Kinetics of hyaluronic acid production by Streptococcus zooepidemicus considering the effect of glucose

Biochemical Engineering Journal 49 (2010) 95–103

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Kinetics of hyaluronic acid production by Streptococcus zooepidemicus considering the effect of glucose Mashitah Mat Don ∗ , Noor Fazliani Shoparwe School of Chemical Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai South, Penang, Malaysia

a r t i c l e

i n f o

Article history: Received 25 June 2009 Received in revised form 26 November 2009 Accepted 5 December 2009

Keywords: Hyaluronic acid Stirred tank bioreactor Kinetics Mathematical models Submerged Biopolymer

a b s t r a c t An unstructured model of hyaluronic acid (HA) fermentation by Streptococcus zooepidemicus considering the effect of glucose was proposed and validated. Experiments were performed in a glucose concentration range of 10–60 g l−1 in a 2 l bioreactor of batch mode. Three different models, namely, the Logistic equations for cell growth, the Logistic incorporated Leudeking–Piret-like equation for glucose consumption, and the Logistic incorporated Leudeking–Piret equation with time delay, t, for HA productions were proposed. The kinetic parameters were estimated by fitting the experimental data to the models. Simulation was made using the estimated kinetics parameter values and was compared with the experimental data. For glucose inhibition, S. zooepidemicus tolerated up to 40 g l−1 glucose. Beyond this concentration, cell growth was inhibited. The Han and Levenspiel model and the Teissier-type model gave the best fit for all the systems studied with R2 of 0.997 and 0.985, respectively. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Hyaluronic acid (HA), a polysaccharide, is a high-value biopolymer with a wide variety of medical and cosmetic applications. HA belongs to a family of glycosaminoglycan, also known as mucopolysaccharide. This polymer is comprised of d-glucuronic acid (GlcUA) and N-acetylglucosamine (GlcNAc) linked by a ␤(13)-glycosidic bond, with the disaccharide repeating units linked by ␤(1-4)-glycosidic bonds [1,2]. Conventionally, it has been extracted from animal tissues such as rooster combs and bovine vitreous humor [3]. However due to limited tissue sources, risks of viral infection and high cost, HA production from microbial sources through the fermentation process has received increased attention, especially when using the gram-positive bacterium Streptococcus zooepidemicus [3–5]. S. zooepidemicus is a catalase-negative, facultative anaerobe but is also aerotolerant [6]. In sheep blood agar plates, colonies of these ␤-hemolytic bacteria will produce a clear zone with HA identified as mucoid or slimy translucent layer surrounding bacteria colonies. Under the microscope, these non-sporulating and non-motile bacteria appear as spherical or ovoid cells that are typically arranged in pairs or chains surrounded by an extensive extracellular capsule [5]. The capsule is composed of HA polymers identical to that found

∗ Corresponding author. Tel.: +60 4 5996468; fax: +60 4 5941013. E-mail addresses: [email protected], [email protected] (M.M. Don). 1369-703X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.bej.2009.12.001

in mammalian connective tissues [7]. The capsule may also protect the bacteria against reactive oxides released by white cells to fight off the bacteria [8]. Batch fermentation is a standard method for HA production, and several studies have been conducted regarding the optimal culture condition for HA production [7,9–12]. However, the principal disadvantages of batch HA fermentation is the inhibition of substrate and by-product concentration, a conventional property of batch fermentation [9]. Ding and Tan [14] reported that the production of aimed metabolites was limited due to the strong inhibition of substrates under high concentrations. Shuler and Kargi [13] previously stated that a high substrate or product concentrations and the presence of inhibitory substances in the medium inhibit growth. As reviewed by other researchers, the majority of growth, substrate consumption and product formation models in the literature involved Monod-like relationships which are taken into account for the inhibition kinetics of many fermentation processes [15,16]. Subsequently, the Logistic model and Luedeking–Piret equations considered by Jian et al. [17] have been developed to interpret the experimental observations of cell growth and the formation of the desired metabolic products. The use of several models that involved more than one substrate state variable has also been reported on HA production by S. zooepidemicus. Cooney et al. [18] proposed a structured model based on two compartments for HA fermentation under anaerobic and aerobic conditions. In addition, Richard and Margaritis [19] proposed an empirical model, whereas Huang et al.

