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Improved phytase production by a thermophilic mould Sporotrichum thermophile in submerged fermentation due to statistical optimization
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Bioresource Technology 99 (2008) 824–830
Improved phytase production by a thermophilic mould Sporotrichum thermophile in submerged fermentation due to statistical optimization Bijender Singh, T. Satyanarayana
*
Department of Microbiology, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India Received 14 November 2006; received in revised form 18 January 2007; accepted 18 January 2007 Available online 12 March 2007
Abstract Culture variables affecting phytase production by a thermophilic mould Sporotrichum thermophile in submerged fermentation were optimized. Soluble starch, peptone, Tween-80 and sodium phytate were identified by Plackett–Burman design as the most significant factors to affect phytase production. The 24 full factorial central composite design of response surface methodology was applied for optimizing the concentrations of the significant variables and to delineate their interactions. Starch, Tween-80, peptone and sodium phytate at 0.4%, 1.0%, 0.3% and 0.3% supported maximum enzyme titres, respectively. An overall 3.73-fold improvement in phytase production was achieved due to optimization. When sodium phytate was substituted with wheat bran (3%), the phytase titre in the former was comparable with that in the latter. 2007 Published by Elsevier Ltd. Keywords: Phytase; Sporotrichum thermophile; Plackett–Burman design; Response surface methodology; Wheat bran
1. Introduction The screening and evaluation of nutritional and other culture variables of a microorganism is an important step in the development of a bioprocess. Since microbial fermentations are affected by cultural variables, a suitable approach has to be applied for their optimization. The conventional methods used in the optimization of the cultural variables are time consuming, tedious and expensive (Vohra and Satyanarayana, 2002; Bogar et al., 2003a,b). These methods also tend to overlook the effects of interactions among them. Optimization of all the variables by statistical experimental designs can eliminate the limitations of ‘one variable at a time’ approach (Stanbury et al., 1997). Several research groups have applied this approach for screening and optimization of the parameters affecting
*
Corresponding author. Tel.: +91 11 24112008; fax: +91 11 24115270. E-mail address: [email protected] (T. Satyanarayana).
0960-8524/$ - see front matter 2007 Published by Elsevier Ltd. doi:10.1016/j.biortech.2007.01.007
phytase production (Sunitha et al., 1999; Vohra and Satyanarayana, 2002; Kaur and Satyanarayana, 2005). The use of statistical approaches helped in enhancing yield of yeast phytase that led to the reduction in the cost of production, and therefore, making the fermentation process economical and cost effective (Vohra and Satyanarayana, 2002; Kaur and Satyanarayana, 2005). Phytases (myo-inositol hexaphosphate phosphohydrolase EC 3.1.3.8) are histidine acid phosphatases, a subclass of acid phosphatases which catalyze the hydrolysis of phosphate moieties from phytic acid (myo-inositol hexakis dihydrogen phosphate), thereby mitigating its anti-nutritional properties (Vohra and Satyanarayana, 2003; Greiner and Konietzny, 2006). The market volume of phytases is in the range of 2 150 milllion which is still rising further (Greiner and Konietzny, 2006). The supplementation of animal feed with phytases enhances the bioavailability of minerals, protein and phosphorus in monogastric animals, besides reducing the phosphorus pollution in the areas of intensive livestock production. In grains, roughly 60–80% of
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830
phosphorus is tied up in phytin, which is not digestible by monogastric animals due to the lack of adequate levels of phytase in their digestive tracts. The phytin thus excreted in the manure reaches the soil, where it is degraded by microflora that leads to the eutrophication of water bodies and thus environmental pollution (Wodzinski and Ullah, 1996). The pollution can be tackled by supplementing animal feeds with phytase. In view of ever increasing demand for phytase, it is essential to produce phytase in a cost-effective manner. In this investigation, an attempt has been made to improve phytase production by a thermophilic mould Sporotrichum thermophile by statistical approaches using Plackett–Burman design and response surface methodology (RSM) in submerged fermentation.
factor and the average of the measurements made at the low level of that factor, which was determined by the following equation: EðXiÞ ¼
2ðRPiþ RPi Þ N
2.1. Source of the strain and inoculum preparation The thermophilic mould, S. thermophile BJTLR50 was isolated from a soil sample collected from Rohtak, Haryana (India), and routinely grown on Emerson’s YpSs (Emerson, 1941) agar medium (Singh and Satyanarayana, 2006). The conidiospores from 6 day-old fully sporulated solid media slopes were harvested by flooding with normal saline containing 0.1% (v/v) Tween-80 and the spore suspension thus prepared was adjusted to 1 · 107 CFU/ml for inoculating the medium. 2.2. Phytase assay Phytase was assayed by measuring the inorganic phosphorus liberated from sodium phytate at pH 5.0 and 60 C according to Fiske and Subbarow (1925). One unit of phytase is defined as the amount of enzyme that liberates 1 nmol of inorganic phosphate per ml per sec under the assay conditions. 2.3. Optimization of phytase production The optimization of medium components for phytase production by S. thermophile was carried out in two stages. 2.3.1. Identification of important nutrient components using Plackett–Burman design The critical cultural variables (15 culture variables and four dummy variables) were screened for phytase production using Plackett–Burman design (Plackett and Burman, 1946) as shown in Table 1. Total number of experiments to be carried out according to Plackett–Burman is n + 1, where n is the number of variables. Each variable is represented at two levels, high and low denoted by (+) and (), respectively. The number of positive and negative signs per experiment or trial are (n + 1)/2 and (n 1)/2, respectively. The effect of each variable is the difference between the average of the measurements made at the high level of that
