Effects of aeration and pH gradient on nisin production.

Enzyme and Microbial Technology 29 (2001) 264 –273

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Effects of aeration and pH gradient on nisin production. A mathematical model M. L. Cabo*, M. A. Murado, MaP. Gonza´lez, L. Pastoriza. Marine Research Institute (CSIC), C/Eduardo Cabello, 6, 36208 Vigo (Pontevedra), Spain Received 28 July 2000; received in revised form 8 March 2001; accepted 22 May 2001

Abstract A study on the effects of aeration and pH on nisin production by Lactococcus lactis showed significant increases as either aeration or pH drop gradient were risen. Nisin production at the maximum biomass point quadrupled when the oxygen saturation percentage increased from 50 to 100%. This suggests the association of this bioproduction with an oxidative metabolic pathway. A procedure based on successive re-alkalizations of the culture with feeding with glucose at regular intervals was proposed. It was shown that pH drop gradient (VpH) enhances nisin production, which increased approximately four-fold, and increases the efficiency of nutrient consumption. By using conventional equations of microbial kinetics, with the only additional assumption that VpH has some effect on the growth rate of the microorganism, a model that describes the results successfully is proposed. This model enables nisin to be typified as a primary metabolite, which tends to acquire secondary nature when the productive period of the culture is prolonged by imposing a stepwise-pH profile. © 2001 Elsevier Science Inc. All rights reserved. Keywords: Nisin; pH; Production; Fermentation

1. Introduction Lactic acid bacteria (LAB) are one of the most important microbial groups for industrial purposes, since their fermentative activity involves a notable preserving ability as a result of the decrease of pH and the antimicrobial activity of metabolites such as lactic acid, ethanol, diacetyl or bacteriocins. Bacteriocins have a high interest for the food industry as they are inocuous, sensitive to digestive proteases and do not induce changes in the organoleptic properties of the food. However, only nisin is allowed as a food additive at the moment, and its use has widely extended during the last decade. Consequently, some studies related to nisin production are being carried out at present. Most of them have addressed the effects of pH, and to a lower extent those of variables such as temperature or aeration. Aeration has a special significance for nisin, as the oxygen tolerance of LAB is associated to different metabolic pathways which give rise to different yields. In this respect, * Corresponding author. Tel.: ⫹34-986-231930; fax: ⫹34-986292762. E-mail address: [email protected] (M.L. Cabo).

some variability was found when results of previous studies were compared. Thus, whereas some authors suggested the use of anaerobic conditions [1,2], some others found that an oxygen enriched-atmosphere (60% O2) enhanced nisin production considerably [3]. Otherwise, it seems clear that the effect of aeration depends on the bacteriocin under study. Thus, the production of amilovorin increases as the oxygen saturation percentage is raised from 40 to 80% [4], whilst the yield of sakacin markedly decreases if the culture is aerated [5]. With regard to pH, it is usually kept constant, the acids that are formed being neutralized. This is accomplished either by buffering the culture medium initially or by continuous addition of alkali [6,7,8]. However, there are quite a few discrepancies regarding which pH is optimum. Such discrepancies do not only depend on the species and culture medium. Although a pH range of 5.8 – 6.0 has been commonly proposed for nisin [1,3,7,9], it is not rare that pH values as high as 6.8 are reached [6,10]. Another matter results from the nutritional support provided by some buffers, e.g. phosphate or citrate, which do not allow to attribute the effects noticed to keeping a constant pH. Furthermore, no clear results have been achieved by the few studies that enabled pH to freely drop. Thus, Geis et al. [11] and Yang

0141-0229/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 1 - 0 2 2 9 ( 0 1 ) 0 0 3 7 8 - 7

