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Optimization of culture conditions for the production of antimicrobial substances by probiotic Lactobacillus paracasei subsp. Tolerans FX-6
Journal of Functional Foods 18 (2015) 244–253
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Optimization of culture conditions for the production of antimicrobial substances by probiotic Lactobacillus paracasei subsp. Tolerans FX-6 Jianyin Miao a,b, Mingbin Xu a, Haoxian Guo a, Liping He a, Xiangyang Gao a, Christina DiMarco-Crook b, Hang Xiao b, Yong Cao a,* a b
College of Food Science, South China Agricultural University, Guangzhou 510642, China Department of Food Science, University of Massachusetts, Amherst, MA 01003, United States
A R T I C L E
I N F O
A B S T R A C T
Article history:
There is an increasing demand from consumers for natural antimicrobial substances that
Received 14 November 2014
can be used for food preservation. This study is focused on establishing the conditions to
Received in revised form 30 June
enhance the production of antimicrobial substances by the probiotic Lactobacillus paracasei
2015
Subsp. Tolerans FX-6 from Tibetan kefir. The culture conditions were optimized by using re-
Accepted 8 July 2015
sponse surface methodology, and remarkably the optimized conditions increased the
Available online
antimicrobial activity by more than 80%. Further characterization revealed that the antimicrobial substances exhibited a broad spectrum of antimicrobial activity against a variety
Keywords:
of Gram-positive and Gram-negative bacteria and fungi. Moreover, the antimicrobial sub-
Probiotic Lactobacillus paracasei
stances were highly tolerant to heat and enzyme treatment. Excitingly, the antimicrobial
subsp. tolerans FX-6
substances were fractionated by HPLC and the sub-fractions generated showed stronger an-
Antimicrobial substances
timicrobial activity than nisin, a highly effective bacteriocin currently used as a food
Response surface methodology
preservative. Overall, these results demonstrate a novel approach to produce effective antimicrobial substances for use as natural preservatives in food. © 2015 Elsevier Ltd. All rights reserved.
1.
Introduction
There is an increasing demand from consumers for natural and minimally processed foods, therefore it is of great interest to develop and produce effective natural antimicrobial substances for food preservation.Various microorganisms, especially lactic acid bacteria have been found to produce antimicrobial substances (Tiwari et al., 2009). Since lactic acid bacteria are naturally found in food, they have drawn particular attention
for their potential in the production of antibacterial substances for food preservation. Nisin, the first lactic acid bacteria bacteriocin, is now approved for use in over 40 countries and has been used as a food preservative for over 50 years. In recent years, several other antimicrobial substances produced by lactic acid bacteria have also been reported (Cálix-Lara et al., 2014; Kawai, Saito, Kitazawa, & Itoh, 1998; Powell, Witthuhn, Todorov, & Dicks, 2007). Nevertheless, more research efforts are needed to develop novel natural antimicrobial substances with broad antimicrobial spectrum and improved yield.
* Corresponding author. College of Food Science, South China Agricultural University, Guangzhou 510642, China. Tel.: +86 020 85286234; fax: +86 020 85286234. E-mail address: [email protected] (Y. Cao). http://dx.doi.org/10.1016/j.jff.2015.07.011 1756-4646/© 2015 Elsevier Ltd. All rights reserved.
Journal of Functional Foods 18 (2015) 244–253
Response surface methodology is an effective statistical technique that has been successfully applied in many areas, including optimizing culture conditions for the production of antimicrobial substances (Kumar, Jain, Ghosh, & Ganguli, 2012; Wang & Liu, 2008), determining processing parameters for enzymatic hydrolysis (Cao, Zhang, Hong, & Ji, 2008; Pinho, Melo, Mansilha, & Ferreira, 2010), and optimizing the extraction process (Barizão et al., 2013; Cho, Choi, Lee, & Eitenmiller, 2010; Khajeh & Ghanbari, 2011; Khajeh & Sanchooli, 2010; Prakash Maran, Manikandan, Thirugnanasambandham, Vigna Nivetha, & Dinesh, 2013; Zhong & Wang, 2010). Box–Behnken design is an example of a response surface methodology that requires fewer data points to evaluate multiple variables and their interactions, making it easier and less laborious to design and carry out the experiments. In the current study, we systematically investigated the antimicrobial substances produced by a newly isolated probiotic strain of Lactobacillus paracasei subsp. tolerans FX-6 from Tibetan kefir. The culture conditions of L. paracasei subsp. tolerans FX-6 were optimized by response surface methodology to enhance the yield and reduce the cost of production. The antimicrobial properties of the active substances were characterized to establish its antibacterial spectrum, stability against high temperature and enzyme degradation. Furthermore, we isolated the active fractions responsible for the antimicrobial activities.
2.
Materials and methods
2.1.
Materials
Probiotic L. paracasei subsp. tolerans FX-6, which was previously isolated from Tibetan kefir (traditional fermented milk from Tibet, China) and identified by 16S rDNA sequence analysis (GenBank accession number KF544958), was stored in the College of Food Science, South China Agricultural University, Guangzhou, China and used for the present study. Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 63589, Salmonella enterica CMCC 9812, Shigella dysenteriae CMCC (B) 50071, Bacillus thuringiensis CMCC 9812, Aspergillus niger ACCC 30005, Aspergillus flavus CGMCC 3. 2890, Rhizopus nigricans AS3.4997, and Penicillium glaucum STL 3501 were all stored in the microbial culture laboratory in the College of Food Science, South China Agricultural University, Guangzhou, China. Nisin was purchased from Beijing Daniel Spulber Biological Technology Co., LTD (Beijing, China).
2.2. Preparation of the antimicrobial substances and antimicrobial activity assay Bacterial broth cultures were adjusted to pH 7.0 and then centrifuged at 3500 × g for 20 min at room temperature. The supernatant was then filtered using a 0.22-µm Millipore filter and lyophilized. The lyophilized substances were used for antimicrobial activity assays. The antimicrobial activity was detected by agar diffusion assay, as described previously (Ouoba, Diawara, Traoré, & Møller, 2004), with some modifications. First, the indicator organisms (0.2 mL, approximate 10 8 cfu/mL) were spread onto
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Luria–Bertani (used for cultivating bacteria: E. coli ATCC 25922; S. aureus ATCC 63589; S. enterica CMCC 9812; S. dysenteriae CMCC (B) 50071; B. thuringiensis CMCC 9812) or potato dextrose (used for cultivating fungi: A. niger ACCC 30005; A. flavus CGMCC 3. 2890; R. nigricans AS3.4997; P. glaucum STL 3501) agar plates. The antimicrobial substances (200 µL, 1 g/mL) were then added into an Oxford cup (a stainless cylinder, outer diameter 7.8 ± 0.1 mm, inner diameter 6.0 ± 0.1 mm, and height 10.0 ± 0.1 mm) placed on the surface of the agar. Bacteria were cultured at 37 °C for 18 h. Fungi were cultured at 30 °C for 24 h. The size of the clear diffusion zone around the cup (including that of the Oxford cup) was measured with a Vernier caliper and reported in millimetres. The experiment was performed in triplicate.
