Optimization of fermentation conditions for P450 BM-3 monooxygenase production by hybrid design methodology

Optimization of fermentation conditions for P450 BM-3 monooxygenase production by hybrid design methodology first-author corresp ‡Corresponding Author ‡Corresponding Author †E-mail:meilh@zju.edu.cn †E-mail:meilh@zju.edu.cn meilh@zju.edu.cn Department of Chemical and Biochemical Engineering, Zhejiang University, Hangzhou 310027, China Department of Chemical and Biochemical Engineering, Zhejiang University, Hangzhou 310027, China Factorial design and response surface techniques were used to design and optimize increasing P450 BM-3 expression in E . coli . Operational conditions for maximum production were determined with twelve parameters under consideration: the concentration of FeCl 3 , induction at OD 578 (optical density measured at 578 nm), induction time and inoculum concentration. Initially, Plackett-Burman (PB) design was used to evaluate the process variables relevant in relation to P450 BM-3 production. Four statistically significant parameters for response were selected and utilized in order to optimize the process. With the 416C model of hybrid design, response surfaces were generated, and P450 BM-3 production was improved to 57.90×10 −3 U/ml by the best combinations of the physicochemical parameters at optimum levels of 0.12 mg/L FeCl 3 , inoculum concentration of 2.10%, induction at OD 578 equal to 1.07, and with 6.05 h of induction. Optimization P450 BM-3 Plackett-Burman (PB) design Hybrid design INTRODUCTION 1 P450 enzymes are found throughout nature and can catalyze the oxidation of various chemicals (Ortiz de Montellano, 1995 1995 ). Because of their important roles in these processes, numerous systems have been developed for the expression of P450s in vitro (Helvig and Capdevila, 2000 2000 ; Nakamura et al., 2001 2001 ; Deeni, 2001 2001 ). But P450 enzymes fermentation levels in the microbial system are too low to catalyze the reactions for industrial preparations. Therefore, it is necessary to improve fermentation conditions to obtain maximum P450 levels. Optimization of enzyme production and activity requires consideration of a large number of potentially influential factors, so it is essential to select the more important ones prior to optimization. The classical method—studying one variable at a time while holding all others constant—is extremely inefficient in many cases. In recent years, a number of statistical experimental designs, which include regression analysis, factorial design and EVOP (evolutionary operation), have been employed for studying interaction and selecting optimal conditions (Kar et al., 2002 2002 ). However, even these either involve a large number of experiments or are too restricted in the number of variables which can be studied. Here, the methodology of Plackett-Burman was used as a tool for the initial screening, and is based on balanced incomplete blocks (Plackett and Burman, 1946 1946 ). Instead of using more extensive factorial design, the statistical procedure would furnish more complete information. Each design of N experiments can be used to study up to N −1 variables. After initial screening, response surface analysis can be limited to the most critical variables for determining their optimum operation conditions. Recently, many statistical experimental designs with response surface methodology have been employed for optimizing enzyme production from microorganism (de Coninck et al., 2000 2000 ; Murthy et al., 2000 2000 ; Kalil et al., 2000 2000 ; Uyar and Baysal, 2004 2004 ; Dahiya et al., 2005 2005 ). Hybrid design is a superior and more accurate modelling technique when compared to the response surface methodology (Roquemore, 1976 1976 ). Response surfaces plotted by 3D plots can provide a good way for visualizing the parameter interaction. Therefore, both techniques can be used together to predict optimum process conditions for microbial