Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 20  |  Issue : 1  |  Page : 51-58

Statistical optimization of L-asparaginase production by Cladosporium tenuissimum


1 Department of Microbiology and Molecular Biology, Al-Neelain University, Khartoum; Department of Basic and Industrial Microbiology, Ege University, İzmir, Türkiye, Sudan
2 Department of Microbiology and Molecular Biology, Al-Neelain University, Khartoum, Sudan
3 Department of Basic and Industrial Microbiology, Ege University, İzmir, Türkiye

Date of Submission07-Sep-2020
Date of Decision01-Oct-2020
Date of Acceptance21-Dec-2020
Date of Web Publication18-Mar-2021

Correspondence Address:
PhD Ahmed A Osman
Department of Microbiology and Molecular Biology, Al-Neelain University, Khartoum, PO Box 12702
Sudan
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/epj.epj_47_20

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  Abstract 


Background L-asparaginase produced by plant and bacteria can be used in the pharmaceutical and food industry. Unlike the bacterial counterparts, fungal L-asparaginase has more stability, more activity, and less adverse effects. Central composite design (CCD) was used to optimize temperature, pH, incubation time, and carbon-to-nitrogen ratio for L-asparaginase production by Cladosporium tenuissimum via submerged fermentation. CCD reduces the number of tests and time for optimization.
Objective To optimize the culture conditions, such as temperature, pH, production time, and the ratio between concentration of carbon and nitrogen sources, for the production of L-asparaginase by isolated C. tenuissimum via submerged fermentation.
Materials and methods Primarily, four significant parameters (temperature, pH, incubation period, and carbon-to-nitrogen ratio) were identified that affect the production process of L-asparaginase via submerged fermentation using the modified Czapek Dox medium. CCD was used to optimize the selected parameters concurrently, and their results were compared.
Results and conclusions The highest L-asparaginase enzyme activity obtained was 2.6471 U/ml at 37°C, pH 6.2, incubation time 72 h, and 2 : 1 carbon-to-nitrogen ratio. The P value of interaction between every two factors was only significant for the interaction between temperature and incubation period (P<0.0281). The most significant factor was temperature followed by pH (P<0.0154) and carbon-to-nitrogen ratio (P<0.0346). Incubation period has no major effect on the production of L-asparaginase, but it has a quadratic effect (P<0.0001). Our results showed the significant role of culture conditions (temperature, pH, incubation period, and carbon-to-nitrogen ratio) in L-asparaginase production and confirmed the need for optimization.

Keywords: enzyme, fungi, L-asparaginase, optimization, production


How to cite this article:
Hamed M, Osman AA, Ateş M. Statistical optimization of L-asparaginase production by Cladosporium tenuissimum. Egypt Pharmaceut J 2021;20:51-8

How to cite this URL:
Hamed M, Osman AA, Ateş M. Statistical optimization of L-asparaginase production by Cladosporium tenuissimum. Egypt Pharmaceut J [serial online] 2021 [cited 2021 May 15];20:51-8. Available from: http://www.epj.eg.net/text.asp?2021/20/1/51/311593




  Introduction Top


L-asparaginase (E.C.3.5.1.1) is an enzyme that catalyzes the deamidation of L-asparagine to L-aspartic acid and ammonia [1],[2]. L-asparaginase is used in pharmaceutical and food industries [3]. It is produced by plant, animal tissues, and microbes [4],[5]. Pharmaceutical L-asparaginase is obtained primarily from bacteria such as Escherichia coli and Erwinia carotovora. However, L-asparaginase enzymes obtained from bacteria have less stability, less enzyme activity, and are associated with undesired adverse effects, especially in the long-term use, causing some immunological reactions [4],[5]. Fungal L-asparaginase is nontoxic and presumed to have an immune-suppressive activity [6].

Optimizing production medium is the most important step in L-asparaginase production [3],[7] which is performed before scaling up production [8],[9]. Design of experiment techniques offer a reliable result in a lesser time with reduced number of experiments [10]. In this work, we described optimization of L-asparaginase production using response surface methodology (RSM) as one of the design of experiment approaches.


  Materials and methods Top


Microorganisms

A total of 55 fungal isolates were obtained from the stocks of the Laboratory of Mycology at the Basic and Industrial Microbiology Department, Ege University, Adnan Mendres University, and Celal Bayar University. Fungal isolates were maintained by cultivation in a slant of potato dextrose agar, Oxoid, Wesel, Germany. These isolates were tested for L-asparaginase production by plate assay using modified Czapek Dox medium.

