Metabolic engineering seeks to rewire the metabolic network of cells for the efficient production of value-added compounds from renewable substrates. However, it remains challenging to evaluate and identify strains with the desired phenotype from the vast rational or random mutagenesis library. One effective approach to resolve this bottleneck is to design an efficient high-throughput screening (HTS) method to rapidly detect and analyze target candidates. L-cysteine is an important sulfur-containing amino acid and has been widely used in agriculture, pharmaceuticals, cosmetics, and food additive industries. However, HTS methods that enable monitoring of L-cysteine levels and screening of the enzyme variants and strains to confer superior L-cysteine biosynthesis remain unavailable, greatly limiting the development of efficient microbial cell factories for L-cysteine production at the industrial scale. Here, we took advantage of the L-cysteine-responsive transcriptional regulator CcdR to develop a genetically encoded biosensor for engineering and screening the L-cysteine overproducer. The in vivo L-cysteine-responsive assays and in vitro electrophoretic mobility shift assay (EMSA) and DNase I footprint analysis indicated that CcdR is a transcriptional activator that specifically interacts with L-cysteine and binds to its regulatory region to induce the expression of target genes. To improve the response performance of the L-cysteine biosensor, multilevel optimization strategies were performed, including regulator engineering by semi-rational design and systematic optimization of the genetic elements by modulating the promoter and RBS combination. As a result, the dynamic range and sensitivity of the biosensor were significantly improved. Using this the excellent L-cysteine biosensor, a HTS platform was established by coupling with fluorescence-activated cell sorting (FACS) and was successfully applied to achieve direct evolution of the key enzyme in the L-cysteine biosynthetic pathway to increase its catalytic performance and to screen the high L-cysteine-producing strains from the random mutagenesis library. These results presented a paradigm of design and optimization of biosensors to dynamically detect metabolite concentrations and provided a promising tool enabling HTS and metabolic regulation to construct L-cysteine hyperproducing strains to satisfy industrial demand.

代谢工程旨在重新连接细胞的代谢网络，以便从可再生基质中高效生产增值化合物。然而，从庞大的合理或随机诱变库中评估和鉴定具有所需表型的菌株仍然具有挑战性。解决这一瓶颈的一种有效方法是设计一种高效的高通量筛选 (HTS) 方法来快速检测和分析目标候选者。L-半胱氨酸是一种重要的含硫氨基酸，已广泛应用于农业、医药、化妆品、食品添加剂等行业。然而，能够监测 L-半胱氨酸水平和筛选酶变体和菌株以赋予优越的 L-半胱氨酸生物合成的 HTS 方法仍然不可用，极大地限制了工业规模生产 L-半胱氨酸的高效微生物细胞工厂的发展。在这里，我们利用 L-半胱氨酸反应性转录调节因子 CcdR 开发了一种基因编码的生物传感器，用于工程和筛选 L-半胱氨酸过度生产者。这体内L-半胱氨酸反应试验和体外电泳迁移率变动分析 (EMSA) 和 DNase I 足迹分析表明，CcdR 是一种转录激活剂，可与 L-半胱氨酸特异性相互作用并与其调控区域结合以诱导靶基因的表达。为了提高L-半胱氨酸生物传感器的响应性能，进行了多级优化策略，包括通过半理性设计的调节工程和通过调节启动子和RBS组合的遗传元件的系统优化。结果，生物传感器的动态范围和灵敏度显着提高。使用这个优秀的L-半胱氨酸生物传感器，结合荧光激活细胞分选（FACS）建立了HTS平台，并成功应用于实现L-半胱氨酸生物合成途径中关键酶的直接进化，提高其催化性能，筛选高产L-半胱氨酸来自随机诱变文库的菌株。这些结果提出了设计和优化生物传感器以动态检测代谢物浓度的范例，并提供了一种有前途的工具，使 HTS 和代谢调节能够构建 L-半胱氨酸高产菌株以满足工业需求。