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Economical production of agricultural γ-polyglutamic acid using industrial wastes by Bacillus subtilis
Biochemical Engineering Journal 146 (2019) 117–123
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Biochemical Engineering Journal journal homepage: www.elsevier.com/locate/bej
Regular article
Economical production of agricultural γ-polyglutamic acid using industrial wastes by Bacillus subtilis
T
Chao Zhanga,b, , Daoji Wua,b, Huixue Rena,b ⁎
a b
School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan, 250101, China Co-Innovation Center of Green Building, Jinan, 250101, China
HIGHLIGHTS
wastes, such as CEGA and chicken manure, were used for γ-PGA production. • Industrial glutamate waste liquor could be used instead of process water for γ-PGA production. • Monosodium could be used instead of glutamate for γ-PGA production. • CEGA • Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%. ARTICLE INFO
ABSTRACT
Keywords: Monosodium glutamate waste liquor Crude extract of glutamic acid γ-Polyglutamic acid
Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for agricultural γ-polyglutamic acid (γ-PGA) production by Bacillus subtilis ZC-5. Crude extract of glutamic acid after isoelectric crystallization (CEGA) could be used instead of glutamate for γ-PGA production. The optimal composition of medium was obtained by response surface methodology. Firstly, Plackett-Burman design was undertaken to assess the effects of eight factors. With statistic regression analysis, the significant factors affecting γ-PGA production were determined as follows: chicken manure, soybean cake and CEGA. Then the Box-Behnken design was adopted for further optimization. The optimal addition amounts of factors were determined as: 62.35 g chicken manure, 25.15 g soybean cake and 15.09 g CEGA. Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%, which was 20.6% higher than the maximum value of the single factor tests. These investigations would lay a foundation for reducing the pollution of industrial wastes, exploring a late-model for agricultural γ-PGA production.
1. Introduction The γ-polyglutamic acid is a natural polymer composed of D- and Lglutamic acid units connected by γ-amide linkages [1–5]. This unusual anionic polymer is water soluble, water-absorbent, metal binding, biodegradable, edible and non-toxic to both human and environments [6]. These unique properties make γ-PGA has wide application prospects in the fields of pharmaceutical, food, cosmetic industries and agriculture [7–9]. Especially in agriculture, γ-PGA has attracted wide attention as an environment-friendly fertilizer synergist [10,11]. Up to now, the high cost of γ-PGA production is the bottleneck of the business in agriculture, which is directly related to the cost of medium. Thus, many studies have been focused on looking for cheaper medium to produce γ-PGA. And waste is one of the most important choices. However, only a few studies have been reported about the use of waste to produce γ-PGA. For example, Potter et al. [12] and Hoppensack et al. ⁎
[11] have shown that Bacillus licheniformis could produce 0.16−0.85 g/L γ-PGA from swine manure liquid in the presence of sodium gluconate, or glycerol and citrate. These investigations reported that the expensive refined supplements were still necessary for fermentation, and the waste in the medium improved the difficulty of downstream [13]. Because of the rapid development of poultry in China, it has caused serious environmental problems [14]. However, chicken manure has a high nutrient value, such as nitrogen, phosphorous and potassium. Soybean cake, a byproduct obtained during the processing of soybean oil, has been shown to be a good medium composition. The utilization of chicken manure and soybean cake provides carbon source and nitrogen source for γ-PGA production, which cuts down the cost of medium. In addition, this process can offset the disposal costs of agricultural wastes [15,16]. PGA producing bacteria are divided into two categories: L-glutamic acid-dependent bacteria and L-glutamic acid-independent bacteria.
Corresponding author at: School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan, 250101, China. E-mail address: [email protected] (C. Zhang).
https://doi.org/10.1016/j.bej.2019.03.013 Received 19 November 2018; Received in revised form 24 February 2019; Accepted 14 March 2019 Available online 15 March 2019 1369-703X/ © 2019 Published by Elsevier B.V.
