International Journal for Biotechnology and Molecular Biology Research Vol. 3(2), pp. 15-21, April 2012 Available online at http://www.academicjournals.org/IJBMBR DOI: 10.5897/IJBMBR12.007 ISSN 2141-2154 ©2012 Academic Journals
Full Length Research Paper
Statistical optimization of protease production by mangrove-derived Trichoderma estonicum and its potential on blood stain removal K. Saravanakumar* and K. Kathiresan Faculty of Marine Sciences, Annamalai University, Parangipettai-608 502, Tamil Nadu, India. Accepted 30 March, 2012
Production of protease by Trichoderma estonicum isolated from mangrove sediment was statistically optimized for culture conditions using a two-step approach. Statistical modeling method of PlackettBurman was used for identification of the important factors that are responsible for protease production while the central composite design was used for further optimization of the important factors. For the identification of the important factors for protease production, six factors such as fructose (g/L), paraffin liquid (%), NaCl (g/L), CaCl2 (g/L), temperature (°C) and pH were tested. Among these, three factors - fructose (g/L), NaCl (g/L), and temperature (°C) - were found to be important for protease production in addition to that one important factor was incubation period also added for the further optimization. After statistical optimization of production medium (fructose 0.5 g/L, NaCl 3.22 g/L, temperature 41°C), protease production was 41.54 IU/mL which was 66.57% higher than the normal production medium in 107 hours of incubation. The crude protease enzyme extract was tested for the blood stain removal. The highest removal activity of 59.7% was observed in enzyme extract, which was greater than that of the commercial detergent with 55.1% removal activity. Therefore Trichoderma derived enzyme is a promising detergent in removal of blood strain. Key words: Trichoderma estonicum, protease, mangroves, blood stain, response surface methodology.
INTRODUCTION Enzymes are industrially important for the bio-catalytic activity. They have significant advantages over chemical catalysts in terms of substrate specificity, high catalytic activity and the ability to work in moderate environmental conditions (Sandhya et al., 2005). Generally, the quantity of enzyme produced from microorganisms is partially inducible in nature, and their production is typically under the optimized level of culture conditions (Beg et al., 2002). Proteases of microbial origin have been used in many industry; they are replaced by fungal proteases (Ogrydziak, 1993; Pavlukova et al., 1998), easily extracted and separated from mycelium (Phadatare et al., 1993). Extracellular protease production from fungal species is significantly influenced by medium composition
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and physical factors such as fermentation period, aeration, inoculum density, pH, and temperature of growth medium (Puri et al., 2002; Genckal and Tari, 2006). Proteases are one of the most important groups of industrial enzymes, with considerable application in the food and detergent industry (Merheb et al., 2007). Cost of the enzyme is one of the main factors determining economy of process. Reducing cost of enzyme production by optimization of fermentation medium and process parameters is the major goal of basic research for industrial applications (Li et al., 2006; Manivannan and Kathiresan, 2007). Proteases are produced by fungi. Trichoderma harzianum and Talaromyces flavus also produce the enzyme when they are grown in the presence of casein and the effect of the enzyme is also known as a safe biodegradable biocontrol agent for control of brown spot disease on bean (Wafaa et al.,
2006). Response surface methodology (RSM) is an effective statistical tool and widely used in optimization process, which includes experimental design, model fitting, validation and condition optimization. An effective statistical design is the basis for response surface optimization and the reported designs include the Plackett-Burman design, the Box-Behnken design, the Graeco-Latin square design and the central composite design (Li et al., 2001; Bayraktar, 2001; Francis et al., 2003). These are the most popular among RSM designs and have the characteristics, uniform precision and repeatability. Production of high quantity enzyme depends on the process parameters and the quantities of medium ingredients. These are the best tools for making enzyme production economically feasible. Trichoderma species are dominant soil fungi especially in coastal mangrove sediment (Kathiresan et al., 2011) and these are yet to receive attention for protease enzyme production using RSM. Hence, the aim of this study is to produce protease enzyme by mangrove-derived Trichoderma species and optimization of conditions of their important media constituents by using RSM. MATERIALS AND METHODS Microorganism and maintenance Trichoderma strains were isolated from soil samples collected from Pichavarm mangrove forest located in south east coast of India (Latitude 11 46N; Longitude 79 49E) by using selective medium (Askew and Laing, 1993). The stock culture for the 10 strains isolated was maintained at 4°C on potato dextrose agar slants. The strains were screened for production of protease.
