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Assisted reproduction technology (ART) procedures are often impacted by post-ovulatory aging (POA), which can lead to reduced fertilization rates and impaired embryo development. This study used RNA sequencing analysis and experimental validation to study the similarities and differences between in vivo- and vitro-matured porcine oocytes before and after POA. Differentially expressed genes (DEGs) between fresh in vivo-matured oocyte (F_vivo) and aged in vivo-matured oocyte (A_vivo) and DEGs between fresh in vitro-matured oocyte (F_vitro) and aged in vitro-matured oocyte (A_vitro) were intersected to explore the co-effects of POA. It was found that "organelles", especially "mitochondria", were significantly enriched Gene Ontology (GO) terms. The expression of genes related to the "electron transport chain" and "cell redox homeostasis" pathways related to mitochondrial function significantly showed low expression patterns in both A_vivo and A_vitro groups. Weighted correlation network analysis was carried out to explore gene expression modules specific to A_vivo. Trait–module association analysis showed that the red modules were most associated with in vivo aging. There are 959 genes in the red module, mainly enriched in "RNA binding", "mRNA metabolic process", etc., as well as in GO terms, and "spliceosome" and "nucleotide excision repair" pathways. DNAJC7, IK, and DDX18 were at the hub of the gene regulatory network. Subsequently, the functions of DDX18 and DNAJC7 were verified by knocking down their expression at the germinal vesicle (GV) and Metaphase II (MII) stages, respectively. Knockdown at the GV stage caused cell cycle disorders and increase the rate of abnormal spindle. Knockdown at the MII stage resulted in the inefficiency of the antioxidant melatonin, increasing the level of intracellular oxidative stress, and in mitochondrial dysfunction. In summary, POA affects the organelle function of oocytes. A_vivo oocytes have some unique gene expression patterns. These genes may be potential anti-aging targets. This study provides a better understanding of the detailed mechanism of POA and potential strategies for improving the success rates of assisted reproductive technologies in pigs and other mammalian species. [ABSTRACT FROM AUTHOR]

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP)., Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip., Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits., Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.