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| Bioinformatics study on the changes of fertility of male mice with their increasing age by microRNA combined with mRNAomics |
| 1. Tangshan Maternal and Child Health Care Hospital，Tangshan, Hebei Province, 063000; 2. Graduate School of Peking Union Medical College; 3.National Research Institute for Family Planning; 4.Tangshan People's Hospital |
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Abstract Through bioinformatic methods screening out the key miRNAs and mRNAs differentially expressed in mouse testicular tissues with their increasing age, to provide new directions for the research of spermatogenesis. Methods: Microarray data on miRNAs and mRNAs of spermatogenesis were searched in NCBI's GEO database, and the datasets were identified as GSE41857 and GSE41858. GEO2R online analysis of the dataset GSE41857 was used to derive the differentially expression of miRNAs related to spermatogenesis, and the Lianchuan Omicstudio was used to predict their target genes. GEO2R online analysis of the dataset GSE41858 was used to derive the differentially expression of mRNAs associated with spermatogenesis. The target genes as mentioned above were intersected with the target genes of the miRNA to obtain the final differentially expressed genes. GO enrichment and KEGG pathway analysis of the differentially expressed genes were performed by DAVID, and STRING was applied to construct a protein interaction network associated with differentially expressed genes. Results: Analysis of the dataset GSE41857 yielded 52 differentially expressed miRNAs with 17,224 target genes. Analysis of the dataset GSE41858 yielded 619 differentially expressed genes, and finally 485 differentially expressed genes of intersection were obtained. GO enrichment analysis of biological processes (BP), mainly included spermatogenesis, cell differentiation, and cilia assembly, etc. Cell composition (CC) mainly included cytoplasm, nucleus, and cytoskeleton, etc. Molecular function (MF) mainly included protein binding, RNA polymerase II sequence-specific DNA binding transcription factor binding, and integrin binding, etc. The pathways obtained by KEGG analysis of differentially expressed genes mainly included axon guidance, hepatitis B, and Hippo signaling pathway, etc. The top 20 hub genes analyzed by cytoscape were Tgfb1/1, Tns3, Trn1, Csk, Zyx, Capn5, Capn1, Crebbp, Plxna1, Plxna3, Plxna4, Plxnb1, Itga9, Lama5, Fras1, Pdlim3, Ccnb2, E2f1, Ncor2, and Inppl1. Conclusion: The miRNA-mRNA regulatory network in mouse spermatogenesis has been constructed in this study, and some miRNAs and novel genes are uncovered, which may be involved in spermatogenesis. The new directions are provided for further elucidation of spermatogenesis.
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