Characterization of glucose isomerase-producing bacteria and optimization of fermentation conditions for producing glucose isomerase using biomass

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-06-01 DOI:10.1016/j.gce.2022.05.003
Aristide Laurel Mokale Kognou , Chonlong Chio , Janak Raj Khatiwada , Sarita Shrestha , Xuantong Chen , Hongwei Li , Yuen Zhu , Zi-Hua Jiang , Chunbao (Charles) Xu , Wensheng Qin
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引用次数: 4

Abstract

Glucose isomerase (GI) is an enzyme with high potential applications. Characterization of GI producing bacteria with interesting properties from an industrial point of view is essential. Bacillus sp., Paenarthrobacter sp., Chryseobacterium sp., Hymenobacter sp., Mycobacterium sp., and Stenotrophomonas sp. were isolated from soil samples. Optimization of enzyme production yield was investigated in various fermentation conditions using response surface methodology. All isolates exhibited maximum GI activity at 40 °C, pH 6–8 after 4 days of incubation. A mixture of peptone/yeast extract or tryptone/peptone enhanced higher enzyme production. The same trend was observed in fermentation medium containing 1% xylose or 2%–2.5% wheat straw. This study advanced the knowledge of these bacterial isolates in promoting wheat straw as feedstock for the bio-based industry.

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产糖异构酶细菌的鉴定及生物质产糖异构酶发酵条件的优化
葡萄糖异构酶(GI)是一种应用前景广阔的酶。从工业角度对具有有趣特性的GI产生菌进行表征是至关重要的。从土壤样品中分离出芽孢杆菌属(Bacillus sp.)、Paenarvonacter sp.、Chrysobacterium sp.、膜壳杆菌属(Hymenobacter sp..)、分枝杆菌属(Mycobacterum sp.)和狭窄单胞菌属(Stenotrophomonas sp.)。采用响应面法研究了不同发酵条件下酶产量的优化。培养4天后,所有分离株在40°C、pH 6-8时均表现出最大的GI活性。蛋白胨/酵母提取物或胰蛋白酶/蛋白胨的混合物提高了较高的酶产量。在含有1%木糖或2%–2.5%麦秆的发酵培养基中也观察到了相同的趋势。本研究在推广麦草作为生物工业原料方面提高了对这些菌株的认识。
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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
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OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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