7
Indian Journal of Biotechnology Vol 16, July 2017, pp 419-425 Sugar cane molasses as culture media component for microbial transglutaminase production Oscar M Portilla 1 *, Vicente Espinosa 1 , Lorenzo Jarquin 2 , Arturo Salinas 2 , Gonzalo Velazquez 3 , Manuel Vazquez 4 1 Universidad Autónoma de San Luis Potosí CARHS, Km 5 Carretera Tamazunchale- San Martín.Tamazunchale, SLP-79960, México, 2 Universidad Politécnica de Guanajuato, Av Universidad Norte, Cortazar, Guanajuato-38483, México, 3 Instituto Politécnico Nacional, CICATA Unidad Querétaro, Cerro Blanco141, Colinas del Cimatario, Querétaro-76090, México, 4 Facultad de Veterinaria, Universidad de Santiago de Compostela, Campus Lugo, Lugo-27002, España Received 24 July 2015; revised 13 August 2015; accepted 20 August 2015 Microbial transglutaminase (MTGase, Ec 2.3.3.13) modifies functional properties like gelation, emulsification, foaming, viscosity and water holding capacity of proteins in high-protein content foods. Sugar cane molasses are mainly composed by noncrystallised sugars. The aim of this work was to evaluate the effect of sugar cane molasses as culture media combined with sodium caseinate and glycerol on both, the MTGase production and the relationship between enzyme and biomass production, when high or low values of nutrient concentrations were used. In the optimal conditions, mathematical models predicted biomass concentrations of 7.5 and 5.3 g/L, enzyme activities of 0.32 and 0.54 U/mL, as well as productivities of 0.0033 U/mLh -1 and 0.012 U/mLh -1 for high and low values of nutrient concentrations, respectively. Keywords: Microbial transglutaminase, food additive, biotechnology, sugar cane molasses, models. Introduction In the cane sugar production, sugar cane molasses (mainly composed by noncrystallised sugars) are produced as wastes. Their discharge to the field is an important environmental problem. Therefore new applications for this waste are needed. The use of sugar cane molasses as culture media components for the microbial transglutaminase (MTGase) production can solve the problem. MTGase catalyses the transamidation of glutamine residues to lysine residues 1 . This enzyme is a food additive that modifies functional properties of proteins including gelation, emulsification, foaming, viscosity and water holding capacity of high protein content foods 2 . Since first findings on MTGase production 3 , most of the studies dealing with the production of this enzyme have been focused in metabolic effect of different amino acids 4,5 , the use of genetically modified microorganisms 6,7 , the effect of solid or liquid media 8-10 , the capacity of different microorganisms from different environments 11-14 , different fermentation conditions like pH or temperature 15-17 , agitation speed 18,19 and dissolved oxygen 20,21 . In biotechnological processes, the formulation of culture media is of critical importance, as it affects the final concentration of product, yield and the volumetric productivity. Likewise, it is important to reduce the budget of culture media as it affects the cost of the biotechnological process, and the final product 12 . Therefore, it is necessary to continue investigating the effect of media composition on the MTGase production. In this regard, efforts on evaluating different substrates seeking for low-cost carbon or nitrogen sources have been made 18-24 . Technical and economical evaluations for MTGase production using alternative substrates have been also studied 23, 25 . The effect agitation and media containing sodium caseinate (SC), glycerol (G) and total noncrystallised sugars (TNS) from sugar cane molasses as an alternative economical nutrient, and glycerol (G) was previously reported 18 , however the optimal conditions for MTGase production varying the concentrations of these nutrients have not been established. The objective in this work was to evaluate the effect of using TNS from sugar cane molasses combined with sodium casinate and glycerol on both the MTGase production and the relationship between enzyme and biomass production, when high values of nutrient concentrations or low values of nutrient concentrations were used. Materials and Methods Raw Material Sugar cane molasses (SCM) were obtained from a local mill in Ciudad Mante, Tamaulipas, Mexico. SCM is mainly composed by: Sucrose, 609 g/L; Glucose, 116 g/L; ___________ *Author for correspondence [email protected] Tel: 0052 4833624500

