Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing

Deng, Huaxiang; Yu, Han; Deng, Yanwu; Qiu, Yulan; Li, Feifei; Wang, Xinran; He, Jiahui; Liang, Weiyue; Lan, Yunquan; Qiao, Longjiang; Zhang, Zhiyu; Zhang, Yunfeng; Keasling, Jay D.; Luo, Xiaozhou

doi:10.1002/advs.202306935

Title: Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing

Journal Article · Tue Feb 06 00:00:00 UTC 2024 · Advanced Science

DOI: https://doi.org/10.1002/advs.202306935 · OSTI ID:2470802

Deng, Huaxiang ^[1]; Yu, Han ^[2]; Deng, Yanwu ^[3]; Qiu, Yulan ^[3]; Li, Feifei ^[3]; Wang, Xinran ^[3]; He, Jiahui ^[3]; Liang, Weiyue ^[1]; Lan, Yunquan ^[3]; Qiao, Longjiang ^[3]; Zhang, Zhiyu ^[3]; Zhang, Yunfeng ^[3]; Keasling, Jay D. ^[4];

^[5]

Chinese Academy of Sciences (CAS), Shenzhen (China); Jiangnan University, Wuxi (China)
Chinese Academy of Sciences (CAS), Shenzhen (China); University of Chinese Academy of Sciences, Beijing (China)
Chinese Academy of Sciences (CAS), Shenzhen (China)
Chinese Academy of Sciences (CAS), Shenzhen (China); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States); Technical University of Denmark, Lyngby (Denmark)
Chinese Academy of Sciences (CAS), Shenzhen (China); University of Science and Technology Beijing (China)

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L^-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: Guangdong Basic and Applied Basic Research Foundation; National Natural Science Foundation of China (NSFC); Natural Sciences Foundation of Jiangsu Province; USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2470802

Journal Information:: Advanced Science, Journal Name: Advanced Science Journal Issue: 14 Vol. 11; ISSN 2198-3844

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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