Chemicals and reagents

All chemicals were purchased from Merck (New Jersey, USA), unless otherwise specified.

Production of L-asparaginase via submerged fermentation

A 250-ml Erlenmeyer flask containing 50 ml sterile modified Czapek Dox medium [components (g/l): glucose: 2.0; L-asparagine, Sigma, St Louis, Missouri, USA: 10; KH2PO4: 1.52; MgSO4.7H2O: 0.52; KCl: 0.52; FeSO4: 0.03; CuNO3.3H2O 0.03; and ZnSO4.7H2O 0.05] was used for production. Cultivation was done by adding 1 ml of the inoculum to the medium. The bottles were incubated at 30°C and 150 rpm. After 96 h, the fungal cell mass was separated by centrifugation at 5000 rpm for 15 min at 4°C. The supernatant was used as a raw enzyme to determine enzyme activity [11],[12].

Effect of different types of carbon and nitrogen sources on L-asparaginase production

The effect of the nitrogen sources (proline, urea, asparagine, ammonium chloride, and sodium nitrate) and carbon sources (glucose, starch, lactose, glycerol, and sucrose) on the enzyme production was assessed by using the best three L-asparaginase producers from the semi-quantitative screening.

L-asparaginase activity assay

L-asparaginase activity test was performed using the Nesslerization method [9]. In the enzyme assay mix, 1 ml Tris-HCl buffer (pH 8.6), 0.9 ml distilled water, and 0.1 ml (40 mM) freshly prepared L-asparagine were used. The enzyme assay mixture was incubated with 0.1 ml culture supernatant for 30 min at 37°C, and the reaction was stopped by adding 0.1 ml of 1.5 M TCA. The reaction mixture was centrifuged at 10 000 rpm for 5 min at 4°C. Ammonia released in the supernatant was determined using a colorimetric technique at 425 nm by adding 0.5 ml of Nessler reagent (45.5 g HgI2 and 35.0 g KI dissolved in 1 l distilled water containing 112 g KOH) to the sample containing 0.2 ml of supernatant and 4.3 ml of distilled water. Blank samples were prepared by adding TCA before the enzyme was added. Ammonia released in the reaction was calculated based on the standard curve obtained using ammonium sulfate. One unit of L-asparaginase activity was defined as the amount of enzyme that releases 1 µM ammonia at 37°C per minute, using asparagine as the substrate.

Experimental design and central composite design-based optimization

A RSM-based central composite rotatable design with four variables was used to study the response pattern and to define the interaction between parameters. The variables with five levels are temperature (27–47°C), pH (4.2–8.2), carbon-to-nitrogen ratio (2 : 10, 2 : 15, 2 : 20, 4 : 10, 6 : 10), and incubation time (24–120 h), which were optimized. The experimental design for central composite rotatable design was carried out as shown in [Table 1]. For detection of pure error sum square, six replicates (run order: 12, 19, 26, 3, 14, 22) at the center of the model were performed. The influence of the variables on the production of L-asparaginase (response) was expressed by second-order polynomial presented in the following equation:
Table 1 Ranges of the independent variables used for the production of L-asparaginase by Cladosporium tenuissimum using response surface methodology

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Where Y is the predicted enzyme yield (response), β0 is the intercept term, βi is the linear effect, βii is the quadratic effect, βij is the interaction effect, and Xi and Xj are independent factors.

Statistical analysis

The statistical analysis was performed using the Design-Expert 7.0 software. Statistical analysis of the observations was evaluated through analysis of variance, R2, and three-dimensional graphs. The observations resulted from RSM were recorded and interpreted with their respective possible surface interaction plots.

Validation of experimental design

The model was validated by selecting one of the experiments recommended by the program to perform validation. After performing the experiment, the result was compared with the result that had been predicted by the model.


  Results and discussion Top


Experimental design and central composite design-based optimization

The present study reveals that 2.6471 U/ml is the highest L-asparaginase level obtained by Cladosporium tenuissimum at 37°C, pH 6.2, incubation time 72 h, and 2 : 1 carbon : nitrogen ratio ([Table 2]) (trial 19), whereas the lowest L-asparaginase level (0.0037 U/ml) was obtained from trial 9 experiment (42°C, pH 5.2, incubation time 48 h, and carbon/nitrogen ratio 0.4 : 1), indicating that L-asparaginase production by C. tenuissimum is influenced by levels of the selected factors. Almost at a similar condition (incubation time 72 h, pH 6.3, and temperature 33°C), Vimal and Kumar [3] achieved the highest level of L-asparaginase production using Aspergillus terreus.
Table 2 Experiments design for central composite rotatable design

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Statistical analysis

Regression analysis was conducted to calculate the effect of each factor and interaction between factors. The model is expressed by the following formula:



Where Y is the predicted enzyme yield, A is the temperature, B is the pH, C is the incubation time, and D is the C versus N.

