A Unified Atlas of Dynamic tRNA Function

动态 tRNA 功能统一图谱

• 批准号: 10349572
• 负责人: Todd Michael Lowe
• 金额: $ 53.88万
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10349572
• 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; public database; 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.

这个项目的广泛目标是更好地理解功能、调节和不同的转录 由每个单独的人类tRNA基因产生。我们建议对全长tRNA和tRNA衍生的 对于人类和首选的哺乳动物模型，小RNA在许多不同组织类型中转录， 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tRNA的综合观点。有了这些新数据，我们将能够 更好地注释和预测tRNA在组织中的表达广度，识别潜在的非典型tRNA 新的功能，并识别人类tRNA基因中潜在的致病突变。TRNA 研究领域也将因能够应用革命性的CRISPR/Cas9基因打靶而受益匪浅 用于研究的单个tRNA基因的技术--我们建议开发的实验资源。因此， 这笔赠款的目的如下： (1)利用现有功能数据创建新的tRNA基因预测模型，具有新的见解 TRNA基因变异和外源基因组特征的重要性。 (2)用最先进的tRNA转录组分析训练新的预测模型，目前还没有 公共数据库。我们将生成一份tRNA表达的比较图谱，这些样本来自 来自人类和小鼠的广泛的健康组织，与NIH的组织分布相匹配 基因-组织表达(GTEx)项目用于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和基因组内其他已发表的功能数据进行整合 TRNA数据库。利用这个数据库的框架，我们将建立健壮的功能正交图 物种之间的图谱，从人和老鼠开始，使研究人员能够识别最好的 在模式生物中研究人类基因的候选对象。 (4)开发人CRISPR/Cas9引导RNA文库，以实现单个tRNA基因靶向和 人物刻画。我们将选择15个特别感兴趣的tRNA的代表性子集进行研究， 基于从目标1和目标2收集的功能预测和表达数据。 这些新数据集成到最广泛使用的tRNA基因数据库中，以及改进的工具 用于预测和测试基因功能，将使tRNA社区的生物医学研究成为可能并加快步伐。 我们的跨学科研究小组非常适合实现这些目标，正如我们已经证明的那样。 在计算分析和分子生物学技术开发方面的专业知识，使研究 TRNA生物学。

项目成果

tRNA methylation resolves codon usage bias at the limit of cell viability.

• DOI: 10.1016/j.celrep.2022.111539
• 发表时间: 2022-10-25
• 期刊: Cell reports
• 影响因子: 8.8

Complete genome sequence of Desulfurococcus fermentans, a hyperthermophilic cellulolytic crenarchaeon isolated from a freshwater hot spring in Kamchatka, Russia.

发酵脱硫球菌的完整基因组序列，这是一种从俄罗斯堪察加半岛的淡水温泉中分离出来的超嗜热纤维素分解菌。

• DOI: 10.1128/jb.01314-12
• 发表时间: 2012
• 期刊: Journal of bacteriology
• 影响因子: 3.2
• 作者: Susanti,Dwi;Johnson,EricF;Rodriguez,JasonR;Anderson,Iain;Perevalova,AnnaA;Kyrpides,Nikos;Lucas,Susan;Han,James;Lapidus,Alla;Cheng,Jan-Fang;Goodwin,Lynne;Pitluck,Sam;Mavrommatis,Konstantinos;Peters,Lin;Land,MiriamL;Hauser,
• 通讯作者: Hauser,

GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes.

• DOI: 10.1093/nar/gkv1309
• 发表时间: 2016-01-04
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Chan PP;Lowe TM
• 通讯作者: Lowe TM

RNAcentral: a comprehensive database of non-coding RNA sequences.

• DOI: 10.1093/nar/gkw1008
• 发表时间: 2017-01-04
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: The RNAcentral Consortium;Petrov AI;Kay SJE;Kalvari I;Howe KL;Gray KA;Bruford EA;Kersey PJ;Cochrane G;Finn RD;Bateman A;Kozomara A;Griffiths-Jones S;Frankish A;Zwieb CW;Lau BY;Williams KP;Chan PP;Lowe TM;Cannone JJ;Gutell R;Machnicka MA;Bujnicki JM;Yoshihama M;Kenmochi N;Chai B;Cole JR;Szymanski M;Karlowski WM;Wood V;Huala E;Berardini TZ;Zhao Y;Chen R;Zhu W;Paraskevopoulou MD;Vlachos IS;Hatzigeorgiou AG;Ma L;Zhang Z;Puetz J;Stadler PF;McDonald D;Basu S;Fey P;Engel SR;Cherry JM;Volders PJ;Mestdagh P;Wower J;Clark MB;Quek XC;Dinger ME
• 通讯作者: Dinger ME

tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes.

• DOI: 10.1093/nar/gkab688
• 发表时间: 2021-09-20
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Chan PP;Lin BY;Mak AJ;Lowe TM
• 通讯作者: Lowe TM