Accepted Manuscript Bioprocess development for the production of sonorensin by Bacillus sonorensis MT93 and its application as a food preservative Lipsy Chopra, Gurdeep Singh, Kautilya Kumar Jena, Himanshu Verma, Debendra K. Sahoo PII: DOI: Reference:

S0960-8524(14)01537-5 http://dx.doi.org/10.1016/j.biortech.2014.10.105 BITE 14148

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

27 August 2014 19 October 2014 20 October 2014

Please cite this article as: Chopra, L., Singh, G., Jena, K.K., Verma, H., Sahoo, D.K., Bioprocess development for the production of sonorensin by Bacillus sonorensis MT93 and its application as a food preservative, Bioresource Technology (2014), doi: http://dx.doi.org/10.1016/j.biortech.2014.10.105

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Title: Bioprocess development for the production of sonorensin by Bacillus sonorensis MT93 and its application as a food preservative

Author names and affiliations.

Lipsy Chopra, Gurdeep Singh, Kautilya Kumar Jena, Himanshu Verma, Debendra K. Sahoo* Biochemical Engineering Research and Process Development Centre CSIR - Institute of Microbial Technology, Chandigarh–160 036, India

Corresponding author.

Debendra K. Sahoo Senior Principal Scientist CSIR - Institute of Microbial Technology Sector–39 A, Chandigarh–160036, INDIA Phone: +91 172 6665324 FAX: +91 172 2690585 Email: [email protected]

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Bioprocess development for the production of sonorensin by Bacillus sonorensis MT93 and its application as a food preservative Lipsy Chopra, Gurdeep Singh, Kautilya Kumar Jena, Himanshu Verma and Debendra K. Sahoo* Biochemical Engineering Research and Process Development Centre CSIR-Institute of Microbial Technology, Sector-39A Chandigarh-160036, INDIA Abstract Media composition and environmental conditions were optimized using statistical tools, Plackett Burman design and Response Surface Methodology, to maximize the yield of a bacteriocin, named as sonorensin, from a new marine isolate Bacillus sonorensis MT93 showing broad spectrum of antimicrobial activity. Under optimized conditions, MT93 produced 15-fold higher yield of sonorensin compared to that under initial fermentation conditions. As oxygen supply is a critical parameter controlling growth and product formation in aerobic bioprocesses and used as a parameter for bioprocess scale up, the effects of oxygen transfer, in terms of volumetric oxygen transfer coefficient (kLa), on production of sonorensin was investigated using optimized medium composition in a bioreactor. Studies on effectiveness of sonorensin against Staphyloccus aureus and Listeria monocytogenes in fruit juice and as a preservative in pasteurized milk demonstrated its potential as a biopreservative in fruit products and shelf life extender of the pasteurized milk. Keywords: Bacillus sonorensis, sonorensin, optimization, volumetric oxygen transfer coefficient, biopreservative * Corresponding author: Email: [email protected]; Tel: +91 172 6665324

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1. Introduction In recent years, bacteriocins have gained attention as a promising therapeutics against antibiotic resistant pathogens such as methicillin resistant S. aureus and as anti-spoilage agent against bacteria like L. monocytogenes. Bacteriocins are ribosomally synthesized and biologically active, peptides that inhibit or kill microorganisms which are usually, but not always, closely related to the producer strain (Klaenhammer, 1988). Besides therapeutic applications, bacteriocins are of great interest to food industry as they are used as food biopreservatives to control food borne and spoilage bacteria especially L.monocytogenes, S. aureus etc. Bacillus is an interesting genus to investigate for antimicrobial activity since Bacillus spp. produce a broad spectrum of bioactive peptides including lipopeptides which act as biosurfactants and peptide antibiotics with great potential for biotechnological and biopharmaceutical application, (Al-Ajlani et al., 2007). These potentials of Bacillus spp. (e.g., Bacillus cereus, Bacillus subtilis) have been recognized for more than 50 years and peptide antibiotics represent the predominant class of antibiotics produced by this genus (Nacleiro et al., 1993). The primary objective of process development is economical production of the desired product and in fermentation based products, optimization of medium composition and culture conditions to maximize product yield become essential. Several reports have described the influence of temperature, pH and media composition on the production and activity of bacteriocins (De Vuyst, 1995; Krier et al., 1998) including those produced by lactic acid bacteria, such as Lactococcus lactis (De Vuyst, 1995), Leuconostoc mesenteroides (Krier et al., 1998). Though good cell growth frequently goes hand in hand with bacteriocin production (Baker, 1996), there are reports where optimal cell not resulting in high bacteriocin production (Bogovic-Matijasic and Rogeljl, 1998). As optimization of various parameters for a bioproduct formation is generally case specific and all the ingredients of a complex medium

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are often not required for the production of bacteriocins (Han et al. 2011), the effects of medium constituents and culture conditions on bacteriocin yield and activity need to be studied to optimize sonorensin production and economize its production cost. Optimization of medium composition by the classical method involves changing one independent variable while fixing all others at a fixed level. This is extremely timeconsuming and expensive for a large number of variables and also may result in wrong conclusions (Adinarayana et al., 2003). Statistical experimental designs such as factorial design and response surface analysis fulfil this requirement. Response surface methodology (RSM) is a collection of statistical techniques for designing experiments, building models, evaluating the effects of factors, and searching optimum conditions of factors for desirable responses (Li et al., 2001). The main advantage of RSM is the small number of experimental trials required to evaluate multiple parameters and taking in to account their interactions (Chow et al., 1998). It has been successfully applied in many areas of biotechnology such as optimization of media compositions and culture conditions for production of enzymes such as proteases and other proteins (Gheshlaghi et al., 2005; Kohli and Sahoo, 2010), conditions of enzymatic hydrolysis (Choudhari and Singhal, 2008), enzyme synthesis (Ismail et al., 1998), lactic acid esterification (Kiran et al., 1999), aqueous two-phase extraction of bacteriocin (Li et al., 2001) etc. Plackett-Burman design (PBD) is a 2-level experimental design that requires fewer runs than a comparable fractional design and can be used to identify the significant factors among many candidate factors. In aerobic microbial cultivation processes, product formation is often limited by oxygen transfer. However the oxygen demand varies from species to species and the oxygen transfer rate (OTR), which is the function of the volumetric mass transfer coefficient, kLa, and the oxygen solubility in the medium, depends on the fluid physical properties, temperature, pressure, solution composition, agitation, oxygen superficial gas velocity and the