M.L. Cabo et al. / Enzyme and Microbial Technology 29 (2001) 264 –273

and Ray [7] obtained the highest contents of nisin and pediocin, respectively, in non-buffered culture media, but De Vuyst and Vandamme [6] did not find significant differences between nisin production in buffered and nonbuffered media. From a metabolic point of view, bacteriocins are generally considered to be primary metabolites, that is, products that are formed at a rate that only depends on the growth rate [12]. These are the cases of nisin [6,9,13,14], mesenterocin [15], enterocin [16], lactostrepcin [17], lactocin [18], lactocin 27 [19] and leucocin LA54A [20]. However, some studies have considered pediocin AcH [21] or propionicin [22] to be secondary metabolites. Additionally, some others did not explicitly stated the secondary nature (lactacin B: 23; helveticin J: 24; lactocin S: 25), but supplied profiles showing production began at the end of the exponential phase and even continued during the subsequent stationary phase. The latter result would be unlikely to be compatible with a primary nature. Even though pH is widely recognized as one of the most relevant factors for bacteriocin production, there are still quite a few questions concerning which procedure is most suitable. To this respect, the objectives of the present work were to define the effects of aeration and pH on nisin production by Lactococcus lactis subsp. lactis in submerged culture. A mathematical model was developed to gain information about the kinetic-metabolic nature of nisin and to facilitate the optimization necessary for upscaling. This would also contribute to the few attempts that have been made to model bacteriocin production [16,26].

265

2.2. Experimental conditions and analyses In order to study the effects of aeration, a number of cultures with different gas transference coefficients were prepared by using 300 ml Erlenmeyer flasks containing different volumes of medium: 50, 100, 150, 200 and 300 ml. Oxygen pressure was determined by galvanometry at each sampling period. Realkalized-cultures were carried out in a 2 L-bioreactor at 30°C, an aeration rate of 1 vol 䡠 vol⫺1 䡠 min⫺1 and continuous-record of pH. Stepwise-pH profiles were obtained by re-alkalizing the cultures repeatedly up to a set pH with 2 N NaOH. Equal volumes of sterile distilled water were added to controls. A volume of 50 ml in 300 ml Erlenmeyer flasks was used in all other studies. Each sample, which comprised the whole experimental unit, was split in two aliquots. One of them was centrifuged (15000 g, 10 min) and the precipitate was collected, washed twice with distilled water, and dried at 106°C until constant weight, which was taken as the biomass of the culture. On the other hand, the supernatant was used to determine reducing sugar [28] and protein [29] contents. The other aliquot was used for extraction and quantification of nisin according to Cabo et al. [30]. All assays were in triplicate.

3. Results & discussion 3.1. Effects of aeration

2. Materials and methods 2.1. Microrganisms and culture media The nisin-producing strain was isolated from salmon sausages and genetically identified as Lactococcus lactis subsp. lactis (IIM Lb. 1.13). Bacteriocin gene was cloned and sequenced. The sequence corresponded to nisin A. The nisin-sensitive indicator strain (Leuconostoc mesenteroides subsp. lysis) was kindly provided by Dr. Ray (University of Wyoming, Laramie, USA). Stock cultures of both strains were stored at -50°C in powdered skimmed milk suspension containing 25% glycerol. Microorganisms were grown in TGE medium [27] at 30°C under orbital shaking at 200 rpm. In pH-constant cultures, the medium was buffered with 0.05 M biphtalateNaOH at a set value. This buffer was chosen in order to avoid to supply nutrients which could mask the effect of pH on production. Biphtalate is a very hardly assimilable carbon source, and only a moderate concentration was added. Inocula were prepared as cell suspensions in sterile distilled water adjusted to an optical density of 0.900 at 700 nm. The inoculum size was always 1% (v/v).

