Prediction of meiosis-essential genes based on dynamic proteomes responsive to spermatogenesis

Abstract

ABSTRACTMammalian meiosis is a specific cell division process during sexual reproduction, whereas a comprehensive proteome of the different meiotic stages has not been systematically investigated. Here, we isolated different types of spermatocytes from the testes of spermatogenesis-synchronized mice and quantified the corresponding proteomes with high-resolution mass spectrometry. A total of 8,002 proteins were identified in nine types of germ cells, and the protein signatures of spermatogenesis were characterized using the dynamic proteomes. A supervised machine learning package, FuncProFinder, was developed to predict meiosis-essential candidate genes based on changes in their protein abundance. Of the candidates without functional annotation, four of the ten genes with the highest prediction scores,Zcwpw1, Tesmin, 1700102P08Rik, andKctd19, were validated as meiosis-essential genes using knockout mouse models. The proteomic analysis of spermatogenic cells provides a solid foundation for studying the mechanism of mammalian meiosis.