| 一株假单胞菌产脱氢酶的产酶条件优化 |
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| 引用本文： | 雒晓芳,王福彬,陈丽华,王冬梅.一株假单胞菌产脱氢酶的产酶条件优化[J].环境科学研究,2018,31(9):1628-1635. |
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| 作者姓名： | 雒晓芳 王福彬 陈丽华 王冬梅 |
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| 作者单位： | 1.西北民族大学实验中心, 甘肃 兰州 730030 |
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| 基金项目： | 中国石油集团川庆钻探工程有限公司技术开发项目（No.cqzt-cqjx-2017-zcj-032）；甘肃省自然科学基金项目（No.17JR5RA287）；西北民族大学基本科研业务费专项（No.31920170169） |
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| 摘 要： | 为提高假单胞菌（Pseudomonas sp.）降解蒽、芘过程中产生的脱氢酶量，以脱氢酶量为指标，采用Box-Behnken试验设计方法和响应面分析法（RSM）对产酶条件（如温度、盐度以及质量梯度）进行了筛选与优化.结果表明:假单胞菌降解蒽和芘模型的P均小于0.05，表明该模型差异性显著，回归效果良好.另外，假单胞菌降解蒽时的最适产酶条件为盐度3.89%、质量梯度5%、温度35.73℃，测得脱氢酶量为（140.353±6.430）μg，与预期值（141.466 μg）接近，优化结果可靠.各因素对脱氢酶量均有显著性影响，按影响程度从大到小依次为温度>质量梯度>盐度.假单胞菌降解芘时的最适产酶条件为盐度0.73%、质量梯度7%、温度34.78℃，测得脱氢酶量为（84.032±0.063）μg，与预期值（86.304 μg）接近，优化结果可靠.各因素对脱氢酶量均有显著性影响，按影响程度从大到小依次为质量梯度>盐度>温度.可见，温度是影响假单胞菌降解蒽物质的主要因素，而质量梯度则是影响芘的主要因素.研究显示，模型对假单胞菌产脱氢酶量的预测可靠，具有良好的工业应用前景.
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| 关 键 词： | 蒽 芘 脱氢酶量 响应面分析法 优化 |
| 收稿时间： | 2017/11/30 0:00:00 |
| 修稿时间： | 2018/6/12 0:00:00 |
Optimization of Enzyme Conditions for Dehydrogenase Activity by a Pseudomonas sp. |
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| LUO Xiaofang,WANG Fubin,CHEN Lihua and WANG Dongmei.Optimization of Enzyme Conditions for Dehydrogenase Activity by a Pseudomonas sp.[J].Research of Environmental Sciences,2018,31(9):1628-1635. |
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| Authors: | LUO Xiaofang WANG Fubin CHEN Lihua and WANG Dongmei |
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| Affiliation: | 1.Center of Experiment, Northwest University for Nationality, Lanzhou 7300302.College of Life Science and Engineering, Northwest University for Nationalities, Lanzhou 730030, China |
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| Abstract: | A high-efficiency Pseudomonas sp. was separated from oil-contaminated soil and its dehydrogenase activities toward anthracene and pyrene were investigated. Based on the amount of dehydrogenase (DA), the Box-Behnken model and response surface methodology (RSM) were adopted to screen and optimize the fermentation conditions, i.e., temperature, salinity and weight percentage of quality gradient. The results showed that the P values of these models were all less than 0.05, indicating the models were significant and fitted well with experimental data. For anthracene, the optimal dehydrogenase conditions were:3.89% salinity, 35.73℃, 5% quality gradient. Under these conditions, the observed DA was (140.353±6.430)μg, which was close to the predicted value of 141.466 μg. Hence, the optimized treatment conditions were reliable. All factors could significantly influence the DA, and the order of importance was temperature > quality gradient > salinity. However, the optimal dehydrogenase conditions for pyrene were:0.73% salinity, 34.78℃, 7% quality gradient. Under these conditions, the observed DA was (84.032±0.063)μg, which was close to the predicted value of 86.304 μg. Hence, the optimized treatment conditions were also reliable. All factors could significantly affect the DA, and the order of importance was quality gradient > salinity > temperature. It can be seen that temperature is the main factor that determines the degradation of anthracene, while quality gradient is the main factor that controls the degradation of pyrene. Our results showed that these models were reliable to predict the DA of the Pseudomonas sp. and were suitable for industrial application. |
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| Keywords: | anthracene pyrene amount of dehydrogenase response surface methodology optimization |
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