The combined effects of macronutrients of media on glucoamylase production by Candida guilliermendii were studied using Design Of Experiment (DOE). A 2P-k factorial design was chosen to explain fifteen medium constituents: pH, Starch, Sucrose, Yeast extract, Peptone, NH4Cl (NH4)2HPO4, NH4NO3, CH4NO2 (NH4)2SO4, CaCl2, MnCl2, FeCl2, ZnCl2 and MgCl2 and analyse the results. This procedure limited the number of actual experiments performed while allowing for possible interactions between components. The p-value of the coefficient for quadratic effect of pH, starch and yeast extract concentration was < 0.001, suggesting that they were the main experimental variables having the highest effect on the production of glucoamylase. It was found that yeast extract had a great effect on glucoamylase production. The optimal combinations of media constituents for maximum which were chosen for further studies on production of glucoamylase were determined as 10 g L-1 starch, 0.45 g L-1 urea, 0.61 g L-1 NH4NO3, 3 g L-1 Yeast extract and 0.1 g L-1 Mg SO4.
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Amylases are among the most important enzymes used in biotechnology, particularly in processes involving starch hydrolysis. Though amylases originate from different sources (plants, animals and micro organisms), the microbial amylases are the most used in industry, due to their reproductivity (Burhan et al., 2003). Natural fermented media (foods, soils and waste) offer sources for isolation of micro-organism strains producing amylases. Many strains used in food industry are isolated from fermented food media (Pandey et al., 2000; Burhan et al., 2003; Gomes et al., 2003).
Various industries, such as food, brewing, textile, pharmacy and confectionaries depend on the various products especially extra-cellular enzymes produced by micro-organisms (Grupta et al., 2003). An extra-cellular amylase, specifically raw starch digesting amylase has found important applications in bioconversion of starches and starch-based substrates (Forgarty, 1983; Okolo et al., 1995).
Optimization of medium by the classical method involves changing one independent variable keeping the other factors constant. The conventional methods for multifactor experimental design are time-consuming and incapable of detecting the true optimum, due especially to the interactions among the factors (Liu and Tzeng, 1998). In fermentation process, the operational variables interact and influence each other. As a result, it is important that the optimization method accounts for the interactions so that a set of optimal experimental condition can be determined (Silva and Roberto, 2001). This limitation of a single factor optimization process can be eliminated by different techniques.
The need of efficient methods for screening large number of variables has led us to the adoption of statistical experimental designs. Statistical methods of Placket-Burman (1946) used in this work have been applied to bacterial culture optimization (Ahuja et al., 2004) and animal cell culture (Ganne and Mignot, 1991).
Such statistical design have already been used in many research works; such as the optimization of amylase and protease production from Aspergillus awamori (Negi and Banerjee, 2006) and the optimization of α-amylase production by Aspergillus niger (Djekrif-Dakhmouche et al., 2006), Aspergillus oryzae (Bennamoun et al., 2004; Francis et al., 2003) and by Bacillus sp. (Saban Tanyildizi et al., 2005). These designs were also used for the selection of amino acids causing the increase of the production of pyoverdine by Pseudomonas fluorescens (Kissalita et al., 1993) and the optimization of the production of carotenoids by Rhodotorula glutinis DBVPG 3853 (Buzzini, 2000).
The present study is aimed to determine better conditions for growth and the glucoamylase productivity, particularly their behaviour toward pH and compositions of media using statistical design. The choice of yeast isolation was justified by the facility of their culture and their harmlessness.
MATERIALS AND METHODS
Microorganism used: Candida guilliermendii was isolated in Laboratory of Microbial Biotechnology (LMB) from traditional Moroccan sourdough using the following medium: soluble starch (5 g L-1), KH2PO4 (3 g L-1); (NH4)2SO4 (1 g L-1); MgSO4 (0.5 g L-1); yeast extract (4 g L-1). pH was adjusted to 5 with HCl 0.1M. The medium was solidified by the addition of 1.5% agar and autoclaved at 121°C for 15 min. Liquid medium was incubated in flask on a rotary shaker set at 105 rpm for 72 h. Incubation was at 30°C.
Growth rate: Growth rate was determined by measuring the absorbance of the suspension at 600 nm.
