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����Optimization of culture conditions for laccase production by Shiraia bambusicola GZ11K2 using response surface methodology
Wen Du, Chunlong Sun, Jianping Yu, Liying Ren, Yanfeng Han, Zongqi Liang 
Wen Du, Institute of Biochemistry and Nutrition, College of Life Science, Guizhou University, Guiyang, China. 
E-mail: duwen6688@163.com 
Chunlong Sun, Institute of Biochemistry and Nutrition, College of Life Science, Guizhou University, Guiyang, China. 
Jianping Yu*( corresponding author ), Institute of Biochemistry and Nutrition, College of Life Science,Guizhou University, Guiyang, China. 
Phone: 00-86-0851-3854677   E-mail: 2857337095@qq.com;  HYPERLINK "mailto:yujp666666@163.com" yujp666666@163.com
Liying Ren*(corresponding author), Institute of Soil and Water Conservation and Environmental Protection, Linyi University, Linyi, China.
Phone: 00-86-0539-8218653         E-mail: 2243979303@qq.com
Yanfeng Han, Institute of Fungus Resources, College of Life Science, Guizhou University, Guiyang, China.    
Zongqi Liang, Institute of Fungus Resources, College of Life Science, Guizhou University, Guiyang, China.
Abstract
Response surface methodology was employed for statistical optimization of laccase production by Shiraia bambusicola GZ11K2 in shaker flask cultivation form a novel fermentation system. The Plackett-Burman design was used in the first step to evaluate the effects of eight factors, including fermentation medium components. Among the variables screened, guaiacol and bamboo extract in the fermentation medium had significant effects on laccase production. The steepest ascent method was used to access the optimal region of the medium composition, followed by an application of response surface. The analysis revealed that the optimized concentration of guaiacol and bamboo extract was 132.6 (�M) and 167.7 (g/L), respectively, which increased the laccase production in a shake flask system by 6.24-fold increase relative to the production of original level (2113 U/L).


Keywords Shiraia bambusicola; Laccase; Medium optimization; Response surface




Introduction
Laccases(p-diphenol: oxygen oxidoreductases, EC 1.10.3.2) are multicopper enzymes that catalyze the oxidation of a broad variety of substrates such as mono-, di-, and polyphenols, aminophenols, methoxyphenols, aromatic amines, and ascorbic acid (Mayer et al., 2002). They have a great biotechnological potential in diverse fields of industrial application, including effluent detoxification, kraft pulp and dye bleaching, polymer synthesis, bioremediation of contaminated soils, baking, wine and beverage stabilization, the manufacture of anticancer drugs, and recently, in nanobiotechnology as part of biosensors for immunoassays (Baldrian, 2006; Kunamneni et al., 2008; Tinoco et al., 2011). 
Laccase widely distributed in higher plants, in some insects and in a few bacteria, whereas the best known laccases are of fungal origin (Gnanamani et al., 2006; Hattori et al., 2005; Liu et al., 2009). In fungi, laccases function in lignin degradation, pathogenesis, detoxification, and fungal development and morphogenesis (Baldrian, 2006; Leonowicz et al., 2001). Therefore, laccases of fungi have attracted considerable attentions for academic and industry. Laccase production occurs in various fungi over a wide range of taxa. Up to date, fungi from the deuteromycetes, ascomycetes as well as basidiomycetes are known potential producers of laccase (Bollag and Leonowicz, 1984). A newly isolated ascomycetes fungus, Shiraia bambusicola Henn., have proven to be laccase producers and gave a preliminary study about the laccase activity (Hu et al., 2011). S. bambusicola is a parasitic fungus of bamboo, which grows primarily in the southern provinces of China. S. bambusicola has the advantages of short fermentation period and higher laccase activity. Therefore, it is necessary to do further research. Many studies have shown that the activation of the laccases production is significantly enhanced by a wide variety of substances, including metal ions, aromatic compounds, and dyes during fermentation process (Liu et al., 2009; Valderrama et al., 2003). Therefore, it is important to study the optimization of culture conditions for the maximal laccase production of different sources.
