Design, optimization and application of whole-cell microbial biosensors with engineered genetic circuits

doi:10.12211/2096-8280.2021-021

Abstract

Abstract:

Whole-cell microbial biosensors with engineered genetic circuits constructed based on the concept of synthetic biology is an important branch of the biosensor. Whole-cell microbial biosensor is mainly composed of the sensing module, the computing module and the output actuating module. It can sense the concentration of specific substances in the environment and then transfer it to specific signal outputs in time according to certain rules, which shows great potential in bioengineering process control, environmental monitoring, food safety, environmental quality monitoring and disease diagnosis and control, etc. With the improvement of various technologies in synthetic biology and the enrichment of genetic elements, more and more whole-cell microbial sensors based on different response mechanisms, different logic gates and logic circuits have been developed. However, the design and construction of genetically engineered whole-cell microbial biosensor still mainly rely on the empirical method of trial-and error-learning. Therefore, how to design and construct high performance genetically engineered microbial whole-cell biosensors and how to tune its response curves by optimizing genetic elements or circuits to meet the detection requirements of different practical application scenarios is the new and important challenge. Here we reviewed the principle, classification and development process of genetically engineered whole-cell biosensor. We also focused on the design and construction of genetic circuit based on transcription factors and riboswitches, discussed optimization strategies for improving biosensor detection performance including dynamic range, specificity and working range, and then summarized its application progress in different detection fields. The optimization strategies are mainly involved in changing the expression level of genetic elements, adjusting the binding affinity between metabolites and genetic elements, restructuring the position of functional domains, etc. Finally, some challenges, such as biological safety, cumbersome design and construction, and inconvenience to enter the sensor market were discussed. It is expected that emerging technologies such as artificial intelligence, synthetic biology, and droplet microfluidics, will accelerate the development of genetic regulatory elements and the design and construction of novel biosensors.

Key words: whole-cell microbial biosensor, transcription factor, riboswitch, genetic circuit, detection

摘要：

基于合成生物学理念构建的基因回路型全细胞微生物传感器作为生物传感器的一大重要分支，能够感知环境中特定的待测物质，并按照一定规律将其转换成特定的信号输出，在生物制造过程监控、环境监测与食品安全、医疗诊断与监护等领域的检测应用中显现出巨大的潜力。随着合成生物学各项技术的日益完善和遗传元件的逐渐丰富，越来越多的基于不同响应机制、不同逻辑门与逻辑回路的全细胞微生物传感器已被陆续开发。然而，目前基因回路型全细胞微生物传感器的设计与构建仍主要依靠假设-试错循环的经验性方法。如何设计与构建具有高响应特性的基因回路型全细胞传感器，以及如何对元件、基因回路的优化提高其传感检测性能以满足不同实际应用场景的检测需求，是目前亟需解决的瓶颈问题。本文将主要对基因回路型全细胞生物传感器的原理、分类以及发展历程进行综述，着重介绍全细胞微生物传感器基因回路的设计与构建原则、传感检测性能的优化策略以及在不同检测领域的应用进展，最后剖析了目前全细胞微生物传感器面临的生物安全性、设计构建烦琐、缺乏高效便捷性、难以进入传感器市场等诸多挑战，以及对人工智能、合成生物学、液滴微流控等新兴技术将加速遗传传感元件的开发和生物传感器的人工设计与构建进行了展望。

关键词: 全细胞生物传感器, 转录因子, 核糖开关, 基因回路, 传感检测

CLC Number:

Q812

Lu YANG, Nan WU, Rongrong BAI, Weiliang DONG, Jie ZHOU, Min JIANG. Design, optimization and application of whole-cell microbial biosensors with engineered genetic circuits[J]. Synthetic Biology Journal, 2022, 3(6): 1061-1080.

杨璐, 吴楠, 白茸茸, 董维亮, 周杰, 姜岷. 基因回路型全细胞微生物传感器的设计、优化与应用[J]. 合成生物学, 2022, 3(6): 1061-1080.

Figures/Tables 10

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评价指标	描述
特异性	在复杂环境中专一性识别待测底物的能力
灵敏度	对待测物浓度变化的敏感程度
动态范围	最大输出信号和最小输出信号的比例
操作范围	输入与输出呈单一正相关或负相关的输入信号浓度范围
稳定性	样品检测过程中保持性能恒定的能力
响应时间	信号输入到信号输出的总时间
安全性	是否存在基因泄露转移、吸收外源DNA或潜在有害突变等风险

评价指标	描述
特异性	在复杂环境中专一性识别待测底物的能力
灵敏度	对待测物浓度变化的敏感程度
动态范围	最大输出信号和最小输出信号的比例
操作范围	输入与输出呈单一正相关或负相关的输入信号浓度范围
稳定性	样品检测过程中保持性能恒定的能力
响应时间	信号输入到信号输出的总时间
安全性	是否存在基因泄露转移、吸收外源DNA或潜在有害突变等风险

领域	检测物	传感元件	操作范围	应用	参考文献
生物制造过程监控	木糖	XylR	0～20 g/L	木糖转运蛋白筛选	[18]
	甘氨酸	甘氨酸核糖开关	20～200 μmol/L	动态调控5-氨基乙酰丙酸合成	[25]
	丙酮酸	PdhR	10～35 nmol/g	动态调控葡萄糖二酸合成	[17]
	衣康酸	YpLysR	16～160 μmol/L	衣康酸高产菌株筛选	[36]
环境监测食品安全	4-甲基苯二酚	ChpR	25～500 nmol/L	农药毒死蜱检测	[81]
	As³⁺ /As⁵⁺	ArsR	0.74～60 μg/L	水样重金属离子检测	[82]
	四环素土霉素	TetR	＞20 μg/kg ＞50 μg/kg	鱼肉四环素、土霉素含量检测	[83]
医疗诊断与监护	硫代硫酸盐	ThsR	0.1～1 mmol/L	小鼠结肠炎诊断	[84]
	茶碱	茶碱核糖开关	0～5 mmol/L	支气管扩张剂药物摄取监测	[85]
	尿酸	HucR	0～5 mmol/L	血液中尿酸稳态维持	[86]
	葡萄糖	GBP	1～10 mmol/L	糖尿病患者血糖监测	[87]

领域	检测物	传感元件	操作范围	应用	参考文献
生物制造过程监控	木糖	XylR	0～20 g/L	木糖转运蛋白筛选	[18]
	甘氨酸	甘氨酸核糖开关	20～200 μmol/L	动态调控5-氨基乙酰丙酸合成	[25]
	丙酮酸	PdhR	10～35 nmol/g	动态调控葡萄糖二酸合成	[17]
	衣康酸	YpLysR	16～160 μmol/L	衣康酸高产菌株筛选	[36]
环境监测食品安全	4-甲基苯二酚	ChpR	25～500 nmol/L	农药毒死蜱检测	[81]
	As³⁺ /As⁵⁺	ArsR	0.74～60 μg/L	水样重金属离子检测	[82]
	四环素土霉素	TetR	＞20 μg/kg ＞50 μg/kg	鱼肉四环素、土霉素含量检测	[83]
医疗诊断与监护	硫代硫酸盐	ThsR	0.1～1 mmol/L	小鼠结肠炎诊断	[84]
	茶碱	茶碱核糖开关	0～5 mmol/L	支气管扩张剂药物摄取监测	[85]
	尿酸	HucR	0～5 mmol/L	血液中尿酸稳态维持	[86]
	葡萄糖	GBP	1～10 mmol/L	糖尿病患者血糖监测	[87]

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