
Research Article


Statistical Assessment of Medium Components by Factorial Design and Surface Methodology of LAsparaginase Production by Isolated Streptomyces radiopugnans MS1 in Submerged Fermentation using Tapioca Effluent 

M. Senthil Kumar,
K. Selvam
and
Singaravel



ABSTRACT

Streptomyces radiopugnans MS1 was isolated from marine environment and identified as a potent strain for producing LAsparaginase from Tapioca effluent an inexpensive substrate. To optimize the LAsparaginase production of this bacterial strain, 23 fermentation variables were screened using a PlackettBurman design and were then further optimized via response surface methodology based on a Box/Behnken design. Four significant variables, i.e., Tapioca Effluent, Corn steep liquor, LAspaparagine and Areation, were selected. The optimum values of the tested variables by response surface methodology were; Tapioca Effluent, 5 (% v/v); Corn steep liquor, 2 (% w/v); LAspaparagine, 0.003 (% w/v); Aeration, 0.7 (vvm) found to be optimum for LAsparaginase production. The experimental result (19.5 IU mL^{1}) in a medium optimized for LAsparaginase production was in good agreement with the predicted value of a quadratic model (19.8 IU mL^{1}). A verification experiment was carried out to examine model validation and revealed more than 99% validity. The model was very satisfactory as the coefficient of determination was 0.99. LAsparaginase production in the optimized medium (19.5±0.33 IU mL^{1}) increased 4.81fold over that of the basal medium (4.05±0.84 IU mL^{1}) in the fermenter.




How
to cite this article:
M. Senthil Kumar, K. Selvam and Singaravel , 2012. Statistical Assessment of Medium Components by Factorial Design and Surface Methodology of LAsparaginase Production by Isolated Streptomyces radiopugnans MS1 in Submerged Fermentation using Tapioca Effluent. Asian Journal of Applied Sciences, 5: 252265. DOI: 10.3923/ajaps.2012.252.265 URL: https://scialert.net/abstract/?doi=ajaps.2012.252.265



Received: November 15, 2011;
Accepted: January 26, 2012;
Published: March 26, 2012


