Two-Stage Semi-Continuous 2-Keto-Gluconic Acid (2KGA) Production by Pseudomonas plecoglossicida JUIM01 From Rice Starch Hydrolyzate

Sun, Lei; Wang, Da-Ming; Sun, Wenjing; Cui, Fengjie; Gong, Jin‐Song; Zhang, Xiaomei; Shi, Jin‐Song; Xu, Zhenghong

doi:10.3389/fbioe.2020.00120

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…However, the products of 2-kGA prepared by this technology exist in liquid form, and hence, the addition of acetic acid would seriously affect the product quality. In 2020, Sun et al 43 developed two-stage semi-continuous fermentation that could be industrially applied due to its high 2-kGA concentration, 2-kGA yield and operational efficiency from glucose. However, this technology cannot achieve the purpose of accurate preparation of 2-kGA and 5-kGA simultaneously, which increased the difficulty of process operation.…”

Section: Mass Balance Of the Assembled Cascade Biotechnologymentioning

confidence: 99%

Cascading and precise regulation of the selective bio-production of 2- or 5-ketogluconic acid from glucose with whole-cell catalysis technology

et al. 2023

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Section: Mass Balance Of the Assembled Cascade Biotechnologymentioning

confidence: 99%

Cascading and precise regulation of the selective bio-production of 2- or 5-ketogluconic acid from glucose with whole-cell catalysis technology

et al. 2023

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“…For example, for xylose, the most important feature was growth on xylitol, a metabolic intermediate in the typical xylose-degrading pathway in yeasts and other fungi (Wohlbach et al 2011, Meng et al 2022, while for sucrose, the most important feature was maltose, another disaccharide containing a glucose moiety (Brown et al 2011) (Figure 3). For galactose, the top features included 2-keto-D-gluconate and L-sorbose, which are generated from glucose or galactose, respectively, by the enzymes acting on an alternative galactose-degrading pathways in some bacteria and fungi (Fekete et al 2004, Tanimura et al 2003, Meng et al 2022, Sun et al 2020, as well as lactose and melibiose, polysaccharides that contain galactose (Figure 3).…”

Section: Top Features For Predicting Growth On a Specific Carbon Sour...mentioning

confidence: 99%

Machine learning illuminates how diet influences the evolution of yeast galactose metabolism

Harrison¹,

Ubbelohde²,

LaBella³

et al. 2023

Preprint

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How genomic differences contribute to phenotypic differences across species is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. In recent years, machine learning has been successfully used in diverse analyses of biological big data. Using a random forest classification algorithm trained on these genomic, metabolic, and/or environmental data, we predicted growth on several carbon sources and conditions with high accuracy from presence/absence patterns of genes and of growth in other conditions. Known structural genes involved in assimilation of these sources were important features contributing to prediction accuracy, whereas isolation environmental data were poor predictors. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.6%) or growth data in 120 other conditions (83.3%) but not from isolation environment data (65.7%). When we combined genomic and growth data, we noted that prediction accuracy was even higher (93.4%) and that, after theGALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol. These data raised the hypothesis that several species in two orders, Serinales and Pichiales (containingCandida aurisand the genusOgataea, respectively), have an alternative galactose utilization pathway because they lack theGALgenes. Growth and biochemical assays of several of these species confirmed that they utilize galactose through an oxidoreductive D-galactose pathway, rather than the canonicalGALpathway. We conclude that machine learning is a powerful tool for investigating the evolution of the yeast genotype-phenotype map and that it can help uncover novel biology, even in well-studied traits.

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“…For enzymatic conversion expensive enzymes are required (Sun et al 2012b). Microbial fermentation presents a high selectivity and yield conversion and is the main industrial 2-KGA (14) production method (Sun et al 2020).…”

Section: -Keto-d-gluconic Acidmentioning

confidence: 99%

“…Research also shows the use of new and robust promoters (Shi et al 2014) and vector modification (Shi et al 2015) to overexpress the enzyme and enhance its level of 2-KGA ( 14) production. Genetic modifications of other species have also been developed to increase the production of 2-KGA ( 14) and may contribute with a framework for the improvement of G. oxydans strains (Zeng et al 2019;Sun et al 2020).…”

Section: -Keto-d-gluconic Acidmentioning

confidence: 99%

The industrial versatility of Gluconobacter oxydans: current applications and future perspectives

Alves-Ribeiro

Oliveira

Lima

et al. 2022

World J Microbiol Biotechnol

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Gluconobacter oxydans is a well-known acetic acid bacterium that has long been applied in the biotechnological industry. Its extraordinary capacity to oxidize a variety of sugars, polyols, and alcohols into acids, aldehydes, and ketones is advantageous for the production of valuable compounds. Relevant G. oxydans industrial applications are in the manufacture of L-ascorbic acid (vitamin C), miglitol, gluconic acid and its derivatives, and dihydroxyacetone. Increasing efforts on improving these processes have been made in the last few years, especially by applying metabolic engineering. Thereby, a series of genes have been targeted to construct powerful recombinant strains to be used in optimized fermentation. Furthermore, low-cost feedstocks, mostly agro-industrial wastes or byproducts, have been investigated, to reduce processing costs and improve the sustainability of G. oxydans bioprocess. Nonetheless, further research is required mainly to make these raw materials feasible at the industrial scale. The current shortage of suitable genetic tools for metabolic engineering modifications in G. oxydans is another challenge to be overcome. This paper aims to give an overview of the most relevant industrial G. oxydans processes and the current strategies developed for their improvement.

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Two-Stage Semi-Continuous 2-Keto-Gluconic Acid (2KGA) Production by Pseudomonas plecoglossicida JUIM01 From Rice Starch Hydrolyzate

Cited by 14 publications

References 20 publications

Cascading and precise regulation of the selective bio-production of 2- or 5-ketogluconic acid from glucose with whole-cell catalysis technology

Cascading and precise regulation of the selective bio-production of 2- or 5-ketogluconic acid from glucose with whole-cell catalysis technology

Machine learning illuminates how diet influences the evolution of yeast galactose metabolism

The industrial versatility of Gluconobacter oxydans: current applications and future perspectives

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