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Genetically Encoded Biosensor Engineering for Application in Directed Evolution

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...

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Autores principales: Mao, Yin, Huang, Chao, Zhou, Xuan, Han, Runhua, Deng, Yu, Zhou, Shenghu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society for Microbiology and Biotechnology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619561/
https://www.ncbi.nlm.nih.gov/pubmed/37449325
http://dx.doi.org/10.4014/jmb.2304.04031
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author Mao, Yin
Huang, Chao
Zhou, Xuan
Han, Runhua
Deng, Yu
Zhou, Shenghu
author_facet Mao, Yin
Huang, Chao
Zhou, Xuan
Han, Runhua
Deng, Yu
Zhou, Shenghu
author_sort Mao, Yin
collection PubMed
description 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.
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spelling pubmed-106195612023-11-02 Genetically Encoded Biosensor Engineering for Application in Directed Evolution Mao, Yin Huang, Chao Zhou, Xuan Han, Runhua Deng, Yu Zhou, Shenghu J Microbiol Biotechnol Review 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. The Korean Society for Microbiology and Biotechnology 2023-10-28 2023-07-14 /pmc/articles/PMC10619561/ /pubmed/37449325 http://dx.doi.org/10.4014/jmb.2304.04031 Text en Copyright © 2023 by the authors. Licensee KMB https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Review
Mao, Yin
Huang, Chao
Zhou, Xuan
Han, Runhua
Deng, Yu
Zhou, Shenghu
Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title_full Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title_fullStr Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title_full_unstemmed Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title_short Genetically Encoded Biosensor Engineering for Application in Directed Evolution
title_sort genetically encoded biosensor engineering for application in directed evolution
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619561/
https://www.ncbi.nlm.nih.gov/pubmed/37449325
http://dx.doi.org/10.4014/jmb.2304.04031
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