Modeling gene expression in yeast using large degenerate libraries

项目总结 DNA和RNA中的短序列元件决定了mRNAs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的序列将如何影响转录、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知识的空白。 流程。此外，随着大量人类基因组的测序，预测影响的能力 蛋白质终极水平上的序列变异对于解释蛋白质的变异是不可或缺的 调控序列。同样，构建具有特定表达和水平的代谢途径 设计合成基因网络需要准确了解调控序列是如何影响的 表情。这个应用程序试图使用酵母酿酒酵母作为测试用例，以了解如何 任何短的调控序列都会影响蛋白质水平。预测模型将在两个库的集合上进行训练 比迄今所描述的更复杂的数量级。库将由 具有100万个随机序列的50个核苷酸的生长报告基因，这些随机序列包含一个DNA元件 它调节转录，或调节剪接或翻译的RNA元件。图书馆将是 转化为酵母，酵母将被置于选择之下，以便它们根据能力生长 每个随机序列对蛋白质表达有贡献。卷积神经网络方法将是 用来学习这些“适合度”表型和它们相关的基因类型之间的关系。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， 这些发现构成了我们对更复杂的真核生物的大部分知识的基础。此外， 酵母中组成调节蛋白的DNA和RNA结合位点的短序列往往是 在大小上可与其他生物相媲美的。酵母常用于合成生物学和新陈代谢 工程学，这里提出的工作将导致新的工具来定量控制其基因 表情。5‘非翻译区文库的初步结果表明，我们可以构建一个 模型来解释观察到的表达可变性的很大一部分，并且该模型扩展到 原生序列元素。该模型允许我们转发工程师5‘URR以增加活动。 此应用程序的特定目标是评估针对上游的随机序列的影响 调控元件、核心启动子元件、5‘非翻译区、内含子和3’非翻译区；学习预测性和 使用卷积神经网络和识别新的功能顺式调节的可解释模型 元素；并在本地序列和组合库上验证我们的模型，并通过工程验证我们的模型 具有用户指定属性的合成序列元素。总而言之，这项建议旨在构建一个 调控序列-功能关系的全面和预测模型，为研究得很好的单个- 真核细胞，为其他生物的类似研究提供了基础。

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.