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Nomenclature Notations OD optical density O2 oxygen HA hyaluronic acid HAMW hyaluronic acid molecular weight HPLC high performance liquid chromatography mM millimolar rpm rotation per minute SBA sheep blood agar R2 correlation coefficient (dimensionless) Ks Monod saturation constant (dimensionless) inhibition constant (dimensionless) Ki ms maintenance coefficient (gglucose gbiomass −1 h−1 ) t time delay (h) t time (h) 0 specific growth rate (h−1 ) maximum specific growth rate (h−1 ) max S substrate concentration (g l−1 ) S0 substrate concentration at time 0 (g l−1 ) T temperature (◦ C) X biomass concentration (g l−1 ) Xt biomass concentration at time t (h) X0 biomass concentration at time 0 (h) Xm maximum biomass concentration (g l−1 ) YHA/glu HA yield per glucose consumed (gHA gglu −1 ) YX/glu biomass yield per glucose consumed (gcell gglu −1 ) YP/X product yield per biomass produced (gproduct gbiomass −1 ) YX/S biomass yield per substrate consumed (gbiomass gsubstrate −1 )

[20] proposed a delayed growth-associated model for HA production. Recently, Liu et al. [21] reported a two-stage culture strategy model to enhance HA production in either a batch or fed-batch culture. The batch culture had a higher specific HA synthesis rate, while the fed-batch culture had a higher specific cell growth rate. The enhanced HA production by this model resulted from the decreased inhibition of cell growth and the increased transformation rate of sucrose to HA. In this study, mathematical models that described growth, substrate utilization and inhibition, and HA production were proposed, taking into account the effect of the initial glucose concentration on the kinetic parameters. To achieve this goal, experiments were performed at an initial glucose concentration range of 10–60 g l−1 in the batch fermentation. 2. Materials and methods 2.1. Microorganism Streptococcus equi subsp. zooepidemicus ATCC 39920 was obtained from the American Type Culture Collection (Rockville, MD) as a freeze-dried culture in ampoules. The strains were maintained by weekly transfer on sheep blood agar (SBA) and stored at 4 ◦ C after being incubated at 37 ◦ C for 24 h. Monthly subculture ensured the availability of sufficient stock cultures for the experimental processes. 2.2. Fermentation medium The composition of the medium used comprised the following (g l−1 ): glucose (10–60), yeast extract 10, KH2 PO4 0.5,

Na2 HPO4 ·12H2 O 1.5 and MgSO4 ·7H2 O 0.5. The medium was prepared and autoclaved at 121 ◦ C for 20 min. Glucose solution was autoclaved separately to avoid caramelisation, and mixed aseptically with the other components upon cooling [22]. 2.3. Cell suspension preparation Cell suspension for the inoculum was prepared by inoculating a stock culture of Streptococcus zooepidemicus onto SBA-plates and incubating overnight at 37 ◦ C. The colonies formed were punched using a sterile cork borer to obtain five round disks 0.85 cm in diameter. The disks were then put into a sampling bottle containing 50 ml of sterile distilled water. The sampling bottle was then vortexed for 3 min so that the cells were evenly distributed [22]. 2.4. Inoculum preparation The seed culture or inoculum was prepared by inoculating 15 ml of cell suspension into a 500 ml Erlenmayer flask containing 135 ml of the fermentation medium. The flask was incubated in a rotary shaker at 37 ◦ C, 250 rpm for 8 h. The inoculum was standardized by measuring the absorbance (optical density) at 600 nm using a spectrophotometer (Thermo Spectonic, Genesys 20). An inoculum with an optical density within 0.6–0.9 was used to inoculate the fermentation medium. 2.5. Fermentation Batch fermentation was carried out in a 2 l bioreactor (BBraun, Biotech International, Germany) with a working volume of 1.5 l. The initial glucose concentration in the medium was varied from 10 to 60 g l−1 . The pH was maintained at 7.0 ± 0.1 using 3 M NaOH and 3 M HCl. The agitation speed and temperature were set at 300 rpm, 37 ◦ C and aeration was maintained at 2.0 l min−1 . 2.6. Analytical procedure Samples were withdrawn at regular time intervals and analyzed for cell, glucose and product concentrations. Cell concentration was determined by measuring the optical density (OD) at 600 nm using a spectrophotometer (Thermo Spectonic, Genesys 20) and the dry cell weight method [21]. A correlation between dry cell weight and OD600 was established. Glucose and HA concentrations were analyzed by high performance liquid chromatography (HPLC) (Model: Perkin Elmer) system equipped with an ultrahydrogel linear column (7.8 mm × 300 mm) and a guard column (6 mm × 40 mm) (Waters Associates, Japan), and detected by refractive index detector. A hyaluronic acid standard was prepared using Streptococcal HA (Sigma, H-9390), and HA concentrations were determined using the method as described in previous research [9,22,23]. For the molecular weight of HA, gel permeation chromatography (GPC) equipment consisting of a pump connected to a 2414 refractive index (RI) detector and 2695 separation module were used. For best resolution, the gel permeation chromatography (GPC) was equipped with an Ultrahydrogel Linear (7.8 mm × 300 mm) and Ultrahydrogel 2000 column. The mobile phase used was degassed with 0.1 M NaNO3 and was operated at a flow rate of 0.8 ml min−1 at ambient temperature. Hyaluronic acid standards of 0.68, 1.6 and 1.8 MDa were prepared and tested. All data was acquired and processed using the Millennium GPC software as attached to the equipment.

