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首页> 外文期刊>Biotechnology Progress >Framework for the Rapid Optimization of Soluble Protein Expression in Escherichia coli Combining Microscale Experiments and Statistical Experimental Design
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Framework for the Rapid Optimization of Soluble Protein Expression in Escherichia coli Combining Microscale Experiments and Statistical Experimental Design

机译：结合微尺度实验和统计实验设计快速优化大肠杆菌中可溶性蛋白表达的框架

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A major bottleneck in drug discovery is the production of soluble human recombinant protein in sufficient quantities for analysis.This problem is compounded by the complex relationship between protein yield and the large number of variables which affect it.Here,we describe a generic framework for the rapid identification and optimization of factors affecting soluble protein yield in microwell plate fermentations as a prelude to the predictive and reliable scaleup of optimized culture conditions.Recombinant expression of firefly luciferase in Escherichia coli was used as a model system.Two rounds of statistical design of experiments (DoE) were employed to first screen (D-optimal design) and then optimize (central composite face design) the yield of soluble protein.Biological variables from the initial screening experiments included medium type and growth and induction conditions.To provide insight into the impact of the engineering environment on cell growth and expression,plate geometry,shaking speed,and liquid fill volume were included as factors since these strongly influence oxygen transfer into the wells.Compared to standard reference conditions,both the screening and optimization designs gave up to 3-fold increases in the soluble protein yield,i.e.,a 9-fold increase overall.In general the highest protein yields were obtained when cells were induced at a relatively low biomass concentration and then allowed to grow slowly up to a high final biomass concentration,>8 g·L~(-1).Consideration and analysis of the model results showed 6 of the original 10 variables to be important at the screening stage and 3 after optimization.The latter included the microwell plate shaking speeds pre-and postinduction,indicating the importance of oxygen transfer into the micro wells and identifying this as a critical parameter for subsequent scale translation studies.The optimization process,also known as response surface methodology (RSM),predicted there to be a distinct optimum set of conditions for protein expression which could be verified experimentally.This work provides a generic approach to protein expression optimization in which both biological and engineering variables are investigated from the initial screening stage.The application of DoE reduces the total number of experiments needed to be performed,while experimentation at the microwell scale increases experimental throughput and reduces cost.
机译：药物发现的主要瓶颈是产生足够量的可溶性人类重组蛋白以供分析。蛋白质产量与影响其的大量变量之间的复杂关系使这一问题更加复杂。在此，我们描述了一种通用的框架。快速鉴定和优化影响微孔板发酵的可溶性蛋白产量的因素，以预测和可靠地优化优化培养条件的规模。萤火虫荧光素酶在大肠杆菌中的重组表达作为模型系统。两轮统计设计（DoE）用于首先筛选（D-最佳设计），然后优化（中央复合面部设计）可溶性蛋白的产量。初始筛选实验的生物学变量包括培养基类型，生长和诱导条件。工程环境对细胞生长和表达的影响，平板几何y，摇动速度和液体填充量作为因素被包括在内，因为它们强烈影响氧气向井中的转移。与标准参考条件相比，筛选和优化设计都使可溶性蛋白质产量提高了3倍，即总体上提高了9倍。通常，以相对较低的生物量浓度诱导细胞，然后使其缓慢生长直至最终的生物量浓度较高，> 8 g·L〜（-1），可以获得最高的蛋白质产量。对模型结果的考虑和分析表明，最初的10个变量中的6个在筛选阶段很重要，而在优化后则有3个。后者包括诱导前和诱导后的微孔板摇动速度，表明氧转移到微孔中的重要性和优化过程，也称为响应面方法（RSM），预测会有一个独特的最佳集合这项工作为蛋白质表达优化提供了一种通用方法，该方法从最初的筛选阶段就研究了生物学和工程变量。DoE的应用减少了需要进行的实验总数，同时在微孔规模进行实验可提高实验通量并降低成本。

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