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NW-G01, produced by _{4})_{2}SO_{4}, peptone and CaCO_{3}. A prediction model has been built in the experiments of central composite design and response surface methodology, and its validation has been further verified. The optimal medium composition was determined (g/L): corn starch 15, glucose 15, peptone 3.80, (NH_{4})_{2}SO_{4} 0.06, NaCl 1.5, CaCO_{3} 1.30, MgSO_{4}·7H_{2}O 0.015, K_{2}HPO_{4}·3H_{2}O 0.015, MnCl_{2}·4H_{2}O 0.015, FeSO_{4}·7H_{2}O 0.015, and ZnSO_{4}·7H_{2}O 0.015. Compared with NW-G01 production (5.707 mg/L) in non-optimized fermentation medium, the production of NW-G01 (15.564 mg/L) in optimized fermentation medium had a 2.73-fold increase.

Agricultural antibiotic produced by different species of actinomyces is a biological product from a natural resource. Agricultural antibiotics have been attracting growing interest with the development of environmentally friendly and safe integrated crop management. In our ongoing screening for new bioactive microbial compounds, a novel hexapeptide antibiotic NW-G01 (

An appropriate fermentation medium is one of the crucial factors in the antibiotic industry, because the medium composition could significantly affect secondary metabolic yield from microorganisms [

To the best of our knowledge, the medium requirements for

The importance of the eleven components, corn starch, glucose, (NH_{4})_{2}SO_{4}, peptone, NaCl, CaCO_{3}, MgSO_{4}·7H_{2}O, K_{2}HPO_{4}·3H_{2}O, MnCl_{2}·4H_{2}O, FeSO_{4}·7H_{2}O and ZnSO_{4}·7H_{2}O for NW-G01 production was investigated by PBD. The results showed the effects of these components on the response and significant levels in

According to statistical analysis of the data by Design expert software, the results showed that only (NH_{4})_{2}SO_{4}, peptone and CaCO_{3} had confidence levels above 95% (^{2} = 0.9285 indicated that 92.85% of the variability in the response could be explained in the model.

PBD results indicated that the effect of (NH_{4})_{2}SO_{4}, peptone and CaCO_{3} were negative. Decreasing the three components concentration might result in higher production of antibiotic NW-G01. Thus, the three variables (NH_{4})_{2}SO_{4} (_{3}), peptone (_{4}) and CaCO_{3} (_{6}) were selected and their optimal levels were identified further using response surface methodology.

RSM using central composite design (CCD) was applied to determine the optimal levels of the three selected variables that affected the production of NW-G01. The respective low and high levels (g/L) with the coded levels for the factors are defined in

The concentrations of the other factors were fixed at zero level as shown in

The experimental results were fitted with the second-order polynomial (

where _{3}, _{4} and _{6} were coded values of (NH_{4})_{2}SO_{4}, peptone and CaCO_{3} concentration, respectively.

The statistical significance of ^{2}, which was calculated to be 0.9850, indicating that 98.50% of the variability in the response could be explained by the model. Normally, a regression model, having an ^{2}-value higher than 0.9, was considered as a high correlation [^{2}-value, therefore, reflected a very good fit between the observed and predicted responses, and it was considered reasonable to use the regression model to analyze trends of the responses. A lower value of coefficient variation (CV = 3.33%) showed the experiments conducted were precise and reliable [

The significance of the regression coefficients was tested by a _{4})_{2}SO_{4} (_{3}) and CaCO_{3} (_{6}_{4}) was not significant with a probability of over 84%. In _{3} and _{3}, _{4} and _{4}, _{6} and _{6}, _{3} and _{4}, _{3} and _{6}, _{4} and _{6}, had a very significant influence on antibiotics NW-G01 production.

The final results showed that among the independent factors, _{3}((NH_{4})_{2}SO_{4}) and _{6}(CaCO_{3}) had significant effects on antibiotic NW-G01 production and the negative coefficient of them showed a linear effect to decrease antibiotics NW-G01 production. The quadratic term of the three factors and the interaction between _{3}((NH_{4})_{2}SO_{4}), _{4}(peptone) and _{6}(CaCO_{3}) also had a significant effect.