ð1Þ
where E(Xi) is the concentration effect of the tested variable. Pi+ and Pi are the phytase activities from the trials where the variable (Xi) under study was present at high and low concentrations, respectively and N is the number of experiments. Standard error (SE) of the concentration effect was the square root of the variance of an effect and the significant level (p-value) of the effect of each concentration was determined using Student’s t-test as given by the equation: tðXiÞ ¼ EðXiÞ =SE
2. Methods
825
ð2Þ
where E(Xi) is the effect of variable Xi. 2.3.2. Optimization of critical variables using response surface methodology The critical variables, starch, peptone, Tween-80 and sodium phytate were further optimized by RSM in the synthetic medium [g/l: starch variable, glucose 1.0, peptone variable, MgSO4 0.5, Tween-80 variable, sodium phytate variable and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0), pH 5.0] using central composite design (CCD) to find out their optimal values and to study their interactions. Each variable in the design was studied at five different levels (Table 2). A 24 factorial design, with eight axial points and six replicates at the center point with a total number of 30 experiments were employed according to the statistical software package Design-Expert (Version 6.0.7, Stat-Ease, Minneapolis, MN, USA) [Table 3]. The behavior of the system was explained by the following quadratic equation Y ¼ b0 þ b1 A þ b2 B þ b3 C þ b4 D þ b11 A2 þ b22 B2 þ b33 C 2 þ b44 D2 þ b1 b2 AB þ b1 b3 AC þ b1 b4 AD þ b2 b3 BC þ b2 b4 BD þ b3 b4 CD
ð3Þ
where Y is predicted response, b0 is intercept, b1, b2, b3, b4 are linear coefficients, b1,1, b2,2, b3,3, b4,4 are squared coefficient, b1,2, b1,3, b1,4, b2,3, b2,4, b3,4 are interaction coefficients A, B, C, A2, B2, C2, AB, AC, AD, BC, CD are independent variables. 2.4. Effect of supplementation of inorganic phosphate in the medium The optimized medium [g/l: Starch 4.0, glucose 1.0, peptone 3.0, MgSO4 0.5, Tween-80 10, sodium phytate 3.0 and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0)] was supplemented with different concentrations (0.005–0.25%) of inorganic phosphate as K2HPO4. The cell-free culture filtrates were used in the enzyme assays.
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2.5. Effect of substitution of sodium phytate with different concentrations of wheat bran Sodium phytate of the medium was substituted with different levels of wheat bran (1–4%) and the cell-free culture filtrates were analyzed for phytase activity. 2.6. Large scale production of phytase The feasibility of phytase production was tested in shake flasks of varied volumes (0.25–2.0 l), and 22 l Biostat C fermenter (B. Braun, Melsungen, Germany) under the optimized conditions. The fermenter containing 10 l medium [g/l: starch 4.0, glucose 1.0, peptone 3.0, MgSO4 0.5, KH2PO4 0.1, Tween-80 10, wheat bran 30 and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0)] was operated at 45 C, 250 rev min1 with 1 vvm of aeration. The pH was maintained at 5.0 ± 0.05 using sterile 2 N HCl/ NaOH. The samples were drawn at the desired intervals aseptically and the cell-free culture filtrates were used in the determination of biomass and soluble protein, and in phytase assays.
Table 1 Regression analysis of the data generated by the Plackett–Burman designa Term
% Contribution
F-value
p-value
Intercept Starch Glucose (NH4)2SO4 NH4NO3 Peptone Tryptone Corn steep liquor Asparagine MgSO4 Æ 7H2O CaCl2 Æ 7H2O Incubation period Sodium phytate Tween-80 FeSO4 Æ 7H2O MnSO4 Æ H2O
100 19.175 0.0146669 3.35114 0.0250117 3.65858 3.04038 0.141516 2.41418 4.89057 0.0208231 3.24642 24.2314 35.4797 0.188207 0.127403
129.38 224.43 0.172 39.22 0.294 42.82 35.59 1.66 28.26 57.24 0.244 38.00 283.61 415.27 2.20 1.43
<0.0001 <0.0001 0.022 0.0008 0.018 0.0006 0.0010 0.0158 0.0018 0.0003 0.0175 0.0008 <0.0001 <0.0001 0.0170 0.0172
a
Coefficient of determination (R2) = 0.995.
4000
3.1. The optimization of medium components for phytase production by S. thermophile Plackett–Burman design is a widely used statistical design for the screening of the medium components (Plackett and Burman, 1946). The design screens important variables that affect enzyme production as well as their significant levels, but does not consider the interaction effects among the variables as in RSM. In RSM, each selected variable is studied at five different levels along with other variables, and therefore, the interactions among the variables at their different levels could be studied. Table 1 presents the effect of each variable along with contribution (%), p-value and F-value. The observations revealed a variation from 12.56 to 8250.75 U/l for phytase production, reflecting the importance of medium optimization to attain higher productivity. The pareto graph was drawn to show the effect of all variables on phytase production (Fig. 1). When the value of concentration effect (EXi) of the tested variable was positive, the influence of variable was greater at the high concentration tested, and when negative, the influence of variable was greater at low concentration. A
0 -1000
MnSO4
KH2PO4
Tween-80
Sodium phytate
CaCl2
Incubation period
MgSO4
Asparagine
Yeast extract
Peptone
Tryptone
-3000
Glucose
-2000 Ammonium nitrate
3. Results and discussion
1000
Ammonium sulphate
Fungal biomass was estimated gravimetrically by filtering the culture through a pre-weighed dry Whatman No. 1 filter paper and drying at 80 C to a constant weight. Soluble protein was determined according to Lowry et al. (1951) using BSA as a standard. All experiments were carried out in triplicates and their mean values are presented.
