PRDM9 and Meiotic Regulatory Networks

PRDM9 和减数分裂调控网络

• 批准号: 8723851
• 负责人: Matthew Aaron Hibbs
• 金额: $ 32.53万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8723851
• 关键词: Alleles; Binding; Biological; Chromatin; Communities; Computer Simulation; DNA Binding; Data; Data Analyses; Data Sources; Development; Event; Evolution; Gametogenesis; Gene Expression; Gene Expression Profile; Genetic Recombination; Genetic Transcription; Genome; Genomics; Germ Cells; Goals; High-Throughput Nucleotide Sequencing; Histones; Hybrids; Imagery; Knockout Mice; Light; Literature; Machine Learning; Measures; Meiosis; Meiotic Recombination; Methyltransferase; Modeling; Molecular; Molecular Genetics; Mus; Outcome; Pattern; Proteins; Regulation; Resources; Role; Sequence Homology; Series; Site; Spermatogenesis; Sterility; Systems Biology; Techniques; Time; Transcriptional Activation; Variant; chromatin immunoprecipitation; chromatin modification; data integration; dosage; gene repression; human ZNF45 protein; mutant; novel; programs; transcriptome sequencing

PROJECT C PRDM9 is a multi-functional protein, expressed uniquely during meiosis, that regulates meiotic recombination, transcription, and gametogenesis. We know that PRDM9 has the capacity to create transcriptionally activating histone H3K4me3 chromatin marks, possesses potentially repressive KRAB and SSXRD domains, and physically binds to recombination hotspot sequences; but we do not understand how these molecular functions cooperate to achieve PRDM9's diverse biological roles. The overall goal of this project is to delineate the molecular networks of PRDM9's transcriptional and H3K4 trimethyltransferase activity through high-throughput sequencing, and to analyze these data in conjunction with other Program Project data to construct a computational systems biology model of F'RDM9 function. In Aim 1, we will determine PRDM9-dependent gene expression changes and histone H3K4me3 mark changes throughout a meiotic germ cell time course. We will analyze these data in conjunction with the PRDMS-DNA binding data of Project B Petkov and the characterization of PRDMS-SET and -KRAB domain mutants of Project D Handel, to distinguish direct from indirect transcriptional targets of PRDMS, and to infer the likely mechanisms of regulation. In Aim 2, we will determine the effects of allelic and dosage variation in PrdmQ by examining gene expression and H3K4me mark profiles from germ cells isolated from an allelic series of mice. We will analyze these data in conjunction with the characterization of these strains by Projects A Paigen and B Petkov to associate each allele with downstream outcomes and to examine allelic interference or dominance patterns. In Aim 3, we will develop and apply a statistical machine learning approach to computationally integrate all Program Project data, including the RNA-seq and ChlP-seq data of this project, with high-throughput data from the literature into a comprehensive model of PRDMS function. Our integrated model will be used to predict novel PRDMS interactors and meiotic proteins, and will serve as a resource for the germ cell community through a public, online visualization interface. Ultimately, this project will determine the temporal and allelic dynamics of PRDMS's transcriptional and chromatin modifying roles, and will place these data in the broader context of PRDMS's multiple functions in recombination, meiosis, and gametogenesis.

项目C PRDM 9是一种多功能蛋白，在减数分裂期间唯一表达，调节减数分裂 重组、转录和配子发生。我们知道PRDM 9有能力创造 转录激活组蛋白H3 K4 me 3染色质标记，具有潜在的抑制性KRAB， SSXRD结构域，并与重组热点序列物理结合;但我们不知道如何结合 这些分子功能合作以实现PRDM 9的多种生物学作用。 该项目的总体目标是描绘PRDM 9的转录和表达的分子网络。 通过高通量测序检测H3 K4三甲基转移酶活性，并分析这些数据 结合其他计划项目数据，构建F 'RDM 9的计算系统生物学模型 功能 目的1：检测PRDM 9依赖性基因表达变化和组蛋白H3 K4 me 3标记 在整个减数分裂生殖细胞时间过程中的变化。我们将结合 项目B Petkov的PRDMS-DNA结合数据以及PRDMS-SET和-KRA B结构域的表征 突变体，以区分PRDMS的直接和间接转录靶标，并推断 可能的监管机制。 在目的2中，我们将通过检查基因来确定PrdmQ中等位基因和剂量变化的影响。 来自分离自等位基因系列小鼠的生殖细胞的H3 K4 me表达和H3 K4 me标记谱。我们将分析 这些数据结合项目A Paigen和B Petkov对这些菌株的表征， 将每个等位基因与下游结果相关联，并检查等位基因干扰或显性模式。 在目标3中，我们将开发和应用统计机器学习方法来计算整合 所有计划项目数据，包括该项目的RNA-seq和ChlP-seq数据，具有高通量数据 从文献中转化为PRDMS功能的综合模型。我们的集成模型将用于 预测新的PRDMS相互作用和减数分裂蛋白，并将作为生殖细胞的资源 社区通过一个公共的，在线的可视化界面。 最终，该项目将确定PRDMS转录和表达的时间和等位基因动态， 染色质修饰的作用，并将这些数据放在更广泛的背景下，PRDMS的多种功能， 重组、减数分裂和配子发生。