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Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for spinosad production improvement.

ABSTRACT: Spinosad is a macrolide antibiotic produced by Saccharopolyspora spinosa with aerobic fermentation. However, the wild strain has a low productivity. In this article, a computational guided engineering approach was adopted in order to improve the yield of spinosad in S. spinosa.Firstly, a genome-scale metabolic network reconstruction (GSMR) for S.spinosa based on its genome information, literature data and experimental data was established. The model was consists of 1,577 reactions, 1,726 metabolites, and 733 enzymes after manually refined. Then, amino acids supplying experiments were performed in order to test the capabilities of the model, and the results showed a high consistency. Subsequently, transhydrogenase (PntAB, EC 1.6.1.2) was chosen as the potential target for spinosad yield improvement based on the in silico metabolic network models. Furthermore, the target gene was manipulated in the parent strain in order to validate the model predictions. At last, shake flask fermentation was carried out which led to spinosad production of 75.32 mg/L, 86.5% higher than the parent strain (40.39 mg/L).Results confirmed the model had a high potential in engineering S. spinosa for spinosad production. It is the first GSMM for S.spinosa, it has significance for a better understanding of the comprehensive metabolism and guiding strain designing of Saccharopolyspora spinosa in the future.

SUBMITTER: Wang X

PROVIDER: S-EPMC4003821 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

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Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for spinosad production improvement.

Wang Xiaoyang X Zhang Chuanbo C Wang Meiling M Lu Wenyu W

Microbial cell factories 20140315 1

<h4>Background</h4>Spinosad is a macrolide antibiotic produced by Saccharopolyspora spinosa with aerobic fermentation. However, the wild strain has a low productivity. In this article, a computational guided engineering approach was adopted in order to improve the yield of spinosad in S. spinosa.<h4>Results</h4>Firstly, a genome-scale metabolic network reconstruction (GSMR) for S.spinosa based on its genome information, literature data and experimental data was established. The model was consist ...[more]

PMID: 24628959

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Project description:BackgroundPichia pastoris has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive in silico model of P. pastoris can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.ResultsA fully compartmentalized metabolic model of P. pastoris (iPP668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of P. pastoris observed during chemostat experiments. Subsequent in silico analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant P. pastoris strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of P. pastoris system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.ConclusionThe genome-scale metabolic model characterizes the cellular physiology of P. pastoris, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through in silico simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of P. pastoris metabolic network to enhance humanized glycoprotein production.

Dataset Information

Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for spinosad production improvement.

Publications

Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for spinosad production improvement.

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