| Literature DB >> 31433066 |
Woo Dae Jang1, Tae Yong Kim2, Hyun Uk Kim1,3,4, Woo Yong Shim2, Jae Yong Ryu1, Jin Hwan Park2, Sang Yup Lee1,3,4.
Abstract
Bacterial cellulose nanofiber (CNF) is a polymer with a wide range of potential industrial applications. Several Komagataeibacter species, including Komagataeibacter xylinus as a model organism, produce CNF. However, the industrial application of CNF has been hampered by inefficient CNF production, necessitating metabolic engineering for the enhanced CNF production. Here, we present complete genome sequence and a genome-scale metabolic model KxyMBEL1810 of K. xylinus DSM 2325 for metabolic engineering applications. Genome analysis of this bacterium revealed that a set of genes associated with CNF biosynthesis and regulation were present in this bacterium, which were also conserved in another six representative Komagataeibacter species having complete genome information. To better understand the metabolic characteristics of K. xylinus DSM 2325, KxyMBEL1810 was reconstructed using genome annotation data, relevant computational resources and experimental growth data generated in this study. Random sampling and correlation analysis of the KxyMBEL1810 predicted pgi and gnd genes as novel overexpression targets for the enhanced CNF production. Among engineered K. xylinus strains individually overexpressing heterologous pgi and gnd genes, either from Escherichia coli or Corynebacterium glutamicum, batch fermentation of a strain overexpressing the E. coli pgi gene produced 3.15 g/L of CNF in a complex medium containing glucose, which was the best CNF concentration achieved in this study, and 115.8% higher than that (1.46 g/L) obtained from the control strain. Genome sequence data and KxyMBEL1810 generated in this study should be useful resources for metabolic engineering of K. xylinus for the enhanced CNF production.Entities:
Keywords: Komagataeibacter xylinus DSM 2325; bacterial cellulose nanofiber (CNF); genome sequence; genome-scale metabolic model; metabolic engineering
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Year: 2019 PMID: 31433066 DOI: 10.1002/bit.27150
Source DB: PubMed Journal: Biotechnol Bioeng ISSN: 0006-3592 Impact factor: 4.530