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Isoleucyl-tRNA synthetase mutant based whole-cell biosensor for high-throughput selection of isoleucine overproducers

Authors :
Zhou Wenjuan
Yanmei Guo
Yu Wang
Jibin Sun
Qinggang Li
Ping Zheng
Xue Sun
Yanhe Ma
Jiuzhou Chen
Source :
Biosensorsbioelectronics. 172
Publication Year :
2020

Abstract

Whole-cell amino acid biosensors can sense the concentrations of certain amino acids and output easily detectable signals, which are important for construction of microbial producers. However, many reported biosensors have poor specificity because they also sense non-target amino acids. Besides, biosensors for many amino acids are still unavailable. In this study, we proposed a new strategy for constructing whole-cell biosensors based on aminoacyl-tRNA synthetases (aaRSs), which take the advantage of their universality and intrinsically specific binding ability to corresponding amino acids. Taking isoleucine biosensor as an example, we first mutated the isoleucyl-tRNA synthetase in Escherichia coli to dramatically decrease its affinity to isoleucine. The engineered cells specifically sensed isoleucine and output isoleucine dose-dependent cell growth as an easily detectable signal. To further expand the sensing range, an isoleucine exporter was overexpressed to enhance excretion of intracellular isoleucine. Since cells equipped with the optimized whole-cell biosensor showed accelerated growth when cells produced higher concentrations of isoleucine, the biosensor was successfully applied in high-throughput selection of isoleucine overproducers from random mutation libraries. This work demonstrates the feasibility of engineering aaRSs to construct a new kind of whole-cell biosensors for amino acids. Considering all twenty proteinogenic and many non-canonical amino acids have their specific aaRSs, this strategy should be useful for developing biosensors for various amino acids.

Details

ISSN :
18734235
Volume :
172
Database :
OpenAIRE
Journal :
Biosensorsbioelectronics
Accession number :
edsair.doi.dedup.....4d53b20114b819f709da412d75856cc7