Metabolic engineering of Synechococcus elongatus PCC 7942 for improvement of 1,3-propanediol and glycerol production based on in silico simulation of metabolic flux distribution

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Abstract

Background: Production directly from carbon dioxide by engineered cyanobacteria is one of the promising technologies for sustainable future. Previously, we have successfully achieved 1,3-propanediol (1,3-PDO) production using Synechococcus elongatus PCC 7942 with a synthetic metabolic pathway. The strain into which the synthetic metabolic pathway was introduced produced 3.48mM (0.265g/L) 1,3-PDO and 14.3mM (1.32g/L) glycerol during 20days of incubation. In this study, the productivities of 1,3-PDO were improved by gene disruption selected by screening with in silico simulation. Methods: First, a stoichiometric metabolic model was applied to prediction of cellular metabolic flux distribution in a 1,3-PDO-producing strain of S. elongatus PCC 7942. A genome-scale model of S. elongatus PCC 7942 constructed by Knoop was modified by the addition of a synthetic metabolic pathway for 1,3-PDO production. Next, the metabolic flux distribution predicted by metabolic flux balance analysis (FBA) was used for in silico simulation of gene disruption. As a result of gene disruption simulation, NADPH dehydrogenase 1 (NDH-1) complexes were found by screening to be the most promising candidates for disruption to improve 1,3-PDO production. The effect of disruption of the gene encoding a subunit of the NDH-1 complex was evaluated in the 1,3-PDO-producing strain. Results and Conclusions: During 20days of incubation, the ndhF1-null 1,3-PDO-producing strain showed the highest titers: 4.44mM (0.338g/L) 1,3-PDO and 30.3mM (2.79g/L) glycerol. In this study, we successfully improved 1,3-PDO productivity on the basis of in silico simulation of gene disruption.

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Hirokawa, Y., Matsuo, S., Hamada, H., Matsuda, F., & Hanai, T. (2017). Metabolic engineering of Synechococcus elongatus PCC 7942 for improvement of 1,3-propanediol and glycerol production based on in silico simulation of metabolic flux distribution. Microbial Cell Factories, 16(1). https://doi.org/10.1186/s12934-017-0824-4

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