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2.7. Proposed kinetics model

Simplifiying Eq. (6) gives:

2.7.1. Microbial growth During fermentation processes, microorganism growth becomes very complex. In order to demonstrate the inhibitory effect, the Logistic equation, a substrate independent method was used as an alternative empirical function [24]. Logistic equations are a set of equations that characterize growth in terms of carrying capacity [13] and the equation is utilized to describe the kinetics of several polysaccharides fermentation systems [25]. It can be described as in Eq. (1):

S = S0 −

dX = 0 X dt



X 1− Xm

(1)

(2)

Eq. (2) can also be generated by assuming that a toxin is generated as a by-product of the growth limit. 2.7.2. Substrate consumption Glucose was used as a substrate for growth and HA production by S. zooepidemicus. A generalized Logistic mass balance on glucose consumption was incorporated to the Leudeking–Piret-like equation (Eq. (3)) [15], where S (g l−1 ) is the substrate concentration, YX/S (gcell biomass gglucose −1 ) is the maximum yield coefficients, and ms (gglucose gcell biomass −1 h−1 ) is the maintenance coefficient. −

dS 1 dX = + mS X YX/S dt dt

(3)

Integration of Eq. (3) leads to:

S dS =

−

t

1

t dX + mS

YX/S 0

S0

Xdt

(4)

1 YX/S

t [X]t0 + mS

Xdt

(5)

0

Substituting Eq. (2) into Eq. (5), and integrating yields the following equation: − [S − S0 ] =

1



YX/S

t + mS

X0 Xm e0 t X0 Xm e0 −  t Xm − X0 + X0 e 0 Xm − X0 + X0 e0



(9)

X0 Xm e0 t YX/S (Xm − X0 + X0 e0 t )

Xm mS Xm − X0 + X0 e0 t X0 − ln YX/S m Xm

(10)

Eq. (10) represents a nonlinear relationship between substrate concentrations (S) and fermentation time (t). The parameters YX/S and ms can subsequently be estimated using a variety of nonlinear schemes as suggested by Razvi et al. [26]. 2.7.3. Product formation Product formation can be described using the Leudeking–Piret kinetics equation [15]. The rate of product formation has been shown to depend upon both the instantaneous biomass concentration, X, and growth rate dX/dt, in a linear fashion, such as: dP dX =˛ + ˇX dt dt

(11)

where the ˛ and ˇ values are empirical constants that may vary with the fermentation condition, as well as with the microbial strain, as shown in Table 1. These kinetic expressions have been proven to be extremely useful and versatile in fitting the product formation data from different types of fermentations, including those of biopolymers [27,28]. Huang et al. [20] stated that HA concentration increased proportionally to the increase in biomass during the fermentation process. This occurred especially during the exponential phase of the cell growth. A significant relationship was found between the specific HA production rate and the specific growth rate, which had been expected earlier with the growth-associated product formation. Thus Eq. (11) can be written as: dP dX = YP/X dt dt



P

(12)



t−t

dP = YP/X 0

dX dt dt

(13)

0−t

Solve Eq. (13) gives: P = YP/X [X]t−t 0−t

(14)

Substituting Eq. (2) into Eq. (14) gives: X0 Xm e0 t dt Xm − X0 + X0 e0 t

(6)

P = YP/X



0

T = em t

X0 Xm e0 t Xm − X0 + X0 e0 t

t−t (15) 0−t

Table 1 Empirical constants of product formation.