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configuration of the reactor (Puthli et al., 2005). Oxygen supply/ aeration efficiency depends on solubilization and diffusion of oxygen into the broth, as well as on the capacity of the bioreactor to satisfy oxygen demands of the microbial population and in aerobic fermentations, it constitutes a decisive factor for microorganism growth and plays an important role in the scale-up of bioprocesses. Biopreservation refers to the extension of the shelf-life and improvement of the safety of foods using microorganisms and/ or their metabolites (Ross et al., 2002). Growth of unwanted bacteria in fruit and vegetable juices during storage may cause spoilage due, in particular, to the formation of ropiness and off-flavours (Settanni and Corsetti, 2007). The application of bacteriocins as biopreservatives for vegetable food matrices started approximately 20 years ago. In recent years, a number of studies are focused on the inhibition of spoilage and/or human pathogen bacteria vehiculated with vegetable foods and beverages by bacteriocins and their application appeared as a good alternative to chemical preservatives and antibiotics. Grande et al. (2005) demonstrated the efficacy of enterocin AS-48 for the control of Alicyclobacillus acidoterrestris in fruit juice. Spore formers and psychrotrophic microorganisms may multiply in food during its storage, thus affecting its safety and quality. Storage of pasteurized milk leads to spoilage problems like bitty cream, off-flavors or sweet curdling which decrease its shelf-life and substantial economic losses. Bacteriocins, such as nisin, are used to control the growth of undesirable microbes including spoilage and pathogenic bacteria in milk and to keep the milk products more acceptable to consumer. We have earlier reported the isolation, purification and characterization of a bacteriocin named as sonorensin, predicted to be the first bacteriocin of sub-family heterocycloanthracin from a new marine isolate Bacillus sonorensis MT93 (Chopra et al., 2014). The objectives of this work were to evaluate the effects of the medium components as well as important process parameters on sonorensin production and to search for the optimal medium

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composition and culture conditions for enhancing sonorensin yield. Furthermore, the predicted model of RSM for sonorensin production was validated both at shake flask as well as 5 L bioreactor scale. The study also reports the effect of volumetric oxygen transfer coefficient (kLa), an important parameter for scale up of an aerobic process like bacteriocin fermentation, on sonorensin production. The efficacy of sonorensin as a biopreservative in fruit products and as shelf life extender of pasteurised milk was also evaluated. Materials and methods 2.1 Reagents and media All the reagents and media components used were either analytical grade or of highest purity grade available in India. 2.2 Bacterial strains Bacillus sonorensis MT93 (accession number HF944961.1) was isolated from marine soil sample collected from Parangipettai (Lat. 11 o 30'N, Long. 079o 47'E), India as described previously (Chopra et al., 2014). Indicator strains S. aureus (MTCC 1430), and L. monocytogenes (MTCC 839) were procured from Microbial Type Culture Collection (MTCC), Chandigarh, India. 2.3 Bacteriocin production The inoculum was prepared in 20 ml of sterile media containing peptone 5.0 g/l, sodium chloride 5.0 g/l beef extract 1.5 g/l and yeast extract 1.5 g/l under shaking at 200 RPM and 30OC for 18 h. Then, 5.0 ml of this inoculum was transferred to 2 L Erlenmeyer flasks containing 500 ml of medium and incubated in a orbital shaker at 200 RPM and 30oC for 34 h. Aliquots of 1ml withdrawn at different time intervals were centrifuged at 10,000×g for 10 min. Then the culture supernatants were filtered through sterile 0.22 µm membranes and stored/used in antimicrobial assay. 2.4 Antimicrobial activity assay

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The antimicrobial activity was detected by agar disk diffusion assay as described by Kimura et al., (1998). The bacteriocin titer was determined by the serial two-fold dilution method described by Mayr-Harting et al., (1972) and its activity was defined as the reciprocal of the dilution after the last serial dilution giving a zone of inhibition and expressed as activity units (AU) per millilitre. 2.5 Experimental design The statistical analysis of the data was performed using Design Expert statistical software version 9.0.3 (Stat–Ease Inc, Minneapolis, MN). 2.5.1 Plackett-Burman experimental design (PBD): The purpose of the first optimization step was to identify important ingredients of the culture medium. The PB design was used to evaluate the relative importance of various nutrients for sonorensin production in batch fermentation. This design was used to screen the important variables affecting the sonorensin production in relatively few experiments as compared to the one factor at a time technique. 11 media components i.e. yeast extract (A), peptone (B), beef extract (C), sucrose (D), tryptone (E), dipotassium hydrogen phosphate (F), sodium bicarbonate (G), glucose (H), soyapeptone (J), magnesium chloride (K) and sodium chloride (L) were selected which were expected to have influence on sonorensin production. Each of the 11 factors were examined in two levels: low (−1) and high (+1) levels based on PlackettBurman matrix design, which is a fraction of a two-level factorial design and allows the investigation of “n−1” variables in at least “n” experiment. The lower and higher levels of each variable and the design matrix are shown in Table 1a & b respectively. 2.5.2 Central Composite Rotatable Design (CCRD) After the identification of the medium components significantly affecting sonorensin yield, a CCD was adopted to optimize the major variables (yeast extract, peptone and beef extract) selected through PB design. A factorial, central composite rotary design (CCRD) for three

