Optimization of Medium Composition for Lipopeptide Production ... - kisti

Korean J. Microbiol. Biotechnol. (2013), 41(1), 52–59 http://dx.doi.org/10.4014/kjmb.1207.07020 pISSN 1598-642X eISSN 2234-7305

Korean Journal of Microbiology and Biotechnology

Optimization of Medium Composition for Lipopeptide Production from Bacillus subtilis N7 using Response Surface Methodology Luo, Yi, Guoyi Zhang, Zhen Zhu, Xiaohui Wang, Wei Ran*, and Qirong Shen Jiangsu Key Lab for Organic Solid Waste Utilization, Nanjing Agricultural University, Nanjing 210095, China Received : July 23, 2012 / Revised : October 31, 2012 / Accepted : November 1, 2012

The nutritional requirements for the maximum production of lipopeptides by Bacillus subtilis N7 (B. subtilis N7) were investigated and optimized using response surface methodology (RSM) under shake flask fermentation. A one-factor-at-a-time experimental setup was used to screen carbon and nitrogen sources. A Plackett–Burman design (PBD) was employed to screen the most critical variables for lipopeptides production amongst ten nutritional elements. The central composite experimental design (CCD) was finally adopted to elucidate the composition of the fermentation medium. Statistical analyses (analysis of variance, ANOVA) of the results showed that KCl, MnSO4 and FeSO4·6H2O were important components and that their interactions were strong. Lipopeptide production was predicted to reach 709.87 mg/L after a 60 h incubation using an optimum fermentation medium composed of glucose 7.5 g/L, peanut oil 1.25 g/L, MgSO4 0.37 g/L, KH2PO4 0.75 g/L, monosodium glutamate 6.75 g/L, yeast extract and NH4Cl (5:3 w/w) 10 g/L, KCl 0.16 g/L, FeSO4·6H2O 0.24 mg/L, MnSO4 0.76 mg/L, and an initial pH of 7.0. Lipopeptide production (706.57 ± 3.70 mg/L) in the optimized medium confirmed the validity of the predicted model. Keywords: Lipopeptides, Bacillus subtilis, optimization, response surface methodology

Introduction Bacillus subtilis lipopeptides (iturin, surfactin and fengycin) synthesized non-ribosomally via muti-enzymes are considered some of the most promising biosurfactants [6, 17]. Iturin A is a cyclic lipopeptide containing heptapeptide (L-Asn-D-Tyr-D-Asn-L-Gln-L-Pro-D-Asn-L-Ser) cycled with a β-amino fatty acid. The surfactin family encompasses structural variants, but all the members of this family are heptapeptides interlinked with a β-hydroxy fatty acid to form a cyclic lactone ring structure. Fengycin are lipodecapeptides with an internal lactone ring in the peptide moiety and with a β-hydroxy fatty acid chain that can be saturated or unsaturated [12]. Because of their amphiphilic *Corresponding author Tel: +86-025-84396212, Fax: +86-025-84396212 E-mail: [email protected]

http://dx.doi.org/10.4014/kjmb.1207.07020

nature, lipopeptides readily associate with and tightly anchor into lipid layers; in addition they exhibit antagonistic activities against several plant pathogens by interfering with biological membrane integrity in a dose-dependent manner [7]. Along with their surface activity and wide spectrum of antibiotic activity, biologically-derived lipopeptides have advantages over synthetic chemical fungicides, specifically low toxicity, low allergic effect on humans and animals, and high biodegradability [14]. To meet the need for lipopeptides, most previous work emphasized the isolation and screening of hyper-producing microorganisms [2] that were cultured via submerged fermentation (SMF) [16] or solid-state fermentation (SSF) [11]. Fermentation processes mostly used organic nitrogen, carbon, and potassium salt. However, few studies have been devoted to investigating trace elements and precursors such as Fe2+, Mn2+ and fatty acids, which are related to synthetase activity in Bacillus [8]. In industrial-scale produc-

Optimization of Medium Composition for Lipopeptides Production by RSM

tion of lipopeptides, a lack of knowledge regarding the sophisticated interactions among various factors leads to complexities and uncertainties in the fermentation process. Response surface methodology (RSM) is a powerful technique for simultaneously testing multiple variables by statistical experimental design and identifying and quantifying interactions between such variables [4]. Therefore, it has been increasingly used for various phases of optimizing the fermentation process [4, 13, 20]. In the present study, we adopted a one-factor-at-a-time experiment to screen carbon and nitrogen sources for lipopeptides production by Bacillus subtilis N7. Furthermore, the Plackett–Burman design technique was applied to screen ten factors for the ones that most inuence the production of lipopeptides. Subsequently, important factors were optimized using the central composite design technique.

