Xiong Bo, Yang Tianrui, Zhang Zixiong, Li Xiang, Yu Huan, Wang Lian, You Zixuan, Peng Wenbin, Jin Luyu, Song Hao
State Key Laboratory of Synthetic Biology, and School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
College of Life and Health Sciences, Northeastern University, Shenyang 110169, China.
Bioresour Technol. 2025 Jun;426:132350. doi: 10.1016/j.biortech.2025.132350. Epub 2025 Mar 5.
Nicotinamide mononucleotide (NMN) is a bioactive compound in NAD(P) metabolism, which exhibits diverse pharmaceutical interests. However, enhancing NMN biosynthesis faces the challange of competing with cell growth and disturbing intracellular redox homeostasis. Herein, we boosted NMN production in Escherichia coli by reprogramming central carbon metabolism with a machine learning (ML)-guided cofactor engineering strategy. Engnieering NMN biosynthesis-related pathway directed carbon flux toward NMN with the NADPH level increased by 73 %, which, although enhanced NMN titer (2.45 g/L), impaired cell growth. A quorum sensing (QS)-controlled cofactor engineering system was thus contructed and optimized by ML models to address redox imbalance, which led to 3.04 g/L NMN with improved cell growth. The final strain S344 produced 20.13 g/L NMN in fed-batch fermentation. This study showed that perturbation on cofactor level is a crucial limiting factor for NMN biosynthesis, and proposed a novel ML-guided strategy to manipulate intracellular redox state for efficient NMN production.
烟酰胺单核苷酸（NMN）是NAD（P）代谢中的一种生物活性化合物，具有多种药学研究价值。然而，提高NMN生物合成面临着与细胞生长竞争以及扰乱细胞内氧化还原稳态的挑战。在此，我们通过机器学习（ML）引导的辅因子工程策略对中心碳代谢进行重新编程，从而提高了大肠杆菌中NMN的产量。对NMN生物合成相关途径进行工程改造，使碳通量导向NMN，同时NADPH水平提高了73%，这虽然提高了NMN产量（2.45 g/L），但损害了细胞生长。因此，构建并通过ML模型优化了群体感应（QS）控制的辅因子工程系统以解决氧化还原失衡问题，这使得NMN产量达到3.04 g/L，同时细胞生长得到改善。最终菌株S344在补料分批发酵中产生了20.13 g/L NMN。这项研究表明，辅因子水平的扰动是NMN生物合成的关键限制因素，并提出了一种新颖的ML引导策略来操纵细胞内氧化还原状态以实现高效NMN生产。
