting.

Construction of customized profiles

GranulosaIMAGE indicates relative gene expression in terms of mRNA abundance under different conditions of folliculogenesis. This manner of presenting expression kinetics provides a basis for the construction of customized profiles by users in the form of simpler illustrations. We present here two examples: (1) temporal kinetics during different physiological states (growth, plateau and atresia), dominance and relevance to LH surge (Fig. 4) and (2) gene dynamics at different intervals and energy states during the post-partum period (Fig. 5).

Figure 4
Figure 4

Illustration of gene dynamics during folliculogenesis as represented in GranulosaIMAGE. Dynamics of four genes (FSHR, LHCGR, AREG and ADAMTS1) were consulted initially in GranulosaIMAGE and then represented graphically vs follicular developmental stage, either destined to ovulate (grey line) or undergoing atresia (green line). The x-axis values are arbitrary units. Users may consult the dynamics of any gene of interest and may illustrate these versus their preferred follicular parameters (development, super-stimulation and effect of age or various post-partum metabolic states).

Citation: Reproduction 151, 6; 10.1530/REP-15-0594

Figure 5
Figure 5

Illustration of gene dynamics during the post-partum period as represented in GranulosaIMAGE. The dynamics of four genes (CCNB1, LHCGR, FOXO1 and PTX3) were consulted in GranulosaIMAGE and then drawn to illustrate the effects of (A) 30, 60, 90 and 120 days post-partum and (B) maternal energy status (BHB level) and vitamin supplementation (pre-LH and irrespective of LH surge) at 60±5 days post-partum. The x-axis values are arbitrary units. Users may consult the dynamics of any gene of interest and illustrate these versus a variety of follicular conditions of interest.

Citation: Reproduction 151, 6; 10.1530/REP-15-0594

The most striking feature of this illustration is the down-regulation of CCNB1 and up-regulation of LHCGR at 60 days post-partum (pre-LH group) in response to vitamin supplementation. In fact, CCNB1 is involved in cell cycle regulation. Its down-regulation in this group indicates a relatively more differentiated cell state in which LH receptors are more abundant. This diagram supports the conclusions drawn by Gagnon et al. (2015), suggesting that vitamin supple­mentation alters post-partum follicular dynamics.

Conclusions

The ovarian follicle is a remarkable structure having diverse functions and highly complex and dynamic physiology. Understanding ovarian physiology is essential in order to optimize female fertility, and huge amounts of data on ovarian tissues therefore have been generated. However, these data are scattered in databases that are difficult to dig in, creating a need for novel ways of integrating and presenting the information for the purpose of advancing knowledge in this field. We present here GranulosaIMAGE, a web-based interface that provides gene expression profiles of granulosa cells from a new perspective. It is an interactive, easy-to-access resource for researchers in the field of ovarian physiology. This is the first step towards integration of various time points of interest in the reproductive cycle. For the moment, GranulosaIMAGE presents only the transcriptomic data that have been produced using EmbryoGENE platform. Although due to intricate technical constraints GranulosaIMAGE does not include the data produced by various groups using microarray platforms other than the EmbryoGENE, we look forward to include RNAseq data in this tool that could be filtered in a relatively more homogenous manner. It also provides a preliminary basis for comparing different follicular tissues such as theca cells, cumulus cells, and oocytes, which are also becoming increasingly available.

Declaration of interest

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

Funding

This study was funded by Natural Science and Engineering Research Council of Canada (NSERC) as part of EmbryoGENE Network and was conducted in collaboration with Boviteq Inc.

Acknowledgements

The authors acknowledge all the researchers who participated in the EmbryoGENE program and whose work has served to construct GranulosaIMAGE.

