[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a...[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.展开更多
Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating c...Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years.However,if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge,the conflicts between stoichiometric and other constraints,such as thermodynamic feasibility and enzyme resource availability,would lead to distorted predictions.In this work,we investigated a prediction anomaly of EcoETM,a constraints-based metabolic network model,and introduced the idea of enzyme compartmentalization into the analysis process.Through rational combination of reactions,we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites.This allowed us to correct the pathway structures of L-serine and L-tryptophan.A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments.Notably,this work also reveals the trade-off between product yield and thermodynamic feasibility.Our work is of great value for the structural improvement of constraints-based models.展开更多
The pit mud(PM)microbiome has a major influence on the yield and flavor quality of Nongxiangxing baijiu.The study of isolated single strains and sub-communities of PM microorganisms is necessary for the maintenance an...The pit mud(PM)microbiome has a major influence on the yield and flavor quality of Nongxiangxing baijiu.The study of isolated single strains and sub-communities of PM microorganisms is necessary for the maintenance and future development of PM.Genome annotation and genome-scale metabolic models of multiple isolated strains of PM microorganisms were used to analyze their metabolic capacity and metabolic interactions,and predict the inter-species interactions that facilitate the biosynthesis of specific metabolites.Sixty-eight PM strains were isolated using multiple culture conditions.At the genus level,Bacillus,Enterococcus,Enterobacter,Acinetobacter,Rummeliibacillus and Lactococcus predominated.The genomes of various pure-strain cultures were annotated,highlighting their metabolic and biogeochemical cycles(e.g.,carbon cycle,nitrogen cycle and sulfur cycle).These strains exhibited a hierarchical trait space,including resource acquisition,resource use and stress tolerance,which facilitates adaptation to the PM environment.Genome-scale metabolic models of the 68 strains were constructed and these predicted that they had the metabolic potential to biosynthesize 223 different metabolites.Community modelling with 17 specific metabolites as the target products indicated that there were 24 active biosynthetic species,two of which were essential(Lysinibacillus mangiferihumi and Providencia manganoxydan)and 22 of which were substitutable,and could combine to form 270 sub-communities.These findings provide an efficient method for the comprehensive analysis of the metabolic functions and interactions of PM microorganisms,as well as theoretical support for the design of artificial microbial communities for the production of specific PM flavor compounds.展开更多
基金supported by the Hebei Provincial Key Research and Development Project(21372803D)。
文摘[Objective]To construct an Escherichia coli mutant strain that accumulates pyruvate by genetic modification guided by the genome-scale metabolic network model.[Methods]Using a genome-scale metabolic network model as a guide,we simulated pyruvate production of E.coli,screened key genes in metabolic pathways,and developed gene editing procedures accordingly.We knocked out the acetate kinase gene ackA,phosphate acetyltransferase gene pta,alcohol dehydrogenase adhE,glycogen synthase gene glgA,glycogen phosphorylase gene glgP,phosphoribosyl pyrophosphate(PRPP)synthase gene prs,ribose 1,5-bisphosphate phosphokinase gene phnN,and transporter encoding gene proP.Furthermore,we knocked in the transporter encoding gene ompC,flavonoid toxin gene fldA,and D-serine ammonia lyase gene dsdA.[Results]A shake flask process with the genetically edited mutant strain MG1655-6-2 under anaerobic conditions produced pyruvate at a titer of 10.46 g/L and a yield of 0.69 g/g.Metabolomic analysis revealed a significant increase in the pyruvate level in the fermentation broth,accompanied by notable decreases in the levels of certain related metabolic byproducts.Through 5 L fed-batch fermentation and an adaptive laboratory evolution,the strain finally achieved a pyruvate titer of 45.86 g/L.[Conclusion]This study illustrated the efficacy of a gene editing strategy predicted by a genome-scale metabolic network model in enhancing pyruvate accumulation in E.coli under anaerobic conditions and provided novel insights for microbial metabolic engineering.
基金funded by the National Key Research and Development Program of China(2018YFA0900300,2020YFA0908301)the National Natural Science Foundation of China(32201188)+1 种基金the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIP-CXRC-060,TSBICIP-PTJS-001,and TSBICIP-PTJS-013)the China Postdoctoral Science Foundation(2022M723341).
文摘Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years.However,if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge,the conflicts between stoichiometric and other constraints,such as thermodynamic feasibility and enzyme resource availability,would lead to distorted predictions.In this work,we investigated a prediction anomaly of EcoETM,a constraints-based metabolic network model,and introduced the idea of enzyme compartmentalization into the analysis process.Through rational combination of reactions,we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites.This allowed us to correct the pathway structures of L-serine and L-tryptophan.A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments.Notably,this work also reveals the trade-off between product yield and thermodynamic feasibility.Our work is of great value for the structural improvement of constraints-based models.
基金supported by Sichuan Natural Science Foundation General Project(2023NSFSC0184)Liquor Making Biological Technology and Application of Key Laboratory of Sichuan Province(NJ2023-07).
文摘The pit mud(PM)microbiome has a major influence on the yield and flavor quality of Nongxiangxing baijiu.The study of isolated single strains and sub-communities of PM microorganisms is necessary for the maintenance and future development of PM.Genome annotation and genome-scale metabolic models of multiple isolated strains of PM microorganisms were used to analyze their metabolic capacity and metabolic interactions,and predict the inter-species interactions that facilitate the biosynthesis of specific metabolites.Sixty-eight PM strains were isolated using multiple culture conditions.At the genus level,Bacillus,Enterococcus,Enterobacter,Acinetobacter,Rummeliibacillus and Lactococcus predominated.The genomes of various pure-strain cultures were annotated,highlighting their metabolic and biogeochemical cycles(e.g.,carbon cycle,nitrogen cycle and sulfur cycle).These strains exhibited a hierarchical trait space,including resource acquisition,resource use and stress tolerance,which facilitates adaptation to the PM environment.Genome-scale metabolic models of the 68 strains were constructed and these predicted that they had the metabolic potential to biosynthesize 223 different metabolites.Community modelling with 17 specific metabolites as the target products indicated that there were 24 active biosynthetic species,two of which were essential(Lysinibacillus mangiferihumi and Providencia manganoxydan)and 22 of which were substitutable,and could combine to form 270 sub-communities.These findings provide an efficient method for the comprehensive analysis of the metabolic functions and interactions of PM microorganisms,as well as theoretical support for the design of artificial microbial communities for the production of specific PM flavor compounds.
