Informatics Publishing Limited

Bashir Sajo Mienda ¹, Mohd Shahir Shamsir ¹, Faezah Mohd Salleh ¹

Affiliations
1 Bioinformatics Research Group (BIRG), Biosciences & Health Sciences Department, Universiti Teknologi Malaysia, Skudai 81310 Johor Bahru, MY

Abstract

The increase availability of genome scale metabolic models of Escherichia coli and computational successes is revolutionizing the field of metabolic engineering and synthetic microbiology. E. coli has been experimentally established to produce D-lactate under micro-aerobic conditions when pyruvate formate lyase (PFL) genes are knocked out. However, investigation on the in silico prediction and for evaluation of the effect of PFL genes knockout on the production of D-lactate using E. coli genome scale metabolic model with regulatory on/off minimization (ROOM) under the OptFlux software platform remained under explored. Here, we demonstrate that metabolic engineering strategies using the OptFlux software platform by gene knockout simulation of pflA/b0902, pflB/b0903, pflC/b3952 and pflD/b3951 have been predicted to increase D-lactate production in E. coli and hence maintaining a growth rate that is 96% of the wild-type model. The deletion of the PFL genes have been established to increase D-lactate production in E. coli. The results obtained in this study is in agreement with the previously established experimental studies. These findings suggests that the OptFlux software platform using ROOM as the simulation algorithm, can prospectively and effectively predict future metabolic engineering targets for increased D-lactate production in E. coli and/or other microbial chemical syntheses.

Keywords

D-Lactate, Escherichia coli Model, Gene Knockout Simulation, Metabolic Engineering, Optflux Software.

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Bashir Sajo Mienda ¹, Mohd Shahir Shamsir ¹, Rosli Md Illias ²

Affiliations
1 Bioinformatics Research Group, Biosciences and Health Sciences Department, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MY
2 Department of Bioprocess Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MY

Abstract

The use of genome-scale models of Escherichia coli to guide future metabolic engineering strategies for increased succinic acid production has received renewed attention in recent years. Substrate selectivity such as glycerol is of particular interest, because it is currently generated as a by-product of biodiesel industry and therefore can serve as a solitary carbon source. However, study on the prediction of gene knockout candidates for enhanced succinate production from glycerol using Minimization of Metabolic Adjustment Algorithm with the OptFlux software platform remained underexplored. Here, we show that metabolic engineering interventions by gene knockout simulation of some pyruvate dissimilating pathway enzymes (lactate dehydrogenase A and pyruvate formate lyase A) using E. coli genome-scale model can reduce acetate flux and enhance succinic acid production under anaerobic conditions. The introduced genetic perturbations led to substantial improvement in succinate flux of about 597% on glycerol and 120% on glucose than that of the wild-type control strain BSKO. We hypothesize that the deletion of pyruvate formate lyase A (pflA) in E. coli can led to no acetate production from glucose, lower acetate production from glycerol and increased succinic acid productivities on both substrates under anaerobic conditions. Our results demonstrate a predicted increase in succinate production (597% higher than the wild-type model) among others, from glycerol after deletion of pflA/b0902 gene in E. coli genome-scale model. This would open up a novel platform for model-guided experimental inquiry and/or allow a comprehensive biological discovery on the metabolic processes of pflA in E. coli for succinate production when glycerol is the substrate.

Keywords

Escherichia coli, Genome-Scale Model, Gene Knockout Simulation, Metabolic Engineering, Optflux Software, Succinic Acid.

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Bashir Sajo Mienda ¹, Mohd Shahir Shamsir ¹, Faezah Mohd Salleh ¹, Rosli Md. Illias ²

Affiliations
1 Bioinformatics Research Group (BIRG), Biosciences and Health Sciences Department, Universiti Teknologi, MY
2 Department of Bioprocess Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MY

Abstract

In silico metabolic engineering interventions have received renewed attention due to the increase in the number of annotated genomes and the development of several genome-scale metabolic models. Using the retrosynthetic metabolic pathway prediction method, we engineered metabolic strategies for the production of 1-butanol by Escherichia coli using the OptFlux software platform. The metabolic engineering model shows that the insertion of nucleotide sugar dehydrogenase enzyme from Leptothrix cholodnii is predicted to catalyse the production of 1-butanol in E. coli. The growth rate and the secretion profile of the mutant model was retained as the wild-type. The result demonstrates that the proposed engineered strain is capable of substantial butanol production increase when 1-butanol gene (nsdh/b3544) is overexpressed under semi-anaerobic conditions with fixed glucose and oxygen uptake rates of 8 mmol g DW^-1 h^-1 and 5 mmol g DW^-1 h^-1, respectively. We anticipate that our in silico results would serve as a starting point for novel in vivo metabolic engineering strategies of 1-butanol production in E. coli.

Keywords

Butanol, Escherichia coli, Metabolic Engineering, Optflux, Prediction and Retrosynthesis.

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