Biotechnology Letters 24: 1217–1222, 2002. © 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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Optimization of thermostable lipase production from a thermophilic Geobacillus sp. using Box-Behnken experimental design Yasser Refaat Abdel-Fattah Department of Bioprocess Development, Genetic Engineering & Biotechnology Research Institute, Mubarak City for Scientific Research & Technology Applications, New Burg El-Arab City, Alexandria, Egypt (Fax: +203-4593423; E-mail:
[email protected]) Received 13 February 2002; Revisions requested 19 February 2002/5 April 2002; Revisions received 21 March 2002/13 May 2002; Accepted 14 May 2002
Key words: Box-Behnken design, Geobacillus thermoleovorans, medium optimization, thermostable lipase
Abstract Thermostable lipase production by Geobacillus thermoleovorans was optimized in shake-flask cultures using Box-Behnken experimental design. An empirical model was developed through response surface methodology to describe the relationship between tested variables (Tween 80, olive oil, temperature and pH) and enzyme activity. Maximum enzyme activity (495 U l−1 ) was attained with Tween 80 at 5 g l−1 ; olive oil at 60 g l−1 ; 70 ◦ C and pH 9. Experimental verification of the model showed a validation of 95%, which is more than 4-fold increase compared to the basal medium. Introduction Thermostable enzymes, such as amylases and proteases, have been investigated from thermophilic bacteria. However, little is known about production of heat-stable lipases (Becker et al. 1997). Mesophilic bacteria or fungi that produce most of thermostable lipases are commercially available. Production of thermostable lipases from thermophilic strains is of importance in industrial processes due to the valuable role of lipases in the enzyme market (Geraats 1994, Markossian et al. 2000). The advantages of running the bioreaction at elevated temperatures include higher diffusion rates and increasing mass transfer effects, increased solubility of lipid substrates in water and reduced risk of cross contamination (Becker et al. 1997). To develop a bioprocess for industrial purpose, it is important to optimize highly significant factors affecting this process. Optimization process involving one-variable-at-a-time method (OVAT) is a time-consuming technique and it neglects the interaction between variables and it does not guarantee attaining optimal point (Strobel & Sullivan 1999). Box-Behnken optimization design abolishes these dis-
advantages, besides it creates empirical model equations that correlate the relationship between variables and response(s) (Box & Behnken 1960). In the present investigation, the objectives were to better understand the relationships between the factors (Tween 80, olive oil, pH, temperature) and the response (enzyme activity), and to determine optimum conditions for thermostable lipase production from Geobacillus thermoleovorans YN [previously named as Bacillus thermoleovorans (Nazina et al. 2001)] using Box-Behnken experimental design.
Materials and methods Microorganism The microorganism used in this study, GEBRICC 1 (Culture Collection of the Genetic Engineering and Biotechnology Research Institute, Alexandria, Egypt), was isolated from a soil sample collected from desert and enriched by growing in an olive oil/nutrient broth medium at 50 ◦ C. The bacterium was purified and identified by 16S rRNA sequencing as Geobacillus thermoleovorans YN (Y Abdel-Fattah, A Gaballa &
1218 N Abdel-Al, unpublished work) (accession number AF385083). Production medium and enzyme preparation
tained from the design. The polynomial equation is in the following form: Y = β 0 + β 1 X1 + β 2 X2 + β 3 X3 + β 4 X4 + β12 X1 X2 + β13 X1 X3 + β14 X1 X4
A 10% (v/v) bacterial suspension was transferred from an overnight nutrient broth seed culture to the production basal medium composed of (g l−1 ): peptone, 5; beef extract, 3; NaCl, 2; olive oil, 60; Tween 80, 10 and pH 8. For the purpose of the experimental design, the production medium was prepared in different formulae as illustrated in Table 1. After 24 h and 48 h the cells were removed by centrifugation and the supernatants were used for measurement of lipase activity.
where Y is the predicted response, β0 is the model constant; X 1 , X 2 , X 3 and X 4 are independent variables; β1 , β2 , β3 and β4 are linear coefficients; β12 , β13 , β14 , β23 , β24 and β34 are cross product coefficients and β11 , β22 , β33 and β44 are the quadratic coefficients.
Lipase assay
Data analysis and optimization
Lipase activity was determined colorimetrically (Vorderwuelbecke et al. 1992) using two solutions. Solution 1 contained 90 mg p-nitrophenyl palmitate dissolved in 30 ml 2-propanol. Solution 2 contained 2 g Triton X 100 and 0.5 g gum arabic dissolved in 450 ml buffer (Tris/HCl, 50 mM, pH 8). The assay reagent was prepared by adding 1 ml solution 1 to 9 ml solution 2 dropwise to get an emulsion that remained stable for 2 h. The assay mixture contained 900 µl of the emulsion and 100 µl appropriately diluted enzyme solution. The liberated p-nitophenol was measured at 410 nm. One unit of enzyme was defined as the amount of enzyme that releases 1 µmol p-nitrophenol from the substrate per minute.