INTRODUCTION
Lasparaginase is an important enzyme as therapeutic agent used in treatment
of acute lymphocytic leukemia (mainly in children), Hodgkin disease, acute myelocytic
leukemia, acute myelomonocytic leukemia, chronic lymphocytic leukemia, lymphosarcoma
treatment, reticlesarcoma and melanosacroma (Stecher et
al., 1999; Verma et al., 2007).
Lasparaginase is widely distributed in both prokaryotic and eukaryotic cells
and has been intensively studied over the past five decades. This enzyme is
existing in many animal tissues, plants and bacteria. Lasparaginase produced
by a large number of microorganisms such as E. coli (Khushoo
et al., 2004; Derst et al., 1994),
Erwinia cartovora (Aghaiypour et al., 2001;
Borisova et al., 2003), Erwinia chrysanthemi
(Kotzia and Labrou, 2007), Enterobacter aerogenes
(Mukherjee et al., 2000), Pseudomonas
aeruginosa (ElBessoumy et al., 2004), Candida
utilis (Kil et al., 1995), Thermus thermophilus
(Pritsa and Kyridio, 2001) Staphylococcus aureus
(Muley et al., 1998) and Filamentous Fungi
(Elzainy and Ali, 2006). Moreover, Lasparaginase was
demonstrated and characterized in higher plants on account of the vital role
of this enzyme in the nitrogen nutrition (Sodek et al.,
1980; ElShora et al., 2005; Cho
et al., 2007). Actinomycetes have been shown to be a good source
for LAsparaginase too.
Nowadays, marine actinomycetes have been widely recognized as a potential source
of new drug candidates in particular marine Streptomyces are promising source
of antitumour drugs and therapeutic enzymes (Jensen et
al., 2005). Marine actinomycetes have been reported to produce functionally
unique metabolites and enzymes that are not found in their terrestrial counterparts.
This is due to their extreme living conditions within the marine environment,
Recent studies have shown that few antitoumor compounds isolated from marine
actinomycetes are under clinical trials (Ye et al.,
2009). Hence, as a new source of antitumour drug candidates, marine actinomycetes
have attracted serious attention in the last decade.
With a view to characterize enzymes with less toxic side effects, several members
of a larger family of homologous lASNases have been thoroughly investigated
over many years (Boyse et al., 1967; Ehrman
et al., 1971; Cammack et al., 1972;
Wriston and Yellin, 1973; Krasotkina
et al., 2004; Kotzia and Labrou, 2005). In
addition, because the antitumour activity of LAsparaginase is also a function
of its halflife in the blood (Fernandes and Gregoriadis,
1997), attempts have been made to increase the halflife, for example by
entrapment of the enzyme in liposomes (Neerunjun and Gregoriadis,
1976) or microcapsules (Chang, 1984) and by covalent
coupling to macromolecules such as dextran (Wileman et
al., 1986), albumin (Poznansky et al., 1982)
or monomethoxypolyethylene glycol (mPEG) (Kamisaki et
al., 1982) which is on the market. Unfortunately, none of these approaches
have managed to eliminate the disadvantages of LAsparaginase treatment, leaving
scientists with the need to identify and characterize new enzymes with better
properties.
Medium composition is one of the most important parameters when enzymes are
produced for industrial purposes because around 3040% of the production cost
is estimated to be the cost of the growth medium (Kirk et
al., 2002; Youssef and AlOmair, 2008). Statistical
approaches have helped to enhance product yield and reduce the cost of production,
thereby making the fermentation process economical and cost effective (Kaur
and Satyanarayana, 2005; Shah et al., 2010).
In the optimization of media compounds, PlackettBurman designs are used as
a screening method in order to select the variables that influence a system.
However, they do not give an optimum value for each variable (Jalbani
et al., 2006; Youssef and Berekaa, 2009)
and further optimization is needed. Response Surface Methodology (RSM) has been
widely used to evaluate and understand the interactions between different physiological
and nutritional parameters (Bandaru et al., 2006;
Charyulu and Gnanamani, 2010). It is an efficient mathematical
approach widely applied in the optimization of the fermentation process (Panda
et al., 2007) and media component, e.g., production of enzymes, biomass,
spore, and other metabolites (Ghosalkar et al., 2008;
