A Unified Atlas of Dynamic tRNA Function

动态 tRNA 功能统一图谱

• 批准号: 10112281
• 负责人: Todd Michael Lowe
• 金额: $ 52.92万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-16 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10112281
• 关键词: Address; Anabolism; Animal Model; Antibiotics; Apoptosis; Atlases; Basic Science; Binding; Biological; Biology; Biomedical Research; Brain; Breast Cancer Cell; CRISPR/Cas technology; Cell Survival; Cell physiology; Cells; Cellular biology; Code; Collection; Communities; Complement; Complex; Computer Analysis; Data; Data Set; Databases; Development; Disease; Drug or chemical Tissue Distribution; Epigenetic Process; Family; Foundations; Gene Expression; Gene Targeting; Generations; Genes; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Grant; Guide RNA; Human; Individual; Information Resources; Interdisciplinary Study; Length; Libraries; Lipids; Malignant Neoplasms; Maps; Measurement; Messenger RNA; Metastatic breast cancer; Modeling; Modification; Molecular Biology Techniques; Mus; Mutation; Nerve Degeneration; Nucleotides; Oncogenic; Organism; Orthologous Gene; Paper; Phenotype; Play; Production; Proteins; Protocols documentation; Publications; Publishing; RNA; Recycling; Regulation; Repression; Research; Research Personnel; Resistance; Resolution; Resources; Ribosomes; Role; Sampling; Serum; Small RNA; Stress; System; Techniques; Testing; Tissues; Training; Transcript; Transfer RNA; Translating; Translations; United States National Institutes of Health; Untranslated RNA; Variant; Virus Diseases; base; cell growth; comparative; epigenomics; experimental study; flexibility; gene complementation; gene function; genetic information; human disease; human tissue; improved; insight; interest; large scale data; mRNA Stability; novel; predictive modeling; predictive test; predictive tools; progressive neurodegeneration; protein expression; sperm cell; tool; transcriptome

The broad objective of this project is to better understand function, regulation, and the varied transcripts produced by each individual human tRNA gene. We propose to sequence full-length tRNA and tRNA-derived small RNA transcripts across many different tissue types for both human and the preferred mammalian model, mouse. These data will complement existing large-scale data sets focusing only on protein coding genes, giving an integrated view of tRNAs in the context of all other genes. With this new data, we will be able to better annotate and predict tRNA expression breadth across tissues, identify atypical tRNAs with potentially novel function, and recognize potentially disease-contributing mutations in human tRNA genes. The tRNA research field will also greatly benefit by being able to apply the revolutionary CRISPR/Cas9 gene targeting technique to individual tRNA genes for study – an experimental resource which we propose to develop. Thus, the aims of this grant are as follow: (1) Creation of new tRNA gene predictive models leveraging existing functional data with new insights in tRNA gene variation and the importance of external genomic features. (2) Train new predictive models with state-of-the-art tRNA transcriptome analyses, currently absent from the public databases. We will generate a comparative atlas of tRNA expression in samples from a broad range of healthy tissues from human and mouse, matching the tissue distribution of the NIH Genotype-Tissue Expression (GTEx) project to complement gene expression data for protein coding genes. (3) Integrate tRNA Atlas data with epigenomic and other published functional data within the Genomic tRNA Database. Using the framework of this database, we will establish robust functional ortholog maps between species, starting with human and mouse, to enable researchers to identify the best candidates for study of human genes in model organisms. (4) Develop a library of human CRISPR/Cas9 guide RNAs to enable individual tRNA gene targeting and characterization. We will select a representative subset of fifteen tRNAs of special interest for study, based on functional predictions and expression data collected from Aims 1 and 2. These new data, integrated into the most widely used tRNA gene database, along with improved tools for predicting and testing gene function, will enable and accelerate biomedical research in the tRNA community. Our interdisciplinary research group is ideally suited to carry out these aims, as we have demonstrated expertise in both computational analysis and development of molecular biology techniques enabling study of tRNA biology.

这个项目的主要目标是更好地了解功能，调节，和不同的成绩单 由每个人的tRNA基因产生。我们建议测序全长tRNA和tRNA衍生的 人和优选的哺乳动物模型的许多不同组织类型的小RNA转录物， 老鼠.这些数据将补充现有的只关注蛋白质编码基因的大规模数据集， 在所有其他基因的背景下给出了一个完整的tRNA视图。有了这些新数据，我们将能够 更好地注释和预测tRNA在组织中的表达宽度， 新的功能，并识别潜在的疾病在人类tRNA基因的突变。的tRNA 研究领域也将大大受益于能够应用革命性的CRISPR/Cas9基因靶向 技术到个别tRNA基因的研究-一个实验资源，我们建议开发。因此，在本发明中， 这项赠款的目的如下： (1)利用现有功能数据创建新的tRNA基因预测模型， tRNA基因变异和外部基因组特征的重要性。 (2)使用最先进的tRNA转录组分析训练新的预测模型，目前缺乏 公共数据库。我们将生成一个tRNA表达的比较图谱， 来自人类和小鼠的广泛健康组织，与NIH的组织分布相匹配 基因型-组织表达（GTEx）项目，以补充蛋白质编码的基因表达数据 基因. (3)将tRNA图谱数据与表观基因组数据和基因组数据库中其他已发表的功能数据整合 tRNA数据库。利用这个数据库的框架，我们将建立强大的功能直向同源物 从人类和老鼠开始， 在模式生物中研究人类基因的候选者。 (4)开发人类CRISPR/Cas9指导RNA文库，以实现单个tRNA基因靶向， 特征化我们将选择15种特别感兴趣的tRNA的代表性子集进行研究， 基于从目标1和2收集的功能预测和表达数据。 这些新的数据，整合到最广泛使用的tRNA基因数据库，沿着改进的工具 用于预测和测试基因功能，将使和加速tRNA社区的生物医学研究。 我们的跨学科研究小组非常适合实现这些目标，正如我们所证明的那样。 在计算分析和分子生物学技术开发方面的专业知识， tRNA生物学