Materials and Methods Microorganism and culture conditions The lipopeptide producing strain B. subtilis N7 was isolated from the cucumber rhizosphere, whose 16s rRNA gene sequence shows 99% identity to Bacillus subtilis DSM 10 based on a BLAST search against all nucleotide sequences in the NCBI database. This strain was stored in nutrient agar (NA) medium at 4oC, and the sequence of the N7 strain was deposited into GenBank with the accession number JQ317780. To prepare the seed culture, a loop of N7 cells from an NA slant culture of fresh NA was inoculated into a 250 ml flask containing 50 ml Luria–Bertani (LB) broth; the flask was shaken for 60 h at 240 rpm and 24 h at 200 rpm on a rotary shaker at 30oC and used as a seed culture for subsequent submerged cultivations. Batch fermentation was carried out with 170 rpm orbital agitation for 60 h at 30oC in 250 ml asks containing 50 ml of fermentation medium inoculated with 1% v/v of seed culture. Basal medium (glucose 5 g/L, peptone 10 g/L, NaCl 5 g/L, pH 7.0) was used for carbon and nitrogen source selection. The original fermentation medium (pH 7.0) consisted of 1% carbon source (glucose, sucrose, starch or potato), 0.5% nitrogen source (yeast extract, peptone, beef extract, trypsin, (NH4)2SO4, NH4Cl and KNO3), sodium glutamate 5 g/L, peanut oil 1 g/L, KH2PO4 1 g/L, MgSO4 0.5 g/L, KCl 0.5 g/L, MnSO4 0.5 mg/L, FeSO4·7H2O 0.15 mg/L, and CuSO4 ·5H2O 0.15 mg/L.

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All the experiments were carried out independently in triplicate, and the results were the average of three replicate experiments. Analysis of samples The production of the lipopeptides was measured using the a previously-reported method [16] with slight modication. High-performance liquid chromatography (HPLC) for quantitative determination of lipopeptides was performed on an Agilent Technologies 1200 series system (Agilent Co., Santa Clara, CA, USA) composed of an Eclipse XDBC18 column (4.6 mm × 250 mm, 5 µm). The injection volume was 20 µl. The sample was eluted with a mobile phase of 3.8 mM triuoroacetate/acetonitrile (3:2, v/v) at a flow rate of 0.8 ml/min. The chromatogram was monitored at 280 nm. Iturin A and surfactin standards obtained from Sigma-Aldrich Chemical (st. Louis, MO, USA) to construct calibration curves from which lipopeptide concentration in the fermentation media was determined. Experimental design and data analysis A one-factor-at-a-time approach and a two-way classification design [9] were used to screen suitable carbon and nitrogen sources. Experiments were conducted in triplicate, and the data were analyzed using SPSS Version 16.0 software (SPSS Inc., Chicago, IL, USA). To identify the important variables for lipopeptides production, different medium components were evaluated using PBD The total number of trials to be carried out according to PBD is k+1, where k is the number of variables. Each variable was studied at two levels, high and low, denoted by (+1) and (-1) signs, respectively (Table 1). One dummy variable was introduced into the PBD matrix, which was used to calculate the standard error (SE) as follows: SE =

( Ed )2 ∑ --------------------

(1)

n

Where Ed is the effect of the dummy variable and n is the Table 1. Carbon source selection for lipopeptide production. Carbon source

Lipopeptides production (mg/L)

Sucrose

228.03 ± 5.70

Glucose

330.87 ± 7.61

Starch

258.91 ± 3.11

Potato

253.08 ± 2.13

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number of dummy variables. In our experiments, the variables with confidence levels above 95% were considered as significantly influencing lipopeptides production. All the experiments were conducted in triplicate. Experimental design and statistical analyses were performed using Minitab 15 Statistical Software® (Minitab Inc., PA, USA). RSM was used to optimize the most signicant variables identied by PBD. The three independent variables were studied at ve different levels (-α, -1, 0, +1, +α, where α = (2k)1/4 and k is the number of variables). The role of each variable, their interactions, and statistical analysis to obtain predicted lipopeptides production is explained by applying the following quadratic equation: Y = β0 + ∑ βi Xi + ∑ βij Xi Xj + ∑ βij Xi2

