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Extracellular ribonuclease production from Aspergillus niger ATCC 26550: process optimization by biostatistical analysis.

Introduction

Ribonuclease (RNase) catalyzes the digestion of RNA and is widely exists in organisms. RNases are nucleases produced by a diversity of organisms including fungi, plants, submammalian vertebrates, and mammalian liver, kidney, brain, placenta, pancreas, milk and semen [1]. Ribonucleases may exhibit activities other than ribonucleolytic activity, such as anti mitogenic [2] antibacterial, antifungal [3], antiproliferative, antiviral activity [1,4], HIV-I reverse transcriptase inhibitory [5], translation inhibitory [6] and angiogenic [7] activities. As the demand for DNA vaccines and biological drugs increases, usage of ribonuclease attached to a solid support (immobilized) is better way which has many advantages over free enzymes [8]. They are widely used in molecular biological study, food and pharmaceutical industry. As an important analytical tool, they have played a major role in study on the structure and function of RNA. In single cell protein production they are used to remove RNA from cell [9]. These enzymes are produced by numerous microorganisms among which the fungi are the most potent producers. RNases have also applied commercially to produce nucleotides for clinical use or for the food industry. Many RNases are highly cytotoxic. Recently, a series of scientific studies showed that RNases had important biological functions, in controlling the tumor formation [10]. In recent years, the RNase functions related to the control of gene expression, cell growth and differentiation, cell protection from pathogens, and apoptosis induction have received special attention [11]. RNases has been produced by many other Aspergillus species such as Aspergillus clavatus and Aspergillus nidulans. Aspergillus is a kind of good producers for RNases [12]. At present, the contradiction between low production and high demands is becoming acute, giving rise to the urgency of addressing the problem of increasing the production of RNase. In this higher production by using biochemical mutants resistant to metabolic inhibitors was a very efficient method for improvement of ribonuclease production by Aspergillus niger [13]. Aside from genetic alteration, culture components also affect the yield of RNase production. However, conventional methods of optimization involve changing variable-to-variable while maintaining all others at a fixed level; these often do not yield reliable results because interactions between different components are neglected. Besides, they are laborious, time-consuming, and impractical. Several strategies have been widely used to enhance RNase production, such as introducing super productive strains, optimizing fermentation operating conditions, and building mathematical models. Recently, many statistical experimental design methods have been developed to eliminate these drawbacks. For example, the Plackett-Burman design (PBD), which ignores interactions among factors, has been applied to minimize the number of fermentation runs [14,15]. Only the most effective factors with high significance levels will be selected for further optimization, while others with lower significance levels or small effects on response value will be omitted in further experiments [16]. In addition, PBD has been widely applied to medium optimization and formulation of multicomponents among others [17]. The steepest ascent experiment, a preliminary experiment to determine the optimum level, moves sequentially along the path of the steepest ascent, that is, in the direction of the maximum increase in response value [18]. Then, the central composite design (CCD) and response surface method (RSM) [19,20] are further adopted to determine the relationship between variables and responses. Moreover, the optimum of each variable will be obtained by differential approximation. This technique has been widely applied to optimizing parameters and factors for fermentation media for various microorganisms [21-23].

The traditional 'One-factor at a time' technique used for optimizing a multivariable system is not only time consuming but also often easily misses the alternative effects between components. Also this method requires a number of experiments to determine the optimum level, which is untrue. These drawbacks of single factor optimization process can be eliminated by optimizing all the affecting parameters collectively by CCD using response surface methodology (RSM). Recently many statistical experimental design methods have been employed in bioprocess optimization. Among them RSM is the one suitable for identifying the effect of individual variables and for seeking the optimum conditions for a multivariable system efficiently. This method has been successfully applied to optimize production media. A detailed account of this technique has been outlined. Basically this optimization process involves three major steps. Performing the statistically designed experiments estimating the coefficients in a mathematical model and predicating the response and checking the adequacy of the model.

