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An efficient high-throughput screening of high gentamicin-producing mutants based on titer determination using an integrated computer-aided vision technology and machine learning
  • +8
  • Xiaofeng Zhu,
  • Congcong Du,
  • Ali Mohsin,
  • Qian Yin,
  • Feng Xu,
  • Zebo Liu,
  • Zejian Wang,
  • Ying-Ping Zhuang,
  • Ju Chu,
  • Xiwei Tian,
  • Mei-Jin Guo
Xiaofeng Zhu
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering

Corresponding Author:zhuxiaofeng1996@live.com

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Congcong Du
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Ali Mohsin
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Qian Yin
South-Central Minzu University Library
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Feng Xu
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Zebo Liu
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Zejian Wang
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Ying-Ping Zhuang
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Ju Chu
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Xiwei Tian
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Mei-Jin Guo
East China University of Science and Technology State Key Laboratory of Bioreactor Engineering
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Abstract

The ‘design-build-test-learn’ (DBTL) cycle has been adopted in rational high-throughput screening for obtaining high-yield industrial strains. However, the mismatch between build and test slows the DBTL cycle due to the lack of high-throughput analytical technologies. In this study, a highly-efficient, accurate, and non-invasive detection method of gentamicin (GM) was developed, which can provide timely feedback for the high-throughput screening of high-yield strains. Firstly, a self-made tool was established to obtain datasets in 24-well ​based on the coloring of cells. Subsequently, the random forest (RF) algorithm was found to have the highest prediction accuracy with 98.5% for the training and 91.3% for verification. Finally, a stable genetic high-yield strain (998U/mL) was successfully screened out in 3005 mutants, which was verified to improve the titer by 72.7% in a 5 L bioreactor. Moreover, the verified new datasets were updated to the model database in order to improve learning ability of DBTL cycle.
23 Aug 2022Published in Analytical Chemistry volume 94 issue 33 on pages 11659-11669. 10.1021/acs.analchem.2c02289