Jaiswal et al., 2011). RSM which includes factorial
design and regression analysis, can be used to help evaluate the effective factors
and build models (De Coninck et al., 2000; Akhir
et al., 2009). It can give information about the interaction between
variables and can be used to select optimum conditions of variables for a desirable
response and multiple responses at the same time (Lo et
al., 2009). Finally, after model building and optimization, the predicted
model is verified.
In this investigation, an attempt has been made to improve LAsparaginase production
from Streptomyces species by statistical approaches using a PlackettBurman
design and RSM in submerged culture.
MATERIALS AND METHODS Sample collection and strain isolation: A total of 254 sediment samples were collected during six separate research expeditions to Bay of Bengal, Tamilnadu, India. Samples of the top 1 cm of sediment were collected in sterile 50 mL strile plastic bags by divers using scuba gear when necessary. Sediment sample depth ranges, numbers, locations and dates were as follows: 0 to 30 m, 42 sediment samples (chennai, Nov 2009); 0 to 30 m, 65 sediment samples (Chidambaram, Dec 2010); 10 to 30 m, 42 sediment samples (Pondicherry, Jan, 2010); 0 to 30 m, 25 sediment samples (Tuticorin, Feb, 2010); 0 to 30 m, 57 sediment samples (Rameshwaram, March, 2010); 0 to 30 m, 23 sediment samples (Kanyakumari, Apr, 2010); Samples were kept at room temperature during the expedition and at 4°C upon return to the laboratory.
Enrichment and screening of slowly growing marine microorganisms: One
gram each of the sieved soil samples was treated with 100 mL of tapioca effluent
and incubated at an ambient temperature for about a week at 200 rpm (Mincer
et al., 2002; Pisano et al., 1986).
The dilutionandheatshock method was carried out as follows with Actinomycete
isolation agar and Starch casein agar medium supplemented with phenol red (0.009%
final concentration) (Himedia, Mumbai, India) (Gulati et
al., 1997). Media were prepared with 100% filtered natural sea water.
The media components included cycloheximide (20 mg L^{1}), pravastatin
(10 mg L^{1}), trimethoprim (2 mg L^{1}) and nalidixic acid
(10 mg L^{1}) to prevent other nonactinomycete bacteria and fungal
growth (Kuster and Williams, 1964).
One millilitter of enriched wet sediment was added to 4 mL of sterile seawater, heated for 6 min at 55°C, vigorously shaken and further diluted (1:4) in sterile seawater and 50 μL of each dilution was suspensions were spread, in triplicate onto agarbased isolation media. Actinomycetes generally appeared after week of incubation at 25 to 28°C. Colonies with pink zones were considered as Lasparaginase producing strains. The colonies were picked and streaked on AIA an SCA by quadrant streak method to isolate the single pure culture. The isolated colonies were sub cultured on AIA and SCA media in refrigerated conditions for further work to carried.
Selection of the significant media components for process modeling:
The purpose of PlackettBurman factorial design was to identify significant
medium components affecting the LAsparaginase production. This factorial design
is important when large numbers of factors are to be considered for optimization.
Thirty two experiments were obtained for 23 factors namely Glucose ,Fructose,
Galactose, Lactose ,Tapioca Effluent, Wheat floor, Tryptone ,Yeast Extract,
Corn steep liquor, peptone, Casein Hydrolysate, LAspaparagine, LGlysine, LCystein,
LSerine, LGlutamine, Lproline, K_{2}HPO_{4}, MgSO_{4},
MnSO_{4}, sodium acetate, pH and Temperature. Each variable was represented
at two levels, upper (“high (1)”) and lower (“low (1)”)
levels of the range covered by each variable and the response (AbdelFettah
et al., 2002) Table 1 shows a 24run PlackettBurman
experimental design (Plackett and Burman, 1946).
This model does not describe the interaction among factors and it is used to screen and evaluate important factors that influence the response. From the regression analysis of the variables, the factors having significant effect on LAsparaginase production were further optimized by Response Surface Methodology (RSM).
Table 1: 
Assigned concentrations of variables at different levels
in Plackett–Burman design for production 

