Enhanced production of human Cytochrome P450 2C9 by Escherichia coli BL21(DE3)pLysS through the novel use of grey relational analysis and Plackett–Burman design

Abstract

Cytochrome P450 (CYP, P450) 2C9 is one of three human microsomal CYPs in subfamily 2C that contribute extensively to the hepatic metabolism of therapeutic drugs. The enhancement of recombinant CYP2C9 expression of a transformed E. coli strain is essential for the development of an efficient large-scale bioprocess. The recombinant CYP2C9 production is influenced by various factors, especially the medium components. The aim of this study is to optimize the culture medium of the recombinant E. coli, to determine the influence order of 11 factors, and to improve the yield of heterologously expressed CYP2C9. Plackett-Burman (PB) design, a statistical methodology, was used to screen 11 nutrients and medium components for the production of human CYP2C9 from E. coli BL21(DE3)pLysS harboring plasmid pCW2C9dH in aerobic shaking flask cultures. The experimental data were subject to statistical analysis for calculating the regression coefficients. The matrix of PB design was also used to construct the series that is needed for grey relational analysis (GRA), an approach totally different from traditional statistical analysis used to calculate the relational grade between two sequence data, to determine the influence priority of 11 parameters. Influence priority: glycerol > d-ALA > IPTG = ampicillin > chloramphenicol = inoculum density > peptone > thiamine > trace elements > NH 4 Cl > MgSO 4 . The coefficient for the individual factor estimated from PB design agrees the conclusion of GRA. This is the first report that combined two powerful analysis methods for the enhanced production of recombinant protein.

Influence of induction conditions on the expression of carbazole dioxygenase components (CarAa, CarAc, and CarAd) from Pseudomonas stutzeri in recombinant Escherichia coli using experimental design

“…The best approach is to use experimental design, which allows the most process information to be gathered at the lowest cost. It also makes it possible to evaluate which variables and interactions influence the expression of recombinant proteins, reducing the total number of experiments that need to be performed to obtain such information [14,15,18,19,49,51].…”

“…These techniques are still not usually employed for the analysis of expression variables related to specific target proteins (construct length and expression system), fermentation conditions (culture media and process time), or protein induction conditions in molecular biology practice. To our knowledge, there are some research groups using experimental design for recombinant protein expression in E. coli: for evaluating the effects of specific induction variables on heterologous expression, such as induction temperature, post-induction time, cell concentration at induction, inducer concentration, host strain, and plasmid [5, 8, 9, 14-16, 19, 28, 30, 32, 36, 37, 47, 49, 51-53]; for culture medium composition [6, 7, 11, 13-15, 18, 19, 22, 27, 28, 34, 35, 38, 40, 41, 45, 48, 51, 52, 54, 55]; for the concentration of antibiotics [14,15,18] used for selective pressure; for seed conditions and inoculum level/density [14,15,37,47]; for bioprocess variables, such as oxygen transfer rate, pH, temperature, fluid hydrodynamics, process time, as well as feeding strategy [11-13, 19, 40, 45, 53].…”