Determinants and consequences of translation elongation rate

平移伸长率的决定因素和后果

• 批准号: 10398813
• 负责人: Liana Faye Lareau
• 金额: $ 31.92万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398813
• 关键词: Address; Affect; Biological Assay; CRISPR interference; CRISPR screen; Cells; Codon Nucleotides; Data; Elongation Factor; Ensure; Gene Expression; Genes; Genetic Transcription; Genome; Goals; Human; Mammalian Cell; Measures; Messenger RNA; Methods; Modeling; Motion; Mus; Neighborhoods; Network-based; Neural Network Simulation; Outcome; Output; Production; Proteins; RNA; RNA Stability; Regulation; Reporter; Research; Ribosomes; Role; Speed; System; Testing; Time; Tissues; Training; Trans-Activators; Transcript; Translating; Translation Initiation; Translations; Variant; Work; Yeasts; base; cell type; density; design; expectation; experimental study; feedforward neural network; genome-wide; in vivo; mRNA Stability; neural network; preference; protein expression; ribosome profiling

Project summary We propose to use quantitative, high-throughput methods to understand the consequences of variation in translation elongation on gene expression. New methods including ribosome profiling provide a genome-wide view of the motion of ribosomes along transcripts. We recently developed a neural network based on ribosome profiling data that captures information about what makes a ribosome move faster or slower, and then used that same information to design synonymous sequences that are not just decoded at different rates but also actually make more or less protein. This raises two questions that we propose to address here: how does slow decoding result in diminished protein expression, and what consequences does this rate variation have in vivo? First, we will expand our preliminary results from yeast, adapting our neural network model to understand the impact of variation in translation elongation in mammalian systems. We will measure changes in translation elongation in different cell types and with differential activity of translation elongation factors. Second, we will investigate and model the interplay between translation initiation and translation elongation rate to understand how translation elongation can be rate-limiting for protein production. We will also identify trans-acting factors that modulate this effect, using a genome wide CRISPR screen. Third, we will develop a more complete neural network model relating gene sequence to ultimate translation output, incorporating not just local sequence context but also positional effects and other factors. This proposal presents new experimental systems that can quickly and sensitively measure the consequences of codon choice and identify factors affecting how different codons determine translation output.

项目总结 我们建议使用定量的、高通量的方法来理解变化的后果 翻译延伸对基因表达的影响。包括核糖体原fi在内的新方法提供了一种全基因组 核糖体沿转录本运动的视图。我们最近开发了一种基于核糖体的神经网络 ProfiLing数据，捕获有关核糖体移动更快或更慢的信息，然后使用 相同的信息来设计同义序列，这些序列不仅以不同的速率解码，而且 实际上或多或少会产生蛋白质。这提出了两个问题，我们建议在这里解决：如何放慢 解码会导致蛋白质表达减少，这种速率变化在体内会产生什么后果？ 首先，我们将从酵母中扩展我们的初步结果，使我们的神经网络模型能够理解 哺乳动物系统中翻译延伸变异的影响。我们将衡量以下变化 翻译伸长因子在不同细胞类型中具有不同的活性。 其次，我们将对翻译启动和翻译延伸之间的相互作用进行调查和建模 以了解翻译延伸如何限制蛋白质生产的速率。我们还将确定 调节这一效应的反式作用因子，使用全基因组CRISPR屏幕。第三，我们将制定一项 更完整的神经网络模型，将基因序列与最终翻译输出联系起来，包括NOT 只是局部序列背景，还有位置效应和其他因素。这项提案提出了新的 能够快速、灵敏地测量密码子选择的后果并识别 影响不同密码子如何决定翻译输出的因素。