References

  • Assidi M, Dufort I, Ali A, Hamel M, Algriany O, Dielemann S & Sirard MA 2008 Identification of potential markers of oocyte competence expressed in bovine cumulus cells matured with follicle-stimulating hormone and/or phorbol myristate acetate in vitro. Biology of Reproduction 79 209222. (doi:10.1095/biolreprod.108.067686)

    • Search Google Scholar
    • Export Citation
  • Conti M, Hsieh M, Park JY & Su YQ 2006 Role of the epidermal growth factor network in ovarian follicles. Molecular Endocrinology 20 715723. (doi:10.1210/me.2005-0185)

    • Search Google Scholar
    • Export Citation
  • Gagnon A, Khan DR, Sirard MA, Girard CL, Laforest JP & Richard FJ 2015 Effects of intramuscular administration of folic acid and vitamin B12 on granulosa cells gene expression in postpartum dairy cows. Journal of Dairy Science 98 77977809. (doi:10.3168/jds.2015-9623)

    • Search Google Scholar
    • Export Citation
  • Gougeon A 1996 Regulation of ovarian follicular development in primates: facts and hypotheses. Endocrine Reviews 17 121155.

  • Hamel M, Dufort I, Robert C, Leveille MC, Leader A & Sirard MA 2010 Genomic assessment of follicular marker genes as pregnancy predictors for human IVF. Molecular Human Reproduction 16 8796.

    • Search Google Scholar
    • Export Citation
  • Hsueh AJ & Rauch R 2012 Ovarian Kaleidoscope database: ten years and beyond. Biology of Reproduction 86 192. (doi:10.1095/biolreprod.112.099127)

    • Search Google Scholar
    • Export Citation
  • Khan DR, Guillemette C, Sirard MA & Richard FJ 2015 Characterization of FSH signalling networks in bovine cumulus cells: a perspective on oocyte competence acquisition. Molecular Human Reproduction 21 688701. (doi:10.1093/molehr/gap079)

    • Search Google Scholar
    • Export Citation
  • Lussier JG, Matton P & Dufour JJ 1987 Growth rates of follicles in the ovary of the cow. Journal of Reproduction and Fertility 81 301307. (doi:10.1530/jrf.0.0810301)

    • Search Google Scholar
    • Export Citation
  • Nivet AL, Bunel A, Labrecque R, Belanger J, Vigneault C, Blondin P & Sirard MA 2012 FSH withdrawal improves developmental competence of oocytes in the bovine model. Reproduction 143 165171. (doi:10.1530/REP-11-0391)

    • Search Google Scholar
    • Export Citation
  • Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W & Smyth GK 2015 limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43 e47. (doi:10.1093/nar/gkv007)

    • Search Google Scholar
    • Export Citation
  • Robert C, Nieminen J, Dufort I, Gagne D, Grant JR, Cagnone G, Plourde D, Nivet AL, Fournier E & Paquet E et al. 2011 Combining resources to obtain a comprehensive survey of the bovine embryo transcriptome through deep sequencing and microarrays. Molecular Reproduction and Development 78 651664. (doi:10.1002/mrd.21364)

    • Search Google Scholar
    • Export Citation
  • Sayasith K, Lussier J & Sirois J 2013 Molecular characterization and transcriptional regulation of a disintegrin and metalloproteinase with thrombospondin motif 1 (ADAMTS1) in bovine preovulatory follicles. Endocrinology 154 28572869. (doi:10.1210/en.2013-1140)

    • Search Google Scholar
    • Export Citation
  • Sirard MA 2014 Toward building the cow folliculome. Animal Reproduction Science 149 9097. (doi:10.1016/j.anireprosci.2014.06.025)

  • Sirard MA, Desrosier S & Assidi M 2007 In vivo and in vitro effects of FSH on oocyte maturation and developmental competence. Theriogenology 68 (Supplement 1) S71S76. (doi:10.1016/j.theriogenology.2007.05.053)

    • Search Google Scholar
    • Export Citation
  • Sugimura S, Ritter LJ, Sutton-McDowall ML, Mottershead DG, Thompson JG & Gilchrist RB 2014 Amphiregulin co-operates with bone morphogenetic protein 15 to increase bovine oocyte developmental competence: effects on gap junction-mediated metabolite supply. Molecular Human Reproduction 20 499513. (doi:10.1093/molehr/gau013)

    • Search Google Scholar
    • Export Citation
  • Tseng GC, , Ghosh D & Feingold E 2012 Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Research 40 37853799. (doi:10.1093/nar/gkr1265)

    • Search Google Scholar
    • Export Citation
  • Webb R, Nicholas B, Gong JG, Campbell BK, Gutierrez CG, Garverick HA & Armstrong DG 2003 Mechanisms regulating follicular development and selection of the dominant follicle. Reproduction Supplement61 7190.

