Lactase production by Saccharomyces fragilis IZ 275 using different carbon sources

Produção de lactase por Saccharomyces fragilis IZ 275 usando diferentes fontes de carbono

Abstract

This study sought to create a better fermentation medium to maximize lactase production by Saccharomyces fragilis IZ 275 using different carbon sources, including reconstituted powdered cheese whey. A factorial design 2⁴ was applied to evaluate the significant effects of variables which compose the fermentation medium. Then, a steepest descent-ascent design was applied to obtain the maximum activity. A Rotational Central Composite Design (RCCD) 2⁴ was made to optimize the fermentation medium. We verified that the cheese whey, a by-product of the dairy industry, can be employed as an excellent fermentation medium by yeast, within the bioeconomy concept and used by the dairy industry as product with additional value. The employed methodology is an efficient tool in the optimization process for β-galactosidase production. In the optimized fermentation medium, the maximum production of β-galactosidase (54.68 U/mL) by S. fragilis IZ 275 is obtained with 14 g/L sucrose, 17.7 g/L reconstituted powdered cheese whey, 5.14 g/L yeast extract and 8.85 g/L peptone.

Keywords:
β-galactosidase; Cheese whey; environmental damage

Resumo

O presente trabalho teve como objetivo estudar a melhor composição do meio de fermentação para máxima produção de lactase por Saccharomyces fragilis IZ 275 usando diferentes fontes de carbono incluindo o soro de queijo em pó reconstituído. Primeiramente, um delineamento fatorial 2⁴ foi aplicado para verificar dentre as variáveis estudadas as que apresentavam efeito significativo. Logo após, foi aplicado um delineamento de máxima inclinação ascendente e máxima inclinação descendente para obter a máxima atividade. Para otimizar o meio de fermentação, foi então, realizado um delineamento Composto Central Rotacional (DCCR) 2^4. Foi observado que o soro de queijo, um subproduto da indústria de lacticínios, e altamente poluidor para o meio ambiente, pode ser empregado como excelente meio de fermentação por leveduras, dentro do conceito de bioeconomia com valor agregado. O emprego da metodologia é uma ferramenta eficiente para otimizar o processo de produção da β-galactosidase. No meio de fermentação otimizado, a máxima produção de β-galactosidase (54,68 U/mL) por S. fragilis IZ 275 é obtida com 14 g/L sacarose, 17,7 g/L soro de queijo em pó reconstituído, 5,14 g/L extrato de levedura e 8,85 g/L peptona.

Palavras-chave:
β-galactosidase; impacto ambiental; soro de queijo

1. INTRODUCTION

β-D-galactosidase (EC 3.2.1.23; β-D-galactoside galactohydrolase) or lacatase has several applications in the food industry (Jones et al., 2017). It is an intracellular enzyme that hydrolyzes lactose, a disaccharide present in milk and dairy products, into its two monosaccharides units, galactose and glucose, which are easily absorbed in most organisms, including humans (Anisha, 2017; Panesar et al., 2006). Some people are lactose intolerant and cannot digest lactose properly due to an inactive intestinal lactase enzyme. Common symptoms of lactose intolerance are intestinal-dysfunction gas, abdominal pain, and diarrhea. The sugar is found in mammalian milk at a concentration of 3-8% (w/v), and has low solubility and sweetness. (Perini et al., 2013; Cardoso et al., 2017).

One of the main sources for the production of lactase are yeasts (Mlichová and Rosenberg, 2006) by the submerged fermentation process (Anisha, 2017). Several factors influence the production of lactase, such as temperature, pH, incubation time and fermentation medium. The optimization of the fermentation medium is critical for β-galactosidase production (Jones et al., 2017). and some works have described these factors that influence the β-galactosidase production (Karlapudi et al., 2018; Venkateswarulu et al., 2017). However, the best composition of fermentation medium for maximizing the lactase production by yeast has not been yet developed.

Cheese whey produced during cheese-making or during the coagulation of milk casein process presents as principal components, lactose (70-72% of the total solids), whey proteins (8%-10%) and minerals (12-15%) (Yadav et al., 2015). Over the last few decades, the dairy industry has explored different alternatives to exploit the valuable components of cheese whey. The world production of whey (El-Tanboly et al., 2017) is estimated to be around 180 to 190 x 10⁶ ton / year, and causes serious socio-economic and environmental problems, since much of this amount is discarded in the environment. Furthermore, Lopes et al. (2018) found that, in comparison to domestic sewage, cheese whey can be 100 times more polluting. One alternative to help remediate these problems would be the application of cheese whey as the fermentation medium for lactase production by microorganisms within the concept of circular economy or bioeconomy (Ranta et al., 2018; López-Gómez et al., 2019). This will promote the integration of economic activities and environmental wellbeing in a sustainable way.

Considering these aspects, we studied the creation of a better composition of fermentation medium to maximize lactase production by Saccharomyces fragilis IZ 275 using different carbon sources, including reconstituted powdered cheese whey.