2.3. Single factor experiments for optimization of the production of the antimicrobial substances The effects of single factors on the production of the antimicrobial substances were examined. The culture parameters were then further optimized using response surface methodology based on the results of single factor experiments. All assays were carried out in triplicate. Batch culture was carried out in 50 mL hermetical Erlenmeyer flasks containing culture broth. Because L. paracasei subsp. tolerans FX-6 was isolated from Tibetan kefir (a traditional fermented milk from Tibet, China), to simulate the original growth conditions of the strain, the sterilized pure milk (Nmyili, Huhehaote, China) was selected as the basic culture medium in the culture experiments. To study the effect of different factors on antimicrobial activity, six factors (culture time, temperature, initial pH, inoculum volume, carbon source, and nitrogen source) were selected for examination in single factor experiments. E. coli ATCC 25922 was selected as the indicator bacterium to assay the antibacterial activity of the lyophilized culture supernatants under different culture conditions. The levels of all factors other than the parameter being examined remained constant (unless otherwise stated, the parameters under examination were: temperature, 30 °C; initial pH, 7.0; inoculum volume (optical density 0.2 at 600 nm), 5% (v/v)). The single factor experiments were undertaken as follows. Culture time: the effect of culture time on antimicrobial activity was investigated using cultures incubated for 12, 24, 48, 60, 72, 84, 96, 108, and 120 h. Initial pH: the initial pH of the basic culture medium was adjusted to 3.0, 5.0, 7.0, or 9.0, and the culture was incubated for the optimal culture time as determined above. Culture temperature: the optimal culture temperature was examined by carrying out growth experiments at 20, 25, 30, 35, 40, or 45 °C, for the optimal culture time and at the optimal initial pH as determined above. Inoculum volume (optical density 0.2 at 600 nm): the effect of the initial inoculum volume was studied using inoculums of 1, 3, 5, 7, 9, or 11% (v/v), with incubation for the optimal culture time, at the optimal initial pH, and the optimal culture temperature as determined above. Carbon source: lactose, glucose, maltose, and sucrose were selected to study the effect of carbon source on antimicrobial activity. Each of the carbon sources was added to the culture medium at a concentration of 2% (w/v), and the cultures were
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Journal of Functional Foods 18 (2015) 244–253
incubated under the optimal conditions as determined above. The optimal carbon source was then selected and studied at 1, 3, 4, or 5% (w/v). Nitrogen source: Peptone, beef extract, yeast powder, fish meal, soybean protein, ammonium nitrate and ammonium sulphate were selected to study the effect of different nitrogen sources on antimicrobial activity. Each of the nitrogen sources was added to the culture medium at a concentration of 2% (w/ v), and the cultures were incubated under the optimal conditions as determined above. The optimal nitrogen source was then selected and studied at 1, 2, 3, 4, or 5% (w/v).
2.4. Response surface methodology and statistical analysis The results of the single factor experiments were analysed by variance analysis. The single factors which had a significant effect on antimicrobial activity (P < 0.05) were selected as significant independent variables. The Box–Behnken design under response surface methodology was employed to further optimize the significant independent variables selected above. Experimental runs were carried out randomly and the data were analysed by the response surface regression procedure to fit the second-order polynomial equation model generated by the Design-Expert software (Version 8.05b, Stat-Ease, Minneapolis, MN, USA). The general polynomial equation (1) (Guo et al., 2012) was as follows: n
n
i=1
i=1
n −1
Y = β 0 + ∑ βi Xi + ∑ βii Xi2 + ∑
n
∑ β XX ij
i
j
2.7. HPLC
Characterization of the antimicrobial substances by
The antimicrobial substances produced at the optimal culture conditions were analysed by HPLC. The substances were filtered through a 0.22-µm membrane filter and separated by a reversed-phase C-18 column (Agela, Tianjin, China) (5 µm, 100 Å, 4.6 mm × 250 mm). Elution was performed using a linear gradient of 5–10% methanol with 0.1% trifluoroacetic acid for 40 min. The flow rate was 1 mL/min. The eluted peaks were monitored at 214 and 280 nm, collected, concentrated, and lyophilized. The antimicrobial activities of the eluted peaks were tested by a microdilution technique using 96-well plates (Rahman & Gray, 2005) with E. coli ATCC 25922 as the indicator bacterium. Nisin (Beijing Daniel Spulber Biological Technology Co., LTD, Beijing, China) was selected as the positive control. All assays were carried out in triplicate. The molecular mass of the active sub-fractions was analysed by ABI 4800 MALDI-TOF-MS (Shanghai Applied Protein Technology Co. Ltd, Shanghai, China).
(1)
i=1 j=i+1
in which Y is the dependent variable (diameter of inhibition zone), Xi and Xj represent the independent variables, β0 is constant, and βi, βii, and βij are coefficients of linear, quadratic and interaction terms, respectively. n represents the number of independent variables. The quality of the model fitting was evaluated by R2 and analysis of variance (ANOVA). Finally, three additional confirmation experiments under the optimal conditions were conducted to verify the validity of the statistical model.
2.5.
(3 mg/mL, pH 2.0; Sigma) at 37 °C for 2 h to test the effect of the enzymes on antimicrobial activity. For the heat treatment test, the antimicrobial substances (200 µL, 1 g/mL) were incubated at 80 or 100 °C for 60 min, or at 121 °C for 20 min. The antimicrobial activity of all the treated samples was determined by agar diffusion assay, as described above, using E. coli ATCC 25922 as the indicator strain. All assays were carried out in triplicate.
Determination of antimicrobial spectrum
The antimicrobial spectrum of the active substances was analysed by agar diffusion assay, as described above. The activity of the antimicrobial substances against the indicator organisms (E. coli ATCC 25922; S. aureus ATCC 63589; S. enterica CMCC 9812; S. dysenteriae CMCC (B) 50071; B. thuringiensis CMCC 9812; A. niger ACCC 30005; A. flavus CGMCC 3. 2890; R. nigricans AS3.4997; P. glaucum STL 3501) was examined using lyophilized culture supernatant from L. paracasei subsp. tolerans FX-6 cultures grown under the optimal culture conditions as determined above. All assays were carried out in triplicate.
2.6. Effect of enzymes and heat treatment on the antimicrobial substances The antimicrobial substances (200 µL, 1 g/mL) were treated with trypsin (3 mg/mL, pH 8.0; Sigma, St. Louis, MO, USA) or pepsin
2.8.
Statistical analysis
All assays were carried out in triplicate and the results were expressed as mean values ± standard deviation (SD). The data were analysed by using SPSS 18.0 statistical software.
3.
