Fermentation Microbiology (FM) | Strain Isolation and Improvement
Microbiol. Biotechnol. Lett. 2021; 49(3): 337-345
https://doi.org/10.48022/mbl.2106.06016
Marisa Dian Pramitasari and Miftahul Ilmi*
Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta 55281, Indonesia
Correspondence to :
Miftahul Ilmi, m.ilmi@ugm.ac.id
Lipase (triacylglycerol lipase, EC 3.1.1.3) is an enzyme capable of hydrolyzing triacylglycerol, to produce fatty acids and glycerol and reverse the reaction of triacylglycerol synthesis from fatty acids and glycerol through transesterification. Applications of lipase are quite widespread in the industrial sector, including in the detergent, paper, dairy, and food industries, as well as for biodiesel synthesis. Lipases by yeasts have attracted industrial attention because of their fast production times and high stability. In a previous study, a lipase-producing yeast isolate was identified as Zygosaccharomyces mellis SG1.2 and had a productivity of 24.56 U/mg of biomass. This productivity value has the potential to be a new source of lipase, besides Yarrowia lypolitica which has been known as a lipase producer with a productivity of 0.758 U/mg. Lipase production by Z. mellis SG1.2 needs to be increased by optimizing the production medium. The aims of this study were to determine the significant component of the medium for lipase production and methods to increase lipase production using the optimum medium. The two methods used for the statistical optimization of production medium were Taguchi and RSM (Response Surface Methodology). The data obtained were analyzed using Minitab 18 and SPSS 23 software. The most significant factors which affected lipase productivity were olive oil and peptones. The optimum medium composition consisted of 1.02% olive oil, 2.19% peptone, 0.05% MgSO4·7H2O, 0.05% KCl, and 0.2% K2HPO4. The optimum medium was able to increase the lipase productivity of Z. mellis SG1.2 to 1.8-fold times the productivity before optimization.
Keywords: Lipase, lipolytic yeast, Taguchi, response surface methodology (RSM)
Lipase (triacylglycerol acyl hydrolase, E.C. 3.1.1.3) is an enzyme that plays a role in the hydrolysis of triglycerides to produce fatty acids and glycerol. Lipases can also reverse reactions to synthesize triacylglycerol from free fatty acids and glycerol [1]. Lipase applications in modern industry are very wide, including the formulation of detergents, dairy products, paper, cosmetics, pharmaceuticals, and biodiesel synthesis [2].
Biodiesel, a biodegradable and non-toxic biofuel has emerged as one of the most potential renewable energies to replace diesel fuel. Biodiesel can be derived from vegetable oils, animal fats, or microbial oils through transesterification or esterification [3]. The application of lipase as a biocatalyst in biodiesel synthesis has been widely reported [3, 4]. Transesterification with a basecatalyzed forms undesirable soap and water. Meanwhile, acid-catalyzed esterification requires large amounts of alcohol [3]. Lipases do not form soap and can esterify FFAs and TAGs in one step without requiring a subsequent washing step. So that lipase is an attractive prospect for the industrial-scale production for the reduction of production costs [5].
The high demand for lipases for use in various fields of the biotechnology industry necessitates the isolation of lipases from new sources [6]. Microorganism lipases have gained special attention in the industrial field due to their stability, selectivity, and broad substrate specificity [7]. Microorganism lipases have previously been studied, such as
Increasing lipase productivity during the fermentation process is very important because it can reduce production costs and minimize production time [12, 13]. Increased lipase productivity can be done by optimizing the growth media [13]. The optimization process with the experimental method is statistically considered more advantageous because it can reduce the number of experiments and the possibility of evaluating the interactions effects between variables [14].
Taguchi statistical model was used to predict significant experimental medium for yeast growth and lipase production [15]. Taguchi DOE mainly involves Orthogonal Arrays (OA) to minimize experimental errors and increasing lipase production yields with a few experimental trials [6]. While RSM (Response Surface Methodology) was used further to optimize lipase production from the optimum medium. Response surface analysis is important tools to determine the optimal process conditions. RSM method has previously been used to study the effect of concentration of carbon and nitrogen sources on lipase production by
The aim of our research was to increase the lipase production of the
Yeast strain
Yeast strain were prepared for lipase production by inoculating one loop of the yeast cell on YMEA media and incubated for 4 days at room temperature [2].
Design of experiment consists of 25 medium variations according to Taguchi based on 5 levels and 5 factors. Factors used along with the level listed in Table 1. The experiments were performed using a 250 ml shake flask containing 50 ml of production medium and incubated in an incubator shaker with agitation speed of 200 rpm and a temperature of 30°C. Lipase activity was measured after 48 h of incubation.
Table 1 . Factors and levels on experimental design Taguchi.
