Background The oocyte development ability of prepubertal animals is significantly lower than that of adult animals. Granulosa cells (GCs) have an important function on regulation of follicular and oocyte development. Therefore, analysis of GC characteristics can be used to explore the developmental mechanism of follicles and oocytes. Results In order to understand the possible reasons for the differences in follicle and oocyte development between lambs and adult sheep, we utilized high-throughput sequencing technique to analyze the transcriptome of GCs from follicle-stimulating hormone (FSH) superstimulated adult ewes and prepubertal lambs. Adult ewes were treated with FSH for 3 days (group A) and lambs were FSH-treated for 2 days (group B) or 3 days (group C). Transcriptome analysis of GCs showed that there were 405 and 159 differentially expressed genes from A vs. B and A vs. C, respectively. The results indicated that prolonging the FSH-treatment of lambs made the GC state of lambs more similar to the adult ewes, but there were still a large number of differentially expressed genes between adult ewes and lambs. Further analysis showed that many differently expressed genes were implicated in cell proliferation and apoptosis, oocyte development and follicular ovulation. Cellular examination demonstrated that fatty acid binding protein 4 (FABP4), which was highly expressed in lamb GCs, had a potential of promoting cell apoptosis. Cytoplasmic phospholipase A2 (PLA2G4A), which was expressed lowly in lamb GCs, may be responsible for reduced synthesis of prostaglandins in cells and impaired follicle/oocyte development. In contrast, glutathione S-transferase β-1 (GSTT2B) and forkhead boxO6 (FOXO6) had no apparent effect on the proliferation and apoptosis of GCs. Conclusions Our study found dramatic transcriptomic differences in GCs between lambs and adult sheep, which may explain the possible reasons for the defects of follicle and oocyte development in lambs compared to adult sheep. Our data provides important information for further understanding the mechanism of follicular development in prepubertal animals and improving their oocyte developmental competence.
Oocyte cryopreservation demonstrates great benefits in the conservation of animal germplasm resources and assisted reproductive technology. However, vitrification causes damages in oocytes, which would lead to the decrease of oocyte quality, and embryonic development post fertilization. Cytoskeleton plays an important role in regulating cell shape, organelle migration, cell division and mechanical signal transduction. Cortical tension is a reflection of the physiological state and contractile ability of cortical cytoskeleton. Appropriate cortical tension is prerequesite for normal oocyte meiosis. In the present study, oocyte cortical tension was examined by evaluating the levels of cortical tension-related protein pERM (Phospho-Ezrin/Radixin/Moesin) and pMRLC (Phospho-Myosin Light Chain 2). We found that the cortical tension of vitrified oocytes was decreased. Increasing cortical tension of vitrified oocytes by adding 10 μg/ml ConA during in vitro culture could significantly improve the polar body extrusion rate and embryo development. Furthermore, increasing the cortical tension could improve spindle positioning, maintain kinetochore-microtubule (KT-MT) attachment, strengthen spindle assembly checkpoint (SAC) activity, and reduce the aneuploidy rate in vitrified oocytes. In conclusion, vitrification induced a remarkable decrease in cortical tension, and increasing the cortical tension could rescue the meiosis defect and improve oocyte quality.
Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.
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