The optimization of fermentation conditions, particularly nutritional and inducers are very important. The statistical methods are believed to be effective and powerful approach for screening key factors rapidly from a multivariable system to optimize fermentation conditions and have been extensively used recently (Majumder et al., 2007; Guo et al., 2009). However, the statistical design and the addition of inducers have been reported in the study of laccase production from S. bambusicola only recently. 
In the present study, the culture medium compositions were optimized applying the Plackett�Burman design (PBD), followed by the paths of steepest ascent, and response surface methodology (RSM) was also employed for maximizing laccase production by S. bambusicola GZ11K2. The major variables affecting the performance of the culture in terms of laccase production were also investigated.
Material and Methods
Strain and Culture media inoculation preparation
Laccase producing mutant strain Shiraia bambusicola GZ11K2, was obtained from institute of biochemistry and nutrition, Guizhou University, China. Both the strains were grown at 26 �C on potato dextrose agar (PDA) slants for preservation. Non-optimized fermentation medium was composed of : K2MnO4 0.005 g/L, guaiacol 120�M, glucose 30 g/L, bamboo extract 160 g/L, SDS 0.010 g/L, copper 2.0 mM, MgSO4 �7H2O 0.300 g/L, KH2PO4 0.500 g/L. 
Fermentation was performed in two stages: seed growth and laccase production. For the seed growth stage, mycelium from a plate culture was inoculated into 50ml of seed medium (potato extracts 200 g/L, glucose 20 g/L) and grown at 28 oC with 150 rpm for 48 h. Then, 5 % (v/v) seed cultures were inoculated into the fermentation medium. The strain was incubated at 28 oC with 150 rpm for 96 h in 250 ml flasks containing 100 ml medium. In all cases, media were sterilized by autoclaving at 121�C for 30 min.
Enzyme assay 
All experiments were carried out in 250ml flasks as batch reactors described before (Guo et al., 2009). Laccase activity was determined by monitoring the absorbance change at 420nm related to the rate of oxidation of 1mM 2,22 -azino-bis-[3-ethylbenz- thiazo-line-6-sulfonate](ABTS) to its cation radical in 25 mM Na-acetate buffer (pH 3.8) at room temperature (Tavares et al., 2005). One unit of enzyme activity was defined as the amount of enzyme required to oxidize 1�mol ABTS per minute. The data presented correspond to mean values, the standard deviation being lower than 15%. 
Experimental design and data analysis
In preliminary experiments, various carbon and nitrogen sources, inorganic salts, inducers, and surfactants were evaluated for their suitability to sustain good laccase production by strain GZ11K2. The addition time of inducers (e.g., guaiacol) was at 60h of cultivation. The results revealed that the major variables affecting the performance of the culture in terms of laccase yield were K2MnO4, guaiacol, glucose, bamboo extract, SDS, copper, MgSO4 �7H2O and KH2PO4. These components were chosen for further optimization.
Plackett�Burman design
In this study, the PBD was used for investigating the significance of variables to laccase produce. To obtain a value for the experimental error, three replicates of the each run were performed. The sas software package version 9.1 (SAS Institute Inc., Cary, NC, USA) was used to generate the PBD and analyse the experiment data.
The PBD was used to select factors that significantly influenced laccase production (Liang et al., 2011). The technique is based on the first-order polynomial model:
Y = �0 + "�i�i                       [Equation 1]  
where Y is the response (laccase production), �0 is the model intercept and �i is the linear coefficient and �i is the level of the independent variable. Eight factors, K2MnO4, guaiacol, glucose, bamboo extract, SDS, copper, MgSO4�7H2O and KH2PO4, were examined to investigate the key ingredients significantly affecting laccase production. Based on PBD, each factor was prepared in two levels: -1 for low level and +1 for high level. A center point was run to evaluate the linear and curvature effects of the variables. Table 1 illustrates the experimental design with eight factors as well as the response, whereas, table 2 shows the design matrix. In the present study, eight assigned variables were screened in twelve experimental runs in addition with three runs at their center points. The factors significant at 95% level (P<0.05) were considered to have significant effect on laccase production and thus used for further optimization by RSM. To move rapidly towards the neighborhood of the optimum response, we used the method of steepest ascent (Zhou et al., 2011).