    • Search Google Scholar
    • Export Citation
  • Wigglesworth K, Lee KB, Emori C, Sugiura K & Eppig JJ 2014 Transcriptomic diversification of developing cumulus and mural granulosa cells in mouse ovarian follicles. Biology of Reproduction 5 23. (doi:10.1095/biolreprod.114.121756)

    • Search Google Scholar
    • Export Citation
  • Wigglesworth K, Lee KB, Emori C, Sugiura K & Eppig JJ 2015 Transcriptomic diversification of developing cumulus and mural granulosa cells in mouse ovarian follicles. Biology of Reproduction 92 23. (doi:10.1095/biolreprod.114.121756)

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  • View in gallery
    Figure 1

    Summary of GranulosaIMAGE workflow. Data from transcriptome studies conducted by EmbryoGENE network scientists on granulosa cells have been deposited in the ELMA database. Granulosa cells were obtained from follicles at various stages of folliculogenesis from cows in different physiological and metabolic states (top panel). This flow diagram provides the working model of data retrieval from ELMA, its normalization, statistical tests and graphical representation on the GranulosaIMAGE web-based resource.

  • View in gallery
    Figure 2

    GranulosaIMAGE homepage. This screenshot illustrates the organization of the homepage containing a brief introduction to the database along with the gene search bar into which the sought probe ID or gene symbol is entered. This page also shows the manner in which a gene search result is displayed and lists the statistical methods used and the publications from which results are included in the database. External links to EmbryoGENE microarray annotation files and the ELMA database are also provided.

  • View in gallery
    Figure 3

    Integrative meta-analysis in GranulosaIMAGE: Expression dynamics of (A) amphiregulin (AREG) and (B) ADAM metallopeptidase with thrombospondin type 1 motif, 1 (ADAMTS1) under different conditions. Significant changes in gene expression profiles are highlighted in pink. (See the expression graphs section for explanation).

  • View in gallery
    Figure 4

    Illustration of gene dynamics during folliculogenesis as represented in GranulosaIMAGE. Dynamics of four genes (FSHR, LHCGR, AREG and ADAMTS1) were consulted initially in GranulosaIMAGE and then represented graphically vs follicular developmental stage, either destined to ovulate (grey line) or undergoing atresia (green line). The x-axis values are arbitrary units. Users may consult the dynamics of any gene of interest and may illustrate these versus their preferred follicular parameters (development, super-stimulation and effect of age or various post-partum metabolic states).

  • View in gallery
    Figure 5

    Illustration of gene dynamics during the post-partum period as represented in GranulosaIMAGE. The dynamics of four genes (CCNB1, LHCGR, FOXO1 and PTX3) were consulted in GranulosaIMAGE and then drawn to illustrate the effects of (A) 30, 60, 90 and 120 days post-partum and (B) maternal energy status (BHB level) and vitamin supplementation (pre-LH and irrespective of LH surge) at 60±5 days post-partum. The x-axis values are arbitrary units. Users may consult the dynamics of any gene of interest and illustrate these versus a variety of follicular conditions of interest.

  • Assidi M, Dufort I, Ali A, Hamel M, Algriany O, Dielemann S & Sirard MA 2008 Identification of potential markers of oocyte competence expressed in bovine cumulus cells matured with follicle-stimulating hormone and/or phorbol myristate acetate in vitro. Biology of Reproduction 79 209222. (doi:10.1095/biolreprod.108.067686)

    • Search Google Scholar
    • Export Citation
  • Conti M, Hsieh M, Park JY & Su YQ 2006 Role of the epidermal growth factor network in ovarian follicles. Molecular Endocrinology 20 715723. (doi:10.1210/me.2005-0185)

    • Search Google Scholar
    • Export Citation
  • Gagnon A, Khan DR, Sirard MA, Girard CL, Laforest JP & Richard FJ 2015 Effects of intramuscular administration of folic acid and vitamin B12 on granulosa cells gene expression in postpartum dairy cows. Journal of Dairy Science 98 77977809. (doi:10.3168/jds.2015-9623)

    • Search Google Scholar
    • Export Citation
  • Gougeon A 1996 Regulation of ovarian follicular development in primates: facts and hypotheses. Endocrine Reviews 17 121155.

