Abstract
Alkaline lipase production by mutant strain of Pseudomonas aeruginosa MTCC 10,055 was optimized in shake flask batch fermentation using response surface methodology. An empirical model was developed through BoxBehnken experimental design to describe the relationship among tested variables (pH, temperature, castor oil, starch and tritonX100). The secondorder quadratic model determined the optimum conditions as castor oil, 1.77 mL.L1; starch, 15.0 g.L1; tritonX100, 0.93 mL.L1; incubation temperature, 34.12 ºC and pH 8.1 resulting into maximum alkaline lipase production (3142.57 U.mL1). The quadratic model was in satisfactory adjustment with the experimental data as evidenced by a high coefficient of determination (R²) value (0.9987). The RSM facilitated the analysis and interpretation of experimental data to ascertain the optimum conditions of the variables for the process and recognized the contribution of individual variables to assess the response under optimal conditions. Hence BoxBehnken approach could fruitfully be applied for process optimization.
alkaline lipase; BoxBehnken design; Pseudomonas aeruginosa; response surface methodology
Computation of interactive effects and optimization of process parameters for alkaline lipase production by mutant strain of Pseudomonas aeruginosa using response surface methodology
Deepali Bisht; Santosh Kumar Yadav; Nandan Singh Darmwal
Center of Excellence, Department of Microbiology, Dr. Ram Manohar Lohia Avadh University, Faizabad, UP, India
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ABSTRACT
Alkaline lipase production by mutant strain of Pseudomonas aeruginosa MTCC 10,055 was optimized in shake flask batch fermentation using response surface methodology. An empirical model was developed through BoxBehnken experimental design to describe the relationship among tested variables (pH, temperature, castor oil, starch and tritonX100). The secondorder quadratic model determined the optimum conditions as castor oil, 1.77 mL.L^{1}; starch, 15.0 g.L^{1}; tritonX100, 0.93 mL.L^{1}; incubation temperature, 34.12 ºC and pH 8.1 resulting into maximum alkaline lipase production (3142.57 U.mL^{1}). The quadratic model was in satisfactory adjustment with the experimental data as evidenced by a high coefficient of determination (R^{2}) value (0.9987). The RSM facilitated the analysis and interpretation of experimental data to ascertain the optimum conditions of the variables for the process and recognized the contribution of individual variables to assess the response under optimal conditions. Hence BoxBehnken approach could fruitfully be applied for process optimization.
Key words: alkaline lipase, BoxBehnken design, Pseudomonas aeruginosa, response surface methodology.
Introduction
The environment friendly characteristics of enzymes from natural sources lead the industries to reflect on enzymes because they are definitely a sustainable alternative to chemicals in industrial processes (Hasan et al., 2010). So, the microorganisms can be the only source to get sufficient amount of enzyme as they can be cultured in large quantities in a reasonably short period by conventional methods of fermentation and they also provide a copious and regular supply of the desired products (Gupta et al., 2002).
Lipases (triacylglycerile hydrolases EC 3.1.1.3) are industrially important enzymes attributed to their regio, stereo, chemoselective reactions and kinetic resolution of racemates (Nelofer et al., 2011). The application spectrum of lipases in new industries is mounting day by day. Recently, these enzymes have been used for biodiesel production (Adamczak et al., 2009), enantioselective deacetylation (Kumar and Gupta, 2008), cyclic resolution of racemic ibuprofen (Liu et al., 2009), production of mediumchain triacylglycerols (Low et al., 2007), and the preparation of diacylglycerolenriched palm olein (Wang et al., 2009).
Lipases are not only attractive as catalysts for the modification and synthesis of useful compounds as discussed above, but they are also used as functional components of mixtures. For example, a large potential market for lipolytic enzymes is in detergent formulations (Macrae and Hammond, 1985). The prelude conditions in favor of the enzymes to be detergent additive are not only its compatibility and stability against various detergent components but also the production level of the enzymes (Joo and Chang, 2006).
The improvement of industrial fermentation processes centered on designing of fermentation medium since its composition can appreciably affect product concentration, yield and volumetric productivity (Kennedy and Krouse, 1999). The classical optimization method (single variable optimization) is not only timeconsuming and tedious but also fails to depict the overall effects of the parameters in the process and overlooks the combined interactions between physicochemical parameters, leading to misinterpretation of results (AbdelFattah et al., 2005; Bas and Boyaci, 2007). To overcome this difficulty, Fisher (1926) developed the basic theory of experimental design which proves the superiority of study of more than one factor at a time over only one factor at a time. The Response Surface Methodology (RSM) appraises the interaction between the response(s) and the independent variables (Chen et al., 2002) and defines the effect of the independent variables, alone or in combination. Moreover, this method is a valuable tool to resolve the optimum operating conditions decisive for the scale up of the process and to reduce the number and outlay of experiments (Gopinath et al., 2003).
