تولید لیپاز توسط باکتری اعماق دریایی Idiomarina zobellii؛ شناسایی فاکتورهای موثر و بهینه‌سازی فرایند تولید

Introduction

Lipases (triacylglycerol acylhydrolases, E.C. 3.1.1.3) catalyze the hydrolysis of fats into fatty acids and glycerol and also esterification reaction for the production of fats. Lipases can perform these reactions at the water-insoluble substrate interface (1). In addition, lipases have the ability to catalyze inter esterification, acidolysis, alcoholysis and aminolysis reactions. In addition to their broad substrate specificity, lipases carry  properties such as good chemoselectivity, regioselectivity and enantioselectivity (2). This wide range of biochemical specifications has  made lipases one of the indispensable components in different biotechnological fields including synthesis of biopolymers, biodiesel, the production of enantio-pure pharmaceuticals, agro-chemicals, and flavour compounds (3). These applications have made lipases one of the most consumed and economical important enzymes in the world enzyme market. This growth in daily applications and demands in new biotechnological and industrial applications have led to daily efforts to discover new lipases with different specificities. Therefore, characterization of more natural sources of lipases holding newer properties is increasingly demanded (4). Many microorganisms and higher eukaryotes can naturally produce lipases. Among microorganisms, bacteria, fungi, yeasts and actinomyces are the most important lipase producers. Microbial lipases are most commercially useful and widely used in various industries (4, 5).

Over the last few decades, microorganisms from extreme marine conditions such as high pressures, hypersaline habitats and extreme temperature have increasingly received  interest for industrial processes due to their high potentials in the production of new bio-products including enzymes and other active metabolites (6-8). Therefore, studies focusing on optimization of growth conditions and production of bioactive products such as enzymes from marine microorganisms are attractive and on demand for biotechnological and pharmaceutical industries. In addition, enzymes from psychrophilic micro-organisms effectively function in the cold, and the biotechnological applications of these cold-adapted enzymes have been considered in the last few decades (9).

Idiomarina zobellii is a gram-negative, psychrotolerant, heterotrophic, aerobic, rod-shaped bacterium which was isolated from the seawater of north-western Pacific Ocean at the depths of 4000-5000 m. Growth occurs at temperature of 4−30°C with its optimum at 20−22°C. The pH range for growth is 5.5−9.5 with its optimum at 7.5−8. This deep-sea bacterium requires NaCl (0.6−15%) for growth, hence it is regarded as moderate halophile (10). Enzymes from halophilic microorganisms such as I. zobellii can survive  at high salt concentrations (11). These adaptations besides the existing robust nature of lipases, make halophilic lipases more attractive for industrial and biotechnological applications. (12). The number of studies on lipase producing halophilic organisms however, is limited. To date, there is more attention to these halophilic microorganisms, especially halo-archaea and their applicability for lipase production (13-15). Submerged fermentation is commonly used for the production of enzymes and other bio-active compounds by micro-organisms (5, 6). Growth conditions such as availability of carbon and nitrogen sources, the presence of activators, stimulators, inhibitors, surfactants, incubation temperature, pH, and the level and source of inoculum can influence the production of lipase (16). Besides, these various factors, the dependency of a microorganism for lipase production varies from one microorganism to another. Moreover, most of the culture mediums and conditions proposed for optimal growth of microorganisms may be improper for the production of lipase. Therefore, the optimization of production conditions is an important step in the development of economically feasible bioprocesses (17).

The one factor at a time method, is a time consuming and costly method to distinguish factors affecting the process; therefore, it has been replaced with Plackett–Burman design (PBD) method (18). After determining the effective factors, Response Surface Methodology (RSM) could be used to investigate the combinatorial interactions of medium components and define the optimum conditions of effective factors for appropriate responses (17).

In this study, for the first time, the nutritional and cultural parameters affecting the production of lipase from a deep-sea bacterium, I. zobellii were investigated using PBD. These factors were used for optimization of growth condition by applying RSM to gain higher lipase production. Some properties such as optimum pH and temperature of lipase from I. zobelli were also investigated.

Materials and Methods

Microorganism and Preparation of inoculum: Live active culture of I. zobelli in a glass plate was obtained from the Iranian Biotechnology Research Center (IBRC-M10598). For pre-culture, a loop full of the bacterial active colony was added aseptically to 25 ml of Marine broth pH 7.5 and the bacterial suspension was incubated at 22°C for 48 h with 120 rpm shaking. A volume of 50 ml of Marine broth as preparation medium collected in a 250 ml Erlenmeyer flask was inoculated with 1 ml of the 48 h old culture and incubated as previously described. The optical density of the culture at 600 nm was recorded every 24 h to obtain the growth kinetics of I. zobellii. Using fresh culture medium, load of the bacterial growth was adjusted at OD=1. This preparation was subsequently used for inoculation of the experimental flasks.

