Determinants and consequences of translation elongation rate

平移伸长率的决定因素和后果

• 批准号: 10617730
• 负责人: Liana Faye Lareau
• 金额: $ 31.87万
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617730
• 关键词: Address; Affect; Biological Assay; CRISPR interference; CRISPR screen; Cells; Codon Nucleotides; Data; Elongation Factor; Ensure; Gene Expression; Genes; Genome; Goals; Human; Mammalian Cell; Measures; Messenger RNA; Methods; Modeling; Motion; Mus; Neighborhoods; Neural Network Simulation; Outcome; Output; Production; Proteins; RNA; RNA Stability; Regulation; Reporter; Research; Ribosomes; Role; Speed; System; Testing; Tissues; Training; Trans-Activators; Transcript; Translating; Translation Initiation; Translations; Variant; Work; Yeasts; cell type; density; design; expectation; experimental study; feedforward neural network; genome-wide; in vivo; mRNA Stability; neural network; posttranscriptional; preference; protein expression; ribosome profiling; translational model

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.

项目摘要 我们建议使用定量，高通量的方法来了解变化的后果， 翻译延伸对基因表达的影响。包括核糖体分析在内的新方法提供了一个全基因组的 核糖体沿着转录物运动的观点。我们最近开发了一个基于核糖体的神经网络 分析数据，捕获关于是什么使核糖体运动得更快或更慢的信息，然后使用 同样的信息来设计同义序列，这些同义序列不仅以不同的速率解码， 实际上会产生或多或少的蛋白质。这就提出了两个问题，我们建议在这里解决： 解码导致蛋白质表达减少，这种速率变化在体内有什么后果？ 首先，我们将扩大我们的初步结果从酵母，适应我们的神经网络模型，以了解 哺乳动物系统中翻译延伸变异的影响。我们将衡量 在不同细胞类型中的翻译延伸和翻译延伸因子的差异活性。 其次，我们将研究和模拟翻译启动和翻译延伸之间的相互作用 速率来理解翻译延伸如何限制蛋白质生产的速率。我们还将确定 使用全基因组CRISPR筛选来研究调节这种效应的反式作用因子。第三，我们会发展一个 将基因序列与最终翻译输出相关联的更完整的神经网络模型， 不仅是局部序列背景，而且还有位置效应和其他因素。该提案提出了新的 实验系统可以快速、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。