Statistical optimization of medium components for the production of lipase by Serratia marcescens SB08
C Venil, N Sangeetha Kamatshi, P Lakshmanaperumalsamy
ccd, lipase, optimization, serratia marcescens
C Venil, N Sangeetha Kamatshi, P Lakshmanaperumalsamy. Statistical optimization of medium components for the production of lipase by Serratia marcescens SB08. The Internet Journal of Microbiology. 2008 Volume 7 Number 1.
The optimization of the fermentation medium and conditions for maximum lipase production was carried out using a new strain,
Lipases are extremely versatile enzymes, showing many interesting properties of industrial applications.They are a class of enzymes which catalyze the hydrolysis of long chain triglycerides and constitute the most important group of biocatalysts for biotechnological applications. Lipases can be divided generally into the following four groups according to their specificity in hydrolysis reaction: substrate specific lipases, regio-selective lipases, fatty acid specific lipases, and stereo -specific lipases. They are obtained from a variety of sources like plants, animals, yeast, bacteria; but among all, microbial lipases are the most popular for industrial use as they are easy to produce and are stable comparatively. Pancreatic lipase of porcine origin is one of the earliest recognized lipases and is still the best-known lipase. Plant lipases are not used commercially; the animal and microbial lipases are used extensively.
Although there have been many papers dealing with the lipase producing yeasts such as
A characteristic trait of many strains of
The demand for the production of highly active preparations of lipolytic enzymes has led to research on lipase producing microorganisms and on culture strategies (Lechner
The interest in microbial lipase production has increased in the last decades, because of its large potential in industrial applications as additives in foods (flavor modification), fine chemicals (synthesis of esters), detergents (hydrolysis of fats), waste water treatment (decomposition and removal of oil substances), cosmetics (removal of lipids), pharmaceuticals (digestion of oil and fats in foods), leather (removal of lipids from animal skins) and medical (blood triglyceride assay) (Elibol and Ozer, 2000; Kamini
Lipases represent an extremely versatile group of bacterial extracellular enzymes that are capable of performing a variety of important reactions, thereby presenting a fascinating field for future research. Wide and constant screening of new microorganisms for their lipolytic enzymes will open novel and simpler routes for the synthetic processes. Consequently, this may pave new ways to solve biotechnological and environmental problems. Hence the present study has been aimed with the objective of screening a novel lipase producing bacteria.
Materials and methods
Microorganism and culture maintenance
The new strain
Production of lipase
One hundred ml of nutrient broth was inoculated with 1 ml of the above 18 hrs bacterial inoculum and was incubated at 30oC for 24 hours. After incubation the crude enzyme was obtained by centrifugation of the culture broth at 10,000 rpm for 10 minutes. The cell free supernatant was assayed for lipase activity.
The lipase assay was carried out by the modified method of Satarik (1991). The fermented broth was centrifuged at 10,000 rpm for 15 minutes. The supernatant was taken for determination of lipase activity. For this, an aliquot of olive oil (250 mg) was transferred into a test tube containing 2 ml of phosphate buffer (pH 6.5) and 1 ml of crude enzyme was added to it. The mixture was vortexed for 15 seconds and incubated at 37oC in a water bath under static conditions for 30 minutes. After stopping the reaction by adding 1 ml concentrated HCl and vortexing for 10 seconds, 3 ml of benzene was added and after vortexing for 90 seconds, the aqueous phase and organic phases were allowed to separate. From this 2 ml of benzene layer was withdrawn and transferred to a tube containing 1 ml of aqueous solution of cupric acetate (5 %) and the mixture after vortexing for 90 seconds was centrifuged at 5,000 rpm for 10 minutes to obtain a clear organic phase. The organic layer (Benzene layer) was withdrawn and used to estimate the liberated free fatty acid by measuring the optical density (OD) against distilled water at 715 nm using spectrophotometer (Model – 3210, Hitachi, Japan). One unit (U) of lipase activity is equal to one micromole of free fatty acid liberated per minute per ml using the assay condition.
Optimization of process parameters
Screening of important nutrient components using Plackett – Burman design
This study was done by Plackett - Burman design for screening medium components with respect to their main effects and not their interaction effects (Plackett and Burman, 1946) on enzyme production by
The effect of each variable was calculated using the following equation
Where E is the effect of tested variable, M+ and M- are responses (enzyme activities) of trials at which the parameter was at its higher and lower levels respectively and N is the number of experiments carried out.
The standard error (SE) of the variables was the square root of variance and the significance level (p – value) of each variables calculated by using Student’s t – test.
where E xi is the effect of tested variable. The variables with higher confidence levels were considered to influence the response or output variable.
Optimizaion of concentrations of the selected medium components using response surface methodology
Response surface methodology is an emprical statistical modeling technique employed for muliple regression analysis using quanitative data obtained from factorial design to solve multivariable equations simultaneously (Rao
According to this design, the total number of treatment combinations is 2
The behavior of the system was explained by the following quadratic equation:
Validation of the experimental model
The statistical model was validated with respect to lipase under the conditions predicted by the model in shake flask conditions. Samples were withdrawn at the desired intervals and lipase assay was determined as described above.