Sugar cane molasses as culture media component for ...nopr.niscair.res.in/bitstream/123456789/43327/1/IJBT 16(3) 419-425.pdf · Sugar cane molasses as culture media component for

Embed Size (px)

Citation preview

Indian Journal of Biotechnology Vol 16, July 2017, pp 419-425

Sugar cane molasses as culture media component for microbial transglutaminase production

Oscar M Portilla1*, Vicente Espinosa1, Lorenzo Jarquin2, Arturo Salinas2, Gonzalo Velazquez3, Manuel Vazquez4 1Universidad Autónoma de San Luis Potosí CARHS, Km 5 Carretera Tamazunchale- San Martín.Tamazunchale, SLP-79960, México,

2Universidad Politécnica de Guanajuato, Av Universidad Norte, Cortazar, Guanajuato-38483, México, 3Instituto Politécnico Nacional, CICATA Unidad Querétaro, Cerro Blanco141, Colinas del Cimatario, Querétaro-76090, México,

4Facultad de Veterinaria, Universidad de Santiago de Compostela, Campus Lugo, Lugo-27002, España

Received 24 July 2015; revised 13 August 2015; accepted 20 August 2015

Microbial transglutaminase (MTGase, Ec 2.3.3.13) modifies functional properties like gelation, emulsification, foaming, viscosity and water holding capacity of proteins in high-protein content foods. Sugar cane molasses are mainly composed by noncrystallised sugars. The aim of this work was to evaluate the effect of sugar cane molasses as culture media combined with sodium caseinate and glycerol on both, the MTGase production and the relationship between enzyme and biomass production, when high or low values of nutrient concentrations were used. In the optimal conditions, mathematical models predicted biomass concentrations of 7.5 and 5.3 g/L, enzyme activities of 0.32 and 0.54 U/mL, as well as productivities of 0.0033 U/mLh-1 and 0.012 U/mLh-1for high and low values of nutrient concentrations, respectively.

Keywords: Microbial transglutaminase, food additive, biotechnology, sugar cane molasses, models.

Introduction In the cane sugar production, sugar cane molasses

(mainly composed by noncrystallised sugars) are produced as wastes. Their discharge to the field is an important environmental problem. Therefore new applications for this waste are needed. The use of sugar cane molasses as culture media components for the microbial transglutaminase (MTGase) production can solve the problem. MTGase catalyses the transamidation of glutamine residues to lysine residues1. This enzyme is a food additive that modifies functional properties of proteins including gelation, emulsification, foaming, viscosity and water holding capacity of high protein content foods2. Since first findings on MTGase production3, most of the studies dealing with the production of this enzyme have been focused in metabolic effect of different amino acids4,5, the use of genetically modified microorganisms6,7, the effect of solid or liquid media8-10, the capacity of different microorganisms from different environments11-14, different fermentation conditions like pH or temperature15-17, agitation speed18,19 and dissolved oxygen20,21. In biotechnological processes, the formulation of culture media is of critical importance, as it affects the final concentration of product, yield and the

volumetric productivity. Likewise, it is important to reduce the budget of culture media as it affects the cost of the biotechnological process, and the final product12. Therefore, it is necessary to continue investigating the effect of media composition on the MTGase production. In this regard, efforts on evaluating different substrates seeking for low-cost carbon or nitrogen sources have been made18-24. Technical and economical evaluations for MTGase production using alternative substrates have been also studied23, 25. The effect agitation and media containing sodium caseinate (SC), glycerol (G) and total noncrystallised sugars (TNS) from sugar cane molasses as an alternative economical nutrient, and glycerol (G) was previously reported18, however the optimal conditions for MTGase production varying the concentrations of these nutrients have not been established. The objective in this work was to evaluate the effect of using TNS from sugar cane molasses combined with sodium casinate and glycerol on both the MTGase production and the relationship between enzyme and biomass production, when high values of nutrient concentrations or low values of nutrient concentrations were used.

