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## Electronic Journal of Biotechnology

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*On-line version* ISSN 0717-3458

### Electron. J. Biotechnol. vol.15 no.4 Valparaíso July 2012

Response surface methodology was used to optimize the fermentation conditions for the production of pristinamycin by immobilization of
Pristinamycin, which was first discovered in France in 1962, is produced by Fermentation of immobilized microbial cells has recently gained much attention among many biotechnological approaches, because of its advantage over conventional free cell systems in respect to retention of high cell density, operational stability, higher efficiency of catalysis, higher volumetric productivity and lower shear stress (Adinarayana et al. 2005; Givry et al. 2008). The adsorption in porous material such as polyurethane foam (PUF) is a very simple immobilization used in a liquid fermentation (de Ory et al. 2004; Quezada et al. 2009). Many examples of microbial cells immobilization on PUFs have been reported in literatures, such as bacteria (de Ory et al. 2004), microalgae (Yamaguchi et al. 1999), basidiomycetes (Guimaraẽs et al. 2005), ascomycetes (Hama et al. 2006) and Cyanobacteria (Chetsumon et al. 1993). It is well known that designing proper culture conditions is a prerequisite in the production of metabolites (Maia et al. 2001). Nevertheless, the optimization of fermentation conditions for pristinamycin production by immobilized cell has not been made so far. The conventional optimization method, one-factor-at-a-time approach, is time consuming and incapable of detecting the true optimum, especially due to the complex interactions among various physicochemical parameters (Wang et al. 2008). Response surface methodology (RSM) has overcome these drawbacks, therefore it can evaluate the relative signiﬁcance of several variables simultaneously, especially in the presence of complex interactions. As a result, RSM is used popularly to solve multivariate problems and has proved to be powerful and useful for the optimization of the target metabolites production (Rahman et al. 2004; Katapodis et al. 2006; Li et al. 2006; Sayyad et al. 2007). In the present study, a surface response methodology was applied to optimize fermentation conditions for pristinamycins production using
The
Commercial polyurethane foam (PUF, Qitai Foam Co. Ltd, Shanghai, P.R. China) was used as the carrier and cut in the same length (1 cm), thickness (0.5 cm) and different width, the size of the PUFs were determinated by volume. The PUFs were soaked and swollen in 95% alcohol for 24 hrs to remove impurities and washed several times with sufficient distilled water to remove the alcohol. The PUFs were dried in vacuum oven at 80ºC for 8-10 hrs and added to the seed medium before sterilization. The fermentation condition before optimization was as follows: for seed culture, 1 ml of spore solution was inoculated into a 250 ml shaking ﬂask containing 30 ml seed medium, in which the size and amount of PUFs was 0.35 cm
To determinate yield of pristinamycin, one volume of the whole fermentation broth containing mycelia and PUFs was directly mixed with two volumes of methanol for 1 hr. After centrifugation (4000 x
The preliminary single-factor experiments revealed that the major variables affecting the pristinamycin production were seed medium volume, carrier amount, seed age, carrier size, inoculum volume of fermentation, fermentation medium volume, shaking speed of both seed culture and fermentation culture. These variables were chosen for further optimization.
PBD was used to screen the most important factors influencing pristinamycin production. The experimental design with the name, symbol code, and level of the variables is shown in Table 1. Each independent variable is represented in two levels, high and low, which are denoted by (+) and (-), respectively. Three dummy variables were studied in 12 experiments to calculate the standard error. Pristinamycin fermentation was carried out in duplication and the average value was taken as the response. Usually, the variable with
To find the neighbourhood of the optimum condition quickly, we used the method of the steepest ascent. The experiments were applied to determine a suitable direction by increasing or decreasing the variables according to the results obtained from the Plackett-Burman design (Gheshlaghi et al. 2005).
To describe the optimum culture conditions to enhance the pristinamycin production, the response surface methodology was performed with central-composite design. The levels of each variable and the design matrix are given in Table 2. The low, middle, and high levels of each variable were designated as -1.682, -1, 0, and 1, 1.682, respectively.
The Design Expert software (Version 7.0.0, Stat-Ease, Minneapolis, USA) was used for the experimental design and the analysis of variance (ANOVA) for the data. The quality of the polynomial model equation was judged statistically by the coefficient of determination R
Based on our previous single-factor experiments, the importance of the eight culture conditions, namely, seed medium volume, carrier amount, inoculum amount, seed age, fermentation medium volume, shaking speed of both seed culture and fermentation culture for the pristinamycin production was analyzed by PBD. The experimental design and corresponding pristinamycin yields were shown in Table 1, whereas Table 3 shows the effects of these factors on the response and signiﬁcant levels. Based on the statistical analysis, fermentation medium volume, with a probability value of 0.022, was determined to be the most signiﬁcant factor, followed by shaking speed of seed culture (0.047), and seed medium volume (0.053), so these three factors were considered in the further optimization. In the results, R
PBD results indicated that the effect of seed medium volume was positive, whereas that of fermentation medium volume and shaking speed of seed culture was negative. Thus it is predicted that increasing seed medium volume (
Based on the identiﬁcation of variables by the PBD and the steepest ascent method, the experiments were performed according to a CCD experimental plan together with experimental results (Table 5). In order to predict the maximum pristinamycin production corresponding to the optimum levels of the three variables, a second-order polynomial model was proposed to calculate the optimum levels of these variables. By applying the multiple regression analysis on experimental data, a second-order polynomial model in coded unit explains the role of each variable and their second-order interactions in producing pristinamycin. All terms, regardless of their significance, were included in the following second-order polynomial equation: Where Furthermore, the results of the second-order response surface model in the form of analysis of variance (ANOVA) were shown in Table 6. The The fitness of the model can be checked by the determination coefficient (R The 3D response surface curve and 2D contour plot are generally the graphical representation of the regression equation. The three dimensional response surface and their corresponding 2D contour plots for the pristinamycin production against any two independent variables while the other independent variable maintained at zero levels were presented in Figure 2 and Figure 3. The graphical representation provides a method to visualize the relation between the response and experimental levels of each variable, and the type of interactions between test variables (Rahulan et al. 2009). The optimum value of each variable was located based on the hump in the three-dimensional plot, or from the central point of the corresponding contour plot. Figure 2 depicted the three dimensional plot and its respective contour plot showing the response surface from the interaction between seed medium volume ( Figure 4 showed the effect of fermentation medium volume ( Figure 3 depicted the effect of seed medium volume (
Based on the quadratic model, the optimal values of each test variables in coded levels were as follows: The RSM design applied in the present investigation have been successfully used in many metabolite production for optimization of immobilization conditions (Sankpal and Kulkarni, 2002; Aybastier and Demir, 2010; Liu et al. 2010). However, to the best our knowledge, there are no reports of optimization of pristinamycin production by immobilized
Response surface methodology used in this investigation suggested the importance of dissolved oxygen supply for pristinamycin production in immobilization fermentation. A highly signiﬁcant quadratic polynomial obtained by the CCD was very useful for determining the optimal conditions with signiﬁcant effects on pristinamycin production. Validation experiments were also performed to verify the accuracy of the model, and the results indicated that the predicted value agreed with the experimental values well. A maximum pristinamycin yield of 213 mg/l was achieved, which was 2.34-fold higher than that before optimization, but it was lower than the reported yield of 412 mg/l (Xu et al. 2009). The less yield may be due to the difference of the strain used: the strain used in this study is a mutant strain, while that in the literature is a recombinant created from genome shuffling. Thus, for the fermentation of immobilized
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