Elucidating the cellular bases of fish fecundity: dynamics and regulation of oogenesis in medaka – DynaMO

The first task of the project will be descriptive and will aim at providing a comprehensive overview of oogenesis over time, including the number and size of oocytes present in the ovary during the reproductive cycles. To this aim, will take advantage in the recent progress in 3D imaging and bio-informatics image analysis. Resulting data will be used to build a model of the oogenesis dynamics thanks to mathematical modeling and numerical simulation approaches. In a second functional task, we will knock-out some specific miRNAs using the CRISPR/Cas9 technology. The most biologically relevant mutants displaying a reduced fecundity will then be subjected to an extensive phenotyping using both histological analyses (3D imaging) and molecular analyses (RNA-seq). The model previously generated will be used to understand how each mutant affect oogenesis dynamics. Finally, the output of mathematical modeling will be analyzed in the light of the molecular phenotyping in order to further understand the regulation of oogenesis by miRNA.

During the course of the project, started 18 months ago, we analyzed the oocyte content in medaka ovaries by three-dimensional (3D) imaging during the reproductive cycles. This allowed the construction of a preliminary mathematical model of the oogenesis dynamics. The protocol elaborated for studying ovaries in 3D, called C-ECi, was published1. This protocol gathers all the steps necessary for sample pre-treatment, samples 3D imaging and bio-informatics image analysis. The C-ECi method is the first procedure elaborated for analyzing fish ovary in 3D and promises to be useful for getting precise quantitative data for studying medaka oogenesis. Through the first part of the DynaMO project, we also selected one miRNA of interest on the base of its expression pattern. The production of a KO mutant for this miRNA will allow the precise analysis of its potential role in the regulation of oogenesis and fecundity in medaka.

1- Lesage M, Thomas M, Bugeon J, Branthonne A, Gay S, Cardona E, Bobe J, Thermes V. C-Eci: A Cubic-Eci Combined Clearing Method For 3D Follicular Content Analysis In The Fish Ovary. Developmental Biology; 2020. doi: doi.org/10.1101/2020.03.05.978189

In the last part of the project, data on the oocyte contents during the reproductive cycles will be completed and compiled in a refined mathematical model. This latter will be also completed with additional data gathered from mutants of fecundity, including a mutant depleted for the known miR-202 regulator (lesage et.al., 2018), and a mutant depleted for the new candidate miRNA identified in the first part of the project.

Lesage M, Thomas M, Bugeon J, Branthonne A, Gay S, Cardona E, Bobe J, Thermes V. C-Eci: A Cubic-Eci Combined Clearing Method For 3D Follicular Content Analysis In The Fish Ovary. Developmental Biology; 2020. doi: doi.org/10.1101/2020.03.05.978189

Female fecundity, i.e. the number of spawned eggs at each reproductive cycle, being the direct output of the oogenetic process occurring in the ovary, is a key factor for the management of both wild and farmed fish. Commitment into oogenesis (frequency, rate) and duration of the overall process greatly vary among fish species. They also significantly vary within a given species in response to extrinsic and intrinsic factors. The mechanisms that govern the cellular dynamics of oogenesis remain however largely unknown. The main reason is that investigating the entire process at the level of the entire organ has been methodologically challenging, if not virtually impossible using classical histology technics based on 2D ovarian sections. For this very reason, we lack a comprehensive overview of the process, and more specifically of the dynamic changes occurring among the different classes/sizes (i.e. oogenetic stages) of oocyte throughout oogenesis, despite several decades of extensive study of the endocrine regulations in the fish ovary. Using the medaka (a small aquarium fish with short generation time widely used to study oogenesis) as a model, we reported a set of 20 new ovarian-specific miRNAs and evidenced the key role of one of them (miR-202) in the determinism of reproductive success and more specifically in fecundity. Besides the preliminary data on the critical role played by miR-202, newly identified miRNAs with predominant ovarian expression suggest an important role of miRNAs in the regulation of fish fecundity. The objective of the DynaMO project is to understand not only the overall dynamics of oogenesis, but also to determine the role of these newly identified miRNAs in the regulation of this process. Our ambition is to go beyond a simple list of key regulatory miRNAs, by providing integrated knowledge of the dynamic of oogenesis in fish and of its regulation by miRNAs. To achieve this aim, we will combine descriptive, functional and modeling approaches, using 3D imaging, genome-editing and mathematics. In the first task of the project we will conduct 3D imaging of the entire intact medaka ovary (using wild-type fish), after tissue clearing, an innovative technology, which is available in the laboratory. This will provide a comprehensive view of oogenesis over time, including the number of oocytes of the different class-sizes present in the ovary. In a second task, carried out simultaneously, we will knock-out specific miRNAs, selected from the pool of newly identified miRNAs, to characterize miRNAs involved in the regulation of fish fecundity. This genome-editing task will be carried out using the CRISPR/Cas9 technology available in the laboratory. The most biologically relevant mutants will then be subjected to extensive histological (3D imaging, task 3) and molecular (RNA-seq and miRNA target prediction, task 4) phenotyping. In the last task of the project, a mathematical model will be built using data from task 1, which will then be used to understand how each mutant affects oogenesis dynamics. Finally, the output of mathematical modeling will be analyzed in the light of the molecular phenotyping performed in task 4 on order to further understand the regulation of oogenesis by miRNA.