To solve the integration part in Eq. (6), we define: (7)

Parameter

Product formation pattern

(8)

If, ˛ = / 0 and ˇ = 0, If, ˛ = / 0 and ˇ = / 0 If, ˛ = 0 and ˇ = / 0

Product formation is growth associated Product formation is mixed-growth-associated Product formation is non-growth-associated

and differentiated Eq. (7) gives: dT = 0 e0 t dt



However, the formation of HA in a culture broth was found to be very low during the lag phase. This is due to the introduction of t, a parameter of the lag time, to describe the delay of HA production correlated to cell growth [20]. Thus Eq. (12) can be modified and solved using integrated form given as:

0

Thus, Eq. (4) can be solved as:

− [S − So ] =



Xm mS Xm − X0 + X0 e0 t ln 0 Xm

S = S0 − +

X0 Xm e0 t Xm − X0 + X0 e0 t

YX/S

X0 Xm e0 t − X0 Xm − X0 + X0 e0 t

So, the final equation for substrate utilization can be written as:



where Xm is the maximum cell biomass concentration that can be obtained with a particular cultivation system (strain, medium and cultivation conditions) corresponding to the maximum carrying capacity, and (1 − (X/Xm )) represents the unused carrying capacity [26]. The integrated form of Eq. (1) using X = X0 (at t = 0) gives a sigmoidal variation of (X) as a function of (t), which may present both the exponential and the stationary phase (Eq. (2)). X =

−



1

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Solving Eq. (15) yields the product formation, P as: P = YP/X



e−0 t

eo (t−t)

X0 Xm X0 Xm − Xm − X0 + X0 e0 (t−t) Xm − X0 + X0 e−0 t

 (16)

This equation also represents a nonlinear relationship between (P) and (t) with time delay (t) when it was combined with the analytical solution of the Logistic equation, namely, Eq. (2). 2.8. Model parameters estimation Kinetic equations which describe the activity of a microorganism on a particular substrate are crucial in understanding various phenomena in biotechnological processes [29,30]. It is important to note that most kinetic models and their integrated forms are nonlinear. This makes parameter estimation relatively difficult [31]. Although some of these models can be linearized, their use is limited because they transform the error associated with the dependent variable, making it normally distributed and therefore giving rise to inaccurate parameter estimations. Hence, the nonlinear least-squares regression is often used to estimate kinetic parameters from nonlinear expressions [30]. The parameter estimates obtained from the linearized kinetics expressions can be used as initial estimates in the iterative nonlinear least-squares regression using the Levenberg–Marquardt method [31]. In this study, the Polymath® Version 5.1 (CACHE Corpn., USA) was employed sequentially in order to estimate the value of the parameters within the nonlinear model equations (i.e. Eqs. (2), (10) and (16) from the experimental results. The parameters were evaluated using the nonlinear least square method of the Levenberg–Marquardt optimization, which was used to minimize the residual sum of squares. Each experiment was repeated twice, and the results were reported as averaged values. 2.9. Model validation Model validation is possibly the most important step in the model building sequence [32]. In order to validate the model, the kinetic parameter values obtained from Section 2.8 were used to simulate the profiles of cell growth, substrate utilization and product formation with other batch experimental data for various initial glucose concentrations. Simulation of the batch fermentation for each initial glucose concentrations in the bioreactor were performed using ordinary differential method of Polymath® Version 5.1 (CACHE Corpn., USA) software. A set of ordinary differential equation (ODE) was solved using the Runge-Kutta Fehlberg (RKF45) algorithm. The profiles from simulation of the models and experimental data were then evaluated using mean squares error (MSE). This performance criterion was chosen because they are easy to identify and have convenient mathematical properties. The MSE value can be calculated by the sum squares errors divided by the length of actual data period [32]:



MSE (%) =

NT (y I=1 i

− fi )2

nt

the Logistic equation (Eq. (2)) was fitted to represent the batch HA fermentation kinetics. By fitting the experimental data to this equation, the values of parameters as shown in Table 2 were obtained. The data showed that the specific growth rate,  increased along with the increase in glucose concentration from 10 to 40 g l−1 . Beyond these values, the 0 values decreased gradually. The optimum 0 was 1.14 h−1 corresponding to the initial glucose concentration of 40 g l−1 . Fig. 1 shows the experimental and theoretical prediction profiles on the growth of the tested strain during HA fermentation. A classical sigmoidal growth trend for the S. zooepidemicus cells, in which an initial lag phase (∼2 h) varied in duration, was observed. This was followed by the exponential growth phase, the stationary phase and the death phase, accordingly. Sikyta [33] reported that the length of the lag phase could be affected by several factors: (1) composition of medium, (2) type and age of the strains, (3) number of cells, and (4) physical factors such as temperature and pH. As shown in Fig. 1, the cell growth of this tested strain was very low in the lag phase. This is in agreement with the results of Huang et al. [20] who reported that the growth and formation of HA in the culture broth were found to be very low during the lag phase due to the adaptation of bacterial cells to the new environmental condition. After a lag phase, the cells entered an exponential phase either for a lower or higher glucose level. It started at 2 h and continued up to 8 h. Once the limiting nutrient glucose started to decrease, the cells biomass stopped increasing exponentially, but it still continued to increase due to the presence of other components that accumulated in the culture media. Cheng et al. [34] reported that yeast extract was the best supplement for N source in HA fermentation and provided convenient growth factors for most streptococci. Furthermore, the salt component of the medium, usually sodium or potassium phosphate and a form of magnesium salt, was necessary for the activation of hyaluronate synthase [22,35], thus stimulating the significant growth of the tested strain. Eventually, a stationary phase was reached, which started at 8 or 9 h and lasted until 12 h. During this phase, the cell concentration was almost constant or had little variations along that period of time. The predicted maximum cell biomass obtained from the Logistic model for each 10,



