Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development

Abstract

Background The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach. Results Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles. Conclusions Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro. Electronic supplementary material The online version of this article (10.1186/s12859-019-2825-2) contains supplementary material, which is available to authorized users.

Capitalizing on transcriptome profiling to optimize and identify targets for promoting early murine folliculogenesis in vitro

“…To compare the transcriptome profile of somatic cells from follicles co-cultured in vitro to those from freshly isolated follicles in vivo at comparable stages of development, we drew on previously published data available online on the Gene Expression Omnibus (GSE 97902) 33 – 35 . This published dataset was used to develop a metabolic model for murine ovarian follicles by updating the previously published dataset Mouse Recon 1 to include human homologues and key ovarian follicle development pathways that were not previously represented 33 , 36 . The dataset used during model development includes transcriptome data from the follicular structure as a whole (including the oocyte and somatic cells) at various stages of follicle development 33 .…”

“…To compare the in vitro follicle culture data to previously published microarray data from freshly isolated follicles of different stages, raw expression data (GSE 97,902) from the Gene Expression Omnibus was downloaded using the package GEOquery 33 , 64 . This dataset reports ovarian follicle gene expression at various stages of development, using RNA collected from freshly isolated follicles from CD-1 mice 33 . From this dataset, the three replicates each of primary, multilayer secondary, and antral follicle transcriptome data were extracted for comparisons to the in vitro co-culture data.…”

Integrated analysis of miRNA–mRNA interaction in ovaries of Turpan Black Sheep during follicular and luteal phases

“…Therefore, the results of the present study indicated a difference of regulatory elements for the transition of reproductive processes between single lamb and prolific sheep. In addition, these differentially expressed genes were mostly enriched in several KEGG pathways, including metabolic pathway (Penalver Bernabe et al., 2019), ovarian steroidogenesis, steroid hormone biosynthesis and oestrogen signalling pathway. Moreover, these pathways play crucial roles in reproductive hormone biosynthesis and follicular development.…”

Clavulanic Acid Production by Streptomyces clavuligerus: Insights from Systems Biology, Strain Engineering, and Downstream Processing

“…This mathematical platform along with the integration of omics data (genomics, transcriptomics, proteomics, metabolomics, etc.) has demonstrated usefulness in guiding experimental efforts and elucidating mechanisms affecting cell phenotypes in many different cell types [ 150 , 151 , 152 , 153 , 154 , 155 , 156 ].…”