2. MATERIAL AND METHODS

2.1. Microorganism and inoculum

Saccharomyces fragilis IZ 275 yeast was used for the study and collected in the Collection of Tropical Cultures (WDCM 69 885 number). The yeast was maintained in tubes containing PDA (Potato Dextrose Agar, Acumedia) and stored at 4°C, and was reactivated in a medium containing (w/v) malt extract (2%) yeast extract (0.5%) autoclaved at 121°C for 15 min and then incubated at 30°C for 48 h on an orbital shaker (Tecnal^®, TE-420). The inoculum was performed by using a Neubauer chamber at a count of 1 x 10⁷ cells/mL. An inoculum concentration of 10% v/v was used in relation to the culture medium.

2.2. Extraction and analysis of β-galactosidase

Samples of 80 mL fermentation medium were centrifuged (Eppendorf centrifuge 5804R, Germany) under conditions of 27,200 x g, 4°C for 5 min. The supernatant was resuspended in 0.1 M phosphate buffer, pH 6.6, and centrifuged again under the above conditions. The new precipitate was solubilized in 10 mL of the same buffer, to which 0.2 mL of chloroform was added. This mixture was incubated at 37°C under agitation of 150 rpm for 17 h. The suspension was then centrifuged and the supernatant used to determine the enzymatic activity. The enzymatic activity was determined using the o-nitrophenyl-β-D-galactopyranoside substrate (ONPG) following the methodology described in the Food Chemical Codex (National Academy of Sciences, 1996) with some modifications. The ONPG (1.25 mM) substrate dissolved in 0.05 M sodium phosphate buffer (pH 7.0) was used. The amount of substrate and enzyme used were 2 mL and 0.5 mL, respectively. At the time zero, 0.5 mL of enzyme solution was added to the ONPG solution and incubated for 5 min. The assay was stopped by the addition of 0.5 mL sodium carbonate 1 M, and the absorbance was determined in spectrophotometer (Biochrom libra S22 Cambridge England) at 420 nm. One enzymatic unit was defined as the quantity of enzyme that would liberate 1 mM of o-nitrophenol from ONPG per minute under the assay conditions. Enzymatic unit was calculated using the following Equation 1:

$\mathrm{U}\mathrm{n}\mathrm{i}\mathrm{t}/\mathrm{m}\mathrm{L}\mathrm{}=\mathrm{}\mathrm{A}\mathrm{}\mathrm{x}\mathrm{}\mathrm{d}\mathrm{i}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}/\mathrm{\varepsilon }\mathrm{}\mathrm{x}\mathrm{}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}\mathrm{}\mathrm{x}\mathrm{}\mathrm{e}\mathrm{n}\mathrm{z}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{}\mathrm{s}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$ (1)

Where A was the absorbance at 420 nm, dilution factor was the fold dilution of the enzyme solution, enzyme solution was the amount of enzyme (mL) undergoing the reaction, ɛ was the extinction coefficient (determined from the o-nitrophenol standard curve) and time was the incubation time (15 min).

2.3. Experiment 1: screening experiments to investigate the composition of medium fermentation

First, initial experiments were performed to evaluate the significant effects of variables which compose the fermentation medium used for β-galactosidase production by Saccharomyces fragilis IZ 275 yeast. A factorial design 2⁴ was applied with eight variables and three replicates at the central point totaling 19 assays. The coded independent variables (x₁, x₂, x₃, x₄, x₅, x₆, x₇ and x₈) and uncoded variables (X₁ = g/L lactose, X₂ = g/L sucrose, X₃ = g/L glucose, X₄ = g/L cheese whey, X₅ = g/L yeast extract, X₆ = g/L peptone, X₇ = g/L MgSO₄ and X₈ = g/L K₂HPO₄) are shown in Table 1 with their variation levels.

The fermentation media were run in 250 mL Erlenmeyer flasks containing 100 mL of each, incubated on the orbital shaker (Tecnal, TE-420) at 180 rpm and with pH initial 6.8, at 30°C temperature for 72 h according to the literature (Kumar et al., 2012). The β-galactosidase production was evaluated in terms by β-galactosidase activity and its response function as follows: Y₁ (β-galactosidase activity, U/mL). The model equation was as follows: Y₁ = β₀ + β₁x₁ + β₂x₂ + β₃x₃ + β₄x₄+ β₅x_{5 +} β₆x_{6 +} β₇x₇₊ β₈x_{8 +} e

Where Y₁ (response function), x₁, x₂, x₃, x₄, x₅, x₆, x₇ and x₈ (coded variables), β (estimated coefficients for each term of the response surface model). The response functions (Y₁) were used to perform regression analyses and analysis of variance (ANOVA) for the regression and were performed using STATISTICA 7.0 software (StatSoft Inc., 2007).

Table 1.
Independent variables and levels of variation in factorial design 2⁴.

Independent Variables	Levels
	-1	0	1
Lactose (g/L) X1	1	5.5	10
Sucrose (g/L) X2	1	5.5	10
Glucose (g/L) X3	1	5.5	10
Cheese whey (g/L) X4	1	5.5	10
Yeast extract (g/L) X5	0.5	2.75	5
Peptone (g/L) X6	0.5	2.75	5
MgSO4 (g/L) X7	0.5	2.75	5
K2HPO4 (g/L) X8	0.5	2.75	5

2.4. Experiment 2: Method of steepest ascent-descent design to investigate the maximum increase of β-galactosidase activity

From the results of factorial design and to obtain the maximum increase of β-galactosidase activity, a steepest descent-ascent design was applied (Montgomery, 2011). The independent variables were stabilized as follows: sucrose and cheese whey ranged from 6 to 16 g/L; yeast extract, peptone and MgSO₄ ranged from 2 to 8 g/L; the variables lactose and glucose ranged from null to 10 g/L and K₂PHO₄ ranged from null to 5 g/L (Table 2).