(2)

where Y is the predicted response, β0 is an offset term, βi is the linear effect, βii is the squared effect, βij is the interaction effect, and Xi is the dimensionless coded value of xi . The statistical software package “Design Expert Version 7.0” (Stat-Ease Inc., Minneapolis, MN, USA) was used for analyzing the experimental data. Shake-flask Fermentation of B. subtilis N7 using optimum medium The standard seed culture was inoculated into a 500 ml Erlenmeyer flasks with 100 ml optimum medium (glucose

7.5 g/L, peanut oil 1.25 g/L, MgSO4 0.37 g/L, KH2PO4 0.75 g/ L, monosodium glutamate 6.75 g/L, yeast extract and NH4Cl (5:3, w/w) 10 g/L, KCl 0.16 g/L, FeSO4·6H2O 0.24 mg/L, MnSO4 0.76 mg/L, and initial pH 7.0). Batch fermentation was carried out with 170 rpm orbital agitation for 60 h at 30oC for 84 h. The profile of cell growth of B. subtilis N7 and lipopeptieds production were detected by regular time interval. All the experiments were carried out independently in triplicate, and the results were the average of three replicate experiments.

Results Carbon and nitrogen source selection Pre-experiments were done to investigate the influence of various carbon and nitrogen sources on lipopeptide production by B. subtilis N7. In the carbon selection experiment, each trial used basal medium, except that glucose was replaced by carbon the sources listed in Table 1. The results showed glucose was superior to other carbon sources. Both the inorganic nitrogen source (A) and organic nitrogen source (B) (Table 2) were evaluated with two-way classification design (Marks 1968). Table 3 presents the ANOVA results of the pre-experiments. The results showed that both inorganic and organic nitrogen

Table 2. Selection of nitrogen source and results in lipopeptide production. Nitrogen source

Lipopeptides production (mg/L) A1 (−)

A2 ((NH4)2SO4)

A3 (NH4Cl)

A4 (KNO3)

Xi

(B1) Peptone

330.87

351.19

357.13

332.52

342.93

(B2) Beef extract

326.27

332.05

345.54

329.24

333.28

(B3) Trypsin

213.53

233.29

235.36

226.40

227.15 367.77

(B4) Yeast extract

362.52

369.67

372.93

365.94

Xj

308.30

321.55

327.74

313.26

The concentration of each nitrogen source was 5 g/L organic nitrogen and 3 g/L inorganic nitrogen, except for the first group that had no inorganic nitrogen source. A: Inorganic nitrogen source, B: organic nitrogen source, xi: mean value of row, xj: mean value of column. Table 3. ANOVA of nitrogen source selection in lipopeptides production. Source A B

Type III Sum of Squares 12780.405 139108.22

df

Mean Square

3

926.802

3

46369.407

A*B

524.641

9

58.293

Error

367.026

32

11.470


F

Sig.

80.805

p < 0.05

4.043E3

p < 0.05

5.082

p < 0.05

55


sources had very significant effects on lipopeptide production; however, NH4Cl was more effective at increasing lipopeptide production than were other inorganic nitrogen sources. Furthermore, lipopeptide production of the B4 group was higher than that of other groups. We also found that the addition of both NH4Cl and yeast extract to the medium had a synergistic effect on lipopeptide production above their independent individual contributions. Plackett-Burman experimental design PBD was used to identify which variables had a significant inuence on lipopeptide production. Eleven variables, Table 4. Range of different variables for the PlackettBurman design. Code

Level

Variables

-1

+1

7.5

12.5

X1

Glucose (g/L)

X2

Peanut oil (g/L)

0.75

1.25

X3

MgSO4 (g/L)

0.37

0.62

X4

KCl (g/L)

0.37

0.62

X5

KH2PO4 (g/L)

0.75

1.25

X6

FeSO4·6H2O (mg/L)

0

0.02

X7

MnSO4 (mg/L)

0.37

0.62

X8

Sodium glutamate (g/L)

3.75

6.75

X9

Yeast extract and NH4Cl (g/L)

6

X10

CuSO4·5H2O (mg/L)

0

X11

Dummy

Table 6. Regression analysis of Plackett-Burman design. Variables T value intercept X1