Results and discussion
3.1. Effects of culture conditions on the activity of the antimicrobial substances The results of assays investigating the effect of the six selected growth factors (culture time, temperature, initial pH, inoculum volume, carbon source, and nitrogen source) are shown in Fig. 1. Based on a comprehensive analysis of these results, the optimum conditions for production of the antimicrobial substances were determined as follows: culture time, 72 h (Fig. 1a); temperature, 30 °C (Fig. 1b); initial pH, 7.0 (Fig. 1c); inoculum volume, 9% (v/v) (Fig. 1d); carbon source, 3% (w/v) glucose (Fig. 1e and f); and nitrogen source, 2% (w/v) yeast powder (Fig. 1g and h). The results of the variance analysis on the six selected growth factors showed that the culture time, inoculum volume, and nitrogen source did not produce significant effects on the antimicrobial activity (P > 0.05). However, the temperature, initial pH, and carbon source significantly affected the antimicrobial activity of the lyophilized culture supernatant (P < 0.05). Temperature, initial pH, and glucose concentration were therefore selected as independent variables for further study.
Journal of Functional Foods 18 (2015) 244–253
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Fig. 1 – Effect of culture time (a), temperature (b), initial pH (c), inoculum volume (d), carbon source (e, f), and nitrogen source (g, h) on antimicrobial activity. Data represent mean ± SD of 3 replicates. Asterisk indicates statistical significance (p < 0.05).
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Table 1 – Levels of the independent variables and Box–Behnken design arrangement. Data represent mean ± SD of 3 replicates. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Variables A: Initial pH
B: Temperature (°C)
C: Glucose (w/v, %)
−1(6) −1(6) 1(8) 1(8) 0(7) 0(7) 0(7) 0(7) −1(6) 1(8) −1(6) 1(8) 0(7) 0(7) 0(7)
−1(25) 1(35) −1(25) 1(35) −1(25) 1(35) −1(25) 1(35) 0(30) 0(30) 0(30) 0(30) 0(30) 0(30) 0(30)
0(3) 0(3) 0(3) 0(3) −1(2) −1(2) 1(4) 1(4) −1(2) −1(2) 1(4) 1(4) 0(3) 0(3) 0(3)
Inhibition zone diameter (mm) 21.80 ± 0.46 22.98 ± 0.23 23.74 ± 0.11 23.02 ± 0.07 20.60 ± 0.24 21.42 ± 0.17 23.60 ± 0.14 23.16 ± 0.07 19.76 ± 0.45 21.35 ± 0.14 22.82 ± 0.06 24.20 ± 0.18 24.31 ± 0.19 24.52 ± 0.23 24.41 ± 0.07
3.2. Optimization of the culture conditions by response surface methodology
Y = 24.41 + 0.11A + 0.62B + 1.33C − 0.48 AB
Based on the results of variance analysis of the single factor experiments, initial pH (A), temperature (B), and glucose concentration (C) were chosen as independent variables. The range and centre point values for the three independent variables are presented in Table 1. For the other factors, culture time was set to 72 h, inoculum volume was set to 9% (v/v), and amount of yeast powder was set to 2% (w/v). A three-factor threelevel Box–Behnken design with 15 runs containing three replications at the centre point was carried out. The experimental data listed in Table 1 were analysed through multiple regression using Design-Expert 8.05b. The relationship between dependent variable (Y) and three independent variables, initial pH (A), temperature (B), and glucose concentration (C), was determined using the following second-order polynomial equation (2).
The model predicted that optimal culture conditions for the three significant independent variables were as follows: an initial pH of 6.84, a temperature of 32 °C, and a glucose concentration of 3.44% (w/v). At the optimal culture conditions, the predicted value of the zone of inhibition was 24.82 mm. The results of ANOVA are shown in Table 2. The model had a very high F-value (F = 53) and a very low P-value (P = 0.0002), which implied that the model fits the experimental data very well (Zhong & Wang, 2010). There is only a very low possibility (0.02%) that a model with an F-value this high could occur by chance. Both the determination coefficient (R2 = 0.9897) and the adjusted determination coefficient (R2Adj = 0.971) were satisfactory; exhibiting a low experimental error and a well-fit regression equation (Liu, Du, Yuan, & Zhu, 2009). The F-value
(2)
− 0.32 AC − 0.053BC − 0.68 A2 − 0.85B2 − 1.54C2
Table 2 – Results of variance analysis. Source
Sum of squares
df
Mean square
F-value
P-value Prob > F
Significant
Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total R2 = 0.9897
30.290602 0.0882 3.0628125 14.177813 0.9025 0.3969 0.011025 1.7220006 2.6390006 8.7046314 0.3164917 0.294425 0.0220667 30.607093 R2Adj = 0.9710
9 1 1 1 1 1 1 1 1 1 5 3 2 14
3.365622 0.0882 3.062813 14.17781 0.9025 0.3969 0.011025 1.722001 2.639001 8.704631 0.063298 0.098142 0.011033
53 1.4 48 224 14 6.3 0.2 27 42 138
0.0002 0.2909 0.0009 <0.0001 0.0129 0.0542 0.6937 0.0034 0.0013 <0.0001
**
8.9
0.1027
* Significant (P < 0.05). ** Highly significant (P < 0.001).
** ** *
* * ** not significant
Journal of Functional Foods 18 (2015) 244–253
(8.9) and P-value (0.1027) of lack-of-fit indicated that the lack-of-fit was not significantly relative to the pure error (Sharma, Singh, & Dilbaghi, 2009). Among the model terms, the temperature (B), glucose concentration (C), and quadratic term of glucose (C2) had highly significant effects on antibacterial activity (P < 0.001).
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The interaction term for initial pH-temperature (AB), quadratic term of initial pH (A2), and quadratic term of temperature (B2) all had significant effects (P < 0.05) (Qiao et al., 2009). The three-dimensional response surface plots of the interactions between two variables among the three factors are shown in Fig. 2.
Fig. 2 – Response surface plots of the effects of three factors on antibacterial activity. (a) Interaction of A (initial pH) and B (temperature). (b) Interaction of A (initial pH) and C (glucose). (c) Interaction of B (temperature) and C (glucose).
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Table 3 – Effects of temperature and enzyme treatment on the activity of the antimicrobial substances against Escherichia coli. Data represent mean ± SD of 3 replicates. Treatment Temperature (°C)
Enzymes
Diameter of inhibitory zone (mm) 80 (60 min) 100 (60 min) 121 (20 min) Pepsin Trypsin
24.35 ± 0.71 23.59 ± 0.42 23.60 ± 0.29 22.85 ± 0.65* 23.43 ± 0.36
* Significant (P < 0.05). **Highly significant (P < 0.001).
3.3.
Confirmation of optimal culture conditions
To confirm the validity of the model, three experiments were carried out at initial pH 6.84, temperature 32 °C, glucose of 3.44% (w/v), culture time of 72 h, inoculum volume of 9% (v/v), and yeast powder concentration of 2% (w/v). The mean diameter of the zone of inhibition generated by the antimicrobial substances from the confirmation experiments was 24.25 mm, a measurement very close to the predicted value of 24.82 mm, indicating the accuracy of the model. Under the optimal culture conditions, the size of the zone of inhibition increased by 82% compared with the zone of inhibition for cultures grown under the original non-optimal conditions (The zone of inhibition was 13.32 mm under the original non-optimal conditions). Our results demonstrated that the optimized conditions produced significant enhancement on the production of the antimicrobial substances.