The 3D response surface curves were then plotted to explain the interactions of medium components and the optimum concentration of each component required for the NW-G01 production (

On the basis of medium optimization, the quadratic model predicted that the maximum production of NW-G01 was 15.387 mg/L, when the model predicted the optimal values of test factors in the coded units were _{3} = −1.68, _{4} = 0.80 and _{6} = 0.74, which were 0.06 g/L (NH_{4})_{2}SO_{4}, 3.80 g/L peptone and 1.30 g/L CaCO_{3}, respectively. To verify the predicted results, validation experiments in shake flasks were performed in triplicate testes. Under the optimized medium, the observed experimental value of average NW-G01 concentration was 15.564 mg/L, suggesting that experimental and predicted values (15.387 mg/L) of NW-G01 yield were in good agreement. The concentration was 5.707 mg/L in non-optimized medium, 2.73-fold increase had been obtained, while the growth of the strain in the two media was comparable. This result therefore corroborated the predicted values and the effectiveness of the model, indicating that the optimized medium favors the production of NW-G01.

The strain 313 was isolated from the soil samples from the northeast of China, which was identified as

Fermentation was performed in two stages: seed growth and antibiotics NW-G01 production. For the seed growth stage, medium from a plate culture was inoculated into 100 mL of seed medium (glucose 20 g/L, peptone 6 g/L, NaCl 2.5 g/L, CaCO_{3} 1 g/L. pH 7.0) in a 500-mL Erlenmeyer flask and grown at 28 °C with 180 rpm on a rotary shaker (ShangHai Fuma Test Equipment Co., Ltd.) for 16 h. Then, 10% (v/v) seed cultures were inoculated into 50-mL production medium in a 250-mL Erlenmeyer flask. The strain was incubated at 28 °C with 180 rpm on a rotary shaker for 108 h. Triplicate experiments were carried out and the mean value was calculated.

In our preliminary experiments, various carbon and nitrogen sources, and inorganic salts were evaluated for the suitability to sustain good NW-G01 production by _{4})_{2}SO_{4}, NaCl, CaCO_{3}, MgSO_{4}·7H_{2}O, K_{2}HPO_{4}·3H_{2}O, MnCl_{2}·4H_{2}O, FeSO_{4}·7H_{2}O, and ZnSO_{4}·7H_{2}O. Components were chosen for further optimization. The amount of every component was changed in different experimental processes and the pH of production medium was 7.0.

After centrifuging the fermentation broth (3000 rpm, 10 min), 1 mL supernatants were filtered (0.45 μm) and analyzed by high performance liquid chromatography (HPLC, Shimadzu 6AD, Kyoto, Japan) using a Sinochrom ODS-BP (5 μm, 4.6 mm × 250 mm) reverse phase column, methanol-water (75/25, v/v) as the mobile phase, flow rate of 1.0 mL/min, monitored by UV detector at 210 nm [

PBD was employed for screening the most significant fermentation parameters affecting NW-G01 production with

Central composite design (CCD) and response surface methodology (RSM) were employed to optimize the three most significant factors ((NH_{4})_{2}SO_{4}, peptone, CaCO_{3}) for enhancing NW-G01 production. The three independent variables were studied at five different levels (−1.682, −1, 0, 1, 1.682) (

The factors were coded according to the following equation:

where _{i}_{i}_{0}_{i}

The response variable (antibiotic production) was explained by the following second-order polynomial equation:

where _{0} was the intercept, _{i} and _{j} were the coded independent factors, _{i} was the linear coefficient, _{ii} was the quadratic coefficient and _{ij} was the interaction coefficient.

Design Expert Version 7.1 (Stat-Ease Inc.: Minneapolis, MN, USA, 2007) was used for the experimental designs and regression analysis of the experimental data. Statistical analysis of the model was performed to evaluate the analysis of variance (ANOVA). The quality of the polynomial model equation was judged statistically by the coefficient of determination ^{2}, and its statistical significance was determined by an

In order to validate the optimization of medium composition, three tests were carried out using the optimized condition, to confirm the result from the analysis of the response surface.