No significant differences either in biomass or nutrient consumption were found amongst the cultures with different initial volumes of medium (Fig. 1). On the contrary, nisin production stepped up with oxygen saturation percentage (pO2), and it even quadrupled within the range assayed (Fig. 2). The relationship between nisin production and pO2 was found to be more than lineal, but it did not fit to exponential equations suitably. This would indicate that production was approximately constant in part of the range of study, and changed markedly from a threshold pO2 value of ⬃80%. It is therefore clear that the suitable conditions for nisin production are far from those defined as typical (no aeration and moderate shaking) by De Vuyst and Vandamme [2] or those proposed much earlier by Hirsch [1], who suggested to apply strict anaerobiosis. However, they come closer to the conditions found by Amiali et al. [3], who found that a pO2 of 60% was optimum for nisin Z production. Although these discrepancies can be ascribed to variations amongst different strains, the results of the present study point to a direct effect of the dissolved oxygen on nisin production, with no correlative increase of biomass. This suggests that such a production is associated with an oxidative metabolic pathway.

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Fig. 1. Comparison amongst L. lactis cultures with different initial volumes of medium: 50 (䡬), 100 (䡺), 150 (䉫), 200 (ƒ) and 300 (F) mL. pO2: oxygen saturation percentage; B: biomass; BT: bacteriocin.

3.2. Effects of pH 3.2.1. Non-buffered versus partially buffered cultures From Fig. 3 a comparison can be made amongst the results for a non-buffered culture with an initial pH of 6.0

Fig. 2. Nisin production in L. lactis cultures subjected to different initial oxygen saturation percentages after 6 h-incubation. Notations follow the conventions of Fig. 1.

Fig. 3. Comparison amongst non-buffered (F) and partially-buffered (pH 4.5: 䡬; pH 5.0: 䡺; pH 6.0: 〫). cultures of L. lactis. G: glucose present. Notations follow the conventions of Fig. 1.

and those obtained for three cultures buffered at pHs 4.5, 5.0 and 6.0 with 0.05 M sodium biphtalate:NaOH. Although these latter cultures were only partially buffered, as a high biphtalate concentration was avoided, a noticeable buffering effect resulted in all cases, and this gave rise to pH profiles clearly different for each culture. The study of such profiles shows that: 1. The highest production of nisin was achieved in the control culture, which reached the lowest final pH. It is clear, however, that production is not enhanced by keeping an acid pH, since the lowest content was obtained in the culture buffered at pH 4.5, which also showed the most stable profile throughout the incubation period. 2. Although the cultures buffered at pHs 5.0 and 6.0 reached the same final pH, nisin production was noticeably higher in the latter. However, the initial pH has to be ruled out as a decisive factor too, since production was higher in the control, with an initial pH of 6.0, than in the culture buffered at that same pH. 3. Neither acid conditions nor initial pH can be considered causal factors, so only the pH drop gradient seems to be able to account for the production of nisin in the different cultures. It would also agree with the fact that production becomes stabilized once the drop of pH stops. Therefore, it can be accepted as a hypothesis that the pH

