Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark.
Nat Commun. 2024 Mar 15;15(1):2356. doi: 10.1038/s41467-024-46486-3.
Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli. iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.
机器学习在大规模转录组数据中的应用，使得对细菌转录组进行分解，从而鉴定出独立调控的基因集成为可能，这些 iModulons 代表了特定的细胞功能。iModulon 的鉴定能够准确识别对跨物种细胞功能转移所必需和充分的基因。我们证明了跨物种转移的存在：1）香草酸盐向原儿茶酸的生物转化，2）丙二酸代谢途径，3）2,3-丁二醇的分解代谢途径，4）在多种假单胞菌中发现的对氨苄青霉素的抗菌抗性转移到大肠杆菌。基于 iModulon 的工程是一种变革性的策略，因为它包含了所转移的细胞功能的所有基因，包括没有功能注释的基因。适应性实验室进化被用来优化所转移的细胞功能，揭示了宿主中的突变。因此，将大数据分析和实验室进化相结合，提高了对系统生物学和合成生物学的理解水平，有助于菌株设计和开发。