2000
Starch
2.7. Determination of biomass and soluble protein
Effect on phytase production
3000
Variables
Fig. 1. Pareto graph showing the effect of variables on phytase production.
p-value less than 0.05 indicated that the model terms are significant. The analysis of the data suggested that phytase production was affected by starch, peptone, magnesium sulphate, sodium phytate and Tween-80 as corroborated further by their F-values and p-values (Table 1). The influence of Tween-80 and sodium phytate was greater at high concentration, while starch and peptone affected enzyme titres at low levels. These variables had confidence level above 95% and higher than other variables, and thus, were considered to significantly affect phytase production by S. thermophile. The variables identified by Plackett–Burman design (starch, peptone, sodium phytate and Tween-80) were further optimized by RSM using central composite design (CCD), and the other variables (Glucose, MgSO4 Æ 7H2O, FeSO4 Æ 7H2O, MnSO4 Æ H2O and CaCl2 Æ 7H2O) were taken at their initial level. The data recorded for phytase
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830 Table 2 Range of variables used for the response surface methodology Independent variables
Starch Peptone Sodium phytate Tween-80
(% (% (% (%
Levels
w/v) w/v) w/v) v/v)
a
1
0
+1
+a
0.05 0.10 0.20 0.25
0.3 0.20 0.30 1.00
0.55 0.30 0.40 1.75
0.80 0.40 0.50 2.50
1.05 0.50 0.60 3.25
production were analyzed using the analysis of variance (ANOVA) as appropriate to the experimental design used. The range of coded as well as actual values of the selected variables for phytase production is presented in Table 2. In the regression equation derived for the optimization of medium components, the phytase activity (Y) was shown as a function of these variables. By applying multiple regression analysis on the experimental data, the following equation was found to explain the production of phytase:
827
where Y, represents the response i.e. phytase production and A, B, C, D, A2, B2, C2, D2, A3, AB, AB, AD, BC, BD and CD, ABC, ABD, BCD are the variables. The predicted levels of phytase production along with the experimental data are given in Table 3. The quality of the model could be checked using various criteria. The coefficient of determination (Adjusted R2) is 0.9889 for phytase production, which explained 98.89% variability in the model. The R2 value should be between 0 and 1. The closer the R2 value is to 1.0, the stronger the model and better it predicts the response (Haaland, 1989). The purpose of statistical analysis is to determine the experimental factors, which generate signals that are large in comparison to the noise. Adequate precision measures signal to noise ratio. An adequate precision of 35.369 for phytase production was recorded. The value of correlation coefficient (predicted R2) for phytase production was 0.9233 suggesting
Y ¼ þ9890:00 1888:67A 853:75D 616:19A2 552:44B2 786:44C 2 737:94D2 þ 541:13AB 12170.32
þ 29:13AC þ 669:88AD þ 550:13BC þ 215:88BD ð4Þ
Table 3 Experimental design and results of CCD of response surface methodology Run Starch Peptone Sodium no. (%) (%) phytate (%)
0.50
0.36
0.41 0.28
0.33 0.19
)
0.24
)
Enzyme activity ± standard deviation.
5600.29 ± 12.48a 5714.85 8090.00 ± 576.56 8386.80 6640.52 ± 213.89 6417.37 7690.87 ± 378.93 7238.12 10230.00 ± 874.37 10230.68 5678.00 ± 308.75 5610.26 6579.00 ± 454.30 6579.68 8490.00 ± 589.50 8418.57 9890.59 ± 540.43 9890.55 7570.98 ± 489.54 7309.28 6790.30 ± 210.00 6779.69 9876.64 ± 777.89 10061.27 5590.27 ± 200.86 5500.62 8400.00 ± 508.20 8501.04 9890.00 ± 608.78 9890.55 3510.51 ± 280.40 3420.84 4608.46 ± 276.90 4867.25 12500.45 ± 950.60 11915.75 10020.89 ± 487.56 9890.55 8560.64 ± 211.19 8529.41 4512.11 ± 209.29 4500.31 8956.65 ± 490.58 8952.18 4800.71 ± 270.41 4802.26 9905.69 ± 698.45 9890.55 8760.11 ± 307.23 9247.53 5498.66 ± 386.20 6048.76 7180.00 ± 287.69 7173.85 9890.78 ± 540.33 9890.55 6750.00 ± 300.45 6823.23 10002.00 ± 789.90 9890.55
%
1 1 1 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 a +a 0 1 1 1 0 0 0
0.45
e(
1 1 1 1 0 1 a 0 0 1 +a 1 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 0 0
6489.40
on
1 1 1 1 0 1 0 +a 0 1 0 1 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 a 0
7909.63
pt
a
1 1 1 1 +a 1 0 0 0 1 0 1 1 1 0 1 a 1 0 1 1 0 0 0 1 1 1 0 0 0
9329.86
Pe
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Tween- Phytase activity (U/l) 80 (%) Experimental Predicted
ch
(%
tar
S
0.10 0.15
Fig. 2a. Response surface graph showing the effect of the interaction of starch and peptone on phytase production.
12170.32 Phytase production (U/l)
652:13ABD 671:63BCD
Phytase production (U/l)
10750.10
þ 6:38CD þ 830:29A3 345:38B2 C þ 818:62ABC
10750.10 9329.86 7909.63 6489.40
0.50 0.45 0.43
So
diu
m
0.36
ph 0.35 yta te (% 0.28 )
0.28 0.19 0.20
0.10
%)
e(
n pto
Pe
Fig. 2b. Response surface graph showing the effect of the interaction of sodium phytate and peptone on phytase production.
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830 14000
5
12000 4 10000 3
8000 6000
2
Biomass (g/l)
a strong correlation between the experimental and predicted values of phytase production. The model F-value of 62.75 and p-value of <0.0001 indicated that the model terms are significant. The three-dimensional plots showed the optimal levels and interactions among the culture variables for enzyme production (Figs. 2a and 2b). The maximum experimental value for phytase production was 12 500 U/l, whereas the predicted response was 11 915.75 U/l indicating a strong agreement between them. The optimum values of the tested variables for starch, Tween-80, peptone and sodium phytate were 0.4%, 1.0%, 0.3% and 0.3%, respectively as shown in response surface graphs.
Phytase production (U/l)
828
4000 1 2000 0 0.00
0.05
0.10 0.15 0.20 Inorganic phosphate (%)
0.25
0 0.30
Fig. 3. Effect of inorganic phosphate on phytase production.