Lipase assay: p-Nitrophenyl palmitate was used as a substrate for the measurement of I. zobellii lipase activity (19). To prepare the substrate solution, 10 ml of p-nitrophenyl palmitate (0.1 M) ethanol solution was added to a mixture of 207 mg of sodium deoxycholate and 100 mg of gum Arabic in 90 ml phosphate buffer (0.05 M, pH 6.5). To start the reaction, 0.1 ml of crude enzyme extract or blank solution (50 mM phosphate buffer, pH 6.5) was added to 2.4 ml of substrate solution. After 15 min incubation in 50 mM phosphate buffer, absorbance at 410 nm was recorded. One enzyme unit (U) was defined as the lipase activity that liberates 1μmol of p-nitrophenyl palmitateper milliliter per minute under standard assay conditions (20).

Temperature and pH effect on the lipase activity: The optimal temperature for the activity of I. zobellii lipase was determined using different temperatures ranging from 30-60°C (with 5°C intervals). For this, the substrate solution was mixed with crude enzyme extract, incubated at the selected temperatures for 15 min with the absorbance level recorded at 410 nm. The pH dependency of I. zobellii lipase was determined using substrate solution with different pH ranging from 7-11 with 0.5 unit intervals in the following buffers: 0.5 M sodium phosphate buffer (7-8.5), 0.5 M Britton-Robinson Buffer (9-11).

Plackett–Burman design: Factors affecting the production of lipase by I. zobellii were determined with experimental designed using the two factorial Plackett–Burman (PB) methods. In this method, n+1 experimental design for n variables and interactions between the different variables were explained statistically (21). Among medium components and culture conditions, 10 parameters including ferric citrate (g/L), ammonium nitrate (g/L), sodium bicarbonate (g/L), yeast extract (g/L), peptone (g/L), MgCl₂ (M), shaking (rpm), pH, incubation temperature (°C) and time (h) were investigated. All experimental patterns and analysis were designed using the statistical software package “Minitab 17” (Stat-Ease Inc., USA) in 2 levels for each factor, high (+) and low (–), with one center point. According to selected levels for each variable, a collection of 15 experiments were designed. All experiments were performed in triplicate and the average was analyzed using Minitab software. Table (1) shows variables and selected levels for each experimental design.

The PB design is based on the following first-order polynomial equation:

Y = ßo + Σ ßi Xi

Where, Y, is the lipase activity; ßo, is the model intercept; ßi, is the linear coefficient; Xi, is the level of the independent variable.

Optimization using response surface methodology (RSM): RSM methodology was used for further optimization of significant variables obtained by Plackett-Burman design. According to screening experiments, among investigated variables, MgCl₂ medium, yeast extract, sodium bicarbonate, ammonium nitrate and the temperature were determined as significant. These variables were used in a Central Composite Design (CCD) to explore their effect on lipase production. A collection of 32 different experiments for three variables each in 5 levels (Table 2) was designed. This design consists of 16 cube points, 6 center points in cube and 10 axial point runs with 6 replications at the center point to evaluate the pure error. The lipase activity (U/ml) of each run was taken as response and the following second order polynomial equation was used to determine the relationship among variables:

Y = ßo + Σ ßi xi + Σ ßii xi2 + Σ ßij xixj,

 i = 1, 2, 3... k

Where, Y, is the predicted response; k, is the number of factor variables; ßo, is the model constant; ßi, is the linear coefficient; ßii, is the quadratic coefficient; ßij, is the interaction coefficient.

Model validation: The Fischer’s F-test was used to determine the statistical significance of polynomial model and the coefficient of determination R² value was checked for the quality of represented model (17, 22).

Optimization of variable’s level: The final goal of optimization process was to create an increment in lipase production by a combination of factor levels. To achieve this, predicted levels for each variable were used in an experiment. The predicted levels of variables were as follows:  temperature 42 °C, MgCl₂ 7.18 (g/L), yeast extract 1.1 (g/L), NH₄NO₃ 3.65 (g/L) and NaHCO₃ 0.11 (g/L). The predicted lipase activity by the model in 95% confidence level was 3.78 U/ml. The “Minitab 17” (Stat-Ease Inc., USA) software package was used for experimental design and statistical analysis of the responses.

Results

Inoculum preparation: Inoculum size and growth profile of bacterial culture can affect enzyme production. The number of bacterial cells in the production media determines the availability of nutrients. The concentration of growth inhibitors in culture increases, with increase in culture time (23). To overcome these effects in lipase production, growth profile of I. zobellii was obtained in order to realize a constant amount of active bacteria for use as an inoculum in all experiments. According to Fig. 1, absorbance at 600 nm increased up to 1.7 for 48 h and then remained nearly constant. After 72 h, absorbance decreases as a result of the death of bacteria. About 1 ml of a 24 h bacterial culture was used with absorbance of 0.8 at 600 nm to inoculate culture medium in all experiments.

Optimization of enzyme assay: It is important to assay each enzyme in its optimum condition. Temperature and pH are two important assay conditions, which can affect enzyme activity. Lipase activities in different pH and temperatures were investigated to obtain the optimum condition of the assay (Fig. 2). The pH profile shows that I. zobellii lipase has the maximum activity at pH 7-8. According to Figure 2b, the optimum temperature for I. zobellii lipase was determined as 50°C.