Plackett–Burman design and response surface methodology had been proved to be effective on optimization for enhancing NW-G01 production with _{4})_{2}SO_{4} 0.06, NaCl 1.5, CaCO_{3} 1.30, MgSO_{4}·7H_{2}O 0.015, K_{2}HPO_{4}·3H_{2}O 0.015, MnCl_{2}·4H_{2}O 0.015, FeSO_{4}·7H_{2}O 0.015, and ZnSO_{4}·7H_{2}O 0.015, which resulted in an overall 2.73-fold increase compared with that using the non-optimized medium. Validation experiments were also carried out to verify the adequacy and the accuracy of the model, and the results showed that the predicted value agreed with the experimental values well. The optimum culture medium obtained in this experiment laid a foundation for further study with large scale batch fermentation in a fermenter for NW-G01 production from

This study was supported by the grant of The National Key Basic Research Program (973 Program, Project No. 2010CB126100) from Science and Technology Ministry of China and the foundation of National Science Program of China (Project No. 31101468).

The molecular structure of Novel Cyclic Hexapeptide Antibiotic (NW-G01).

Response surface curve for NW-G01 production by _{4})_{2}SO_{4} and peptone concentrations, when CaCO_{3} concentration was maintained at 1.00 g/L.

Response surface curve for NW-G01 production by _{4})_{2}SO_{4} and CaCO_{3} concentrations, when peptone concentration was maintained at 3.00 g/L.

Response surface curve for NW-G01 production by _{3} concentrations, when (NH_{4})_{2}SO_{4} concentration was maintained at 0.90 g/L.

The Plackett-Burman design for screening variables in NW-G01 production.

Factors(g/L) | Code | Low | High | Effect | Coefficient | NW-G01 Production
| |
---|---|---|---|---|---|---|---|

Level (−) | Level (+) | ||||||

Intercept | 3.18 | 22.73 | 0.0004 | ||||

Corn starch | _{1} |
10 | 20 | 0.41 | 0.2 | 1.37 | 0.3046 |

Glucose | _{2} |
10 | 20 | 0.84 | 0.42 | 1.90 | 0.1163 |

(NH_{4})_{2}SO_{4} |
_{3} |
2 | 4 | −4.83 | −2.41 | 65.93 | <0.0001 |

Peptone | _{4} |
3 | 6 | −1.67 | −0.83 | 7.89 | 0.0262 |

NaCl | _{5} |
1 | 2 | 0.28 | 0.14 | 0.15 | 0.1000 |

CaCO_{3} |
_{6} |
1 | 2 | −2.15 | −1.07 | 13.08 | 0.0085 |

MgSO_{4}·7H_{2}O |
_{7} |
0.01 | 0.02 | 0.57 | 0.29 | 1.40 | 0.2341 |

K_{2}HPO_{4}·3H_{2}O |
_{8} |
0.01 | 0.02 | 1.19 | 0.59 | 4..01 | 0.0854 |

MnCl_{2}·4H_{2}O |
_{9} |
0.01 | 0.02 | −0.31 | −0.16 | −1.12 | 0.4643 |

FeSO_{4}·7H_{2}O |
_{10} |
0.01 | 0.02 | −0.88 | −0.44 | −1.65 | 0.1493 |

ZnSO_{4}·7H_{2}O |
_{11} |
0.01 | 0.02 | −0.57 | −0.28 | −1.68 | 0.1919 |

^{2} = 92.85%, ^{2}_{adj} = 88.77%;

Statistically significant at 95% of confidence level.

The Plackett-Burman design variables (in coded levels) with NW-G01 production as response.

Run | Variable Level | NW-G01 (mg/L) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

| ||||||||||||

_{1} |
_{2} |
_{3} |
_{4} |
_{5} |
_{6} |
_{7} |
_{8} |
_{9} |
_{10} |
_{11} | ||

1 | −1 | 1 | −1 | 1 | 1 | −1 | 1 | 1 | 1 | −1 | −1 | 7.64 |

2 | −1 | −1 | −1 | 1 | −1 | 1 | 1 | −1 | 1 | 1 | 1 | 1.73 |

3 | −1 | 1 | 1 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 1 | 0.89 |

4 | 1 | 1 | −1 | −1 | −1 | 1 | −1 | 1 | 1 | −1 | 1 | 6.15 |

5 | 1 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 1 | −1 | 1 | 0.05 |

6 | −1 | −1 | 1 | −1 | 1 | 1 | −1 | 1 | 1 | 1 | −1 | 0.04 |

7 | 1 | 1 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 1 | −1 | 3.57 |

8 | 1 | −1 | 1 | 1 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 0.26 |

9 | −1 | 1 | 1 | 1 | −1 | −1 | −1 | 1 | −1 | 1 | 1 | 0.83 |

10 | 1 | 1 | 1 | −1 | −1 | −1 | 1 | −1 | 1 | 1 | −1 | 2.54 |

11 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | 6.74 |

12 | 1 | −1 | −1 | −1 | 1 | −1 | 1 | 1 | −1 | 1 | 1 | 7.74 |

Levels of the factors tested in the central composite design (CCD).

