以代谢重编程和机器学习为指导的辅助因子工程来促进大肠杆菌 (Escherichia coli) 中的尼古丁胺胺单核酸生产
Bo Xiong1, Tianrui Yang1, Zixiong Zhang1
1State Key Laboratory of Synthetic Biology, and School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
Bioresource technology
|March 7, 2025
概括
研究人员使用机器学习对大肠杆菌进行了改造,以提高尼古丁胺胺单核酸 (NMN) 的产生. 这一策略优化了辅因子水平,将NMN产量提高到20.13g/L,同时改善了细胞生长.
科学领域:
- 生物技术是生物技术.
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
背景情况:
- 尼古丁胺胺单核酸 (NMN) 是NAD(P) +代谢的关键前体,具有重要的制药应用.
- 在NMN生物合成中的挑战包括与细胞生长的竞争和细胞内氧化还原平衡的破坏.
- 高效的NMN微生物生产对其治疗潜力至关重要.
研究的目的:
- 通过重编程中央碳代谢来增强大肠杆菌中的NMN生产.
- 为了解决NMN生物合成期间细胞生长和氧化还原失衡的局限性.
- 开发一种以机器学习为指导的战略,以优化NMN生产.
主要方法:
- 在大肠杆菌中重新编程中央碳代谢.
- 实施一个以机器学习 (ML) 为指导的辅助因素工程策略.
- 构建和优化由定数感应 (QS) 控制的辅因子工程系统.
- 使用料批发发酵来进行大规模的NMN生产.
主要成果:
- 最初的工程增加了NADPH水平73%和NMN标位到2.45g/L,但细胞生长受损.
- 通过ML模型优化的QS控制系统改善了NMN产量至3.04g/L,增强了细胞生长.
- 最后的工程菌株S344在料批发发酵中获得了20.13g/L的显著NMN产量.
结论:
- 辅因子水平的扰动是NMN生物合成的关键限制因素.
- 一个新的ML引导策略可以有效地操纵细胞内氧化还原状态,以有效地产生NMN.
- 这项研究为微生物NMN生产提供了一个强大的平台,有可能用于制药应用.
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