Materials and Methods

Raw Material Sugar cane molasses (SCM) were obtained from a local

mill in Ciudad Mante, Tamaulipas, Mexico. SCM is mainly composed by: Sucrose, 609 g/L; Glucose, 116 g/L;

___________ *Author for correspondence [email protected] Tel: 0052 4833624500

INDIAN J BIOTECHNOL, JULU 2017

420

Fructose, 158 g/L. Glycerol and all other reagents used were obtained from Sigma (USA). The strain Streptoverticillium ladakanum NRRL 3191 was obtained from the NRRL Culture Collection (Peoria, IL, EUA). Culture Conditions

The strain NRRL 3191 was reactivated in 100 mL of culture media containing glucose (5 g/L), yeast extract (4 g/L) and malt extract (10 g/L). After 72 h of incubation, cells were separated by centrifugation. Supernatant-free bacteria were resuspended in a fresh culture media containing glycerol at 50 % and used as stock culture in 1.5 mL cryovials at -80 °C and used as needed. For experimental trials, a 1 mL sample of stock culture was taken and bacteria were reactivated in the same media above described for 72 h before inoculated in the fresh culture for experimental trials. Culture formulation was prepared according to Junqua et al26, and modified according to the experiments described in Table 1. Glycerol and sodium caseinate were added as inductors for the production of transglutaminase. The three media components (noncrystallised sugars from molasses, glycerol and sodium caseinate) were mixed and sterilized at 121 ºC for 20 min. Then it was inoculated and incubated at 26 °C in 250 mL Erlenmeyer flasks in an orbital shaker at 400 rpm for up to 120 h18.

Experimental Design A block factor named nutrient concentration

values, whose levels were high (HRNC) and low (LRNC) was set. In each level of the block the effect of G, SC and TNS was tested using a Box Behnken structure. The independent variables are given in Table 1. The responses were biomass (g/L) and enzyme activity (U/mL). Both variables were followed during 120 h drawing a 2 mL aliquot every 24 h and immediately processed. Analytical Methods

Samples were centrifuged at 4000 rpm. Supernatants were analysed for enzyme activity (EA)

(Grossowicz et al, 1950). One unit of activity EA was defined as the amount that causes the formation of 1 μ mol hydroxamate in 1 min at 37ºC18. The precipitated was washed twice using distilled water and maintained during 24 h at 105 °C to calculate biomass concentration (B) in g/L.

Statistical analysis and models Data analysis was performed in Excel 2013

(Microsoft Corporation, Redmond, Washington, EUA). The analysis of variance (ANOVA) was carried out with α = 0.05. Models were obtained by second-order multiple regression analysis using least squares regression methodology.

Results and Discussion

Effects on Biomass Production Results are summarized in Table 2. Biomass played

an important role on the MTGase production12,7. The higher concentration of biomass was 17.7 and 13.3 g/L for HRNC and LRNC, respectively (Table 2).

Results of ANOVA are showed in Table 3. Besides the effect of block, the three factors SC, G and TNS gave a significant effect. Block, SC and TNS showed a significant effect only at 24 h, meaning that by changing the concentrations from high to low, the concentration of biomass was statistically lowered. The positive main effect of SC and TNS indicates that when increasing the concentration of each nutrient, the biomass concentration is higher. This behaviour is explained by the strong dependence of biomass on the nitrogen and carbon sources26, however, TNS is metabolised faster than G18. Adding the block effect to a base model previously established12, and considering only the active effects obtained in the ANOVA, a simplified model (Eq. 1) can be established for predicting biomass at 24 h:

24 8.518 2.207 6.17 0.565 (Eq. 1)

The prediction was 7.5 g/L when using HRNC at SC (60 g/L) and TNS (60 g/L), while 5.3 g/L are reached when LRNC is used at CS (40 g/L) and TNS (40 g/L). Figure 1 shows both the effect of block and factors studied on the biomass concentration at 24 h. These results indicate the critical importance of the first hours of fermentation for the MTGase production considering that this period is the onset of cellular differentiation. They are also important because the MTGase production is improved when the differentiation phase is extended17. Besides, reducing

Table 1 Levels and values of independent variables considered in the study.