Materials and Methods

Microorganism and Culture Conditions

Aspergillus niger ATCC26550 the source material for ribonuclease production was obtained from NCL. Pune, India. Organism was propagated on potato dextrose agar medium (PDA) at 30[degrees]C and maintained at 4[degrees]C. Conidiospores of A.niger grown for 7days on PDA medium were harvested in sterile condition for use as the inoculum.

Ribonuclease Production in Shake Flask Cultures

The basal minimal medium (pH 5.5) used for ribonuclease production contained: glucose, 3%: beef extract, 0.5%: peptone, 1%: MgS[O.sub.4] x 7[H.sub.2]O, 0.05%: Ca[Cl.sub.2] x 2[H.sub.2]O, 0.01%. The production medium (50-ml in a 250-ml Erlenmeyer flask) was inoculated with 5% of seed culture and flasks were placed on a rotary shaker at 180 rpm with 5 cm amplitude at 30[degrees]C for 120h to produce RNase. After fermentation, the fungal bio mass was separated from the culture fluid by filtration and filtrate was used to determine the extracellular ribonuclease activity.

Assay of Ribonuclease

RNase activity was assayed in a reaction mixture containing 200ul citrate buffer (100mM, pH 3.5), 200[micro]l yeast RNA (substrate, 5mg/ml in the same buffer), 200[micro]l of suitably diluted enzyme solution. After incubation at 30[degrees]C for 15min, the reaction was terminated by 200[micro]l of 25% (v/v) perchloric acid solution containing 0.75 % (w/v) phosophotungstic acid and 0.6%(w/v) bovine serum albumin at 0-4[degrees]C. The undigested RNA was precipitated by centrifugation at 4000g for 10min at 4[degrees]C and the acid soluble nucleotides were estimated spectrophotometrically at 260nm [24]. One international unit of enzyme activity was defined as the amount of enzyme required to liberate 1[micro]mol of acid soluble oligonucleotides per minute to increase one unit of [A.sub.260] value in the reaction mixture. The extinction coefficient of 10,600 l [mol.sub.- 1] [cm.sup.-1] for hydrolyzed RNA was used in the assay activity.

Experimental Design for Ribonuclease Production (Response Surface Methodology)

Earlier one-at-a-time approach had been followed to identify the parameters (variables) having significant effect on ribonuclease production from Aspergillus niger. Subsequently, a statistical approach, response surface methodology was employed to study the interaction of these parameters.

In preliminary experiments, culture conditions, various carbon and nitrogen sources, and inorganic salt were evaluated for their suitability to sustain good ribonuclease production by strain Aspergillus niger ATCC26550. The data obtained indicated that the major variables affecting the performance of the culture in terms of ribonuclease yield were glucose, beef extract, peptone, MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O, and pH. These six medium components were chosen for further optimization.

Plackett-Burman Design

Plackett-Burman is an efficient and effective approach to the systematic investigation and evaluation of the effects of medium components. Each independent variable was tested at two levels, a high (+1) level and a low (-1) level. (Table 1) shows the factors and their levels used in the experimental design, where as (Table 2) show the detail of the design.

Path of the Steepest Ascent (Descent) Experiment

PBD served as a local approximation in a small region close to the initial operating conditions but far from where the process exhibited curvature. The path of steepest ascent (descent) was adopted to determine a suitable direction by increasing or decreasing the concentrations of variables according to the sign of the main effects to improve production. For the purpose of maximizing RNase production, we used the direction of the steepest ascent.

Central composite designs and response surface method

Optimization of the media constituents for overproduction of RNase by Aspergillus niger was done by central composite experimental design (CCD), where a [2.sup.3] factorial design was employed with different combinations of three independent variables had

nc = 8 : ([+ or -]1, [+ or -]1, [+ or -]1), na = 6 : ([+ or -][alpha], 0, 0), [n.sub.0] = 6: (0, 0, 0)

Where the value of a = [nc.sup.1/4] = [8.sup.1/4] = 1.682 to make the design rotatable.

