Folliculogenesis in Mammalian Models: A Computational Biology Study.

folliculogenesis (F) has been proposed as an emerging technology to support follicle growth and oocyte development. It holds a great deal of attraction from preserving human fertility to improving animal reproductive biotechnology. Despite the mice model, where live offspring have been achieved,in medium-sized mammals, F has not been validated yet. Thus, the employment of a network theory approach has been proposed for interpreting the large amount of F information collected to date in different mammalian models in order to identify the controllers of the system. The WoS-derived data generated a scale-free network, easily navigable including 641 nodes and 2089 links. A limited number of controllers (7.2%) are responsible for network robustness by preserving it against random damage. The network nodes were stratified in a coherent biological manner on three layers: the input was composed of systemic hormones and somatic-oocyte paracrine factors; the intermediate one recognized mainly key signaling molecules such as PI3K, KL, JAK-STAT, SMAD4, and cAMP; and the output layer molecules were related to functional F endpoints such as the FSH receptor and steroidogenesis. Notably, the phenotypes of knock-out mice previously developed for hub.BN indirectly corroborate their biological relevance in early folliculogenesis. Finally, taking advantage of the STRING analysis approach, further controllers belonging to the metabolic axis backbone were identified, such as mTOR/FOXO, FOXO3/SIRT1, and VEGF, which have been poorly considered in F to date. Overall, this study identifies new metabolic sensor molecules controlling F serving as a basis for designing innovative diagnostic and treatment methods to preserve female fertility.