2.5. Experiment 3: Rotational Central Composite Design (RCCD) 2⁴ to optimize the medium fermentation to β-galactosidase production and model validation

From the results of steepest ascent-descent design and to optimize the fermentation medium for β-galactosidase production, a third experiment was performed. In this third step, a Rotational Central Composite Design (RCCD) 2⁴ was applied with two central points and eight axial points, for orthogonal, totaling 26 assays. The coded independent variables (x₁, x₂, x₃, x₄) and uncoded variables (X₁ = sucrose g/L, X₂ = cheese whey g/L, X₃ = yeast extract g/L and X₄ = peptone g/L) are shown in Table 3 with their variation levels. The coded independent variables (x₅, x₆, x₇, x₈) and uncoded variables (X₅ = lactose g/L, X₆ = glucose g/L, X₇ = MgSO₄ g/L and X₈ = K₂HPO₄ = g/L) that did not show significance in the factorial design were stabilized according to the steepest ascent-descent design (Table 2).

For each assay, the fermentation media were run in 250 mL Erlenmeyer flasks containing 100 mL of each, incubated on the orbital shaker (Tecnal, TE-420) at 180 rpm and with pH initial 6.8 at 30°C temperature for 72 h. β-galactosidase production was evaluated in terms of β-galactosidase activity by its response function as follows: Y₂ (β-galactosidase activity, U/mL). The model equation was as follows: Y₂ = β₀ + β₁x₁ + β₂x₂ + β₃x₃ + β₄x₄ + β₁₁ x₁ ² + β₂₂ x₂ ² + β₃₃ x₃ ² + β₄₄ x₄ ² + β₁₂ x₁x₂ + β₁₃ x₁x₃ + β₁₄ x₁x₄ + β₂₃ x₂x_{3 +} + β₂₄ x₂x₄ + β₃₄ x₃x₄ +e.

Where Y₂ (response function), x₁, x₂, x₃ and x₄ (coded variables), β (estimated coefficients for each term of the response surface model) and e = pure error. The response functions (Y₂) were used to perform regression analyses and analysis of variance (ANOVA) for the regression. The equation model was fitted to experimental data to yield the proposed model. Response surface graphs were generated. All executed analysis and response surfaces were performed with STATISTICA 7.0 software (StatSoft Inc., 2007). After response surface analysis for maximum β-galactosidase activity, the proposed model was validated by performing new assays in triplicate. The results (Y_exp.) were compared with the estimated response (y^₁) by Student’s t-test (p < 0.05).

Table 2.
Steepest ascent-descent design to the maximum increase in terms of β-galactosidase production.

Assays	Sucrose g/L	Whey Cheese g/L	Yeast extract g/L	Peptone g/L	MgSO4 g/L	Lactose g/L	Glucose g/L	K2PO4 g/L	β-galactosidaseactivity U/mL
1	6	6	2	2	2	10	10	5	17.21
2	8	8	4	4	4	10	10	5	34.84
3	12	12	6	6	6	10	10	5	26.98
4	14	14	7	7	7	10	10	5	39.81
5	16	16	8	8	8	10	10	5	27.03
6	6	6	2	2	2	1	1	0.5	1.57
7	8	8	4	4	4	1	1	0.5	13.51
8	12	12	6	6	6	1	1	0.5	19.61
9	14	14	7	7	7	1	1	0.5	23.36
10	16	16	8	8	8	1	1	0.5	20.84
11	6	6	2	2	2	5.5	5.5	2.75	10.67
12	8	8	4	4	4	5.5	5.5	2.75	18.61
13	12	12	6	6	6	5.5	5.5	2.75	16.25
14	14	14	7	7	7	5.5	5.5	2.75	18.61
15	16	16	8	8	8	5.5	5.5	2.75	13.46
16	6	6	2	2	2	null	null	null	2.46
17	8	8	4	4	4	null	null	null	7.09
18	12	12	6	6	6	null	null	null	13.29
19	14	14	7	7	7	null	null	null	14.12
20	16	16	8	8	8	null	null	null	17.39

Table 3.
Independent variables and levels of variation in RCCD 2⁴.