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factors with replicates at the centre point and star points was used for optimization of fermentation medium. The variables were used at five coded levels (-α, -1, 0, +1, +α) each as shown in Table 2. All experiments were carried out at 30 oC and pH 7 for 34 h. The CCRD experiments contained a total of 20 experimental trials that include eight trials for factorial design, six trials for axial points (two for each variable) and six trials for replication of the central points (Table 3). The central values (zero level) chosen for experimental design were: yeast extract, 13 g/l; peptone, 6.5 g/l; and beef extract, 13 g/l. CCRD experimental designs for optimizing process parameters were also conducted and are presented in Table 4. The three process conditions chosen were temperature, pH and rate of agitation. The statistical software package, Design-Expert 9.0.3 was used for the analysis of experimental data and to plot response surface. ANOVA was used to estimate the statistical parameters for optimization of fermentation medium (Supplementary Material, Table S1) and fermentation conditions (Supplementary Material, Table S2). The relationship among the three variables was determined by fitting the second order polynomial equation to bacteriocin responses obtained from 20 experiments Y = β0 + β1x1 + β2x2 + β3x3 + β11x21 + β22x22 + β33x23 +β12x1x2 + β13x1x3 + β23x2x3 where Y represents predicted response, β0 is the intercept term, β1x1, β2x2, β3x3 are linear coefficients, β11x21, β22x22, β33x23 are the quadratic coefficients and β12x1x2, β13x1x3, β23x2x3 are the coefficients of interaction effect. The experiments were performed in triplicates with the mean values taken for analysis. 2.6

Production of sonorensin in a bioreactor

In order to validate the optimised medium composition in a bioreactor, production of sonorensin was conducted using 5.0 L bioreactor (Bioflow 310, New Brunswick Scientific, USA) with a working volume of 2.0 L. Production experiments were carried out using

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optimised media at 40oC, initial pH of 7.5 and agitation rate of 200 RPM. Following the inoculation of the culture medium with 5% (v/v) of the seed culture, dissolved oxygen concentration (DOC) of the fermented broth was continuously monitored using a dissolved oxygen probe (Mettler-Toledo International, Inc., Switzerland). The influence of rates of aeration and agitation on cell growth and sonorensin production in bioreactor cultivation was investigated at agitation rates in the range of 50 to 200 rpm (keeping aeration rate constant at 0.25 vvm) and aeration in the range of 0.25 to 1.0 vvm (keeping agitation rate constant at 200 rpm), respectively. Samples from the cultivation broth were withdrawn at regular time intervals and processed for analysis of sonorensin production and cell growth. The batches were harvested after 34 h. 2.7

Determination of volumetric oxygen transfer coefficient (kLa)

The volumetric oxygen transfer coefficient (kLa) was measured at 40oC in bioreactor in cellfree sterile medium by the static gassing-out method (Pal et al., 2013). Initially, nitrogen was sparged into the sterile medium until the dissolved oxygen concentration fell to zero and then, the humidified air was sparged into the bioreactor while monitoring the dissolved oxygen concentration and this process was continued till oxygen concentration in the liquid reached saturation. The rate of increase in DO concentration, dCL/dt = kLa (C* - CL)………………… (1) Where CL and C*, respectively, are DOC in fermentation media and at saturation condition, dCL/dt is the change in DOC over a time‘t’, kLa is volumetric oxygen transfer coefficient. Since, C*, the equilibrium (saturated) concentration of oxygen is constant at fixed temperature and pressure, the integration of above equation would yield, ln [(C*-CL)/C*]= - kLa.t ………………………………………………………….... (2) A plot between ln [(C*-CL)/C*] versus ‘t’ would produce a straight line and the value of its slope would be volumetric mass transfer coefficient, kLa.

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In our study, the value of kLa was determined at agitation rates in the range of 50 to 200 rpm (keeping aeration rate constant at 0.25 vvm) and aeration rates in the range of 0.25 to 1.0 vvm (keeping agitation rate constant at 200 rpm) by plotting ln(C* − CL) against time (t) in each case. 2.8

Determination of preservative effect of sonorensin

The fresh fruit juice (mixture of orange and pineapple) was filter sterilized (0.2µm) and inoculated with S. aureus and L. monocytogenes separately at 108 CFU/ml. Initial counts of inoculated samples were recorded followed by the addition of purified sonorensin (Chopra et al., 2014) and nisin (Himedia, India) separately into above said fruit juice at different concentrations. After 24 h, 36 h and 48 h, samples were withdrawn and platted on petri discs the plate count was recorded and compared with the control (fruit juice without any preservative). In order to study the role of sonorensin in prolonging shelf life of pasteurised milk, purified sonorensin and nisin were added separately to the pasteurised milk samples and the time taken for the spoilage/souring of milk was determined and compared with that of control milk (pasteurised milk without any preservative). 3

Results and discussion

3.1

Plackett-Burman (PB) design

The influence of eleven variables namely yeast extract (A), peptone (B), beef extract (C), sucrose (D), tryptone (E), dipotassium hydrogen phosphate (F), sodium bicarbonate (G), glucose (H), soyapeptone (J), magnesium chloride (K) and sodium chloride (L) on the production of sonorensin was investigated in 12 runs using Plackett-Burman design (Table 1b). The corresponding response for sonorensin production showed variations ranging from 2000 to 8000 AU/ml of sonorensin activity in these 12 trials. Among the variables screened, yeast extract, beef extract and peptone were observed to be the most effective factors for sonorensin production and were selected for further optimization. According to the regression

