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中的短序列元素决定了mRNA和蛋白质的水平和组成，至关重要的是，我们可以准确地建模任何给定序列如何影响转录，剪接或翻译。这样的顺式调节模型将填补我们对这些核心基因表达的知识的空白过程。另外，随着大量人类基因组的测序，预测影响的能力蛋白质最终水平上的序列变化将是解释的不可或缺的一部分调节序列。同样，建造具有明确表达水平和的代谢途径合成基因网络的工程需要准确了解调节序列如何影响表达。该应用程序旨在将酿酒酵母的酵母糖疗法用作测试案例，以了解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中进行培训两个比迄今为止表征的数量级要复杂得多。库将生成成长记者基因，具有50个核动肽的一百万个随机序列，这些核苷酸构成了DNA元素调节转录或调节剪接或翻译的RNA元素。图书馆将是变成酵母，将酵母放在选择下，以使它们根据能力生长每个随机序列有助于蛋白质表达。卷积神经网络方法将是用于学习这些“健身”表型及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，它一直是基因表达的大多数原始发现的来源，这些发现构成了我们对更复杂的真核生物知识的基础。此外，酵母中的简短序列包括调节蛋白的DNA和RNA结合位点倾向于与其他生物的大小相当。酵母经常用于合成生物学和代谢工程以及此处提出的工作将导致新颖的工具用于定量控制其基因表达。使用5'非翻译区域（UTRS）的库的初始结果表明，我们可以构建一个解释表达式观察到的可变性的很大一部分的模型，并且该模型延伸至天然序列元素。该模型使我们能够将工程师5'UTR提高活性。该应用的具体目的是评估针对上游的随机序列的影响调节元素，核心启动子元素，5'UTR，内含子和3'UTR；学习预测和使用卷积神经网络的可解释模型，并识别新型功能顺序调节元素；并通过本机序列和组合库验证我们的模型，并通过工程具有用户指定属性的合成序列元素。总而言之，该提议旨在构建研究良好的单人的调节序列 - 功能关系的全面和预测模型细胞真核生物，为其他生物的类似研究提供了基础。