MFB: Cracking the codes: understanding the rules of mRNA localization and translation

严格控制基因表达是正常细胞功能的关键。全面了解蛋白质生产的组成部分将对生物技术和生物医学研究产生革命性的影响。然而，我们基于一组条件预测蛋白质输出的能力很差，因为考虑遗传密码的影响、其转化为蛋白质输出的影响以及细胞位置对蛋白质产生的影响的模型是无效的。该提案将应用RNA测序技术的最新发展，以产生捕获RNA生命周期新方面的改进数据，然后建立和测试新的机器学习模型，以确定它们是否预测蛋白质输出。该项目还将为RNA生物学的本科生、研究生和博士后提供跨学科培训，并向当地高中生推广现代RNA测序技术。预测信使RNA （mRNA）的蛋白质输出是RNA领域的主要挑战。RNA测序的最新发展提供了组织和单细胞转录组的高分辨率视图，并深入了解哪些mRNA被积极翻译，从而导致tRNA丰度和mRNA亚细胞位置影响给定mRNA的蛋白质合成，并且可能是组织甚至细胞类型特异性的。然而，由于缺乏审讯方法，这两个参数背后的机制仍然不清楚。该项目将核糖体分析与新型tRNA测序技术相结合，以促进我们对控制蛋白质生产的这些关键变量的理解。此外，该项目寻求开发一种“RNA通行证”方法，利用定位RNA的顺序修饰和长读测序来更好地定义定位对翻译的影响。总的假设是，对mRNA含量、亚细胞位置和tRNA丰度之间关系的精确理解将更好地预测蛋白质的产生。机器学习方法将识别生物体水平的规则和组织特异性的翻译优化模式，使用公共蛋白质组学数据集验证对蛋白质合成的预测影响，并在多种生物环境中测试合成基因构建的规则。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Tight control of gene expression is key to normal cell function. A complete understanding of the components of protein production would be transformative for biotechnology and biomedical research. However, our ability to predict protein output based on a set of conditions is poor, due to ineffective models that account for the impact of the genetic code, its conversion to protein output, and the influence of cellular location on protein production. This proposal will apply recent developments in RNA sequencing technology to produce improved data that captures new aspects of the RNA lifecycle, and then build and test new machine learning models to determine whether they are predictive of protein output. The project will also provide interdisciplinary undergraduate, graduate, and postdoc training in RNA biology, and conduct outreach about modern RNA sequencing techniques to local high school students.Predicting the protein output from a messenger RNA (mRNA) is a major challenge for the RNA field. Recent developments in RNA sequencing provide high resolution views of tissue and single-cell transcriptomes and insights into which mRNAs are actively translated, leading to an emerging appreciation that tRNA abundances and mRNA subcellular location influence protein synthesis for a given mRNA, and may be tissue- and even cell type-specific. However, the mechanisms underlying these two parameters remain unclear due to the lack of methods for their interrogation. This project combines ribosome profiling with novel tRNA sequencing techniques to advance our understanding of these key variables that control protein production. In addition, the project seeks to develop an “RNA passport” method leveraging sequential modification of localized RNAs and long-read sequencing to better define the impact of localization on translation. The overall hypothesis is that a refined understanding of the relationship between mRNA content, its subcellular location, and tRNA abundance will better predict protein production. A machine learning approach will identify organism-level rules and tissue-specific patterns of translational optimization, validate the predicted impact on protein synthesis using public proteomic datasets, and test the rules on synthetic gene constructs in multiple biological contexts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.