Analysis of the miRNA Expression Profiles in the Zearalenone-Exposed TM3 Leydig Cell Line.

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2019
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Abstract
Zearalenone (ZEN), an important environmental pollutant, can cause serious harm to human and animal health. The aim of our study was to examine the effect of zearalenone (ZEN) on miRNA expression profiles in the mouse Leydig cell line (TM3 Leydig cell line) by miRNA sequencing. The effect of ZEN on the viability of TM3 Leydig cells was verified by Cell Counting Kit-8 (CCK-8). MiRNA sequencing was performed 24 h after the exposure of TM3 Leydig cells with 50 μmol/L of ZEN. Bioinformatics predicted the miRNA target genes, performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and conducted miRNA-gene-pathway mapping to show the relationship between miRNA, the target gene, and the signalling pathway. The expression levels of miRNA and the miRNA target genes associated with ZEN toxicology were verified by quantitative real-time polymerase chain reaction. The miRNA sequencing revealed a significant change ( < 0.05) in the 197 miRNAs in the ZEN-treated and control groups, among which 86 were up-regulated and 111 were down-regulated. GO analysis of the target genes of these miRNAs indicated various biological functions. KEGG analysis showed that the predicted miRNA target genes were involved in signalling pathways, such as cancer, apoptosis, and oxidation, namely, the Ras signalling pathway, Rap1 signalling pathway, PI3K-AKT signalling pathway, Foxo signalling pathway, and AMPK signalling pathway. These results suggest that ZEN, as an estrogen-like toxin, is regulated by microRNAs. Our results can help to examine the toxicological effects of ZEN-regulated miRNAs on germ cells.
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wang2019analysisinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Mingyang;Wu, Weiwei;Li, Lin;He, Jianbin;Huang, Sheng;Chen, Si;Chen, Jia;Long, Miao;Yang, Shuhua;Li, Peng;
Journal International journal of molecular sciences
Year 2019
DOI
E635
URL
Keywords
semantic segmentation cnn multi-task learning depth completion

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