3.4. Effects of enzyme and high temperature treatments on antimicrobial activity Resistance to enzyme degradation and high temperature treatment is an important trait for antimicrobial substances that need to be used in a wide range of food products because the antimicrobial activity should not be significantly decreased during food processing and storage. The effects of enzymes and high temperature treatments on the antimicrobial substances against E. coli were analysed, and the results are shown in Table 3. Our results showed that the antimicrobial substances were highly heat-tolerant, with antibacterial activity showing no significant changes even after incubation at 100 °C for 60 min or 121 °C for 20 min. The treatment with pepsin or trypsin did not cause significant change in the antimicrobial activity. Overall, these results indicated that the antimicrobial substances produced showed high resistance to high temperature thermal treatments and degradation by digestive enzymes, therefore establishing the antimicrobial substances as a promising candidate for use as preservatives in many food products.
3.5. Antimicrobial spectrum of the antimicrobial substances The antimicrobial spectrum of the antimicrobial substances was determined by agar diffusion assay, and the results are shown in Fig. 3 and Table 4. We observed that the antimicrobial substances had a wide antibacterial spectrum. This was evidenced by the significant inhibition of both Gram-positive (S. aureus and B. thuringiensis) and Gram-negative (E. coli,
Fig. 3 – Antimicrobial activity spectrum of the antimicrobial substances (all assays were carried out in triplicate).
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Table 4 – Antimicrobial activity spectrum of the antimicrobial substances. Indicator bacterium
Antimicrobial activity
Escherichia coli Staphylococcus aureus Bacillus thuringiensis Salmonella enterica Shigella dysenteriae Aspergillus flavus Aspergillus niger Rhizopus nigricans Penicillium glaucum
+++ +++ +++ +++ +++ + +++ ++ ++
+: diameter of inhibitory zone 5–10 mm; ++: diameter of inhibitory zone 10–20 mm; +++: diameter of inhibitory zone >20 mm.
Salmonella enteric and S. dysenteriae) bacteria. In addition, the antimicrobial substances also showed an inhibition of fungi (A. flavus, A. niger, R. nigricans, and P. glaucum), which was a little weaker than that of bacteria. Some of the reported antimicrobial peptides did not have antifungal activity, such as Pardaxin (Oren & Shai, 1996), Melittin (Oren & Shai, 1996) and Ceratotoxin (Marri, Dallai, & Marchini, 1996). The main reason is that fungi are multicellular organisms with a hard layer of cell wall, which makes them resistant to many chemical drugs. The antimicrobial substances from lactic acid bacteria usually have relatively narrow antimicrobial spectra based on previous reports. Many of the antimicrobial substances produced from lactic acid bacteria had no significant inhibitory activity against Gram-negative bacteria (Gao, Jia, Gao, & Tan, 2010). Other antimicrobial substances from lactic acid bacteria have shown inhibition on bacteria but not on fungi (Messaoudi et al., 2012). It was also reported that certain antimicrobial substances from lactic acid bacteria only had antifungal activity but no activity on bacteria (Hassan & Bullerman, 2008). Our results demonstrated that the antimicrobial substances obtained in the current study possessed inhibitory effects against the majority of food borne pathogens and spoilage bacteria.
3.6. Analysis of the active components in antimicrobial substances The antimicrobial substances were separated into several fractions by HPLC. Each fraction was collected, concentrated and tested for antimicrobial activity by microdilution technique with E. coli as an indicator strain. Three active fractions were identified (Fig. 4). Three active fractions were all dual wavelength absorption, and the absorption in 280 nm is stronger than in 214 nm. The peak at about 3 min was also dual wavelength absorption, but the peak did not have antimicrobial activity. Table 5 shows the results of the antimicrobial activity of HPLCisolated active fractions and nisin (positive control) against E. coli. Active components in peak 1 (62.5 µg/mL), peak 2 (500 µg/mL) and peak 3 (125 µg/mL) showed significant antibacterial activity. Among the active peaks, the antibacterial activity of peak 1 and peak 3 were significantly stronger than the antimicrobial activity of the positive control nisin (250 µg/mL). So we analysed the molecular mass of peak 1 and peak 3 by MALDITOF-MS. Peak 1 had the same molecular mass as bacteriocin
Table 5 – The inhibitory activities of sub-fractions isolated from antimicrobial substances against Escherichia coli. Data represent mean ± SD of 3 replicates. Active peaks
Antimicrobial activity (µg/mL)
Peak 1 Peak 2 Peak 3 Nisin
62.5 ± 2.03 500 ± 4.67 125 ± 2.71 250 ± 3.52
F1 (2113.842 Da), which was a bacteriocin we had found in previous research (Miao et al., 2014). The molecular mass of peak 3 was 2727.9849 Da. Nisin is produced by Lactococcus lactis subsp. lactis and was the first bacteriocin approved for use in food (da Silva Malheiros, Daroit, & Brandelli, 2010). In the current study, the antimicrobial substances were produced by L. paracasei subsp. tolerans FX-6 isolated from Tibetan kefir, a traditional fermented milk from Tibet, China. The long history of human consumption of kefir suggests that antimicrobial substances should be safe for human consumption. Most importantly, our results demonstrated that the sub-fractions of the antimicrobial substances produced by L. paracasei subsp. tolerans FX-6 had much stronger antimicrobial effects than nisin, a widely used food preservative. Further study is warranted to identify the chemical structures of the active components responsible for the antimicrobial activities.
4.
Conclusions
In conclusion, we have developed a method to produce the antimicrobial substances from the fermentation of probiotic L. paracasei subsp. tolerans FX-6. Our results demonstrated that the antimicrobial substances had high tolerance against thermal treatment and digestive enzyme degradation, and it possessed potent antimicrobial activity against a wide spectrum of pathogens and spoilage microorganisms. The active components of the antimicrobial substances showed stronger efficacy than the commercially used food preservative nisin. Overall, our results provide a solid basis to use the antimicrobial substances from L. paracasei subsp. tolerans FX-6 as a labelfriendly natural food preservative.
Conflict of interest The authors have declared that there is no conflict of interest.
Acknowledgements The authors would like to express their sincere gratitude to the National Natural Science Foundation of China (No. 31171768) for the financial support of this research.
