Bioinformatics analysis of human spermatogonia differentiation process
10.3760/cma.j.cn101441-20230109-00018
- VernacularTitle:人类精原细胞分化过程的生物信息学分析
- Author:
Chenxing YIN
1
;
Pengtao LI
;
Jinglan SONG
;
Nan SHI
;
Ying CHEN
;
Peng XING
;
Nana MENG
;
Kang ZHANG
;
Yuzhen WANG
Author Information
1. 保定市妇幼保健院生殖医学科，保定 071000
- Publication Type:Journal Article
- Keywords:
Spermatogenesis;
Spermatogonia;
Bioinformatics;
Infertility, male;
Differentiation
- From:
Chinese Journal of Reproduction and Contraception
2023;43(12):1237-1243
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To clarify the key genes and biological processes in spermatogenesis, integrate and analyze the differentially expressed genes (DEGs) and key signaling pathways in the differentiation process of human spermatogonia (SPG), and to explore the early molecular mechanism of spermatogenesis, increase the molecular biology understanding of SPG differentiation process.Methods:The transcriptome data of human undifferentiated spermatogonia and differentiated spermatogonia (dSPG) were downloaded from the public database. Hisat2 and StingTie were used to screen DEGs. DEGs were analyzed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) functional enrichment analysis. MACS2 software was used to analyze the open chromatin regions (OCRs) in ATAC-seq data.Results:A total of 8 532 DEGs were screened, including 4 127 up-regulated genes and 4 405 down-regulated genes. They regulate the differentiation of SPG through some important biological processes, such as cell cycle, cytokine-mediated signaling pathways, organic matter metabolism, cell movement, and methylation. KEGG enrichment analysis showed that some important signaling pathways including FoxO signaling pathway and JAK-STAT signaling pathway played an important role in SPG differentiation. GO enrichment analysis showed that methylation played an important role in the differentiation of SPG, and the expression of methylation-related genes was significantly different. The TDRDs family was significantly enriched, and 9 TDRDs genes were found to be more active in dSPG. Conclusion:In this study, the differentially expressed genes during SPG differentiation were identified by bioinformatics analysis, and the differences in transcription and chromatin levels of key genes were clarified, which laid an important theoretical foundation for the study of SPG differentiation mechanism.