Variable Units Coded levels

Block (values of nutrient

concentration) High

(HRNC) Low

(LRNC) Sodium caseinate (SC)

g/L -1, 0, 1 20, 40, 60 10, 25, 40

Glycerol (G) g/L 1, 0, 1 20, 40, 60 5, 15, 25 Total noncrystallised sugars (TNS)

g/L 1, 0, 1 30, 45, 60 20, 30, 40

PORTILLA et al.: MICROBIAL TRANSGLUTAMINASE PRODUCTION FROM SUGAR CANE

421

the concentration of nutrients impacts positively the process cost, which is a considerable budget in the production of the MTGase12, 23, 25.

Effect on the Enzyme Production Table 3 shows the regression results for the enzyme

production. Block effect indicates that enzyme

activity was increased by lowering the nutrient concentrations. Also, within each block, the activity increased when lowering the level of each factor, indicating a greater difficulty for the microorganism to reach mycelium differentiation17. At 96 and 120 h, the active effects are block, SC, G and TNS. By using

Table 2 Box-Behnken design and results for biomass (B) and enzyme activity (EA) obtained in the kinetics.

Block SC G TNS B24h B48h B72h B96h B120h EA48h EA72h EA96h EA120hHRNC 0 1 1 6.72 7.2 8 8.37 4.37 0.04 0 0 0 HRNC 0 -1 1 8.81 7.11 8.8 9.36 8.84 0.17 0.02 0.11 0.17 HRNC -1 0 -1 4.98 7.4 11.07 17.61 17.7 0.33 0.1 0.26 0.26 HRNC 0 0 0 6.39 6.84 7.95 7.66 1.53 0.15 0.01 0.1 0.16 HRNC -1 -1 0 5.25 8.59 12.69 13.49 11.67 0.36 0.21 0.37 0.39 HRNC -1 0 1 6.59 5.68 8.56 7.75 13.5 0.02 0 0.1 0.1 HRNC 1 -1 0 6.62 6.15 9.09 12.17 10.11 0.14 0.1 0.12 0.16 HRNC 0 0 0 6.21 6.58 7.2 7.94 7.26 0.22 0.06 0.1 0.16 HRNC -1 1 0 5.79 4.92 3.05 8.41 13.7 0.14 0.03 0.1 0.12 HRNC 0 0 0 5.96 5.96 7.76 2.94 6.53 0.21 0.06 0.12 0.17 HRNC 0 1 -1 5.86 5.74 8.88 8.72 9.67 0.18 0.06 0.12 0.14 HRNC 1 0 1 8.06 12.36 8.66 9.58 10.65 0.12 0 0.08 0.01 HRNC 1 0 -1 5.66 7.94 8.54 11 9 0.34 0.09 0.09 0.11 HRNC 0 -1 -1 5.92 6.92 8.74 8.38 9.24 0.32 0.16 0.19 0.22 HRNC 1 1 0 6.76 4.73 6.98 8.8 7.92 0.12 0.02 0.03 0.03 LRNC 0 1 1 3.1 6.36 10.92 12.5 12.51 0.25 0.29 0.28 0.35 LRNC 0 -1 1 4.73 6.86 9.11 11.11 11.03 0.27 0.41 0.4 0.42 LRNC -1 0 -1 3.33 7.58 9.48 8.5 8.68 0.27 0.33 0.35 0.35 LRNC 0 0 0 4 4.43 7.74 4.37 7.15 0.36 0.36 0.39 0.37 LRNC -1 -1 0 3.03 6.3 6.04 7.61 8.25 0.28 0.41 0.42 0.45 LRNC -1 0 1 4.13 6 10.48 11.45 11.15 0.13 0.2 0.21 0.29 LRNC 1 -1 0 5.16 7.16 11.04 7.95 7.04 0.41 0.41 0.41 0.41 LRNC 0 0 0 4.12 7.12 10.35 11.02 10.85 0.31 0.35 0.39 0.38 LRNC -1 1 0 3.74 6.68 10.99 11.54 11.67 0.21 0.31 0.27 0.29 LRNC 0 0 0 4.56 7.84 11.35 11.22 11.33 0.32 0.38 0.39 0.31 LRNC 0 1 -1 3.9 13.33 12.29 11.68 10.25 0.32 0.33 0.36 0.32 LRNC 1 0 1 5.24 7.58 7.99 9.62 10.8 0.25 0.26 0.27 0.25 LRNC 1 0 -1 4.47 8.54 10.94 9.04 7.85 0.34 0.47 0.38 0.39 LRNC 0 -1 -1 4.22 8.89 7.99 7.72 6.88 0.59 0.44 0.34 0.31 LRNC 1 1 0 4.74 8.39 7.33 7.72 6.86 0.3 0.22 0.15 0.17

