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

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.

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。

近年来，一类参与生物体内诸多重要信号通路调控的基因元件被发现，成为构建灵敏高效的代谢物生物传感器的重要材料。然而此类元件在配体响应和基因调控过程中快速变化的动力学状态和复杂的作用机制，使得人们很难深入清晰地解析其作用机理，极大地限制了此类元件在生物传感器构建中的应用。本项目以此类元件中的TnaC色氨酸生物传感器的工程化改造为目标，建立基于FACS-seq深度突变扫描技术高通量分析该传感器突变文库的方法，并根据该分析结果深入解析TnaC前导肽的天然响应机理，为该传感器的性能改造提供指导。.本项目首先利用构建的TnaC色氨酸传感器体系，建立了基于FACS-seq高通量分析的完整实验操作流程和数据分析框架，在该框架下对包含1450个成员的TnaC突变文库进行了分析。随后根据FACS-seq分析结果绘制出了TnaC传感器的“序列-性能”图谱，该图谱的深度分析结果，除了能够验证已报道的TnaC功能外，还发现了其诸多未知的响应过程的新结构和新特点，并结合文献调研与实验验证提出了这些新发现背后可能存在的动力学机制。在此基础上，根据TnaC响应过程的关键节点，建立了描述其整个动力学机制的随机过程模型，该模型能够准确预测与实验结果相一致的传感器响应性能，并对改造TnaC传感器的性能指标提供了指导方案，据此获得了检测范围有明显扩展的TnaC传感器突变体。最后，通过系统发育分析揭示了模型预测的tnaC基因功能模块化的现象在肠道细菌中的普遍存在性，并对TnaC前导肽调控大肠杆菌环境耐受性的生物学意义进行了探讨。.综上，本工作通过系统生物学的方法从分子机制层面全面获知了TnaC色氨酸传感器的新机制，揭示了该传感器的响应动力学过程，同时高效地构建了具有不同响应性能参数的上千种TnaC突变体的文库，为该传感器在微生物细胞工厂中的应用提供了理论基础。同时，本项目也拓展了FACS-seq深度突变扫描方法在以复杂动力学响应过程为特征的调控元件的功能解析及其工程化改造中的应用。

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