3.2. Validation of the model The model was validated by repeating the experiments under the optimized conditions that resulted in the phytase production of 12 105.89 U/l (Predicted response 11 915.75 U/l), thus proving the validity of the model. The statistical optimization resulted in about 3.73-fold improvement in phytase production. S. thermophile secreted 12 500 U/l phytase in the optimized medium, which is 3.73- and 1.91-fold higher than that in unoptimized medium and optimized by one variable at a time approach, respectively. By using statistical optimization, 1.3-fold increase in phytase production by Rhizomucor pusillus (Chadha et al., 2004), 1.8-fold higher phytase production in Mucor racemosus (Bogar et al., 2003b), 1.7-fold higher phytase production by A. ficuum (Bogar et al., 2003a) and 1.75-fold higher phytase production by a yeast Pichia anomala in synthetic medium (Vohra and Satyanarayana, 2002) and 5-fold higher phytase production in cane molasses medium (Kaur and Satyanarayana, 2005) were achieved, respectively. These observations clearly suggested that the nutritional and physical requirements of the microbes are differ from one another, and therefore, need to be optimized for each strain. 3.3. Effect of supplementation of inorganic phosphate in the medium Supplementation of inorganic phosphate in the medium enhanced phytase synthesis up to 0.01% and decreased thereafter (Fig. 3). Phytase production was enhanced with the incorporation of low levels of inorganic phosphate in the medium but declined at high levels, which could be due to the repression of enzyme synthesis, as high phosphate levels are known to repress the synthesis of acid phosphatases and phytases, while limiting phosphate conditions result in their expression (Vohra and Satyanarayana, 2003; Vats and Banerjee, 2004). Han et al. (1987) observed similar trend for phytase production by A. ficuum on semisolid substrate using soybean meal, where 10 mg P/ 100 g substrate in the growth medium caused a high phytase secretion as compared to the control, while higher phosphate levels repressed phytase production. Kim et al.
(1999) have reported similar observations on extracellular phytase production by Aspergillus sp. 5990. In a survey of phytase producing microorganisms, A. ficuum was found to produce highest amount of phytase, when the inorganic phosphorus content was in the range of 0.0001–0.005% with 8% corn starch (Shieh et al., 1969). Nampoothiri et al. (2004) have also recorded enhanced phytase secretion by Thermoascus aurantiacus at low level of inorganic phosphate followed by reduction at higher levels. These observations suggested that the lower concentrations of inorganic phosphate stimulate phytase synthesis, while higher concentrations repress it. 3.4. Effect of substitution of sodium phytate with wheat bran The use of agricultural residues in microbial fermentations is being encouraged due to their ease of availability and inexpensive (Pandey et al., 2001), and contains carbon and nitrogen that are utilized by microbes. Wheat bran also contains phytic acid, an inducer of phytase. Substitution of sodium phytate with wheat bran (3%) resulted in a sustained secretion of phytase (12 098.72 U/l). There was a slight enhancement beyond 3%, but as the wheat bran level increased, the fermentation began to resemble to semi-solid state fermentation. As sodium phytate is expensive, an attempt was made to substitute it with wheat bran that resulted in a sustained secretion of phytase. This could be either due to the slow release of phosphate from wheat bran by the mould phytase preventing the repression/inhibition of phytase or could be regarded as an indication of phytase induction by the presence of phytic acid in wheat bran (Papagianni et al., 1999). These results are consistent with those of Nampoothiri et al. (2004) and Papagianni et al. (1999), where wheat bran supported maximum phytase production by T. aurantiacus and A. niger, respectively. 3.5. Large scale production of phytase Phytase production was sustainable (10 996–11 930 U/l) in Erlenmeyer flasks of varied volumes (0.25–2 l) and in 22 l fermenter, suggesting the feasibility of scale up of phy-
7
12000
6
10000
5
8000
4
6000
3
4000
2
2000
1
2.0
1.5
1.0
0.5
0 0
10
20
30 40 50 Fermentation time (h)
60
70
0
829
New Delhi, India) by awarding Junior/Senior Research Fellowship during the course of this investigation.
2.5
Soluble protein (mg/ml)
14000
Biomass (g/l)
Phytase production (U/l)
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830
0.0
Fig. 4. Phytase production by S. thermophile in 22 l fermenter under optimized conditions.
tase production. There was a slight decline in enzyme production in 2 l Erlenmeyer flasks (10 996 U/l), which could be due to the reduction in dissolved oxygen and inadequate mixing with increased volume (Uma Maheshwar Rao and Satyanarayana, 2003). A high proportion of phytase is produced during the exponential growth phase reaching maximum after 48 h followed by a decline (Fig. 4). Further, there was a reduction in fermentation period in the laboratory fermenter which appeared to be due to better mixing of nutrients and improved dissolved oxygen due to sparging of air into the fermenter (Uma Maheshwar Rao and Satyanarayana, 2003; Kaur and Satyanarayana, 2005). Similar observations have been recorded for the production of a-amylase by Geobacillus thermoleovorans (Uma Maheshwar Rao and Satyanarayana, 2003) and phytase by Pichia anomala (Kaur and Satyanarayana, 2005).
4. Conclusions Plackett–Burman design made possible to consider a large number of variables and to identify the most important variables that affect phytase production. Plackett–Burman and RSM designs have been proved to be effective in optimizing phytase production by S. thermophile in submerged fermentation, which resulted in a 3.73-fold enhancement in phytase production. Sodium phytate could be substituted with wheat bran that resulted in sustained secretion of phytase, and thus making the fermentation process cost-effective and more economical. The phytase productivity by S. thermophile was higher in solid state fermentation (2333.33 U/Kg/h) than that in submerged fermentation (126 U/l/h).
Acknowledgements BS gratefully acknowledges the financial assistance from the Council of Scientific and Industrial Research (CSIR,
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Vohra, A., Satyanarayana, T., 2002. Statistical optimization of the media components by response surface methodology to enhance phytase production by Pichia anomala. Process Biochem. 37, 999–1004. Wodzinski, R.J., Ullah, A.H.J., 1996. Phytase. Adv. Appl. Microbiol. 42, 263–310.