Table 3 Coefficients, p values, and R2 obtained for each ANOVA for: B24h Block EA96h Block EA120h HRNC EA48h LRNC Coefficient p value Coefficient p value Coefficient p value Coefficient p value Interception 8.518 0.00 -0.064 0.16 0.16 0.00 0.33 0.00 Block -2.207 0.00 0.208 0.00 - - - - SC 0.617 0.00 -0.034 0.04 -0.07 0.00 0.05 0.01 G -0.196 0.19 -0.066 0.00 -0.08 0.00 -0.06 0.01 TNS 0.565 0.00 -0.040 0.02 -0.06 0.00 -0.08 0.00 SC*SC -0.085 0.70 -0.011 0.64 0.04 0.06 -0.01 0.60 G*G 0.015 0.95 -0.004 0.88 0.02 0.35 0.01 0.51 TNS*TNS 0.186 0.40 -0.020 0.41 -0.02 0.19 0.06 0.02 SC*G -0.191 0.36 0.009 0.70 0.00 0.98 -0.07 0.01 SC*TNS 0.095 0.65 0.023 0.33 0.01 0.46 0.04 0.08 G*TNS -0.418 0.06 -0.022 0.33 -0.04 0.04 -0.01 0.53 R2 0.89 0.85 0.97 0.96 B24h Block. Biomass at 24 h considering the block in the analysis. EA96h Block. Enzyme activity at 96 h considering the block in the analysis. EA120h HRNC. Enzyme activity at 120 h excluding block in the analysis. EA48h LRNC. Enzyme activity at 48 h excluding block in the analysis.

INDIAN J BIOTECHNOL, JULU 2017

422

the regression coefficients from Table 3, and including block as part of the model (Eq. 2) it is possible to predict the enzyme activity at 96 h of fermentation (EA96h):

96 0.064 0.208 0.0340.066 0.040 (Eq. 2)

On these conditions, the model predicts 0.3 U/mL when HRSNC were used at SC, 20 g/L; G, 20 g/L y TNS, 30 g/L, while enzyme activity was increased up to 0.5 U/mL when LRNC were used at SC, 10 g/L; G, 5 g/L and TNS 20 g/L. Optimal Conditions for MTGase Production

To establish the optimal condition for MTGase production, enzyme activity response was subjected to ANOVA at both HRNC and LRNC. Biomass response was processed similarly and no active effect was found, therefore only enzyme activity is herein reported. Results are showed in Table 3.

When HRNC were used, effects appear active at 72, 96 and 120 h. Results for 120 h are herein presented as the three factors are active, and the quadratic effect of TNS is active and R2 is the highest (0.97). The simplified model for this set of experiments is showed in Eq. 3.