Independent Variables	Levels
	-1.48	-1	0	1	1.48
Sucrose (g/L) X1	10.29	11.5	14	16.5	17.70
Cheese Whey (g/L) X2	10.29	11.5	14	16.5	17.70
Yeast Extract (g/L) X3	5.14	5.75	7	8.25	8.85
Peptone (g/L) X4	5.14	5.75	7	8.25	8.85

3. RESULTS AND DISCUSSION

3.1. Factorial design 2⁴ to investigate the composition of fermentation medium

The first experiment was performed using a factorial design of 2⁴ for evaluating the effects of significant variables which compose the fermentation medium of Saccharomyces fragillis IZ 275 during β-galactosidase production. According to ANOVA and regression analysis, only the independent variables X₄ (cheese whey) and X₅ (yeast extract) were shown to have a significant effect on the response function Y₁ (β-galactosidase activity, U/mL). None of the other independent variables had a significant effect and the coefficient of determination (R²) was 0.75. The proposed model could be described as follows:

Y₁ = 6.23 + 5.58 x₄ + 8.72x₅. Thus, the β-galactosidase activity was 20.53 U/mL. The highest β-galactosidase activity (Y₁) was obtained in the assay 16 (Y₁ = 28.42 U/mL) (Table 4) when the independent variables were shown in the maximum levels. This correspond to using X₁ (lactose); X₂ (sucrose); X₃ (glucose) and X₄ (cheese whey) at a concentration of 10 g/L and while using X₅ (yeast extract), X₆ (peptone), X₇ (MgSO₄) and X₈ (K₂HPO₄) at 5 g/L concentration. This observation suggested that, although not significant to the model, the investigated variables influenced the β-galactosidase activity. It was decided to make a new regression analysis and ANOVA including only the significant independent variables. Our results confirmed that the response function Y₁ decreased (data not shown), indicating that the other independent variables (x₁, x₂, x₃, x₆, x₇ and x₈) were not in their optimal regions earlier and they were important to explain the model. The complete model can now be described as follows: Y₁ = 6.23 + 0.21x₁ + 3.23x₂ + 0.94x₃ + 5.58x₄ + 8.72x₅ + 3.81x₆ + 4.70x₇ - 0.55x₈.

3.2. Method of steepest ascent-descent design to investigate the maximum increase in the β-galactosidase production

According to the results of the steepest ascent-descent design (Table 2), it was observed that assay 4 yielded maximum value for β-galactosidase activity (39.81 U/mL). This corresponds when, the concentration of independent variables sucrose (X₂) and cheese whey (X₄) were 14 g/L, yeast extract (X₅), peptone (X₆) and MgSO₄ (X₇) were 7 g/L and lactose (X₁), glucose (X₃) and K₂HPO₄ (X₈) were at a concentration of 10 g/L, 10 g/L and 5 g/L, respectively i.e. when present at their maximum levels. Also, the values of β-galactosidase activity were lower (1.57 U/mL and 2.46 U/mL, respectively) when the levels of independent variables decreased, as shown in assays 6 and 16. The results from the steepest ascent-descent design and from factorial design 2⁴ suggest that the non-significant independent variables (lactose, sucrose, glucose, peptone, MgSO₄ and K₂HPO₄) contribute to the increase in β-galactosidase production. Therefore, to optimize the composition of fermentation medium for the β-galactosidase production and the model validation, all variables were considered.

3.3. Rotational Central Composite Design (RCCD) 2⁴ to optimize the fermentation medium to the β-galactosidase production by Saccharomyces fragilis IZ 275 and model validation

ANOVA and regression analysis were used to evaluate the effects of variables X₁ (sucrose, g/L), X₂ (whey cheese, g/L), X₃ (yeast extract, g/L) and X₄ (peptone, g/L). The linear effects of variables X₁, X₂, X₃ and X₄ were found to be significant. The quadratic effects of variables X₂ and X₃ and the interactions among the variables X₁X₂, X₁X₄ and X₂X₃ were also significant. According to Table 5 (ANOVA), the model showed a lack of fit, which was not significant (p > 0.05) and an R² of 0.77, indicating that 77% of the experimental results adequately matched the proposed model. Thus, the model can be used for predictive purposes and can be described as follows: Y₂ = 43.54 + 4.97x₁ + 4.69x₂ - 5.61x₂ ² - 4.96x₃ + 4.55x₃ ² + 2.51x₄ - 2.84x₁x₂ - 5.24x₁x₄ - 2.66x₂x₃.

Table 4.
Factorial Design 2⁴ to evaluate the fermentation medium and response function Y₁ (β-galactosidase activity, U/mL).

