The new strain Serratia marcescens SB08 was isolated from soil and the culture was maintained at 4oC and subcultured every two weeks. Nutrient broth medium was prepared and the pH of the medium was adjusted to 7.0 and was autoclaved at 121°C for 15 minutes. A loopful of bacterial culture (Serratia marcescens SB08) was inoculated and incubated at 30°C for 18 hrs.

Production of lipase

Enzyme assay

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 Serratia marcescens SB08. The medium components were screened for eleven variables at two levels, maximum (+) and minimum (-). According to the Plackett – Burman design, the number of positive signs (+) is equal to (N+1) / 2 and the number of negative signs (-) is equal to (N-1) / 2 in a row. A column should contain equal number of positive and negative signs. The first row contains (N+1) /2 positive signs and (N-1) / 2 negative signs and the choice of placing the signs is arbitrary. The next (N-1) rows are generated by shifting cyclically one place (N-1) times and the last row contains all negative signs. The medium was formulated as per the design and the flask culture experiments for lipase enzyme was assayed as described earlier. Response was calculated as the rate of enzyme production and expressed as U/mL. All experiments were performed in triplicates and the average of the rate of enzyme production was considered as the response.

The effect of each variable was calculated using the following equation

Figure 1

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.

Figure 2

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 et al., 2000). The screened medium components affecting enzyme production were optimized using cenral composite design (CCD) (Box and Wilson, 1951; Box and Hunter, 1957).

According to this design, the total number of treatment combinations is 2k +2k + n0 where ‘k’ is the number of independent variables and n0 the number of repetitions of the experiments at the center point. For statistical calculation, the variables X_i have been coded as x_i according to the following transformation:

Figure 3

where x_i is dimensionless coded value of the variable X_i , X ₀ the value of the X_i at the center point, and δX is the step change. A 2k-factorial design with eight axial points and six replicates at the center point with a total number of 30 experiments was employed for optimizing the medium components.

The behavior of the system was explained by the following quadratic equation:

Figure 4

where Y is the predicted response, β ₀ the intercept term, β_i the linear effect, β_ii the squared effect, and β_ij is the interaction effect. The regression equation was optimized for maximum value to obtain the optimum conditions using Design Expert Version 7.1.5 (State Ease, Minneapolis, MN).

Validation of the experimental model

Purification of the lipase from Serratia marcescens SB08 was done by ammonium sulphate precipitation followed by dialysis. The enzyme from the cell free supernatant was precipitated by ammonium sulphate at 80% saturation and kept overnight at 4°C. The precipitate was collected by centrifugation at 15,000 rpm for 15 min and was dissolved in a minimum quantity of 0.2M phosphate buffer at pH 7.5. The solution was dialyzed against the same buffer overnight at 4°C. The dialyzed enzyme was used for further studies.

The homogenity of the dialysate was checked by Sodium Dodecyl Sulphate Poly Acrylamide Gel Electrophoresis (SDS PAGE). SDS PAGE was performed as described by Laemmli (1970). Ten percent acrylamide gel was prepared according to standard procedure and the gel was run at 60 volts till the dye crossed the stacking gel margin and then increased to 140 volts till the end. The unit was switched off when the dye front reached the bottom of the running gel. The gel was then transfered to distilled water and then to Coomassie Brilliant Blue staining solution. The staining was carried out for 2 hrs followed by the transfer of gel to the destaining solution. Destaining was carried out until the bands became clear and the background completely destained.

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.

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).

Figure 5

Table – 1 Plackett – Burman experimental design for evaluating factors influencing lipase by SB08

Figure 6

Figure – 1 Pareto chart for Plackett Burman design for 11 medium factors on Lipase production by SB08

Figure 7

Table – 2 Analysis of variance for lipase production by SB08

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.

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.

Figure 8

Table – 3 Experimental plan for optimization of lipase production using central composite design

Figure 9

Table – 4 ANOVA for the experimental results of the central composite design (quadratic model)

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 p values denotes the significance of the coefficients and also important in understanding the pattern of the mutual interactions between the variables.

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.

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

Figure 10

Figure – 2 Three dimensional response surface plot for the effect of (A) pH, yeast extract; (B) incubation time, yeast extract; (C) yeast extract, CaCl on lipase production by SB08

Figure 11

Figure 12

Figure 13

Figure – 3 Perturbation graph showing the optimum values of the tested variables

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 Serratia marcescens SB08 lipase was 52 KDa.

The authors are thankful to Bharathiar University, Coimbatore, Tamil Nadu, India for providing the infrastructure facilities for this study.