|Year : 2019 | Volume
| Issue : 2 | Page : 165-171
Optimization of parameters for lipase production by Aspergillus niger NRRL-599 using response surface methodology
Rania A. Abd El Aal1, Yousseria M Shetaia2, Mona S Shafei1, Sanaa K Gomaa1, Hassaan A El Menoufy1, Heba A El-Refai1
1 Chemistry of Natural and Microbial Products Department, Pharmaceutical Industries Researches Division, National Research Centre, Dokki, Giza, Egypt
2 Ein-Shams University, Faculty of Science, Microbiology Department, Cairo, Egypt
|Date of Submission||26-Dec-2018|
|Date of Acceptance||03-Mar-2019|
|Date of Web Publication||05-Jul-2019|
Sanaa K Gomaa
Department of Chemistry of Natural and Microbial Products, National Research Centre, 33 Street El-Behoos, Dokki, Giza, 12311
Source of Support: None, Conflict of Interest: None
Background and objective Lipases are characterized to catalyze precise chemical transformation. That is they have widespread applications in detergents, cosmetics, food, organics synthesis, and pharmaceutical industries. In recent years, the study and development of lipase production in solid state fermentation, also using sequential statistical strategy as optimization for some vital factors are gaining more attention. This may be attributed to several advantages using techniques such as simplicity of fermentation media, no need for complex machinery. Equipment and control system, compactness of fermentation vessel due to lower water volume, high yields, less energy needed, and lower capital.
Materials and methods Aspergillus niger NRRL-599 strain was tested for lipase production using different agroindustrial wastes including wheat bran, wheat germ cake oil, jojoba cake oil, almond cake oil, and olive oil as substrates under solid state fermentation. For further optimization, lipase activity was studied using two sequential optimization.
Results and conclusion Olive oil waste was the most suitable substrate for lipase production (125 U/ml). Using one variable at a time, maximum lipase activity (200 U/ml) and specific enzyme activity (357.1 U/mg) were recorded in the presence of 5% w/v olive oil, 48 h inoculum age, 4% v/v inoculum size after 14 days fermentation at 30°C. The screening of the seven physiological factors using Plackett–Burman design showed that only three variables; that is tween 80, moisture content, and inoculum age affected significantly lipase production. Optimization by Box–Behnken design resulted in the highest lipase activity (420 U/ml) in which the most effective variables were inoculum size, inoculum age, moisture content, and tween 80 1%.
Keywords: Aspergillus niger, lipase production, olive oil: agroindustrial wastes, Plackett–Burman, solid state fermentation
|How to cite this article:|
El Aal RA, Shetaia YM, Shafei MS, Gomaa SK, El Menoufy HA, El-Refai HA. Optimization of parameters for lipase production by Aspergillus niger NRRL-599 using response surface methodology. Egypt Pharmaceut J 2019;18:165-71
|How to cite this URL:|
El Aal RA, Shetaia YM, Shafei MS, Gomaa SK, El Menoufy HA, El-Refai HA. Optimization of parameters for lipase production by Aspergillus niger NRRL-599 using response surface methodology. Egypt Pharmaceut J [serial online] 2019 [cited 2022 Dec 9];18:165-71. Available from: http://www.epj.eg.net/text.asp?2019/18/2/165/262145
| Introduction|| |
Enzymes are important for their various industrial applications. The demand for the industrial market for enzymes continues to grow due to the improvement of new technologies for production, the use of genetic engineering and continuous emergence of new fields of applications . According to a new research report by Global Market Insights Inc. the enzyme market is expected to cross USD 9.5 billion by 2024. This is due to the wide range of biotechnological applications of microbes and their enzymes in antibiotic synthesis, biopolymers, food, paper, vitamins, and textile industry. Lipases have widespread applications in detergents, cosmetic, food, organic synthesis, and pharmaceutical industries .
In recent years, the study and development of lipase production in solid state fermentation (SSF) are gaining more attention. This may be attributed to several advantages using techniques such as simplicity of fermentation media, no need for complex machinery, equipment and control system, compactness of fermentation vessel due to lower water volume, high yields, less energy needed .
Lipases are produced from different sources, however, the most suitable of which are from microbes including bacteria , fungi , and yeast . High-quality lipases can be produced in lower cost and shorter time ,.
In addition, SSF has attracted great attention because of the availability of the agroindustrial wastes used as a cheap carbon source instead of the commercial synthetic media .