enzyme production. The aim of this work was to increase the production of P450 BM-3 by optimizing the fermentation conditions. Therefore, Plackett-Burman design based on screening design and factorial design was used to determine the relevant factors, and then the factors having more significant effect were further optimized by hybrid design methodology and response surface analysis. MATERIALS AND METHODS 1 Microorganism 2 The basis system involving the construction of plasmid and the expression of P450 BM-3 is described elsewhere (Li et al., 2000 2000 ; 2005 2005 ). pET28a(+) expresses P450 BM-3 with three sites mutagenesis (D168N, A225V, K440N) in E . coli BL21. Chemicals 2 All chemicals used were of analytical grade. The yeast extract, tryptone, kanamycin, NADH (reduced nicotinamide adenine dinucleotide) and IPTG (isopropyl-β-D-thiogalactoside) were purchased from Shanghai Sangon Biological Engineering & Technology and Service Co. Ltd. (Shanghai, China). Medium and culture conditions 2 The strain was grown aerobically on LB medium containing 30 μg/ml kanamycin overnight at 37 °C in tube. Aliquots of the cell culture from the tube were transferred to 50 ml LB medium supplemented with 30 μg/ml kanamycin and 0.1 mg/L FeCl 3 in a 250 ml flask. The shaking flask was incubated at 180 r/min and 37 °C. After reaching an optical density at 578 nm (OD 578 ) of 0.8, P450 BM-3 expression was induced by adding 0.5 mmol/L IPTG for 5 h. E . coli cells containing P450 BM-3 were harvested. The most important influential parameters were selected by Plackett-Burman (PB) design and optimized by hybrid design to maximize the P450 BM-3 production. Enzyme preparation 2 For enzyme preparation, the cells were centrifuged at 8000 r/min for 15 min, washed once with 0.1 mol/L potassium phosphate buffer (pH 7.4), then resuspended in the same buffer (5 ml per 50 ml medium), and broken up under ice-cooling with Sonifier (output level 200 W, three 2 min duty cycles of 20% each). The suspension was centrifuged for 20 min at 8000 r/min. The crude extracts were used directly for the activity assay. Activity assay of P450 BM-3 2 The activity assay was carried out as modified according to Schwaneberg et al.( 1999 1999 ). For all assay procedures, after addition of 25 μl of 10- p -NCA ( p -nitrophenoxydecanoic acid) solution in DMSO (dimethyl sulfoxide) (25 mmol/L), 1725 μl Tris/HCl buffer (pH 8.2, 0.1 mol/L), and 500 µl of P450 BM-3 extract solution, the samples were preincubated for 5 min. Then the reaction was started by adding 250 μl aqueous NADH solution (6 mmol/L). The formation of p -nitrophenolate was determined from the absorption at 410 nm [with an extinction coefficient=13.2 L/(mmol∙cm)]. The enzyme activity was defined as the initial rate of p -nitrophenolate formation, with the unit of activity being the amount of enzyme that produces 1 μmol of p -nitrophenolate per minute under the assay conditions. Experimental design 2 1. Plackett-Burman (PB) design 3 PB designs are very useful for screening process variables from a large number of factors, and require fewer runs than other factorial designs (Stowe and Mayer, 1966 1966 ). The influence of twelve variables on P450 BM-3 activity was investigated using the methodology of PB design. Each of the independent variables was tested at two levels, a high level (+) and a low level (−), as shown in Table 1 1 . 