In the light of above facts, the present study deals with statistical optimization for improving alkaline lipase production from mutant strain of P. aeruginosa 10,055 using BoxBehnken design.
Materials and Methods
Microorganism and lipase production
A promising mutant strain of P. aeruginosa MTCC 10,055 was developed by chemical mutagenesis in our laboratory (Bisht et al., 2012). The culture was maintained on nutrient agar slants containing 1% (v/v) tributyrin and stored at 4 ºC.
The composition of production medium containing (g.L^{1}), NH_{4})_{2}SO_{4}, 1.0; KH_{2}PO_{4}, 0.6; MgSO_{4}, 0.4; yeast extract, 0.2; castor oil, 2.0; starch, 20; tritonX100, 1.0; gum arabic, 5.0; and initial of pH 9.0 was determined after using 'Onevariableatatime' approach. Fifty milliliters of the production medium was taken in 250 mL Erlenmeyer flask, inoculated with 0.5% (v/v) inoculum (OD_{610} 1.0) and incubated at 35 ºC under shaking (120 rpm) for 28 h. After incubation, the fermenting broth was centrifuged at 12,000 g for 10 min at 4 ºC and the cellfree supernatant was used for lipase assay.
Enzyme assay
Lipase activity was determined spectrophotometrically as described by Winkler and Stuckman (1979) with slight modifications. The substrate solution containing 10 mL of isopropanol with 30 mg of pnitrophenyl palmitate was mixed with 90 mL of TrisHCl buffer (50 mM, pH 9.0), containing 0.4% TritonX 100 and 100 mg of gum arabic. Freshly prepared substrate solution (2.4 mL) was in cubated at 37 ºC with 25 µL of suitably diluted cellfree supernatant for 15 min. After incubation absorbance was measured at 410 nm by using a spectrophotometer (UV1601, Shimadzu) against a control with heat inactivated enzyme. One unit of enzyme is defined as the amount of enzyme liberating 1 µg of pnitrophenol.mL.min under the assay conditions.
Study of interactions among the medium components using BoxBehnken design
A response surface methodology using a BoxBehnken design (Box and Behnken, 1960) was adopted to appraise the interactions occurring among the factors viz., pH (A), temperature (B), castor oil (C), starch (D) and tritonX100 (E). The factors at three different levels (1, 0, + 1) with minimum, central and maximum values and the treatment schedule for the model is given in Table 1 and Table 2, respectively. Six replicates (run) at the center of the design were used for estimation of the pure error and sum of squares.
Statistical analysis
The average of maximum alkaline lipase activity was taken as the dependent variable (response). A secondorder polynomial equation, fitted to the data by multiple regression procedure, resulted in an empirical model which is as under
For analysis of design based on five factors following model equation was used,
Where Y is the predicted response for alkaline lipase produced; β_{0 }is the value of the fitted response at the center point of the design; β_{1}, β_{2}, β_{3}, β_{4 }and β_{5 }are the linear coefficients; β_{11}, β_{22}, β_{33}, β_{44 }and β_{55 }are the quadratic coefficients; β_{12}, β_{13}, β_{14}, β_{15, }β_{23}, β_{24}, β_{25}, β_{34}, β_{35 }and β_{45 }are the interaction coefficients; while 'ε' is the random error. The software package DesignExpert 8.0.5.2 (Stat Ease, Inc., Minneapolis, USA) was used to obtain the coefficients of Eq. (2) based on the data provided in Table 2. The responses under different combinations as defined by the design (Table 2) were analyzed using analysis of variance (ANOVA) to estimate the statistical parameters.
Results and Discussion
In recent years, statistical experimental designs have been proved to be an effective tool for optimization of process parameters in biotechnological processes. There are several reports for optimization of culture media, using statistical approaches (Kumari et al., 2009; Faiza et al., 2011; Salihu et al., 2011). In the present investigation RSM was used for the optimization of alkaline lipase production by an improved strain of P. aeruginosa after optimization of the medium by "onevariableatatime" approach.
