Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region

Li, Hanshuang; Long, Chunshen; Xiang, Jinzhu; Liang, Pengfei; Li, Xueling; Zuo, Yongchun

doi:10.1093/bib/bbaa342

Cited by 15 publications

(12 citation statements)

References 54 publications

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“…Another marker, DPPA2 , acts as the main activator of the ZGA transcription program by directly regulating the mouse ZGA transcription factor Dux ( 53 ). DPPA2 contributes to the transcriptional regulation of ZGA in humans and mice and is significantly activated in mouse 2-cell embryos and 4–8-cell embryos ( 54 ). Our study indicated that DPPA2 is highly expressed at the 4-cell stage and continues to be highly expressed at the 8-cell and morula stages.…”

Section: Discussionmentioning

confidence: 99%

Arginine Regulates Zygotic Genome Activation in Porcine Embryos Under Nutrition Restriction

et al. 2022

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Arginine has a positive effect on pre-implantation development in pigs. However, the exact mechanism by which arginine promotes embryonic development is undefined. Here, single-cell RNA sequencing technology was applied to porcine in vivo pre-implantation embryos from the zygote to morula stage, it was found that that the expression of arginine metabolism-related genes clearly changed from the 2-cell stage to the 4-cell stage, when zygotic genome activation (ZGA) occurs in porcine embryos. Further analysis showed that arginine metabolism-related genes are significantly correlated with key ZGA genes. To determine the function of arginine in porcine embryos during ZGA, the in vitro fertilization embryos were cultured in PZM-3 medium (0.12 mM arginine, Control group), a modified PZM-3 medium (0 mM arginine, Block group) and a modified PZM-3 medium supplemented with arginine (0.12 mM arginine, Block + Arg group). The results showed that the 4-cell arrest rate was significantly increased in the Block group compared to the Control group (P < 0.05). The 4-cell arrest rate in the Block + Arg group was significantly decreased than that in the Block group (P < 0.05). Meanwhile, the expression of ZGA marker genes and SIRT1 protein in 4-cell embryos was significantly decreased in the Block group compared to the Control group, and their expression was significantly increased in the Block + Arg group. In addition, we observed that the glutathione (GSH), ATP levels, and lipid droplet contents were significantly increased, and the reactive oxygen species (ROS) level was decreased in the Block + Arg group compared to the Block group. Compared with Control group, spermine content in culture medium and the mRNA expression of ornithine decarboxylase1 (ODC1) of embryos in the Block group were significantly decreased (P < 0.05), and those in the Block + Arg group were significantly increased compared with the Block group (P < 0.05). Moreover, when difluoromethylornithine (an inhibitor of ODC1) was added to the modified PZM-3 medium supplemented with arginine, the effect of arginine on ZGA was inhibited. In summary, our findings demonstrated that arginine may regulate ZGA under nutrition restriction in porcine embryos by promoting polyamine synthesis.

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Section: Discussionmentioning

confidence: 99%

Arginine Regulates Zygotic Genome Activation in Porcine Embryos Under Nutrition Restriction

et al. 2022

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“…Differential expression analysis was performed by R package DEseq2 [ 53 ]. For each comparison, genes with a Benjamini and Hochberg-adjusted p -value (false discovery rate, FDR) < 0.05 and the absolute of Log2 (fold change) > 1 were regarded as differential expression genes (DEGs) [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

Long

et al. 2021

IJMS

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Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

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“…KEGG database 4 : KEGG was established in 1995 by the Kanehisa Laboratories at the Bioinformatics Center, Kyoto University, Japan, and is now one of the most commonly used international bioinformatics databases ( Kanehisa and Goto, 2000 ). KEGG is a database used to understand the high-level functions and practicability of biological systems from molecular-level information ( Li H. et al, 2020 ; Wang et al, 2021a ) (especially large-scale molecular datasets generated by genome sequencing and other high-throughput techniques), of which the data information can be roughly classified into four major categories: system information, genetic information, chemical information, and medical information.…”

Section: Three Factorsmentioning

confidence: 99%

Application of Machine Learning for Drug–Target Interaction Prediction

Song

2021

Front. Genet.

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Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.

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Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region

Cited by 15 publications

References 54 publications

Arginine Regulates Zygotic Genome Activation in Porcine Embryos Under Nutrition Restriction

Arginine Regulates Zygotic Genome Activation in Porcine Embryos Under Nutrition Restriction

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

Application of Machine Learning for Drug–Target Interaction Prediction

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