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. 2019 Jun 10;20(1):307.
doi: 10.1186/s12859-019-2825-2.

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

Affiliations

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

Beatriz Peñalver Bernabé et al. BMC Bioinformatics. .

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.

Keywords: Cell-type specific metabolic models; Genome-scale modeling; Metabolic communities; Metabolism; Ovarian follicle development; Secreted metabolites.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Reconstruction of the ovarian follicle metabolic model, OvoFol Recon 1, based on transcriptomic data. a Follicle sizes and age of the mice that the follicles were collected from; b Heatmap of the top significantly expressed genes for each follicle stage and follicle cell type; c Bi-partite graph of the Mice Recon 2 metabolic model, showing enzymes in purple and metabolites in green; d Bi-partite graph of OvoFol Recon 1 metabolic model constructed using FASTCORE from Mouse Recon 2. PND, post-natal day; PMSG, pregnant mare serum gonadotropin; PREANTRAL, pre-antral follicles between 200 and 300 μm; ANTRAL, antral follicles, between 400 and 600 μm; PMD, primordial; PR, primary; 2LS, two layered secondary; MLS, multi-layer secondary; SC, secondary follicle; SA, small antral; LA, large antral; CC, cumulus cells; MR, mural cells; CCTR, cumulus granulosa cells in large antral follicles during the acquisition of oocyte competence; NSN, non-surrounded oocyte nucleolus; SN, surrounded oocyte nucleolus
Fig. 2
Fig. 2
Ovarian follicle metabolic communities in OvoFol Recon 1 based on metabolite flow between enzymes (from Infomap). The sizes of the communities, defined as clusters of highly connected enzymes based on the flow of information through them, and the width of the edges between communities are proportional to the information that flows through them. Communities are color-coded based on the proportion of enzymes that pertain to a given location. For instance, Community 1 is largely composed of cytoplasmic enzymes (purple), followed by mitochondrial enzymes (yellow)
Fig. 3
Fig. 3
Top metabolic pathways in each follicle cell type based on Z-scores, which accounts for the transcriptional activity of the genes that encode the corresponding enzymes in the metabolic pathway and the flow of information between those enzymes, during ovarian follicle maturation. Metabolic pathways are divided based on the abundance of scientific references during ovarian follicle development. OPM, oocyte primordial; OPR, oocyte primary; OSC, oocyte secondary; OSA, oocyte small antral; OLA, oocyte large antral; SPM, somatic primordial; SPR, somatic primary; SSC, somatic secondary; SSA, somatic small antral; SLA, somatic large antral; CcSA, cumulus small antral; CcLA, cumulus large antral; MrSA, mural small antral; MrLA, mural large antral; CcNSN, cumulus granulosa cell in large antral follicles that present a non-surrounded oocyte nucleolus; CcSN, cumulus granulosa cell in large antral follicles that present a surrounded oocyte nucleolus
Fig. 4
Fig. 4
Top metabolites (a) and genes (b) encoding enzymes in each follicle cell type during follicle development. Cc, cumulus cells; Mr., mural cells; PM, primordial; PR, primary; SC, secondary; SA small antral; LA, large antral follicle. Metabolite and enzyme full names can be found in Additional file 2
Fig. 5
Fig. 5
Predicted exo- and endo-metabolism in each follicle cell type during follicle maturation. PMD, primordial follicle; PR, primary; SC, secondary; SA, small antral follicle; LA, large antral follicle; NSN, non-surrounded oocyte nucleolus; SN, surrounded oocyte nucleolus. Extracellular reactions and metabolite full names can be found in Additional file 2

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