Bioresource Technology 99 (2008) 824–830
Improved phytase production by a thermophilic mould Sporotrichum thermophile in submerged fermentation due to statistical optimization Bijender Singh, T. Satyanarayana
*
Department of Microbiology, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India Received 14 November 2006; received in revised form 18 January 2007; accepted 18 January 2007 Available online 12 March 2007
Abstract Culture variables affecting phytase production by a thermophilic mould Sporotrichum thermophile in submerged fermentation were optimized. Soluble starch, peptone, Tween-80 and sodium phytate were identified by Plackett–Burman design as the most significant factors to affect phytase production. The 24 full factorial central composite design of response surface methodology was applied for optimizing the concentrations of the significant variables and to delineate their interactions. Starch, Tween-80, peptone and sodium phytate at 0.4%, 1.0%, 0.3% and 0.3% supported maximum enzyme titres, respectively. An overall 3.73-fold improvement in phytase production was achieved due to optimization. When sodium phytate was substituted with wheat bran (3%), the phytase titre in the former was comparable with that in the latter. 2007 Published by Elsevier Ltd. Keywords: Phytase; Sporotrichum thermophile; Plackett–Burman design; Response surface methodology; Wheat bran
1. Introduction The screening and evaluation of nutritional and other culture variables of a microorganism is an important step in the development of a bioprocess. Since microbial fermentations are affected by cultural variables, a suitable approach has to be applied for their optimization. The conventional methods used in the optimization of the cultural variables are time consuming, tedious and expensive (Vohra and Satyanarayana, 2002; Bogar et al., 2003a,b). These methods also tend to overlook the effects of interactions among them. Optimization of all the variables by statistical experimental designs can eliminate the limitations of ‘one variable at a time’ approach (Stanbury et al., 1997). Several research groups have applied this approach for screening and optimization of the parameters affecting
*
Corresponding author. Tel.: +91 11 24112008; fax: +91 11 24115270. E-mail address: [email protected] (T. Satyanarayana).
0960-8524/$ - see front matter 2007 Published by Elsevier Ltd. doi:10.1016/j.biortech.2007.01.007
phytase production (Sunitha et al., 1999; Vohra and Satyanarayana, 2002; Kaur and Satyanarayana, 2005). The use of statistical approaches helped in enhancing yield of yeast phytase that led to the reduction in the cost of production, and therefore, making the fermentation process economical and cost effective (Vohra and Satyanarayana, 2002; Kaur and Satyanarayana, 2005). Phytases (myo-inositol hexaphosphate phosphohydrolase EC 3.1.3.8) are histidine acid phosphatases, a subclass of acid phosphatases which catalyze the hydrolysis of phosphate moieties from phytic acid (myo-inositol hexakis dihydrogen phosphate), thereby mitigating its anti-nutritional properties (Vohra and Satyanarayana, 2003; Greiner and Konietzny, 2006). The market volume of phytases is in the range of 2 150 milllion which is still rising further (Greiner and Konietzny, 2006). The supplementation of animal feed with phytases enhances the bioavailability of minerals, protein and phosphorus in monogastric animals, besides reducing the phosphorus pollution in the areas of intensive livestock production. In grains, roughly 60–80% of
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830
phosphorus is tied up in phytin, which is not digestible by monogastric animals due to the lack of adequate levels of phytase in their digestive tracts. The phytin thus excreted in the manure reaches the soil, where it is degraded by microflora that leads to the eutrophication of water bodies and thus environmental pollution (Wodzinski and Ullah, 1996). The pollution can be tackled by supplementing animal feeds with phytase. In view of ever increasing demand for phytase, it is essential to produce phytase in a cost-effective manner. In this investigation, an attempt has been made to improve phytase production by a thermophilic mould Sporotrichum thermophile by statistical approaches using Plackett–Burman design and response surface methodology (RSM) in submerged fermentation.
factor and the average of the measurements made at the low level of that factor, which was determined by the following equation: EðXiÞ ¼
2ðRPiþ RPi Þ N
2.1. Source of the strain and inoculum preparation The thermophilic mould, S. thermophile BJTLR50 was isolated from a soil sample collected from Rohtak, Haryana (India), and routinely grown on Emerson’s YpSs (Emerson, 1941) agar medium (Singh and Satyanarayana, 2006). The conidiospores from 6 day-old fully sporulated solid media slopes were harvested by flooding with normal saline containing 0.1% (v/v) Tween-80 and the spore suspension thus prepared was adjusted to 1 · 107 CFU/ml for inoculating the medium. 2.2. Phytase assay Phytase was assayed by measuring the inorganic phosphorus liberated from sodium phytate at pH 5.0 and 60 C according to Fiske and Subbarow (1925). One unit of phytase is defined as the amount of enzyme that liberates 1 nmol of inorganic phosphate per ml per sec under the assay conditions. 2.3. Optimization of phytase production The optimization of medium components for phytase production by S. thermophile was carried out in two stages. 2.3.1. Identification of important nutrient components using Plackett–Burman design The critical cultural variables (15 culture variables and four dummy variables) were screened for phytase production using Plackett–Burman design (Plackett and Burman, 1946) as shown in Table 1. Total number of experiments to be carried out according to Plackett–Burman is n + 1, where n is the number of variables. Each variable is represented at two levels, high and low denoted by (+) and (), respectively. The number of positive and negative signs per experiment or trial are (n + 1)/2 and (n 1)/2, respectively. The effect of each variable is the difference between the average of the measurements made at the high level of that