10 -

including 10 medium components in the original fermentation medium and 1 dummy variable, were screened in PB experiments (Table 4). Table 5 shows the PB experimental design for 20 trials with two levels of each variable and their corresponding effects on lipopeptide production. Confidence levels were accepted only when above 95% (p< 0.05). Based on statistical analysis, three variables were signicant in terms of lipopeptide production: X4 (KCl), X6 (FeSO4·6H2O), and X7 (MnSO4). Among them, FeSO4· 6H2O and MnSO4 had positive effects, while KCl had a negative one. Although the independent effects of X2 (peanut oil) and X9 (yeast extract and NH4Cl) were insignificant, they were chosen for use at their high levels based on their positive effects and secondary role in lipopeptides production. In our PB experiments, the effects of X3 (MgSO4) and X10 (CuSO4·5H2O) were neither positive nor significant to lipopeptide production by B. subtilis N7. Hence, these two variables were not considered for use as medium components. Concentrations of the variables (excluding the three significant variables) in the fermentation medium were as follows: glucose 7.5 g/L, peanut oil 1.25 g/L, KH2PO4

P> t

Variables T value

P> t

116.75

0.005

X6

34.83

0.018

-4.91

0.128

X7

38.80

0.016

0.02

X2

9.63

0.066

X8

4.54

0.138

-

X3

-2.15

0.277

X9

10.06

0.063

X4

-15.57

0.041

X10

-1.39

0.396

X5

-7.69

0.082

Variable X9 is a combination of yeast extract and NH4Cl in the ratio of 5:3.

Table 5. Plackett-Burman experimental design matrix for screening of important variables for lipopeptides production. Experiment no.

X1

X2

1

1

2

1

3

-1

4

1

5

1

1

6

1

1

7

-1

1

8

-1

-1

1

9

-1

-1

-1

10

1

-1

-1

-1

1

11

-1

1

-1

-1

-1

12

-1

-1

-1

-1

-1

-1

X3

X4

X5

X6

X7

X8

-1

1

-1

-1

-1

1

1

-1

1

-1

-1

-1

1

1

-1

1

-1

-1

-1

-1

1

1

-1

1

-1

-1

-1

1

1

-1

1

-1

1

-1

1

1

-1

1

1

1

-1

1

1

1

1

-1

1

1

1 1

1

-1

1

1

1

-1

-1

-1

X11


X9

X10

1

1

-1

1

481.92

1

1

1

-1

181.47

1

1

-1

228.81

-1

1

1

244.59

-1

-1

-1

307.71

-1

-1

1

394.50

-1

1

-1

-1

670.65

1

1

-1

1

1

276.15

-1

1

1

-1

1

347.16

1

1

-1

631.20

-1

1

1

689.55

-1

-1

165.69

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Luo et al.

0.75 g/L, sodium glutamate 3.75 g/L, yeast extract and NH4Cl (5:3, w/w) 10 g/L. Central composite design X4 (KCl), X6 (FeSO4·6H2O) and X7 (MnSO4) were selected and further optimized using CCD. Each variable was studied at five coded levels (-1.68, -1, 0, 1, and 1.68), and all variables were taken at a central coded value of zero. Table 7 shows the experimental design matrix for optimization of lipopeptide production. By applying quadratic regression analyses (ANOVA) on the experimental data, results of the CCD were tted to a second-order polynomial equation as follows: Y = 699.27 − 46.33A + 4.23B + 39.43C - 94.68AB + 48.78AC − 25.66BC − 113.98A2 - 43.63B2 − 107.97C2

(3)

Table 7. Experimental design matrix for optimization of lipopeptide production using CCD. Run 1

0.3

0.1

5


Sum of Squares

Model

478171

A-KCl

29316.36

df

Mean Square

F Value

p-value Prob > F

9 53130.11 310.9238 < 0.0001 1 29316.36 171.5629 < 0.0001

B-FeSO4·6H2O 244.1839

1 244.1839 1.428994

1 21237.56 124.2848 < 0.0001

0.2595

C-MnSO4

21237.56

AB

71712.53

1 71712.53 419.6703 < 0.0001

AC

19032.98

1 19032.98 111.3833 < 0.0001

BC

5266.972

1 5266.972 30.82295

A2

187231.5

1 187231.5 1095.702 < 0.0001

B2

27436.04

1 27436.04 160.559

C2

168000.8

1 168000.8 983.1612 < 0.0001

Residual

1708.782 10 170.8782

363.93

Lack of Fit

1337.914

5 267.5827 3.607514

370.8686

5 74.17372

479879.8 19

Observed Predicted 353.65

Table 8. Variance analysis for the regression equation. Source

where Y represents lipopeptides production (mg/L), and A, B and C represent KCl, FeSO4·6H2O and MnSO4·H2O respectively.