Lipase production is influenced by several environmental factors such as the composition of fermentation media, pH, temperature as well as the microbial strain . Classical methods of optimization do not define the relation between factors and their effects on each other; in addition, the classical methods require a large number of experiments . Plackett–Burman design was used to distinguish the vital variables and their main effects . Box–Behnken design shows the interaction between the independent variables .
Our objective was to produce extracellular lipase by Aspergillus niger NRRL-599 from available agroindustrial waste using SSF technique by applying two sequential factorial designs.
| Materials and methods|| |
All the chemicals used during this study were of analytical grade and were obtained from Sigma Aldrich Chemical Co. (St Louis, Missouri, USA).
Different substrates, namely jojoba oil cake, wheat germ oil cake, almond oil cake, wheat bran, and olive oil waste were obtained from Industries and Nutrition Division, Fats and Oils Department.
A. niger NRRL-599 was obtained from Natural and Microbial Products Department (National Research Centre, Cairo, Egypt) and was routinely grown on potato-dextrose agar medium (Sigma Aldrich Chemical Co.) at 30°C and preserved at 80°C in 50% (v/v) glycerol.
In order to prepare the inoculum, 4 ml spore suspension of 7-day old cultures (after adjusting the amount of spores otherwise stated) was transferred into 250 ml Erlenmeyer flasks containing 100 ml of sterilized nutrient broth medium which has the following composition (g/l): a beef extract, 5.0; glucose, 5; peptone, 5.0; and sodium chloride, 5.0. The pH of the medium was initially adjusted at pH 7.0. The flasks were incubated on a reciprocal shaker (MAXQ 481 R HP) at 200 rpm for 4 days at 30°C .
Solid state fermentation
Different substrates were used, namely jojoba oil cake, wheat bran, wheat germ oil cake, almond oil cake, and olive oil waste. Fermentation was carried out with 5 g of a substrate in 250 ml Erlenmeyer flasks. The moisture content was adjusted with 20 ml tap water/5 g waste. After sterilization (121°C, 15 min) the flasks were allowed to cool down then each flask was inoculated with 4 ml of inoculum and incubated at 30°C at 7, 11, 14, and 18 days .
At the end of fermentation, the crude enzyme was extracted by mixing the fermented substrate with 100 ml of tap water, then shaking the mixture in a reciprocal shaker at 200 rpm for 15 min, the obtained extract was filtered, and the supernatant was used as crude lipase enzyme .
Lipase activity assay
Lipase hydrolysis activity was assayed using 1 ml of culture filtrate mixed with 3 ml emulsion of olive oil in Arabic gum (10% w/v) and 2.5 ml of deionized water in 1 ml of 0.1 M tris-HCL buffer (pH 7.5). After incubation for 2 h at 37°C and 160 rpm, the reaction was stopped by addition of 10 ml of 99% acetone or ethanol solution. The fatty acids produced due to hydrolysis were titrated with 0.05 N NaOH. A control assay was carried out by boiling the enzyme mixture .
One unit of lipase activity was defined as the amount of enzyme preparation necessary to produce 1 μm of free fatty acids under the assay conditions.
The protein content was determined according to Lowry method .
Effect of levels of seven variables (time course, moisture content, pH, waste concentration, temperature, inoculum age, and emulsifying agent) were studied for optimization of maximum lipase activity using the Box–Behnken design . The most critical parameters by A. niger were studied with JMP 8 software (SAS Institute Inc. Cary,North Carolina, USA) using the Plackett–Burman design. For each variable, high (+), low (−), and medium (0) levels were tested. Each trial represents the average value of enzyme activity (U/ml) and the protein level (mg/ml) which was taken as an independent response.
Response surface methodology
For further optimization of lipase activity, significant parameters were studied using the Box–Behnken. The experimental design was comprised of 13 runs at three levels. Each run with three replicates and the average value of enzyme activity and protein content were represented as a dependent response.
The significance of the model was determined by analysis of variance, the regression equation was obtained, a P value less than 0.05 indicates that the model term is significant. The fit of the model (R2) was studied and the results closer to show a better correlation between experimental and predicted values. The values of the coefficients were calculated and the optimum concentrations were predicted using JMP.8 software.
It was performed under conditions predicted by the experiment model. The experiments were examined in triplicate at an interval of 14 days.