ð1Þ
where E(Xi) is the concentration effect of the tested variable. Pi+ and Pi are the phytase activities from the trials where the variable (Xi) under study was present at high and low concentrations, respectively and N is the number of experiments. Standard error (SE) of the concentration effect was the square root of the variance of an effect and the significant level (p-value) of the effect of each concentration was determined using Student’s t-test as given by the equation: tðXiÞ ¼ EðXiÞ =SE
2. Methods
825
ð2Þ
where E(Xi) is the effect of variable Xi. 2.3.2. Optimization of critical variables using response surface methodology The critical variables, starch, peptone, Tween-80 and sodium phytate were further optimized by RSM in the synthetic medium [g/l: starch variable, glucose 1.0, peptone variable, MgSO4 0.5, Tween-80 variable, sodium phytate variable and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0), pH 5.0] using central composite design (CCD) to find out their optimal values and to study their interactions. Each variable in the design was studied at five different levels (Table 2). A 24 factorial design, with eight axial points and six replicates at the center point with a total number of 30 experiments were employed according to the statistical software package Design-Expert (Version 6.0.7, Stat-Ease, Minneapolis, MN, USA) [Table 3]. The behavior of the system was explained by the following quadratic equation Y ¼ b0 þ b1 A þ b2 B þ b3 C þ b4 D þ b11 A2 þ b22 B2 þ b33 C 2 þ b44 D2 þ b1 b2 AB þ b1 b3 AC þ b1 b4 AD þ b2 b3 BC þ b2 b4 BD þ b3 b4 CD
ð3Þ
where Y is predicted response, b0 is intercept, b1, b2, b3, b4 are linear coefficients, b1,1, b2,2, b3,3, b4,4 are squared coefficient, b1,2, b1,3, b1,4, b2,3, b2,4, b3,4 are interaction coefficients A, B, C, A2, B2, C2, AB, AC, AD, BC, CD are independent variables. 2.4. Effect of supplementation of inorganic phosphate in the medium The optimized medium [g/l: Starch 4.0, glucose 1.0, peptone 3.0, MgSO4 0.5, Tween-80 10, sodium phytate 3.0 and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0)] was supplemented with different concentrations (0.005–0.25%) of inorganic phosphate as K2HPO4. The cell-free culture filtrates were used in the enzyme assays.
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2.5. Effect of substitution of sodium phytate with different concentrations of wheat bran Sodium phytate of the medium was substituted with different levels of wheat bran (1–4%) and the cell-free culture filtrates were analyzed for phytase activity. 2.6. Large scale production of phytase The feasibility of phytase production was tested in shake flasks of varied volumes (0.25–2.0 l), and 22 l Biostat C fermenter (B. Braun, Melsungen, Germany) under the optimized conditions. The fermenter containing 10 l medium [g/l: starch 4.0, glucose 1.0, peptone 3.0, MgSO4 0.5, KH2PO4 0.1, Tween-80 10, wheat bran 30 and micronutrient solution 10 ml (g/l: MnSO4 1.0, FeSO4 1.0, CaCl2 2.0, KCl 5.0)] was operated at 45 C, 250 rev min1 with 1 vvm of aeration. The pH was maintained at 5.0 ± 0.05 using sterile 2 N HCl/ NaOH. The samples were drawn at the desired intervals aseptically and the cell-free culture filtrates were used in the determination of biomass and soluble protein, and in phytase assays.
Table 1 Regression analysis of the data generated by the Plackett–Burman designa Term
% Contribution
F-value
p-value
Intercept Starch Glucose (NH4)2SO4 NH4NO3 Peptone Tryptone Corn steep liquor Asparagine MgSO4 Æ 7H2O CaCl2 Æ 7H2O Incubation period Sodium phytate Tween-80 FeSO4 Æ 7H2O MnSO4 Æ H2O
100 19.175 0.0146669 3.35114 0.0250117 3.65858 3.04038 0.141516 2.41418 4.89057 0.0208231 3.24642 24.2314 35.4797 0.188207 0.127403
129.38 224.43 0.172 39.22 0.294 42.82 35.59 1.66 28.26 57.24 0.244 38.00 283.61 415.27 2.20 1.43
<0.0001 <0.0001 0.022 0.0008 0.018 0.0006 0.0010 0.0158 0.0018 0.0003 0.0175 0.0008 <0.0001 <0.0001 0.0170 0.0172
a
Coefficient of determination (R2) = 0.995.
4000
3.1. The optimization of medium components for phytase production by S. thermophile Plackett–Burman design is a widely used statistical design for the screening of the medium components (Plackett and Burman, 1946). The design screens important variables that affect enzyme production as well as their significant levels, but does not consider the interaction effects among the variables as in RSM. In RSM, each selected variable is studied at five different levels along with other variables, and therefore, the interactions among the variables at their different levels could be studied. Table 1 presents the effect of each variable along with contribution (%), p-value and F-value. The observations revealed a variation from 12.56 to 8250.75 U/l for phytase production, reflecting the importance of medium optimization to attain higher productivity. The pareto graph was drawn to show the effect of all variables on phytase production (Fig. 1). When the value of concentration effect (EXi) of the tested variable was positive, the influence of variable was greater at the high concentration tested, and when negative, the influence of variable was greater at low concentration. A
0 -1000
MnSO4
KH2PO4
Tween-80
Sodium phytate
CaCl2
Incubation period
MgSO4
Asparagine
Yeast extract
Peptone
Tryptone
-3000
Glucose
-2000 Ammonium nitrate
3. Results and discussion
1000
Ammonium sulphate
Fungal biomass was estimated gravimetrically by filtering the culture through a pre-weighed dry Whatman No. 1 filter paper and drying at 80 C to a constant weight. Soluble protein was determined according to Lowry et al. (1951) using BSA as a standard. All experiments were carried out in triplicates and their mean values are presented.