100

(17)

where fi , yi , and nt are the model data, experimental data and length of actual data period, respectively. 3. Results and discussion 3.1. Microbial growth To investigate the behavior of S. zooepidemicus growth in aerobic batch fermentation, the fermentation medium supplemented with varying initial concentrations of glucose (10–60 g l−1 ) was chosen. The most popular kinetic expression for microbial growth,

Fig. 1. Fitting of the experimental data to the model describing cell growth over time at 10–60 g l−1 of initial glucose concentrations.

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Table 2 Kinetic parameters values for growth, substrate consumption and product formation at different initial glucose concentrations. Parameter estimation

Initial glucose concentration (g l−1 ) 10

Cell biomass X0 (g l−1 ) Xm (g l−1 ) o (h−1 ) R2 (correlation coefficient)  (variance) Glucose consumption S0 (g l−1 ) ms (gglucose gcellbiomass −1 h−1 ) YX/S (gcellbiomass gglucose −1 ) R2 (correlation coefficient)  (variance) HA production YP/X (gHA gglucose −1 ) t (h) R2 (correlation coefficient)  (variance)

20

30

40

50

60

0.036 1.86 0.61 0.997 2.40 × 10−3

0.022 2.05 0.84 0.996 4.68 × 10−3

0.029 2.21 1.06 0.998 4.25 × 10−3

0.036 2.24 1.14 0.998 3.15 × 10−3

0.052 2.17 1.00 0.995 5.67 × 10−3

0.048 1.94 0.86 0.974 2.59 × 10−2

10.27 0.04 0.16 0.998 1.97 × 10−1

20.26 0.51 0.22 0.997 2.73 × 10−1

30.23 0.65 0.30 0.974 6.84 × 10−1

41.44 1.56 0.37 0.996 6.60 × 10−1

51.24 0.99 0.34 0.985 7.42 × 10−1

61.21 0.86 0.32 0.980 7.56 × 10−1

0.310 0.49 0.991 8.97 × 10−4

0.310 0.87 0.996 1.47 × 10−3

0.934 0.86 0.996 3.14 × 10−3

0.958 0.90 0.998 1.85 × 10−3

0.889 1.40 0.994 5.07 × 10−3

0.572 2.16 0.907 1.11 × 10−2

20, 30, 40, 50, and 60 g l−1 of the initial glucose concentration was 1.86, 2.05, 2.21, 2.24, 2.17, and 1.94 g l−1 , respectively (Table 2). The highest cell biomass was obtained (2.24 g l−1 ) at the initial glucose concentration of 40 g l−1 . The predicted results also gave a relatively good agreement with the experimental data, with R2 values above 0.9 (Table 2). This showed that the proposed Logistic model was sufficient to describe both the exponential and stationary phase of S. zooepidemicus growth in a batch culture. According to Charalampopoulus, Vazquez and Pandiella [36], a number of models had been successfully used to describe the sigmoidal curves of bacterial growth, including the Logistic model which fitted cell growth over time and took into account growth inhibition in the stationary phase of growth. 3.2. Substrate consumption The rate of glucose consumption was mainly a function of three factors: growth rate, HA production and rate of substrate uptake for cell maintenance (ms ). The experimental data were fitted to Eq. (10). The parameter values of the glucose uptake model for each initial glucose concentration are shown in Table 2. Fig. 2 shows that the fitting results were satisfactory and gave good agreement to the model used, with correlation coefficient (R2 ) values above 0.9. As can be seen from Fig. 2 and the predicted values of yield coefficient (YX/S ) in Table 2, the yield significantly increased with the increase in glucose concentration from 10 to 40 g l−1 . Beyond this, an opposite trend was observed, suggesting that a significant inhibition would have occurred for the glucose concentration higher than 50 g l−1 . Generally, when energy and carbon sources were in excess, microorganisms tend to dispense the excess energy and carbon through the formation of storage compounds or extracellular products. Some of the products, such as acetic acid, lactic acid, hydrogen peroxide, and ethanol, are toxic to cell growth [9,21,22]. Hence, a maintenance coefficient (ms ) was used to describe the specific rate of substrate uptake for cellular maintenance, which was found to vary for each initial glucose concentration tested (Table 2). Zheng et al. [37] reported that the maintenance coefficient (ms ) might vary with the specific growth rate, , particularly in aerobic culture and energy sufficient conditions. Shuler and Kargi [13] later stated that cellular maintenance represented energy expenditures necessary to repair damaged cellular components, to transfer some nutrients and products in and out of the cells, for motility, and to adjust the osmolarity of the cells at interior volume. Sinclair and Kristiansen [38] also reported that the values of the mainte-