Detection of significant factors using Plackett–Burman design: Different medium components and culture conditions including ferric citrate, ammonium nitrate, sodium bicarbonate, yeast extract, peptone, MgCl₂, shaking, pH and incubation temperature, and time were selected as independent variables and were investigated in PB design. Using this method, a set of 15 different experiments were designed. Results of these experiments with respect to lipase activity were shown in Table 1. The following equation shows the effect of each studied variable in lipase production from I. zobellii as a mathematical model:

Activity (U/ml) = -1.712 + 0.0277 temperature+ 0.01 pH - 0.00083 Incubation time - 0.00017 Shaking+ 0.0250 peptone + 0.708 yeast extract + 0.0918 MgCl₂ - 2.29 NaHCO₃ + 0.224 [NH₄]NO₃+ 1.08 ferric citrate + 0.017 Ct Pt

Fig. 1- Growth curve of I. zobellii during different incubation time

Fig. 2- Activity of I. zobellii lipase in different pH (a) and temperatures (b)

Table 1- The Placket-Burman design for detection of significant factors affecting lipase production in I. zobellii

The F- and P-values of the given model were 7.78 and 0.004, respectively, so the model seems significant. The confidence of determination (R²) of PB design is 81.2%. This means that the represented mathematical model can fit 81.2% of total variables in the range of studied values.

The significance of each variable at the designed confidence level was determined with p-value and F-value of ANOVA analysis. The significant variables should have low p-value (less than 0.05) and high f-value (24). Standardized effects of variables were shown in Pareto chart (Fig. 3). According to this chart, five parameters including ammonium nitrate, sodium bicarbonate, yeast extract, MgCl₂, and incubation temperature were determined as significant. This implies that ferric citrate and peptone used in the culture of I. zobellii as carbon and nitrogen sources have no effects on lipase production and could be replaced by other components. Among three significant factors, yeast extract and ammonium nitrate have the highest and lowest effects, respectively.

Optimization of lipase production using RSM: The Response Surface Methodology (RSM) can be used to investigate interactions between significant parameters in the production of lipase. In this study, significant variables determined with PB design (temperature, yeast extract, sodium bicarbonate, ammonium nitrate and MgCl₂) were used to inquire the combined effects of these independent variables. A set of 32 experiments were designed using five levels for each parameter in the CCD.

Fig. 3- Pareto chart of the standardized effect of variables for detection of significant factors for lipase production from I. zobellii.

Table 2 represents observed and predicted values for each experiment in the CCD. The observed lipase activity varied widely from 1.18 to 3.96 U/ml among different experiments. The following second order polynomial equation represents linear, square and 2-way interaction effects in the production of lipase.

Lipase activity (U/ml)= -19.11 + 0.054 A + 2.24 B + 15.55 C - 0.181 D + 37.8 E + 0.000205 A*A - 0.1409 B*B - 4.82 C*C - 0.03999 D*D - 212.5 E*E - 0.00233 A*B - 0.0312 A*C + 0.00164 A*D - 0.131 A*E - 0.477 B*C + 0.0748 B*D + 1.25 B*E - 0.186 C*D + 4.9 C*E + 0.63 D*E

Where, A; temperature, B; MgCl₂, C; yeast extract, D; [NH₄]NO₃ and E; NaHCO₃.

Table 2- Data for the central composite design of significant factors in the production of lipase from I. zobellii.

The F- and P-values for the model were 2.9 and 0.037, respectively, implying that the model terms are significant. The coefficient of variations (R²) always lies between 0 and 1 and it indicates the ability of the model to explain the variability in the response (25). The stronger model has R² close to 1 while in a weak model, it is close to zero (26). Considering R² value (0.8405), demonstrated model can explain 84.05% of the total variability within the range of studied values.

Analysis of Response Surface Methodology (RSM): Interactive effects of two variables at a time on lipase production in I. zobellii were investigated by 3D surface plots. In each plot, one variable remains constant while two other variables change according to defined values in CCD.  The surface of the curved sheet shows the changes in the lipase production. According to the 3D surface plots, different temperatures have no effect on lipase production when other factors remain constant (Fig. 4a, 4b, 4c and 4d). In each given temperature, we can see a maximum lipase production in response to the variations in second factors. Moreover, there was no significant difference in the maximum lipase production in response to the different temperatures. In the presence of different concentrations of MgCl₂, a maximum point in lipase production can be seen in response to variations in other factors (Fig. 4e, 4f and 4g). In the presence of different concentrations of NaHCO₃ and(NH₄)NO₃, lipase production decreased when the concentration of yeast extract increased from 1.2 (Fig. 4h and 4i). According to Fig. 4j, there is an optimal concentration of NaHCO₃ and  (NH₄)NO₃ for lipase production.

Fig. 4- Surface plot for investigation of interactive effects of variables on lipase production.