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通过响应曲面法利用新分离的巨大芽孢杆菌菌株B69同时生产淀粉酶和蛋白酶

Contemporaneous Production of Amylase and Protease through CCD Response Surface Methodology by Newly Isolated Bacillus megaterium Strain B69.

作者信息

Saxena Rajshree, Singh Rajni

机构信息

Amity Institute of Microbial Biotechnology, Amity University, Sector 125, Noida, Uttar Pradesh 201303, India.

出版信息

Enzyme Res. 2014;2014:601046. doi: 10.1155/2014/601046. Epub 2014 Nov 12.

Abstract

The enormous increase in world population has resulted in generation of million tons of agricultural wastes. Biotechnological process for production of green chemicals, namely, enzymes, provides the best utilization of these otherwise unutilized wastes. The present study elaborates concomitant production of protease and amylase in solid state fermentation (SSF) by a newly isolated Bacillus megaterium B69, using agroindustrial wastes. Two-level statistical model employing Plackett-Burman and response surface methodology was designed for optimization of various physicochemical conditions affecting the production of two enzymes concomitantly. The studies revealed that the new strain concomitantly produced 1242 U/g of protease and 1666.6 U/g of amylase by best utilizing mustard oilseed cake as the substrate at 20% substrate concentration and 45% moisture content after 84 h of incubation. An increase of 2.95- and 2.04-fold from basal media was observed in protease and amylase production, respectively. ANOVA of both the design models showed high accuracy of the polynomial model with significant similarities between the predicted and the observed results. The model stood accurate at the bench level validation, suggesting that the design model could be used for multienzyme production at mass scale.

摘要

世界人口的大幅增长导致了数百万吨农业废弃物的产生。生产绿色化学品(即酶)的生物技术工艺能对这些原本未被利用的废弃物进行最佳利用。本研究阐述了新分离的巨大芽孢杆菌B69在固态发酵(SSF)中利用农业工业废弃物同时生产蛋白酶和淀粉酶的情况。采用Plackett-Burman和响应面方法的二级统计模型用于优化同时影响两种酶生产的各种物理化学条件。研究表明,新菌株在20%底物浓度和45%水分含量条件下,以芥子油籽饼为底物,培养84小时后,能同时产生1242 U/g的蛋白酶和1666.6 U/g的淀粉酶。与基础培养基相比,蛋白酶和淀粉酶的产量分别提高了2.95倍和2.04倍。两个设计模型的方差分析均显示多项式模型具有较高的准确性,预测结果与观察结果之间存在显著相似性。该模型在实验室水平验证中表现准确,表明该设计模型可用于大规模多酶生产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c44c/4244924/b581e2883ba2/ER2014-601046.001.jpg

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