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analysis results of of Plackett-Burman design using Design-Expert 9.0.3, a first-order model could be obtained as shown: Y1 = +7117.65 + 541.67 * A + 625.00 * B + 375.00 * C - 41.67 * D - 1208.33 * E - 625.00 * F + 125.00 * G - 375.00 * H - 375.00 * J + 208.33 * K - 291.67 * L……………………. (3) where Y1 is the sonorensin activity (AU/ml), A-L are the variables (g/l) as mentioned before. The goodness of fit of the model could be checked by the coefficient of determination (R2), which provides a measure of how much variability in the observed response values can be explained by the experimental factors and their interactions. The R2 value is always between 0 and 1. The closer the R2 value to 1.00, the stronger the model is and the better it predicts the response. The linear regression model of sonorensin production was evaluated by the coefficient of determination, R2, which was 0.9729 indicating that 97.29 % of the variability in the response could be explained by the model. The high value of the adjusted determination coefficient (adjusted R2 = 0.9134) also indicated high significance of the model. These measures indicated that the accuracy and general ability of the polynomial model was good and that analysis of the response trends using the model was reasonable. In addition, the variable with confidence level above 95 % is considered as significant parameter. In the present case it was clear from equation (2) that yeast extract (A), peptone (B), beef extract (C), tryptone (E), dipotassium hydrogen phosphate (F), glucose (H), soyapeptone (J), and sodium chloride (L) were the significant factors with confidence levels above 95%. Among these parameters, yeast extract (A), peptone (B), beef extract (C) had positive effects on sonorensin production, while tryptone (E), dipotassium hydrogen phosphate (F), glucose (H) and soyapeptone (J) showed negative effects. Sodium chloride (L) had positive effects at low (-1) level but at high level produced negative impact on sonorensin production. 3.2

Central composite design

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The response surface methodology based on the central composite design was employed to determine the optimal levels of three variables i.e. yeast extract, peptone and beef extract. The respective low and high levels with the coded levels for the factors are presented in Table 2. The sodium chloride was chosen as a constant component and its concentration was fixed at low level. The design matrix and the experimental results were represented in Table 3. Adequacy and fitness of sonorensin activity were evaluated by ANOVA and calculations of regression coefficients are presented in Table S1(Supplementary Material). ANOVA analysis of sonorensin activity indicated that the confidence levels were greater than 95 %. The high F-value and a very low probability (p > F = 0.0001) indicated that the present model for sonorensin production showed good agreement between predicted and experimental results. The coefficient of determination (R2) was 0.9966 indicating that 99.66 % of the variability in the response could be explained by the model. The value of the adjusted determination coefficient (adjusted R2 = 0.9936) was also high. As the coefficient of variation (CV) indicates the degree of precision with which the treatments are compared (Zhou et al., 2011), the lower value of CV (2.84 %) demonstrated that the performed experiment was highly reliable. From the ANOVA analysis for sonorensin production, except for interaction terms AB and BC, all the other linear (A, B and C) or quadratic (A2, B2 and C2) and interaction term AC were statistically significant (p < 0.05) (Supplementary Material ,Table S1). Multiple regression analysis was used to analyze the data and a second-order polynomial equation was derived for optimization of medium composition for sonorensin production as follows: Y2 = +16657.00 + 1475.84 * A +1658.86 * B + 4079.68 * C + 155.50 * AB + 484.50 * AC + 164.50 * BC - 1475.09 * A2 - 2373.82 * B2 - 1242.45 * C2 ……………………………... (4) where Y2 is sonorensin activity, A is peptone (g/l), B is beef extract (g/l) and C is yeast extract (g/l).

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The 3D response surface plots described by the regression model were drawn to illustrate the interactive effects of each independent variable on sonorensin activity (Fig. 1 a-c). The significant effect of high concentration of yeast extract (24 g/l) on sonorensin production, as evident from Fig. 1, could be due to the availability of larger quantity of free amino acids, short peptides and growth factors (from yeast extract) that stimulated bacteriocin production (Aasen et al., 2000). The sonorensin activity gradually increased with addition of peptone and beef extract and reached maximum at concentration of 6.5 g/l of peptone and 13 g/l of beef extract. Based on the results obtained, optimized fermentation medium was found to be composed of (g/l): yeast extract 24.0 g/l, peptone 6.5 g/l, beef extract 13.0 g/l and sodium chloride 5.0 g/l. The cultivation of B. sonorensis MT93 in this medium resulted in 20480 AU/ml of sonorensin activity. 3.3

Optimization of bioprocess parameters

Following the optimization of media components, important bioprocess parameters such as pH, temperature and rate of agitation for sonorensin production were optimized at flask level using CCRD. Table 4 summarizes the CCRD matrix and the experimental and predicted values of sonorensin production. The sonorensin production varied markedly from 0 to 23680 AU/ml at different level of fermentation conditions. Based on the experimental results, a quadratic model for sonorensin production with variables such as temperature (A), pH (B) and agitation (C) was derived. The ANOVA for the selected quadratic model of the design for the optimization of above said fermentation parameters is presented in Table S2 (Supplementary Material). The overall second-order polynomial equation for sonorensin production at optimized environmental conditions could be presented as follows: Y3 = +23089.14 + 8164.75 * A + 562.35 * B + 251.35 * C + 960.00 * AB + 160.00 * AC + 160.00 * BC - 4280.93 * A2 - 8467.00 * B2 - 773.68 * C2……………………………….. (5) where Y3 is sonorensin activity, A is temperature (oC), B is pH and C is agitation (RPM).

