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Statistical optimization of process variables for antibiotic activity of Xenorhabdus bovienii.

Fang XL, Han LR, Cao XQ, Zhu MX, Zhang X, Wang YH - PLoS ONE (2012)

Bottom Line: A 2(5-1) factorial central composite design was chosen to determine the combined effects of the five variables, and to design a minimum number of experiments.The experimental and predicted antibiotic activity of X. bovienii YL002 was in close agreement.After optimization, the antibiotic activity was improved by 23.02% as compared with that of unoptimized conditions.

View Article: PubMed Central - PubMed

Affiliation: Research and Development Center of Biorational Pesticides, Northwest A & F University, Yangling, Shaanxi, People's Republic of China.

ABSTRACT
The production of secondary metabolites with antibiotic properties is a common characteristic to entomopathogenic bacteria Xenorhabdus spp. These metabolites not only have diverse chemical structures but also have a wide range of bioactivities of medicinal and agricultural interests. Culture variables are critical to the production of secondary metabolites of microorganisms. Manipulating culture process variables can promote secondary metabolite biosynthesis and thus facilitate the discovery of novel natural products. This work was conducted to evaluate the effects of five process variables (initial pH, medium volume, rotary speed, temperature, and inoculation volume) on the antibiotic production of Xenorhabdus bovienii YL002 using response surface methodology. A 2(5-1) factorial central composite design was chosen to determine the combined effects of the five variables, and to design a minimum number of experiments. The experimental and predicted antibiotic activity of X. bovienii YL002 was in close agreement. Statistical analysis of the results showed that initial pH, medium volume, rotary speed and temperature had a significant effect (P<0.05) on the antibiotic production of X. bovienii YL002 at their individual level; medium volume and rotary speed showed a significant effect at a combined level and was most significant at an individual level. The maximum antibiotic activity (287.5 U/mL) was achieved at the initial pH of 8.24, medium volume of 54 mL in 250 mL flask, rotary speed of 208 rpm, temperature of 32.0°C and inoculation volume of 13.8%. After optimization, the antibiotic activity was improved by 23.02% as compared with that of unoptimized conditions.

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Residual diagnostics of contour surface of the quadratic model.(A) The predicted vs. observed antibiotic activity of Xenorhabdus bovienii YL002. (B) Plot of internally studentized residuals vs. predicted responses.
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pone-0038421-g002: Residual diagnostics of contour surface of the quadratic model.(A) The predicted vs. observed antibiotic activity of Xenorhabdus bovienii YL002. (B) Plot of internally studentized residuals vs. predicted responses.

Mentions: A regression model can be used to predict future observations on the response Y (antibiotic activity) corresponding to particular values of the variables. In predicting new observations and estimating the mean response at a given point, one must be careful about extrapolating beyond the region containing the original observations. It is very possible that a model that fits well in the region of the original data will no longer fit well outside the region [31]. Fig. 2A showed the observed antibiotic activity versus the predicted ones from the model Equation (2). Point above or below the diagonal line represented areas of over or under prediction. There were no significant violations of the model, and the predicted data of the response from the empirical model was in agreement with the observed ones in the range of the operating variables. It is necessary to check the fitted model to ensure that it provides an adequate approximation to the real system. Unless the model shows an adequate fit, proceeding with the investigation and optimization of the fitted response surface likely give poor or misleading results [31]. The residuals from the least squares fit play an important role in judging model adequacy. The assumptions for randomness, normality and constant variances of the residuals were all verified by the normal probability plot and the residual plot. A linear pattern demonstrated normality in the error term and there were no signs of any problems in our data. Fig. 2B showed a plot of residuals versus the predicted response, where residual scatters were randomly displayed, suggesting that the variance of the original observation was constant for all values of Y.


Statistical optimization of process variables for antibiotic activity of Xenorhabdus bovienii.

Fang XL, Han LR, Cao XQ, Zhu MX, Zhang X, Wang YH - PLoS ONE (2012)

Residual diagnostics of contour surface of the quadratic model.(A) The predicted vs. observed antibiotic activity of Xenorhabdus bovienii YL002. (B) Plot of internally studentized residuals vs. predicted responses.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3368850&req=5

pone-0038421-g002: Residual diagnostics of contour surface of the quadratic model.(A) The predicted vs. observed antibiotic activity of Xenorhabdus bovienii YL002. (B) Plot of internally studentized residuals vs. predicted responses.
Mentions: A regression model can be used to predict future observations on the response Y (antibiotic activity) corresponding to particular values of the variables. In predicting new observations and estimating the mean response at a given point, one must be careful about extrapolating beyond the region containing the original observations. It is very possible that a model that fits well in the region of the original data will no longer fit well outside the region [31]. Fig. 2A showed the observed antibiotic activity versus the predicted ones from the model Equation (2). Point above or below the diagonal line represented areas of over or under prediction. There were no significant violations of the model, and the predicted data of the response from the empirical model was in agreement with the observed ones in the range of the operating variables. It is necessary to check the fitted model to ensure that it provides an adequate approximation to the real system. Unless the model shows an adequate fit, proceeding with the investigation and optimization of the fitted response surface likely give poor or misleading results [31]. The residuals from the least squares fit play an important role in judging model adequacy. The assumptions for randomness, normality and constant variances of the residuals were all verified by the normal probability plot and the residual plot. A linear pattern demonstrated normality in the error term and there were no signs of any problems in our data. Fig. 2B showed a plot of residuals versus the predicted response, where residual scatters were randomly displayed, suggesting that the variance of the original observation was constant for all values of Y.

Bottom Line: A 2(5-1) factorial central composite design was chosen to determine the combined effects of the five variables, and to design a minimum number of experiments.The experimental and predicted antibiotic activity of X. bovienii YL002 was in close agreement.After optimization, the antibiotic activity was improved by 23.02% as compared with that of unoptimized conditions.

View Article: PubMed Central - PubMed

Affiliation: Research and Development Center of Biorational Pesticides, Northwest A & F University, Yangling, Shaanxi, People's Republic of China.

ABSTRACT
The production of secondary metabolites with antibiotic properties is a common characteristic to entomopathogenic bacteria Xenorhabdus spp. These metabolites not only have diverse chemical structures but also have a wide range of bioactivities of medicinal and agricultural interests. Culture variables are critical to the production of secondary metabolites of microorganisms. Manipulating culture process variables can promote secondary metabolite biosynthesis and thus facilitate the discovery of novel natural products. This work was conducted to evaluate the effects of five process variables (initial pH, medium volume, rotary speed, temperature, and inoculation volume) on the antibiotic production of Xenorhabdus bovienii YL002 using response surface methodology. A 2(5-1) factorial central composite design was chosen to determine the combined effects of the five variables, and to design a minimum number of experiments. The experimental and predicted antibiotic activity of X. bovienii YL002 was in close agreement. Statistical analysis of the results showed that initial pH, medium volume, rotary speed and temperature had a significant effect (P<0.05) on the antibiotic production of X. bovienii YL002 at their individual level; medium volume and rotary speed showed a significant effect at a combined level and was most significant at an individual level. The maximum antibiotic activity (287.5 U/mL) was achieved at the initial pH of 8.24, medium volume of 54 mL in 250 mL flask, rotary speed of 208 rpm, temperature of 32.0°C and inoculation volume of 13.8%. After optimization, the antibiotic activity was improved by 23.02% as compared with that of unoptimized conditions.

Show MeSH
Related in: MedlinePlus