casein in 0.05 M phosphate buffer (pH 7.0) and incubated for 10 min at 45°C in a water bath. After 10 min, the reaction was terminated by the addition of 5.0 ml of 10% trichloroacetic acid (TCA) and incubated at 30°C for 30 min. Then the reaction mixture was centrifuged at 10,000 rpm for 20 min at 30°C. Absorbance was read at 275 nm using UV spectrophotometer. The enzyme activity is expressed as mg of tyrosine released per min under standard assay conditions. Destaining of blood clot in cotton cloth by using crude enzyme of fungal strain T. estonicum White cotton cloth pieces, measuring 4 cm2 sizes were stained with freshly collected blood from a goat. Stained clothes were incubated for an hour at 100°C to fix the stain and the initial weight of the cloth pieces was recorded. Each of these stained cloth pieces was placed to analyze for destaining in Erlenmeyer flask containing either one ml of commercial detergent or one ml of culture filtrate in 100 ml distilled water. All the flasks were incubated at 60°C for 15 min. After incubation, cloth pieces were rinsed with water and dried at 80°C for 4 h. The final weight of these cloth pieces was taken. The difference between initial weight and final weight and percentage of blood clot removal efficiency was calculated. Selection of the significant factors for protease production using Plackett-Burman design For screening the important medium components, Plackett-Burman design (2-level factorial) (Plackett and Burman, 1946) was used. The variables chosen for this experiment were fructose (g/L), paraffin liquid (%), NaCl (g/L), CaCl2 (g/L), temperature (°C) and pH. The experimental design for the screening of the variables is given in Table 1. All the variables were denoted as numerical factors and investigated at two widely spaced intervals designated as -1 (low level) and +1 (high level). The effect of individual parameters on protease production was calculated by the following equation: E = (ΣM+ - ΣM-)/N
Primary screening method of extracellular protease Protease assay was performed by growing 10 strains of Trichoderma sp. on Glucose yeast peptone (GYP) agar medium amended with 0.4% gelatin and pH was adjusted to 6. After incubation, plates were flooded with saturated aqueous ammonium sulphate. The protease production was indicated by the presence of clearing zone around the fungal colony. The primary screening showed positive activity in all the 10 strains and one strain (CAS 8) exhibited the highest activity. This strain was morphologically identified as Trichoderma estonicum and has been deposited in the microbial culture collection center at CAS in Marine Biology, Annamalai University. Protease enzyme production T. estonicum, CAS 8 was grown in potato dextrose broth with soya bean meal (0.5%, w/v) (Sen and Satyanarayana, 1993) for 4 days on a rotary shaker (125 rpm) at 30°C. After 4 days, the fungal biomass was removed by filtration and the filtrate was centrifuged at 10,000 rpm for 20 min at 4°C and this was used as crude enzyme. Determination of protease enzyme activity The enzyme activity was estimated with casein as substrate (Hagihara et al., 1958). The culture filtrate (1.0 ml) obtained from soya bean meal amended medium was mixed with 5 ml of 1% (w/v)
(1)
Where, E is the effect of parameter under study and M (+) and M (-) are responses of protease activities in trials, and N is the total number of trials. Optimization of protease enzyme production by T. estonicum using central composite design (CCD) Formulation of the medium was determined for the optimum levels of significant variables for protease production. In RSM, a model of CCD was adopted to increase the total protease production. The previously screened significant factors such as fructose (g/L), NaCl (g/L), temperature (°C) and an important factor (incubation period) of each factor was assessed at five coded levels (-2, -1, 0, +1 and +2) and in this statistical model, a total of 30 experimental run were carried out. In the whole experiment, minimum and maximum actual values of the production medium was used and presented in Table 2. The response value (Y) in each trial was the average of the duplicates. Statistical analysis and modeling The data obtained from RSM on protease production were subjected to analysis of variance (ANOVA). The experimental results of RSM were fit via the response surface regression procedure, using the following second order polynomial equation: Yi = β0 + Σi βiXi + Σi βiiXi2 + Σij βijXiXj
(2)
Table 1. Screening of significant parameters influencing protease production and predicted and actual protease activity by using Plack ettBurman (2-level factorial) experimental design.