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drop gradient is a variable that has a positive effect on nisin production. 4. With regard to biomass production, it should be pointed out that although it was roughly proportional to nisin production in each culture, they did not follow a parallel variation in the different cultures. Thus, the highest content of nisin was achieved in the control, but the highest biomass occurred in the culture buffered at pH 6.0. 5. Finally, the highest carbohydrate consumption took place in the culture buffered at the highest pH (in agreement with the values for biomass) but it was very low, and between 50 and 60% of the initial glucose content was at least left over. It should also be pointed out that the content of nitrogen -the excess of which was even higher than that of carbon- remained essentially the same in all the cultures (results not shown). To verify the hypothesis proposed, two trials should be carried out next. Firstly, to compare the production of nisin, at a set incubation time, in a number of non-buffered cultures subjected to different pH drop gradients accomplished by allowing the pH to drop from different initial values. Secondly, to impose a stepwise-pH profile by re-alkalizing the culture repeatedly up to about the initial pH value. The latter would be expected to lead to contents higher than in non-buffered and partially buffered cultures. 3.2.2. Effects of pH drop gradient The results obtained for three cultures subjected to different pH drop gradients accomplished by using initial pH values of 6.0, 6.5 and 7.0 are shown in Fig. 4. The use of this moderate interval prevents possible interferences due to a loss of nisin activity at alkaline pH [31,32,33]. As expected, the highest nisin content was achieved in that culture subjected to the steepest pH gradient within the pH range of production (i.e. that with the highest initial pH). Correlative values were also noticed amongst pH gradient, biomass production and carbohydrate consumption (the latter was still moderate, with a maximum efficiency of ⬃37%). 3.2.3. Stepwise-pH profiles Subsequently, a comparison was made between a control with free drop of pH (from an initial value of 6.0) and a culture re-alkalized repeatedly up to the initial pH value once the lower steady pH was reached. In accordance with the profiles obtained previously, an incubation period of six hours was fixed for re-alkalization, and was maintained as long as the producing strain was able to bring about the decrease of pH. The results of this study confirmed once again the hypothesis proposed on the causal effect of the pH gradient. Thus, the active period was prolonged in the culture subjected to a stepwise-pH profile, and nisin production was about twice as high as that of the control (Fig. 5). With regard to nutrient consumption (Table 1), yields

Fig. 4. Comparison amongst L. lactis cultures subjected to different pH drop gradients by using initial pH values of: 7.0 (〫), 6.5 (䡬) and 6.0 (control: F). Notations follow the conventions of Figs. 1 and 3.

and efficiencies make evident that a stepwise-pH pattern also contributes to the fact that the process is much more balanced as far as availabilities and requirements are concerned. Thus, glucose is practically depleted, but only ⬃20% of the available protein content is consumed. This suggests the need to either balance the composition of the culture medium or carry out glucose fed-batch processes. If proteins are only source of nitrogen (nutrients), and have no specific role (e.g. as inductors) in the biosynthesis of nisin, excess protein puts up the price of the process unnecessarily, and can also become a hurdle in case nisin is to be purified. Moreover, the fact that the culture is in stationary phase during the last three hours of each step leads to think about reducing the interval between re-alkalizations, since it would likely lead to a further enhancement of the process. 3.2.4. Effects of initial pH on stepwise-pH cultures Previous results have shown that bacteriocin (BT) is produced as long as the gradient of pH (VpH: decrease in pH per unit of time) is negative and that the higher the absolute value of the gradient, the higher nisin production. Therefore, it can be provisionally assumed that BT ⬀ 兩-VpH兩. Adjusting the pH repeatedly up to 6.0 every 6 h lead to a longer period of production, so production doubled, and to a higher efficiency in nutrient consumption. A direct procedure to test such a hypothesis lies in comparing the effects of pH steps with different magnitudes or frequencies. These two resources increase the drift of pH throughout the incubation period. The effects of the former are shown in Fig. 6 by comparing three cultures with an

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Fig. 5. Comparison between L. lactis cultures with no control of pH (F) and re-alkalized repeatedly up to pH 6.0 (䡬). Gc: glucose consumed. Prc: protein consumed. Notations follow the conventions of previous figures.

Fig. 6. Comparison amongst L. lactis cultures with no re-alkalization (●) and with several re-alkalizations up to pH levels of 6.0 (䡬) and 7.0 (〫). Notations like in previous figures.

initial pH of 6.0 subjected to different pH profiles. The pH of the control dropped freely, whilst it was adjusted up to values of 6.0 and 7.0 in the other two once was close to leveling off. Re-alkalizing at different pH levels enhanced bacteriocin production, which was about three-fold (pH 6.0) and four-fold (pH 7.0) as high as that of the control. Furthermore, nutrient consumption efficiencies defined a much more balanced process as far as availabilities and requirements are concerned (Table 2). On the other hand, a com-

parison between two cultures with an initial pH of 6.0 that were re-alkalized every 3 and 6 h is shown in Fig. 7. Reducing the re-alkalization period from 6 to 3 h led to double nisin production. The results of both tests confirmed the hypothesis. Although not previously described for bacteriocins, it has been reported for at least two proteins (GroE and DnaK) related to the thermal shock response of E. coli that the

