RESUMO
BACKGROUND: Glycerol is a desirable alternative substrate for 2,3-butanediol (2,3-BD) production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis, a "generally recognized as safe" organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis. RESULTS: B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study. CONCLUSIONS: The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.
Assuntos
Bacillus subtilis/metabolismo , Butileno Glicóis/isolamento & purificação , Engenharia Metabólica/métodos , Redes e Vias MetabólicasRESUMO
BACKGROUND: Flux Balance Analysis (FBA) is a well-known bioinformatics tool for metabolic engineering design. Previously, we have successfully used single-level FBA to design metabolic fluxes in Bacillus subtilis to enhance (R,R)-2,3-butanediol (2,3-BD) production from glycerol. OptKnock is another powerful technique for devising gene deletion strategies to maximize microbial growth coupling with improved biochemical production. It has never been used in B. subtilis. In this study, we aimed to compare the use of single-level FBA and OptKnock for designing enhanced 2,3-BD production from glycerol in B. subtilis. RESULTS: Single-level FBA and OptKnock were used to design metabolic engineering approaches for B. subtilis to enhance 2,3-BD production from glycerol. Single-level FBA indicated that deletion of ackA, pta, lctE, and mmgA would improve the production of 2,3-BD from glycerol, while OptKnock simulation suggested the deletion of ackA, pta, mmgA, and zwf. Consequently, strains LM01 (single-level FBA-based) and MZ02 (OptKnock-based) were constructed, and their capacity to produce 2,3-BD from glycerol was investigated. The deletion of multiple genes did not negatively affect strain growth and glycerol utilization. The highest 2,3-BD production was detected in strain LM01. Strain MZ02 produced 2,3-BD at a similar level as the wild type, indicating that the OptKnock prediction was erroneous. Two-step FBA was performed to examine the reason for the erroneous OptKnock prediction. Interestingly, we newly found that zwf gene deletion in strain MZ02 improved lactate production, which has never been reported to date. The predictions of single-level FBA for strain MZ02 were in line with experimental findings. CONCLUSIONS: We showed that single-level FBA is an effective approach for metabolic design and manipulation to enhance 2,3-BD production from glycerol in B. subtilis. Further, while this approach predicted the phenotypes of generated strains with high precision, OptKnock prediction was not accurate. We suggest that OptKnock modelling predictions be evaluated by using single-level FBA to ensure the accuracy of metabolic pathway design. Furthermore, the zwf gene knockout resulted in the change of metabolic fluxes to enhance the lactate productivity.