ZCWPW1 is recruited to recombination hotspots by PRDM9, and is essential for meiotic double strand break repair

Wells, Daniel; Bitoun, Emmanuelle; Moralli, Daniela; Zhang, Gang; Hinch, Anjali Gupta; Donnelly, Peter; Green, Catherine; Myers, Simon

doi:10.1101/821678

Cited by 9 publications

(16 citation statements)

References 76 publications

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“…Immunofluorescence images in this study revealed that Zcwpw1 functioned in DNA repair and synapsis. Very recently, Zcwpw1 was identified as a histone H3K4me3 reader required for the repair of PR domain zinc finger protein 9-dependent DNA DSBs and synapsis by three independent research groups ( 49 , 50 , 51 , 52 ), which was consistent with our observations. Tesmin and 1700102P08Rik have also recently been identified as essential genes in spermatogenesis ( 53 , 54 ), yet their mechanisms of involvement in meiosis-essential biological events still require further exploration.…”

Section: Discussionsupporting

confidence: 92%

Prediction and Validation of Mouse Meiosis-Essential Genes Based on Spermatogenesis Proteome Dynamics

Fang

et al. 2021

Molecular & Cellular Proteomics

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The molecular mechanism associated with mammalian meiosis has yet to be fully explored, and one of the main reasons for this lack of exploration is that some meiosis-essential genes are still unknown. The profiling of gene expression during spermatogenesis has been performed in previous studies, yet few studies have aimed to find new functional genes. Since there is a huge gap between the number of genes that are able to be quantified and the number of genes that can be characterized by phenotype screening in one assay, an efficient method to rank quantified genes according to phenotypic relevance is of great importance. We proposed to rank genes by the probability of their function in mammalian meiosis based on global protein abundance using machine learning. Here, nine types of germ cells focusing on continual substages of meiosis prophase I were isolated, and the corresponding proteomes were quantified by high-resolution mass spectrometry. By combining meiotic labels annotated from the MGI mouse knockout database and the spermatogenesis proteomics dataset, a supervised machine learning package, FuncProFinder, was developed to rank meiosis-essential candidates. Of the candidates whose functions were unannotated, four of ten genes with the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. Therefore, mammalian meiosis-essential genes could be efficiently predicted based on the protein abundance dataset, which provides a paradigm for other functional gene mining from a related abundance dataset.

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Section: Discussionsupporting

confidence: 92%

Prediction and Validation of Mouse Meiosis-Essential Genes Based on Spermatogenesis Proteome Dynamics

Fang

et al. 2021

Molecular & Cellular Proteomics

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“…Immunofluorescence images in this study divulged that Zcwpw1 played a role of DNA repair and synapsis. Recently, Zcwpw1 was identified as a histone H3K4me3 reader required for synapsis and repair of PRDM9-dependent DSBs by other three research groups [37][38][39][40] , which was consistent with our observations. Whereas, knockout of the other two genes, Tesmin and 1700102P08Rik, were found have no effect on DNA repair and synapsis, indicating they could be involved in presently unknown molecular events inspected by midPachytene checkpoint.…”

