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大肠杆菌表达及分泌内皮抑素蛋白:采用响应面法优化培养条件

Expression and Secretion of Endostar Protein by Escherichia Coli: Optimization of Culture Conditions Using the Response Surface Methodology.

作者信息

Mohajeri Abbas, Abdolalizadeh Jalal, Pilehvar-Soltanahmadi Younes, Kiafar Farhad, Zarghami Nosratollah

机构信息

Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.

Department of Biotechnology, Zahravi pharmaceutical company, Tabriz, Iran.

出版信息

Mol Biotechnol. 2016 Oct;58(10):634-647. doi: 10.1007/s12033-016-9963-9.

Abstract

Endostar as a specific drug in treatment of the nonsmall cell lung cancer is produced using Escherichia coli expression system. Plackett-Burman design (PBD) and response surface methodology (RSM) are statistical tools for experimental design and optimization of biotechnological processes. This investigation aimed to predict and develop the optimal culture condition and its components for expression and secretion of endostar into the culture medium of E. coli. The synthetic endostar coding sequence was fused with PhoA signal peptide. The nine factors involved in the production of recombinant protein-postinduction temperature, cell density, rotation speed, postinduction time, concentration of glycerol, IPTG, peptone, glycine, and triton X-100-were evaluated using PBD. Four significant factors were selected based on PBD results for optimizing culture condition using RSM. Endostar was purified using cation exchange chromatography and size exclusion chromatography. The maximum level of endostar was obtained under the following condition: 13.57-h postinduction time, 0.76 % glycine, 0.7 % triton X-100, and 4.87 % glycerol. The predicted levels of endostar was significantly correlated with experimental levels (R 2 = 0.982, P = 0.00). The obtained results indicated that PBD and RSM are effective tools for optimization of culture condition and its components for endostar production in E. coli. The most important factors in the enhancement of the protein production are glycerol, glycine, and postinduction time.

摘要

恩度作为一种治疗非小细胞肺癌的特效药物,是采用大肠杆菌表达系统生产的。Plackett-Burman设计(PBD)和响应面法(RSM)是用于生物技术过程实验设计和优化的统计工具。本研究旨在预测和开发最佳培养条件及其成分,以便在大肠杆菌培养基中表达和分泌恩度。合成的恩度编码序列与PhoA信号肽融合。使用PBD评估了重组蛋白生产中涉及的九个因素——诱导后温度、细胞密度、转速、诱导后时间、甘油、IPTG、蛋白胨、甘氨酸和曲拉通X-100的浓度。根据PBD结果选择四个显著因素,使用RSM优化培养条件。通过阳离子交换色谱法和尺寸排阻色谱法纯化恩度。在以下条件下获得了恩度的最高水平:诱导后时间13.57小时、0.76%的甘氨酸、0.7%的曲拉通X-100和4.87%的甘油。恩度的预测水平与实验水平显著相关(R² = 0.982,P = 0.00)。所得结果表明,PBD和RSM是优化大肠杆菌中恩度生产培养条件及其成分的有效工具。提高蛋白质产量的最重要因素是甘油、甘氨酸和诱导后时间。

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