Food Sci. Biotechnol. 21(3): 653-659 (2012) DOI 10.1007/s10068-012-0085-2
RESEARCH ARTICLE
Optimization for the Maximum Bacteriocin Production of Lactobacillus brevis DF01 Using Response Surface Methodology Yu Mi Lee, Jun Soo Kim, and Wang June Kim
Received: 9 September 2011 / Revised: 27 February 2012 / Accepted: 28 February 2012 / Published Online: 30 June 2012 © KoSFoST and Springer 2012
Abstract To enhance the production of bacteriocin DF01, produced by Lactobacillus brevis DF01, cultivation conditions and medium composition were optimized by using response surface methodology (RSM). The selected 5 factors based on MRS medium were glucose, yeast extract, MgSO4, temperature, and initial pH. Fractional factorial design (FFD) was effective in searching for the main factors. By a 25-1 FFD, glucose, yeast extract, and initial pH were found to be significant factors and had positive effects on bacteriocin production. The effects of the 3 main factors on bacteriocin production were further investigated by a central composite design (CCD). RSM revealed that the maximum bacteriocin production was achieved at yeast extract concentration of 14.56 g/L, glucose concentration of 28.95 g/L, and initial pH of 6.8. After RSM, the titer of bacteriocin was increased by 4-fold. Keywords: bacteriocin, response surface methodology, Lactobacillus brevis
Introduction Lactic acid bacteria (LAB) are widely used as starters in industry of fermented foods, such as dairy, meat, and vegetable foods. LAB produces various antimicrobial compounds, including bacteriocins. Bacteriocins are proteinaceous antimicrobial compounds, which have an Yu Mi Lee, Wang June Kim () Department of Food Science and Technology, Dongguk University-Seoul, Seoul 100-715, Korea Tel/Fax: +82-2-2260-3373 E-mail:
[email protected] Jun Soo Kim Dong Suh Foods Corporation, 411-1 Cheongcheon-2-dong, Bupyong-gu, Incheon 403-032, Korea
ability to inhibit many Gram-positive bacteria, including spoilage and pathogenic bacteria (1,2). Studies of LAB bacteriocins widely have been conducted in recent years because of their potential use as biopreservatives to eliminate spoilage and food-borne pathogenic bacteria (3,4). Bacteriocins are ribosomally synthesized, hydrophobic or amphiphilic peptides, and digested by protease (1,5). They usually have comparatively narrow antimicrobial spectra that are lethal to bacteria only closely related to the producer strain. In recent years, a large number of bacteriocins produced by LAB have been described, but relatively few have been characterized at the molecular level (1,6,7). In previous study, we isolated bacteriocin producing Lactobacillus brevis DF01 from dongchimi. Dongchimi is one of the water based kimchi, fermented in brine by LAB. The carbon dioxide and organic acids produced by LAB in dongchimi provide tart and refreshing flavor (8,9). In addition, it has been known that a strain DF01 inhibited the growth of tyramine-producing Lactobacullus curvatus KFRI 166. The antimicrobial compound produced by L. brevis DF01 was secreted at maximum level in the late exponential phase in MRS broth, and its activity was remained constant during the stationary phase. The activity of bacteriocin DF01 was totally inactivated by αchymotrypsin, pronase E, proteinase K, trypsin, and αamylase. Bacteriocin DF01 had a bacteriolytic characteristic mode of action (10). The growth of bacteria and the accumulation of their metabolites are strongly influenced by growth environment and medium compositions such as carbon sources, nitrogen sources, growth factors, and minerals. Search for the major factors and optimization of them for biotechnological processes including multivariables is difficult. The traditional ‘one-factor-at-a-time approach’ that was used in medium optimization to obtain high yields of the desired metabolites
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disregards the complex interactions among various physicochemical parameters (11,12). Statistically based experimental designs such as factorial design and response surface analysis fulfill this requirement. Response surface methodology (RSM), an experimental strategy for find out the optimum conditions among the multivariable system is a much more efficient technique for optimization of microorganism’s metabolites production (13,14). RSM is a gathering of statistical techniques for designing experiments, evaluating the effects of factors, and searching optimum conditions of factors to achieve a desirable goal (15,16). This method has been successfully applied to the optimization of medium composition, conditions of enzymatic hydrolysis, and parameters of food preservation and fermentation processes (14,17,18). In this study, we combined traditional non-statistical method and statistical method based experimental designs to optimize the medium for production of bacteriocin by L. brevis DF01. The one-factor-at-a-time experiment was used to select the medium ingredients supposed to have the effects on bacteriocin production. And the RSM, which included factorial design, was applied to build models to evaluate the effective factors and study their interaction and select optimum conditions.