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The Model F-value of 102.81 implied that the model for sonorensin production was significant. The goodness of the model was checked by the coefficient of determination (R2) which was calculated to be 0.9893. This value of R2 (0.9893) reflected a good agreement between the experimental and the predicted responses and hence, it was considered reasonable to use the regression model to analyze trends of the responses. The value of “p > F” less than 0.05 indicated that the model terms were also significant. Furthermore, the linear term A, and quadratic terms A2 and B2 were also observed to be significant. The 3D response surface plots were drawn to illustrate the interactive effects of each fermentation parameter (variable) on sonorensin activity (Fig. 2 a-c). From the 3D response surface plots, the optimal values of the independent variables and the corresponding sonorensin activity could be predicted, and the interaction between each pair of independent variables could be understood. High level factorial point (+1) temperature (40oC) and zero level point of pH (7.5) improved to the level of maximum sonorensin production. There was no sonorensin production under low level star point of pH (3.98) and temperature (13.18 oC). The agitation rate had little effect on sonorensin production. Further, interaction of low level factorial points (-1) of temperature and pH could not support the production of sonorensin due to the failure of bacterial growth under extreme environmental conditions. So, it could be reasonably speculated that the optimum temperature might induce the associated genes expression and ⁄ or activate the key enzymes that dominate sonorensin synthesis by MT93. Higher and lower values of pH were not beneficial to sonorensin production (Fig. 2). This result suggested that the broth pH could play a crucial role in the production of bacteriocins, which showed good agreement with previous reports (Buckland et al. 1989; Yang et al. 2001). Perturbation graph showed that temperature played an important role in sonorensin production and the sonorensin concentration increased to maximum at higher temperature (40 oC). Response

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surface graph for sonorensin activity suggested that highest level of sonorensin production could be obtained at 40oC and pH 7.5 (Fig.2a). The above results are in accordance with the production of bacillocin 490 by B. licheniformis (Martirani et al., 2002) and lichenin by B. licheniformis 26 L-10/3RA (Pattnaik et al., 2001) where high level of bacteriocin production was observed at suboptimal growth temperature. However, in case of Lactobacillus strains higher volumetric production of bacteriocin has been reported to be achieved by lowering the growth temperature from 30oC to 25oC (Todorov and Dicks, 2004; Delgado et al., 2005). Investigations on the effect of pH on sonorensin production indicated maximum sonorensin activity when initial pH of the medium was pH 7.5. In previous studies, optimum bacteriocin production by B. licheniformis strains were reported to be between the pH 6.5 and 7.0 (Pattnaik et al., 2001; Martirani et al., 2002) and maximum micrococcin GO5 production was obtained at pH 7.0 to 9.0 (Kim et al., 2006). Using optimized medium composition and under optimised process conditions, B. sonorensis MT93 produced 23,680 AU/ml of sonorensin activity against S. aureus (MTCC 1430) which was 15-fold higher than the sonorensin activity obtained using initial growth media. 3.4

Model validation

The model for sonorensin production was validated by performing shake flask fermentations with the predicted optimum medium composition and process parameters: yeast extract 24.0 g/l, peptone 6.5 g/l, beef extract 13.0 g/l and sodium chloride 5.0 g/l at 40 oC and pH 7.5. The actual experimental data indicated production of 23,680 U/ml of sonorensin which was in well agreement with the model predicted value of sonorensin activity (24,712 U/ml). However, for economical production of a biomolecule such as sonorensin, the bacteriocin needs to be produced in large scale using bioreactors. 3.5

Production of sonorensin at bioreactor scale

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In order to verify the feasibility of using optimized medium composition (obtained at flask level) for large scale production, sonorensin production was carried out in a 5 L bioreactor using this optimised medium. A maximum sonorensin activity of 32,000 AU/ml was obtained in bioreactor at the end of 34 h, at aeration and agitation rates of 0.25 vvm and 200 RPM, respectively, indicating that optimum medium composition obtained by statistical experimental design in shake flasks could also be equally important at bioreactor scale. Furthermore, this higher production of sonorensin (32,000 AU/ml) in bioreactor over that in shake flask (23,680 AU/ml) could be due to high mass transfer rates, specifically due to high oxygen transfer on account of better control of bioprocess parameters. In aerobic bioprocesses, oxygen is a key substrate for cell growth and maintenance, protein secretion and formation of metabolites. Furthermore, the variation in agitation intensity or oxygen availability might have positive or negative influence on physiological state of the microorganisms, potentially leading to an impact upon cell growth and protein secretion among other effects (James et al., 2007). It could be observed from a typical profile of cell growth, sonorensin production and dissolved oxygen concentration (DOC) (Fig. 3) that DOC remained at very low level after 8 h of inoculation. Hence, the effects of oxygen availability in terms of rates of aeration and agitation on cell growth and production of sonorensin were investigated by cultivating B. sonorensis MT93 in a bioreactor to elucidate the implication of oxidative metabolism in sonorensin production. Investigations on the effect of aeration rate on cell growth and sonorensin production (Fig.4a) showed a high level of sonorensin activity (32,000 AU/ml) at 34 h where as the growth of B. sonorensis MT93 (2.8 x 10 10 CFU/ml) at 20 h was highest at aeration rate of 0.25 vvm. Increase in aeration rate above 0.25 vvm resulted in a decrease in sonorensin activity from 32,000 AU/ml (at 0. 25 vvm) to 23040 AU/ml, 19200 AU/ml and 17920 AU/ml at aeration rates of 0.5 vvm, 0.75 vvm and 1.0 vvm respectively. However, the maximum cell density of B. sonorensis MT93 was observed at