Determination of significant variables by BoxBehnken design
Multiple regression analysis on the experimental data, results in following coefficients for alkaline lipase production (Y), considering, temperature (A), initial pH (B), starch (C), castor oil (D) and tritonX100 (E) was obtained after the analysis of ANOVA
Alkaline lipase activity (Y U.mL^{1}) = 2362.50  288.63 × A  341.81 × B  320.75 × C  116.31 × D  63.00× E  18.75 × AB + 24.25 × AC  33.75 × AD  63.75 × AE + 597.75 × BC + 4.75 × BD + 97.00 × BE + 51.50 × CD  57.00 × CE + 20.75 × DE  796.52 × A^{2}  377.27 × B2  409.35 × C^{2}  454.60 × D^{2}  503.69 × E^{2}
The analysis of variance for the quadratic regression model demonstrates the aptness of the model for alkaline lipase production. The computed Fvalue (938.78) for the present model implies significance of the model (Table 3). There is only a 0.01% chance that a large "model Fvalue" could occur due to noise. In general, calculated Fvalues should be several times more than tabulated value, if the model was a good prediction of experimental results and estimated factors effects were real (Dutta et al., 2004). A high Fvalue and a very low probability (PF = 0.0001) indicated that the present model was in a good prediction of experimental results. R^{2}, or determination coefficient, is the proportion of variation in the response attributed to the model rather than to random error (Henika, 1972). The R^{2 }value always lies between 0 and 1 and for a good fit of model, R^{2 }should be at least 0.80 (Joglekar and May, 1987). Similarly, Doddapaneni et al. (2007) suggested that closer the value of R^{2 }to 1.0, the stronger the model and the better its prediction efficiency of the responses. The R^{2 }(0.9987) for this model implied that 99.87% of the sample variation for lipase activity was attributed to the independent variables, and only about 0.13% of the total variation was not explained by the model. However, its value closer to 1.0 suggested that model represents better correlation between experimental and predicted values.
The "Lack of Fit Fvalue" of 4.51 demontrate that the Lack of Fit is not significant relative to the pure error. There is a 5.12% chance that a "Lack of Fit Fvalue" this large could occur due to noise. Nonsignificant lack of fit is good as we want the model to fit (Table 3). Adequate Precision measures the signal to noise ratio and a ratio greater than 4 is desirable. An adequate precision of 127.274 indicated low signal to noise ratio. The coefficient of variation (CV) is the ratio of the standard error of estimate to the mean value of the observed response, expressed as a percentage. A model can be considered practically reproducible if the CV is not greater than 10% (Joglekar and May, 1987). Here, a relatively lower value of the coefficient of variation (CV = 1.81%) indicates precision and reliability of the conducted experiments (Table 4). On the basis of results obtained from ANOVA it can be concluded that the model is highly significant and sufficient to represent the actual relationship between the response and the significant variables and can be used successfully to navigate the design space.
In our study thirteen model terms were found to be significant and the variables with prevalent effects were the linear terms of A (p < 0.0001), B (p < 0.0001), C (p < 0.0001), D (p < 0.0001), E (p < 0.0001) and the quadratic terms of A^{2 }(p < 0.0001), B^{2 }(p < 0.0001), C^{2 }(p < 0.0001), D^{2 }(p < 0.0001), E^{2 }(p < 0.0001) followed by interaction effects of AE (p < 0.0001), BC (p < 0.0001), and BE (p < 0.0001). The value of 'p' less than 0.0500 indicates the 'significance' of the model terms. The results indicated that the effect order of the linear terms on the yield of alkaline lipase were as follows, pH (F; 2620.82), starch (F; 2307.78) temperature (F; 1868.66) and castor oil (F; 303.47).
The secondorder model can be plotted as a threedimensional surface representing the response (lipase production) as a function of the two factors at a time while maintaining other three factors at fixed levels (centre point) to understand both the main and the interaction effects of these two factors. The three dimensional response surfaces obtained after analysis showed different shapes which indicated variation in the combined effect of independent variables on lipase production. Figure 1a illustrates the interaction effect of pH with incubation temperature. According to the plot, the optimal value (1810 U.mL^{1}) lied towards the value at central point of the pH (8.5) and temperature (35 ºC). However, further increase in pH and temperature resulted in lowest enzyme production (515 U.mL^{1}) of test variables.
The effect of pH and temperature is shown in Figure 1b. The response curve analysis indicated that maximum enzyme units were produced at temperature 30 ºC with 15 g.L^{1 }of starch. Supplementation of starch at elevated level (25 g.L^{1}) and incubation at higher temperature (40 ºC) had negative effect on the response. Figure 1c depicts the interaction of temperature and castor oil on lipase production. The observation of interactions of castor oil and temperature indicated that lipase production decreased with increase in temperature (3040 ºC) and castor oil concentration (1.03.0 mL.L^{1}).