Concentration levels of the three factors MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O, and pH were considered as the three independent variables for optimization. The concentration of other media constituents was kept constant throughout the investigation. The variables levels [X.sub.i] were coded as [X.sub.i] according to the following equation such that [X.sub.0] corresponded to the central value:

[x.sub.i] = [[[X.sub.i] - [X.sub.0]]/[DELTA][X.sub.i]] i = 1,2,3 ...., k .... (1)

Where [X.sub.i] is the dimensionless value of an independent variable, [X.sub.i] the real value of an independent variable, [X.sub.0] the real value of the independent variable at the centre point, and [DELTA][X.sub.i] is the step change. The experimental plan and levels of independent variables are shown in Table 4. [X.sub.1] (pH) had a lower limit of 4.15 and upper limit 5.84, [X.sub.2] (MgS[O.sub.4] x 7[H.sub.2]O) was varied between 0.45 and 0.83 g/l. The lower and upper limits of [X.sub.3] (Ca[Cl.sub.2] x 2[H.sub.2]O) were 0.07 and 0.106 g/l, respectively. The response surface methodology (RSM) was used to analyze the experimental design. The response variable was fitted by a second order model in order to correlate the response variable to the independent variables. The general formula of the second degree polynomial equation is:

[Y.sub.i] = [[beta].sub.0] + [summation of][[beta].sub.ixi]+ [summation of][[beta].sup.2.sub.ijxi] + [summation of][[beta].sub.ijxixj] .... (2)

Where [Y.sub.i] is the predicted response, [x.sub.i][x.sub.j] are input variables which influence the response variable Y; [[beta].sub.0] is the offset term; [[beta].sub.i] is the ith linear coefficient; [[beta].sub.ii] the ith quadratic coefficient and [[beta].sub.ij] is the ijth interaction coefficient. The second order polynomial coefficients were calculated using the Minitab software. The statistical analysis of the model was performed in the form of analysis of variance (ANOVA). This analysis included the Fisher's F-test (overall model significance), its associated probability p(F), correlation coefficient R, determination coefficient [R.sup.2] which measures the goodness of fit of regression model. It also includes the Student's t-value for the estimated coefficients and the associated probabilities p(t). For each variable, the quadratic models were represented as contour plots (2D). The optimal combination was determined from the contour plots.

CCD was used for investigation the region of the response surface in the neighbourhood of the optimum. For three factors, a fractional factorial design with six replications of centre points and six star points, which allows curvature estimation, is typically recommended to have a total number of 20 runs [25]. The star points for this design had a value of 1.68179, which can maintain rotatability. The actual values of the variables and the design matrix are shown in (Tables 3 and 4). Using software Minitab15.0, we obtained the response surface model, which is a second-order model that was confirmed by statistical analysis.

Results and Discussion

Plackett-Burman Design (PBD) for screening important medium factors for RNase production

The importance of the six medium components, glucose, beef extract, peptone, MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O, and pH 5.5 for RNase production was investigated using PBD. (Table 5) shows the effects of these components on RNase production. The effects of MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O and pH were (- ) 56.12, (-) 60.05, and (-) 43.22, respectively, and all have confidence levels 95%. Hence, they were considered as the most significant factors that effects RNase production. Other had effects and low confidence level s (P>0.05) and were considered insignificant. In the pareto chart (Fig. 1), the largest effects (most important factors) are presented in the upper portion and then progress down to the smallest effects (least important factors). In addition, it directly shows that the most important factors determining RNase production were MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O and pH.

[FIGURE 1 OMITTED]

Path of steepest ascent experiment

PBD results indicated that the MgS[O.sub.4] x 7[H.sub.2]O effect was negative, Ca[Cl.sub.2] x 2[H.sub.2]O effect was negative and pH also negative effect. Thus, decreasing three significant factor concentrations could result in a higher production of ribonuclease. The path of steepest ascent started from the centre of the PBD and moved along the path in which the concentration of MgS[O.sub.4] x 7[H.sub.2]O decreased to 0.1 g/l, Ca[Cl.sub.2] x 2[H.sub.2]O decreased to 0.02g/l and pH 1.0 [26]. (Table 6) shows the result of steepest ascent experiment.