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Currently, γ-PGA is mainly generated by glutamic acid-dependent Bacillus subtilis. B. subtilis can produce γ-PGA with lower nutrition requirements, and so it is very economical to be applied in industrial fermentors [17–19]. We therefore chose L-glutamic acid-dependent B. subtilis ZC5 for the production of γ-PGA in this study. For glutamic aciddependent bacterium, L-glutamic acid plays an important role in the production of γ-PGA. Whereas, its raw material cost is up to 50%, which is not suitable for mass production [17]. In China, the industry of monosodium glutamate is the most important pillar industries of the national economy. The industry produces a large amount of monosodium glutamate waste liquor (MGWL). Most of MGWL is disposed of by landfilling, which causes serious environmental problems. Zhang et al. (2012) had shown that B. subtilis NX-2 could produce 29.8 ± 0.26 g/L γ-PGA by adding 20 g/L glutamate to the fermentation medium dissolved in MGWL. But it could not solve this problem of high price of culture medium completely because it still needed 20 g/L exogenous glutamate in the medium. In this study, crude extract of glutamic acid after isoelectric crystallization could be a replacement for glutamate, and MGWL could be used instead of process water for γ-PGA production. Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for γ-PGA production by solid-state fermentation, which was never investigated previously. Compared with former investigations, solid-state fermentation provided numerous advantages for γ-PGA production, such as low cost substrates, a reduced energy requirement, environmental friendly process and reduced wastewater production. Moreover, the solid-state fermented mixture was used directly as fertilizer synergist or seed manure, and can eliminate the downstream process [20]. In this study, the goal was to increase γ-PGA production by B. subtilis ZC-5 through the optimization of medium, and evaluating the effect of solid-state fermented mixture as seed manure.
autoclaving at 121 ℃ for 20 min. 2.4. Solid-state fermentation A loopful of cells from the slant was transferred into a 250 ml conical flask containing 30 ml SM and cultivated at 37 ℃ for 24 h in a rotary shaker at 180 r/min. The culture in the conical flask was used as seed culture. Twenty grams of fermentation medium in 250 ml conical flasks were inoculated with 2 ml of the seed culture previously prepared, mixed thoroughly to achieve uniformity, sealed with eight layers of gauze and then cultivated in a static condition with relative humidity of more than 80% at 37 ℃ for 48 h. 2.5. Plackett-Burman design The Plackett-Burman design (PB) was the first step in optimization. It could determine which factors significantly affect the production of γPGA. Significance analyses of eight factors, such as chicken manure, soybean cake, wheat bran, CEGA, MGWL, MgSO4•7H2O, K2HPO4 and MnSO4•H2O, were evaluated by the PB (Table 1). According to the previous single-factor optimization experiments, they were used as the basis for selecting the level of PB test (Data not presented). Then, a total of eight factors were screened in 12 experimental runs. The experiment was designed and analyzed using Design-Expert software (Version 8.0.6, Stat-Ease, Inc, USA). All experiments were performed in triplicate. 2.6. Box-Behnken design Box-Behnken design (BBD) was used to determine optimal concentrations of factors using Design-Expert software (Version 8.0.6, StatEase, Inc, USA), and to understand the relationship among various factors. The three factors were studied at three levels (Table 2) and twenty sets of experiments were carried out (Table 6).
2. Materials and methods 2.1. Strain
2.7. Time course of fermentation
B. subtilis ZC-5 (CICC 20646) was stored in our laboratory. The strain was maintained on slant medium at 5℃.
Fermentation experiment was carried out in a 2-L conical flask, in which 1 kg of FM media was initially used for fermentation. The flask was inoculated with 4% inoculum, mixed thoroughly. In addition, the humidity and temperature of the flask were maintained at 65% and 37 ℃, respectively. The fermentation process lasted for 60 h. Samples were taken every four hours for measuring the yield of γ-PGA and the number of living bacteria (10^10cfu/g).