Variables | Units | Symbol Code | Level | ||||
---|---|---|---|---|---|---|---|

| |||||||

−1.682 | −1 | 0 | 1 | 1.682 | |||

(NH_{4})_{2}SO_{4} |
g/L | _{3} |
0.06 | 0.4 | 0.9 | 1.4 | 1.74 |

Peptone | g/L | _{4} |
1.32 | 2 | 3 | 4 | 4.68 |

CaCO_{3} |
g/L | _{6} |
0.33 | 0.6 | 1 | 1.4 | 1.67 |

Central composite design matrix for the experimental design and predicted responses for NW-G01 production.

Run | Coded Level | NW-G01 Production(mg/L) | |||
---|---|---|---|---|---|

| |||||

_{3} |
_{4} |
_{6} |
Observed | Predicted | |

1 | 0 | 0 | 0 | 13.33 | 13.57 |

2 | −1 | −1 | 1 | 11.61 | 11.29 |

3 | 1.682 | 0 | 0 | 3.54 | 4.02 |

4 | 0 | −1.682 | 0 | 10.15 | 10.50 |

5 | 0 | 0 | −1.682 | 19.94 | 20.22 |

6 | 0 | 0 | 0 | 13.25 | 13.57 |

7 | −1.682 | 0 | 0 | 15.46 | 15.65 |

8 | 0 | 0 | 0 | 13.47 | 13.57 |

9 | 0 | 0 | 0 | 13.72 | 13.57 |

10 | 1 | −1 | 1 | 5.07 | 4.85 |

11 | 0 | 0 | 0 | 13.96 | 13.57 |

12 | 1 | 1 | −1 | 9.74 | 9.58 |

13 | −1 | 1 | −1 | 17.22 | 14.64 |

14 | −1 | 1 | 1 | 14.72 | 5.19 |

15 | 1 | 1 | 1 | 5.62 | 13.60 |

16 | 1 | −1 | −1 | 13.99 | 16.97 |

17 | 0 | 1.682 | 0 | 9.62 | 9.93 |

18 | 0 | 0 | 1.682 | 10.49 | 10.89 |

19 | −1 | −1 | −1 | 18.04 | 17.99 |

20 | 0 | 0 | 0 | 13.80 | 13.57 |

Analysis of variance (ANOVA) for the second-order polynomial model.

Source | SS | DF | MS | Prob > | |
---|---|---|---|---|---|

Model | 338.6167 | 9 | 37.6241 | 222.43 | <0.0001 |

Residual | 1.6915 | 10 | 0.1692 | ||

Lack of Fit | 1.2958 | 5 | 0.2592 | 3.27 | 0.1095 |

Pure Error | 0.3957 | 5 | 0.0791 | ||

Cor Total | 340.3083 | 19 |

SS, sum of squares; DF, Degree of freedom; MS, mean square. ^{2} = 0.9850, ^{2}_{adj} = 0.9706, ^{2}_{pred} = 0.9591, CV = 3.33%, PRESS = 10.51.

Regression results of the central composite design.

Factor | Coefficient | |
---|---|---|

Intercept | 13.57 | |

_{3} |
−3.46 | <0.0001 |

_{4} |
−0.17 | 0.1617 |

_{6} |
−2.77 | <0.0001 |

_{3}_{4} |
−0.75 | 0.0004 |

_{3}_{6} |
−0.51 | 0.0055 |

_{4}_{6} |
1.09 | <0.0001 |

_{3}^{2} |
−1.32 | <0.0001 |

_{4}^{2} |
−1.18 | <0.0001 |

_{6}^{2} |
0.70 | <0.0001 |

Statistically significant at 95% of confidence level.