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解淀粉芽孢杆菌SS35产羧甲基纤维素酶的优化

Optimization of carboxymethylcellulase production from Bacillus amyloliquefaciens SS35.

作者信息

Singh Shuchi, Moholkar Vijayanand S, Goyal Arun

机构信息

Center for Energy, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India.

Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India.

出版信息

3 Biotech. 2014 Aug;4(4):411-424. doi: 10.1007/s13205-013-0169-6. Epub 2013 Sep 6.

DOI:10.1007/s13205-013-0169-6
PMID:28324477
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4145630/
Abstract

In this paper, we have attempted optimization of production of enzyme carboxymethylcellulase or endoglucanase from the bacterium Bacillus amyloliquefaciens SS35. Optimization has been carried out in two stages using statistical experimental design, viz. medium optimization and optimization of fermentation parameters. For medium optimization, Plackett-Burman design followed by central composite design (CCD) was used, while for optimization of fermentation parameters one-variable-at-a-time method followed by CCD was used. Carbon and nitrogen sources in the medium have been revealed to be the significant factors for enzyme production (carboxymethylcellulose 18.05 g/L; yeast extract 8 g/L and peptone 2 g/L). The inorganic salts have been found to be insignificant components of medium. Optimum fermentation parameters for optimized medium were: initial medium pH 5.65, incubation temperature = 40 °C, shaking speed = 120 rpm, and inoculum size = 6.96 %, v/v. Interestingly, the influence of all four parameters was almost independent with no interlinks. Secondly, the overall effect of all parameters was also low, as indicated by linear, square and interaction regression coefficients that were at least one order of magnitude lower than the intercept in the model equation. These results essentially meant that medium components dominate overall enzyme production process in comparison to fermentation parameters.

摘要

在本文中,我们尝试对解淀粉芽孢杆菌SS35产羧甲基纤维素酶或内切葡聚糖酶进行优化。利用统计实验设计分两个阶段进行优化,即培养基优化和发酵参数优化。对于培养基优化,采用了Plackett-Burman设计,随后进行中心复合设计(CCD);而对于发酵参数优化,采用了一次一个变量法,随后进行CCD。结果表明,培养基中的碳源和氮源是产酶的重要因素(羧甲基纤维素18.05 g/L;酵母提取物8 g/L和蛋白胨2 g/L)。已发现无机盐是培养基中的非重要成分。优化培养基的最佳发酵参数为:初始培养基pH 5.65、培养温度 = 40°C、振荡速度 = 120 rpm、接种量 = 6.96%,v/v。有趣的是,所有四个参数的影响几乎相互独立,没有相互联系。其次,所有参数的总体影响也较低,线性、平方和交互回归系数表明其至少比模型方程中的截距低一个数量级。这些结果本质上意味着与发酵参数相比,培养基成分在整个产酶过程中起主导作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/c70c69fb69e2/13205_2013_169_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/394614c6f317/13205_2013_169_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/3bb5fa7ed868/13205_2013_169_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/c70c69fb69e2/13205_2013_169_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/394614c6f317/13205_2013_169_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/3bb5fa7ed868/13205_2013_169_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ab5/4145630/c70c69fb69e2/13205_2013_169_Fig3_HTML.jpg

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