2 2 Level −1 +1 Yeast extract (g/L) 5.0 7.5 Tryptone (g/L) 10 15 NaCl (g/L) 10 15 Original pH 7.0 7.5 Kanamycin (μg/ml) 30 45 FeCl 3 (mg/L) 0.10 0.15 Induction at OD 578 0.8 1.2 IPTG (mmol/L) 0.50 0.75 Induction time (h) 5 7 Induction temperature (°C) 30 35 Inoculum concentration (%) 1 2 Medium volume in 250 ml flask 50 75 2. Hybrid design 3 After the four relevant variables were selected by screening, hybrid design was used to obtain a quadratic model with the P450 BM-3 activity as response. The empirical polynomial model for four factors was in the following form: $$y = \beta _0 + \sum\limits_{i = 1}^k {\beta _i X_i } + \sum\limits_{i = 1}^{j - 1} {\sum\limits_{j = 1}^k {\beta _{ij} X_i X_j } + \sum\limits_{i = 1}^k {\beta _{ii} X_i^2 } }$$ , where y was the predicted response as the dependent variable; X i ( i =1, 2, 3 and 4) were the controlling variables or input predictors; and β 0 , β i ( i =1, 2, 3 and 4), β ij ( i =1, 2, 3 and 4; j = i , …, 4) and β ii were the regression coefficients. They were estimated by multiple non-linear regression analysis. Surfaces were then built using the quadratic model for the statistically significant variables. Table 2 2 presents the values in actual and coded form of the variables used to optimize the P450 BM-3 activity. The first three variables were studied at five different levels, and the last variable at four different levels. All the variables were taken with the central coded value considered as zero. 2 Coded values ( X i ) 4 Variables ( x i ) FeCl 3 (mg/L) Induction at OD 578 Induction time (h) Inoculum concentration (%) −1.4697 0.088 0.71 4.530 − −1.0509 − − − 0.975 −1.0000 0.100 0.80 5.000 1.000 0.0000 0.125 1.00 6.000 1.500 0.5675 − − − 1.784 1.0000 0.150 1.20 7.000 2.000 1.4697 0.162 1.29 7.470 − 1.7654 − − − 2.383 RESULTS AND DISCUSSION 1 Screening design 2 Table 3 3 presents the PB design matrix and the results obtained for P450 BM-3 activity from experiments. A, B, C, E, F, G, I, J, K, M, N, O are yeast extract, tryptone, NaCl, original pH, kanamycin, FeCl 3 , induction at OD 578 , IPTG, induction time, induction temperature, inoculum concentration, and medium volume in 250 ml flask, respectively; D, H, L are dummy variables Run A B C D E F G H I J K L M N O P450 BM-3 activity (×10 −3 U/ml) 1 + + + + − + − + + − − + − − − 37.88 2 + + + − + − + + − − + − − − + 35.35 3 + + − + − + + − − + − − − + + 27.78 4 + − + − + + − − + − − − + + + 29.85 5 − + − + + − − + − − − + + + + 41.34 6 + − + + − − + − − − + + + + − 45.76 7 − + + − − + − − − + + + + − + 37.60 8 + + − − + − − − + + + + − + − 40.53 9 + − − + − − − + + + + − + − + 32.30 10 − − + − − − + + + + − + − + + 29.55 11 − + − − − + + + + − + − + + − 27.61 12 + − − − + + + + − + − + + − − 39.47 13 − − − + + + + − + − + + − − + 25.25 14 − − + + + + − + − + + − − + − 49.24 15 − + + + + − + − + + − − + − − 25.83 16 − − − − − − − − − − − − − − − 23.67 Dummy variables were used as the measure of variability for PB design. They gave a direct estimate of the standard error of a factor effect. The effects of the variables on each response are presented in Table 4 4 . P450 BM-3 production was affected by FeCl 3 concentration, inoculum concentration, induction at OD 578 , and induction time. 2 Variable Effect 2 Relative significance Code Factor (+) to (−) t -test Confidence level (%) A Yeast extract (g/L) 3.60 0.799 78.16 B Tryptone (g/L) 0.15 0.033 51.29 C NaCl (g/L) 4.14 0.919 81.37 D Dummy 2.72 − − E Original pH 3.09 0.686 74.84 F Kanamycin (µg/ml) 0.04 0.009 50.35 G FeCl 3 (mg/L) −4.48 −0.994 83.20 H Dummy 4.56 − − I Induction at OD 578 −6.43 −1.427 91.30 J IPTG (mmol/L) 1.95 0.432 66.41 K Induction time (h) 4.79 1.063 84.77 L Dummy 5.72 − − M Induction temperature (°C) 1.31 0.292 61.29 N Inoculum concentration (%) 4.29 0.952 82.19 O Medium volume in 250 ml flask −3.87 −0.860 79.83 As shown in Table 4 4 , an increase in FeCl 3 concentration led to a decrease in P450 BM-3 activity. Increasing inoculum concentration and induction time, however, led to an increase in P450 BM-3 activity. In relation to the compositions of medium, increasing the concentrations of yeast extract, tryptone and NaCl resulted in an increase in P450 BM-3 activity, although this effect did not reach statistical significance. Table 4 4 also shows that original pH, the concentrations of kanamycin and IPTG, and induction temperature did not exert statistically significant effects on P450 BM-3 activity. These results determine which at the variables have more significant effect on the responses. So the