Study On The Genes In Spermatogenesis Based On Data Integration Analysis

Approximately one seventh human couples suffer from fertility disorders in the world and within around 50% of such cases are caused by male infertility.Clinical studies have shown that male infertility is mainly due to the abnormal genes during the development of spermatogenesis.Therefore,reveal the etiology of spermatogenesis and elucidate its molecular mechanism,it could provide a guiding significance for the diagnosis,treatment of male infertility.Spermatogenesis is a dynamic biological process which can be divided into three major stages: SSCs divide into A,B type spermatogonia,which subsequently differentiate into spermatocyte;spermatocyte undergoes two consecutive meiotic divisions to produce round spermatids;spermatids then experience a serious of differentiation spermiogenesis process to form mature sperm.The spermatogenesis involves complex gene expression regulation,and any one of them can cause to produce abnormal sperm and lead to male infertility.To identify the dynamic process of spermatogenesis systematically,conventional experiment method that only focuses on single gene research have not been achieved.With the development of sequencing data and related research literature,it is possible to make a comprehensive analysis of genes in spermatogenesis.This study mainly focuses on data of spermatogenesis,with the combination of the text mining technology,and uses the systematic biological method to analyze and predict the related genes of spermatogenesis.Here we integrated analysis the transcriptome sequencing data and texting information of spermatogenesis to detect the stage-specific genes in mouse.Based on the protein-protein network,we construct predict algorithm(SGNet)for digging the novel factors in spermatogenesis.Furthermore,molecular experiments were conducted to validate the function of candidate genes in spermatogenesis.1.Integrate analysis of transcriptomic data of spermatogenesis in mouseThe establishment of the RNA-Seq data platform in spermatogenesis based on the GEO,SRA database could help to analyze the gene expression in each stage.We integrated the same stage genes from different independent studies based on two methods and obtained the stagespecific genes.The results showed that,8508 and 6939 genes expressed in two or more stages and 247 and 150 genes expressed in five or more stages in FC and IN methods respectively.Especially,the gene Inhba,Thbs2 expressed in all seven stages of spermatogenesis.The analysis in correlation indicated that these genes have high functional similarities,which may exert functions synergistically.The functional enrichment analysis showed that these genes were highly associated with the development of spermatogenesis.2.Spermatogenesis genes detection based on text mining technologyWith the combination of phase keywords and species as search terms,we extracted genes that related to spermatogenesis from the PubMed database with text mining technology and found 1241(64.6%)genes expressed in two or more stages,and six genes by all seven stages.Among them,Sycp3 and Pou5f1 are well known to be highly related to spermatogenesis.Our functional enrichment analysis within phase genes showed that these genes were highly related to the biological process of spermatogenesis and meiosis.3.Prediction of key markers that related to spermatogenesisBased on protein-protein network,with stage-specific differential expression and text mining genes as positive sets,we developed a computational analysis approach(SGNet)for detecting the key genes combined with the measures such as topological properties,shortest path length,functional similarity and so on.The 52 genes were predicted by SGNet,including 5 TFs.Then we used CTD database as golden stander of predicted genes.Results revealed that 84% of genes have been previously reported to associate with male infertility,abnormal development of testicular tissue and other reproductive diseases.Our qRT-PCR experiments certificated that the expression of Smc1 a and Tubgcp family genes in spermatogenesis were consistent with our prediction algorithm.