Optimization using response surface methodology: Response surface methodology was adopted for improving LAsparaginase find the interactive effects of the four variables found to be significant from the PlackettBurman experiments. Box/Behnken design of response surface method was used to obtain data that fits a full second order polynomial model. Table 5 represents the design matrix of 29 trials experiment. Regression analysis was performed on the data obtained. A secondorder polynomial equation was used to fit the data by multiple regression procedure. Using this design, factors of highest confidence level percentage are prescribed into three levels, coded /1, 0 and /1 for low, middle and high concentration (or value). For predicting the optimal point, a second order polynomial function was fitted to correlate relationship between variables and response (asparaginase activity). The three dimensional graphical representation of model equation represents the individual and interactive effect of the test variables on the response. Statistical analysis of the data: The data on enzyme activity was subjected to multiple linear regression using DesignExpert^{®} software version 8.0.6.(StatEase,Inc., 2021 East Hennepin Ave., Suite 480, Minneapolis, MN 55413) to estimate tvalue, pvalue and confidence level. The significance level (pvalue) was determined using the Students ttest. The ttest for any individual effect allows an evaluation of the probability of finding the observed effect purely by chance. If this probability is sufficiently small, the idea that the effect was caused by varying the level of the variable under test is accepted. Confidence level is an expression of the pvalue in percent. Scaleup studies in a fermenter: After optimization studies in shaken flasks, protease was produced in a 3.5liter Laboratory Fermenter L1523, (Bioengineering AG ,Sagenrainstrasse 7 CH8636 Wald, Switzerland). Two percent of the 24 h seed culture was inoculated into the optimal medium and fermented at pH 6.5 and 28°C with 350 rpm agitation. The dissolved oxygen concentration was maintained at 80% air saturation throughout the fermentation process. RESULTS AND DISCUSSION Isolation and identification of the isolated strain: From 254 marine sediment samples, 29 Actinomycetes were obtained. The number of actinomyycetes isolated from East coastal region of India (Chennai3 colonies, Chidambaram6 colonies, Pondicherry2 colonies, Tuticorin5 colonies, Rameshwaram8 colonies, Kanyakumari5 colonies). According by morphology and chemotaxonomy about 89% of the isolates were presumed to be in genus streptomyces and 11% non streptomyces. The streptomyces strains were screened LAsparaginase activity on phenol red incorporated medium and 6 (3, 6, 10, 14, 23 and 28) strains were found to produces significant range of LAsparaginase. Farther than six strains, strain 2 (Genebank accession No. HQ623051) was expressed premier activity. It was identified as Streptomyces radiopugnans MS1 based on partial 16 sRNA gene sequence homology and a standard method for bacterial identification. These isolate was maintained in AIA and SCA media at 20°C.
Plackettburman designs: The first optimization step identified the
significant factors for LAsparaginase production from Streptomyces radiopugnans
MS1 using a 24run PlackettBurman design (Table 1, 2).
The statistical significance of the ratio, between the of mean square variation, due to regression and the mean square residual error, was tested using Analysis of Variance (ANOVA). ANOVA is a statistical technique that subdivides the total variation of a set of data into component associated to specific sources of variation for the purpose of testing hypotheses for the modeled parameters. According to the ANOVA , the F values for all regressions were high, what indicates that most of the variations on the response variable can be explained by the regression equation.
Table 3 represents the effect of each variable along with
the mean squares, Fvalues and pvalues. The observed LAsparaginase production
varied from 0.087 to 19.675 IU mL^{1}, reflecting the importance of
medium optimization to attain higher yields. Values of "Prob>F" less than
0.0500 indicate model terms are significant. In general, larger magnitudes of
t and smaller of p, indicates that the corresponding coefficient term (Myers
and Montgomery, 2002). The Model Fvalue of 844.94 implies the model is
significant. There is only a 0.01% chance that a "Model FValue" this large
could occur due to noise. The adequate precision which measures the signal to
noise ratio was 136.121 for response which indicates an adequate signal. A ratio
of >4 is desirable. This model can be used to navigate the design space.
The ‘Pred RSquared’ of 0.9939 is in reasonable agreement with ‘Adjusted
RSquared’ of 0.9983.
The model of equation can be shows as:
Table 2: 
PlackettBurman experimental design for evaluation of factors
affecting Lasparaginase activity 