Section: Dissucssionsupporting

confidence: 92%

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

Fang

Wei

et al. 2020

Preprint

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Mammalian meiosis is a cell division process specific to sexual reproduction, whereas a comprehensive proteome related to different meiotic stages has not been systematically investigated. Here, we isolated different types of germ cells 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, while the protein signatures of spermatogenesis were characterized by the dynamic proteomes. A supervised machine learning package, FuncProFinder, was developed to predict meiosis-essential candidates based on the proteomic dataset. Of the candidates with unannotated functions, four of the ten genes at the top prediction scores, Zcwpw1, Tesmin, 1700102P08Rik and Kctd19, were validated as meiosis-essential genes by knockout mouse models. The proteomic analysis towards spermatogenic cells indeed setups a solid evidence to study the mechanism of mammalian meiosis. The proteome data are available via ProteomeXchange with identifier PXD017284.Up to now, transcriptional gene expression in thousands of germ cells covering various developmental stages of spermatogenesis were quantified at single cell level 9-15 , resulting in very detail transcriptome landscape throughout spermatogenesis, yet few studies explored the data for further functional excavating. As gene expression at protein level are downstream of transcription, proteomic abundance change of genes could be more directly associated with phenotype or functional change. Importantly, multiple studies clarified a poor correlation between mRNA and protein abundance in testes 16,17 , therefore, a global proteomic profiling of gene expression in spermatogenesis is of great meaningful to unravel functional molecules of meiosis. However, the report regarding systematic profiling of proteomics during meiosis was limited. Only one type of meiotic cells-pachytene spermatocytes was quantified in previous proteomics studies 7, 17 , leaving protein expression remained unknown in most of the stages in meiosis. Therefore, in contrast to meiotic dependence of transcriptomes in details, quantified profiling of meiotic proteome has remained a large room to be improved, as well as digging for functional molecules from a big omics dataset.In this work, to understand the molecular basis of mouse meiosis and predict meiosis-essential proteins, 7 consecutive types of meiotic cells plus pre-meiotic spermatogonia and post-meiotic round spermatids were isolated and the proteins in each cell-type were identified and quantified by high-resolution mass spectrometry with a label-free mode. The meiosisdependent signatures were characterized by protein abundance changes. Furthermore, a supervised ensemble machine learning package, FuncProFinder, was developed to predict the meiosis-essential proteins. The meiosis-related phenotypes for the five proteins at the top scores of the prediction, Pdha2, Zcwpw1, Tesmin, Kctd19 and 1700102P08Rik were verified by knoc...

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“…In addition to specifying the locations of DSBs, PRDM9 has recently been discovered to play a second role, in the downstream repair of DSBs (Huang et al 2020; Mahgoub et al 2020; Wells et al 2020). In mice and humans, DSBs at which PRDM9 is bound on both homologs are more likely to be efficiently repaired and to result in a crossover; in contrast, DSBs at which PRDM9 is only bound on one of the two homologs are delayed in their repair (Hinch et al 2019; Wells et al 2020). If these “asymmetric” DSBs are overwhelming in number—as is the case in certain hybrid crosses in mice—this delay can lead to asynapsis and infertility (Davies et al 2016; Gregorova et al 2018).…”

Section: Introductionmentioning

confidence: 99%

PRDM9 losses in vertebrates are coupled to those of paralogs ZCWPW1 and ZCWPW2

Cavassim

Baker

Hoge

et al. 2021

Preprint

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In most mammals and likely throughout vertebrates, the gene PRDM9 specifies the locations of meiotic double strand breaks; in mice and humans at least, it also aids in their repair. For both roles, many of the molecular partners remain unknown. Here, we take a phylogenetic approach to identify genes that may be interacting with PRDM9, by leveraging the fact that PRDM9 arose before the origin of vertebrates, but was lost many times, either partially or entirely--and with it, its role in recombination. As a first step, we characterize PRDM9 domain composition across 379 vertebrate species, inferring at least eight independent losses. We then use the interdigitation of PRDM9 orthologs across vertebrates to test whether it co-evolved with any of 241 candidate genes co-expressed with PRDM9 in mice or associated with recombination phenotypes in mammals. Accounting for the phylogenetic relationship among species, we identify three genes whose presence and absence is unexpectedly coincident with that of PRDM9: ZCWPW1, which was recently shown to be recruited to sites of PRDM9 binding and to facilitate double strand break repair; TEX15, which has also been suggested to play a role in repair; and ZCWPW2, the paralog of ZCWPW1. ZCWPW2 is expected to be recruited to sites of PRDM9 binding as well; its tight coevolution with PRDM9 across vertebrates suggests that it is a key interactor, with a role either in recruiting the recombination machinery or in double strand break repair.

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ZCWPW1 is recruited to recombination hotspots by PRDM9, and is essential for meiotic double strand break repair

Cited by 9 publications

References 76 publications

Prediction and Validation of Mouse Meiosis-Essential Genes Based on Spermatogenesis Proteome Dynamics

Prediction and Validation of Mouse Meiosis-Essential Genes Based on Spermatogenesis Proteome Dynamics

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

PRDM9 losses in vertebrates are coupled to those of paralogs ZCWPW1 and ZCWPW2

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