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BMC Bioinformatics. 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.

Author information

1
Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
2
Luxembourg Center for Systems Biology, University of Luxembourg, Esch-sur-Alzette, Luxembourg, L-4365, Luxembourg.
3
Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
4
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
5
Women's Health Research Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
6
Department of Chemical and Biological Engineering, Northwestern University Feinberg School of Medicine, Evanston, IL, 60208, USA.
7
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA. ldshea@umich.edu.

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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