African Journal of
Microbiology Research

  • Abbreviation: Afr. J. Microbiol. Res.
  • Language: English
  • ISSN: 1996-0808
  • DOI: 10.5897/AJMR
  • Start Year: 2007
  • Published Articles: 5123

Full Length Research Paper

Studies on optimization of L-glutaminase production under submerged fermentation from marine Bacillus subtilis JK-79

Jambulingam Kiruthika
  • Jambulingam Kiruthika
  • Department of Industrial Biotechnology, Government College of Technology, Thadagam Road, Coimbatore-641013, Tamil Nadu, India.
  • Google Scholar
Saranya Murugesan
  • Saranya Murugesan
  • Department of Industrial Biotechnology, Government College of Technology, Thadagam Road, Coimbatore-641013, Tamil Nadu, India.
  • Google Scholar

  •  Received: 25 March 2019
  •  Accepted: 06 May 2019
  •  Published: 31 January 2020


In the current study, a marine strain Bacillus subtilis JK-79 capable of producing L-glutaminase enzyme, was analyzed for maximal L-glutaminase production. The Plackett-Burman design (PBD) was applied to determine the significant variables. Optimum conditions of the significant variables on L-glutaminase production were determined by the Central Composite Design (CCD) of response surface methodology. Maximum L-glutaminase production of 691.27 U/ml under flask condition was obtained at the predicted optimal values of pH 6.9, fructose 2.1% (w/v), yeast extract 1.25% (w/v), and glutamine 2.47% (w/v). Statistical optimization has enhanced L-glutaminase enzyme production upto 3.48 fold when compared with the basal SWG medium. The results of this study revealed that marine B. subtilis JK-79 could be a promising source for L-glutaminase production.


Key words: Bacillus subtilis JK-79, L-gltaminase, response surface methodology, submerged fermentation.