KCl FeSO4·6H2O MnSO4 (mg/L) (g/L) (mg/L)

ANOVA results for the model are summarized in Table 8. The results of the ANOVA showed that the model is significant. Furthermore, the lack of fit value of 0.0927 implies that the lack of fit is not significant relative to the pure error. The coefcient of determination (R2) for lipopeptide production was calculated to be 0.9946, indicating that 99.46% of the total variation was explained by the model. The plot of predicted values vs. experimental values in Fig. 1 also shows that all the predicted values of the RSM model were close to the experimental values. The adequate precision ratio, which measures the signal-to-noise ratio, was calcu-

0.0002 < 0.0001

0.0927

2

0.2

0.2

7.5

710.72

699.27

Pure Error

3

0.2

0.2

7.5

703.61

699.27

Cor Total

AB, AC, and BC represent the interaction effect of variables A, B, and C; A2, B2, and C2 are the squared effects of the variables. R2 = 0.9964; Adj. R2 = 0.9932; CV = 2.52%; adequate precision ratio = 50.298.

4

0.37

0.2

5

0.1

0.1

308.00

298.96

10

6

0.1

7

0.3

8

0. 3

0.2

9

0.2

0.2

10

0.2

7.5

385.87

397.43

0.1

5

370.71

364.79

0.1

10

582.61

591.67

7.5

447.94

454.80

7.5

705.38

699.27

0.2

7.5

699.46

699.27

11

0.2

0.2

3.3

318.79

327.56

12

0.2

0.2

7.5

690.05

699.27

13

0.2

0.03

7.5

583.00

568.75

14

0.1

0.3

5

621.73

613.93

15

0.3

0.3

5

244.65

234.35

16

0.3

0.3

10

352.28

359.46

17

0.2

0.2

690.10

699.27

18

0.2

0.37

570.50

582.97

19

0.1

0.3

10

552.95

543.92

20

0.2

0.2

11.7

470.76

460.21

7.5 7.5


Fig. 1. Comparison between the experimental values and the predicted values of the RSM model.


lated as 50.298. Because a ratio greater than 4 is desirable, the ratio of 50.298 indicates an adequate signal. A relatively lower value of the coefcient of variation (CV=2.52%) indicated good precision and reliability of the experiment. The results showed KCl and MnSO4 had significant independent effects and a synergistic effect on protein production. Although the independent effect of FeSO4·6H2O was insignificant, its squared effect and interaction effects with other

57

variables were significant. Three-dimensional response plots in Fig. 2 were created to show the interactions of medium components and find the optimum range of the components required for maximum lipopeptide production. The shapes of contour plots indicate the nature and extent of the interactions. The prominent interaction between KCl and FeSO4·6H2O (Fig. 2B) is shown by the elliptical nature of the contour plot, while less prominent interactions are shown in Figs. 2A and 2C. It is evident from the response surface plot that maximum lipopeptide production required an increasing concentration of MnSO4 and a decreasing concentration of KCl, whereas high concentrations of the MnSO4 variables have a significantly negative influence on the response. Response surface estimation for maximum production According to Eq. (2), the optimum medium composition was determined to be KCl 0.16 g/L, FeSO4·6H2O 0.24 mg/ L, and MnSO4 0.76 mg/L. The uncoded values of the test variables were as follows: glucose 7.5 g/L, peanut oil 1.25 g/L, MgSO4 0.37 g/L, KH2PO4 0.75 g/L, monosodium glutamate 6.75 g/L, yeast extract and NH4Cl (5:3 w/w) 10 g/ L, initial pH 7.0. The predicted maximum lipopeptide production was 709.87 mg/L. Shake-flask fermentation of B. subtilis N7 using optimum medium To confirm the model adequacy for predicting maximum lipopeptide production and suitability for cell growth of B. subtilis N7 in optimum medium, three replicate experiments using the optimum medium composition were performed.