2000
Starch
2.7. Determination of biomass and soluble protein
Effect on phytase production
3000
Variables
Fig. 1. Pareto graph showing the effect of variables on phytase production.
p-value less than 0.05 indicated that the model terms are significant. The analysis of the data suggested that phytase production was affected by starch, peptone, magnesium sulphate, sodium phytate and Tween-80 as corroborated further by their F-values and p-values (Table 1). The influence of Tween-80 and sodium phytate was greater at high concentration, while starch and peptone affected enzyme titres at low levels. These variables had confidence level above 95% and higher than other variables, and thus, were considered to significantly affect phytase production by S. thermophile. The variables identified by Plackett–Burman design (starch, peptone, sodium phytate and Tween-80) were further optimized by RSM using central composite design (CCD), and the other variables (Glucose, MgSO4 Æ 7H2O, FeSO4 Æ 7H2O, MnSO4 Æ H2O and CaCl2 Æ 7H2O) were taken at their initial level. The data recorded for phytase
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830 Table 2 Range of variables used for the response surface methodology Independent variables
Starch Peptone Sodium phytate Tween-80
(% (% (% (%
Levels
w/v) w/v) w/v) v/v)
a
1
0
+1
+a
0.05 0.10 0.20 0.25
0.3 0.20 0.30 1.00
0.55 0.30 0.40 1.75
0.80 0.40 0.50 2.50
1.05 0.50 0.60 3.25
production were analyzed using the analysis of variance (ANOVA) as appropriate to the experimental design used. The range of coded as well as actual values of the selected variables for phytase production is presented in Table 2. In the regression equation derived for the optimization of medium components, the phytase activity (Y) was shown as a function of these variables. By applying multiple regression analysis on the experimental data, the following equation was found to explain the production of phytase:
827
where Y, represents the response i.e. phytase production and A, B, C, D, A2, B2, C2, D2, A3, AB, AB, AD, BC, BD and CD, ABC, ABD, BCD are the variables. The predicted levels of phytase production along with the experimental data are given in Table 3. The quality of the model could be checked using various criteria. The coefficient of determination (Adjusted R2) is 0.9889 for phytase production, which explained 98.89% variability in the model. The R2 value should be between 0 and 1. The closer the R2 value is to 1.0, the stronger the model and better it predicts the response (Haaland, 1989). The purpose of statistical analysis is to determine the experimental factors, which generate signals that are large in comparison to the noise. Adequate precision measures signal to noise ratio. An adequate precision of 35.369 for phytase production was recorded. The value of correlation coefficient (predicted R2) for phytase production was 0.9233 suggesting
Y ¼ þ9890:00 1888:67A 853:75D 616:19A2 552:44B2 786:44C 2 737:94D2 þ 541:13AB 12170.32
þ 29:13AC þ 669:88AD þ 550:13BC þ 215:88BD ð4Þ
Table 3 Experimental design and results of CCD of response surface methodology Run Starch Peptone Sodium no. (%) (%) phytate (%)
0.50
0.36
0.41 0.28
0.33 0.19
)
0.24
)
Enzyme activity ± standard deviation.
5600.29 ± 12.48a 5714.85 8090.00 ± 576.56 8386.80 6640.52 ± 213.89 6417.37 7690.87 ± 378.93 7238.12 10230.00 ± 874.37 10230.68 5678.00 ± 308.75 5610.26 6579.00 ± 454.30 6579.68 8490.00 ± 589.50 8418.57 9890.59 ± 540.43 9890.55 7570.98 ± 489.54 7309.28 6790.30 ± 210.00 6779.69 9876.64 ± 777.89 10061.27 5590.27 ± 200.86 5500.62 8400.00 ± 508.20 8501.04 9890.00 ± 608.78 9890.55 3510.51 ± 280.40 3420.84 4608.46 ± 276.90 4867.25 12500.45 ± 950.60 11915.75 10020.89 ± 487.56 9890.55 8560.64 ± 211.19 8529.41 4512.11 ± 209.29 4500.31 8956.65 ± 490.58 8952.18 4800.71 ± 270.41 4802.26 9905.69 ± 698.45 9890.55 8760.11 ± 307.23 9247.53 5498.66 ± 386.20 6048.76 7180.00 ± 287.69 7173.85 9890.78 ± 540.33 9890.55 6750.00 ± 300.45 6823.23 10002.00 ± 789.90 9890.55
%
1 1 1 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 a +a 0 1 1 1 0 0 0
0.45
e(
1 1 1 1 0 1 a 0 0 1 +a 1 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 0 0
6489.40
on
1 1 1 1 0 1 0 +a 0 1 0 1 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 a 0
7909.63
pt
a
1 1 1 1 +a 1 0 0 0 1 0 1 1 1 0 1 a 1 0 1 1 0 0 0 1 1 1 0 0 0
9329.86
Pe
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Tween- Phytase activity (U/l) 80 (%) Experimental Predicted
ch
(%
tar
S
0.10 0.15
Fig. 2a. Response surface graph showing the effect of the interaction of starch and peptone on phytase production.
12170.32 Phytase production (U/l)
652:13ABD 671:63BCD
Phytase production (U/l)
10750.10
þ 6:38CD þ 830:29A3 345:38B2 C þ 818:62ABC
10750.10 9329.86 7909.63 6489.40
0.50 0.45 0.43
So
diu
m
0.36
ph 0.35 yta te (% 0.28 )
0.28 0.19 0.20
0.10
%)
e(
n pto
Pe
Fig. 2b. Response surface graph showing the effect of the interaction of sodium phytate and peptone on phytase production.
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830 14000
5
12000 4 10000 3
8000 6000
2
Biomass (g/l)
a strong correlation between the experimental and predicted values of phytase production. The model F-value of 62.75 and p-value of <0.0001 indicated that the model terms are significant. The three-dimensional plots showed the optimal levels and interactions among the culture variables for enzyme production (Figs. 2a and 2b). The maximum experimental value for phytase production was 12 500 U/l, whereas the predicted response was 11 915.75 U/l indicating a strong agreement between them. The optimum values of the tested variables for starch, Tween-80, peptone and sodium phytate were 0.4%, 1.0%, 0.3% and 0.3%, respectively as shown in response surface graphs.
Phytase production (U/l)
828
4000 1 2000 0 0.00
0.05
0.10 0.15 0.20 Inorganic phosphate (%)
0.25
0 0.30
Fig. 3. Effect of inorganic phosphate on phytase production.
