Presentation Information
[P03-328]Elucidation and Optimization of Intestinal Metabolism in Escherichia coli Nissle 1917 via Systems Metabolic Engineering
○Jungyeon Kim1 (1. Seoul National University (Korea))
Keywords:
Escherichia coli Nissle 1917 (EcN),Systems metabolic engineering,Intestinal metabolism,Multi-omics analysis,Metabolic bottleneck removal
[Purpose]
To elucidate the intestinal metabolic activities of Escherichia coli Nissle 1917 (EcN) and to enhance its metabolic performance using a systems metabolic engineering approach.
[Method]
In silico analysis and fermentation profiling were conducted to characterize substrate utilization, particularly mucin metabolism. Multi-omics analyses (including transcriptomics and metabolomics) were employed to identify key metabolic pathways associated with intestinal colonization and growth. Targeted metabolic engineering was performed by deleting the otsAB genes involved in trehalose synthesis to relieve metabolic bottlenecks.
[Results]
EcN was found to actively metabolize mucin-derived substrates. Fucose metabolism significantly contributed to intestinal colonization by enhancing flagellar biosynthesis and nutrient uptake systems. Multi-omics analysis revealed that excessive intracellular trehalose accumulation, linked to galactose metabolism, limited cellular growth. The engineered ΔotsAB strain showed improved metabolic performance, with a 1.47-fold increase in growth rate and a 1.37-fold increase in substrate consumption compared to the wild-type strain.
[Consideration]
These results indicate that intrinsic metabolic pathways of EcN play a critical role in determining its intestinal behavior. In particular, the identification and removal of metabolic bottlenecks can significantly improve probiotic performance. The study highlights the importance of integrating systems-level analyses with metabolic engineering for rational strain design.
[Conclusion]
This study provides a systems-level understanding of EcN intestinal metabolism and demonstrates that targeted metabolic engineering can effectively enhance its growth and substrate utilization, offering a promising strategy for improving probiotic functionality in vivo.
To elucidate the intestinal metabolic activities of Escherichia coli Nissle 1917 (EcN) and to enhance its metabolic performance using a systems metabolic engineering approach.
[Method]
In silico analysis and fermentation profiling were conducted to characterize substrate utilization, particularly mucin metabolism. Multi-omics analyses (including transcriptomics and metabolomics) were employed to identify key metabolic pathways associated with intestinal colonization and growth. Targeted metabolic engineering was performed by deleting the otsAB genes involved in trehalose synthesis to relieve metabolic bottlenecks.
[Results]
EcN was found to actively metabolize mucin-derived substrates. Fucose metabolism significantly contributed to intestinal colonization by enhancing flagellar biosynthesis and nutrient uptake systems. Multi-omics analysis revealed that excessive intracellular trehalose accumulation, linked to galactose metabolism, limited cellular growth. The engineered ΔotsAB strain showed improved metabolic performance, with a 1.47-fold increase in growth rate and a 1.37-fold increase in substrate consumption compared to the wild-type strain.
[Consideration]
These results indicate that intrinsic metabolic pathways of EcN play a critical role in determining its intestinal behavior. In particular, the identification and removal of metabolic bottlenecks can significantly improve probiotic performance. The study highlights the importance of integrating systems-level analyses with metabolic engineering for rational strain design.
[Conclusion]
This study provides a systems-level understanding of EcN intestinal metabolism and demonstrates that targeted metabolic engineering can effectively enhance its growth and substrate utilization, offering a promising strategy for improving probiotic functionality in vivo.
Comment
To browse or post comments, you must log in.Log in