4.3 Article

Genome shuffling of Saccharomyces cerevisiae for enhanced glutathione yield and relative gene expression analysis using fluorescent quantitation reverse transcription polymerase chain reaction

Journal

JOURNAL OF MICROBIOLOGICAL METHODS
Volume 127, Issue -, Pages 188-192

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.mimet.2016.06.012

Keywords

Genome shuffling; Saccharomyces cerevisiae; Glutathione; FQ RT-PCR

Funding

  1. China Postdoctoral Science Foundation [2015M582063]
  2. National High Technology Research and Development Program of China (863 Program) [2013AA102109]
  3. Open Research Fund of State Key Laboratory of Biological Fermentation Engineering of Beer

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Genome shuffling is an efficient and promising approach for the rapid improvement of microbial phenotypes. In this study, genome shuffling was applied to enhance the yield of glutathione produced by Saccharomyces cerevisiae YS86. Six isolates with subtle improvements in glutathione yield were obtained from populations generated by ultraviolet (UV) irradiation and nitrosoguanidine (NTG) mutagenesis. These yeast strains were then subjected to recursive pool-wise protoplast fusion. A strain library that was likely to yield positive colonies was created by fusing the lethal protoplasts obtained from both UV irradiation and heat treatments. After two rounds of genome shuffling, a high-yield recombinant YSF2-19 strain that exhibited 32- and 33-fold increases in glutathione production in shake flask and fermenter respectively was obtained. Comparative analysis of synthetase gene expression was conducted between the initial and shuffled strains using FQ (fluorescent quantitation) RT-PCR (reverse transcription polymerase chain reaction). Delta CT (threshold cycle) relative quantitation analysis revealed that glutathione synthetase gene (GSH-I) expression at the transcriptional level in the YSF2-19 strain was 9.9-fold greater than in the initial YS86. The shuffled yeast strain has a potential application in brewing, other food, and pharmaceutical industries. Simultaneously, the analysis of improved phenotypes will provide more valuable data for inverse metabolic engineering. (C) 2016 Elsevier B.V. All rights reserved.

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