Materials and Methods Bacterial strains The bacteriocin DF01 producing strain used in this study was Lactobacillus brevis DF01, which was previously isolated from dongchimi, Korean traditional fermented food. L. curvatus KFRI 166, a tyramine producing strain, was used as an indicator strain for the bacteriocin activity assay. All microorganisms were stored in 15% glycerol stock at −80oC, and subcultured twice in MRS broth before use. Both strains were cultured in MRS broth (BD, Sparks, MD, USA) at 37oC. Bacteriocin assay Agar-well diffusion assay was used to determine antimicrobial activity of L. brevis DF01 (19). Bacteriocin titer was inferred from the area of the inhibition zone. L. brevis DF01 was cultivated in MRS broth for 24 h at 37oC. Cell-free culture supernatant was obtained by centrifugation at 10,000×g for 15 min at 4oC. The supernatant was neutralized to pH 6.5 with 5 N NaOH, and subsequently filtered though a 0.22-µm pore size cellulose acetate filter (Advantec DISMIC-13CP; Toyo-Rochi, Tokyo, Japan). Each aliquot containing 100 µL of the cellfree supernatant was placed in wells (diameter of 9 mm) cut in MRS agar plate pre-inoculated with the indicator microorganism. The plates were kept at 4oC for 4 to 6 h to allow diffusion of the supernatants and then were incubated at 37oC overnight. The antimicrobial activity was
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demonstrated by clear zone around the well. The bacteriocin titer, that expressed as arbitrary units (AU/mL), was determined by the serial 2-fold dilution method against L. curvatus KFRI 166. AU was defined as the reciprocal of the highest dilution showing a clear zone of growth inhibition (20,21). Culture condition The MRS broth consists peptone (1%), beef extract (1%), yeast extract (0.5%), dextrose (2%), polysorbate 80 (0.1%), ammonium citrate (0.2%), sodium acetate (0.5%), magnesium sulfate (0.01%), manganese sulfate (0.005%), and dipotassium phosphate (0.2%). For selection of the best carbon source on the bacteriocin production, some carbon sources were tested individually (lactose, fructose, sucrose, mannose, dextrin, mannitol, and glucose). Each carbon source replacing the 2% glucose was added at 2% of the MRS medium. To study the effect of different nitrogen sources, a modified MRS medium was used as a basal medium and yeast extract, beef extract, peptone, and tryptone, were all purchased from BD and added individually at different percentage (Table 1). To investigate the influence of the NaCl based on the amount of added salt in dongchimi, different concentrations of NaCl were added to the medium (about 0.5, 1, and 1.5%). To test the effect of the initial pH, MRS media were adjusted to different pH values (Table 2) with 5 N NaOH. After inoculation of L. brevis DF01 (1% of inoculums), incubation was performed at 37oC except Table 1. Effect of the combination of multiple nutrient sources on growth and antimicrobial activity1)
1)
Nutrient source2)
Growth (OD600)
Antimicrobial activity (AU/mL)
MRS (normal) MRS with 1% YE MRS with 3% YE MRS with 0.5% BE+1% YE MRS with 1.5% BE+1% YE MRS with 0.5% TP+1% YE MRS with 1.5% TP+1% YE MRS with 1.5% PP+1% YE MnSO4 0.01% MgSO4 0.02% Sodium acetate 1% Ammonium citrate 0.4% Polysorbate 80 0.2% NaCl 0.5% NaCl 1% NaCl 2%
2.57 2.84 3.04 2.57 1.91 2.50 1.18 2.46 3.17 1.48 1.30 2.35 2.10 2.88 3.35 2.76
320 640 160 320 320 320 320 320 320 640 320 320 320 640 640 640
Cells were grown at 37oC; Cell growth, pH, and bacteriocin activity were measured every 3 h time intervals, and the values when bacteriocin activity was maximum are presented. 2) TP, tryptone; PP, peptone; YE, yeast extract; BE, beef extract
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Optimized Bacteriocin Production Using RSM Table 2. Effect of the initial pH and temperature on growth and antimicrobial activity Growth (OD600 )
Bacteriocin activity1) (AU/mL)
Initial pH
pH 6.5 pH 7.5 pH 8.5
2.98 2.83 0.09
320 640 -
Temp.