Standard order
Fructose (g/L)
Paraffin liquid (%)
Nacl (g/L)
Cacl2 (g/L)
Temperature (°C)
pH
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1
1 1 5 5 1 1 5 5 1 1 5 5 1 1 5 5
1 1 1 1 5 5 5 5 1 1 1 1 5 5 5 5
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
30 60 60 30 60 30 30 60 30 60 60 30 60 30 30 60
5 5 10 10 10 10 5 5 10 10 5 5 5 5 10 10
Where Yi is the predicted response, XiXj are independent variables, β0 is the offset term, βi is the ith linear coefficient, βii is the ith quadratic coefficient, and β ij is the ij th interaction coefficient. However, in this experiment, the independent variables were coded as X1, X2, X3 and X4. Thus, the second order polynomial equation can be presented as follows. Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β11 X12 + β22 X22 + β33 X32 + β44 X42 + β12 X1 X2 + β13X1X3 + β14 X1 X4 + β23 X2X3 + β24 X2X4 + β34 X3 X4 (3) Statistical software, namely, the design expert (8.0.6 package) was used for the regression analysis and to plot the response surface graphs of the experimental data. The statistical significance of the model equation and the model terms was evaluated via the Fisher’s test. The quality of fit in the second-order polynomial model equation was expressed via the coefficient of determination (R 2) and the adjusted R2. The fitted polynomial equation was then expressed in the form of three-dimensional surface plots, in order to illustrate the relationship between the responses and the experimental levels of each of the variables utilized in this experiment. The point optimization method was employed in order to optimize the level of each variable for maximum protease production. The combination of different optimized variables, which yielded the maximum response, was determined in an attempt to verify the validity of the model.
RESULTS Screening of important factors for production using Plackett-Burman design
protease
The experiment was conducted in 16 runs to study the
Protease activity (IU/ml) Observed Predicted 21.84 19.82 24.52 21.33 15.6 12.02 18.6 25.75 9.6 14.23 34.68 27.96 22.5 18.65 12.6 20.15 16.3 18.37 16.3 19.88 12.3 10.56 26.6 24.29 12.56 12.77 25.1 26.50 12.95 17.19 26.2 18.70
effect of the selected variables. Table 1 represents the results of the screening experiments using the PlackettBurman design. The model F value of 808.14 implies that the model was significant. The value of p< 0.0001 indicates that model terms were significant. The magnitude of the effects indicated the level of the significance of the variables on protease production. Fructose (g/L), NaCl (g/L), temperature (°C) and incubation period range, concentration were identified respectively as the most significant variables influencing protease production (Table 3). Optimization of significant factors for protease enzyme production by T. estonicum using CCD The design matrix and the corresponding results of RSM experiments to determine the effects of four independent variables (fructose (g/L), NaCl (g/L), temperature (°C) and incubation period ranges (h) are shown in Table 3 along with the mean experimental and predicted values. The regression analysis of the optimization study indicated that the model terms, X1, X2, X3, X4, X12, X22, X32, X42, X2X3 and X2X4, were significant (Table 4) (P < 0.05). These results indicated that the fructose (g/L), NaCl (g/L), temperature (°C) and incubation period range, and interactions of the parameters had the direct association with protease production and lack of fit (0.00) also suggested that the obtained experimental data was a good fit with the model. The regression equation coefficients were calculated
Table 2. Central composite design matrix for the experimental design and predicted responses for Protease activity (U/ml).
