Sublancin 168, as a distinct S-linked antimicrobial glycopeptide produced by
Sublancin 168 is a novel and distinct S-linked bacteriocin glycopeptide consisting of 37 amino acids and is produced by
Recently, there has been an increasing interest in response surface methodology processes for improving productivities of natural bioactive agents [
The aim of this current work was to efficiently produce sublancin 168 via optimizing the variables of medium compositions and culture conditions by using statistical tools in shake-flasks. In the first step, Plackett-Burman design as an effective technique was used to screen remarkable variables. Subsequently, the steepest ascent was utilized to approach the optimal region. At last, Box-Behnken design and response surface analysis were employed to ascertain the optimum levels of the factors which significantly effect sublancin 168 productions. In this study, the sublancin 168 production at a high level is achieved through adopting chemometric and statistical methodology.
Yeast extract and tryptone were purchased from Difco (Detroit, USA). Corn powder and soybean meal were purchased from Sinopharm Chemical Reagent Beijing Co., Ltd. (China) and Shandong Litong Biotechnology Co., Ltd. (China), respectively, and were passed through 60-mesh sieve. Other chemicals used were of chemical grade.
Seed culture of
The optimal nitrogen and carbon sources effecting sublancin 168 production were screened by one variable at a time (OVAT) approach. The evaluations of different simple and complex nitrogen (yeast extract, peptone, soybean meal, urea, and (NH4)2SO4) and carbon sources (corn powder, glycerol, sucrose, lactose, starch, maltose, and glucose) on sublancin 168 production were performed one by one (Table
Effects of different carbon sources and nitrogen sources on the yield of sublancin 168.
Carbon sources | Nitrogen sources | ||
---|---|---|---|
Sources | Yield (mg/L) | Sources | Yield (mg/L) |
Corn powder | 67.66 ± 3.56 | Yeast extract | 28.89 ± 4.72 |
Glycerol | 28.96 ± 4.39 | Peptone | 50.11 ± 4.56 |
Sucrose | 30.55 ± 4.90 | Soybean meal | 58.40 ± 5.33 |
Lactose | 29.61 ± 5.03 | Urea | 8.72 ± 3.18 |
Starch | 50.65 ± 4.85 | (NH4)2SO4 | 32.93 ± 2.10 |
Maltose | 28.26 ± 5.10 | ||
Glucose | 21.40 ± 5.49 |
Each experiment was repeated three times, and all of the data were expressed as means ± standard deviations.
The Plackett-Burman design is a powerful tool for rapidly screening and determining the important variables that has significant influence on the production response. This method was very useful for picking the most important factors from a long list of candidate factors [
Variables and test levels for Plackett-Burman experiment.
Number | Variables | Code levels | Estimate |
|
|
Significance | |
---|---|---|---|---|---|---|---|
−1 | 1 | ||||||
|
Peptone (g/L) | 8 | 12 | 2.33 | 1.01 | 0.3851 | |
|
Corn powder (g/L) | 20 | 30 | 27.20 | 11.83 | 0.0013 | * |
|
Starch (g/L) | 10 | 20 | 3.19 | 1.39 | 0.2594 | |
|
Soybean meal (g/L) | 24 | 36 | 21.09 | 9.17 | 0.0027 | * |
|
KH2PO4 (g/L) | 3 | 6 | 3.21 | 1.39 | 0.2575 | |
|
(NH4)2SO4 (g/L) | 3 | 6 | −0.96 | −0.42 | 0.7038 | |
|
Incubation temperature (°C) | 28 | 34 | 11.11 | 4.83 | 0.0169 | * |
|
Initial pH | 6.5 | 8.5 | −1.91 | −0.83 | 0.0466 | |
|
Incubation time (h) | 28 | 40 | 0.38 | 0.17 | 0.8786 | |
|
Inoculum size (%) | 1 | 3 | −2.34 | −1.01 | 0.3834 |
* indicates model terms are significant.