Microsoft Excel 97 was used to fit the quadratic response surface model to the experimental data through the multiple regression analysis. Optimization of the culture conditions in terms of Tween 80, olive oil, temperature and pH was analyzed using the predictive polynomial model through solver function. Using Statistica V software, three dimensional plots were created by holding two variables of the model as constants. All data are the mean of triplicates.
Experimental design In the present work, the effects of Tween 80, olive oil, temperature and pH on lipase activity was evaluated. Levels of these factors were optimized for maximum lipase production (the response) using one of the response surface methodologies, the Box-Behnken statistical design (Box & Behnken 1960). Table 1 represents a 27-trial experimental design, where each variable was tested in three different coded levels: low (−1), middle (0) and high (+1). The coded values correspond for Tween 80: −1 (5 g l−1 ), 0 (10 g l−1 ), +1 (15 g l−1 ). For olive oil: −1 (40 g l−1 ), 0 (60 g l−1 ), +1 (80 g l−1 ). For temperature ( ◦ C): −1 (50), 0 (60), +1 (70). For pH: −1 (7), 0 (8), +1 (9). Once the lipase activity (U l−1 ) was measured, a second-order polynomial model was fitted to the response data ob-
+ β23 X2 X3 + β24 X2 X4 + β34 X3 X4 + β11 X21 + β22 X22 + β33 X23 + β44 X24 ,
Results and discussion Application of Box-Behnken design and data analysis A preliminary experiment was carried out to monitor the growth of G. thermoleovorans and enzyme production in the basal production medium (Figure 1). The maximum specific growth rate of G. thermoleovorans was 0.76/h and the maximum lipase activity was attained after 24 h. Further experiments were carried out to obtain a quadratic model consisting of 24 trials plus three replicates at the center point (trials 9, 18 and 27). The design of this experiment is given in Table 1 together with the experimental results. Enzyme activity was measured at 24 and 48 h, however only the 24 h results were used for the optimization process. Regression analysis was performed to fit the response function (lipase activity) with the experimental data. The analysis of variance for the four variables (Tween 80, olive oil, temperature and pH) indicated that enzyme activity can be well described by a polynomial model with a relatively high coefficient of
1219 Table 1. Box-Behnken design for four variables and experimental results with respect to lipase synthesis by G. thermoleovorans YN. Trial No.
Tween 80 (g l−1 )
Olive oil (g l−1 )
Temperature ( ◦ C)
pH
Resulting lipase activity (U l−1 )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
15 15 5 5 10 10 10 10 10 15 15 5 5 10 10 10 10 10 15 15 5 5 10 10 10 10 10
80 40 80 40 60 60 60 60 60 60 60 60 60 80 80 40 40 60 60 60 60 60 80 80 40 40 60
60 60 60 60 70 70 50 50 60 60 60 60 60 70 50 70 50 60 70 50 70 50 60 60 60 60 60
8 8 8 8 9 8 9 7 8 9 7 9 7 8 8 8 8 8 8 8 8 8 9 7 9 7 8
73 108 96 84 314 176 162 130 120 103 160 350 80 109 65 91 55 130 71 204 310 67 150 48 144 63 109
determination (R 2 = 0.95). The statistical analysis of the full model in Table 2 shows that Tween 80, temperature and pH each had a significant effect on lipase synthesis. The probability value of the coefficient of linear effect of olive oil (χ2 ) was very high, 0.98. In addition, the interaction coefficients of this variable with other variables were high, which indicates the insignificance of these coefficients. On the other hand, the quadratic effect of olive oil shows high significance (P = 0.00038) and must be included in the model. Non-significant interaction coefficients are eliminated and the reduced model can be expressed as follows: Fig. 1. Growth () and lipase activity () monitoring of G. thermoleovorans YN when grown in basal medium (nutrient broth supplemented with olive oil at 60 g l−1 and Tween 80 at 10 g l−1 and pH 8).