Central composite design and response method

The data shown in (Table 4) were explained by the following second-order polynomial equation [27,28]. Response results shown in (Table 4) were analyzed using Minitab 15.0 software. The t-test and P values were used to identify the effect of each factor on RNase production (Table 7). MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O, and pH and the interaction of the three selected variables had a significant effect on RNase yield (p<0.05), as well as quadratic terms of MgS[O.sub.4] x 7[H.sub.2]O, Ca[Cl.sub.2] x 2[H.sub.2]O, and pH. The fit of the model was checked by the coefficient of determination [R.sup.2], which was calculated to be 0.9815, indicating that 98.15% of the variability in the response can be explained by the model.

The model can be shown as follows:

Y (units / ml) = -5468 + 2572 [X.sub.1] + 11173 [X.sub.2] - 45578 [X.sub.3] - 508 [X.sup.2.sub.1] - 28931 [X.sup.2.sub.2]-351871 [X.sup.2.sub.3] + 1920 [X.sub.1][X.sub.2] + 47650 [X.sub.2][X.sub.3] + 16805 [X.sub.1][X.sub.3] (3)

Where Y is the response, that is RNase production, and [X.sub.1], [X.sub.2], and [X.sub.3] are the coded values of pH, MgS[O.sub.4] x 7[H.sub.2]O, and Ca[Cl.sub.2] x 2[H.sub.2]O, respectively.

The corresponding analysis of variance (ANOVA) was presented in (Table 8). The predicted optimum levels of pH, MgS[O.sub.4] x 7[H.sub.2]O, and Ca[Cl.sub.2] x 2[H.sub.2]O concentration of fermentation were obtained by applying the regression analysis to the equation (3). The predicted and experimental RNase production at the optimal level of fermentation conditions were also determined by using equation (3). (Figures 2-4) represent the contour plots for the optimization of fermentation conditions of amount of RNase. The effect of the pH and MgS[O.sub.4] x 7[H.sub.2]O concentrations on the RNase production showed in (Figure 2).

[FIGURE 2 OMITTED]

An increase in the pH with MgS[O.sub.4] x 7[H.sub.2]O concentration up to the optimum point increased the RNase production to a maximum level and a further increase in the pH with MgS[O.sub.4] x 7[H.sub.2]O concentration the trend is reversed. The interaction effect of the pH and Ca[Cl.sub.2] x 2[H.sub.2]O concentration on the RNase production in (Figure 3) clearly indicates a proper combination for production of RNase. An increase in the pH with Ca[Cl.sub.2] x 2[H.sub.2]O concentration increased the RNase production gradually but at a higher pH and Ca[Cl.sub.2] x 2[H.sub.2]O concentration the trend is reversed. The optimum for maximum RNase production lies near the centre point of the pH and Ca[Cl.sub.2] x 2[H.sub.2]O. A similar effect on the response was observed for the MgS[O.sub.4] x 7[H.sub.2]O at any level of the Ca[Cl.sub.2] x 2[H.sub.2]O concentration an increase in the MgS[O.sub.4] x 7[H.sub.2]O with Ca[Cl.sub.2] x 2[H.sub.2]O concentration up to the optimum point increased the RNase production to maximum level and a further increase in the MgS[O.sub.4] x 7[H.sub.2]O with Ca[Cl.sub.2] x 2[H.sub.2]O concentration decreased the RNase production is shown in (Figure 4).

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

Therefore, an optimum was observed near the central value of pH, MgS[O.sub.4] x 7[H.sub.2]O, and Ca[Cl.sub.2] x 2[H.sub.2]O concentration. The optimum conditions for maximum RNase production were observed at a pH 4.49, MgS[O.sub.4] x 7[H.sub.2]O concentration of 0.403g/l and Ca[Cl.sub.2] x 2[H.sub.2]O concentration of 0.073g/l. A maximum RNase production of 825.7units/ml was obtained at these optimum parameters.