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mV
MPa
350 35.0 300 30.0 250
peak 3
200
25.0
peak 1 20.0
150 15.0
peak 2 100
10.0 50 5.0 0 0.0 2.5
5.0
7.5
10.0
12.5
min
A
M=2727.9849 Da
B Fig. 4 – Analysis of the components of the antimicrobial substances by HPLC. Elution was carried out on by a reversedphase C-18 column (Agela, Tianjin, China) (5 µm, 100 Å, 4.6 mm × 250 mm) with a linear gradient of 5–10% methanol with 0.1% trifluoroacetic acid for 40 min. The eluted peaks were monitored at 214 and 280 nm (The red line and the black line represent the 214 nm and 280 nm, respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Optimization of culture conditions for the production of antimicrobial substances by probiotic Lactobacillus paracasei subsp. Tolerans FX-6 Jianyin Miao a,b, Mingbin Xu a, Haoxian Guo a, Liping He a, Xiangyang Gao a, Christina DiMarco-Crook b, Hang Xiao b, Yong Cao a,* a b
College of Food Science, South China Agricultural University, Guangzhou 510642, China Department of Food Science, University of Massachusetts, Amherst, MA 01003, United States
A R T I C L E
I N F O
A B S T R A C T
Article history:
There is an increasing demand from consumers for natural antimicrobial substances that
Received 14 November 2014
can be used for food preservation. This study is focused on establishing the conditions to
Received in revised form 30 June
enhance the production of antimicrobial substances by the probiotic Lactobacillus paracasei
2015
Subsp. Tolerans FX-6 from Tibetan kefir. The culture conditions were optimized by using re-
Accepted 8 July 2015
sponse surface methodology, and remarkably the optimized conditions increased the
Available online
antimicrobial activity by more than 80%. Further characterization revealed that the antimicrobial substances exhibited a broad spectrum of antimicrobial activity against a variety
Keywords:
of Gram-positive and Gram-negative bacteria and fungi. Moreover, the antimicrobial sub-
Probiotic Lactobacillus paracasei
stances were highly tolerant to heat and enzyme treatment. Excitingly, the antimicrobial
subsp. tolerans FX-6
substances were fractionated by HPLC and the sub-fractions generated showed stronger an-
Antimicrobial substances
timicrobial activity than nisin, a highly effective bacteriocin currently used as a food
Response surface methodology
preservative. Overall, these results demonstrate a novel approach to produce effective antimicrobial substances for use as natural preservatives in food. © 2015 Elsevier Ltd. All rights reserved.
1.
Introduction
There is an increasing demand from consumers for natural and minimally processed foods, therefore it is of great interest to develop and produce effective natural antimicrobial substances for food preservation.Various microorganisms, especially lactic acid bacteria have been found to produce antimicrobial substances (Tiwari et al., 2009). Since lactic acid bacteria are naturally found in food, they have drawn particular attention
for their potential in the production of antibacterial substances for food preservation. Nisin, the first lactic acid bacteria bacteriocin, is now approved for use in over 40 countries and has been used as a food preservative for over 50 years. In recent years, several other antimicrobial substances produced by lactic acid bacteria have also been reported (Cálix-Lara et al., 2014; Kawai, Saito, Kitazawa, & Itoh, 1998; Powell, Witthuhn, Todorov, & Dicks, 2007). Nevertheless, more research efforts are needed to develop novel natural antimicrobial substances with broad antimicrobial spectrum and improved yield.
* Corresponding author. College of Food Science, South China Agricultural University, Guangzhou 510642, China. Tel.: +86 020 85286234; fax: +86 020 85286234. E-mail address: [email protected] (Y. Cao). http://dx.doi.org/10.1016/j.jff.2015.07.011 1756-4646/© 2015 Elsevier Ltd. All rights reserved.
Journal of Functional Foods 18 (2015) 244–253
Response surface methodology is an effective statistical technique that has been successfully applied in many areas, including optimizing culture conditions for the production of antimicrobial substances (Kumar, Jain, Ghosh, & Ganguli, 2012; Wang & Liu, 2008), determining processing parameters for enzymatic hydrolysis (Cao, Zhang, Hong, & Ji, 2008; Pinho, Melo, Mansilha, & Ferreira, 2010), and optimizing the extraction process (Barizão et al., 2013; Cho, Choi, Lee, & Eitenmiller, 2010; Khajeh & Ghanbari, 2011; Khajeh & Sanchooli, 2010; Prakash Maran, Manikandan, Thirugnanasambandham, Vigna Nivetha, & Dinesh, 2013; Zhong & Wang, 2010). Box–Behnken design is an example of a response surface methodology that requires fewer data points to evaluate multiple variables and their interactions, making it easier and less laborious to design and carry out the experiments. In the current study, we systematically investigated the antimicrobial substances produced by a newly isolated probiotic strain of Lactobacillus paracasei subsp. tolerans FX-6 from Tibetan kefir. The culture conditions of L. paracasei subsp. tolerans FX-6 were optimized by response surface methodology to enhance the yield and reduce the cost of production. The antimicrobial properties of the active substances were characterized to establish its antibacterial spectrum, stability against high temperature and enzyme degradation. Furthermore, we isolated the active fractions responsible for the antimicrobial activities.
2.
Materials and methods
2.1.
Materials
Probiotic L. paracasei subsp. tolerans FX-6, which was previously isolated from Tibetan kefir (traditional fermented milk from Tibet, China) and identified by 16S rDNA sequence analysis (GenBank accession number KF544958), was stored in the College of Food Science, South China Agricultural University, Guangzhou, China and used for the present study. Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 63589, Salmonella enterica CMCC 9812, Shigella dysenteriae CMCC (B) 50071, Bacillus thuringiensis CMCC 9812, Aspergillus niger ACCC 30005, Aspergillus flavus CGMCC 3. 2890, Rhizopus nigricans AS3.4997, and Penicillium glaucum STL 3501 were all stored in the microbial culture laboratory in the College of Food Science, South China Agricultural University, Guangzhou, China. Nisin was purchased from Beijing Daniel Spulber Biological Technology Co., LTD (Beijing, China).
2.2. Preparation of the antimicrobial substances and antimicrobial activity assay Bacterial broth cultures were adjusted to pH 7.0 and then centrifuged at 3500 × g for 20 min at room temperature. The supernatant was then filtered using a 0.22-µm Millipore filter and lyophilized. The lyophilized substances were used for antimicrobial activity assays. The antimicrobial activity was detected by agar diffusion assay, as described previously (Ouoba, Diawara, Traoré, & Møller, 2004), with some modifications. First, the indicator organisms (0.2 mL, approximate 10 8 cfu/mL) were spread onto
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Luria–Bertani (used for cultivating bacteria: E. coli ATCC 25922; S. aureus ATCC 63589; S. enterica CMCC 9812; S. dysenteriae CMCC (B) 50071; B. thuringiensis CMCC 9812) or potato dextrose (used for cultivating fungi: A. niger ACCC 30005; A. flavus CGMCC 3. 2890; R. nigricans AS3.4997; P. glaucum STL 3501) agar plates. The antimicrobial substances (200 µL, 1 g/mL) were then added into an Oxford cup (a stainless cylinder, outer diameter 7.8 ± 0.1 mm, inner diameter 6.0 ± 0.1 mm, and height 10.0 ± 0.1 mm) placed on the surface of the agar. Bacteria were cultured at 37 °C for 18 h. Fungi were cultured at 30 °C for 24 h. The size of the clear diffusion zone around the cup (including that of the Oxford cup) was measured with a Vernier caliper and reported in millimetres. The experiment was performed in triplicate.