The Plackett-Burman design was established by SAS software package (version 9.1.3, SAS Institute Inc., Cary, NC, USA) in terms of the following first-order model:
In addition to the variables of real interest, the Plackett-Burman design considers insignificant dummy variables, which are introduced to evaluate the experimental error and the variance of the first-order model. In this work, 10 variables were checked in 20 trials (Table
Experimental design and results of the Plackett-Burman design.
Trials | Variable levels | Yield (mg/L) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|
|
|
|
|
Observed | Predicted | |
1 | 1 | −1 | 1 | 1 | −1 | −1 | −1 | −1 | 1 | −1 | 88.38 ± 3.84 | 90.91 |
2 | 1 | 1 | −1 | 1 | 1 | −1 | −1 | −1 | −1 | 1 | 117.96 ± 2.37 | 115.41 |
3 | −1 | 1 | 1 | −1 | 1 | 1 | −1 | −1 | −1 | −1 | 97.42 ± 6.78 | 96.55 |
4 | −1 | −1 | 1 | 1 | −1 | 1 | 1 | −1 | −1 | −1 | 101.79 ± 3.41 | 98.33 |
5 | 1 | −1 | −1 | 1 | 1 | −1 | 1 | 1 | −1 | −1 | 102.43 ± 3.74 | 99.73 |
6 | 1 | 1 | −1 | −1 | 1 | 1 | −1 | 1 | 1 | −1 | 92.45 ± 3.96 | 94.17 |
7 | 1 | 1 | 1 | −1 | −1 | 1 | 1 | −1 | 1 | 1 | 107.47 ± 9.89 | 104.83 |
8 | 1 | 1 | 1 | 1 | −1 | −1 | 1 | 1 | −1 | 1 | 121.38 ± 2.26 | 124.57 |
9 | −1 | 1 | 1 | 1 | 1 | −1 | −1 | 1 | 1 | −1 | 119.62 ± 2.27 | 117.05 |
10 | 1 | −1 | 1 | 1 | 1 | 1 | −1 | −1 | 1 | 1 | 88.76 ± 5.85 | 90.81 |
11 | −1 | 1 | −1 | 1 | 1 | 1 | 1 | −1 | −1 | 1 | 119.63 ± 5.56 | 123.21 |
12 | 1 | −1 | 1 | −1 | 1 | 1 | 1 | 1 | −1 | −1 | 79.81 ± 4.77 | 80.87 |
13 | −1 | 1 | −1 | 1 | −1 | 1 | 1 | 1 | 1 | −1 | 118.85 ± 5.84 | 120.81 |
14 | −1 | −1 | 1 | −1 | 1 | −1 | 1 | 1 | 1 | 1 | 76.33 ± 3.15 | 77.53 |
15 | −1 | −1 | −1 | 1 | −1 | 1 | −1 | 1 | 1 | 1 | 82.21 ± 2.79 | 80.17 |
16 | −1 | −1 | −1 | −1 | 1 | −1 | 1 | −1 | 1 | 1 | 77.19 ± 4.41 | 76.27 |
17 | 1 | −1 | −1 | −1 | −1 | 1 | −1 | 1 | −1 | 1 | 62.37 ± 5.76 | 61.05 |
18 | 1 | 1 | −1 | −1 | −1 | −1 | 1 | −1 | 1 | −1 | 106.22 ± 7.25 | 104.95 |
19 | −1 | 1 | 1 | −1 | −1 | −1 | −1 | 1 | −1 | 1 | 90.56 ± 2.95 | 90.05 |
20 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | −1 | 60.31 ± 3.20 | 63.93 |
Each experiment was repeated three times, and all of the data were expressed as means ± standard deviations.