The relationship between predicted and actual values are presented in [Figure 1], whereas [Figure 2] describes the approximate linear model for probability. According to the analysis of variance ([Table 3]), the most significant factor was temperature followed by pH (P<0.0154) and carbon-to-nitrogen ratio (P<0.0346). Incubation period has no major effect on the production of L-asparaginase (P<0.0686) but it has a quadratic effect (P<0.0001). These results were in agreement with the previous results of Gurunathan and Sahadevan [2], Kumar et al. [9], and Prakasham et al. [10], in which the authors reported a significant main effect of temperature on L-asparaginase production.
Figure 1 Regression plot for L-asparaginase production detected and predicted values.

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Figure 2 Normal plot of residuals.

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Table 3 Optimization of L-asparaginase production by Cladosporium tenuissimum

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Contrary to the findings of Vimal and Kumar [3] and Prakasham et al. [10], in our study, pH showed significant effect on L-asparaginase production, supporting the previous results of Gurunathan and Sahadevan [2], Dias and Sato [7], Kumar et al. [9], and Talluri et al. [13]. The R2 was 0.9679, which is very close to 1.0, and the adjusted R2 (0.8671) and projected R2 (0.8591) were close to each other, reflecting that the model is suitable for prediction.

[Figure 3] shows the main effect of each factor separately on the production of L-asparaginase by C. tenuissimum. [Figure 3]b, c, and d shows a curve with a top in the middle of the curve, indicating that the range of factors used in optimization is well maintained. Moreover, it reveals that the optimum level of the factor for the production of maximum L-asparaginase is the medium level in the range. Although the curve of temperature ([Figure 3]a) shows a top near to the left of the curve, the optimum level for maximum production was not missed. The three-dimensional graphs ([Figure 4]) were done by plotting the response of the regression model; it shows the interaction effect between parameter on the overall produced enzyme level. Moreover, it shows clearly the optimum level of the parameters required for the maximum yield of L-asparaginase enzyme. However, Gurunathan and Sahadevan [2],[4] showed that there was no effect of interaction between any of these variables for l-asparaginase production by A. terreus in submerged fermentation.
Figure 3 Effect of different parameters on the production of l-asparaginase by Cladosporium tenuissimum (a) temperature, (b) pH, (c) incubation time, and (d) carbon versus nitrogen source.

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Figure 4 Three-dimensional response surface plot for the effect of temperature and pH (a), temperature and incubation time (b), temperature and carbon : nitrogen ratio (c), incubation time and pH (d), carbon : nitrogen ratio and pH (e), carbon : nitrogen and incubation time (f).

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Maximum activity and model validation

From the experiment proposed by the program, 3.25 U/ml L-asparaginase activity was obtained. The model predicted that we would obtain 2.903 U/ml, and for the validation of the model, the result should be between 2.2 and 3.8 U/ml. Our result of 3.25 U/ml is fitting the range, indicating that our model is valid. As shown in [Figure 5], the yellow region determines the levels of factors that produce maximum L-asparaginase level. Maximum L-asparaginase activity could be obtained from an experiment carried out under any selected levels in the yellow zone ([Table 4]).
Figure 5 The levels of factors that produce maximum L-LA level. R1: maximum L-LA level, X1: optimum temperature, X2: optimum pH.

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Table 4 Analysis of variance for L-asparaginase production

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  Conclusion Top


The type of carbon and nitrogen sources present in the production environment can affect the level of L-asparaginase produced by the fungi. Glucose is the best carbon source, whereas urea was found to be the best nitrogen source for L-asparaginase production by C. tenuissimum. Optimization strategy used here can also be used to set up optimization for production of other enzymes.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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Talluri PVSSL, Lanka SS, Saladi VR. Statistical optimization of process parameters by central composite design (CCD) for an enhanced production of L-asparaginase by myroides gitamensis bsh-3, a novel species. Avicenna J Med Biotechnol 2019; 11:59–66.  Back to cited text no. 13
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

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