Table 1 Yields (production [AU/ml]/substrate consumed [g/l]) and efficiencies (substrate consumed [g/l])/initial substrate content [g/l]) for control and re-alkalized (up to pH 6.0) cultures of L. lactis Maximum yield

Control Re-alkalized

Table 2 Yields (production [AU/mL]/substrate consumed [g/l]) and efficiencies (substrate consumed [g/l])/initial substrate content [g/l]) of nutrient consumption in non-re-alkalized (control) and re-alkalized cultures Yield

Final efficiency

BT/Glucose

BT/Protein

Glucose

Protein

8.32 5.52

81.58 30.06

0.29 0.96

0.045 0.27

Control Stepwise at pH ⫽ 6 Stepwise at pH ⫽ 7

Efficiency

BT/Glucose

BT/Protein

Glucose

Protein

11.07 9.45 11.63

134.01 76.39 89.29

0.27 0.94 0.94

0.033 0.189 0.188

M.L. Cabo et al. / Enzyme and Microbial Technology 29 (2001) 264 –273

Fig. 7. Comparison amongst L. lactis cultures re-alkalized every 3 (䡬) and 6 h (●). Notations follow the conventions of previous figures.

biosynthesis of some metabolites is induced by the rates of extracelular pH variation between certain intervals [34,35]. Similarly, thermal and osmotic stresses were reported to have favourable effects on the production of amilovorin by Lactobacillus amylovorus [4,36]. It should be pointed out that the usual criterion of a constant pH being needed to optimize nisin production seems rather a consequence of a habit convenient in microbiological practice than an empirically-based conclusion. Sometimes, for instance when the culture medium is buffered with chemicals that can be nutritional sources, it is even difficult to uphold that a (relatively) constant pH is the only factor responsible for the noticed effects -which are not always of a great significance-. It is true that stabilizing the pH at a value higher than that reached at the stationary phase in non-buffered cultures contributes to step up nisin production. It seems, however, of a greater importance (Fig. 3) that nisin production in partially-buffered cultures cannot be suitably explained unless the role ascribed to the pH drop gradient is admitted. Recent results obtained by different authors can also be accounted for in terms of the effect of the gradient of pH. By comparing several cultures of Pediococcus acidilactidi with decreasing final pH values, Biswas et al. [27] found that biomass and pediocin production were highest in those cultures with the lowest final pH (i.e. with the highest pH gradient). These authors suggested that acid conditions promoted the activity of enzymes involved in the post-translational changes of the bacteriocin, which were responsible for it to turn into the active form. Furthermore, they pointed out that, even though biomass was notable, pediocin was not produced if the final pH had not decreased sufficiently. In disagreement with the usual view, they concluded that pe-

269

diocin is a secondary metabolite, since it was still produced during the stationary phase in cultures with sufficient pH drop. On the other hand, studies on lactocin production by Lactobacillus casei led to point out that it was a primary metabolite [18]. These authors also found that, within the initial pH range 4 –10, biosynthesis was maximum between 6.5–7.5. At higher pH values, decreases could be due to alkaline denaturation of lactocin, whilst the lower gradient of pH imposed on cultures would explain decreases at lower pH values. The latter seems even clearer when the inhibitory effects of NaNO2 and NaCl are considered. Thus, it was shown that lactocin production decreases as NaNO2 concentration increases, but also that the final pH of the culture increases (i.e. pH decreases) correlatively with the increase in nitrite concentration. Without coming in mechanisms about the role of pH (post-translational changes or any other), it is clear, from a strictly kinetic viewpoint, that the use of models that dissociate bacteriocin production from biomass production does not seem suitable for most results. In fact, although Biswas et al. [27] noticed an apparently independent variation of both variables, the final pH (i.e. VpH) also had an effect on biomass production (see below for a further discussion). 3.2.5. A descriptive model of the effect of pH on nisin production Graphs shown in Fig. 6 suggest, on the one hand, that both biomass and bacteriocin productions are logistic-type processes and, on the other hand, that the (negative) pH gradient has effects formally similar on both of them. It seems therefore reasonable that a model aiming to describe the effect of VpH on bacteriocin production must, firstly, describe the effects of VpH on the growth of the microorganism. In this respect, a conventional starting point consists in using: 1: To formulate the growth, the logistic equation: X⫽