Leukemia leads to the lympo-proliferative disorders in particular acute lymphoblastic leukemia (ALL). According to facts and statistics, Leukemia and Lymphomo Society (2018), 60,300 people are expected to be diagnosed with leukemia in United States and 24,370 people are expected to die from leukemia (14,270 males and 10,100 females). In 2010 to 2014, leukemia was the sixth most common cause of cancer deaths in both men and women (American Cancer Society’s Cancer Facts and Figures, 2018). The treatment of ALL is very complex with drugs and there is reluctance for use in children  and adults as it results in severe toxic reactions (Douer, 2008; Noura et al., 2014). In recent years, cancer therapy is highly relied on enzymes as they are low molecular weight protein molecules specific in their action and have less or no toxic effects. The enzymatic approach was reported to be more promising cancer therapy, due to the discovery of several enzymes with effective anti-cancer activity on various types of cancer (Vellard, 2003; Pandian et al., 2014).
L-glutaminase an amidohydrolase enzyme (E.C has been found to be promising in  the  treatment
of ALL (Souba, 1993; Vijayan et al., 2017). Cancer cells, especially ALL cells cannot synthesize L-glutamine, as they lack properly functioning glutamine biosynthetic machinery and therefore require large amount of L-glutamine for their rapid growth. These cells depend on the exogenous supply of L-glutamine for their survival and rapid cell division. Hence, the use of L-glutaminase deprives the tumor cells from L-glutamine and causes selective death of L-glutamine dependant tumor cells. Thus, it can act as a possible candidate for enzyme therapy (Hensley et al., 2013; El-Ghonemy, 2014). In recent years, L-glutaminase in combination with or as an alternative to L-asparginase could be used as in enzyme therapy for cancer particularly leukemia (Sabu, 2003).
The production of L-glutaminase by bacteria has attracted great attention owing to their cost effective and eco-friendly nature (Chandrasekaran, 1996; Unissa et al., 2014). Microbial L-glutaminase can be produced by both submerged and solid state fermentation. The improvement in production of L-glutaminase by a microbial strain can be achieved by determining the optimum physico-chemical parameters. Selection of appropriate carbon and nitrogen source is one of the most critical stages in the development of an efficient and economical production process. The methodologies used for screening of the components fall into two major categories: classical and statistical. The screening of various cultural parameters by classical method involves changing one independent variable (component of medium, parameter) at a time while fixing all other at certain level. This single dimensional search is simple, easy and useful technique to evaluate the individual effects of the media components and process conditions (Iyer and Singhal, 2008, 2009, 2010a, b) and has been commonly used for optimization.
However, the interaction between components influencing the production of a particular metabolite cannot be studied by varying one factor at a time (OFAT). Moreover, it often does not allow determination of actual optimum level of different components for a particular fermentation. The statistical methods provide an alternative solution to optimize a particular process by considering mutual interactions among the variables and give an estimate of the combined effect of these variables on the final result. Moreover, statistical methods are less laborious and rapid; thus are preferred over conventional OFAT approach for process optimization (Montgomery, 2000). Various statistical designs ranging from 2-factorial to multi-factorial are available for optimization of process parameters (Montgomery, 2000; Myers et al., 2009).
There has been much work reported on the effect of growth medium on the production of L-glutaminase by different marine bacteria. Various carbon and nitrogen sources were found to improve the production of L-glutaminase. Extracellular L-glutaminase producing Beauveria species BTMF S10 was isolated from marine sediment   (Keerthi   et   al.,   1999)   and   the  yield  was improved when the medium was supplemented with 1% yeast extract and sorbitol, 9% sodium chloride and 0.2% methionine at pH 9, 27°C. Optimum production of L-glutaminase from Streptomyces rimosus was observed at 27°C, pH 9 and glucose and malt extract as carbon and nitrogen sources, respectively (Sivakumar et al., 2006). Iyer and Singhal (2008, 2009), observed that the carbon and nitrogen sources for L-glutaminase production varied with the organisms. Supplementation of sucrose and yeast extract as carbon and nitrogen source improved L-glutaminase production by Zygosaccharomyces rouxii while higher L-glutaminase production was noticed in Providencia species with glucose and urea as carbon and nitrogen source, respectively. Recently, there are several reports which confirm the fact that the carbon and nitrogen sources significantly affect the production of L-glutaminase by microorganisms (Sathish and Prakasham, 2013; Krishnakumar et al., 2011; Pandian et al., 2014; Jesuraj et al., 2017).
In the present investigation, the potent marine strain Bacillus subtilis JK-79 was evaluated for maximal production of the L-glutaminase enzyme. In this context, different production media, carbon and nitrogen sources were tested for the highest yield of the L-glutaminase production.


Microorganism and culture maintenance
The strain B. subtilis JK-79 (KC492745) used in this study was isolated from marine soil collected from Parangipettai costal area (Kiruthika and Saraswathy, 2014).
L-Glutaminase assay and protein estimation
L-glutaminase was assayed by the method of Imada et al. (1973). One international unit of L-glutaminase was defined as the amount of enzyme that liberates 1 µmol of ammonia under optimal conditions. The enzyme yield was expressed as Units/ml (U/ml of culture supernatant). Protein content in the crude enzyme source was estimated by Lowry’s method (Lowry et al., 1951) using bovine serum albumin as the standard and the values were expressed as mg/ml.
Optimization by statistical design
Identifying significant variables by Plackett-Burman Design (PBD)
The Plackett-Burman Design (PBD; Mini Tab, trial version 17) was used to identify the significant variables affecting the L-glutaminase production. A total of eight factors such as pH (A), temperature (B), agitation speed (C), inoculum size (D), incubation time (E), carbon source (F), nitrogen source (G), and glutamine level (H), were considered and their high and low levels are shown in Table 1.
The responses from the 12 individual experiments were utilized for generating regression co-efficient values and the significant variables were further optimization by central composite design (CCD).
Response surface methodology
The significant variables screened through PBD technique were subjected to CCD Software Design Expert Release 9 (Stat-Ease INC. Minneapolis MN, USA), a popular second order experimental design for developing sequential experimentation and predicting the levels of factors to get an optimal response through regression analysis. The effects of four independent  variables   viz.  pH,  carbon source, nitrogen source and glutamine on the production of L-glutaminase were studied at five different levels (-2, -1, 0, 1, 2). A full factorial central composite design was performed to build a total of 30 experiments, having 24 = 16 cube points and 6 centre points (4 in cube and 2 in axial positions) and 4 × 2= 8 star points. The second-degree polynomial equation was used to determine the relationship between the independent variables and the response.