Results and discussion
The main objective was to utilize agroindustrial waste for lipase production. Maximum lipase activity and specific activity were detected in presence of olive oil waste (125 U/ml and 62.1 U/mg protein, respectively) followed by wheat bran (110 U/ml and 73.3 U/mg protein, respectively) after 14 days of fermentation ([Figure 1]).
|Figure 1 Effect of different wastes on lipase activity at different time.|
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Rapp , studied the effect of natural fats on stimulation of lipase production by Fusarium oxysporum. Similar results were shown by Ali et al. , who stated that lipase could be produced by Exiguobacterium spp. BBXS-7 using vegetable oil as a carbon source. Burkert et al. , reported that olive oil and soybean oil showed higher lipase activity using Geotrichum spp.
Effect of inoculum age
The influence of inoculum age on lipase production was analyzed using olive oil cake, after 72 h of fermentation (data not shown). The maximum lipase activity and specific activity (200 U/ml) were determined after 72 h inoculum age showed the best results in terms of lipase activity and specific activity (200 U/ml and 357.1 U/mg protein, respectively). These results agreed with Vardanega et al. , who found that 72 h was suitable for lipase production by SSF of soybean meal using Penicillium spp. Similarly, Penicillium notatum of 72 h old was used for the production of lipase using different agricultural wastes . However, the age of inoculum depends on the process, cultivation conditions, medium composition, and the microorganism .
Effect of inoculum size on lipase production
In the present investigation, different inoculum sizes (1, 2, 3, 4, 5, and 6) from 72 h old culture were used to study the effect of inoculum size in lipase production (data not shown). The lipase activity and specific activity (200 U/ml and 181.8 U/mg protein, respectively) were increased with increasing the inoculum size until it reached a maximum at 4% v/v. Increasing the inoculum size than 4% v/v decreases the production of enzyme occurred. This may be attributed to the fact that there is an increase in cell mass formation and a decrease in nutrients .
Optimization of lipase production by Plackett–Burman design
Extracellular lipase produced by different microorganisms is greatly influenced by the physical factors such as temperature, pH, and incubation period. Several studies were carried out to increase the enzyme production by optimization of several variables at the same time ,. Thus, the experimental design with seven variables (time course, moisture content, pH, olive oil waste, temperature, inoculums age, and tween 80) was carried out to study the main effect of each factor on lipase production by A. niger. The results in [Table 1] showed a variation in lipase activity from 0 to 250 U/ml. The results indicated that the medium optimization is necessary for high enzyme production. The maximum enzyme activity and Specific enzyme activity (SEA) (250 U/ml and 1315.8 U/mg protein, respectively) were observed in trial number 2 after 16 days of fermentation, whereas no enzyme activity in trial numbers 1, 5, and 8.
|Table 1 Matrix of the Plackett–Burman design experiments on nine variables|
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The main effects of the seven examined variables on lipase production were calculated and shown graphically in [Figure 2]. The results obtained showed that emulsifying agent tween 80, moisture content, inoculum age had positive effects on lipase production whereas the other variables had negative effects.
|Figure 2 Effect of culture conditions and medium composition on lipase (U/ml) produced by Aspergillus niger NRRL-599.|
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Increase in moisture content causes a decrease in the substrate porosity enhances the substrate stickiness, changes the particle structure of substrate, and decreases both the gas volume and exchange. On the other hand, the decrease in moisture contents results in a decrease in nutrients solubility and improper swelling as well as higher water tension ,.
Tween 80 at 1.5% was found to be the most effective variable during lipase production by A. niger NRRL-599 because it served as a carbon source and an inducer. These results agreed with Salihu et al.  who found that the presence of tween 80 led to higher lipase production from Penicillium citrinum. The straight line of regression with data points across indicates the stability of the model, the agreement between predicted and actual values and thus the assumption of data point presentation is confirmed (data not shown).
The three variables [inoculums age (days), moisture content (ml), and tween 80 (%)] identified by Plackett–Burman design experiment having positive main effects on enzyme activity and SEA were further tested and optimized through Box–Behnken design methodology . This method allows the interaction of three independent variables at three different levels [low (−1), medium (0), high (+1)] were listed in [Table 2] with 13 trials.
|Table 2 Experimental range and levels of the independent variables by using Box–Behnken design|
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Box–Behnken designed results were shown in [Table 3]. Maximum lipase activity (420 U/ml) was shown in trial number 12 with tween 80 1%, moisture content 4 ml(v/v), and inoculum age 5 days. The F value (Fisher’s statistical analysis) and P value (>0.0001) were used for determining the significance of the model. Low values of P indicate the high significance of the corresponding coefficient whereas large t and F values indicate the significance of corresponding coefficients .Subjecting our model to analysis of variance analysis in [Table 4]. The correlation coefficient (R2) measures how much the variability of the observed response can be explained by the experimental parameters and their interactions .
