Dynamic transcriptomic profiling and screening of peripheral blood reveals key regulatory genes for superovulation response in dairy cows

Abstract Background Multiple ovulation and embryo transfer (MOET) is a key technique in dairy industry, substantially improving reproductive efficiency. However, the lack of effective selection tools leads to a large phenotypic variation in superovulation response. To investigate the key regulatory genes related to superovulation, this study performed RNA-seq of peripheral blood mononuclear cells (PBMCs) to construct and characterize the dynamic transcriptome profiles. Methods Twenty-four donors underwent the same superovulation protocol, and PBMCs were collected from them on day 0 (un-stimulated stage, UN), day 4 (CIDR-embedded stage, CIDR), day 6 (FSH-stimulated stage, FSH), and day 16 (superovulated stage, SUP). Following RNA sequencing, analyses of differentially expressed genes (DEGs) across superovulation stages, identification of phase major expressed genes (PMEGs), time-series analysis (TSA), and weighted gene co-expression network analysis (WGCNA) were performed. Results Across superovulation process, the three comparisons for UN vs. CIDR, CIDR vs. FSH and FSH vs. SUP identified 1,703 up-regulated DEGs (URGs) and 2,599 down-regulated DEGs (DRGs), 940 URGs and 715 DRGs, 389 URGs and 509 DRGs, respectively. There were 214, 114, 47 and 24 SMEGs identified in UN, CIDR, FSH, and SUP, participating in different biological pathways. TSA classified genes into six clusters. Clusters C1 (1,239 genes) and C4 (1,862 genes) were enriched for pathways related to follicle stimulating hormone response, while clusters C2 (1,783 genes) and C6 (1,710 genes) were enriched for pathways related to the synchronous estrus response. WGCNA identified 19 co-expression modules, of which six core modules were significantly associated with the superovulation stages and identified a total of 1,245 hub genes. The key regulatory genes of stage FSH were identified by integrating the results of previous sections. Ten key genes and other 190 eligible genes were concluded in protein-protein interaction network diagram, enriched in cell cycle, DNA damage repair, and energy metabolism. Conclusions Collectively, this study provides detailed dynamic transcriptomic profiles of peripheral blood across superovulation stages. Through integrated analyses of DEGs, SMEGs, TSA, and WGCNA, numerous key genes were screened out. These findings provide valuable molecular targets for improving superovulation efficiency and offer a basis for developing effective selection tools in MOET procedures.