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基于序列深度突变扫描高通量改造胞内色氨酸新生肽生物传感器响应特性

批准号21676156 学科分类生物化工与轻化工 ( B0807 )
项目负责人张翀 负责人职称副教授 依托单位清华大学
资助金额70.00
万元
项目类别面上项目 研究期限2017 年 01 月 01 日 至
2020 年 12 月 31 日
中文主题词生物传感器;代谢工程;深度测序;高通量筛选;色氨酸
英文主题词biosensor;metabolic engineering;deep sequencing;high throughput screening;tryptophan

摘要

中文摘要 色氨酸新生肽(TnaC)是一种基于色氨酸诱导抗转录终止机制的核糖体干扰肽。申请人前期构建了基于新生肽的色氨酸生物传感器,能在单细胞层次响应胞内色氨酸浓度。该传感器在代谢工程领域高产目标化合物菌株的高通量筛选和胞内代谢动态调控等方面具有广泛的用途。本项目将以此传感器为模式体系,研究从分子层面改造生物传感器响应特性的新方法,以期获得一系列适合于实际应用需求的、具有不同响应特性的胞内色氨酸传感器。项目将利用序列深度突变扫描方法,构建传感器双位点深度突变扫描序列库,利用荧光流式细胞分选在不同效应物浓度下的突变文库,进一步基于二代测序关联分析突变体基因型与表型,从而精细刻画传感器突变体的效应物浓度-荧光强度曲线,全面理解序列突变对生物传感器响应特性的影响规律。项目的实施将为代谢工程领域生物传感器工程化改造提供方法借鉴,同时从序列-功能关系角度深入理解影响色氨酸新生肽生物传感器响应特性的分子机制。
英文摘要 Tryptophan nascent peptide (TnaC) is a kind of ribosome arrest peptide that can initiate anti-transcriptional termination with the induction of tryptophan. Based on this mechanism, by fusing a gfp gene downstream of TnaC, we previously constructed a tryptophan biosensor, which can quantitatively response the concentration of intracellular tryptophan at the single cell level. This biosensor could be potentially applied in the field of metabolic engineering for the high-throughput screening of strains with high tryptophan production, or for the dynamic control of intracellular metabolites with tryptophan as intermediate. However, to meet the practical demand of metabolic engineering, a series of engineered tryptophan biosensors with different response properties are urgently needed. By using this newly constructed tryptophan biosensor as model system, this project will focus on developing novel method for the engineering of biosensors with proper responsive properties based on the method of deep mutational scanning. We will first construct a deep mutational pool with double sites mutation of TnaC, and then screen and separate the mutation pools under different tryptophan concentrations by using fluorescence activated cell sorting (FACS). With the information of next generation sequencing (NGS) of each sub-libraries, we will then have the ability to fine tune the responsive curves of each mutants in the library based on genotype-phenotype association (GPA), which could consequently deepen the understanding of the responsive characteristics of our tryptophan biosensor. This project could shed light on the methods for the engineering of biosensors applied for metabolic engineering, and also provide a new way to understand the molecular mechanisms that affecting the responsive properties of tryptophan biosensor based on nascent peptide.
结题摘要

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