Identification of significant factors using pbd
The influence of eight variables, in the production of l-glutaminase by the strain b. Subtilis jk-79 was investigated in 12 runs using pbd. Table 2 represents the PBD for the selected eight variables and the corresponding response for L-glutaminase production (U/ml). Variations were observed in the production of L-glutaminase ranging from 150 to 666.67 U/ml. On the basis of analysis of variance (ANOVA), the most effective factors with high significance were in the order glutamine (H), fructose (F), yeast extract (G) and pH (A) (Table 3). The first order polynomial equation was derived representing L-glutaminase production as a function of independent variables.
Y = 353.9 + 94.8 H + 77.5 G + 81.5 F - 53.7 A
Where, Y is the response (L-glutaminase production U/ml); H, G, F and A are independent variables of glutamine, fructose, yeast extract and pH, respectively with their corresponding co-efficients.
Statistical analysis of PBD demonstrated that the model F value of 13.2 is significant and among the significant factors, glutamine showed a remarkable effect on the production of L-glutaminase by B. subtilis JK-79. In the present model, R2 was 97.24%, and only 2.77% variability was not explained. Thus, the present R2 value reflected reliability of the model for predicting L-glutaminase production. The value of the adjusted determination coefficient (Adj R2 = 0.8987) confirmed the significance of the model as well.
Optimization using CCD
RSM using CCD was adopted to understand the interactive effects of these four significant variables. Table 4 illustrates the details of actual and coded values employed in the CCD. The experimental trials were performed based on the CCD (Table 5) and the results obtained were fitted to a second order polynomial equation to explain the dependence of L-glutaminase production with the independent variables.
Y = +683.08 -13.88 × A + 10.05 × B - 32.83 × C - 5.88 × D + 3.05 × AB - 8.86 × AC + 27.78 × AD + 12.41 × BC - 20.47 × BD + 11.88 × CD – 159.62 × A2 - 27.55 × B2 – 49.30 × C2 - 100.93 × D2
Where, Y is the response of L-glutaminase production, A, B, C and D are the coded values of pH, fructose, yeast extract and glutamine, respectively.
The analysis of variance of the quadratic regression model (Table 6) suggested that the model was very significant which was evident from the Fisher’s F-test (Fmodel = 588.38) and a low probability value (Pmodel<0.0001). The p value for “lack of fit” (0.0957) also indicated that the quadratic model adequately fitted the data. In this model, A, B, C, D, AC, AD, BC, BD, CD, A2, B2, C2, and  D2 are significant model terms. R2 was found to be 0.9982 indicating that the model was reliable. 
3D response surfaces were generated to understand the interaction between independent variables. Figure 1i-vi shows the response surfaces and contour plots generated for the variation in the yields of L-glutaminase as a function of concentrations of two variables with the other two variables at their central value. The coordinates of the central point within the highest contour levels in each of the figures correspond to the optimum concentrations of the respective components. Evaluation of response surface curves and contour plots indicate the range of optimum conditions within the experimental area covered or show the way to conduct further experiments to achieve better results.
From the Figure 1i, it was evident that when the concentration of glutamine and nitrogen source were held at  their   middle   values,   the   pH   showed   a parabolic response at the different concentrations of fructose with the highest yield of L-glutaminase obtained in the range of pH 7. Very low and high pH values were not favorable for enzyme production. Variation in fructose concentration has also followed a parabolic curve and optimum yield was in the range of 2.0 to 2.25%.
Similarly, the response behavior was analyzed between pH and yeast extract with the other two factors (glutamine and fructose) kept at their middle values. The production of L-glutaminae was affected by pH and followed a parabolic curve. Extreme conditions of pH decreased the production of the enzyme and the optimum pH was 7. However, the concentration of yeast extract also affected the response and the maximum L-glutaminase production was obtained in the range of 1 to 1.5% (Figure 1ii).