nance coefficient (ms ) can range from as little as 0.02 to as high as 4.0 gsubstrate gcell −1 h−1 , depending on the environmental conditions surrounding the cell and on its rate of growth. This clearly showed that the Logistic incorporated Leudeking–Piret-like equation was capable of predicting the experimental results of glucose consumption with a good amount of accuracy (>90%) for each of the glucose concentration tested. 3.3. Product formation For HA production, the experimental data obtained from batch fermentation studies with different glucose concentration were fitted to Eq. (16). The results showed that HA fermentation by S. zooepidemicus with glucose as a substrate was highly growthassociated even though a lag time appeared in the profiles. The fitting results were satisfactory and gave good agreement to the model used, with correlation coefficient (R2 ) values above 0.9 (Fig. 3). The parameter estimation for this model (Table 2) showed

Fig. 2. Fitting of the experimental data to the model describing glucose consumption over time at 10–60 g l−1 of initial glucose concentrations.

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Fig. 4. Effect of initial glucose concentration on the molecular weight of HA (HAMW ) produced by S. zooepidemicus in a batch culture (conditions: temp. 37 ◦ C, pH 7.0, aeration rate 2 l min−1 , agitation 300 rpm).

Fig. 3. Fitting of the experimental data to the model describing HA production over time at 10–60 g l−1 of initial glucose concentrations.

that the values of YP/X increased along with the increase in substrate concentration up to 40 g l−1 . However, with further increase in the initial glucose concentration, the values of YP/X gradually decreased. This indicated that HA production has been inhibited as the glucose concentration was increased from 50 to 60 g l−1 . The lag time t, which described the time delay of HA production correlated to cell growth, varied from 0.49 to 2.16 h, which is in the same range of magnitude to the value of t = 0.75 h previously reported [20]. Nickel et al. [39] stated that a 56-kDa ectoprotein kinase from the plasma membrane of group C streptococci does not shed its HA to the medium simultaneous to cell growth; rather, it needs a lag time for HA to appear in the medium, whose function is to regulate the formation and shedding of HA. During the exponential phase, the cell biomass and HA concentrations increased exponentially. According to Almeida and Oliver [40], the percentage of capsulated Streptococcus cells (S. uberis) increased from as low as 10% to over 90% during the exponential phase, but the numbers fell at the end of the batch culture. In another study which used phase contrast microscopy with India ink preparation, Van De Rijn [41] found that the largest capsule appeared at the mid-exponential growth phase. Armstrong and Johns [42], for their part, reported that the HA concentration increased in the broth after the cessation of growth due to glucose exhaustion. This is due to the release of HA capsules from the cells during the HA biosynthesis. Glucose is the primary carbon and energy source, and its effect on the molecular weight of hyaluronic acid (HAMW ) produced by S. zooepidemicus was carried out at standard conditions. Only values obtained at the 10 h of fermentation were considered as maximum HA concentrations were detected for most of the glucose levels tested. As can be seen in Fig. 4, the HAMW reached its maximum value of (2.52 ± 0.001) × 106 Da at initial glucose concentration of 40 g l−1 , and then decreased with an increasing glucose concentration. Similar as the observation of Armstrong and Johns [42], the HAMW increased by 17–21% when the initial glucose level was doubled, and reduced as the level increased to 60 g l−1 . According to Armstrong [43], microorganisms were able to detect changes in environmental osmotic pressure and may response by altering their metabolism. Glucose above 40 g l−1 represents an environment of higher osmotic pressure than at 10, 20 and 30 g l−1 .

