Integrative approaches for decoding the function and regulation of unconventional RNA translation

解码非常规 RNA 翻译功能和调控的综合方法

基本信息

批准号：
10649568
负责人：
Yiwen Chen
金额：
$ 35.64万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-10 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10649568
关键词：
3&apos Untranslated Regions 5&apos Untranslated Regions Adoption Affinity Chromatography Amino Acids Anemia Biological Biological Process Biology CRISPR/Cas technology Cancer Center Catalogs Cell Proliferation Cell Survival Cell physiology Cells Code Complex Coupled Cycloheximide Data Data Analyses Data Set Development Developmental Process Diagnosis Disease Distant Embryonic Development Estrogens Eukaryota Event Freezing Genes Genetic Transcription Human Individual Informatics Internet Knock-out Maps Mass Spectrum Analysis Measures Mediating Medicine Mental disorders Messenger RNA Meta-Analysis Methods Molecular Open Reading Frames Osteoporosis Pathway interactions Physiological Physiological Processes Physiology Plants Play Protein Isoforms Proteins Public Domains Publishing Quality Control RNA Regulation Research Research Personnel Ribosomes Role Signal Transduction Site Small RNA Source Techniques Therapeutic Translating Translation Initiation Translations Untranslated RNA Update Validation Variant Visualization biological adaptation to stress college computational platform computer framework computerized tools data portal design experimental study fungus genome-wide human disease inhibitor insight lactimidomycin malignant breast neoplasm malignant neurologic neoplasms nervous system disorder novel polypeptide public health relevance ribosome profiling transcriptome user-friendly

项目摘要

PROJECT SUMMARY/ABSTRACT Recent studies based on ribosome profiling (ribo-seq) technique have revealed an unanticipated, complex translational landscape in metazoans, with extensive translation beyond the conventional annotated translation events. Some of these novel open reading frame (ORF)-encoded polypeptides produced by unannotated translation events have been shown to play important developmental or physiological roles. However, the functions of most unannotated ORFs remain unknown, and the critical first step toward decoding their functions is to systematically catalogue those that undergo active translation. Ribo-seq data arguably provide the best source of information for this task, given the genome-wide coverage and sensitivity of the ribo-seq technique. There are variations of ribo-seq technique that are based on the use of different translational inhibitors. Regular ribo-seq (rRibo-seq) utilizes cycloheximide (CHX), a translation elongation inhibitor, to freeze all translating ribosomes. In contrast to CHX, the use of translation inhibitor harringtonine or lactimidomycin, which has a much stronger effect for capturing the initiating ribosomes, enables global mapping of translation initiating sites (TISs) by sequencing (TI-seq). Despite the broad applicability and wide adoption of rRibo-seq and TI-seq, a comprehensive and integrated computational platform that enables de novo prediction of novel ORFs from different types of ribo-seq data, and allows for interactive exploration, visualization and meta- analysis of in-house and publically available ribo-seq datasets to study unannotated ORFs is lacking. To fill this gap, we propose to develop an integrated computational platform to facilitate the study of unannotated ORFs in eukaryotes using different types of ribo-seq data. This computational platform will have three core components: first, a new computational toolkit that provides a comprehensive informatic solution to both low-level and high- level analysis of data from different types of ribo-seq experiments; second, a computational framework that enables user-friendly interactive exploration and visualization of quality control and analysis results as well as ribo-seq signal tracks of individual datasets; third, a web data portal that dynamically updates and analyzes the published ribo-seq datasets from metazoa, plants and fungi, and allows a general user to perform meta- analysis of unannotated ORFs across different datasets, species and biological contexts. Furthermore, as a biological application, we will combine this computational platform with both large- and small-scale experimental approaches to uncover novel ORFs whose expression is regulated by estrogen and that are important for estrogen-dependent cell proliferation or survival, and to dissect the molecular mechanisms underlying their biological function. The study proposed here builds upon strong preliminary data. Given our expertise in computational and experimental biology, and the highly complementary expertise and support provided by our collaborators from UT MD Anderson Cancer Center, Baylor College of Medicine and UT Southwestern, we are ideally situated to tackle this project.

项目摘要/摘要基于核糖体分析（Ribo-Seq）技术的最新研究表明，意外，复杂后生动物的翻译景观，超出传统注释翻译的广泛翻译事件。这些新型的开放阅读框（ORF）编码的多肽由未注释的多肽翻译事件已显示起着重要的发育或生理作用。但是，大多数未注释的ORF的功能仍然未知，而解码其功能的关键第一步是系统地分类那些经过主动翻译的人。 Ribo-Seq数据可以说是最好的鉴于Ribo-Seq技术的全基因组覆盖范围和灵敏度，该任务的信息来源。基于不同翻译抑制剂的使用，核糖序列技术的变化有所不同。常规的Ribo-Seq（rribo-Seq）使用翻译伸长抑制剂的环己酰亚胺（CHX）来冻结所有翻译核糖体。与CHX相比，使用翻译抑制剂Harringtonine或乳霉素的使用，该抑制剂在捕获启动核糖体方面具有更强的效果，可以启用翻译的全局映射通过测序（Ti-Seq）启动位点（TISP）。尽管Rribo-seq广泛适用和广泛采用和Ti-Seq，一个全面而综合的计算平台，可以从头开始预测新颖的预测来自不同类型的Ribo-Seq数据的ORF，并允许进行交互式探索，可视化和元缺乏对内部和公开可用的Ribo-Seq数据集进行研究，以研究未注释的ORF。填写这个差距，我们建议开发一个集成的计算平台，以促进对未注释的ORF的研究真核生物使用不同类型的Ribo-Seq数据。该计算平台将具有三个核心组件：首先，一种新的计算工具包，为低级和高级提供了全面的信息解决方案来自不同类型的Ribo-Seq实验的数据的水平分析；第二，一个计算框架启用用户友好的交互式探索和质量控制和分析结果的可视化以及单个数据集的ribo-seq信号跟踪；第三，一个动态更新和分析的网络数据门户来自Metazoa，植物和真菌的Ribo-Seq数据集，允许一般用户执行元用户在不同数据集，物种和生物环境中对未注释的ORF分析。此外，作为生物应用，我们将把这个计算平台与大小规模相结合发现的实验方法，发现其表达受雌激素调节的新型ORF，而这是对于雌激素依赖性细胞增殖或生存重要，并剖析分子机制其生物学功能的基础。这里提出的研究基于强大的初步数据。鉴于我们计算和实验生物学方面的专业知识，以及高度互补的专业知识和支持由UT MD安德森癌症中心，贝勒医学院和UT的合作者提供西南航空，我们本地可以解决这个项目。