Abstract:

Microbial cell factories are pivotal in green biomanufacturing, with applications spanning various sectors, including food production, chemical engineering, pharmaceuticals, and energy. However, traditional metabolic engineering strategies, which reply on static regulation and are hindered by the inherent latency in real-time metabolic flux monitoring, face significant limitations in constructing microbial systems that efficiently synthesize target products. These constraints severely hinder the high-yield biosynthesis of bio-based compounds. Transcription factor-based biosensors (TFBs), which are cornerstone tools in synthetic biology and metabolic engineering, offer innovative solutions by dynamically linking real-time perception of metabolite concentration signals or environmental cues with autonomous regulation of target gene expression. This integration allows for intelligent optimization and efficient construction of microbial production systems. This review systematically examines the molecular architecture, functional classification, and signal transduction mechanisms of TFBs, focusing on the rational design of ligand-recognition modules and the reconfiguration of signal-output components. Key strategies for constructing TFBs are summarized, including directed evolution and rational redesign of transcription factor ligand-binding domains (LBD), modular engineering of responsive promoters, and optimization of ribosome binding sites (RBS) for reporter genes. The review also highlights cutting-edge applications of TFBs in microbial cell factories, such as high-throughput screening platforms, identification of metabolic engineering targets, and dynamic regulation of metabolic pathways. Despite their transformative potential, several challenges remain, including the scarcity of metabolite-responsive elements, narrow ligand detection ranges, insufficient substrate recognition specificity, time-consuming transcription-dependent processes, and poor robustness of sensor components under industrial conditions. To address these bottlenecks, future research must prioritize the integration of synthetic biology with artificial intelligence (AI)-driven big data modeling. Such interdisciplinary efforts will accelerate the development of customizable, standardized plug-and-play modular components to overcome limitations like the shortage of responsive elements. Concurrently, the establishment of scalable validation platforms across “lab-scale, pilot-scale, and industrial production” stages is essential to validate system scalability, laying the foundation for next-generation TFBs capable of supporting large-scale industrial biomanufacturing. These advancements are set to enhance the efficiency and intelligence of microbial cell factories while expanding their applications in critical areas such as food safety testing, environmental monitoring, and medical diagnostics and therapeutics. By offering critical insights into the design and application of TFBs, this review aims to drive the evolution of microbial cell factories into multifunctional, smart bioproduction systems that integrate precision, adaptability, and industrial robustness, ultimately fostering sustainable innovation in the bioeconomy. {L-End}

Key words: transcription factor biosensors, microbial cell factory, high-throughput screening, target mining, dynamic regulation

摘要：

微生物细胞工厂作为绿色生物制造的重要实现形式，广泛应用于食品、化工、医药和能源等领域。然而，利用传统代谢工程策略改造微生物细胞工厂生产目标产品时，仍面临静态代谢调控的局限性与代谢通量实时监测的滞后性等问题，制约着生物基产品的高效生物合成。基于转录因子生物传感器通过实时感知代谢物浓度信号或环境信号，自动调控目的基因表达，为微生物细胞工厂的高效构建与智能化调控提供了创新性解决方案。本文介绍了基于转录因子生物传感器的组成、分类及作用机制，围绕传感器配体识别模块的设计和信号输出模块的元件重构，总结了基于转录因子生物传感器的构建策略，对基于转录因子生物传感器在微生物细胞工厂中的应用进展进行了综述，包括高通量筛选、代谢工程靶点挖掘以及动态调控。聚焦目前基于转录因子生物传感器面临的代谢物响应元件匮乏、检测范围受限、配体识别特异性不足、转录依赖的耗时性和传感器元件鲁棒性缺陷等挑战，对未来的研究方向进行展望，为未来基于转录因子生物传感器的构建与应用提供借鉴。

关键词: 转录因子生物传感器, 微生物细胞工厂, 高通量筛选, 靶点挖掘, 动态调控