Figure 1d represents the response for the interaction of tritonX100 with incubation temperature. Maximum lipase was produced at higher level of tritonX100 (1.5 mL.L^{1}) at lower temperature (30 ºC) in the design space. However, lower concentration of tritonX100 (0.5 mL.L^{1}) had negative effect on lipase production at higher temperature (40 ºC). The interaction effect of pH and starch was remarkable, where both the factors supported maximum lipase production (2903 U.mL^{1})at1 coded value i.e. 8.5 and 15 g.L^{1 }for pH and starch, respectively (Figure 1e). This accorded a run number of 21, which is considered as the optimal condition of test variables.
Figure 1f depicts the production of lipase with respect to tritonX100 vs. pH. Maximum lipase production with these variables was observed at concentration of 0.5 mL.L^{1 }tritonX100 at pH 8.5. However, further elevation in pH (9.5) decrease the lipase production at the same concentration of tritonX100 (0.5 mL.L^{1}).
The results obtained as well as predicted by BoxBehnken design showed that a combination of temperature of 35 ºC, pH 8.5, starch (15 g.L^{1}), castor oil (2 mL.L^{1}) and tritonX100 (1 mL.L^{1}) would favor maximum lipase production (2903 U.mL^{1}).
With an aim to test the desirability of the model, the optimum values of the variables were determined as temperature, 34.12 ºC; pH, 8.1; starch, 11.29 g.L^{1}; castor oil, 1.77 mL.Land tritonX100, 0.93 mL.Lyielding a maximum lipase production of 3007.25 U.mL^{1}.
Comparison of observed and predicted response and validation of the model
A regression model could be used to predict future observations on the response Y (alkaline lipase activity) corresponding to particular values of the variables. Figure 2 illustrates the observed lipase activities vs. predicted values by the empirical model Eq. (2). A high degree of similarity was observed between the predicted data of the response (2836.19 U.mL^{1}) from the empirical model and the experimental values (2903 U.mL^{1}) in the range of the operating variables reflecting the applicability of RSM to optimize the process of enzyme production.
The suitability of the model was validated at shake flask level by additional independent experiments under the optimal conditions predicted by the equation. The model indicated that the selected levels of pH and starch were limiting as the response is maximum at their lowest values. So within the design space the response surface graphs were unable to illustrate the accurate results. Therefore, further decrease in medium pH along with decrease in starch concentration should be worked out for validation. Table 5 shows the predicted and observed responses of the validation experiments. Under the optimum conditions, obtained by point prediction, the mutant strain was able to produce 3142.57 U.mL^{1}, which is 104.5% of the predicted value. The results showed actual values were closer to the predicted values, supporting the data and the model as valid. However, other tested combinations did not reveal improvement in the response. Thus, by optimizing the fermentation parameters using RSM, the yield of alkaline lipase increased from 2362.5 U.mL^{1 }to 3142.57 U.mL^{1 }by optimizing the fermentation parameters using RSM.
Conclusion
The present study conclusively demonstrates the application of a BoxBehnken design for determination of optimal medium composition for alkaline lipase production by a mutant strain of P. aeruginosa. The initial medium pH and supplementation of castor oil had most significant effect on lipase production. The interactions of pH and temperature, pH and castor oil and castor oil and tritonX100 were found to be crucial on the response. Alkaline lipase production (3142.57 U.mL^{1}) using this optimized medium and culture conditions was about 1.3fold higher than medium obtained by onevariableatatime approach (2362.5 U.mL^{1}). The model was capable to foresee accurately the lipase activity by altering culture conditions. Application of such approach can be of immense significance for industrial bioprocess.
Acknowledgments
The researchers are thankful to Government of Uttar Pradesh, India for providing research fellowship under the scheme "Centre of Excellence" to the department. The assistance of Dr. Sunil Kumar Tyagi, Associate Professor, Narendra Dev University of Agriculture and Technology, Kumarganj, Faizabad, U.P. in the interpretation of data obtained by RSM studies is appreciatively acknowledged.
Submitted: March 17, 2012
Approved: July 2, 2012
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Send correspondence to:N.S. DarmwalCenter of Excellence, Department of MicrobiologyDr. Ram Manohar Lohia Avadh UniversityFaizabad 224001 UP, IndiaEmail:
Publication Dates

Publication in this collection
09 Apr 2013 
Date of issue
2013
History

Received
17 Mar 2012 
Accepted
02 July 2012