冉

K K ; being: c ⫽ ln ⫺1 1 ⫹ e c⫺␮t X0

冊

(1)

X: biomass (x0: initial biomass) K: maximum biomass ␮: specific growth rate (biomass formed per unit of present biomass and per unit of time, dimensions T⫺1). 2: for the accumulation of bacteriocin, the classic model of Luedeking and Piret [12] r p ⫽ ␣ r X ⫹ ␤ X;

where

(2)

rp and rx: production rates for P (product) and X (biomass), respectively. ␣ and ␤: parameters to be experimentally determined The latter model is commonly expressed dividing both terms by biomass: rp rX ⫽ ␣ ⫹ ␤; X X

that is:

rp ⫽ ␣␮ ⫹ ␤ X

(3)

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This enables microbial metabolites to be classified in accordance to the following criterion: Primary metabolites: the production rate only depends on the rate of biomass production (growth rate): ␤ ⫽ 0. Secondary metabolites: the production rate only depends on the amount of biomass in the medium: ␣ ⫽ 0. Mixed metabolites: the production rate depends, at the same time, on the growth rate and on the biomass present: ␣ ⫽ 0 and ␤ ⫽ 0. In order to propose a functional expression that relates VpH and X, a simple and reasonable assumption would be to accept that VpH has an effect on the growth rate rx, so that: R X ⫽ r X共1 ⫹ bVpH兲;

where:

(4)

dX dt Rx: growth rate induced by pH variation b: constant ratio to be experimentally determined. ⌬pH VpH: decrease in pH per unit of time: VpH ⫽ ⌬t In differential terms, the logistic equation is expressed as rx: growth rate at constant pH: rX ⫽

冉

dX K⫺X rate ⫽ ⫽ ␮X dt K

冊

(5)

Fig. 8. Lower side: simulation of the pH gradient effect on L. lactis cultures. Upper side: simulation of biomass formation at constant pH (䡬) and with stepwise-pH profile (F). See text for details.

therefore, the model that is put forward would be:

冉

冊

dX K⫺X (1 ⫹ bVpH) rate ⫽ ⫽ ␮X dt K

(6)

If ␮ was constant, it could be expressed in the integral form: X⫽

K 1⫹e

关c⫺␮共1⫹bVpH兲t兴

(7)

However, the model implies that ␮ is not constant, so only a numerical solution can be attained. The growth of the microorganism can be simulated by generating a logistic growth (X) with equation (1) and an arbitrary set of values for the parameters, and by subsequently calculating the rates of growth at constant pH (rx) by numeric derivation, and then those at variable pH (Rx) by using equation (4). Finally, the biomass at variable pH (XR) is obtained by numeric integration of Rx. It is clear that to apply equation (4) requires the simulation of a stepwise-pH profile similar to those imposed in previous assays. Since such assays showed asymptotic drops of pH, an adequate resource to obtain such a profile is to assume a variation that is described by means of a von Bertalanffy-type equation: pH ⫽ pH f ⫹ ␣ e ⫺ct;