Standard order
Fructose (g/L)
Nacl (g/L)
Temperature (°C)
Incubation Hours
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 28 29 30
0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 -0.5 1.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
1 1 5 5 1 1 5 5 1 1 5 5 1 1 5 5 3 3 -1 7 3 3 3 3 3 3 3 3 3 3
30 30 30 30 60 60 60 60 30 30 30 30 60 60 60 60 45 45 45 45 15 75 45 45 45 45 45 45 45 45
0 0 0 0 0 0 0 0 120 120 120 120 120 120 120 120 60 60 60 60 60 60 -60 180 60 60 60 60 60 60
Protease activity(IU/ml) Experimental Predicted 0.32 1.35083 0.20 1.922083 0.23 0.740417 0.0 7.165833 0.10 4.877083 0.05 1.6525 0.21 3.465833 0.12 3.39375 12.56 14.69875 26.35 26.36917 13.26 15.0925 29.26 29.91542 18.9 15.21917 15.6 20.39208 8.5 12.11042 15.6 20.43583 7.6 6.292083 25.6 17.89042 15.5 14.82375 25.3 16.95875 9.6 7.547083 11.26 4.295417 0 -6.42458 29.26 26.66708 31.26 31.26 31.26 31.26 31.26 31.26 31.26 31.26 31.26 31.26 31.26 31.26
Table 3. Statistical parameters for selected the linear polynomial model using Plackett-Burman design.
Term Constant A-Fructose (g/L) B-Paraffin liquid (%) C-NaCl (g/L) D-CaCl2 (g/L) E-Temperature (°C) pH
Coefficient 19.26563 3.809375 -0.84688 0.258125 -0.72688 3.05563 -0.49
SE Coefficient 1.81 1.81 1.81 1.81 1.81 1.81 1.81
and the data was fit to a second-order polynomial equation. The response, protease production (Y) by T. estonicum can be expressed in terms of the following regression equation: Y = 31.26+ 2.90 X1 + 0.53X2 + -0.81 X3 + 8.27X4 -4.79X12 2 2 2 -3.84X2 -6.33X3 -5.28X4 +0.79 X1X2 -1.62X1X3 +2.10X1X4 -0.88X2X3 -0.42X2X4 1.43X3X4
Actual 32.74 2.49 -0.19 0.52 -3.85 0.19 -0.65
Probability 0.026 0.013 0.04 0.025 0.026 0.056 0.025
Where; X1 is fructose (g/L), X2 is NaCl (g/L), X3 is temperature (°C) and X4 is incubation period (h). The regression equation obtained from the ANOVA 2 showed that the R (multiple correlation coefficient) was 0.907 (a value >0.80 indicates fitness of the model). This is an estimate of the fraction of overall variation in the data accounted by the model and thus the model is
Table 4. Analysis of variance table (ANOVA) for response surface methodology of main effects and interacting effects of parameters in quadratic model for protease production.
Source Model A-Fructose (g/L) B-NaCl (g/L) o C-Temperature ( C) D-Incubation hours AB AC AD BC BD CD 2 A B2 2 C D2 Residual Lack of fit Pure error Core total
Sum of squares 4109.85 201.78 6.83 15.86 1642.58 9.93 42.21 70.51 12.26 2.88 32.57 629.89 404.91 1100.66 766.02 420.65 420.65 0 4530.51
Df 14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 10 5 29
Mean square 293.56 201.78 6.83 15.86 1642.58 9.93 42.211 70.51 12.26 2.88 32.57 629.89 404.91 1100.66 766.02 28.04 42.06 0
F value 10.46 7.19 0.24 0.56 58.57 0.35 1.50 2.51 0.43 0.10 1.16 22.46 14.43 39.24 27.31
p-value Probability > F *** < 0.0001 * 0.0170 * 0.0086 * 0.0037 < 0.0001*** * 0.005 0.0088* 0.0037* * 0.0084 * 0.0030 * 0.0082 * 0.003 0.017* *** < 0.0001 0.0001***
Statistically significant *** (P
