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Systematic Dissection of Sequence Elements Controlling σ70 Promoters Using a Genomically-Encoded Multiplexed Reporter Assay in E. coli

机译:在大肠杆菌中使用基因组编码的多重报告基因分析系统分析σ70启动子的序列元素

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摘要

Promoters are the key drivers of gene expression and are largely responsible for the regulation of cellular responses to time and environment. In E. coli, decades of studies have revealed most, if not all, of the sequence elements necessary to encode promoter function. Despite our knowledge of these motifs, it is still not possible to predict the strength and regulation of a promoter from primary sequence alone. Here we develop a novel multiplexed assay to study promoter function in E. coli by building a site-specific genomic recombination-mediated cassette exchange (RMCE) system that allows for the facile construction and testing of large libraries of genetic designs integrated into precise genomic locations. We build and test a library of 10,898 σ70 promoter variants consisting of all combinations of a set of eight −35 elements, eight −10 elements, three UP elements, eight spacers, and eight backgrounds. We find that the −35 and −10 sequence elements can explain approximately 74% of the variance in promoter strength within our dataset using a simple log-linear statistical model. Simple neural network models explain greater than 95% of the variance in our dataset by capturing nonlinear interactions with the spacer, background, and UP elements.
机译:启动子是基因表达的关键驱动力,主要负责调节细胞对时间和环境的反应。在大肠杆菌中,数十年的研究揭示了编码启动子功能所必需的大多数(即使不是全部)序列元素。尽管我们对这些基序有所了解,但仍然不可能仅凭一级序列来预测启动子的强度和调控。在这里,我们通过构建位点特异性基因重组介导的盒式交换(RMCE)系统,开发了一种新型的多重分析方法来研究大肠杆菌中的启动子功能,该系统可方便地构建和测试整合到精确基因组位置的大型遗传设计文库。我们建立并测试了一个包含10,898个σ70启动子变体的库,该变体由一组八个-35元素,八个-10个元素,三个UP元素,八个间隔子和八个背景的所有组合组成。我们发现,使用简单的对数线性统计模型,−35和-10序列元素可以解释我们数据集中启动子强度变异的大约74%。简单的神经网络模型通过捕获与间隔物,背景和UP元素的非线性相互作用,解释了我们数据集中超过95%的方差。

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