Revealing Differences in Metabolic Flux Distributions between a Mutant Strain and Its Parent Strain Gluconacetobacter xylinus CGMCC 2955

Abstract

A better understanding of metabolic fluxes is important for manipulating microbial metabolism toward desired end products, or away from undesirable by-products. A mutant strain, Gluconacetobacter xylinus AX2-16, was obtained by combined chemical mutation of the parent strain (G. xylinus CGMCC 2955) using DEC (diethyl sulfate) and LiCl. The highest bacterial cellulose production for this mutant was obtained at about 11.75 g/L, which was an increase of 62% compared with that by the parent strain. In contrast, gluconic acid (the main byproduct) concentration was only 5.71 g/L for mutant strain, which was 55.7% lower than that of parent strain. Metabolic flux analysis indicated that 40.1% of the carbon source was transformed to bacterial cellulose in mutant strain, compared with 24.2% for parent strain. Only 32.7% and 4.0% of the carbon source were converted into gluconic acid and acetic acid in mutant strain, compared with 58.5% and 9.5% of that in parent strain. In addition, a higher flux of tricarboxylic acid (TCA) cycle was obtained in mutant strain (57.0%) compared with parent strain (17.0%). It was also indicated from the flux analysis that more ATP was produced in mutant strain from pentose phosphate pathway (PPP) and TCA cycle. The enzymatic activity of succinate dehydrogenase (SDH), which is one of the key enzymes in TCA cycle, was 1.65-fold higher in mutant strain than that in parent strain at the end of culture. It was further validated by the measurement of ATPase that 3.53–6.41 fold higher enzymatic activity was obtained from mutant strain compared with parent strain.

Reconstruction of a Genome-scale Metabolic Network of Komagataeibacter nataicola RZS01 for Cellulose Production

“…For quantitative assessment of the model’s accuracy at predicting the growth rate, we compared simulated growth phenotypes, obtained using glucose and ammonium as the sole carbon and nitrogen sources, respectively, with in vivo growth data 29 . The experimental data served as constraints to simulate cell growth parameters, including the glucose uptake rate, gluconic acid production rate, acetic acid production rate, and bacterial cellulose production rate.…”

Genomic and metabolic analysis of Komagataeibacter xylinus DSM 2325 producing bacterial cellulose nanofiber

“…A sampling of metabolic flux distributions was conducted using Artificially Centered Hit‐and‐Run sampling (Kaufman & Smith, ) that is implementable via COBRApy. Random sampling was conducted to generate 3,000 sample points with constraints reflecting 70–85% of the maximal CNF production rate (i.e., maximal CNF production rate of 6.338 mmol/gram dry cell weight or gDCW/hr) as well as the glucose uptake rate of 8.45 mmol/gDCW/hr based on a previous report (Zhong et al, ). Subsequently, pairwise Pearson correlation coefficients between reactions of interest and the CNF production rate in the KxyMBEL1810 were calculated using SciPy (https://www.scipy.org/).…”

Preparation and characterization of a novel bacterial cellulose/chitosan bio-hydrogel

“…xylinus was activated in cellulose medium at 30 C for 24 h. 31 Then, G. xylinus suspension was inoculated at 5% (v/v) in the same medium; the medium was supplemented with chitosan chloride at concentrations of 5, 8, and 10 g L À1 , respectively. BC pellicles and BC/chitosan composite pellicles were formed after 10 days.…”