In [Figure 3] R2 of the model is 0.94, that is 94%. The predicted R2 is in acceptable agreement with the adjusted R2 as shown by the actual predicted plot.
|Figure 3 Predicted lipase activities versus experimentally observed lipase activity for the Box–Behnken design.|
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The model P value of 0.013 is significant for lipase model and similarly, the model F value of 1.6 the effect of each parameter on lipase activity and interaction between the three variables were illustrated in [Figure 4].
|Figure 4 Contour plot of lipase production by Aspergillus niger showing the interactive effects of different concentrations of X1 and X2 (a), X3 and X2 (b), and X1 and X3 (c), Y: lipase activity (U/ml).|
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Three-dimensional aided in the visual determination of the maximum levels of each of the three parameters when they interact. It is well known that the three-dimensional responses have either elliptical or circular representation, which is considered either significance or negligible . Figure 4 showed that, as the level of these parameters changes, their interactive effect on the response also varies. The interaction between tween 80 (X1) and moisture content (X2) was examined while maintaining the inoculum age (X3) was constant ([Figure 4]a) at maximum tween 80 concentration and moisture content, lipase production was 350 U/ml. In [Figure 4]b, lipase activity was found to be maximum (420 U/ml) revealing the effect of the interaction between the moisture content and inoculum age. Although [Figure 4]c showed the effect of interaction between tween 80 concentrations and inoculum age on lipase activity (400 U/ml).
The quadratic model represents Yactivity (U/ml) as a function of tween 80 (X1), moisture content (X2), and inoculum age (X3). The production of lipase enzyme Yactivity (U/ml) was predicted by the following model equation:
A verification experiment was performed in triplicates, the results showed an actual maximum lipase production 125% when compared with that produced by the basal condition.
| Conclusion|| |
This study was investigated the lipase activity of A. niger spp. on agroindustrial residues as a substrate, which expressed that olive oil waste was the best for lipase production on solid medium. By optimizing the culture parameters using Plackett–Burman design, the lipase activity was further increased. This paper throws light on the exploitation of agroindustrial waste for production of an important industrial enzyme using a low cost method as SSF.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Colla LM, Primaz AL, Costa JAV. Surface response methodology for the optimization of lipase production under submerged fermentation by filamentous fungi. Braz J Microbiol 2016; 47:461–467.
Marques TA, Baldo C, Borsato D, Buzato TB, Celligol MAPC. Production and partial characterization of a thermostable alkaline and organic solvent tolerant lipase from Trichoderma atroviride
676. Int J Sci Res 2014; 3:77–83.
Gunasekaran V, Das D. Lipase fermentation: progress and prospects. Indian J Biotechnol 2005; 4:437–445.
Rabbani M, Bagherinejad MR, Sadeghi HM-M., Shariat ZS, Etemadifar Z, Moazen F et al.
Isolation and characterization of novel thermophilic lipase-secreting bacteria. Braz J Microbiol 2013; 44:1113–1119.
Ramos-Sánchez LB, Cujilema-Quitio MC, Julian-Ricardo MC, Cordova J, Fickers P. Fungal lipase production by solid-state fermentation . J Bioproces Biotech 2015; 5:2–9.
Michele PP, Degani de Oliveira KS, Trevisol TC, Fernandes de Medeiros B. Isolation of lipase-producing yeasts from industrial oily residues in different culture media. Int J Curr Microbiol App Sci 2018; 7:2348–2362.
Uppada SR, Gupta AK, Dutta JR. Statistical optimization of culture parameters for lipase production from Lactococcus lactis
and its application in detergent industry. Int J Chem Tech Res 2012; 4:1509–1517.
Kamini NR, Fuji T, Kurosu T, Lefuji H. Production, purification and characterization of an extracellular lipase from the yeast, Cryptococcus
SP S-2. Process Biochem 2000; 36:317–324.
Rehman S, Haq NB, Ijaz AB, Muhammad A. Optimization of process parameters for enhanced production of lipase by Penicillium notatum
using agricultural wastes. Afr J Biotechnol 2011; 10:19580–19589.
Cihangir N, Sarikaya E. Investigation of lipase production by a new isolated of Asperigillus sp. World J Microbies Biotechnol 2004; 20:193–197.
Ebrahimipour G, Sadeghi H, Zarinviarsagh M. Statistical methodologies for the optimization of lipase and biosurfactant by Ochrobactrum intermedium
strain MZV101 in an identical medium for detergent applications. Molcules 2017; 22:1460–1475.
