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Developing a high-throughput screening method for threonine overproduction based on an artificial promoter

Overview of attention for article published in Microbial Cell Factories, August 2015
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Title
Developing a high-throughput screening method for threonine overproduction based on an artificial promoter
Published in
Microbial Cell Factories, August 2015
DOI 10.1186/s12934-015-0311-8
Pubmed ID
Authors

Ya’nan Liu, Qinggang Li, Ping Zheng, Zhidan Zhang, Yongfei Liu, Cunmin Sun, Guoqiang Cao, Wenjuan Zhou, Xiaowei Wang, Dawei Zhang, Tongcun Zhang, Jibin Sun, Yanhe Ma

Abstract

L-Threonine is an important amino acid for animal feed. Though the industrial fermentation technology of threonine achieved a very high level, there is still significant room to further improve the industrial strains. The biosensor-based high-throughput screening (HTS) technology has demonstrated its powerful applications. Unfortunately, for most of valuable fine chemicals such as threonine, a HTS system has not been established mainly due to the absence of a suitable biosensor. In this study, we developed a HTS method to gain high-yielding threonine-producing strains. Novel threonine sensing promoters including cysJp and cysHp were discovered by proteomic analyses of Escherichia coli in response to extracellular threonine challenges. The HTS method was constructed using a device composed of the fused cysJp and cysHp as a promoter and a linked enhanced green fluorescent protein gene as a reporter. More than 400 strains were selected with fluorescence activated cell sorting technology from a library of 20 million mutants and tested within 1 week. Thirty-four mutants have higher productivities than the starting industrial producer. One mutant produced 17.95 % more threonine in a 5-L jar fermenter. This method should play a functional role for continuous improvement of threonine industry. Additionally, the threonine sensor construction using promoters obtained by proteomics analyses is so convenient that it would be easily extended to develop HTS models for other biochemicals.

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The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 1%
Brazil 1 1%
Unknown 84 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 22%
Student > Ph. D. Student 18 21%
Other 7 8%
Professor 5 6%
Student > Doctoral Student 4 5%
Other 13 15%
Unknown 20 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 27%
Agricultural and Biological Sciences 21 24%
Engineering 8 9%
Chemical Engineering 3 3%
Chemistry 2 2%
Other 5 6%
Unknown 24 28%