Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, National Engineering Laboratory for Industrial Enzymes, Tianjin 300457, China.
Haihe Laboratory of Synthetic Biology, Tianjin 300308, China.
Biosensors (Basel). 2024 Sep 25;14(10):455. doi: 10.3390/bios14100455.
The biosensors based on transcription factors (TFs) are widely used in high throughput screening of metabolic overproducers. The unsatisfactory performances (narrow detection and dynamic ranges) of biosensors limit their practical application and need more improvement. In this study, using the TF LysG (sensing lysine) as an example, a biosensor optimization method was constructed by growth-coupled screening of TF random mutant libraries. The better the performance of the biosensor, the faster the strain grows under screening pressure. A LysG-based biosensors were obtained, which were about 2-fold of the control in the detection and dynamic ranges. A lysine high-producer was screened effectively using the optimized biosensor with the production at 1.51 ± 0.30 g/L in flasks (2.22-fold of the original strain). This study provided a promising strategy for optimizing TF-based biosensors and was of high potential to be applied in the lysine high-producers screening process.
基于转录因子 (TFs) 的生物传感器广泛应用于代谢过度产生物的高通量筛选。生物传感器的性能不理想(检测范围和动态范围较窄)限制了它们的实际应用,需要进一步改进。在这项研究中,以 TF LysG(感应赖氨酸)为例,通过 TF 随机突变文库的生长偶联筛选构建了生物传感器优化方法。生物传感器的性能越好,在筛选压力下菌株的生长速度就越快。获得了基于 LysG 的生物传感器,其检测和动态范围比对照约提高了 2 倍。使用优化后的生物传感器有效筛选到赖氨酸高产菌株,摇瓶产量为 1.51±0.30 g/L(比原始菌株提高 2.22 倍)。本研究为基于 TF 的生物传感器的优化提供了一种很有前途的策略,在赖氨酸高产菌株的筛选过程中具有很高的应用潜力。