Assays	x1(X1)	x2(X2)	x3(X3)	x4(X4)	x5(X5)	x6(X6)	x7(X7)	x8 (X8)	Y1
1	-1(1)	-1(1)	-1(1)	-1(1)	-1(0.5)	-1(0.5)	-1(0.5)	-1(0.5)	1.80
2	1(10)	-1(1)	-1(1)	-1(1)	-1(0.5)	1(5)	1(5)	1(5)	1.01
3	-1(1)	1(10)	-1(1)	-1(1)	1(5)	-1(0.5)	1(5)	1(5)	6.22
4	1(10)	1(10)	-1(1)	-1(1)	1(5)	1(5)	-1(0.5)	-1(0.5)	6.49
5	-1(1)	-1(1)	-1(1)	-1(1)	1(5)	1(5)	1(5)	-1(0.5)	10.63
6	1(10)	-1(1)	1(10)	-1(1)	1(5)	-1(0.5)	-1(0.5)	1(5)	0.20
7	-1(1)	1(10)	1(10)	-1(1)	-1(0.5)	1(5)	-1(0.5)	1(5)	0.71
8	1(10)	1(10)	1(10)	-1(1)	-1(0.5)	-1(0.5)	1(5)	-1(0.5)	2.25
9	-1(1)	-1(1)	-1(1)	1(10)	1(5)	1(5)	-1(0.5)	1(5)	10.74
10	1(10)	-1(1)	-1(1)	1(10)	1(5)	-1(0.5)	1(5)	-1(0.5)	12.51
11	-1(1)	1(10)	-1(1)	1(10)	-1(0.5)	1(5)	1(5)	-1(0.5)	8.21
12	1(10)	1(10)	-1(1)	1(10)	-1(0.5)	-1(0.5)	-1(0.5)	1(5)	0.92
13	-1(1.0)	-1(1)	1(10)	1(10)	-1(0.5)	-1(0.5)	1(5)	1(5)	1.19
14	1(10)	-1(1)	1(10)	1(10)	-1(0.5)	1(5)	-1(0.5)	-1(0.5)	0.66
15	-1(1)	1(10)	1(10)	1(10)	1(5)	-1(0.5)	-1(0.5)	-1(0.5)	11.31
16	1(10)	1(10)	1(10)	1(10)	1(5)	1(5)	1(5)	1(5)	28.42
17	0(5.5)	0(5.5)	0(5.5)	0(5.5)	0(2.75)	0(2.75)	0(2.75)	0(2.75)	7.05
18	0(5.5)	0(5.5)	0(5.5)	0(5.5)	0(2.75)	0(2.75)	0(2.75)	0(2.75)	6.89
19	0(5.5)	0(5.5)	0(5.5)	0(5.5)	0(2.75)	0(2.75)	0(2.75)	0(2.75)	6.93

• Considering the complete model, the value of β-galactosidase activity was found to be 32.87 U/mL, and therefore an increase of 62% in the response was obtained.

Table 5.
ANOVA for β-galactosidase activity produced by Saccharomyces fragilis IZ 275.

Source of variation	SS	DF	MS	F	p value*
X1	126.06	1	126.06	919.43	0.021*
X12	20.46	1	20.46	149.21	0.052
X2	112.21	1	112.21	818.41	0.222*
X22	76.03	1	76.03	554.53	0.027*
X3	125.64	1	125.64	916.34	0.021*
X32	50.11	1	50.11	365.49	0.033*
X4	32.11	1	32.11	234.17	0.042*
X42	0.40	1	0.40	2.94	0.336
X1X2	32.18	1	32.18	234.72	0.041*
X1X3	7.09	1	7.09	51.68	0.088
X1X4	109.68	1	109.68	799.98	0.022*
X2X3	28.34	1	28.34	206.72	0.044*
X2X4	0.43	1	0.43	3.12	0.328
X3X4	0.19	1	0.19	1.39	0.448
Lack of Fit	215.14	10	21.51	156.92	0.062
Pure Error	0.14	1	0.14
Total SS	936.20	25

In Table 6, the β-galactosidase activity in assay 21 was found to be greater (52.84 U/mL) during which the conditions were as follows: 14 g/L sucrose, 14 g/L cheese whey, 5.14 g/L yeast extract and 7 g/L peptone. Other variables (lactose, glucose, MgSO₄ and K₂HPO₄) were stabilized at maximum levels. Assay 1 showed a lower value for β-galactosidase activity (29.62 U/mL), for which all variables analysed were at a minimum level, except for lactose, glucose, MgSO₄ and K₂HPO_4.

After analyzing the mathematical model, response function Y₂ and response surface (Figure 1a), it was observed that there was a region with maximum β-galactosidase activity when x₁(X₁) was -1.48 (10.29 g/L) and x₂ is between -1 and +1 or X₂ was between 11.5 and 16.5 g/L, suggesting that the cheese whey, rich in lactose, promotes the production of enzyme by Saccharomyces fragilis IZ 275. In Figure 1b, there is a region in which the β-galactosidase activity is greater when x₁ and x₃ is -1.48 or X₁ is 10.29 g/L and x₃ is 5.14 g/L. Figure 1c indicates two regions with maximum β-galactosidase activity when x₁ is +1.48 or 17.70 g/L and x₄ is -1.48 or 5.14 g/L and the other regions in which x₁ is -1.48 (10.29 g/L) and x₄ +1.48 (8.85 g/L). In Figure 1d, there is a region which the maximum β-galactosidase activity is observed when cheese whey, x₂, ranged from 0 to 1.48, regardless of yeast extract concentration; In Figure 1e, the maximum β-galactosidase activity is observed when the x₂ ranged from 0 (center point) to + 1.48 and in the Figure 1f when peptone, x₄, is 1.48 or X₄ is 8.85 g/L.

An assay in triplicate, which coincided with the points (X₁ = 13.99 g/L; X₂ = 17.7 g/L; X₃ = 5.14 g/L and X₄ = 8.85 g/L), was performed experimentally, and the result was YM _exp. = 54.68 U/mL. The results, which did not differ by the t-test (p > 0.05), confirm the validity of the proposed model. The results showed that for maximum β-galactosidase production by Saccharomyces fragilis IZ 275, the following conditions should be used: X₁ = 14 g/L, X₂ = 17.7 g/L, X₃ = 5.14 g/L, X₄ = 7 g/L, X₅ and X₆ = 10 g/L, X₇ = 7 g/L and X₈ = 5 g/L obtaining the value of 52.84 U/mL β-galactosidase activity.