120 0.16 0.07 0.080.06 0.040 (Eq.3)

Solving Eq. 3, the enzyme activity at 120 h (EA120h) is 0.32 U/mL when HRNC were used. When eliminating the block effect, the enzyme activity is 1.6 fold increased. Figure 2 shows the effect of factors analysed independently from block factor. The quadratic effect of TNS gave a soft curvature showed in Figure 2a. The best conditions predicted were: SC, 20 g/L; G 20 g/L and TNS, 35.4 g/L.

On the other hand when LRNC were used, the active effects were SC, G, TNS, the interaction between G and TNS and the quadratic effect of SC. The simplified model is showed in Eq. 4.

48 0.33 0.05 0.06 0.08 0.06 0.07

(Eq. 4) When eliminating the block effect in LRNC, enzyme activity was increased 1.35 folds, reaching 0.54 U/mL at SC, 30.3 g/L; G, 5 g/L and TNS, 20 g/L. A higher enzyme activity was reached at LRNC conditions at 48 h (Figure 2b), where the G and TNS interaction means that G can be replaced by TNS for the MTGase

Fig. 2 Effect of nutrient concentration on enzyme activity. HRNC at 120 h a). LRNC at 48 h.

Fig. 1 Block effect on biomass concentration. HRNC a). LRNC b).

PORTILLA et al.: MICROBIAL TRANSGLUTAMINASE PRODUCTION FROM SUGAR CANE

423

production. Besides, the optimal concentration of CS was presented as a quadratic effect of this factor. With these results, the concentration of nutrients was significantly reduced and enzyme activity was almost doubled when compared to previously reported results18; besides MTGase production was surpassed from those obtained with other microorganisms14,16, or those carried out using Streptomyces strains in solid substrates9. Furthermore, no complex media prepared by chemical acid hydrolysis is necessary24. Enzyme and Biomass Relationship

In previous reports no analysis of the relationship between enzyme and biomass production were carried out27,28. When picking the best condition at HRNC and the best one at LRNC, it is possible to closely analyse the relationship between enzyme and biomass production. Results are shown in Figure 3.

The kinetic behaviour of biomass production is analysed by Eq. 5.

… (Eq. 5)

Where X is the predicted biomass; X0 is the initial biomass; Xm is the maximum biomass; Rx is the biomass generation rate and Rd is the cell death rate. Results for NRNC conditions are showed in Figure 3a, where the stationary phase (13.49 at 96 h) is reached far after the LRNC conditions (8.89 at 48 h). Besides, the cellular death rate was lower at HRNC conditions than at LRNC conditions.

The kinetic behaviour for enzyme production is analysed by Eq. 6.

… (Eq. 6)

Where P is the higher enzyme activity; P0 is the initial enzyme activity; Pm is the maximum enzyme activity; Re is the enzyme generation rate and Rdn is the enzyme denaturation rate. Results are showed in Figure 3b, where at LRNC 0.59 U/mL were obtained at 48 h, while at HRNC 0.39 U/mL were obtained at 120 h. Besides denaturation took place faster at higher enzyme concentration. Furthermore, productivity was 3.6 times higher at LRNC (0.012 U/mLh-1) than at HRNC (0.0033 U/mLh-1). All results are summarised in Table 4. The kinetic parameters obtained can be used in computer simulations of processing plants to optimize the economics of transglutaminase production.

These results can be explained considering that extending the stationary phase allowed increasing two-fold the MTGase production17. It has been reported that Enterobacter sp. C2361 produced more biomass than Providencia sp. C1112, while the later produced higher concentrations of MTGase22. Enzyme activity using TNS might be improved by changing fermentation variables such as pH, temperature17or ionic strength stress29, 30.

All the results herein described showed that MTGase production is strongly affected by high nutrient concentration “values”, while at those conditions biomass is favoured. Microbial transglutaminase is an extracellular enzyme. It increases with growth stress. Then limitation of nutrients increases the transglutaminase production.