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aeration rate of 1.0 vvm (Fig. 4a). These results are in accordance with the production of Gramicidin S by Bacillus brevis where high level of Gramicidin S production and less cell density were observed at low aeration rates (Vandamme et al, 1981). Since, keeping agitation rate constant at 200 rpm, it was not possible to decrease aeration rate below 0.25 vvm (condition yielding maximum amount of sonorensin) due to limitation of bioreactor instrumentation, the effect of lower oxygen availability was investigated by keeping air flow rate at 0.25 vvm and varying the rates of agitation in the range 50-200 rpm. At constant aeration rate of 0.25 vvm, decrease in the rate of agitation from 200 rpm to 50 rpm led to decrease in sonorensin production though the value of sonorensin activity varied in a narrow range of 28140-32000 AU/ml under experimental conditions. However, the growth of B. sonorensis MT93 was significantly increased as the rate of agitation was increased from 50 to 200 RPM (Fig. 4b). It was also observed that the conditions to achieve maximum growth and maximum sonorensin production were different, maximum growth of 2.8 x 1010 CFU/ml was observed at 20 h while maximum sonorensin activity of 32,000 AU/ml was obtained at 34 h with a moderate aeration rate (0.25 vvm) under experimental conditions. However, as the effects of aeration (air flow) and agitation rates on oxygen transfer is dependent on the bioreactor design, volumetric oxygen transfer coefficient, kLa (a proportionality constant relating oxygen transfer rate to oxygen concentration gradient), representing the oxygen transfer capacity of system, is often used to study the effects of oxygen supply. Furthermore, kLa is also an important parameter used for bioprocess scale up. Hence, it was thought to interpret the effects of oxygen supply in terms of kLa. The static gassing out method was used for determination of volumetric oxygen transfer coefficient (kLa) at different aeration and agitation rates using sterile cell free optimised media (Table 5). The kLa was found to be significantly affecting sonorensin productivity with highest rates of sonorensin production

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being obtained at kLa value of 7.254±0.2 h-1 (0.25 vvm and 200 RPM), with a maximum activity of 32000 AU/ml obtained at 34 h of cultivation. 3.6

Biopreservative efficiency of sonorensin

The evaluation of preservative effect of purified sonorensin against S. aureus and L. monocytogenes in fresh fruit juice (Table 6) showed its effect to be increasing with the increase in the concentration of sonorenin. The reduction in the counts of L. monocytogenes was higher as compared to that of S. aureus. However, in case of control (without sonorensin), no reduction was observed in the counts of S. aureus and L. monocytogenes. On comparing the preservative efficiency of sonorensin with that of nisin (standard bacteriocin), it was observed that the results obtained with sonorensin were similar to those obtained with nisin. In another set of experiments, sonorensin showed its potential as a shelf life extender of pasteurised milk (Verka, India). The pasteurized milk (as control) stored under refrigerated condition was soured at 48 h while the souring of the milk occurred at 6 h when the same pasteurized milk sample was kept at 30 oC. In case of milk samples supplemented with sonorensin, it remained fresh up to 28 h at 30 oC and 10 days under refrigeration, results similar to those obtained with nisin. These results clearly indicated that sonorensin possessed desirable characteristics of a biopreservative, similar to that of nisin. 4. Conclusions The optimization of medium composition and process parameters for the production of sonorensin by B. sonorensis MT93 in shake flasks resulted in 15 fold increase in sonorensin activity (23,680 AU/ml). Production of sonorensin in a bioreactor further increased its production to 32000 AU/ml proving the chosen methods to be powerful tools for optimization of sonorensin production. Furthermore, sonorensin, predicted to be the first bacteriocin of subfamily of heterocycloanthracin, demonstrated its efficacy as food

18

biopreservative. The information obtained is considered fundamental and useful for development of an efficient large scale process for production of bacteriocins like sonorensin. Acknowledgements LC and GS acknowledge their fellowships from DST and CSIR, Government of India, respectively. References 1. Aasen, I.M., Moretro, T., Katla, T., Axelsson, L., Storrø, L., 2000. Influence of complex nutrients, temperature and pH on bacteriocin production by Lactobacillus sakei CCUG42687. Appl. Microbiol. Biotechnol. 53, 159–166. 2. Adinarayana, K., Ellaiah, P., Srinivasulu, B., Devi, R.B., Adinarayana, G., 2003. Response surface methodological approach to optimize the nutritional parameters for neomycin production by Streptomyces marinensis under solid-state fermentation. Process Biochem. 38, 1565–1572. 3. Al-Ajlani, M.M., Sheikh, M.A., Ahmad, Z., Hasnain, S., 2007. Production of surfactin from Bacillus subtilis MZ-7 grown on pharmamedia commercial medium. Microb. Cell Fact. 6 (17), 1-8. 4. Baker, R., Winkowski, K., Montville, T., 1996. pH-controlled fermentors to increase production of Leuconocin S by Leuconostoc paramesenteroides. Process Biochem., 31, 225-228. 5. Bogovic-Matijasic, B., Rogelj, I., 1998. Bacteriocin complex of Lactobacillus acidophilus LF221- production studies in MRS media at different pH values and effect against Lactobacillus helveticus ATCC 15009. Process Biochem. 33, 345-352. 6. Buckland, B., Gbewonyo, K., Hallada, T., Kaplan, L. and Masurekar, P., 1989. Production of lovastatin, an inhibitor of cholesterol accumulation in humans. In Novel

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15. Ismail, A., Soultani, S., Ghoul, M., 1998. Optimization of the enzymatic synthesis of butyl glucoside using response surface methodology. Biotechnol. Prog. 14, 874–878. 16. James, E.R., van Zyl, W.H., Gorgens, J.F., 2007. Increased production of Hepatitis surface antigen productivity by recombinant Aspergillums niger through optimization of agitation and dissolved oxygen concentration. Appl. Microbiol. Biotechnol. 75, 279-288. 17. Kim, M.H., Kong, Y.J., Beak, H., Hyun, H.H., 2006. Optimization of culture conditions and medium composition for the production of micrococcin GO5 by Micrococcus sp. GO5. J. Biotechnol. 121, 54–61. 18. Kimura, H., Sashihara, T., Matsusaki, H., Sonomoto, K., Ishizaki, A., 1998. Novel bacteriocin of Pediococcus sp. ISK-1 isolated from well-aged bed of fermented rice bran. Ann. N Y Acad. Sci. 864, 345–348. 19. Kiran, K.R., Karanth, N.G., Divakar, S., 1999. Preparation of stearoyl lactic acid ester catalyzed by lipases from Rhizomuco miehei and porcine pancreas optimization using response surface methodology. Appl. Microbiol. Biotechnol. 52, 579–584. 20. Klaenhammer, T.R., 1998. Bacteriocins of lactic acid bacteria. Biochimie 70, 337– 349. 21. Kohli, N., Sahoo D.K., 2010. A novel solvent-stable alkaline protease from newly isolated Stenotrophomonas maltophila: production, purification and characterization. J. Biotechnol. 150, 362. 22. Krier, A.M., Revol-Junelles, P., 1998. Germain, Influence of temperature and pH on production of two bacteriocins by Leuconostoc mesenteroides subsp mesenteroides FR52 during batch fermentation. Appl. Microbiol. Biotechnol. 50, 359–363. 23. Li, C., Bai J., Cai, Z., Ouyang, F., 2001. Optimization of a cultural medium for bacteriocin production by Lactococcus lactis using response surface methodology. J.