The data obtained from the present study draws attention towards two points: 1) the variables studied are critical for lactase production by Saccharomyces fragilis IZ 275; and, 2) cheese whey, a by-product of the milk and dairy industry, is an important medium for the growth of yeast. Thus, cheese whey could be re-used in the fermentation industry instead of being discarded as a pollutant as per the concept of the circular economy or bioeconomy (Ranta et al., 2018; López-Gómez et al., 2019). The maximum β-galactosidase activity (28.42 U/mL) obtained in factorial design was observed with 10 g/L of lactose, sucrose, glucose and cheese whey and of variables yeast extract, peptone, MgSO₄ and K₂HPO₄ 5 g/L. In the steepest ascent-descent design, the maximum β-galactosidase activity obtained (39.81 U/mL) for sucrose and cheese whey were 14 g/L, yeast extract, peptone and MgSO₄ were 7 g/L, lactose and glucose were 10 g/L, and for K₂HPO₄ was 5 g/L . In the optimized conditions, the maximum β-galactosidase activity was 52.84 U/mL and obtained with 17.7 cheese whey, 14 g/L of sucrose, 5.14 g/L of yeast extract, 7 g/L peptone and MgSO₄, 10 g/L lactose and glucose and 5 g/L K₂HPO₄. The study clearly showed that the Central Rotational Composite Design (CRCC) and Response Surface (RSM) are efficient tools to optimize the composition of fermentation medium and lactase production increased 53.78% after optimization.

Table 6.
RDCC to optimize the medium fermentation to the β-galactosidase production by Saccharomyces fragilis IZ 275 and response function Y₂ (β-galactosidase activity, U/mL).

Assays	(x1) X1	(x2) X2	(x3) X3	(x4) X4	Lactose	Glucose	MgSO4	K2HPO4	Y2
1	(-1) 11.5	(-1) 11.5	(-1) 5.75	(-1) 5.75	10	10	7	5	29.62
2	(-1) 11.5	(-1) 11.5	(-1) 5.75	(1) 8.25	10	10	7	5	41.06
3	(-1) 11.5	(-1) 11.5	(1) 8.25	(-1) 5.75	10	10	7	5	33.29
4	(-1) 11.5	(-1) 11.5	(1) 8.25	(1) 8.25	10	10	7	5	35.73
5	(-1) 11.5	(1) 16.5	(-1) 5.75	(-1) 5.75	10	10	7	5	42.89
6	(-1) 11.5	(1) 16.5	(-1) 5.75	(1) 8.25	10	10	7	5	51.97
7	(-1) 11.5	(1) 16.5	(1) 8.25	(-1) 5.75	10	10	7	5	35.47
8	(-1) 11.5	(1) 16.5	(1) 8.25	(1) 8.25	10	10	7	5	35.91
9	(1) 16.5	(-1) 11.5	(-1) 5.75	(-1) 5.75	10	10	7	5	51.09
10	(1) 16.5	(-1) 11.5	(-1) 5.75	(1) 8.25	10	10	7	5	39.92
11	(1) 16.5	(-1) 11.5	(1) 8.25	(-1) 5.75	10	10	7	5	41.23
12	(1) 16.5	(-1) 11.5	(1) 8.25	(1) 8.25	10	10	7	5	42.28
13	(1) 16.5	(1) 16.5	(-1) 5.75	(-1) 5.75	10	10	7	5	49.35
14	(1) 16.5	(1) 16.5	(-1) 5.75	(1) 8.25	10	10	7	5	43.33
15	(1) 16.5	(1) 16.5	(1) 8.25	(-1) 5.75	10	10	7	5	44.02
16	(1) 16.5	(1) 16.5	(1) 8.25	(1) 8.25	10	10	7	5	41.67
17	(-1.48) 10.29	(0)14	(0) 7	(0) 7	10	10	7	5	39.66
18	(1.48) 17.70	(0)14	(0) 7	(0) 7	10	10	7	5	42.19
19	(0) 14	(-1.48) 10.29	(0) 7	(0) 7	10	10	7	5	32.07
20	(0) 14	(1.48) 17.70	(0) 7	(0) 7	10	10	7	5	43.85
21	(0) 14	(0) 14	(-1.48) 5.14	(0) 7	10	10	7	5	52.84
22	(0) 14	(0) 14	(1.48) 8.85	(0) 7	10	10	7	5	45.42
23	(0) 14	(0) 14	0	(-1.48) 5.14	10	10	7	5	36.69
24	(0)14	(0) 14	(0)7	(1.48) 8.85	10	10	7	5	50.66
25	(0) 14	(0) 14	(0)7	(0) 7	10	10	7	5	42.19
26	(0) 14	(0) 14	(0)7	(0) 7	10	10	7	5	42.72

• X₁ (sucrose, g/L); X₂ (cheese whey, g/L), X₃ (yeast extract, g/L), X₄ (peptone, g/L).

Figure 1.
Response surface to β-galactosidase activity as function cheese whey and sucrose (a), sucrose and yeast extract (b); peptone and sucrose (c); cheese whey and yeast extract (d) and peptone and yeast extract (e).

