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Statistical optimization of [beta]-1,3 glucan production from the novel strain Bacillus cereus LVK13 (KC 898956).

Exopolysaccharides (EPSs) are either soluble or insoluble polymers obtained from various sources such as bacteria, yeast, fungi, or cereal plants. Many of these utilized in bioindustries are of microbial origin. Among this, [??]-Glucan, a homopolysaccharide of glucose bonded via [??]-(1,3 or 1,4 or 1,6)-D-glycosidic linkage is widely used in food and pharmaceutical industries due to its high anticancer and immunomodulatory effects (1).

EPSs can be produced by bacteria under all conditions, but the yield and chemical nature of EPS are strain dependent and affected by the nutritional and environmental conditions. The EPSs can be mass produced by fermentation of microorganisms on suitable media that aids increased yield of the product with stable costs independent of seasonal variations (2,3). The structure, composition and viscosity of EPS depend on a number of factors such as the composition of culture medium, type of strain, and fermentation conditions (4).

Jung et al (2007) suggests the need for intensive research on bacterial glucan since the industries are using only the glucan from mushrooms and yeast lysate even they are low in purity and yield due to the availability of diminutive information about bacterial glucans (5).

It is a prerequisite to design an appropriate production medium in an efficient fermentation process to achieve higher cell density and yield of the desired metabolic product or enzyme (6). Plackett-Burman design is used as the preliminary step to screen key factors among the process variables (7), which is generally followed by the steepest ascent (descent) method and central composite design (8). Statistical analysis offers tools for optimizing medium components and perhaps response surface methodology (RSM) is the most widely used statistical methods for optimizing medium components. RSM can be used to find out most favourable conditions, ranges of controllable variables, and polynomial equations generation and also to assess relationships between controllable variables and observed results (9,10).

In the current research, the fermentation media for the production of [??]-1,3 glucan by the novel bacterial strain Bacillus cereus LVK13 (KC 898956) (11) was optimized by Plackett-Burman design and Response surface methodology.

MATERIALS AND METHODS

Microorganism and maintenance

Bacillus cereus LVK13 isolated from the agricultural field soil of Sathyamangalam, Tamil Nadu, India with NCBI gene repository accession number KC 898956 was used throughout this study. The isolate was maintained in nutrient agar slants containing the following ingredients (g/l); Peptone--5, yeast extract--1.5, beef extract--1.5, NaCl--5, agar--15 at 4[degrees]C for further studies.

Production conditions

Seed culture was prepared by inoculating loopful of isolate on to seed medium containing (g/l): peptone--5, yeast extract--5 incubated at 30oC for 20 hours on a rotary shaker at 180 rpm. Approximately 10% (v/v) of seed culture was transferred to production medium containing (g/1); Sucrose--100, K[H.sub.2]P[O.sub.4]--1.74, Ca[Cl.sub.2].2[H.sub.2]O--0.015, [K.sub.2]HP[O.sub.4]--0.49, Mn[Cl.sub.2].4[H.sub.2]O--0.01, [Na.sub.2]S[O.sub.4].10[H.sub.2]O-3.7, Citrate--0.21, Mg[Cl.sub.2].6[H.sub.2]O--0.25, N[H.sub.4]Cl--1.5, Fe[Cl.sub.3].6[H.sub.2]O--0.024. 10% sucrose as carbon source was added to the media in order to induce glucan production. The initial pH of the medium before sterilization was adjusted to 6.5. Glucan was produced by shake flask culture at 30oC on a rotary shaker at 180 rpm and the product was analyzed periodically up to 96 hours (11).

Optimization of [??]-glucan production Plackett-Burman design

The relative importance of various components of the medium that influences the production of glucan was evaluated using Plackett-Burman design based on the first-order polynomial model:

Y = [beta]0 + [summation][beta] iXi

Where Y is the response, ao is the model intercept, [??]i is the linear coefficient and Xi is the level of the independent variable. In the present work, 11 assigned variables were screened with 14 treatment combinations. Each independent variable was tested at high (+1) level and low (-1) level (Table 1) using Minitab 17 statistical software package. From regression analysis, the variables which are significant at the 95% level (p<0.05) were considered to have a greater impact on glucan production.

Response surface methodology

CCD was used for determining the optimum concentration of the significant factors that are screened using Plackett-Burman design. The second-order polynomial model used to correlate the relationship between the glucan yield and medium components was

Y = [[beta].sub.0] + [summation]f[[beta].sub.i][X.sub.i] + [summation][[beta].sub.ii][X.sub.i.sup.2] + [summation] [summation][[beta].sub.ij][X.sub.i][X.sub.j]

Where Y is the predicted response, [??]o, [??]i, [??]ii, [??]ij are the model constant, linear coefficient, quadratic coefficient and interaction coefficient respectively. Xi and Xj are the coded independent variables or factors.

