Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Abstract

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Keywords: High-throughput screening; biosensor; directed evolution; microbial cell factory; mutagenesis.

Fig. 1. Mechanisms and engineering strategies of the genetically encoded biosensor.

(A) The structure and signal transduction mechanism of TCBs. (B) Engineering strategies for changing the specificity and inducer dose curve of TCBs by domain swapping and expression level optimization. (C) Engineering strategies of TFBs. LBD or DBD engineering is often performed to change biosensor specificity, while promoter engineering is performed to regulate the expression level of TFs and reporter genes, which could manipulate the dynamic range, sensitivity, and leakage expression level of TFBs. (D) The mechanism of RBS-based riboswitch and ribozyme-based riboswitch.

Fig. 2. Genetically encoded biosensor-based directed evolution.

(A) Directed evolution of enzymes with the assistance of FACS and genetically encoded biosensors. (B) Library construction and high-throughput screening to direct the evolution of the metabolic pathway with the assistance of biosensors. (C) Whole genome-directed evolution with the assistance of whole-cell biosensors in multi-well plates to avoid the accumulation of mutations in biosensors. (D) Continuous directed evolution with simultaneous mutation and selection in the cell growth process. Higher IPP production would generate lower expression levels of MutD5 and RFP (red fluorescence protein), resulting in the accumulation of positive mutations after screening the low red fluorescent cells.