It is very important to determine the composition of fermentation medium for maximum lactase production. There are few studies described in the literature that show the β-galactosidase production using different fermentations. Bosso et al. (2019) worked with microfiltrated cheese whey permeate as substrate for Saccharomyces fragilis IZ 275 yeast for the production of beta-galactosidase. Kumari et al. (2019) concluded that the use of cheese whey for β-galactosidase production improves the economics of the process, and the problems associated with its disposal. Manera et al. (2008) obtained by CRCC the maximum β-galactosidase (10.7 U/mL) activity by K. marxianus CCT 7082 and used as fermentation medium lactose, yeast extract and (NH₄)₂SO₄. Venkateswarulu et al. (2017) concluded that the submerged fermentation with Bacillus subtilis strain VUVD001 produced lactase activity of 63.54 U/mL in optimized medium. The authors observed that the activity was threefold higher in comparison to an unoptimized medium and confirmed that the designed medium was useful for producing higher yields of lactase. Ahmed et al. (2016) revealed that the strain Lactobacillus sp. KLSA 22 isolated from cheese whey was the best potential source of lactase. Cheese whey, a dairy waste, is utilized as a carbon substrate for the enhanced enzyme production at low-cost. Obviously, the value of β-galactosidase activity can range according to microorganisms, culture conditions and fermentation medium; however, it was clear from this work that cheese whey is an excellent culture medium for Saccharomyces fragilis IZ 275 and could be employed by the food industry for β-galactosidase production. In a recent study, Murari et al. (2019) optimized bioethanol production from cheese whey using Kluyveromyces marxianus URM 7404. The authors described that cheese whey can be employed for ethanol production, reducing the environmental damages caused by this by-product.

4. CONCLUSION

The experimental design methodology applied in this study and the response surface are efficient tools in the optimization of fermentation medium for β-galactosidase production by Saccharomyces fragilis IZ 275. In the optimized fermentation medium, the maximum production of β-galactosidase is obtained. Cheese whey, a by-product of the dairy industry, can be employed as an excellent fermentation medium by yeast, within the bioeconomy concept and can be used by the dairy industry as a by-product with additional value.

5. ACKNOWLEDGMENTS

The authors thank Capes/CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for financial support.