Table 3: 
ANOVA for plackettburman experimental design analysis of
variance table [Partial sum of squaresType III] 

Std. Dev. 0.036; RSquared 0.9995; Mean 2.84; Adj RSquared
0.9983; C.V. % 1.28; Pred RSquared 0.9939; PRESS 0.11 Adeq Precision 136.121 
The Pareto chart of standardization histogram graph (Fig. 1) showed that only Tapioca Effluent, Corn steep liquor, LAspaparagine, Areation, Glucose, LProline, peptone, Casein Hydrolysate, LSerine, Temp, sodium acetate, KH2SO4, Agitation, Yeast Extract Wheat floor and pH (significance level (Threshold of t value = 2.364), crosses the critical value and was considered to significantly influence LAsparaginase production by Streptomyces radiopugnans MS1. MgSO_{4}, MnSO_{4}, Lactose, LGlysine, Incubation period, LGlutamine and inculum size (Fig. 13).
Optimization by response surface methodology: BoxBehnken is the most
accepted statistical technique for bioprocess optimization to examine the Lasparaginase
activity.

Fig. 1: 
Pareto chart of standardadized effects of twenty three factor
screening design for the production of Lasparaginase. Values of "Prob >
F" less than 0.0500 indicate model terms are significant. In this study
M, H,T, L,O, W, J,G, X, B,Q, P, E, K, F and A are significant model terms
and R, S, N, U, D, V and C are Non significant (Negative effect) 

Fig. 2: 
Parity plot showing the distribution of Actual vs. predicted
values of LAsparaginase activity 

Fig. 3: 
Residual distribution plot for Lasparaginase activity 
Table 4: 
Experimental design obtained by applying the Box/Behnken
design methodology for four factors 

The design matrix and the corresponding results of the experiments to determine
the effect of Tapioca Effluent, Corn steep liquor, LAspaparagine and Areation
are depicted in Table 4 and 5.
At the model level, The Model Fvalue of 742.17 implies the model is significant (Table 6). There is only a 0.01% chance that a "Model FValue" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant.In this case A, B, C, D, AB, AC, BC, CD, A^{2}, B^{2}, C^{2}, D^{2} are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The "Lack of Fit Fvalue" of 0.29 implies the Lack of Fit is not significant relative to the pure error. There is a 94.66% chance that a "Lack of Fit Fvalue" this large could occur due to noise. Nonsignificant lack of fit is good. The "Pred RSquared" of 0.9955 is in reasonable agreement with the "Adj RSquared" of 0.9973."Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 96.042 indicates an adequate signal. This model can be used to navigate the design space.
Presenting experimental results in the form of surface plots (Fig.
4ad) showed that near to moderate levels of Tapioca Effluent,
Corn steep liquor, LAspaparagine and Areation supported high asparaginase activity.
From statistical analysis, it can be concluded that among the test variables, Tapioca Effluent had the most significant effect on Lasparaginase activity and specific activity. The optimal levels of the four components as obtained from the maximum point of the polynomial model were estimated using the Solver function of DesignExpert^{®} software version 8.0.6 tools and found to be: Tapioca Effluent, 5 (% v/v); Corn steep liquor, 2 (% w/v); LAspaparagine,0.003 (% w/v); Aeration, 0.7 (vvm) with a predicted activity of 19.87.
Validation of the model: Optimal conditions realized from the optimization
experiment were verified experimentally and compared with the calculated data
from the model. The estimated Lasparaginase activity was 19.5 IU mL^{1},
where the predicted value from the polynomial model was 19.8 IU mL^{1}.
This verification revealed a high degree of accuracy of the model of more than
99%, which is an evidence for the model validation under the investigated conditions.

Fig. 4(ad): 
Effect of tapioca effluent, corn steep liquor, Laspaparagine
and areation on the production of Lasparaginase enzyme 
Table 6: 
ANOVA for response surface quadratic model. Analysis of variance
table [Partial sum of squaresType III] 

Std. Dev. 0.044; RSquared 0.9987; Mean 3.38; Adj RSquared
0.9973; C.V. % 1.31; Pred RSquared 0.9955 ;PRESS 0.092; Adeq Precision
96.042. Values of "Prob > F" less than 0.0500 indicate model terms are
significant. In this study A, B, C, D, AB, AC, BC, CD, A^{2}, B^{2},
C^{2}, D^{2} are significant model terms. The "Lack of Fit
Fvalue" of 0.29 implies the Lack of Fit is not significant relative to
the pure error. There is a 94.66% chance that a "Lack of Fit Fvalue" this
large could occur due to noise. Nonsignificant lack of fit is good 
In this study, the isolated strain MS1 gave 4.05±0.84 IU mL^{1} in simple basal medium that was enhanced to 19.5±0.33 IU mL^{1} by use of statistical optimization tool. Thus, it can be said that the isolate MS1 is an excellent Lasparaginase producer. In the present study a Actinomycetes strain was isolated from Bay of bengal. The strain was identified as Streptomyces radiopugnans MSl by biochemical and 16S rRNA study. Sequential statistical strategies, PlackettBurman design followed by: BoxBehnken were used successfully to find the optimum values of the significant factors to achieve maximum LAsparaginase production. The predicted yield was 19.8 IU mL^{1}. On experimentation, 19.5 IU mL^{1} LAsparaginase yield was obtained. The experimental values were found to be very close to the predicted values and hence, the model was successfully validated. The LAsparaginase production showed about 5 fold increases over the basal medium. The isolated strain can be used for the production of LAsparaginase enzyme that could be of industrial value.

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