The 3D response surface and contour plot between pH and glutamine is represented in Figure 1iii. From these
plots it was evident that lowest yield of L-glutaminase was obtained in the extreme pH conditions, that is, highly acidic and highly alkaline pH. The L-glutaminase production increased considerably when the pH approached its middle values and the optimum was in the range of 7. The L-glutaminase production was also affected by glutamine concentration and followed a parabolic curve. The yield was minimum at the very low and high concentration of glutamine and the highest production was obtained in the range of 2.5 and 3%.
The fructose and yeast extract concentration affected the L-glutaminase production and the optimum enzyme production was obtained in the fructose and yeast extract concentration of 2 to 2.5% and 1 to 1.5%, respectively (Figure 1iv). In the response behaviour of different concentrations of fructose and yeast extract (Figure 1v), the response pattern was found to be parabolic and the optimum production of L-glutaminase was obtained at the middle values. Figure 1vi demonstrates that the production pattern of  L-glutaminase  was  parabolic  with respect to glutamine concentration. In the case of yeast extract, the highest yield of the L-glutaminase was attained at the mid concentration and further increase in yeast extract concentration did not significantly change the production of L-glutaminase.
The optimum values of the independent variables were predicted using point prediction tool of design of expert software (Figure 2). Maximum L-glutaminase production of 688.5 U/ml under flask condition was obtained at the predicted optimal values of pH 6.9, fructose 2.1% (w/v), yeast extract 1.25% (w/v), and glutamine 2.47% (w/v). The maximum experimental L-glutaminase production was 691.27 U/ml thus indicating a strong correlation between them.
The L-glutaminase production under submerged fermentation by marine B. subtilis JK-79 was determined in optimized and unoptimized base medium, that is, Sea Water Glutamine (SWG) medium (Kiruthika and Saraswathy, 2014) and a 3.48 fold increase in glutaminase  production   was   obtained   by  phase-wise optimizing the medium (Figure 3).  
Several authors have reported an increased fold of L-glutaminase production by the application of RSM. Iyer and Singhal (2008) used a face centered central composite design (FCCCD) to enhance the L-glutaminase production and specific activity by 2.94 and 3.58 folds, respectively with Z. rouxii. Similarly, the authors have employed One Factor at a Time (OFAT) and FCCCD to design the optimized medium for Providencia spp. (Iyer and  Singhal,  2009).  Sathish  and Prakasham (2010) found that a hybrid methodology adopted resulted in a significant improvement (47%) in the L-glutaminase yield by B. subtilis RSP-GLU.
Suresh et al. (2013) reported optimization of medium components through OFAT approach and FCCCD for the submerged production of L-glutaminase by Serratia marcescens. Pandian et al. (2014) reported the statistical optimization of medium composition for the production of glutaminase from Alcaligenes faecalis KLU102. RSM was used  for  optimization  and  the  bacterium  grown  in  the optimized medium [arabinose (2%), skim milk (4%) and sodium chloride (2%)] yielded L-glutaminase activity of 1.34 IU/mg.
Jesuraj et al. (2017) has reported statistical optimization of L-glutaminase production by mutated strain Aeromonas veronii by PBD and CCD. The model was found to be a perfect fit in terms of maximizing enzyme yield, with the productivity improving at every stage to a fourfold output of enzyme (591.11 ±7.97 IU/mL) compared to the native strain (135±3.51 IU/mL).


Statistical optimization has enhanced L-glutaminase production under submerged fermentation by marine B. subtilis JK-79 (KC492745) (Kiruthika and Saraswathy, 2014) upto 3.48 fold when compared with the basal SWG medium. Thus, application of PBD and RSM for optimization studies proved to be an effective method for improving the L-glutaminase production. Results of this study revealed that marine B. subtilis JK-79 could be a promising source for L-glutaminase production.


The authors have not declared any conflict of interests.


The authors thank the Principal, G. C. T and the Management, K. C. T for providing the necessary facilities and fund to carry out this research work.


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