Conditions which reduced the HAMW in the presence of excess glucose could be due to the availability and channeling of common resources into other competing metabolic pathways. Mausolf [44] reported that when the activated sugar monomers UDP-GlcUA and UDP-GlcNAc were present at high concentrations, HA chain elongation is thought to persist. However, according to Jagannath and Ramachandran [45], the nature, levels and complexity of the carbon source could also alter the strength of the glycolytic process and regulate the HA molecular weight. Nevertheless, the most frequently reported HAMW were in the range of 1 × 106 –2.5 × 106 Da with initial glucose concentration of 10–60 g l−1 in a batch culture. Above this level the HAMW is considered high [10,42,45,46]. 3.4. Model validation In order to prove the reliability of the microbial growth, substrate utilization and product formation models for different initial glucose concentration, the parameters estimated in Table 2 as described in Section 2.8 were used to simulate the model using 4th/5th order Runge-Kutta method. Comparison between the simulated and experimental values were made and only one response curve was shown in Fig. 5, but the models have been checked for R2 and variance () for all the studied substrate concentration during the entire course of fermentation. The error analysis

Fig. 5. Validation of the experimental data and simulation values for biomass, glucose consumption and HA production at 40 g l−1 glucose concentration (() biomass, () glucose consumption, (䊉) HA production and (–) simulated values)).

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Table 3 Mean squares error (MSE) values of microbial growth, substrate consumption and HA production at different glucose concentrations. MSE (%)

Cell biomass Glucose consumption HA production

Initial glucose concentration (g l−1 ) 10

20

30

40

50

60

0.550 8.505 0.241

0.899 6.883 0.322

0.287 7.810 0.472

0.445 9.205 0.354

0.607 9.434 0.589

0.618 6.593 0.507

between experimental and simulated values was also determined using mean squares error (MSE) and shown in Table 3. The value of error for each initial glucose concentration was less than 10%. It implies that the model proposed adequately represents the real process, and it can be used to describe the batch profile of HA fermentation by S. zooepidemicus. 3.5. Comparison with the substrate inhibition model Based on results of Sections 3.1–3.3, it is evident that at higher initial glucose concentration, inhibition effect was seen for the Streptococcus cells growth. The specific growth rate increased along with increase in substrate concentration from 10 to 40 g l−1 . Above this, a reverse trend was observed. In most biotechnological processes, higher concentrations of substrates or products often lead to inhibitory effects which result in the poor utilization of the substrate [47]. These effects also decrease both the cell growth and product yield. The literature provided several expressions which relate the specific growth rate to the substrate concentration [15–17,19,24,25]. Aside from substrate limitation, inhibition by substrate was likewise quite often found in the biotechnology process. This has also been examined by many authors [29,48–51]. When the substrate inhibited the microbial growth, the original Monod model became unsatisfactory. In this case, the Monod derivatives that provided correction for substrate inhibition (by incorporating the inhibition, Ki ) can be used to describe the kinetic growth of the fermentation process. Several substrate inhibition kinetic models (Eqs. (18)–(24)) were tested and compared in this work (Table 4). All the models were fitted to the experimental data with varying initial glucose concentrations. In order to determine the model kinetic parameters and fitting constants, values of specific growth rate, 0 of the Logistic model (Table 2) were used to simulate the models using a nonlinear regression technique. The estimated values for max , KS , Ki , S0 , m and n as returned by the fitting algorithm are shown in Table 5, where Ks is the Monod half saturation constant, Ki is the substrate inhibition concentration, S0 is the maximum substrate inhibition concentration at which no growth was observed, and n

Fig. 6. Comparison of the experimental data and simulations from substrate inhibition models at different initial glucose concentrations.

and m are the constants which accounted for the relation between  and S, respectively. It is clear that growth models that incorporate the substrate inhibition parameter gave better fits to the experimental data with R2 > 0.9. Among the models that took into account the substrate inhibition factor, the Han and Lenvenspiel type model showed the best fit of the experimental data (R2 = 0.997), as compared to Teisser-type (R2 = 0.985) model. The graphical outputs showing the fits of the experimental data by the models are shown in Fig. 6. According to Annuar et al. [52], the R2 (correlation coefficient) is frequently used to judge whether the model represents correctly the data, implying that, if the correlation coefficient is close to one, then the regression model is correct. However, many examples exist where the correlation coefficients are close enough to one but the model is still not appropriate. Hence, the mean square error (MSE) was used with R2 for the comparison of various inhibition models representing the same dependent variable used in this study. A model with small MSE represents the data more accu-

Table 4 Substrate inhibition kinetic models used in this study. Names of model

Substrate inhibition kinetic models

Andrew

 = max

Han and Lenvenspiel

 = max

Aiba

=



S



Competitive substrate inhibition Non-competitive substrate inhibition (if Ki > Ks ) Edward Teissier-type substrate inhibition