3.2. Validation of the model The model was validated by repeating the experiments under the optimized conditions that resulted in the phytase production of 12 105.89 U/l (Predicted response 11 915.75 U/l), thus proving the validity of the model. The statistical optimization resulted in about 3.73-fold improvement in phytase production. S. thermophile secreted 12 500 U/l phytase in the optimized medium, which is 3.73- and 1.91-fold higher than that in unoptimized medium and optimized by one variable at a time approach, respectively. By using statistical optimization, 1.3-fold increase in phytase production by Rhizomucor pusillus (Chadha et al., 2004), 1.8-fold higher phytase production in Mucor racemosus (Bogar et al., 2003b), 1.7-fold higher phytase production by A. ficuum (Bogar et al., 2003a) and 1.75-fold higher phytase production by a yeast Pichia anomala in synthetic medium (Vohra and Satyanarayana, 2002) and 5-fold higher phytase production in cane molasses medium (Kaur and Satyanarayana, 2005) were achieved, respectively. These observations clearly suggested that the nutritional and physical requirements of the microbes are differ from one another, and therefore, need to be optimized for each strain. 3.3. Effect of supplementation of inorganic phosphate in the medium Supplementation of inorganic phosphate in the medium enhanced phytase synthesis up to 0.01% and decreased thereafter (Fig. 3). Phytase production was enhanced with the incorporation of low levels of inorganic phosphate in the medium but declined at high levels, which could be due to the repression of enzyme synthesis, as high phosphate levels are known to repress the synthesis of acid phosphatases and phytases, while limiting phosphate conditions result in their expression (Vohra and Satyanarayana, 2003; Vats and Banerjee, 2004). Han et al. (1987) observed similar trend for phytase production by A. ficuum on semisolid substrate using soybean meal, where 10 mg P/ 100 g substrate in the growth medium caused a high phytase secretion as compared to the control, while higher phosphate levels repressed phytase production. Kim et al.
(1999) have reported similar observations on extracellular phytase production by Aspergillus sp. 5990. In a survey of phytase producing microorganisms, A. ficuum was found to produce highest amount of phytase, when the inorganic phosphorus content was in the range of 0.0001–0.005% with 8% corn starch (Shieh et al., 1969). Nampoothiri et al. (2004) have also recorded enhanced phytase secretion by Thermoascus aurantiacus at low level of inorganic phosphate followed by reduction at higher levels. These observations suggested that the lower concentrations of inorganic phosphate stimulate phytase synthesis, while higher concentrations repress it. 3.4. Effect of substitution of sodium phytate with wheat bran The use of agricultural residues in microbial fermentations is being encouraged due to their ease of availability and inexpensive (Pandey et al., 2001), and contains carbon and nitrogen that are utilized by microbes. Wheat bran also contains phytic acid, an inducer of phytase. Substitution of sodium phytate with wheat bran (3%) resulted in a sustained secretion of phytase (12 098.72 U/l). There was a slight enhancement beyond 3%, but as the wheat bran level increased, the fermentation began to resemble to semi-solid state fermentation. As sodium phytate is expensive, an attempt was made to substitute it with wheat bran that resulted in a sustained secretion of phytase. This could be either due to the slow release of phosphate from wheat bran by the mould phytase preventing the repression/inhibition of phytase or could be regarded as an indication of phytase induction by the presence of phytic acid in wheat bran (Papagianni et al., 1999). These results are consistent with those of Nampoothiri et al. (2004) and Papagianni et al. (1999), where wheat bran supported maximum phytase production by T. aurantiacus and A. niger, respectively. 3.5. Large scale production of phytase Phytase production was sustainable (10 996–11 930 U/l) in Erlenmeyer flasks of varied volumes (0.25–2 l) and in 22 l fermenter, suggesting the feasibility of scale up of phy-
7
12000
6
10000
5
8000
4
6000
3
4000
2
2000
1
2.0
1.5
1.0
0.5
0 0
10
20
30 40 50 Fermentation time (h)
60
70
0
829
New Delhi, India) by awarding Junior/Senior Research Fellowship during the course of this investigation.
2.5
Soluble protein (mg/ml)
14000
Biomass (g/l)
Phytase production (U/l)
B. Singh, T. Satyanarayana / Bioresource Technology 99 (2008) 824–830
0.0
Fig. 4. Phytase production by S. thermophile in 22 l fermenter under optimized conditions.
tase production. There was a slight decline in enzyme production in 2 l Erlenmeyer flasks (10 996 U/l), which could be due to the reduction in dissolved oxygen and inadequate mixing with increased volume (Uma Maheshwar Rao and Satyanarayana, 2003). A high proportion of phytase is produced during the exponential growth phase reaching maximum after 48 h followed by a decline (Fig. 4). Further, there was a reduction in fermentation period in the laboratory fermenter which appeared to be due to better mixing of nutrients and improved dissolved oxygen due to sparging of air into the fermenter (Uma Maheshwar Rao and Satyanarayana, 2003; Kaur and Satyanarayana, 2005). Similar observations have been recorded for the production of a-amylase by Geobacillus thermoleovorans (Uma Maheshwar Rao and Satyanarayana, 2003) and phytase by Pichia anomala (Kaur and Satyanarayana, 2005).
4. Conclusions Plackett–Burman design made possible to consider a large number of variables and to identify the most important variables that affect phytase production. Plackett–Burman and RSM designs have been proved to be effective in optimizing phytase production by S. thermophile in submerged fermentation, which resulted in a 3.73-fold enhancement in phytase production. Sodium phytate could be substituted with wheat bran that resulted in sustained secretion of phytase, and thus making the fermentation process cost-effective and more economical. The phytase productivity by S. thermophile was higher in solid state fermentation (2333.33 U/Kg/h) than that in submerged fermentation (126 U/l/h).
Acknowledgements BS gratefully acknowledges the financial assistance from the Council of Scientific and Industrial Research (CSIR,
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