Zare Hamze, Mir Mohammad Sadeghi Hamid, Akbari Vajihe
Department of Pharmaceutical Biotechnology and Isfahan Pharmaceutical Research Center, Faculty of Pharmacy, Isfahan University of Medical Sciences, Isfahan, Iran.
Avicenna J Med Biotechnol. 2019 Apr-Jun;11(2):162-168.
Expression of heterologous proteins at large scale is often a challenging job due to plasmid instability, accumulation of acetate and oxidative damage in bioreactors. Therefore, it is necessary to optimize parameters influencing cell growth and expression of recombinant protein.
In the present study, the optimal culture conditions for expression of reteplase by ( BL21 (DE3) in a bench-top bioreactor was determined. Response Surface Methodology (RSM) based on Box-Behnken design was used to evaluate the effect of three variables (., temperature, shaking speed and pH) and their interactions with cellular growth and protein production. The obtained data were analyzed by Design Expert software.
Based on results of 15 experiments, a response surface quadratic model was developed which was used to explain the relation between production of reteplase and three investigated variables. The high value of "R-Squared" (0.9894) and F-value of 51.99 confirmed the accuracy of this model. According to the developed model, the optimum fermentation conditions for reteplase expression were temperature of 32, shaking speed of 210 , and pH of 8.4. This predicted condition was applied for the production of reteplase in the bioreactor leading to a protein yield of 188 .
Our results indicate the significant role of culture conditions (., pH, temperature and oxygen supply) in protein expression at large scale and confirm the need for optimization. The proposed strategy here can also be applied to experimental set-up of optimization for fermentation of other proteins.
由于质粒不稳定、生物反应器中乙酸积累和氧化损伤,大规模表达异源蛋白通常是一项具有挑战性的工作。因此,有必要优化影响细胞生长和重组蛋白表达的参数。
在本研究中,确定了在台式生物反应器中由大肠杆菌BL21(DE3)表达瑞替普酶的最佳培养条件。基于Box-Behnken设计的响应面方法(RSM)用于评估三个变量(即温度、振荡速度和pH)及其与细胞生长和蛋白质生产的相互作用。所得数据用Design Expert软件进行分析。
基于15次实验的结果,建立了一个响应面二次模型,用于解释瑞替普酶产量与三个研究变量之间的关系。“决定系数”的高值(0.9894)和F值51.99证实了该模型的准确性。根据建立的模型,瑞替普酶表达的最佳发酵条件为温度32℃、振荡速度210 rpm和pH 8.4。将该预测条件应用于生物反应器中瑞替普酶的生产,导致蛋白质产量为188 mg/L。
我们的结果表明培养条件(如pH、温度和氧气供应)在大规模蛋白质表达中的重要作用,并证实了优化的必要性。这里提出的策略也可应用于其他蛋白质发酵优化的实验设置。