  • Hamel M, Dufort I, Robert C, Leveille MC, Leader A & Sirard MA 2010 Genomic assessment of follicular marker genes as pregnancy predictors for human IVF. Molecular Human Reproduction 16 8796.

    • Search Google Scholar
    • Export Citation
  • Hsueh AJ & Rauch R 2012 Ovarian Kaleidoscope database: ten years and beyond. Biology of Reproduction 86 192. (doi:10.1095/biolreprod.112.099127)

    • Search Google Scholar
    • Export Citation
  • Khan DR, Guillemette C, Sirard MA & Richard FJ 2015 Characterization of FSH signalling networks in bovine cumulus cells: a perspective on oocyte competence acquisition. Molecular Human Reproduction 21 688701. (doi:10.1093/molehr/gap079)

    • Search Google Scholar
    • Export Citation
  • Lussier JG, Matton P & Dufour JJ 1987 Growth rates of follicles in the ovary of the cow. Journal of Reproduction and Fertility 81 301307. (doi:10.1530/jrf.0.0810301)

    • Search Google Scholar
    • Export Citation
  • Nivet AL, Bunel A, Labrecque R, Belanger J, Vigneault C, Blondin P & Sirard MA 2012 FSH withdrawal improves developmental competence of oocytes in the bovine model. Reproduction 143 165171. (doi:10.1530/REP-11-0391)

    • Search Google Scholar
    • Export Citation
  • Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W & Smyth GK 2015 limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43 e47. (doi:10.1093/nar/gkv007)

    • Search Google Scholar
    • Export Citation
  • Robert C, Nieminen J, Dufort I, Gagne D, Grant JR, Cagnone G, Plourde D, Nivet AL, Fournier E & Paquet E et al. 2011 Combining resources to obtain a comprehensive survey of the bovine embryo transcriptome through deep sequencing and microarrays. Molecular Reproduction and Development 78 651664. (doi:10.1002/mrd.21364)

    • Search Google Scholar
    • Export Citation
  • Sayasith K, Lussier J & Sirois J 2013 Molecular characterization and transcriptional regulation of a disintegrin and metalloproteinase with thrombospondin motif 1 (ADAMTS1) in bovine preovulatory follicles. Endocrinology 154 28572869. (doi:10.1210/en.2013-1140)

    • Search Google Scholar
    • Export Citation
  • Sirard MA 2014 Toward building the cow folliculome. Animal Reproduction Science 149 9097. (doi:10.1016/j.anireprosci.2014.06.025)

  • Sirard MA, Desrosier S & Assidi M 2007 In vivo and in vitro effects of FSH on oocyte maturation and developmental competence. Theriogenology 68 (Supplement 1) S71S76. (doi:10.1016/j.theriogenology.2007.05.053)

    • Search Google Scholar
    • Export Citation
  • Sugimura S, Ritter LJ, Sutton-McDowall ML, Mottershead DG, Thompson JG & Gilchrist RB 2014 Amphiregulin co-operates with bone morphogenetic protein 15 to increase bovine oocyte developmental competence: effects on gap junction-mediated metabolite supply. Molecular Human Reproduction 20 499513. (doi:10.1093/molehr/gau013)

    • Search Google Scholar
    • Export Citation
  • Tseng GC, , Ghosh D & Feingold E 2012 Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Research 40 37853799. (doi:10.1093/nar/gkr1265)

    • Search Google Scholar
    • Export Citation
  • Webb R, Nicholas B, Gong JG, Campbell BK, Gutierrez CG, Garverick HA & Armstrong DG 2003 Mechanisms regulating follicular development and selection of the dominant follicle. Reproduction Supplement61 7190.

    • Search Google Scholar
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  • Wigglesworth K, Lee KB, Emori C, Sugiura K & Eppig JJ 2014 Transcriptomic diversification of developing cumulus and mural granulosa cells in mouse ovarian follicles. Biology of Reproduction 5 23. (doi:10.1095/biolreprod.114.121756)

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  • Wigglesworth K, Lee KB, Emori C, Sugiura K & Eppig JJ 2015 Transcriptomic diversification of developing cumulus and mural granulosa cells in mouse ovarian follicles. Biology of Reproduction 92 23. (doi:10.1095/biolreprod.114.121756)

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