The experimental design protocol for RSM was developed using Minitab 17 statistical software package. The analysis of variance (ANOVA) table was generated and the effect and regression coefficients of individual linear and quadratic interactions were determined. The importance of all the terms in the polynomial was predicted statistically by computing the F value at a probability (p) of 0.05. Regression coefficient was used to make statistical calculations to create response surface curves from the regression models.

In order to test the model accuracy, [R.sup.2], adjusted [R.sup.2] ([Radj.sup.2]) and predicted [R.sup.2] ([Rpred.sup.2]) were assessed. Kolmogorov-Smirnov normality test was performed for normality assumption and the outliers were checked by studentized residual values. Using Minitab response optimizer, the second-order polynomial equation was maximized under a global solution of desirability equal to one to obtain the optimal levels of the independent variables. The accuracy of the values was verified by comparing the predicted values obtained with the mathematical model and the measured values obtained after the experiments under the same conditions.

RESULTS AND DISCUSSION

Assessment of factors affecting glucan production

The first optimization step was a 14 run Plackett-Burman design to find out the significant factors affecting glucan production by Bacillus cereus LVK 13 (KC 898956). A wide variation in glucan content from 0.52-3.95 g/l was obtained with three replicates (Table 2). This variation reflected the significance of factors.

The analysis of regression coefficients and the t value of the three medium components (Table 3) demonstrated that sucrose (X1), ammonium chloride (X4) and manganese chloride (X9) had significant effects on glucan production. KN[O.sub.3] (X5) and Glucose (X2), NaN[O.sub.3] (X6) and Fe[Cl.sub.3] (X11) were found to be insignificant with positive coefficients. Neglecting the variables that were insignificant, the first-order model equation for glucan production is:

Y = 0.37 + 0.1127 X1 + 0.0127 X2 - 0.0594 X3 + 0.0261 X4 + 0.0183 X5 + 0.0061 X6 + 0.648 X7-1.14 X8 + 13.0 X9 + 8.40 X10-0.65 X11

Pareto Chart (Fig. 1) states the effects of different variables on the response. Based on this, X1, X4 and X9 were selected for further optimization that had the most significant effects on [??]-1,3 glucan production.

Optimization of culture conditions by RSM

The three components, X1, X4 and X9 determined by Plackett Burman Design was further optimized to maximize the production of glucan using response surface methodology. The run order was provided by Minitab 17 statistical package. The corresponding measured and predicted values are analyzed for the variance. Table 4 shows that the quantity of other variables were same as those in the standard media. Data were analyzed using Minitab 17 and the mathematical expression relating the glucan yield with variables is shown below:

Y = 0.015 + 0.4127 X1-0.0552 X4-29.4 X9 0.01102 X1*X1 + 0.00282 X4*X4 + 484 X9*X9 + 0.00004 X1* X4 + 1.036 X1* X9 + 0.342 X4* X9

To test the fit of the model equation, the regression based determination [R.sup.2] coefficient was evaluated. The [R.sup.2] value provides a measure of how much variability in the observed response values can be interpreted by the experimental factors and their interactions. The [R.sup.2] value is always between 0 and 1 (12,13). The model conferred a high determination coefficient ([R.sup.2]=0.9563) explaining 95.63% of the variability in the glucan production. The adjusted determination coefficient ([Radj.sup.2]) and predicted determination coefficient ([Rpred.sup.2]) were 0.9474 and 0.9329 respectively.

The [Radj.sup.2] corrects the [R.sup.2] value for the sample size and for the number of terms in the model. Normality test was performed for judging the model adequacy which showed a p value of >0.010. Hence, confirming the normality assumption. The studentized residual values were calculated for checking the outliers. All the values are within the range of -2 and +2, thereby affirming the model (Fig. 2). According to Anderson et al (2005) studentized residual values greater than-3.5 and +3.5 are regarded as outliers (14).

The correlation plot was made between the measured values of glucan content and the predicted values determined by the model (Fig. 3). The [R.sup.2] value was found to be 94.2% and the Pearson correlation of predicted yield and expected yield was 0.958. For each variable, model coefficients were predicted by regression analysis (Table 5). The significance of each coefficient was determined by t and P values and the larger t and the smaller P value indicate the high significance of the corresponding coefficient (15,16). A value of p <0.05 implies that the model is significant. The results revealed that sucrose, ammonium chloride and manganese chloride have a significant effect on glucan production. Positive coefficients of sucrose, ammonium chloride and manganese chloride indicated a linear effect of the increase in glucan production.