Factors | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |
---|---|---|---|---|---|
Olive oil | 1% | 1.5% | 2% | 2.5% | 3% |
Peptone | 1% | 2% | 3% | 4% | 5% |
MgSO4•7H2O | 0.03% | 0.04% | 0.05% | 0.06% | 0.07% |
KCl | 0.03% | 0.04% | 0.05% | 0.06% | 0.07% |
K2HPO4 | 0.1% | 0.15% | 0.2% | 0.25% | 0.3% |
The most significant factor will be further optimized using the RSM method. Design of experiment consists of 13 medium variations according to CCD (Central Composite Design) in RSM based on 5 levels and 2 factors. Experimental design of RSM method were shown in Table 2. The experiments were performed using a 250 ml shake flask containing 50 ml of production medium and incubated in an incubator shaker with agitation speed of 200 rpm and a temperature of 30°C. Lipase activity was measured after 48 h of incubation.
Table 2 . Experimental design of RSM method.
Running | Olive oil (%) | Peptone (%) |
---|---|---|
1 | 1 | 2 |
2 | 0.3 | 2 |
3 | 1 | 2 |
4 | 1 | 2 |
5 | 0.5 | 1 |
6 | 1 | 0.6 |
7 | 1 | 2 |
8 | 1.5 | 3 |
9 | 1.5 | 1 |
10 | 1.7 | 2 |
11 | 1 | 3.4 |
12 | 0.5 | 3 |
13 | 1 | 2 |
Optimum medium validation. Validation was performed by growing the strain
Measurement of lipase activity and productivity. A total of 20% of the medium was taken and centrifuged at 4000 rpm for 10 minutes. The pellets from the centrifuge were washed with distilled water and dried in an oven at 70°C. Extracellular lipase contained in the supernatant will be tested for lipase activity. Lipase activity was determined based on lipid content analysis which was carried out by measuring the free fatty acid content using colorimetry. Lipase activity of the supernatant was determined by incubating 100 μl filtrate with 1 ml isooctane containing 0.25 M oleic acid and 0.25 M ethanol for 20 min at 30°C. The amount of oleic acid at 0 min and after 20 min was determined using the cupric-acetate pyridine colorimetric assay [17]. The absorbance was measured using a spectrophotometer at a wavelength of 715 nm.
Productivity is defined as the number of units of relative enzyme activity per milligram of biomass. Productivity is calculated based on the following equation:
Data analysis. The data obtained will be analysed using Taguchi and Response Surface Methodology (RSM) on Minitab software version 18 and Analysis of Variance (ANOVA) on SPSS version 23. The results of the analysis will be presented in the form of tables and graphs.
Screening of significant factors Taguchi method aims to determine the factors that have a significant effect on lipase production from
Table 3 . ANOVA of Mean Plot for SNR.
Factors | Degrees of freedom | Sum of squares | Contribution | Mean square | |||
---|---|---|---|---|---|---|---|
Olive oil | 4 | 898.71 | 37.64% | 224.68 | 6.56 | 0.048* | Significant |
Peptone | 4 | 610.68 | 25.58% | 152.67 | 4.46 | 0.088 | |
MgSO4•7H2O | 4 | 313.59 | 13.14% | 78.40 | 2.29 | 0.221 | |
KCl | 4 | 345.69 | 14.48% | 86.42 | 2.52 | 0.196 | |
K2HPO4 | 4 | 81.74 | 3.42% | 20.43 | 0.60 | 0.685 | |
Error | 4 | 136.94 | 5.74% | 34.23 | |||
Total | 24 | 2387.34 | 100.00% |
R-squared = 94.26%.
Optimization of significant factors using RSM method aims to determine the optimum solution levels of the factors examined in improving lipase production. Experimental design used in the method of RSM is a Central Composite Design (CCD) with 13 variations of production medium.
The suitability of the equation model with the actual situation can be seen based on the results of the ANOVA statistical analysis presented in Table 4. The equation model has a significant probability value (
Table 4 . ANOVA of polynomial equation model.
Factors | Degrees of freedom (df) | Sum of squares | Mean square | k | ||
---|---|---|---|---|---|---|
Model | 5 | 47067.1 | 9413.4 | 17.53 | 0.001* | Significant |
Linear | 2 | 4157.3 | 2078.6 | 3.87 | 0.074 | |
Olive oil | 1 | 562.4 | 562.4 | 1.05 | 0.340 | |
Peptone | 1 | 3594.9 | 3594.9 | 6.69 | 0.036 | |
Square | 2 | 41474.2 | 20737.1 | 38.61 | 0.000 | |
Olive oil*olive oil | 1 | 25285.7 | 25285.7 | 47.08 | 0.000 | |
Peptone*Peptone | 1 | 21576.1 | 21576.1 | 40.17 | 0.000 | |
2-Way Interaction | 1 | 1435.7 | 1435.7 | 2.67 | 0.146 | |
Olive oil*Peptone | 1 | 1435.7 | 1435.7 | 2.67 | 0.146 | |
Error | 7 | 3759.5 | 537.1 | |||
Lack-of-Fit | 3 | 233.7 | 77.9 | 0.09 | 0.963 | Not significant |
Pure Error | 4 | 3525.8 | 881.4 | |||
Total | 12 | 50826.5 |
R-squared = 92.6%.