Conclusion

Thus, the present study using RSM with CCD enables to find the importance of factors at different levels. A high similarity was observed between the predicted and experimental results, which reflected the accuracy and applicability of RSM to optimize the process for RNase production. Following this results the RNase production was increased by 57.4% compared to that under single variables optimized conditions.

Acknowledgment

The authors wish to thank the University Grants Commission (UGC) for financial supports and to the School of Biochemical Engineering, Institute of Technology, Banaras Hindu University, and Varanasi, India for providing Laboratory and technical support.

References

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Gundampati Ravi Kumar, Swati Singh, Neeraj Gupta and M. Debnath Das *

School of Biochemical Engineering, Institute of Technology, Banaras Hindu University, Varanasi-221005, India.

* Corresponding Author E-mail: debnathmira@yahoo.co.in, ravi 33102000@yahoo.com,
Table 1: Six Medium Factors and Their Levels
used in Plackett- Burman Design

Variable Low level High level
code Variables -l(g/l) +1(g/l)

X1 Glucose 30 37.5
X2 Ratio of Nitrogen 0.33 0.55
X3 Total Nitrogen 15 20
X4 MgS[O.sub.4] x 7[H.sub.2]O 0.5 0.65
X5 Ca[Cl.sub.2] x 2[H.sub.2]O 0.1 0.2
X6 pH 5.5 7

Table 2: Matrix of Plackett-Burmam Design and results of evolution
of factors affecting RNase production by Aspergillus niger ATCC 26550.

 Activity Predicted
Run no: X1 X2 X3 X4 X5 X6 (Units/ml) Response

1 + + - + - - 464.8 458.7
2 + + + - + + 406.8 395.1
3 - - - - - - 531.4 524.3
4 - + + - + - 456.6 453.9
5 + + - + + - 392.7 398.7
6 - - - + + + 368.5 364.9
7 - + + + - + 398.1 414.6
8 - - + + + - 392.7 391.7
9 + - + - - - 481.6 492.3
10 - + - - - + 489.4 487.1
11 + - - - + + 392.7 405.5
12 + - + + - + 405.0 393.0

Table 3: Selected nutrient ranges for RNase production Optimization.

Variable code Variables (g/L) -1.68179 -1 0

[X.sub.1], pH 4.15 4.5 5.0
[X.sub.2] MgS[O.sub.4] x 7[H.sub.2]O 0.36 0.4 0.45
[X.sub.3] Ca[Cl.sub.2] x 2[H.sub.2]O 0.07 0.08 0.09

Variable code +1 +1.68179

[X.sub.1], 5.5 5.84
[X.sub.2] 0.5 0.83
[X.sub.3] 0.1 0.106

Table 4: CCD matrix employed for pH, MgSO4.7H2O, and CaCl2.2H2O
independent variables.

 Activity Predicted
Run no: X1 X2 X3 (Units/ml) Response

1 -1.00000 1.00000 1.00000 374.1 362.18
2 0.00000 0.00000 0.00000 722.9 722.86
3 0.00000 0.00000 1.68179 576.8 565.37
4 1.00000 1.00000 -1.00000 396.4 348.87
5 0.00000 0.00000 0.00000 722.9 722.86
6 -1.68179 0.00000 0.00000 503.3 473.56
7 0.00000 1.68179 0.00000 405.2 436.17
8 1.00000 -1.00000 1.00000 461.5 449.63
9 0.00000 0.00000 0.00000 722.9 722.86
10 0.00000 -1.68179 0.00000 630.2 600.40
11 0.00000 0.00000 -1.68179 668.7 681.31
12 1.68179 0.00000 0.00000 223.0 253.91
13 0.00000 0.00000 0.00000 722.9 722.86
14 0.00000 0.00000 0.00000 722.9 722.86
15 0.00000 0.00000 0.00000 722.9 722.86
16 -1.00000 1.00000 -1.00000 540.5 551.52
17 -1.00000 -1.00000 -1.00000 789.3 792.86
18 1.00000 -1.00000 -1.00000 387.1 398.17
19 1.00000 1.00000 1.00000 500.0 495.63
20 -1.00000 -1.00000 1.00000 461.5 508.18

Table 5: Analysis of Plackett-Burman Design on optimization of
culture medium in shake flask culture.