The graphical depiction provides a method to visualize the relationship between the response and experimental levels of each variable and the type of interactions between test variables to deduce the optimum conditions. One such response surface representing glucan production in the present study was a function of the concentration of sucrose and ammonium chloride with manganese chloride at an optimum level (Fig. 4). The steep slope shows that glucan production is sensitive to that factor.

The model predicted a maximum glucan yield of 4.31 g (Dry weight) per litre of the fermentation medium by solving the regression equation and analyzing the response surface plot using Minitab software. The optimum levels of the remarkable variables are sucrose--80 g/l; ammonium chloride--1.5 g/l and manganese chloride--0.03 g/l. To validate the predicted model, three experiments were conducted using this optimum medium composition.

Glucan yield of 4.12 g/l was obtained at this medium composition, which agreed well with the predicted value 4.31 g/l. As a result, the developed model was considered accurate and reliable for the production of [??]-1,3 glucan from Bacillus cereus LVK13 (KC 898956).

CONCLUSION

The three components, sucrose, ammonium chloride and manganese chloride were determined through Plackett Burman Design to be the most significant variables forming essential nutrients for the growth and production of [??]-1,3 glucan. The optimized concentration of sucrose, ammonium chloride and manganese chloride are statistically analyzed by Response Surface Methodology using CCD and a significant mathematical model with a co-efficient determination of [R.sup.2] = 0.95. The interactive effects of the variables were determined to be significant. The optimum concentration of the process variables is: sucrose (80 g/l), ammonium chloride (1.5 g/l) and manganese chloride (0.03 g/l). Using the optimized components and concentrations, the glucan yield reaches 4.12 g/l. The results show a close concordance between the expected and obtained production level.

REFERENCES

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Lakshminarayanan Vijayakumar [1] *, Ponnuswamy Ramalingam [2], Sundaramurthi Pavithra [1] and Rengaraju Balakrishnaraja [1]

[1] Department of Biotechnology, Bannari Amman Institute of Technology, Sathyamangalam--638401, Tamil Nadu, India.

[2] Department of Biotechnology, Kumaraguru College of Technology, Coimbatore--641006, Tamil Nadu, India.

(Received: 18 October 2015; accepted: 06 December 2015)

* To whom all correspondence should be addressed. Tel: +91-9442982665; E-mail: lrvijayakumaar@gmail.com

Caption: Fig. 1. Pareto chart showing the effect of media components on glucan production

Caption: Fig. 2. Residual Plots for yield

Caption: Fig. 3. Linear correlation plot between measured Vs. predicted glucan yield

Caption: Fig. 4. Response surface curves of glucan production showing interaction between various factors
Table 1. Range of variables used in the Plackett-
Burman design and Response Surface Methodology

S.No   Variables              Code Levels (g/l)

                                     -1     0      +1

1      Sucrose                X1     4      8      12
2      Glucose                X2     4      8      12
3      Galactose              X3     4      8      12
4      N[H.sub.4]Cl           X4    0.5    1.5    2.0
5      KN[O.sub.3]            X5    0.5    1.5    2.0
6      NaN[O.sub.3]           X6    0.5    1.5    2.0
7      K[H.sub.2]P[O.sub.4]   X7    0.5    1.75   3.0
8      [K.sub.2]HP[O.sub.4]   X8    0.1    0.5    1.0
9      Mn[Cl.sub.2]           X9    0.01   0.03   0.05
10     Mg[Cl.sub.2]           X10   0.1    0.25   0.4
11     Fe[Cl.sub.3]           X11   0.02   0.06   0.1

Table 2. Plackett-Burman design for 11 variables

Run     X1   X2   X3   X4   X5   X6   X7   X8   X9   X10   X11
Order

1       1    1    -1   1    1    -1   1    -1   -1   -1     1
2       -1   -1   1    1    1    -1   1    1    -1    1     0
3       -1   1    1    1    -1   1    1    -1   1    -1     0
4       -1   1    1    -1   1    -1   0    -1   1     1     1
5       0    0    0    0    0    0    -1   0    0     0    -1
6       -1   -1   -1   1    1    1    0    1    1    -1     1
7       1    1    -1   1    -1   -1   0    1    1     1     0
8       -1   1    -1   -1   -1   1    1    1    -1    1     1
9       1    1    1    -1   1    1    0    1    -1   -1     0
10      1    -1   -1   -1   1    1    1    -1   1     1     0
11      1    -1   1    1    -1   1    0    -1   -1    1     1
12      1    -1   1    -1   -1   -1   1    1    1    -1     1
13      -1   -1   -1   -1   -1   -1   0    -1   -1   -1     0
14      0    0    0    0    0    0    -1   0    0     0    -1