Central composite designs and response surface methodology
To describe the nature of the response surface in the optimum region, a central-composite design (CCD) and RSM was performed. The levels of each factor and the design matrix are given in Table 3. The low, middle, and high levels of each variable were designated as -1.41421, -1, 0, and 1, 1.41421, respectively.
Statistical analysis
All experiments were done in triplicate, and the average laccase production was taken as the response. The P-B and CCD experimental design and statistical analysis of the data were done with the SAS software package. Statistical analysis of the models was used to evaluate the analysis of variance (ANOVA) (Gao et al., 2009).
Results and discussion
Screening of parameters using Plackett�Burman design 
The laccase productions are given in Table 1. Using the SAS software (version 9.1), a first-order model was fit to the data obtained from the experiment. When the sign of the concentration effect of the tested variable was positive, the influence of the variable upon the laccase productions was greater at a high concentration, and when it was negative, the influence of the variable was greater at a low concentration (Fang et al., 2010). The analysis of variance (ANOVA) for the P-B experiment was calculated, and the maximum and minimum effect of each variable on the laccase productions was determined by Student�s t-test (Table 2). 
The effects of X2 (guaiacol) and X4 (bamboo extract) had confidence levels >95% and were considered to influence the laccase productions significantly. Others had no obvious effects and obvious effects and the low confidence level, so they were considered insignificant. In the results, R2 was found to be 0.9052, means that model could explain 90.52% of the total variations in the system. A first-order model equation was derived representing laccase production as a function of the independent variables:
Y (U/l)=7521.75+1089.75X2-960.25X4             [Equation 2]  
Optimization by the path of steepest ascent experiment
The results of PBD indicated that the guaiacol effect was positive, whereas that of bamboo extract was negative. Thus, increasing guaiacol amount and decreasing bamboo extract concentration should result in a higher laccase production. The center point of the PBD has been considered as the origin of the path. The laccase production was obtained; these experiments (Table 3) showed the maximum laccase production. This was obtained when the parameters were 150 �M guaiacol and 172.5 g/L bamboo extract. It suggested that this point might be near the region of the maximum laccase response. This point was chosen for further optimization.
Further optimization using central composite designs
The CCD was conducted in the vicinity of the optimum to locate the true optimum concentrations of guaiacol (X1) and bamboo extract (X2) for laccase production. The levels of the variables for the CCD experiments were selected according to the results of the previous experiments. The design matrix and the corresponding experimental data are given in Table 4. The experimental results of the CCD were fit with a second-order polynomial equation:
Y(U/l) = 12912.2 � 839.98X1 � 210.69X2 � 704.04X1X1 + 39.25X1 X2 � 666.04X2 X2 
[Equation 3]
The fit of the model Y was evaluated by the coefficient of determination R2, which was 0.9904, indicating that 99.04% of the variability in the response could be explained by the model (Table 5). The statistical significance of the second-order model equation was evaluated by an F-test ANOVA, which revealed that this regression is statistically significant (P < 0.0001) at the 99% confidence level. Table 5 shows the significance of the regression coefficients of the model, indicating that guaiacol and bamboo extract had a highly significant effect (P << 0.005) on laccase production, as well as the quadratic terms of guaiacol and bamboo extract. The contour plot described by the model is represented in Figure 1, which shows that the maximum laccase production was approximately 13183 (U/L). 
