Cross-species regulatory network analysis identifies FOXO1 as a driver of ovarian follicular recruitment
Year: 2024
Type: article
Abstract: The transcriptional regulation of gene expression in the latter stages of follicular development in laying hen ovarian follicles is not well understood. Although differentially expressed genes (DEGs) have been identified in pre-recruitment and pre-ovulatory stages, the master regulators driving these DEGs remain unknown. This study addresses this knowledge gap by utilizing Master Regulator Analysis (MRA) combined with the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) for the first time in laying hen research to identify master regulators that are controlling DEGs in pre-recruitment and pre-ovulatory phases. The constructed ARACNe network included 10,466 nodes and 292,391 edges. The ARACNe network was then used in conjunction with the Virtual Inference of Protein-activity by Enriched Regulon (VIPER) for the MRA to identify top up- and down-regulated master regulators. VIPER analysis revealed FOXO1 as a master regulator, influencing 275 DEGs and impacting pathways related to apoptosis, proliferation, and hormonal regulation. Additionally, CLOCK, known as a crucial regulator of circadian rhythm, emerged as an upregulated master regulator in the pre-ovulatory stage. These findings provide new insights into the transcriptional landscape of laying hen ovarian follicles, offering a foundation for further exploration of follicle development and enhancing reproductive efficiency in avian species.
Source: Scientific Reports
Authors: Ashley E. Kramer, Alberto Berral-González, Kathryn M. Ellwood, Shanshan Ding, Javier De Las Rivas, Aditya Dutta
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,590
Funders: National Science Foundation, U.S. Department of Agriculture, University of Delaware Research Foundation, University of Delaware, Delaware IDeA Network of Biomedical Research Excellence, Ministerio de Ciencia, Innovación y Universidades, National Institutes of Health, National Institute of Food and Agriculture, Irving Medical Center, Columbia University, College of Agriculture and Natural Resources, Michigan State University, Instituto de Salud Carlos III, National Institute of General Medical Sciences, Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values, Delaware-CTR ACCEL Program
Awards: G1266426425, G1451002057, G1498668426, G1539057555, G1657775071, G1883723449, G226277090, G2581928502, Collaborative Research: Nonparametric Learning in High-Dimensional Survival Analysis for causal inference and sequential decision making, G274435500, Análisis proteo-transcriptómico por técnicas de célula única del microambiente inmune y estromal de la médula ósea para descifrar la respuesta a tratamiento en mieloma múltiple, G3115553707, G3524313248, A Unified Multi-Stage Approach to Generalized Sequential Decision Making Problems with Covariates, Delaware INBRE, G6718912342, RCN: A Network to Promote Comparative Genome to Phenome Analysis, G8899652413, G9000004866, G9000093202, G9000125231, G9000149682
OpenAlex ID: W4405842578