Run       Y
Order   ([??]-1,3
        glucan
        yield)

1        3.11
2        1.9
3        2.12
4        1.56
5        2.79
6        1.29
7        2.65
8        1.85
9        0.96
10       3.95
11       2.05
12       1.93
13       0.52
14       2.86

Table 3. Statistical analysis of a Plackett -
Burman design showing coefficient values and t
and P values for each variable for glucan
production

Term                   Coefficient     t        P

Constant                 0.01429     139.33   0.005
Sucrose                  0.01429     31.55    0.020
Glucose                  0.01429      3.56    0.174
Galactose                0.01429     -16.62   0.038
N[H.sub.4]Cl             0.01429     13.71    0.046
KN[O.sub.3]              0.01429      9.62    0.066
NaN[O.sub.3]             0.01429      3.21    0.192
K[H.sub.2]P[O.sub.4]     0.01429     34.00    0.019
[K.sub.2]HP[O.sub.4]     0.01429     -15.92   0.040
Mn[Cl.sub.2]             0.01429     18.14    0.035
Mg[Cl.sub.2]             0.01429     23.50    0.027
Fe[Cl.sub.3]             0.01429     -1.81    0.322

Table 4. CCD experimental design matrix of three
variables in real units and amount of [??]-1,3 glucan
production

Run     X1   X4   X9        Yield (g/l)

Order                  Predicted   Expected

1       0    0    0      2.39        2.38
2       1    1    -1     3.53        3.54
3       0    0    0      2.38        2.38
4       1    1    -1     3.54        3.54
5       -1   -1   1      1.39        1.36
6       -1   1    -1     1.29        1.29
7       1    1    1      4.29        4.29
8       1    -1   1      3.95        3.27
9       1    -1   1      3.93        3.27
10      -1   1    -1     1.29        1.29
11      1    -1   -1     3.24        3.26
12      1    -1   1      3.94        2.45
13      0    0    0      2.37        2.38
14      1    1    1      4.31        4.29
15      -1   -1   -1     1.17        1.15
16      0    0    0      2.35        2.38
17      0    0    0      2.33        2.38
18      -1   -1   -1     1.15        1.15
19      -1   -1   -1     1.12        1.15
20      1    -1   -1     3.27        3.26
21      0    0    0      2.36        2.38
22      0    0    0      2.35        2.38
23      0    0    0      2.39        2.38
24      0    0    0      2.36        2.38
25      0    0    0      2.39        2.38
26      0    0    0      2.37        2.38
27      -1   -1   1      1.36        1.36
28      1    -1   -1     3.26        3.26
29      4    -1   1      1.33        1.36
30      1    1    1      4.28        4.29
31      -1   1    1      1.84        1.85
32      -1   1    -1     1.29        1.29
33      -1   1    1      1.85        1.85
34      1    1    -1     3.56        3.56
35      0    0    0      2.38        2.38
36      -1   1    1      1.83        1.85
37      0    0    0      2.37        2.38
38      0    1    0      2.65        2.64
39      0    0    -1     2.23        2.21
40      1    0    0      2.63        2.63
41      0    0    0      2.37        2.38
42      0    0    1      2.47        2.45
43      0    0    0      2.38        2.38
44      0    0    -1     2.23        2.21
45      0    0    1      2.45        2.47
46      0    0    -1      2.2        2.21
47      -1   0    0      1.31        1.32
48      1    0    0      2.64        2.63
49      1    0    0      2.63        2.63
50      0    0    0      2.38        2.38
51      -1   0    0      1.32        1.32
52      0    -1   0      1.97        1.32
53      0    1    0      2.64        2.64
54      0    -1   0      1.96        1.97
55      -1   0    0      1.32        1.32
56      0    -1   0      1.96        1.97
57      0    0    0      2.37        2.38
58      0    1    0      2.63        2.64
59      0    0    0      2.35        2.38
60      0    0    1      2.44        2.47

Table 5. Regression analysis of CCD for glucan
production

Term       Coefficient     t       P

Constant     2.2970      58.39   0.000
X1           1.0723      30.63   0.000
X4           0.1947      5.56    0.000
X9            0.483      6.96    0.000
X1*X1        -0.325      -2.61   0.012
X4*X4        0.1587      2.35    0.023
X9*X9        0.1937      2.87    0.006
X1*X4        0.0013      0.03    0.975
X1*X9        0.0829      2.12    0.039
X4*X9        0.0512      1.31    0.197
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Author:Vijayakumar, Lakshminarayanan; Ramalingam, Ponnuswamy; Pavithra, Sundaramurthi; Balakrishnaraja, Ren
Publication:Journal of Pure and Applied Microbiology
Article Type:Report
Date:Dec 1, 2015
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