Purification of the lipase
Purification of the lipase from
Determination of molecular weight by SDS PAGE
Molecular weights of the enzymes were determined by interpolation from a linear semi-logarithmic plot of relative molecular weight versus the Rf value (relative mobility) using standard molecular weight markers.
Plackett – Burman design
The influence of eleven medium factors namely pH, temperature, agitation, inoculum concentration, incubation time, sucrose, peptone, KH2PO4, yeast extract, NaCl and CaCl2 in the production of lipase was investigated in 12 runs using Plackett – Burman design. Table 1 represents the Plackett–Burman design for 11 selected variables and the corresponding response for lipase production. Variations ranging from 41.46 to 236.09 U / mL in the production of lipase in the 12 trials were observed by Plackett – Burman design.
The Pareto chart illustrates the order of significance of the variables affecting lipase production (figure 1). Among the variables screened, the most effective factors with high significance level indicated by Pareto chart were in the order of CaCl2, incubation time, pH and yeast extract. They were identified as most significant variables in lipase production and selected for further optimization while temperature, agitation, inoculum concentration, sucrose, peptone, KH2PO4, and NaCl which exhibited less significance level were omitted in further experiments.
Statistical analysis of the Plackett – Burman design demonstrates that the model F value of 0.87 is significant. The values of p < 0.05 indicate model terms are significant (Table 2).
Regression analysis was performed on the results and first order polynomial equation was derived representing lipase production as a function of the independent variables.
Lipase = 99.00 + 10.83 A + 17.83 E + 7.67 J + 18.33 L
The magnitude of the effects indicates the level of the significance of the variable on lipase production. Consequently, based on the results from this experiment, statistically significant variables i.e. CaCl2, incubation time, pH and yeast extract with positive effect were further investigated with central composite design to find the optimal range of these variables.
Central composite design
Based on Plackett – Burman design CaCl2, incubation time, pH and yeast extract were selected for further optimization using response surface methodology. To examine the combined effect of these factors, a central composite design (CCD) was employed within a range of -2 to +2 in relation to production of lipase. The results obtained from central composite design are given in table 3.
The results obtained from the central composite design were fitted to a second order polynomial equation to explain the dependence of lipase production on the medium components.
Y = 176.17 + 3.75 A + 29.08 B + 8.08 C + 3.08 D + 3.63 AB + 8.25 AC + 0.38 AD + 13.12 BC - 1.00 BD + 13.38 CD -1.71 A2 - 25.08 B2 - 35.46 C2 - 11.83 D2
where Y is the predicted response of lipase production, A, B, C and D are the coded values of pH, incubation time, yeast extract and CaCl2 respectively.
The analysis of variance of the quadratic regression model suggests that the model is very significant as was evident from the Fisher’s F – test (Table 4). The model’s goodness of fit was checked by determination coefficient (R2). In this case, the value of R2 value (0.915) (multiple correlation coefficient) closer to 1 denotes better correlation between the observed and predicted responses. The coefficient of variation (CV) indicates the degree of precision with which the experiments are compared. The lower reliability of the experiment is usually indicated by high value of CV. In the present case a low CV (3.48) denotes that the experiments performed are highly reliable. The
The fitted response for the above regression model was plotted in figure 2. 3D graphs were generated for the pair wise combination of four factors for lipase production. Graphs highlight the roles played by various factors affecting the production of lipase. The 3D response surface plots described by the regression model were drawn to illustrate the effects of the independent variables and combined effects of each independent variable upon the response variable.
Validation of the model
The maximum experimental response for lipase production was 241.06 U/mL whereas the predicted value was 251.83 U/mL indicating a strong agreement between them. The optimum values of the tested variables are pH 7.0, incubation time 51 h, yeast extract 3.0 g/L and CaCl2 0.13 g/L as shown in perturbation graph (Figure 3). The model was also validated by repeating the experiments under the optimized conditions, which resulted in the lipase production of 243.91 U/mL (Predicted response 251.83 U/mL), thus proving the validity of the model.
To optimize industrial conditions for lipase production, scale – up study was carried out in a jar fermentor by using medium under optimum conditions. The maximum production of 262.91 U/mL lipase was achieved. The results are encouraging for optimization under pilot scale or industrial scale conditions
Molecular weight of lipase
The partial purification of the lipase crude extract that was affected by the ammonium sulfate (80%) precipitation showed that most of the enzyme activity was preserved in the precipitate. SDS-PAGE showed that the enzyme is one band with electrophoretic mobility of 0.48. By using different standard proteins with known molecular weights, it was discovered that the apparent molecular weight of
Enormous interest on lipase production has been evolved due to its widespread applications in oleochemical, detergent, food processing and in fine chemical manufacturing industries (Macrae and Hammond, 1985; Pandey
Lipase production by
In this study, with optimum factors designed by statistical methods, the maximum production of 241.06 U/mL was observed in
The authors are thankful to Bharathiar University, Coimbatore, Tamil Nadu, India for providing the infrastructure facilities for this study.