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Modeling gene expression in yeast using large degenerate libraries

使用大型简并文库模拟酵母中的基因表达

基本信息

批准号：
10172925
负责人：
STANLEY FIELDS
金额：
$ 35.09万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10172925
关键词：
3&apos Untranslated Regions 5&apos Untranslated Regions Affect Alternative Splicing Binding Sites Biological Biological Assay Biology Cells Chemicals Complex Computer Models DNA DNA Binding Data Data Set Disease Elements Engineering Ensure Eukaryota Gene Expression Gene Expression Process Gene Library Genes Genetic Transcription Genetic Variation Genome Genome engineering Genotype Growth Human Human Genetics Human Genome Individual Introns Investigation Knowledge Lead Learning Libraries Messenger RNA Metabolic Pathway Modeling Mutation Nucleic Acid Regulatory Sequences Nucleotides Organism Pharmaceutical Preparations Phenotype Process Property Proteins RNA RNA Binding RNA Splicing RNA-Binding Proteins Regulation Regulatory Element Reporter Genes Research Saccharomyces cerevisiae Source Specific qualifier value Sum Synthetic Genes Testing Training Translating Translations Untranslated RNA Untranslated Regions Validation Variant Work Yeasts base combinatorial convolutional neural network deep learning design fitness genetic regulatory protein human disease improved member metabolic engineering next generation sequencing novel novel sequencing technology predictive modeling promoter protein expression scale up synthetic biology tool

项目摘要

PROJECT SUMMARY Short sequence elements in DNA and RNA determine the levels and composition of mRNAs and proteins, making it critical that we can accurately model how any given sequence will affect transcription, splicing or translation. Such models of cis-regulation will fill in gaps in our knowledge of these core gene expression processes. Additionally, as large numbers of human genomes are sequenced, the ability to predict the effects of sequence variation on the ultimate levels of proteins will be integral to the interpretation of variation in regulatory sequences. Similarly, the construction of metabolic pathways with defined levels of expression and the engineering of synthetic gene networks require accurate knowledge of how regulatory sequences affect expression. This application seeks to use the yeast Saccharomyces cerevisiae as a test case for learning how any short regulatory sequence affects protein levels. A predictive model will be trained on a set of libraries two orders of magnitude more complex than have been characterized to date. Libraries will be generated of a growth reporter gene with a million random sequences of 50 nucleotides that comprise either a DNA element that regulates transcription or an RNA element that regulates splicing or translation. The libraries will be transformed into yeast, and the yeast will be placed under selection such that they grow according to the ability of each random sequence to contribute to protein expression. A convolution neural network approach will be used to learn the relationship between these “fitness” phenotypes and their associated genotypes. Although yeast is a single-celled eukaryote, it has been the source of most of the original findings on gene expression, and these findings form the basis for much of our knowledge of more complex eukaryotes. Furthermore, the short sequences in yeast that comprise the DNA- and RNA-binding sites of regulatory proteins tend to be comparable in size to those of other organisms. Yeast is used often in synthetic biology and metabolic engineering, and the work proposed here will result in novel tools for quantitatively controlling its gene expression. Initial results with a library of 5' untranslated regions (UTRs) indicate that we can construct a model to account for a large fraction of the observed variability in expression, and that the model extends to native sequence elements. The model allowed us to forward engineer 5' UTRs to have increased activity. Specific aims of this application are to assess the effects of random sequences targeted to upstream regulatory elements, core promoter elements, 5' UTRs, introns and 3' UTRs; to learn predictive and interpretable models using convolutional neural networks and to identify novel functional cis-regulatory elements; and to validate our models on native sequences and combinatorial libraries, and by engineering synthetic sequence elements with user-specified properties. In sum, the proposal seeks to construct a comprehensive and predictive model of regulatory sequence–function relationships for a well-studied single- celled eukaryote, providing a basis for similar studies on other organisms.

项目摘要 DNA和RNA中的短序列元件决定mRNA和蛋白质的水平和组成，这使得我们能够准确地模拟任何给定序列如何影响转录、剪接或翻译.这种顺式调控模型将填补我们对这些核心基因表达的知识空白流程.此外，随着大量人类基因组的测序，在蛋白质的最终水平上的序列变异的解释将是不可分割的，调节序列类似地，构建具有确定表达水平的代谢途径，合成基因网络的工程设计需要精确了解调控序列如何影响表情这个应用程序试图使用酵母酿酒酵母作为一个测试案例，学习如何任何短的调节序列都会影响蛋白质水平。预测模型将在一组库上训练，数量级比迄今为止所描述的更为复杂。库将由一个具有50个核苷酸的一百万个随机序列的生长报告基因，所述随机序列包含DNA元件调节转录的RNA元件或调节剪接或翻译的RNA元件。图书馆将转化成酵母，酵母将被置于选择之下，使它们根据能力生长。每个随机序列的组合有助于蛋白质表达。卷积神经网络方法将是用于了解这些“适应性”表型与其相关基因型之间的关系。虽然酵母是一种单细胞真核生物，它是大多数关于基因表达的原始发现的来源，这些发现为我们了解更复杂的真核生物奠定了基础。而且酵母中包含调节蛋白的DNA和RNA结合位点的短序列往往是在大小上与其他生物体相当。酵母通常用于合成生物学和代谢生物学。工程，这里提出的工作将导致定量控制其基因的新工具表情利用5'非翻译区（UTR）文库的初步结果表明，我们可以构建一个该模型解释了观察到的表达变化的很大一部分，并且该模型扩展到天然序列元件。该模型允许我们向前工程化5'UTR以具有增加的活性。本申请的具体目的是评估靶向上游的随机序列的影响，调控元件、核心启动子元件、5'UTR、内含子和3' UTR;以学习预测和使用卷积神经网络的可解释模型，并确定新的功能性顺式调节元素;并验证我们的模型对天然序列和组合库，并通过工程具有用户指定属性的合成序列元素。总括而言，这项建议旨在建立一个全面和预测模型的调控序列功能关系，为一个良好的研究单一的，细胞真核生物，为其他生物的类似研究提供了基础。