Xu Pei, Lin Nuo-Qiao, Zhang Zhi-Qian, Liu Jian-Zhong
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China.
Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou, 510399, China.
Adv Biotechnol (Singap). 2024 Mar 1;2(1):9. doi: 10.1007/s44307-024-00018-8.
Engineering microbial cell factories have achieved much progress in producing fuels, natural products and bulk chemicals. However, in industrial fermentation, microbial cells often face various predictable and stochastic disturbances resulting from intermediate metabolites or end product toxicity, metabolic burden and harsh environment. These perturbances can potentially decrease productivity and titer. Therefore, strain robustness is essential to ensure reliable and sustainable production efficiency. In this review, the current strategies to improve host robustness were summarized, including knowledge-based engineering approaches, such as transcription factors, membrane/transporters and stress proteins, and the traditional adaptive laboratory evolution based on natural selection. Computation-assisted (e.g. GEMs, deep learning and machine learning) design of robust industrial hosts was also introduced. Furthermore, the challenges and future perspectives on engineering microbial host robustness are proposed to promote the development of green, efficient and sustainable biomanufacturers.
工程化微生物细胞工厂在生产燃料、天然产物和大宗化学品方面已取得了很大进展。然而,在工业发酵中,微生物细胞常常面临各种可预测和随机的干扰,这些干扰源自中间代谢产物或终产物的毒性、代谢负担以及恶劣的环境。这些扰动可能会降低生产率和滴度。因此,菌株鲁棒性对于确保可靠且可持续的生产效率至关重要。在本综述中,总结了当前提高宿主鲁棒性的策略,包括基于知识的工程方法,如转录因子、膜/转运蛋白和应激蛋白,以及基于自然选择的传统适应性实验室进化。还介绍了计算辅助(如基因组规模代谢模型、深度学习和机器学习)设计鲁棒的工业宿主。此外,提出了工程化微生物宿主鲁棒性方面的挑战和未来展望,以促进绿色、高效和可持续生物制造企业的发展。