2.3. Single factor experiments for optimization of the production of the antimicrobial substances The effects of single factors on the production of the antimicrobial substances were examined. The culture parameters were then further optimized using response surface methodology based on the results of single factor experiments. All assays were carried out in triplicate. Batch culture was carried out in 50 mL hermetical Erlenmeyer flasks containing culture broth. Because L. paracasei subsp. tolerans FX-6 was isolated from Tibetan kefir (a traditional fermented milk from Tibet, China), to simulate the original growth conditions of the strain, the sterilized pure milk (Nmyili, Huhehaote, China) was selected as the basic culture medium in the culture experiments. To study the effect of different factors on antimicrobial activity, six factors (culture time, temperature, initial pH, inoculum volume, carbon source, and nitrogen source) were selected for examination in single factor experiments. E. coli ATCC 25922 was selected as the indicator bacterium to assay the antibacterial activity of the lyophilized culture supernatants under different culture conditions. The levels of all factors other than the parameter being examined remained constant (unless otherwise stated, the parameters under examination were: temperature, 30 °C; initial pH, 7.0; inoculum volume (optical density 0.2 at 600 nm), 5% (v/v)). The single factor experiments were undertaken as follows. Culture time: the effect of culture time on antimicrobial activity was investigated using cultures incubated for 12, 24, 48, 60, 72, 84, 96, 108, and 120 h. Initial pH: the initial pH of the basic culture medium was adjusted to 3.0, 5.0, 7.0, or 9.0, and the culture was incubated for the optimal culture time as determined above. Culture temperature: the optimal culture temperature was examined by carrying out growth experiments at 20, 25, 30, 35, 40, or 45 °C, for the optimal culture time and at the optimal initial pH as determined above. Inoculum volume (optical density 0.2 at 600 nm): the effect of the initial inoculum volume was studied using inoculums of 1, 3, 5, 7, 9, or 11% (v/v), with incubation for the optimal culture time, at the optimal initial pH, and the optimal culture temperature as determined above. Carbon source: lactose, glucose, maltose, and sucrose were selected to study the effect of carbon source on antimicrobial activity. Each of the carbon sources was added to the culture medium at a concentration of 2% (w/v), and the cultures were
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Journal of Functional Foods 18 (2015) 244–253
incubated under the optimal conditions as determined above. The optimal carbon source was then selected and studied at 1, 3, 4, or 5% (w/v). Nitrogen source: Peptone, beef extract, yeast powder, fish meal, soybean protein, ammonium nitrate and ammonium sulphate were selected to study the effect of different nitrogen sources on antimicrobial activity. Each of the nitrogen sources was added to the culture medium at a concentration of 2% (w/ v), and the cultures were incubated under the optimal conditions as determined above. The optimal nitrogen source was then selected and studied at 1, 2, 3, 4, or 5% (w/v).
2.4. Response surface methodology and statistical analysis The results of the single factor experiments were analysed by variance analysis. The single factors which had a significant effect on antimicrobial activity (P < 0.05) were selected as significant independent variables. The Box–Behnken design under response surface methodology was employed to further optimize the significant independent variables selected above. Experimental runs were carried out randomly and the data were analysed by the response surface regression procedure to fit the second-order polynomial equation model generated by the Design-Expert software (Version 8.05b, Stat-Ease, Minneapolis, MN, USA). The general polynomial equation (1) (Guo et al., 2012) was as follows: n
n
i=1
i=1
n −1
Y = β 0 + ∑ βi Xi + ∑ βii Xi2 + ∑
n
∑ β XX ij
i
j
2.7. HPLC
Characterization of the antimicrobial substances by
The antimicrobial substances produced at the optimal culture conditions were analysed by HPLC. The substances were filtered through a 0.22-µm membrane filter and separated by a reversed-phase C-18 column (Agela, Tianjin, China) (5 µm, 100 Å, 4.6 mm × 250 mm). Elution was performed using a linear gradient of 5–10% methanol with 0.1% trifluoroacetic acid for 40 min. The flow rate was 1 mL/min. The eluted peaks were monitored at 214 and 280 nm, collected, concentrated, and lyophilized. The antimicrobial activities of the eluted peaks were tested by a microdilution technique using 96-well plates (Rahman & Gray, 2005) with E. coli ATCC 25922 as the indicator bacterium. Nisin (Beijing Daniel Spulber Biological Technology Co., LTD, Beijing, China) was selected as the positive control. All assays were carried out in triplicate. The molecular mass of the active sub-fractions was analysed by ABI 4800 MALDI-TOF-MS (Shanghai Applied Protein Technology Co. Ltd, Shanghai, China).
(1)
i=1 j=i+1
in which Y is the dependent variable (diameter of inhibition zone), Xi and Xj represent the independent variables, β0 is constant, and βi, βii, and βij are coefficients of linear, quadratic and interaction terms, respectively. n represents the number of independent variables. The quality of the model fitting was evaluated by R2 and analysis of variance (ANOVA). Finally, three additional confirmation experiments under the optimal conditions were conducted to verify the validity of the statistical model.
2.5.
(3 mg/mL, pH 2.0; Sigma) at 37 °C for 2 h to test the effect of the enzymes on antimicrobial activity. For the heat treatment test, the antimicrobial substances (200 µL, 1 g/mL) were incubated at 80 or 100 °C for 60 min, or at 121 °C for 20 min. The antimicrobial activity of all the treated samples was determined by agar diffusion assay, as described above, using E. coli ATCC 25922 as the indicator strain. All assays were carried out in triplicate.
Determination of antimicrobial spectrum
The antimicrobial spectrum of the active substances was analysed by agar diffusion assay, as described above. The activity of the antimicrobial substances against the indicator organisms (E. coli ATCC 25922; S. aureus ATCC 63589; S. enterica CMCC 9812; S. dysenteriae CMCC (B) 50071; B. thuringiensis CMCC 9812; A. niger ACCC 30005; A. flavus CGMCC 3. 2890; R. nigricans AS3.4997; P. glaucum STL 3501) was examined using lyophilized culture supernatant from L. paracasei subsp. tolerans FX-6 cultures grown under the optimal culture conditions as determined above. All assays were carried out in triplicate.
2.6. Effect of enzymes and heat treatment on the antimicrobial substances The antimicrobial substances (200 µL, 1 g/mL) were treated with trypsin (3 mg/mL, pH 8.0; Sigma, St. Louis, MO, USA) or pepsin
2.8.
Statistical analysis
All assays were carried out in triplicate and the results were expressed as mean values ± standard deviation (SD). The data were analysed by using SPSS 18.0 statistical software.
3.