Cultivation and production of glucoamylase by Candida guilliermendii
Enzyme assays: The fermented broth was taken after 72 h and centrifuged at 7000 rpm for 10 min and then substrate free supernatant was used for estimation of enzyme activity.
Glucoamylase activity was determined by measuring the reducing sugar formed by the enzymatic hydrolysis of starch using the method of Somogyi and Nelson (Nelson, 1944). 0.25 mL soluble starch (1%), 0.15 mL phosphate buffer (0.1M), 0.1 mL enzyme solution were mixed and incubated at 40°C in water bath for 30 min, the reaction was stopped by 2 mL of Somogiy reactive and 1.5 mL of distilled water, followed by boiling for 15 min to develop blue colour. The absorbance measured at 540 nm with a spectrophotometer. The activity was measured against the control in which no enzyme was added. A calibration curve of absorbance and concentration of glucose was established with known amount of glucose.
One unit (μmol/L/min) of amylase was defined as the amount of μmol of reducing sugar per litre of enzymes per min, measured as glucose under the conditions of assay.
Total protein concentration was measured by the method of Bradford. The samples were read at 595 nm against the blanks with the same compositions as the samples
Experimental design: The Plackett-Burman experimental design assumes that there are no interactions between the different media constituents, xi in the range of variables under consideration (Plackett and Burman, 1946). A linear approach is considered to be sufficient for screening.
Y = β0 + Σ βixi (I = 1, . . . , k)
where, Y is the estimated target function and βi are the regression coefficients. The Plackett-Burman experimental design is a fractional factorial design and the main effect (the contrast coefficient) of such a design may be simply calculated as the difference between the average of measurements made at the high level (+1) of the factor and the average of measurements at the low level (-1)
|Table 1:||Summary of variables for the (Plakett-Burman) design for the optimization of parameters|
|Table 2:||Experimental range and levels of the independent variables|
|Table 3:||Matrix of the experimental design using Plackett-Burman method for screening of nutrients|
The whole factors tested are represented in Table 1 and 2. The matrix used in this study is represented in Table 3, comprising 16 experiments and 15 factors. Each line represents the various experiments and each column represents the various factors. The last line is always taken on the level (-1). In order to determine non controlled residues and allow for the estimation of the experimental errors, each experiment was repeated twice (32 experiments).
RESULTS AND DISCUSSION
Experimental design (Table 4) was carried out according to the design for 72 h at 30°C, under agitation on rotary shaker at 105 rpm. The fermented samples were extracted and assayed for biomass, glucoamylase activity, protein and final pH. The results were analyzed by statistical software. The design and results of experiments carried out with the placket-Burman design are given in Table 5.
The analysis of variance (ANOVA) was calculated for each response for the determination of significant parameters. ANOVA consists of classifying and cross-classifying statistical results and testing whether the means of a specified classification differ significantly. This was carried out by Fishers statistical test for the analysis of variance. The F-value standing for the ratio of the mean square due to regression upon the mean square due to error indicates the influence of each controlled factor on the tested model.
The effect of the studied factors on the production of biomass: The effect of the factors upon the biomass is reported in Table 6.
Values of Probability > F less than 0.05 (0.01) indicated that model terms were significant. In this response (biomass production) the model was found significant. The biomass production model determination coefficient R2 (0.998) strongly suggested that the fitted model could explain 99.8% of the total variation. Noises slightly affect the model. This implies a satisfactory representation of the process by the model.
The variation in pH from 5 to 7 leads to a negative effect on the production of biomass (PP 99%). These results may be explained by the fact that adequate development of yeasts necessitates a rather acid pH, the optimal growth pH of which is 5 (Botton et al., 1990; Martinilli and Kinghorn, 1997).
Of the carbon sources tested, starch showed a great effect on the production of biomass (99%), while sucrose had no effect on this production, which leads us to conclude that starch constitutes an adequate carbon source, which stimulated the growth of the cell yeasts.
|Table 4:||Experimental design (Plackett-Burman) used to optimize the parameters for the production of glucoamylase by Candida guilliermendii|
|Table 5:||Observed responses and calculated values|
|Table 6:||Experimental design results of the biomass, proteins and glucoamylase production|
|* Signification degree, ** High signification degree and *** Very high signification degree|
Earlier researchers reported similar findings wherein soluble starch was the best carbon supplement for amylase production in Myceliophora thermophila D14 (Sadhukhan et al., 1990), in Aspergillus fumigatus (Goto et al., 1998) and in Aspergillus oryzae (Bennamoun et al., 2004).