27oC 37oC 45oC
2.92 2.98 0.11
160 320 -
Medium condition
1)
-, no antimicrobial activity
tests for temperature influence. During 40 h of growth cycle, samples were taken at every 3 h intervals and measured for changes in culture pH, bacterial growth, optical density (OD) at 600 nm using a spectrophotometer (UV/Vis SMART Plus 1900PC; Woongki, Seoul, Korea). The un-inoculated media were used as a blank. Antimicrobial activity against L. curvatus KFRI 166 was also examined at every 3 h by using agar-well diffusion, as described previously. Cell-free supernatant was applied using serial 2-fold dilution. Sterile MRS broth was used as diluents and each diluted solution of 100 µL was loaded on well (9 mm of diameter) of MRS agar lawn containing L. curvatus KFRI 166. The antimicrobial activity of bacteriocin DF01 was expressed as AU/mL. All experiments were done in duplicate. Experimental design The optimization of culture medium was carried out by combination of traditional nonstatistical technology and statistical technology based experimental designs. Before RSM was applied, approximate conditions for cultivating L. brevis DF01 were determined by varying one-factor-at-a-time while keeping the others constant. It would be elucidated to select medium components. The preliminary experiments revealed that the carbon sources as glucose, the nitrogen sources as yeast extract, NaCl, MgSO4, and pH were supposed to have positive effects on bacteriocin production. Fractional factorial design (FFD) The purpose of the first optimization step was to identify which ingredient(s) of the medium is the most important components. FFD are very useful in indentifying the important nutrients and interactions between 2 or more nutrients in relatively few experiments as compared to the one-factor-at-a-time technique. The number of experiments can be reduced by using FFD without losing the information about the main effect (12,16). Five variables (i.e., glucose, yeast extract, NaCl, MgSO4, and pH) which were expected to have influence of bacteriocin production, were selected by preliminary experiments. The variables were identified by
Table 3. Coded and real values of variables and 2-level FFD of variables (in coded levels) with titer as response values Level of variables
Variables X1: Yeast extract (g/L) X2: NaCl (g/L) X3: Initial pH X4: MgSO4 (g/L) X5: Glucose (g/L) Runs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-1
0
1
2 5 5.5 0.06 10
5 10 6.5 0.1 20
8 15 7.5 0.14 30
Variables X1
X2
X3
X4
X5
Antimicrobial activity
-1 -1 -1 -1 -1 1 -1 1 1 1 1 1 -1 0 0 0 -1 0 1 1
1 1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 0 0 0 -1 0 -1 1
1 -1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 0 0 0 1 0 1 -1
-1 -1 1 1 1 1 -1 -1 1 -1 -1 1 -1 0 0 0 1 0 -1 1
1 -1 1 -1 -1 1 1 -1 -1 1 -1 1 -1 0 0 0 1 0 1 -1
160 1,280 320 640 640 640 320 640 320 640 320 640 80 320 320 320 80 640 640 640
using a 2-level FFD. The variables considered for the design are listed in Table 3. According to the 2-level 5 variable concept, a 25-1 fractional design was considered. A 25 FFD leading to 16 runs (i.e., 24 =16 runs) and 4 runs were center point runs for statistical reasons. Total 20 runs were performed (Table 3) and runs were randomized for statistical reasons. The fractional factorial analysis was conducted by Minitab program (version 14; Minitab Com., State College, PA, USA). Each independent variable was investigated at a high (+1) and a low (-1) level. The variables are coded according to the following equation: Xi – Xo xi = ---------------∆Xi
where, xi is the coded value of an independent variable, Xi is the real value of an independent variable, Xo is the real value of an independent variable at the center point, and ∆Xi is the step change value. If the difference was significant between the mean of the center points and that
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of the factorial points (p