variables selected for further optimization were the following: FeCl 3 concentration, inoculum concentration, induction at OD 578 , and induction time. Hybrid design 2 Table 5 5 shows the experimental design and the results for P450 BM-3 activity from experiments and predicted responses. Based on the principle of saving costs, yeast extract, tryptone, NaCl, kanamycin and IPTG were fixed at 5 g/L, 10 g/L, 10 g/L, 30 µg/ml and 0.5 mmol/L, respectively, and medium volume was 50 ml in 250 ml flask. The original pH of medium and induction temperature were fixed at 7.5 and 35 °C according to their effects of PB design. Run FeCl 3 Induction at OD 578 Induction time Inoculum concentration Observed response (×10 −3 U/ml) Predicted response (×10 −3 U/ml) 1 0 0 0 1.7654 56.44 56.44 2 −1 −1 −1 0.5675 33.33 34.37 3 1 −1 −1 0.5675 44.92 43.88 4 −1 1 −1 0.5675 41.93 40.89 5 1 1 −1 0.5675 41.29 42.33 6 −1 −1 1 0.5675 39.92 38.88 7 1 −1 1 0.5675 35.53 36.57 8 −1 1 1 0.5675 48.79 49.83 9 1 1 1 0.5675 40.49 39.45 10 1.4697 0 0 −1.0509 46.29 46.29 11 −1.4697 0 0 −1.0509 40.27 40.27 12 0 1.4697 0 −1.0509 36.59 36.59 13 0 −1.4697 0 −1.0509 48.14 48.14 14 0 0 1.4697 −1.0509 59.39 59.39 15 0 0 −1.4697 −1.0509 50.45 50.45 16 0 0 0 0 57.20 57.20 Date were analyzed by non-linear multiple regression using SAS software (SAS Institute Inc., USA). The equation with the optimized coefficients is given by: $$\begin{gathered}y = 57.2 + 0.5769X_1 + 0.1481X_2 + 1.3311X_3 \\{\text{ }} - 0.8670X_4 - 6.9926X_1^2 - 2.0175X_1 X_2 \\{\text{ }} - 2.9550X_1 X_3 - 1.3999X_1 X_4 - 7.4162X_2^2 \\{\text{ }} + 1.1075X_2 X_3 + 3.880X_2 X_4 - 1.6038X_3^2 \\{\text{ }} - 1.6274X_3 X_4 + 0.2472X_4^2 \\\end{gathered}$$ , where y is the output response (i.e. P450 BM-3 activity), X 1 , X 2 , X 3 and X 4 are the coded values of FeCl 3 concentration, induction at OD 578 , induction time and inoculum concentration, respectively. Table 6 6 shows the analysis of variance (ANOVA) for P450 BM-3 activity. The response has a very high correlation coefficient of 0.995. Source Sum of squares Mean square Degree of freedom F -value Model 921.98 65.86 14 7.61 Residual 8.65 8.65 1 − Total 930.63 − 15 − Correlation coefficient 0.995 − − − To study the interactions of variables and analyze the P450 BM-3 activity, three-dimensional response surfaces are plotted in Fig. 1 1 . The response surfaces were plotted to define the optimal conditions for P450 BM-3 activity. All possible combinations of the four variables were studied. Figs. 1a, 1d 1a, 1d and 1e 1e revealed that P450 BM-3 activity was maximal when the coded value of induction at OD 578 was at the central point, which corresponded to the actual value of 1.0. And Figs. 1a, 1b 1a, 1b and 1c 1c showed that when the coded value of FeCl 3 was at the central point, which was equal to the actual value of 0.125 mg/L, high P450 BM-3 activity was obtained. Figs. 1c, 1e 1c, 1e and 1f 1f revealed that P450 BM-3 activity increased greatly when inoculum concentration was increased. The optimal operating conditions obtained from the polynomial model were FeCl 3 of 0.12 mg/L, inoculum volume of 2.10 ml in 100 ml medium, when OD 578 equal to 1.07, IPTG added to the cultures, and in 6.05 h of induction with 57.41×10 −3 U/ml of predicted P450 BM-3 activity. Under the conditions, the P450 BM-3 production in the experimental result was 57.90×10 −3 U/ml. While when the initial conditions were adopted, which corresponded to FeCl 3 of 0.10 mg/L, inoculum volume of 1.0 ml in 100 ml medium, when OD 578 equals 0.8, IPTG was added to the cultures, and in 5 h of induction, P450 BM-3 activity was at a lower level of 37.62×10 −3 U/ml. Thus by optimizing the fermentation process parameters using hybrid design, P450 BM-3 production was increased from 37.62×10 −3 to 57.90×10 −3 U/ml. CONCLUSION 1 The methodology of Plackett-Burman is very useful for selecting relevant variables for further optimization. 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