The results of this simulation (Fig. 8) show a biomass profile with a series of waves, which correspond to each pH cycle and are similar to those noticed in experimental studies (Figs. 6 and 7). The experimental results shown in Fig. 6 were then processed according to this procedure, and the Newton method was applied to calculate the coefficients (non-linear minimum squares). Assuming that the value for b was the same for all the three cases, the estimates of biomass by means of:

t⫽0

t

X

t⫽0

X

(9)

made evident a satisfactory correspondence with the experimental values (a value for b ⫽ 5.205 and a linear correlation coefficient between observed and expected values of 0.988 were obtained. Fig. 9, upper side). The numeric integration of equation (2) with respect to time, once the values for Rx and XR obtained from (4) and (9) were introduced, should also be the simultaneous solution for bacteriocin (BT) production under all the conditions assayed. That is:

with:

␣ ⫽ 共 pH 0 ⫺ pH f兲; being:

冘 r (1 ⫹ bVpH) ⫽ 冘 r t

XR ⫽

冘 ␣R t

(8)

pH0: initial pH or upper level of the pH range. pHf: final pH or lower (asymptotic) level of the pH range. c: rate of pH variation (it can vary in each pH cycle). t: time (it is zeroed at the beginning of each pH cycle).

BT ⫽

t⫽0

X

⫹ ␤XR

(10)

However, when the constancy of ␣ and ␤ for all the three cases is imposed as a restriction to fits, the correlation between observed and expected values is acceptable (r ⫽ 0,969), but production estimates (dotted lines in Fig. 9,

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Table 3 Estimates for the parameters of equation [10] from fits to experimental results for cultures subjected to different pH profiles Specific ␣ and ␤ values

␣ ␤

Common ␣ and ␤ values

Free pH drop

Stepwise at pH 6

Stepwise at pH 7

39.71 28.20

179.01 0

110.14 4.95

34.64 33.26

r*

0.969

0.977

K (at constant pH) ␮ (at constant pH)

0.441 0.321

* Correlation coefficient between expected and experimental values.

Fig. 9. Fits of models (9) and (10) to experimental results (symbols) for cultures subjected to different pH profiles: free pH drop (䡺) and stepwise-pH up to levels of 6.0 (E) and 7.0 (䉫). In lower figure, dotted lines correspond to fits by assuming ␣ and ␤ to be constants (mixed metabolite) in all cases, whilst unbroken lines correspond to fits by accepting specific ␣ and ␤ values for each case, i.e. primary metabolite if pH freely drops and mixed metabolite if a stepwise-pH profile is imposed.

lower side) are unsatisfactory, especially for the control culture. This can be sorted out if such a restriction is omitted, that is, if three independent fits are accomplished (unbroken lines in Fig. 9, lower side), which give rise to an overall correlation coefficient of r ⫽ 0.977 (with residues distributed without bias). Thus, the value of ␤ becomes null for the control and increases correlatively with the upper level of the pH step, i.e. with 兩VpH兩. Results are shown in Table 3. Although no model including the effects of pH on bacteriocin production was found, all models so far admit a relationship between production rate and growth rate (primary metabolites). However, this relationship is often corrected with a negative term that shows the decrease of activity noticed during the last stages of culture (ascribed to a physical adsorption process). This was the case for ente-

rocin [16], lactocin [13] and amilovorin [36] productions. On the contrary, such a decrease was not noticed in the present study. Moreover, plantaricin production [26] can be described by the Luedeking-Piret equation if the concentration of bacteriocin in the medium is accepted as a factor, so an auto-induction phenomenon would be admitted. The results of the present study show that the effects of pH on biomass and bacteriocin production can be adequately described by using conventional models of microbial kinetics with only one additional assumption, which is that the pH gradient (and not the absolute value of pH) is the factor that has some effect on the growth rate of the microorganism. It is also shown that nisin production, which is metabolically primary when pH drops freely, tends to become secondary when a stepwise-pH profile is imposed on the culture. The higher the gradient of pH, the more noticeable this trend. Although due to other factors, similar secondarizations were described for nisin production by Lactococcus lactis, in response to an increase of sucrose in the medium [6], and for plantaricin production by Lactobacillus plantarum, as a result of variations in the culture medium [26]. Finally, although different pH profiles yielded different bacteriocin productions with equal biomass [27], the present approach would be still valid. It would suffice to assume that VpH has an effect, with different coefficients, on biomass and therefore on the specific productivity of bacteriocin. The description of the system would therefore require, on the one hand, the equation [6] for biomass, and, on the other hand, a modification of the Luedeking-Piret model that includes a term (1 ⫹ gVpH) for bacteriocin production, where g would be a parameter representing the specific effect of VpH on productivity:

冉

冊

dX K⫺X ⫽ ␮X (1 ⫹ bVpH) dt K r p ⫽ 共 ␣ r X ⫹ ␤ X兲共1 ⫹ gVpH兲

(6) (11)

These formulae would also account for Parente and Ricciardi’s results [16], who proposed a model for enterocin production and found that the effect of the initial pH on

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Table 4 Yields and efficiencies of nutrient consumption for re-alkalized cultures with and without discontinuous feeding with glucose (units like in Table 2) Yield

Efficiency

BT/Glucose BT/Protein Glucose Protein Stepwise at pH ⫽ 7 11.63 Stepwise at pH ⫽ 7 ⫹ glucose 6.51

89.29 60.67

0.94 0.48

0.19 0.38

estimates of maximum biomass and on specific growth rates was less marked than on estimates of bacteriocin production. 3.2.6. Effect of glucose feeding on fed-batch cultures This model clearly suggests specific ways to enhance nisin production, and perhaps that of other bacteriocins too. Bearing in mind that the stepwise-pH profile leading to maximum efficiencies made only ⬃20% of the available protein content be consumed, whereas glucose was thoroughly depleted, it is to be expected that adding glucose at regular intervals would contribute to further enhancements of production. To verify this hypothesis, two cultures were subjected to a stepwise-pH profile with an upper level of 7.0, and at the same times as re-alkalizations, one of them was fed with a glucose solution. An intake of 1.15 g䡠L⫺1䡠h⫺1 was provided, which was the average glucose consumption during the productive phase in previous cultures. Equal volumes of distilled water were added to the other culture (control) As shown in Table 4 and Fig. 10, the results of this study confirmed the hypothesis and made evident that adding glucose increased bacteriocin production by a factor of 1.5 respect to a re-alkalized control (and of ⬃ 5 if not realkalized). The ability to recover the acid pH of the final stage of the culture was also lost much more gradually in the fed-batch culture. Moreover, the efficiency of protein consumption increased markedly too, but the apparent efficiency of glucose consumption decreased as a result of keeping the feeding rate constant with regard to the initial volume of the culture, even though it decreased due to samplings. The results of the present study therefore indicate that an adequate procedure for bacteriocin production should at least: a) To establish optimum levels for pH variation. b) To define the interval of re-alkalization in terms of a fixed minimum pH. In this respect, both the increase and the drop of pH might even be controlled by imposing the whole pH profile on the culture. c) To establish a glucose feeding rate for the fed-batch culture. Finally, it must be pointed out that subsequent results in our laboratory have confirmed that enhancements in biomass and/or bacteriocin production, in response to stepwise-pH profiles, is a characteristic rather widespread in lactic acid bacteria.

Fig. 10. Comparison amongst (up to pH 7.0) cultures re-alkalized with (䡬) and without (thick unbroken line) discontinuous feeding with glucose. Dotted line represents the amount of glucose accumulated. Notations follow the conventions of previous figures.

Acknowledgment The authors thank J. J. R. Herrera, L. Pastrana, L. Iglesias, and C. Sua´rez for their help in this work. This research was supported by The Xunta de Galicia (project XUGA40204B-96). Author M. L. Cabo was a pre-doctoral fellow of The Ministry of Education and Science.

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