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Biotechnol. 93, 27–34. 24. Martirani, L., Varcamonti, M., Naclerio, G., De Felice, M., 2002. Purification and partial characterization of bacillocin 490, a novel bacteriocin produced by a thermophilic strain of Bacillus licheniformis. Microb. Cell Fact. 1, 1–5. 25. Mayr-Harting, A., Hedges, A.J., Berkeley, R.C.W., 1972. Methods for studying bacteriocins. Meth. Microbiol. 7A, 315-422. 26. Nacleiro, G., Ricca, E., Sacco, M., De Felice, M., 1993. Antimicrobial activity of a newly identified bacteriocin of Bacillus cereus. Appl. Environ. Microbiol. 59, 4313– 4316. 27. Pal, S., Choudhary, V., Kumar, A., Biswas, D., Mondal, A.K., Sahoo, D.K., 2013. Studies on xylitol production by metabolic pathway engineered Debaryomyces hansenii. Biores. Technol. 147:449-455. 28. Pattnaik, P., Kaushik, J.K., Grover, S., Batish, V.K., 2001. Purification and characterization of a bacteriocin-like compound (Lichenin) produced anaerobically by Bacillus licheniformis isolated from water buffalo. J. Appl. Microbiol. 91, 636–645. 29. Puthli, M.S., Rathod, V.K., Pandit, A.B., 2005. Gas---liquid mass transfer studies with triple impeller system on a laboratory scale bioreactor, Biochem. Eng.J. 23: 25 30. Ross, R.P., Morgan, S., Hill, C., 2002. Preservation and fermentation: past, present and future. Internat. J. of Food Microbiol. 79, 3–16. 31. Settanni, L., Corsetti, A., 2008. Application of bacteriocins in vegetable food biopreservation. Internat. J of Food Microbiol. 121:123–138. 32. Todorov, S.D., Dicks, L.M.T., 2004. Effect of medium components on bacteriocin production by Lactobacillus pentosus ST151BR, a strain isolated from beer produced by the fermentation of maize, barley and soy flour. World J. Microb. Biotechnol. 20, 643–650.

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33. Vandamme, E.J., Leyman, D., de Visscher, P., de Buyser, D., Vansteenkiste, G., 1981. Effect of aeration and pH on Gramicidin S production by Bacillus brevis. J. Chem. Tech. Biotechnol. 31, 247-257. 34. Yang, X.F., Yang, H.W., Jian, H. and Liu, Z., 2001. Effect of fermentation conditions on antibiotic production of Xenorhabdus nematophilus. J. Chin. Microbiol. 28, 12–16. 35. Zhou, J.Y., Yu, X.J., Ding, C., Wang, Z.P., Zhou, Q.Q., Pao, H., Cai, W.M., 2011. Optimization of phenol degradation by Candida tropicalis Z-04 using PlackettBurman design and response surface methodology. J. Environ. Sci. 23, 22-30.

23

Figure captions Figure 1: Response surface 3D plots for sonorensin activity. Interaction of (a) yeast extract with peptone; (b) beef extract with peptone and (c) yeast extract with beef extract. Figure 2: Response surface 3D plots for sonorensin activity. Interaction of (a) pH with temperature; (b) agitation with temperature and (c) agitation with pH. Figure 3: Time profile of growth of B. sonorensis MT93, dissolved oxygen concentration (DOC) and sonorensin activity in bioreactor cultivation using optimized medium at aeration rate of 0.25 vvm and agitation rate of 200 rpm. Figure 4: (a) Effect of aeration on sonorensin production ( ) and growth of B. sonorensis MT93 ( )in bioreactor cultivation using optimized medium at agitation rate of 200 RPM; (b) Effect of agitation on sonorensin production ( ) and growth of B. sonorensis MT93 ( ) in bioreactor cultivation using optimized medium at 0.25 vvm.

24

Table 1a:

Levels of the variables tested in the Plackett-Burman design

Variables

Levels of variables -1

+1

A-Yeast extract

5

20

B-Peptone C-Beef extract

5 5

20 20

D-Sucrose E-Tryptone

1 5

5 20

F-Dipotassium hydrogen phosphate G-Sodium bicarbonate

1 1

5 5

H-glucose J-Soyapeptone

1 5

5 20

K-Magnesium chloride L-Sodium chloride

3 3

15 15

Table 1b: Run

A

The matrix of Plackett–Burman design and the observed and predicted values B

1 2

1 -1

3 4

1 -1 1 -1 1 -1

5 6

1 1

7 8

1 1

C

D

-1 -1 1 1

E

F

G

H

J

K

L

Actual

Predicted

values

values

-1 1 -1 -1

-1 -1

1 1

1 -1

-1 1

1 1

5000 7000

5000 7000

1 -1 1 -1

-1 1

-1 1

1 1

-1 -1

1 -1

4000 3500

4000 3500

1 1

1 -1

-1 1

-1 1

-1 -1

1 1

-1 1

5000 4000

5000 4000

- 1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1

1 -1

-1 -1

1 -1

1 -1

1 -1

4000 6000

4000 6000

1 1

1 -1 1 -1 -1 -1

9 10

1 -1 -1 1

1 1

1 -1

-1 1

1 1

1 1

1 -1

-1 -1

-1 -1

-1 1

6000 4000

6000 4000

11 12

-1 -1 1 1

1 1

-1 1 1 -1 -1 -1

-1 1

1 -1

1 1

1 1

-1 -1

2000 8000

2000 8000

25

Table 2

Variables and their levels for optimization of fermentation medium

Factor

Low level Low level star point Factorial (-1)