Results and discussion
3.1. Effects of culture conditions on the activity of the antimicrobial substances The results of assays investigating the effect of the six selected growth factors (culture time, temperature, initial pH, inoculum volume, carbon source, and nitrogen source) are shown in Fig. 1. Based on a comprehensive analysis of these results, the optimum conditions for production of the antimicrobial substances were determined as follows: culture time, 72 h (Fig. 1a); temperature, 30 °C (Fig. 1b); initial pH, 7.0 (Fig. 1c); inoculum volume, 9% (v/v) (Fig. 1d); carbon source, 3% (w/v) glucose (Fig. 1e and f); and nitrogen source, 2% (w/v) yeast powder (Fig. 1g and h). The results of the variance analysis on the six selected growth factors showed that the culture time, inoculum volume, and nitrogen source did not produce significant effects on the antimicrobial activity (P > 0.05). However, the temperature, initial pH, and carbon source significantly affected the antimicrobial activity of the lyophilized culture supernatant (P < 0.05). Temperature, initial pH, and glucose concentration were therefore selected as independent variables for further study.
Journal of Functional Foods 18 (2015) 244–253
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Fig. 1 – Effect of culture time (a), temperature (b), initial pH (c), inoculum volume (d), carbon source (e, f), and nitrogen source (g, h) on antimicrobial activity. Data represent mean ± SD of 3 replicates. Asterisk indicates statistical significance (p < 0.05).
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Table 1 – Levels of the independent variables and Box–Behnken design arrangement. Data represent mean ± SD of 3 replicates. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Variables A: Initial pH
B: Temperature (°C)
C: Glucose (w/v, %)
−1(6) −1(6) 1(8) 1(8) 0(7) 0(7) 0(7) 0(7) −1(6) 1(8) −1(6) 1(8) 0(7) 0(7) 0(7)
−1(25) 1(35) −1(25) 1(35) −1(25) 1(35) −1(25) 1(35) 0(30) 0(30) 0(30) 0(30) 0(30) 0(30) 0(30)
0(3) 0(3) 0(3) 0(3) −1(2) −1(2) 1(4) 1(4) −1(2) −1(2) 1(4) 1(4) 0(3) 0(3) 0(3)
Inhibition zone diameter (mm) 21.80 ± 0.46 22.98 ± 0.23 23.74 ± 0.11 23.02 ± 0.07 20.60 ± 0.24 21.42 ± 0.17 23.60 ± 0.14 23.16 ± 0.07 19.76 ± 0.45 21.35 ± 0.14 22.82 ± 0.06 24.20 ± 0.18 24.31 ± 0.19 24.52 ± 0.23 24.41 ± 0.07
3.2. Optimization of the culture conditions by response surface methodology
Y = 24.41 + 0.11A + 0.62B + 1.33C − 0.48 AB
Based on the results of variance analysis of the single factor experiments, initial pH (A), temperature (B), and glucose concentration (C) were chosen as independent variables. The range and centre point values for the three independent variables are presented in Table 1. For the other factors, culture time was set to 72 h, inoculum volume was set to 9% (v/v), and amount of yeast powder was set to 2% (w/v). A three-factor threelevel Box–Behnken design with 15 runs containing three replications at the centre point was carried out. The experimental data listed in Table 1 were analysed through multiple regression using Design-Expert 8.05b. The relationship between dependent variable (Y) and three independent variables, initial pH (A), temperature (B), and glucose concentration (C), was determined using the following second-order polynomial equation (2).
The model predicted that optimal culture conditions for the three significant independent variables were as follows: an initial pH of 6.84, a temperature of 32 °C, and a glucose concentration of 3.44% (w/v). At the optimal culture conditions, the predicted value of the zone of inhibition was 24.82 mm. The results of ANOVA are shown in Table 2. The model had a very high F-value (F = 53) and a very low P-value (P = 0.0002), which implied that the model fits the experimental data very well (Zhong & Wang, 2010). There is only a very low possibility (0.02%) that a model with an F-value this high could occur by chance. Both the determination coefficient (R2 = 0.9897) and the adjusted determination coefficient (R2Adj = 0.971) were satisfactory; exhibiting a low experimental error and a well-fit regression equation (Liu, Du, Yuan, & Zhu, 2009). The F-value
(2)
− 0.32 AC − 0.053BC − 0.68 A2 − 0.85B2 − 1.54C2
Table 2 – Results of variance analysis. Source
Sum of squares
df
Mean square
F-value
P-value Prob > F
Significant
Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total R2 = 0.9897
30.290602 0.0882 3.0628125 14.177813 0.9025 0.3969 0.011025 1.7220006 2.6390006 8.7046314 0.3164917 0.294425 0.0220667 30.607093 R2Adj = 0.9710
9 1 1 1 1 1 1 1 1 1 5 3 2 14
3.365622 0.0882 3.062813 14.17781 0.9025 0.3969 0.011025 1.722001 2.639001 8.704631 0.063298 0.098142 0.011033
53 1.4 48 224 14 6.3 0.2 27 42 138
0.0002 0.2909 0.0009 <0.0001 0.0129 0.0542 0.6937 0.0034 0.0013 <0.0001
**
8.9
0.1027
* Significant (P < 0.05). ** Highly significant (P < 0.001).
** ** *
* * ** not significant
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(8.9) and P-value (0.1027) of lack-of-fit indicated that the lack-of-fit was not significantly relative to the pure error (Sharma, Singh, & Dilbaghi, 2009). Among the model terms, the temperature (B), glucose concentration (C), and quadratic term of glucose (C2) had highly significant effects on antibacterial activity (P < 0.001).
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The interaction term for initial pH-temperature (AB), quadratic term of initial pH (A2), and quadratic term of temperature (B2) all had significant effects (P < 0.05) (Qiao et al., 2009). The three-dimensional response surface plots of the interactions between two variables among the three factors are shown in Fig. 2.
Fig. 2 – Response surface plots of the effects of three factors on antibacterial activity. (a) Interaction of A (initial pH) and B (temperature). (b) Interaction of A (initial pH) and C (glucose). (c) Interaction of B (temperature) and C (glucose).
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Table 3 – Effects of temperature and enzyme treatment on the activity of the antimicrobial substances against Escherichia coli. Data represent mean ± SD of 3 replicates. Treatment Temperature (°C)
Enzymes
Diameter of inhibitory zone (mm) 80 (60 min) 100 (60 min) 121 (20 min) Pepsin Trypsin
24.35 ± 0.71 23.59 ± 0.42 23.60 ± 0.29 22.85 ± 0.65* 23.43 ± 0.36
* Significant (P < 0.05). **Highly significant (P < 0.001).
3.3.
Confirmation of optimal culture conditions
To confirm the validity of the model, three experiments were carried out at initial pH 6.84, temperature 32 °C, glucose of 3.44% (w/v), culture time of 72 h, inoculum volume of 9% (v/v), and yeast powder concentration of 2% (w/v). The mean diameter of the zone of inhibition generated by the antimicrobial substances from the confirmation experiments was 24.25 mm, a measurement very close to the predicted value of 24.82 mm, indicating the accuracy of the model. Under the optimal culture conditions, the size of the zone of inhibition increased by 82% compared with the zone of inhibition for cultures grown under the original non-optimal conditions (The zone of inhibition was 13.32 mm under the original non-optimal conditions). Our results demonstrated that the optimized conditions produced significant enhancement on the production of the antimicrobial substances.
