Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing
Abstract 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 identi...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202306935 |
| _version_ | 1797215327599198208 |
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| author | Huaxiang Deng Han Yu Yanwu Deng Yulan Qiu Feifei Li Xinran Wang Jiahui He Weiyue Liang Yunquan Lan Longjiang Qiao Zhiyu Zhang Yunfeng Zhang Jay D. Keasling Xiaozhou Luo |
| author_facet | Huaxiang Deng Han Yu Yanwu Deng Yulan Qiu Feifei Li Xinran Wang Jiahui He Weiyue Liang Yunquan Lan Longjiang Qiao Zhiyu Zhang Yunfeng Zhang Jay D. Keasling Xiaozhou Luo |
| author_sort | Huaxiang Deng |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T11:28:18Z |
| format | Article |
| id | doaj.art-f3fe2bdd4eed45598f464ac030087d70 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-24T11:28:18Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-f3fe2bdd4eed45598f464ac030087d702024-04-10T13:10:12ZengWileyAdvanced Science2198-38442024-04-011114n/an/a10.1002/advs.202306935Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux BalancingHuaxiang Deng0Han Yu1Yanwu Deng2Yulan Qiu3Feifei Li4Xinran Wang5Jiahui He6Weiyue Liang7Yunquan Lan8Longjiang Qiao9Zhiyu Zhang10Yunfeng Zhang11Jay D. Keasling12Xiaozhou Luo13Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Infrastructure for Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaCenter for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaShenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 P. R. ChinaAbstract 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.https://doi.org/10.1002/advs.202306935biofoundrydirected evolutionmachine learningpathway debottlenecking |
| spellingShingle | Huaxiang Deng Han Yu Yanwu Deng Yulan Qiu Feifei Li Xinran Wang Jiahui He Weiyue Liang Yunquan Lan Longjiang Qiao Zhiyu Zhang Yunfeng Zhang Jay D. Keasling Xiaozhou Luo Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing Advanced Science biofoundry directed evolution machine learning pathway debottlenecking |
| title | Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing |
| title_full | Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing |
| title_fullStr | Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing |
| title_full_unstemmed | Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing |
| title_short | Pathway Evolution Through a Bottlenecking‐Debottlenecking Strategy and Machine Learning‐Aided Flux Balancing |
| title_sort | pathway evolution through a bottlenecking debottlenecking strategy and machine learning aided flux balancing |
| topic | biofoundry directed evolution machine learning pathway debottlenecking |
| url | https://doi.org/10.1002/advs.202306935 |
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