Centre point High level High level (0) factorial (+1) star point

(-α)

(+α)

(A) Fermentation medium (g/l) A-Peptone

0.61

3

6.5

10

12.39

B-Beef extract

1.23

6

13

20

24.77

C-Yeast extract 1.23

6

13

20

24.77

(B) Fermentation conditions A-Temperature

13.18

20

30

40

46.81

B-pH

3.3

5

7.5

10

11.7

C-Agitation

31.8

100

200

300

368.2

26

Table 3

Run

Experimental design and results of central composite design for optimization of fermentation medium

A

B

C

Activity (AU/ml) Actual

Predicted

1

+1

+1

-1

10204

10127.15

2 3

0 0

0 0

-α 0

6400 16640

6281.64 16657.00

4 5

0 +1

0 -1

0 +1

16640 15360

16657.00 15626.80

6 7

-1 -α

+1 0

+1 0

15360 10204

15352.84 10002.76

8 9

0 0

0 0

0 0

16640 16640

16657.00 16657.00

10 11

0 -1

0 -1

0 -1

16640 5120

16657.00 5155.76

12 13

0 +1

-α +1

0 +1

7680 19200

7152.96 19584.52

14 15

0 0

0 0

+α 0

20480 16640

20004.00 16657.00

16 17

-1 +α

-1 0

+1 0

11520 15360

12017.13 14966.87

18 19

+1 0

-1 +α

-1 0

7680 12800

7833.47 12732.67

20

+1

-1

-1

6400

6827.44

27

Table 4

Experimental design and results of central composite design for optimization of fermentation conditions

Run

A

B

C

Activity (AU/ml) Actual

Predicted

1 2

0 0

+α 0

0 0

0 23040

86.60 23089.14

3 4

0 +1

-α +1

0 +1

0 20480

-1804.93 19825.98

5 6

+1 0

-1 0

-1 0

16000 23040

15958.57 23089.14

7 8

0 -α

0 0

-α 0

21120 0

20478.11 -2750.59

9 10

0 0

0 0

+α 0

22400 23040

21323.56 23089.14

11 12

-1 0

+1 0

+1 0

0 23040

1256.48 23089.14

13 14

-1 +1

+1 -1

-1 +1

0 16000

753.77 16461.27

15 16

-1 -1

-1 -1

+1 -1

0 0

1731.77 1869.06

17 18

+1 +α

+1 0

-1 0

19200 23680

18683.28 24712.26

19 20

0 0

0 0

0 0

23040 23040

23089.14 23089.14

28

Table 5

Summary of experimental results obtained in batch cultures

Experimental condition

Aeration (vvm)

-1 Agitation (RPM) KLa (h )

CFU/ml (10 10)

Activity U/ml)

0.25

50

1.704±0.1

1.34

28140

0.25

100

1.992±0.1

2.38

29440

0.25

150

4.032±0.1

2.65

30720

0.25

200

7.254±0.2

2.8

32000

0.5

200

14.523±0.3

3.1

23040

1

200

29.354±0.4

3.3

17920

29

Table 6 Comparison of preservative effect of sonorensin and nisin against S. aureus and L. monocytogenes in fruit juice after 48 h Preservative effect of sonorensin Preservative (U) Control

S. aureus CFU/ml %*

L. monocytogenes CFU/ml %*

Preservative effect of nisin

S. aureus CFU/ml %*

L. monocytogenes CFU/ml %*

9.72*1011

0

4.8*10 11

0

9.72*10 11

0

4.8*10 11

0

200

7.95*1011

18.2

3.53*1011

26.4

7.89*10 11

18.8

3.49*10 11

27.2

400

4.92*1011

49.4

1.87*1011

61.2

4.84*10 11

50.2

1.80*10 11

62.4

600

2.10*1011

78.7

4.7*10 10

90.2

2.0*1011

79.4

4.03*10 10

91.6

800

1.06*1011

89.1

4.3*109

99.1

9.03*10 10

90.7

2.48*109

99.5

1000

6.3*10 10

93.5

1.92*10 9

99.6

4.64*10 10

95.2

4.8*108

99.9

*Reduction of population (%) = Reduction in microbial count Total count in control

30

* 100

B: beef extract = 13 (g/L) A: peptone = 6.5 (g/ L)

C: yeast extract = 13 (g/L)

(a) (c)

(b)

Fig. 1

31

C: agitation = 200RPM A: temperature = 30oC

B:

(a)

pH (b)

(c) Fig. 2

32

=

7.5

100 3.5

30000

3.0

80 25000

2.5 60

20000

2.0 15000

1.5

40 10000

1.0 Cell Growth (O.D.600nm)

0.5

20 5000

Dissolved Oxygen Concentration(%) Sonorensin Activity (AU/ml)

0.0

0 0

5

10

15

20

25

30

Time (h)

Fig. 3

33

35

0

(a)

(b) Fig. 4

34

Highlights •

Optimization of sonorensin production by RSM led to15 fold increase in its yield

•

Sonorensin production in a bioreactor validated optimized medium composition

•

Sonorensin production was influenced by medium composition and process parameters

•

Sonorensin showed its potential as a biopreservative

35