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Updated: Oct 16, 2026

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform
Published on: January 30, 2017
Embryonic yield-related serum proteomic analysis at bovine superovulation
Yongli Yue1, Anping Xie1, Ruilin Fang1
1The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China.
Abstract:
Superovulation is a key technology for accelerating genetic improvement in cattle, but its efficiency is limited by the unpredictable ovarian response among donors. A non-invasive method to predict this response could enhance superovulation outcomes. As serum proteome may mirror ovarian function, this study aimed to identify potential serum protein biomarkers at critical time points to predict embryo yield in donor cows. In this study, blood samples were collected from 33 donor cows undergoing superovulation at two time points: before FSH administration (designated BS) and at estrus detection (designated E). Based on the total number of embryos recovered, the cows were subsequently assigned to high embryonic yield (HEY) and low embryonic yield (LEY) groups. Proteomic sequencing was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 352 embryos were collected. Proteomic analysis identified 1,620 proteins, corresponding to 11,726 annotated entries. A total of 154 and 102 differentially expressed proteins were identified between HEY and LEY at BS and E, respectively (|log2FC| ≥0.263, P < 0.05, FDR<0.01). Thirteen proteins were shared between the two time points, including consistently upregulated vitamin D-binding protein (GC). The enriched pathways were mainly metabolism-related. Notably, elevated TCN2 and DNMT1 at BS (AUC = 0.96 and 1.00, respectively), and AGRN, RCN3, and WNT7B at E (AUC = 1.00, 0.80, and 0.84, respectively), were associated with the HEY group, suggesting their potential as biomarkers for predicting embryo yield in bovine superovulation. Collectively, this study identified multiple serum protein biomarkers, providing novel molecular insights into the serum protein response to superovulation and laying a foundation for the development of non-invasive prediction strategies.