Term Effect Coef SE Coef T-value Pr>(t)

Constant 431.69 4.043 106.78 0.000
X1 -15.52 -7.76 4.043 -1.92 0.113
X2 6.08 3.04 4.043 0.75 0.486
X3 -16.45 -8.22 4.043 -2.03 0.098
X4 -56.12 -28.06 4.043 -6.94 0.001
X5 -60.05 -30.03 4.043 -7.43 0.001
X6 -43.22 -21.61 4.043 -5.34 0.003

R-Sq=96.56%; RSq (adjust) =92.43%.

Table 6: Design results of path of steepest ascent experiment.

 Activity Predicted
Run no: X1 X2(g/l) X3(g/l) (Units/ml) Response

1 4.500 0.500 0.100 374.1 362.18
2 5.000 0.450 0.090 722.9 722.86
3 5.000 0.450 0.106 576.8 565.37
4 5.500 0.500 0.080 396.4 348.87
5 5.000 0.450 0.090 722.9 722.86
6 4.159 0.450 0.090 503.3 473.56
7 5.000 0.534 0.090 405.2 436.17
8 5.500 0.400 0.100 461.5 449.63
9 5.000 0.450 0.090 722.9 722.86
10 5.000 0.365 0.090 630.2 600.40
11 5.000 0.450 0.073 668.7 681.31
12 5.840 0.450 0.090 223.0 253.91
13 5.000 0.450 0.090 722.9 722.86
14 5.000 0.450 0.090 722.9 722.86
15 5.000 0.450 0.090 722.9 722.86
16 4.500 0.500 0.080 540.5 551.52
17 4.500 0.400 0.080 789.3 792.86
18 5.500 0.400 0.080 387.1 398.17
19 5.500 0.500 0.100 500.0 495.63
20 4.500 0.400 0.100 461.5 508.18

Table 7: Regression coefficients and their significance for
response surface quadratic.

Term Coef StDev Coef T P

Constant -5468 2065.2 -2.648 0.024
X1 2572 415.4 6.190 0.000
X2 11173 4027.2 2.774 0.020
X3 -45578 20135.8 -2.264 0.047
X1X1 -508 31.6 -16.090 0.000
X2X2 -28931 3156.5 -9.166 0.000
X3X3 -351871 78911.9 -4.459 0.001
X1X2 1920 423.7 4.532 0.001
X1X3 16805 2118.3 7.933 0.000
X2X3 47650 21182.6 2.249 0.048

R-Sq= 98.15%; R-Sq (adjust) = 96.48, S=29.9567, PRESS= 71102.7

Table 8: ANOVA of regression model.

Source DF Seq SS Adj SS Adj MS F P

Regression 9 475047 475047 52783.1 58.82 0.000
Linear 3 107030 54079 18026.4 20.09 0.000
Square 3 288573 288573 96190.9 107.19 0.000
Interactions 3 79455 79455 26484.9 29.51 0.000
Residual error 10 8974 8974 897.4
Lack of fit 5 8974 8974 897.4 * *
Pure error 5 0 0 0.0
Total 19 484022

DF, Degree of freedom; SS, sum of squares; MS, mean square.
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Author:Kumar, Gundampati Ravi; Singh, Swati; Gupta, Neeraj; Das, M. Debnath
Publication:International Journal of Biotechnology & Biochemistry
Date:May 1, 2011
Words:4996
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