References



(K S + S) 1 + S/Ki 1−

S S•

n 

Andrew [46]

(19)

Han and Levenspiel [43]

(20)

Aiba et al. [44]

(21)

Shuler and Kargi [13]

(22)

Shuler and Kargi [13]

(23)

Edward [16]

(24)

Edward [16]



S S + KS (1 −

(18)

m (S/S • ))

max S exp−S/Ki KS + S max S = KS (1 + (S/Ki )) + S max S = KS + S + (S 2 /Ki ) S  = max S + KS + (S 2 /Ki )(1 + S/KS )  = max [exp(−S/Ki ) − exp(−S/KS )]

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M.M. Don, N.F. Shoparwe / Biochemical Engineering Journal 49 (2010) 95–103

Table 5 Estimated parameter values of substrate inhibition models as returned by the numerical calculations for the batch fermentation of S. zooepidemicus at different initial glucose concentration. Parameter

Model Andrew model

Equations Ks Ki max m n S• Correlation coefficient) (R2 ) Variance () Means square error (MSE) (%)

(18) 1.70 101.99 1.14 – – – 0.710 8.84 × 10−2 5.53

Han and Lenvenspiel model (19) 40.54 – 2.19 0.77 0.48 71.92 0.997 4.21 × 10−4 0.18

Aiba model (20) 9.08 86.86 1.92 – – – 0.802 5.63 × 10−2 1.90

rately than the models with larger MSE. It is also found that the Han and Lenvenspiel type model, and the Teisser-type model both have much lower MSE at 0.18% and 0.57%, respectively. As indicated by the inhibition constant Ki on cell growth (Table 5), glucose provided a stronger inhibition which predominantly corresponded to the culture of 50 and 60 g l−1 initial glucose concentrations (Fig. 6). According to a few researchers, glucose above a concentration of 50 g l−1 may inhibit growth due to the reduction in water activity [22,23,42,53]. Being a monosaccharide, glucose’s osmotic pressure will be higher; hence, it could cause more substrate inhibition [22]. In fact, Cooney et al. [18] reported that, at a high glucose concentration, lactic acid bacteria such as S. zooepidemicus would produce large quantities of lactate from the catabolism of glucose (75–85%). Consequently, growth will be inhibited. 4. Conclusion The results clearly showed that the proposed unstructured mathematical model satisfactorily predicted the cell growth, substrate utilization, and HA production for the fermentation with an initial substrate concentration of 10–60 g l−1 . For the substrate inhibition, the Han and Levenspiel model and the Teissier-type model gave the best fit for all the systems studied with R2 of 0.997 and 0.985, respectively. All the models tested not only adequately described the relationship between the principal state variables or quantitatively explained the behavior of a system, but could also be used for the design, optimal control, and economic analysis of HA fermentation processes. Acknowledgements The authors would like to thank the Malaysian Ministry of Higher Education for its financial support via the Fundamental Research Grant Scheme (FRGS: Acc Number: 6070018). References [1] J.H. Fessler, L.I. Fessler, Electron microscopy visualization of the polysaccharide hyaluronic acid, Proc. Natl. Acad. Sci. 56 (1966) 141–147. [2] S. Hirano, P. Hofmann, The hexosaminidic linkage of hyaluronic acid, J. Org. Chem. 27 (1962) 395–403. [3] M. O’Regan, I. Martini, F. Crescenzi, C. De Luca, M. Lansing, Molecular mechanisms and genetics of hyaluronan biosynthesis, Int. J. Biol. Macromol. 16 (1994) 283–286. [4] P. Prehm, Hyaluronan, in: E.J. Vandamme, S. De Bacts, A. Steinbuchel (Eds.), Biopolymers, Polysaccharides. I. Polysaccharides from Prokaryotes, Wiley–VCH, 2001, pp. 379–406. [5] B. Fong Chong, L.M. Blank, R. McLaughlin, L.K. Nielsen, Microbial hyaluronic acid production, Appl. Microbiol. Biotechnol. 66 (2005) 341–351. [6] B. Fong Chong, L.K. Nielsen, Aerobic cultivation of Streptococcus zooepidemicus and the role of NADH oxidase, Biochem. Eng. J. 16 (2003) 153–162.

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(22) 11.10 131.32 1.56 – – – 0.935 1.47 × 10−2 0.76

(23) 21.20 128.50 1.95 – – – 0.614 3.39 × 10−3 21.54

Teissier-type model (24) 41.40 55.00 2.90 – – – 0.985 9.31 × 10−3 0.57

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