Parkhey P, Gupta P, Eswari JS. Optimization of cellulase production from isolated cellulolytic bacterium: comparison between genetic algorithms, simulated annealing, and response surface methodology. Chem Eng Commun 2016; 204:28–38.
Holmes B, Popoff M, Kiredjian M, Kersters K. Ochrobactrum anthropi
gen. Nov., sp. Nov. from human clinical specimens and previously known as group vd. Intern J Syst Bacteriol 1988; 38:406–416.
Freire DM, Teles EM, Bon EP, Sant’ Anna GL Jr. Lipase production by Penicillium restrictum in a laboratory scale fermentor: media composition, agitation and aeration. Appl Biotechnol 1997; 63:409–421.
Niaz M, Iftikhar T, Zia MA. Effect of nutritional factors on lipase biosynthesis by Aspergillus niger
in solid state fermentation. Ext J Appl Sci 2013; 1:9–16.
Nadia N, Nehad ZA, Elsayed AE, Essam MA, Hanan MA. Optimization of lipase synthesis by Mucor racemosus
production in a triple impeller bioreactor. Malays J Microbiol 2010; 6:7–15.
Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with folin phenol reagent. J Biol Chem 1951; 193:265–275.
Box GEP, Behnken DW. Some new three level designs for the study of quantitative variables. Technometrics 1960; 2:455–475.
Rapp P. Production, regulation and some properties of lipase activity from Fusarium oxysporum and sp vasinfectum
. Enz Microb Technol 1995; 17:832–838.
Ali CH, Zhang J-J, Mbadinga SM, Liu J-F, Yang S-Z, Gu J-D et al.
Screening, isolation and optimization of an extracellular lipase producing Exiguobacterium
sp.BBXS-7 segregated from waste cooking oil contaminated sites. J Taiwan Inst Chem Eng 2015; 52:7–13.
Burkert JFM, Maugeri F, Rodrigues MI. Optimization of extracellular lipase production by Geotrichum
sp. using factorial design. Bioresour Technol 2004; 91:77–84.
Vardanega R, Remonatto D, Arbter F, Di Luccio M. A systematic study on extraction of lipase obtained by solid-state fermentation of soybean meal by a newly isolated strain of Penicillium sp
. Food Bioprocess Tech 2009; 3:461–465.
Maldonado RR, Burkert JFDe-M, Aguiar-Oliveira E, Durrant L, Mazutti M, Filho FM et al.
Elucidation of the effects of inoculum size and age on lipase production by Geotrichum candidum
. Biotecnol Apl 2014; 31:216–221.
Helal SE, Abdelhady H, AAbouTaleb K, Hassan MG, Amer MM. Evaluation of factors affecting the fungal lipase production using one factor at a time approach and response surface methodology. Egypt J Microbiol 2017; 52:1–17.
Cardenas F, Alvarez E, Castro Alvarez MS, Sanchez- Montero JM, Valmaseda M, Elson SW et al.
Screening and catalytic activity in organic synthesis of novel fungal and yeast lipases. J Mol Catal B Enzym 2001; 14:111–123.
Sun SY, Xu Y. Solid state fermentation for whole cell synthetic lipase production from Rhizopus chinensis and identification of the functional enzyme. Process Biochem 2008; 43:219–224.
Mahanta N, Gupta A, Khare SK. Production of protease and lipase by solvent tolerant Pseudomonas aeruginosa
PseA in solid-state fermentation using Jatropha curcas
seed cake as substrate. Bioresour Technol 2008; 99:1729–1735.
Salihu A, Alam MZ, AbdulKarim MI, Salleh HM. Evaluation of Nutritional components by Plackett-Burman design for Penicillium citrinum
lipase production using palm oil mill effluent. Afr J Biotechnol 2011; 10:18704–18708.
Li XY, Liu ZQ, Chr ZM. Production of phytase by a marine yeast Kodamaea ohmeri
BG3 in an oats medium: optimization by response surface methodology. Bioresour Technol 99:6386–6390.
Reddy L, Wee Y, Yun J, Ryu H. Optimization of alkaline protease production by batch culture of Bacillus sp
.RKY3 through Placket Burman and response surface methodological approaches. Bioresour Technol 2008; 99:2242–2249.
Abu ML, Hisham MN, Siti NO, Abu Bakar S. Optimization of physical conditions for the production of thermostable T1 lipase in Pichia guilliermondii
strain SO using response surface methodology. BMC Biotechnol 2017; 17:78–88.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]