Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China; Key Laboratory of Systems Microbial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
College of Life Science, Hebei Normal University, Shijiazhuang, 050024, China.
Metab Eng. 2022 Sep;73:144-157. doi: 10.1016/j.ymben.2022.07.007. Epub 2022 Jul 31.
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.
代谢工程旨在重新构建细胞的代谢网络，以从可再生基质高效生产增值化合物。然而，从庞大的合理或随机诱变文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，以快速检测和分析目标候选物。L-半胱氨酸是一种重要的含硫氨基酸，已广泛应用于农业、制药、化妆品和食品添加剂行业。然而，仍然缺乏能够监测 L-半胱氨酸水平以及筛选赋予优越 L-半胱氨酸生物合成能力的酶变体和菌株的 HTS 方法，这极大地限制了高效微生物细胞工厂在工业规模上生产 L-半胱氨酸的发展。在这里，我们利用 L-半胱氨酸响应型转录调节因子 CcdR 开发了一种用于工程和筛选 L-半胱氨酸高产菌的基因编码生物传感器。体内 L-半胱氨酸响应测定和体外电泳迁移率变动分析 (EMSA) 和 DNase I 足迹分析表明，CcdR 是一种转录激活因子，它特异性地与 L-半胱氨酸相互作用并结合到其调节区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对调节剂进行工程改造以及通过调节启动子和 RBS 组合对遗传元件进行系统优化。结果，生物传感器的动态范围和灵敏度得到了显著提高。利用这个出色的 L-半胱氨酸生物传感器，通过与荧光激活细胞分选 (FACS) 耦合建立了 HTS 平台，并成功应用于直接进化 L-半胱氨酸生物合成途径中的关键酶以提高其催化性能，并从随机诱变文库中筛选出高 L-半胱氨酸产生菌株。这些结果提出了一种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，可实现 HTS 和代谢调控，以构建 L-半胱氨酸高产菌株以满足工业需求。