Optimum level predictions for each optimization factor are shown in the contour plot graph (Fig. 3) and the surface plot graph (Fig. 4). Prediction of lipase productivity further to determine the optimum level of each factor was analysed using the response optimizer in Minitab which is presented in Fig. 5. As shown in Fig. 5, the maximum lipase production (y) that is predicted to be 217.0994 U/mg of biomass.
Inorder to verify the adequacy of the predicted value, confirmation experiments were performed. Stages of the verification carried out by repeating the experiment using the optimum conditions of the medium. Experimental verification data is 227.9 (U/mg biomass) which is still in the 95% CI and PI range. It can be said that the equation model developed was reasonably accurate.
Validation of the optimum medium aimed to compare the lipase productivity profile from
Olive oil was the most significant factor on lipase production from
Other factors had no significant effect on lipase production of
The suitability of the equation model with the actual situation can be seen based on the results of the ANOVA statistical analysis presented in Table 4. The equation model has a
Correspondence between the experimental results and the predicted results can be illustrated by the graph normal probability plot (Fig. 2) shows the distribution of the residual point approaching a straight line so it can be assumed the experimental results closer to the predicted value. These data points are increasingly approaching normality line indicates normal data distribution means that the experimental results will approach predicted results [27].
Optimum level predictions for each optimization factor are shown in the contour plot graph (Fig. 3) and the surface plot graph (Fig. 4). The two graphs show the combination of factors that influence the response value. The optimum point for both olive oil and peptone factors is predicted to be in the code range 0 and 0.5. Code 0 is the middle level value for each factor, these were olive oil with a concentration of 1% and peptone with a concentration of 2%. Optimum lipase productivity is predicted to be more than 200 U/mg of biomass. Prediction of lipase productivity further to determine the optimum level of each factor was analysed using the response optimizer in Minitab which is presented in Fig. 5.
Prediction graph of the response optimizer (Fig. 5) shows the maximum response (y) that is predicted to be 217.0994 U/mg of biomass. The optimum solution point for each factor and the comparison between the predicted and experimental productivity values can be seen in Table 5. The desirability (d) value on the prediction graph is 0.7764. The desirability value closer to the value of 1 indicates the model's ability to produce the desired response more optimal [26, 28].
Table 5 . Comparison between the predicted and experimental productivity values.
Olive oil (%) | Peptone (%) | Lipase productivity (U/mg biomass) | Standard error | 95% CI | 95% PI | |
---|---|---|---|---|---|---|
Prediction | Experimental | |||||
1.02 | 2.19 | 217.1 | 227.9 | 10.3 | 192.7; 241.5 | 157.1; 277.1 |
In order to verify the adequacy of the predicted value, confirmation experiments were performed. Stages of the verification carried out by repeating the experiment using the optimum conditions of the medium. The verification results are then compared with the predicted optimum productivity value. Experimental verification data is 227.9 (U/mg biomass) which is still in the 95% CI and PI range. If the verification results are still in the range of CI and PI, it can be concluded that the model obtained is in accordance with the predicted [29, 30]. This results show that the verification was successful, and the equation was adequate to describe lipase productivity by response surface methodology.
As shown in Fig. 6, lipase productivity profile from
Lipase productivity after optimized increased by 1.8-fold of the basal medium is 128.857 ± 22.916 U/mg biomass to 231.992 ± 10.366 U/mg of biomass after 48 h of incubation. The optimum composition of the medium after optimization consists of olive oil 1.02%, peptone 2.19%, MgSO4·7H2O 0.05%, KCl 0.05% and K2HPO4 0.2%.
Oliveoil concentration in the medium after optimization decreased from the initial concentration of 2% to 1.02%. Research related to the addition of olive oil in lipase production has been widely studied. Nwachukwu
Peptone concentration in the medium after optimization decreased from the initial concentration of 3% to 2.19%. The addition of peptone as organic nitrogen sources into the production medium was able to increase lipase production by
In conclusion, optimization of medium production for lipase production by yeast
The experiment of this study was funded by RTA Grant year 2020 (2488/UN1.P.III/DIT-LIT/PT/2020), and the publication was funded by RTA Grant year 2021 (3143/UN1.P.III/DIT-LIT/PT/2021). Both grants were from the Universitas Gadjah Mada, Indonesia.
The authors have no financial conflicts of interest to declare.
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