Y = 136.96 − 22.38X1 + 32.41X3 + 47.08X4 − 94.03X1X3 − 81.75X1X4 + 26.5X3 X4 − 64.39X22 + 19.12X32 + 34.81X42,
1220 Table 2. Regression coefficient of the full polynomial model representing relationships between lipase activity and independent variables (Tween 80, olive oil, temperature and pH). Coefficient symbol β0 β1 β2 β3 β4 β12 β13 β14 β23 β24 β34 β11 β22 β33 β44
Estimatea 119.6 −22.38 −0.17 32.41 47.08 −11.55 −94.03 −81.75 2.15 5.3 26.5 19.53 −57.88 25.63 41.32
P-value 2.3 × 10−7 8.6 × 10−3 0.98 6.8 × 10−4 2.6 × 10−5 0.37 6.3 × 10−6 2.5 × 10−5 0.86 0.68 5.3 × 10−2 9.3 × 10−3 1.6 × 10−4 3.4 × 10−2 2.3 × 10−3
a Estimates are the polynomial model coefficients.
where X 1 , X 2 , X 3 and X 4 represent codified values for Tween 80, olive oil, temperature and pH, respectively. Although the elimination of non-significant coefficients does not enhance the coefficient of determination of the polynomial model, it is worth mentioning that the lack of fit is insignificant in the reduced model. Accordingly, it can be assumed that the model accurately represents data in the experimental region (Strobel & Sullivan 1999). This was confirmed by the residual analysis of the data (data not shown). For more discription of the relationship between bioprocess variables and the response partial-effects are illustrated in three dimensional plots. Finding the optimum point of the variables The optimum level of each variable has been obtained using the linear optimization algorithm of Microsoft Excel. As represented by coded levels, the optimum point was obtained using the following conditions: Tween 80 at 5 g l−1 , olive oil at 60 g l−1 , 70 ◦ C and pH 9. Based on the model, the effect of each factor can be predicted separately and presented graphically as a partial-effect function. Partial-effect plot describes how the response moves as the level of that variable changes. The overlayed partial-effects of variables on lipase activity are presented in Figure 2 where temperature and pH have significant effects on
Fig. 2. Partial-effects plot as a function of Tween 80 (), olive oil (), temperature () and pH () coded levels (−1, 0 and +1).
enzyme production. With olive oil, the measured response increased gradually until the coded value of olive oil reached (0) and decreased after the olive oil concentration became higher than its coded level of (0). However, the difference between responses at minimum and maximum olive oil concentrations is relatively small (430 and 495 U l−1 ). Therefore, the partial effect of olive oil was less significant than other variables. Increasing the concentration of Tween 80 decreased the lipase activity though the maximum lipase activity (495 U l−1 ) was higher than in the results reported before (Handelsmann & Shoham 1994, Schmidt-Dannert et al. 1994, Markossian et al. 2000). On the other hand, Lee et al. (1999) reported an enzyme activity titre of 520 U/l for B. thermoleovorans using olive oil medium, which is higher than the activities attained by other thermophilic bacilli. The main results of this study are presented in Figure 3, which represents the expected lipase response and correlation between variables in threedimensional plots. Figure 3a shows non-additive effects of temperature and pH due to the significant interaction between them. In Figures 3b and c it is to be seen that the effects of pairs of factors were additive since there are no interactions except the temperaturepH interaction. By additivity of the two-factor effects, it is ment that the effect of one factor on the response does not depend on the level of the other factor. In Figure 3b it is obvious that maximum lipase activity was attained at moderate levels of olive oil (60 g l−1 ) and 70 ◦ C. In Figure 3c illustrates that increasing pH value to pH 9 at moderate level of olive oil led to maximum lipase activity. The optimum point deduced from
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Fig. 3. Continued.
Cho et al. 2000 found that maximum lipase was obtained in a medium supplemented with olive oil at pH 8, which is in accordance to the results of this study. Validation of optimum point The adequacy of the model was examined by an additional experiment using the derived optimal conditons. The predicted value was 495 U l−1 and in the experimental the value was 472 ± 10 U l−1 . This is approx. 95% of the predicted value, which indicates that the generated model is an adequate prediction of the enzyme activity.
Fig. 3. The response surface of lipase activity by G. thermoleovorans YN as a function of (a) temperature and pH, (b) olive oil and temperature, (c) olive oil and pH levels in the culture environment, where circular dots represent the actual measured lipase activity data points.
Figure 3 is in accordance with the mathematically calculated optimum point. Other three dimensional plots presenting the effect of Tween 80 on lipase activity (not illustrated) indicated that increasing Tween 80 concentration in culture medium decreases the lipase activity. The inhibitory effect of Tween 80 (60% reduction of activity) has been reported by Lee et al. (1999). Dharmsthiti & Luchai (1999) and Markossian et al. (2000) showed that maximum lipase activity level was attained in the presence of olive oil at 70 ◦ C.
Conclusion The response surface methodology allowed the development of an imperical polynomial model for the production of a thermophilic lipase by a G. thermoleovorans YN strain. The model was able to foresee acurately the lipase activity by changing provided culture conditions. Application of such models is of great importance for industrial bioprocess.
Acknowledgements I would like to thank Dr Ehab El-Helow for his valuable suggestions and revising the manuscript. I also express my gratitude to Dr Nadia Abdel-Al who
1222 isolated the strain used in this work. This research was supported by the US-Egypt Joint Science and Technology Fund.
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