Genetic circuit-enabled synthetic biosensors： design and applications Synthetic Biology Journal    DOI: 10.12211/2096-8280.2025-031

Fig. 9 Strategies for increasing the specificity of genetic circuit-enabled synthetic biosensors
(a) Semi-rational design and saturation mutagenesis of the transcription factor LysG to prevent its binding to L-lysine (L-Lys) while retaining its ability to bind L-arginine (L-Arg) and L-histidine (L-His)^[191]; (b) Semi-rational design and saturation mutagenesis of the TynA-FeaR sensing system for specific detection of phenylethylamine. (DA: dopamine; PEA: phenylethylamine; Tyra: tyramine; Trypta: tryptamine)^[192]; (c) Coupling transcription factors ZraR and ZntR through an AND gate for specific respond to Zn²⁺^[7,21].

Extracts from the Article

部分天然感知元件存在靶标非特异性，其非特异性识别机制可能导致检测信号失真。针对该局限性，研究者已建立多维度优化策略：在生物识别元件改造层面，定向进化与高通量筛选技术展现出显著优势。Corte等［191］通过半理性设计策略对LysG转录因子进行功能重编程，在消除其L-赖氨酸结合能力的同时保留对L-组氨酸和L-精氨酸的特异性识别，成功构建了高选择性L-组氨酸生物传感器（图9a）；Rottinghaus等［192］按照相似的思路对原本交叉响应多种配体的TyrR转录因子以及TynA-FeaR传感系统进行工程化改造，他们结合蛋白结构分析，理性选择关键结合残基附近的氨基酸位点进行突变，产生了分别特异性检测多巴胺（DA）、苯乙胺（PEA）、酪胺（Tyra）和色胺（Trypta）的突变体（图9b）；Nishikawa等［81］开发的Sensor-seq平台创新性地实现了转录因子结合域的系统性突变文库构建与配体特异性筛选流程集成，加速了转录因子的高通量特异性优化。在核糖开关领域，Bose等［193］通过定点突变c-di-GMP核糖开关配体结合口袋的两个关键碱基，引入新型氢键网络使配体结合特异性提升超过10倍。除上述方法外，基因线路工程为提升生物传感器特异性提供了系统生物学解决方案［26］，Wang等［21］创新性地将ZraR（响应Zn2?/Pb2?）与ZntR（响应Zn2?/Cd2?）两种广谱金属感应元件偶联构建AND逻辑门，虽然二者均存在交叉响应，但这种逻辑整合实现了传感器对Zn2?的特异性识别（图9c）。此外，基于HrpRS调控系统［194］、分裂型T7 RNA聚合酶［195］及重组酶级联［147］的正交逻辑门技术已形成模块化工具库，为复杂环境下的精准检测提供技术支持。

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• Table 1  Typical genetic circuit-enabled synthetic biosensors and applications
• Fig. 1  Mechanisms and applications of transcriptional regulatory protein-enabled synthetic biosensors(a-c) Mechanisms of allosteric transcription factors, transmembrane transcription factors, and two-component systems-enabled biosensors; (d) Response curves of biosensors based on activation-type and repression-type transcription factor; (e) Gene circuit of a thiosulfate-sensing sensor based on the two-component system ThsRS^[54]; (f) Gene circuit of a transcription factor-enabled biosensor for monitoring glucose uptake^[14]; (g) Gene circuit of a target-dependent RNA polymerase-enabled biosensor^[95].
• Fig. 2  Mechanisms of riboswitches-enabled synthetic biosensors(a) Riboswitch-enabled sensors based on transcriptional activation and transcriptional repression.(b) Riboswitch-enabled sensors based on translational activation and translational repression..
• Fig. 3  Mechanisms and applications of riboregulator-enabled synthetic biosensors(a) Mechanism of the toehold switch^[124]; (b) A ribozyme-based sensing platform RENDR^[125]; (c) Gene circuit of a sensor for reporting bacterial DNA transfer in soil using split ribozymes^[133]; (d) Mechanism of the group I intron-enabled RNA trans-splicing system SENTR and its application for intracellular mRNA detection ^[122].
• Fig. 4  Mechanisms of CRISPR-enabled synthetic biosensors(a) RNA sensing enabled by reprogrammed tracrRNA in type II CRISPR system^[17]; (b) RNA sensing enabled by RNA-triggered RNA processing^[142]; (c) RNA sensing enabled by strand displacement reactions^[143].
• Table 2  Advantages and limitations of genetic circuit-enabled synthetic biosensors based on different signal recognition mechanisms
• Fig. 5  Typical biomemory modules for recording biosensing signals(a) Gene circuit for a biomemory module based on toggle switch; (b) Gene circuit of a zinc ion memory module based on the recombinase flip mechanism^[153]; (c) Principle of the CAMERA recording system based on CRISPR/Cas^[158]; (d) Gene circuit of the DNA sensing and recording module (CATCH) based on CRISPR/Cas^[159].
• Fig. 6  Circuit designs and applications of multilayered biocomputing modules(a) Gene circuit of a dual-parameter biosensor based on an AND logic gate and its response heatmap^[104]; (b) Gene circuit of a metabolic control sensor integrating a multi-input genetic logic circuit ^[163];
• Fig. 7  Synthetic biosensing systems with multimodal outputs for integration with various detection platforms(a) A agarose hydrogel-encapsulated arsenic ion sensor for visual array display on a smartphone platform^[32]; (b) A sensor that visualizes biosensing signals through the formation of bacterial spatial motility patterns^[36]; (c) A sensing system coupling a fluorescent sensor with a photoelectric chip to convert optical signals into wireless signals^[48].
• Fig. 8  Strategies for increasing the sensitivity of genetic circuit-enabled synthetic biosensors(a) Typical response curves and characteristics of genetic circuit-enabled synthetic biosensors.; (b) Dynamic range optimization based on promoter engineering; (c) Limit of detection optimization by gradient regulation of biorecognition components concentration and target enrichment technology; (d) Dynamic range optimization using sensing signal amplifiers based on activator transcription factors, positive feedback loops and cascade repressors ^[7]; (e) Leakage optimization utilizing antisense RNA-mediated translation blocking, decoy DNA binding site competitive inhibition, and output protein degradation tags^[1].
• Fig. 10  Examples of improving the response speed of genetic circuit-enabled synthetic biosensors(a) An E. coli-based sensor with a synthetic electron transfer chain that express an electrical protein switch enable rapid detection of an endocrine disruptor 4-hydroxytamoxifen (4-HT)^[6]; (b) Reversible response kinetics curve and sensing mechanism of a protein phosphorylation signaling network with rapid activation and deactivation^[197].
• Fig. 11  Examples of stability and biosafety optimization for genetic circuit-enabled synthetic biosensors(a) A plasmid copy number control platform in Escherichia coli based on dynamic regulation of translation initiation factors^[204]; (b) A biocontaining CRISPR-Cas9 dual-modal kill switch (CRISPRks) for E. coli under the dual control of chemical inducer anhydrotetracycline (aTc) and temperature changes^[212].