3.4. Effects of enzyme and high temperature treatments on antimicrobial activity Resistance to enzyme degradation and high temperature treatment is an important trait for antimicrobial substances that need to be used in a wide range of food products because the antimicrobial activity should not be significantly decreased during food processing and storage. The effects of enzymes and high temperature treatments on the antimicrobial substances against E. coli were analysed, and the results are shown in Table 3. Our results showed that the antimicrobial substances were highly heat-tolerant, with antibacterial activity showing no significant changes even after incubation at 100 °C for 60 min or 121 °C for 20 min. The treatment with pepsin or trypsin did not cause significant change in the antimicrobial activity. Overall, these results indicated that the antimicrobial substances produced showed high resistance to high temperature thermal treatments and degradation by digestive enzymes, therefore establishing the antimicrobial substances as a promising candidate for use as preservatives in many food products.
3.5. Antimicrobial spectrum of the antimicrobial substances The antimicrobial spectrum of the antimicrobial substances was determined by agar diffusion assay, and the results are shown in Fig. 3 and Table 4. We observed that the antimicrobial substances had a wide antibacterial spectrum. This was evidenced by the significant inhibition of both Gram-positive (S. aureus and B. thuringiensis) and Gram-negative (E. coli,
Fig. 3 – Antimicrobial activity spectrum of the antimicrobial substances (all assays were carried out in triplicate).
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Table 4 – Antimicrobial activity spectrum of the antimicrobial substances. Indicator bacterium
Antimicrobial activity
Escherichia coli Staphylococcus aureus Bacillus thuringiensis Salmonella enterica Shigella dysenteriae Aspergillus flavus Aspergillus niger Rhizopus nigricans Penicillium glaucum
+++ +++ +++ +++ +++ + +++ ++ ++
+: diameter of inhibitory zone 5–10 mm; ++: diameter of inhibitory zone 10–20 mm; +++: diameter of inhibitory zone >20 mm.
Salmonella enteric and S. dysenteriae) bacteria. In addition, the antimicrobial substances also showed an inhibition of fungi (A. flavus, A. niger, R. nigricans, and P. glaucum), which was a little weaker than that of bacteria. Some of the reported antimicrobial peptides did not have antifungal activity, such as Pardaxin (Oren & Shai, 1996), Melittin (Oren & Shai, 1996) and Ceratotoxin (Marri, Dallai, & Marchini, 1996). The main reason is that fungi are multicellular organisms with a hard layer of cell wall, which makes them resistant to many chemical drugs. The antimicrobial substances from lactic acid bacteria usually have relatively narrow antimicrobial spectra based on previous reports. Many of the antimicrobial substances produced from lactic acid bacteria had no significant inhibitory activity against Gram-negative bacteria (Gao, Jia, Gao, & Tan, 2010). Other antimicrobial substances from lactic acid bacteria have shown inhibition on bacteria but not on fungi (Messaoudi et al., 2012). It was also reported that certain antimicrobial substances from lactic acid bacteria only had antifungal activity but no activity on bacteria (Hassan & Bullerman, 2008). Our results demonstrated that the antimicrobial substances obtained in the current study possessed inhibitory effects against the majority of food borne pathogens and spoilage bacteria.
3.6. Analysis of the active components in antimicrobial substances The antimicrobial substances were separated into several fractions by HPLC. Each fraction was collected, concentrated and tested for antimicrobial activity by microdilution technique with E. coli as an indicator strain. Three active fractions were identified (Fig. 4). Three active fractions were all dual wavelength absorption, and the absorption in 280 nm is stronger than in 214 nm. The peak at about 3 min was also dual wavelength absorption, but the peak did not have antimicrobial activity. Table 5 shows the results of the antimicrobial activity of HPLCisolated active fractions and nisin (positive control) against E. coli. Active components in peak 1 (62.5 µg/mL), peak 2 (500 µg/mL) and peak 3 (125 µg/mL) showed significant antibacterial activity. Among the active peaks, the antibacterial activity of peak 1 and peak 3 were significantly stronger than the antimicrobial activity of the positive control nisin (250 µg/mL). So we analysed the molecular mass of peak 1 and peak 3 by MALDITOF-MS. Peak 1 had the same molecular mass as bacteriocin
Table 5 – The inhibitory activities of sub-fractions isolated from antimicrobial substances against Escherichia coli. Data represent mean ± SD of 3 replicates. Active peaks
Antimicrobial activity (µg/mL)
Peak 1 Peak 2 Peak 3 Nisin
62.5 ± 2.03 500 ± 4.67 125 ± 2.71 250 ± 3.52
F1 (2113.842 Da), which was a bacteriocin we had found in previous research (Miao et al., 2014). The molecular mass of peak 3 was 2727.9849 Da. Nisin is produced by Lactococcus lactis subsp. lactis and was the first bacteriocin approved for use in food (da Silva Malheiros, Daroit, & Brandelli, 2010). In the current study, the antimicrobial substances were produced by L. paracasei subsp. tolerans FX-6 isolated from Tibetan kefir, a traditional fermented milk from Tibet, China. The long history of human consumption of kefir suggests that antimicrobial substances should be safe for human consumption. Most importantly, our results demonstrated that the sub-fractions of the antimicrobial substances produced by L. paracasei subsp. tolerans FX-6 had much stronger antimicrobial effects than nisin, a widely used food preservative. Further study is warranted to identify the chemical structures of the active components responsible for the antimicrobial activities.
4.
Conclusions
In conclusion, we have developed a method to produce the antimicrobial substances from the fermentation of probiotic L. paracasei subsp. tolerans FX-6. Our results demonstrated that the antimicrobial substances had high tolerance against thermal treatment and digestive enzyme degradation, and it possessed potent antimicrobial activity against a wide spectrum of pathogens and spoilage microorganisms. The active components of the antimicrobial substances showed stronger efficacy than the commercially used food preservative nisin. Overall, our results provide a solid basis to use the antimicrobial substances from L. paracasei subsp. tolerans FX-6 as a labelfriendly natural food preservative.
Conflict of interest The authors have declared that there is no conflict of interest.
Acknowledgements The authors would like to express their sincere gratitude to the National Natural Science Foundation of China (No. 31171768) for the financial support of this research.
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Journal of Functional Foods 18 (2015) 244–253
mV
MPa
350 35.0 300 30.0 250
peak 3
200
25.0
peak 1 20.0
150 15.0
peak 2 100
10.0 50 5.0 0 0.0 2.5
5.0
7.5
10.0
12.5
min
A
M=2727.9849 Da
B Fig. 4 – Analysis of the components of the antimicrobial substances by HPLC. Elution was carried out on by a reversedphase C-18 column (Agela, Tianjin, China) (5 µm, 100 Å, 4.6 mm × 250 mm) with a linear gradient of 5–10% methanol with 0.1% trifluoroacetic acid for 40 min. The eluted peaks were monitored at 214 and 280 nm (The red line and the black line represent the 214 nm and 280 nm, respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Journal of Functional Foods 18 (2015) 244–253
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