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

作者：Huaxiang Deng, Han Yu, Y. Deng, Yulan Qiu, Feifei Li, Xinran Wang, Jiahui He, Wei-Yue Liang, Yunquan Lan, Longjiang Qiao, Zhiyu Zhang, Yunfeng Zhang, Jay D. Keasling, Xiaozhou Luo · 发表于：Advanced Science · 年份：2024 · DOI：10.1002/advs.202306935 · 被引用次数：28 · 研究领域：Evolution and Genetic Dynamics、CRISPR and Genetic Engineering、Microbial Metabolic Engineering and Bioproduction

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.