In general, some variations of the optimum culture condition for the system exist between the actual optimum and the initial estimate. In such case, the single steepest ascent experiment was performed to optimize the variables influenced sublancin 168 production significantly [
Through the Plackett-Burman design experiment, the significant variables were selected as follows: soybean meal, corn powder, and incubation temperature. After that, the Box-Behnken design, a type of response surface methodology, was used to determine the optimum level of these selected variables for producing sublancin 168 as highly as possible. With the help of the statistical software package “Design Expert 8.0.5b” (Shanghai TechMax Co., Ltd., Shanghai, China), the experimental design was analyzed and 15 experiments in all were formulated. The central values of every variable were coded 0. The maximum and minimum ranges of the variables were set up, and the whole experiment program in terms of their coded and actual values is shown in Table
To investigate the behaviour of sublancin 168 accumulation, batch fermentations were conducted in a 5 L bioreactor (NBS Co., USA). The prepared seed culture was inoculated (2%, v/v) into the optimal medium with an initial pH 7.0. According to the preexperiment results (data not shown), the bioreactor was operated with optimized temperature, airflow at 1.5 vvm, and stirring at 500 rpm, and the pH was uncontrolled during fermentation.
Isolation and purification of sublancin 168 were carried out as previously described [
During this study, each experiment was repeated three times, and all of the data were expressed as means ± standard deviations.
According to the fermentation result (data not shown) obtained by using the method reported in the literatures [
The Plackett-Burman design was employed to evaluate the relative importance of cultivation parameters and different medium components (Figure
Pareto chart of standardized effects on sublancin 168 production.
Based on (
Even though Plackett-Burman design allows for the rapid selection of the significant variables affecting productivity of sublancin 168, the optimum levels of the variables cannot be predicted by this method. The method of steepest ascent is a procedure for moving sequentially along the path of steepest ascent and in the direction of the maximum increase in the response. In order to move the variables rapidly to the general vicinity of the optimum levels, the path of steepest ascent was used to find the proper direction to change the variables by increasing the incubation temperature and the concentration of soybean meal and corn powder to improve the production of sublancin 168. The results showed that the sublancin 168 production reached a yield plateau during the fifth step (Table
Experimental design and corresponding response of steepest ascent.
Experiment number | Corn powder (g/L) | Soybean meal (g/L) | Incubation temperature (°C) | Yield (mg/L) |
---|---|---|---|---|
0 | 12 | 8 | 25 | 72.7 ± 2.97 |
0 + 1Δ | 16 | 12 | 27 | 80.3 ± 4.11 |
0 + 2Δ | 20 | 16 | 29 | 89.5 ± 2.42 |
0 + 3Δ | 24 | 20 | 31 | 117.5 ± 3.58 |
0 + 4Δ | 28 | 24 | 33 | 122.6 ± 1.64 |
0 + 5Δ | 32 | 28 | 35 | 109.0 ± 4.17 |
Each experiment was repeated three times, and all of the data were expressed as means ± standard deviations.
Experimental design and results of Box-Behnken optimization experiment.
Trials |
|
|
|
Yield (mg/L) | |
---|---|---|---|---|---|
Observed | Predicted | ||||
1 | 22.00 | 24.00 | 36.00 | 73.66 ± 1.62 | 73.43 |
2 | 34.00 | 28.00 | 32.00 | 86.35 ± 2.78 | 86.78 |
3 | 28.00 | 28.00 | 28.00 | 77.37 ± 1.83 | 77.57 |
4 | 28.00 | 24.00 | 32.00 | 124.39 ± 1.92 | 124.15 |
5 | 22.00 | 28.00 | 32.00 | 71.97 ± 1.26 | 71.81 |
6 | 34.00 | 24.00 | 28.00 | 94.52 ± 3.73 | 94.75 |
7 | 28.00 | 28.00 | 36.00 | 92.97 ± 5.06 | 93.37 |
8 | 28.00 | 20.00 | 28.00 | 115.92 ± 2.90 | 115.53 |
9 | 22.00 | 20.00 | 32.00 | 86.35 ± 2.72 | 85.93 |
10 | 28.00 | 24.00 | 32.00 | 124.22 ± 1.24 | 124.15 |
11 | 28.00 | 24.00 | 32.00 | 123.85 ± 1.63 | 124.15 |
12 | 34.00 | 20.00 | 32.00 | 92.67 ± 3.56 | 93.37 |
13 | 34.00 | 24.00 | 36.00 | 90.80 ± 2.90 | 90.82 |
14 | 22.00 | 24.00 | 28.00 | 91.98 ± 3.72 | 91.95 |
15 | 28.00 | 20.00 | 36.00 | 77.48 ± 2.35 | 77.28 |
Each experiment was repeated three times, and all of the data were expressed as means ± standard deviations.