Among the nitrogen sources, yeast extract gives positive effect on the production of the biomass, followed by (NH4)2HPO4 and NH4NO3. On the contrary, NH4Cl CH4N2O (NH4)2SO4 and Peptone have no effect. The effect of yeast extract on the production of biomass is very significant, because is contains amino acids and ammonium ions (NH4), which stimulate effect on the growth (Scriban, 1993; Djekrif-Dakhmouche et al., 2006).
All salts tested give positive effect on the production of the biomass production. Maximum effect was given by MgCl2 followed by ZnCl2, FeCl2, MnCl2 and CaCl2.
The effect of the factors on the production of Glucoamylase activity: Coefficient of determination R2 of glucoamylase activity production is (>0.999) which strongly suggested that the fitted model could explain 99.9% of the total variation.
Variation in pH from 5 to 7 had a positive effect on glucoamylase production. These results could be explained by optimum pH for Glucoamylase activity. Mediums which record high Glucoamylase activity had their pH ranged between 6.8 and 7.8. Similar results found by Ichikawa et al. (2004) show that optimum pH for glucoamylase activity produced by Thermoactinomyces vulgaris R-47 was 6.8.
Also Starch increases the glucoamylase production, with a significant value (0.99). Therefore, the presence of starch, as enzyme substrate has inductive effect (Madihah, 2000; Murai et al., 1998; Hassan et al., 1998; Tani et al., 2000), its remarkable efficiency in the production of enzyme, being an inexhaustible source of carbon compared to other carbon sources (Mctigue et al., 1994) and because of its role in stabilizing the enzyme (Aguilar et al., 2000; Santamaria et al., 1999).
Yeast extract have highest positive effects among the nitrogen sources on the production of glucoamylase, followed by CH4N2O, NH4NO3, NH4Cl (NH4)2HPO4 and Peptone respectively. While (NH4)2SO4 has a negative effect. Previous results have shown that yeast extract (Haasum et al., 1991; Han et al., 2005) ammonium nitrate (Hernández et al., 2006), ammonium phosphate (Jun et al., 2001) are good nitrogen supplements for glucoamylase production.
All tested salts give negative effect on the production of glucoamylase activity. On the basis of analyzing the results, we may suggest that microorganisms necessitate a low level of salts in order to produce enzymes because salts may be a limiting factor (Baig et al., 1984; Mctigue et al., 1994; Pedersen and Nielsen, 2000). Also, it may be explained by the fact that yeast extract provides sufficient oligoelements (Belitz and Grosch, 1987; Souci, 1994).
The effect of the factors on the production of proteins: Based on results, the coefficient of determination, R2 was found to be 0.999, indicating that the sample variation of 99.9% can be explained by the model.
Variation of pH from 5 to 7 had a small positive effect on proteins production. These results may conclude that the optimum pH of proteins production is rather acid.
Production of proteins was stimulated by starch, with a significant value (0.99), its efficiency in the production of proteins and enzymes (Dharani Aiyer, 2004; Santos and Martins, 2003; Kiran et al., 2005).
Among the nitrogen sources tested, yeast extract seems to be the most influencing factor in terms of protein production; followed by Peptone (NH4)2HPO4 and CH4N2O, While (NH4)2SO4 and NH4NO3, had a negative effect. As reported below, yeast extract induces the production of proteins. Similar results reported that yeast extracts induce the production of proteins and enzymes (Djekrif-Dakhmouche et al., 2006; Teodoro and Martins, 2000).
The use of an experimental design where the main point was to reveal the influence of concentrations of macro nutrients on glucoamylase production allowed the rapid screening of large experimental domain in search of the best culture conditions for optimization of glucoamylase production. The significant achievement of the present study lies in the fact that the yeast extract, urea and starch were found to be highly significant for the enhancement of glucoamylase production. The optimization of the medium resulted in a reduced cost of medium constituent. The chosen method of optimization of medium composition was efficient, relatively simple and time and material saving.
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