Fig. 3 Kinetcs for biomass production a) and enzyme activity b).

INDIAN J BIOTECHNOL, JULU 2017

424

The highest enzyme activity is reached at the stationary phase. Low concentrations of nutrients allow reaching the stationary phase in less time, therefore the highest enzyme production is reached faster. By lowering the concentration of the nutrients it is possible to increase the productivity of the MTGase production system. Using sugar cane molasses as carbon sources allowed to decrease the concentration of glycerol. This result can be a way of revalorising sugar cane molasses for biotechnological purposes.

References 1 Strop P, Versatility of microbial transglutaminase, Bioconjug

Chem, 25 (2014) 855-862. 2 Gaspar A L & de Góes-Favoni S P, Action of microbial

transglutaminase (MTGase) in the modification of food proteins: A review, Food Chem, 171 (2015) 315-322.

3 Ando H, Adachi M, Umeda K, Matsuura A, Nonaka M et al, Purification and characteristics of a novel transglutaminase derived from microorganisms, Agric Biol Chem, 53 (1989) 2613-2617.

4 Zhu Y, Rinzema A, Tramper J, de Bruin E & Bol J, Fed-batch fermentation dealing with nitrogen limitation in microbial transglutaminase production by Streptoverticillium mobaraense, Appl Microbiol Biotechnol, 49 (1998) 251-257.

5 Zhu Y, Rinzema A, Bonarius H P J, Tramper J & Bol J, Microbial transglutaminase production by Streptoverticillium mobaraense: analysis of amino acid metabolism using mass balances, Enzyme Microb Technol, 23 (1998) 216-226.

6 Lin S J, Hsieh Y F, Lai L A, Chao M L & Chu W S, Characterization and large-scale production of recombinant Streptoverticillium platensis transglutaminase, J Ind Microbiol Biotechnol, 35 (2008) 981-990.

7 Li H, Cui Y, Zhang L, Luo X, Fan R et al, Production of a transglutaminase from Zea mays in Escherichia coli and its impact on yoghurt properties, Int J Dairy Technol, 68 (2015) 54-61.

8 de Souza C F, Heck J X & Ayub M A, Solid state bioreactor production of transglutaminase by Amazonian Bacillus circulans BL32 strain, J Ind Microbiol Biotechnol, 35 (2008) 1677-1685.

9 Nagy V & Szakacs G, Production of transglutaminase by Streptomyces isolates in solid-state fermentation, Lett Appl Microbiol, 47 (2008) 122-127.

10 De Barros Soares L H, Assmann F & Ayub M A Z, Production of transglutaminase from Bacillus circulans on solid-state and submerged cultivations, Biotechnol Lett, 25 (2003) 2029-2033.

11 Kim H S, Jung S H, Lee I S & Yu T S, Production and characterization of a novel microbial transglutaminase from Actinomadura sp. T-2, J Microbiol Biotechnol, 10 (2000) 187-194.

12 De Souza C F V, Flôres S H & Ayub M A Z, Optimization of medium composition for the production of transglutaminase by Bacillus circulans BL32 using statistical experimental methods, Process Biochem, 41 (2006) 1186-1192.

13 Macedo J A, Sette L D, & Sato H H, Optimization of medium composition for transglutaminase production by a Brazilian soil Streptomyces sp, Electron J Biotechnol, 10 (2007) 618-626.

14 Bahrim G, Iancu C, Buţu N & Negoiţǎ T G, Production of a novel microbial transglutaminase using Streptomyces sp. polar strains, Rom Biotechnol Lett, 15 (2010) 5197-5203.

15 Meiying Z, Guocheng D & Jian C, pH control strategy of batch microbial transglutaminase production with Streptoverticillium mobaraense, Enzyme Microb Technol, 31 (2002) 477-481.

16 de Souza C F, De Matos G S, Flôres S H & Ayub M A, Environmental effects on transglutaminase production and cell sporulation in submerged cultivation of Bacillus circulans, Appl Biochem Biotechnol, 158 (2009) 302-312.