Validation of the optimized condition
The optimal concentration for the two components obtained from the maximum point of the model was 132.6 (�M) for guaiacol (X1) and 167.7 (g/L) for bamboo extract (X2). The model predicted a maximum response of 13183 U/L laccase productions for this point. To verify the predicted results, validation experiment was performed in triplicate tests. Under the optimized condition, the observed experimental titer of average laccase was 13020 � 144 U/L, suggesting that experimental and predicted values (13183 U/L) of laccase yield was in good agreement. And the laccase yield was 2113 U/L in non-optimized media; 6.24-fold increase had been obtained, while the growth of the strain in the two medium was comparable (data not shown). This result therefore corroborated the predicted values and the effectiveness of the model, indicating that the optimized medium favors the laccase production.
Nutritional requirements of S. bambusicola play an important role during metabolite synthesis process (Yang et al., 2009). Amongst various nutritional requirements, nutrition source and inducer are generally regarded as important factors of metabolism, and several examples of the production of metabolites in media with optimized contents of these components are also described in the literature (Yang et al., 2009; Hu et al., 2008; Cai et al., 2008). When provided a mixture of energy sources, microorganisms preferentially utilize those they can metabolize most effectively. Secondary metabolites have been reported to be repressed by the presence of glucose during their production. Carbohydrates such as glycerol, maltose, lactose and some others are known to have interference with the production of secondary metabolites (Ripa et al., 2009). The mechanism for catabolite repression in S. bambusicola has not been unraveled, but appears to be unique in the way it regulates the rate of nutrition consumption (Chen et al., 2006). For the majority of S. bambusicola, the preferred carbon source is glucose, nitrogen source are potato and corn extract, especially for the hypocrellin production (Mayer et al., 2002; Tinoco et al., 2011; Ripa et al., 2009). Thus, it is not surprising that bamboo extract is a significant factor, glucose is a better factor, as shown by PBD results. Just as microorganisms have preferred energy sources, they also have preferred inducer. It was reported that complex inducer could increase the laccase production. Tavares et al. (2005) reported a cooperative effect between the inducers on laccase production, and nearly 104 times enhancement of laccase activity was obtained using complex-inducer-supplemented medium. Liu et al. (2009) obtained the maximum laccase production under the induction of copper sulphate and methylene blue.
In this study, as shown by PBD and RSM results, guaiacol as an important factor has a positive effect, and lacking guaiacol may block laccase synthesis. On the basis of data analysis, we appropriately increased the amount of guaiacol and finally obtained a satisfactory result. The RSM designs applied in the present investigation have been successfully applied in many recent biotechnological researches (Su et al., 2010; Giordano et al., 2010; Acikel et al., 2010). However, to the best of our knowledge, no single report was obtained on laccase production from S. bambusicola optimization using the RSM designs.
Conclusion
This study proved that statistical experimental designs offer an efficient and feasible approach for laccase fermentation medium optimization. A maximum laccase production of 13020 U/L was achieved with the following optimized factors: 132.6 �M guaiacol and 167.7 g/L bamboo extract, because we screened a lot of factors, applied RSM designs, using the mutant strain and so on. The results of validation experiments showed that the predicted value agreed with the experimental values well, and 6.24-fold increase compared to the original medium was obtained. The results also give a basis for further study with large scale fermentation.
Acknowledgments 
This work was financially supported by the National Natural Science Foundation of China (No.30960004; 41201228); Natural Science Foundation of Guizhou Province�2008-2266; 2011-3094; 2010-4008; 2009-7012-1; 2011-204), Natural Science Foundation of Guizhou University (2007-035; 2010-102).
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Figure 1  3D surface graph of guaiacol (X1) vs. bamboo extract (X2) for laccase production (Y1) 
Table `!  Plackett Burman experimental design for screening of significant variables affecting laccase production from S. bambusicola GZ11K2
Table a!  Levels of the variables and statistical analysis of Plackett Burma�������	�B�C�D���������ԝ����#�L�N�R�S�e�q����
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Table c!  Experimental results of the path of steepest ascent (descent)
Table d!  Design and responses of the central composite design 
Table e!  Analysis of variance for the experimental results of the central composite design 

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