As illustrated in Table
To determine the optimum levels of these important independent variables (soybean meal, corn powder, and incubation temperature) according to the above results, a 3-factor Box-Behnken design with 3 levels involving 3 replicates at center point was introduced to fit a second-order response surface. Table
Based on
Analysis of variances of the quadratic polynomial model.
Source | SS | DF | MS |
|
|
---|---|---|---|---|---|
Model | 4776.00 | 9 | 530.67 | 2507.25 | <0.0001 |
Lack of fit | 0.90 | 3 | 0.30 | 3.92 | 0.2100 |
Pure error | 0.15 | 2 | 0.08 | ||
|
|||||
Total | 4777.05 | 14 |
Results of regression analysis of the second-order polynomial model.
Factor | Coefficient estimate | Standard error |
|
|
---|---|---|---|---|
Intercept | 124.15 | 0.27 | 2507.25 | <0.0001 |
|
5.05 | 0.16 | 963.01 | <0.0001 |
|
−5.47 | 0.16 | 1130.53 | <0.0001 |
|
−5.61 | 0.16 | 1189.84 | <0.0001 |
|
2.02 | 0.23 | 76.82 | 0.0003 |
|
3.65 | 0.23 | 251.72 | <0.0001 |
|
13.51 | 0.23 | 3449.98 | <0.0001 |
|
−21.51 | 0.24 | 8068.33 | <0.0001 |
|
−18.31 | 0.24 | 5847.41 | <0.0001 |
|
−14.91 | 0.24 | 3877.87 | <0.0001 |
The model coefficient calculated from the regression analysis for each significant variable is shown in Table
Three-dimensional (3D) response surface plots (Figure
3D response surface curves (a) and 2D contour plots (b) predicting for sublancin 168 production by
The model reveals that the corn powder concentration (
The availability of the regression model of the sublancin 168 production using the calculated optimal medium compositions and culture condition, namely, 22.99 g/L soybean meal, 28.49 g/L corn powder, and temperature at 30.8°C, was validated with triplicate experiments. The mean maximal value of sublancin 168 production was 129.72 mg/L, which agreed with the predicted value (125.88 mg/L) well. As a result, the model was considered to be accurate and reliable for predicting the sublancin 168 production by
Using the optimal medium and temperature, sublancin 168 reached repeatable yield of 135.4 mg/L in bioreactor batch fermentation after about 48 h of cultivation. Although the temperature and medium components were coincident in flask and bioreactor fermentations, the yield of sublancin 168 in bioreactor fermentation (135.4 mg/L) was higher than that in the shake-flask culture (129.72 mg/L), probably mainly due to the differences of aeration conditions. Even so, the working conditions of the bioreactor require further optimizations in future experiments to furtherly improve sublancin yield.
In
As a summary, response surface methodology combined with Plackett-Burman design and steepest ascent enabled us to optimize the sublancin 168 yield produced by
The authors declare that there is no conflict of interests.
Shengyue Ji and Weili Li contributed equally to this work.
This research was supported by the National Science and Technology R&D Program of China (Grant no. 2011BAD28B05–3) and the Provincial Agriculture Special Fund Project of China (Grant no. 2011NYTT03).