five

RPA and RAD51 ChIP-seq from genetically modified Dmc1-knockout B6xCAST PRDM9-Humanized/CAST mouse testes

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://www.ncbi.nlm.nih.gov/sra/SRP453150
下载链接
链接失效反馈
官方服务:
资源简介:
Mammalian meiotic recombination proceeds via repair of hundreds of programmed DNA double-strand breaks (DSBs). This process requires choreographed binding of RPA, DMC1 and RAD51 to single-stranded DNA (ssDNA) substrates and in vivo binding maps of these proteins provide insights into the underlying molecular mechanisms. When assayed in F1-hybrid mice, these maps can distinguish the broken chromosome from the homologous chromosome used as template for repair, which reveals further mechanistic detail and enables the structure of the recombination intermediates to be inferred. By applying CRISPR/Cas9 mutagenesis directly on F1-hybrid embryos, we have extended this powerful analysis technique to explore the molecular detail of recombination when a key component is knocked-out. As a proof-of-concept, we have generated biallelic knockouts of Dmc1 and built maps of meiotic binding of RAD51 and RPA in these knockout hybrid mice. Dmc1 mutants undergo meiotic arrest and comparison of these maps with those from wild-type mice is informative about the structure and timing of recombination intermediates in both genotypes. We confirm a complete abrogation of strand exchange in Dmc1 mutants, and observe a redistribution of RAD51 binding across both the distal and proximal ends of the resected DNA. We observe unexpected RPA and DMC1 binding in the wild-type, which suggests multiple rounds of strand invasion with template-switching in mouse. The methodology used involves direct phenotyping of hybrid “founder” mice following CRISPR mutagenesis and provides a high-throughput approach for the analysis of gene function during meiotic recombination, at low animal cost. Overall design: Comparison of RPA and RAD51 ChIP-seq data from wild-type and genetically modified hybrid mice across tens of thousands of hotspots genome-wide
创建时间:
2024-01-16
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作