Evolved Bacterial Biosensor for Arsenite Detection in Environmental Water

• Published in: Environmental science & technology Vol. 49; no. 10; pp. 6149 - 6155
• Main Authors: Li, Luzhi, Liang, Junting, Hong, Wei, Zhao, Yun, Sun, Shuang, Yang, Xiao, Xu, An, Hang, Haiying, Wu, Lijun, Chen, Shaopeng
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 19.05.2015
• Subjects: Arsenites - analysis; Arsenites - metabolism; Bacteria; Bacteria - chemistry; Bacteria - metabolism; Biosensing Techniques - methods; Biosensors; Chemical compounds; Fluorescence; Mutation; Water; Water - chemistry; Water Pollutants, Chemical - analysis
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/acs.est.5b00832

Arsenic, a ubiquitous presence in the biosphere, often occurs from both natural and anthropogenic sources. Bacterial biosensors based on genetically engineered bacteria have promising applications in detecting the chemical compound and its toxicity. However, most of the bacteria biosensors take advantage of the existing wild-type substrate-induced promoters, which are often low in specificity, affinity and sensitivity, and thus limiting their applications in commercial or field use. In this study, we developed an in vivo evolution procedure with a bidirectional selection scheme for improving the sensitivity of an arsenite-responsive bacterial biosensor through optimization of the inducible operon. As a proof of concept, we evolved the arsenite-induced arsR operon for both low background and high expression through three successive rounds of fluorescence activated cell sorting (FACS) with bidirectional strategy. An arsR operon variant with 12-fold higher activity over the control was isolated, confirming multiple rounds of construction and screening of mutation library, as described here, can be efficiently applied to bacterial biosensor optimization. The evolved arsenite-responsive biosensor demonstrated an excellent performance in the detection of low trace arsenite in environmental water. These results indicate that the technologies of directed evolution could be used to improve the performance of bacterial biosensors, which will be helpful in promoting the practical application of bacterial biosensors.