Fig. 2. Response surface and contour plots of lipopeptide production by B. subtilis N7 showing the effect of two variables (other variables were kept at a fixed coded level). A: combined effect of MnSO4 and FeSO4·6H2O on the production of lipopeptides. B: combined effect of FeSO4·6H2O and KCl on the production of lipopeptides. C: combined effect of MnSO4 and KCl on the production of lipopeptides.

Fig. 3. Time course of cell growth and lipopeptides production of B. subtilis N7.

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The time course of the cultivation is shown in Fig. 3. This study showed that when B. subtilis N7 was cultivated at the aforementioned condition, the cell growth occurred before lipopeptides production accumulation. Lipopeptides production accumulated rapidly during cell growth stationary phase (36-60 h). The average maximum yield of lipopeptides (706.57 ± 3.70) was obtained after 60 h cultivation, which was 52.6% higher than the original fermentation medium (463.04 g/L). During cells apoptosis period (7284 h), the lipopeptides production dramatically decreased by 30.05%.

Discussion In our study, RSM was used to optimize the medium composition for lipopeptide production by B. subtilis N7. RSM has proven to be a valuable tool in exploring all the main nutritional factors and to obtain their optimum levels in a process. This method offers a number of advantages; for example, we can determine the effects of variables and select the most influential factors with less experimentation. Furthermore, we can develop a system model to predict experimental lipopeptide production. After carbon and nitrogen source selections, glucose, yeast extract and NH4Cl, and eight other variables were tested by PB experiments. Among these, KCl, FeSO4·6H2O and MnSO4 were identified as the most significant factors for lipopeptides production. Earlier studies found MgSO4·7H2O, CaC12· 2H2O, Na2HPO4, and KH2PO4 are not related to lipopeptide production, but FeSO4·7H2O and MnSO4·H2O supplied at different moladties during stationary growth phase influenced lipopeptide production [5]. Bernheimer and Avigad [3] also reported an increase in Mn2+ from 0.33 g/L to 2.6 g/ L improved surfactin production. However, few studies have reported KCl as being a significant factor for lipopeptide production. Furthermore, the interactions of KCl and FeSO4·6H2O, KCl and MnSO4, as well as FeSO4·6H2O and MnSO4 were found to be significant based on RSM analysis. The effects of KCl seem to be related to permeation pressure by offering a buffer environment in cooperation with KH2PO4 for cell growth. In shake flask cultures, permeation pressure and broth pH are hard to control; however, maintaining a suitable environment for B. subtilis N7 growth is very important because changes may inhibit biomass formation and promote acidic byproduct production in the fermentation process and ion form inactivation [1]. Ear-


lier studies indicate that Mn2+ can affect metabolites of Bacillus by regulating the metabolic flux of glucose and making the microorganism utilize more glucose for forming metabolites instead of biomass [15, 18]. Here, we found that an increase in the concentration of Mn2+ increased lipopeptide production to a maximum value; then, further increases in Mn2+ concentrations inhibited lipopeptide production based on the response surface plot. Iron is also considered an essential element for cellular growth and other bioenergetic pathways [19]. An ironenriched culture of B. subtilis ATCC 21332 exhibited the ability to emulsify kerosene and achieved a maximum emulsion index (E24) of 80% for culture supplemented with 4.0 mM Fe2+. In our study, a high concentration of FeSO4 was one of the key factors for high lipopeptide production. Because several enzymes in amino acid biosynthesis pathways are iron-dependent, iron limitation may cause amino acid starvation [10] and subsequently influence the gene expression involved in amino acid biosynthesis associated with pathways essential for lipopeptide production. Furthermore, the addition of peanut oil and the combination of organic with inorganic nitrogen sources contributed to lipopeptide production improvement by RSM, although they were not considered as key factors. The results of verification experiments indicated that this optimum medium led to a noticeable improvement in lipopeptide production by B. subtilis N7. However, due to the highly complex nature of biological systems, the actual contribution of the significant factors to the mechanisms for enhancing lipopeptide production may be more complicated than we have discussed in this paper, and lipopeptides production conditions are under further investigation.

Acknowledgments This work was supported by the National Basic Research Program of China (Grant no. 2011CB100503) and the National Department of Public Benefit Research Foundation of the Ministry of Agriculture of China (Grant no. 201103004).

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March 2013 | Vol. 41 | No. 1