17 Chen K, Zhang D, Liu S, Wang N S, Wang M et al, Improvement of transglutaminase production by extending differentiation phase of Streptomyces hygroscopicus: Mechanism and application, Appl Microbiol Biotechnol, 97 (2013) 7711-7719.

18 Portilla-Rivera O M, Téllez-Luis S J, de León J A R & Vázquez M, Production of microbial transglutaminase on media made from sugar cane molasses and glycerol, Food Technol Biotechnol, 47 (2009) 19-26.

19 Bagagli M P & Sato H H, Two-staged temperature and agitation strategy for the production of transglutaminase from a Streptomyces sp. isolated from Brazilian soils, Appl Biochem Biotech, 170 (2013) 1057-1065.

20 de Souza C F V, Rodrigues R C & Ayub M A Z, Effects of oxygen volumetric mass transfer coefficient on transglutaminase production by Bacillus circulans BL32, Biotechnol Bioproces Eng, 14 (2009) 571-576.

21 Yan G, Du G, Li Y, Chen J & Zhong J, Enhancement of microbial transglutaminase production by Streptoverticillium mobaraense: Application of a two-stage agitation speed control strategy, Process Biochem, 40 (2005) 963-968.

22 H-Kittikun A, Bourneow C & Benjakul S, Hydrolysis of surimi wastewater for production of transglutaminase by Enterobacter sp. C2361 and Providencia sp. C1112, Food Chem, 135 (2012) 1183-1191.

23 Guerra-Rodríguez E & Vázquez M, Technical and economical evaluation of microbial transglutaminase production on enzymatic hydrolysates of potato (Solanum tuberosum), CYTA–J Food, 11 (2013) 277-284.

24 Rodríguez-Castillejos G C, Tellez-Luis S J, Vázquez M, Lois-Correa J A & Ramírez J A, Evaluation of sorghum grain hydrolysates and dried distillers grains with solubles for the production of microbial transglutaminase, CYTA – J Food, 12 (2014) 115-120.

25 Vazquez M V & Rodríguez M E G, Computer simulation of microbial transglutaminase production from potato wastes, in Procedia Food Science, 11th Int Congress Eng Food, held on 22-26 may 2011 (Int Assoc Eng Food, Athens, Grecee) 2011, 736-741.

26 Junqua M, Duran R, Gancet C & Goulas P, Optimization of microbial transglutaminase production using experimental designs, Appl Microbiol Biotechnol, 48 (1997) 730-734.

27 Moreno L, Dominguez J, Tellez S, Enriquez J & Portilla O, Study of the transglutaminase production by Streptoverticillium ladakanum using sodium caseinate, glycerol and noncrystallized carbohydrates from sugar cane molasses as nutrients, in New Biotechnology, 15th Eur Congress on Biotechnol, held on 23-26 september 2012 (European Fed Biot, Istanbul, Turkey) 2012, S73.

PORTILLA et al.: MICROBIAL TRANSGLUTAMINASE PRODUCTION FROM SUGAR CANE

425

28 Jarquín E, Salinas A, Espinosa V, & Portilla O, Estudio sobre la producción de transglutaminasa microbiana modificando las concentraciones de nutrientes, in Cong Int Acad J,Celaya, Mexico, 5-7 Noviembre 2014, 6(2014) 2326-2331.

29 Zhang L, Zhang L, Yi H, Du M, Zhang Y et al, Enhancement of transglutaminase production in Streptomyces mobaraensis DSM 40587 by non-

nutritional stress conditions: Effects of heat shock, alcohols, and salt treatments, Kor J Chem Eng, 29 (2012) 913-917.

30 Zhang L, Zhang L, Han X, Du M, Zhang Y et al, Enhancement of transglutaminase production in Streptomyces mobaraensis as achieved by treatment with excessive MgCl2, Appl Microbiol Biotechnol, 93 (2012) 2335-2343.