6. REFERENCES

• AHMED, S.; LINGAPPA, K.; DIVATAR, M.; GAJARE, S.; SHIVALEE, A.; FARHEEN, A.; IRFANA, M. Optimization of Lactase Production under Submerged Fermentation by Lactobacillus sp. KLSA 22. International Journal of Pure & Applied Bioscience, v. 4, p. 212-220, 2016.
• ANISHA, G. S. β-galactosidases. In: PANDEY, A.; NEGI, S.; SOCCOL, C. R. (eds.). Current Developments in Biotechnology and Bioengineering: Production, Isolation and Purification of Industrial Products. Amsterdam: Elsevier, 2017. p. 395- 442.
• BOSSO, A.; SETTI, A. C. I.; TOMAL, A. B.; GUEMRA, S.; MORIOKA, L. R. I.; SUGUIMOTO, H. H. Substrate consumption and beta-galactosidase production by Saccharomyces fragilis IZ 275 grown in cheese whey as a function of cell growth rate. Biocatalysis and Agricultural Biotechnology, v. 21, 2019. https://doi.org/10.1016/j.bcab.2019.101335
  » https://doi.org/10.1016/j.bcab.2019.101335
• CARDOSO, B. B.; SILVÉRIO, S. C.; ABRUNHOSA, L.; TEIXEIRA, J. A.; RODRIGUES, L. R. β-galactosidase from Aspergillus lacticoffeatus: A promising biocatalyst for the synthesis of novel prebiotics. International Journal of Food Microbiology, v. 257, p. 67-74, 2017. https://doi.org/10.1016/j.ijfoodmicro.2017.06.013
  » https://doi.org/10.1016/j.ijfoodmicro.2017.06.013
• EL-TANBOLY, E.; MAHMOUD, E.; KHORSHID. Recovery of cheese whey, a by-product from the dairy industry for use as an animal feed. Journal of Nutritional Health & Food Engineering, v. 6, n. 5, p. 148-154, 2017. https://doi.org/10.15406/jnhfe.2017.06.00215
  » https://doi.org/10.15406/jnhfe.2017.06.00215
• JONES, G. K.; HOO, Y.; LEE, K. Production Technology of Lactase and Its Application in Food Industry Application. Journal of the Science of Food and Agriculture, v. 1, p. 4-6, 2017. https://dx.doi.org/10.26855/jsfa.2017.01.002
  » https://dx.doi.org/10.26855/jsfa.2017.01.002
• KARLAPUDI, A. P.; KRUPANIDHIA, S.; RAJESWARA, R. E.; INDIRA, M.; BOBBY, MD. N.; VENKATESWARULU, T. C. Plackett-Burman design for screening of process components and their effects on production of lactase by newly isolated Bacillus sp. VUVD101 strain from Dairy effluent. Beni-Suef University Journal of Basic Applied Sciences, v. 7, p. 543-546, 2018. https://doi.org/10.1016/j.bjbas.2018.06.006
  » https://doi.org/10.1016/j.bjbas.2018.06.006
• KUMAR, M. D. J.; SUDHA, M.; DEVIKA, S.; BALAKUMARAN, M. D.; KUMAR, M. R.; KALAICHELVAN, P. T. Production and optimization of β-galactosidase by Bacillus sp MPTK 121, Isolated from Dairy Plant Soil. Annals of Biological Research, v. 4, p. 1712-1718, 2012.
• KUMARI, S.; PANESAR, P. S.; KAUR, R.; BERA, M. B. Statistical modeling β-galactosidase production from novel yeast isolate using cheese whey. Journal of Scientific & Industrial Research, v. 78, p. 81-85, 2019.
• LOPES, A. C.; HIKICHI, S.; ANDRADE, R.; AMORIM, J.; DUARTE, W. New Alcoholic Fermented Beverages - Potentials and Challenges. In: GRUMEZESCU, A. M.; HOLBAN, A. M. (Eds.). Fermented Beverages. [S.l.]: Elsevier, 2018. p. 577-063. https://doi.org/10.1016/B978-0-12-815271-3.00014-2
  » https://doi.org/10.1016/B978-0-12-815271-3.00014-2
• LÓPEZ-GÓMEZ, J. P.; LATORRE-SÁNCHEZB, M.; UNGERA, P.; SCHNEIDERA, R.; LOZANOB, C. C.; VENUSA, J. Assessing the organic fraction of municipal solid wastes for the production of lactic acid. Biochemical Engineering Journal, v. 150, p. 107-251, 2019. https://doi.org/10.1016/j.bej.2019.107251
  » https://doi.org/10.1016/j.bej.2019.107251
• MANERA, A. P.; ORES, J. C.; RIBEIRO, V. A.; BURKERT, C. A. V.; KALIL, S. J. Optimization of the culture medium for the production of β-galactosidase from K. marxianus CCT 7082. Food Technology and Biotechnology, v. 46, p. 66-72, 2008.
• MLICHOVÁ, Z.; ROSENBERG, M. Current trends of β-galactosidase application in food technology. Journal of Food and Nutrition Research, v. 45, p. 47-54, 2006.
• MONTGOMERY, D. C. Applied statistics and probability for engineers. 3^rd ed. New York: John Wiley & Sons, 2011.
• MURARI, C. S.; MACHADO, W. R. C.; SCHUINA, G. L.; DEL BIANCHI, V. L. Optimization of bioethanol production from cheese whey using Kluyveromyces marxianus URM 7404. Biocatalysis and Agricultural Biotechnology, v. 20, p. 101-182, 2019. https://doi.org/10.1016/j.bcab.2019.101182
  » https://doi.org/10.1016/j.bcab.2019.101182
• NATIONAL ACADEMY OF SCIENCES. Food Chemicals Codex. 4^th ed. Washington, DC: National Academy Press, 1996. p. 802-803.
• PANESAR, P. S.; PANESAR, R.; SINGH, R. S.; KENNEDY, J. F.; KUMAR, H. Microbial production, immobilization and applications of β-D-galactosidase. Journal of Chemical Technology and Biotechnology, v. 81, p. 530-43, 2006. https://doi.org/10.1002/jctb.1453
  » https://doi.org/10.1002/jctb.1453
• PERINI, B. L. B.; SOUZA, H. C. M.; KELBERT, M.; APATI, G. P.; PEZZIN, A. P.; SCHNEIDER, A. L. S. Optimization of Lactase Production under Submerged Fermentation by Lactobacillus sp. KLSA 22. Chemical Engineering Transactions, v. 32, p. 991-996, 2013. https://doi.org/10.3303/CET1332166
  » https://doi.org/10.3303/CET1332166
• RANTA, V.; AARIKKA-STENROOS, L.; RITALA, P.; MÄKINEN, S. M. Exploring institutional drivers and barriers of the circular economy: A cross-regional comparison of China, the US, and Europe. Resources, Conservation and Recycling, v. 135, p. 70-82, 2018. https://doi.org/10.1016/j.resconrec.2017.08.017
  » https://doi.org/10.1016/j.resconrec.2017.08.017
• STATSOFT INC. Statistica: data analysis software system. Version, v.7. 2007. p. 1984-2004.
• VENKATESWARULU, T. C.; PRABHAKAR, K. V.; KUMAR, R. B.; KRUPANIDHI, S. Modeling and optimization of fermentation variables for enhanced production of lactase by isolated Bacillus subtilis strain VUVD001 using artificial neural networking and response surface methodology. 3 Biotech, v. 7, p. 1-7, 2017. https://doi.org/10.1007/s13205-017-0802-x
  » https://doi.org/10.1007/s13205-017-0802-x
• YADAV, J.; SHANKAR, S.; YAN, S.; PILLI, S.; KUMAR, L.; TYAGI, R.; SURAMPALLI, R. Cheese whey: A potential resource to transform into bioprotein, functional/nutritional proteins and bioactive peptides. Biotechnology Advances, v. 3, p. 756-774, 2015. https://doi.org/10.1016/j.biotechadv.2015.07.002
  » https://doi.org/10.1016/j.biotechadv.2015.07.002

Publication Dates

• Publication in this collection
  26 June 2020
• Date of issue
  2020

History

• Received
  24 Sept 2019
• Accepted
  19 Feb 2020