2.2. Industrial wastes The chicken manure was collected from chicken farms around JiNan, China. It contained 54% moisture. The characteristic parameters of dry chicken manure were as follow: 37% total carbon, 3.0% total nitrogen, 2.1% phosphorus, 1.6% potassium and 1.1% magnesium. The MGWL, glutamic acid fermentation broth (glutamic acid, 8 g/ L), crude extract of glutamic acid after isoelectric crystallization (glutamate mass fraction, 80%) and crude monosodium glutamate after crystallization (glutamate mass fraction, 90%) were collected from Bioengineering Experiment Center of Shandong Jianzhu University, Jinan, China, and stored at 4 ℃.
2.8. Analytical methods At the end of the fermentation, fermented mixture (10 g) was transferred to a 500 ml conical flask. Then 10-fold volume of water was added in the conical flask and the mixture was mixed on a rotary shaker at 150 r/min for 1 h (room temperature). The mixed liquid was centrifuged for 20 min at 10,000 r/min and the resulting supernatant was
2.3. Media
Table 1 Range of different factors investigated with PB.
Slant medium, in g/L: L-glutamate, 20; glucose, 20; (NH4) 2SO4, 10; NaCl, 5; agar 18. Seed medium (SM), in g/L: L-glutamate, 20; glucose, 20; (NH4) 2SO4, 10; MgSO4•7H2O, 0.1; MnSO4•H2O, 0.03; K2HPO4, 2. The pH was adjusted to 7.0 by HCl or NaOH. The fermentation medium without crude extract of glutamic acid for B. subtilis ZC-5, in g: L-glutamate, 20; chicken manure, 50; soybean cake, 30; wheat bran, 10; MGWL, 15; MgSO4•7H2O, 0.4; MnSO4•H2O, 0.2; K2HPO4, 0.4. Fermentation medium (FM), in g: CEGA, 15.09; chicken manure, 62.35; soybean cake, 25.15; wheat bran, 10; MGWL, 15; MgSO4•7H2O, 0.4; MnSO4•H2O, 0.2; K2HPO4, 0.4. These media were sterilized by 118
Code
Factors (g)
Low level (-1)
High level (+1)
X1 X2 X3 X4 X5 X6 X7 X8 X9, X10, X11
Chicken manure MgSO4•7H2O Wheat bran K2HPO4 Soybean cake CEGA MGWL MnSO4•H2O Dummy variable
30 0.3 8 0.2 20 10 12 0.1 –
70 0.6 16 0.8 30 20 18 0.3 +
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Table 2 Levels of factors used in the BBD.
Table 5 BBD experiments design matrix and results of γ-PGA production.
Factors
Level (g)
(A) Chicken manure (B) Soybean cake (C) CEGA
−1
0
1
55 20 10
60 25 15
65 30 20
Code
Chicken manure (A)
Soybean cake (B)
CEGA (C)
γ-PGA (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
−1 1 −1 1 −1 1 −1 1 0 0 0 0 0 0 0 0 0
−1 −1 1 1 0 0 0 0 −1 1 −1 1 0 0 0 0 0
0 0 0 0 −1 −1 1 1 −1 −1 1 1 0 0 0 0 0
5.05 6.25 4.77 5.83 4.77 6.24 4.63 6.05 6.31 5.77 5.84 5.75 5.82 5.83 5.85 5.83 5.91
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.23 0.14 0.13 0.20 0.10 0.13 0.17 0.16 0.13 0.15 0.13 0.14 0.16 0.13 0.15 0.17 0.15
Table 6 ANOVA of RSM.
Fig. 1. Effect of glutamic acids from different sources on γ-PGA production. Table 3 PB experiment design and response values. Trials
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
γ-PGA /%
1 2 3 4 5 6 7 8 9 10 11 12
+ – + – – – + + + – + –
+ + – + – – – + + + – –
– + + – + – – – + + + –
+ – + + – + – – – + + –
+ + – + + – + – – – + –
+ + + – + + – + – – – –
– + + + – + + – + – – –
– – + + + – + + – + – –
– – – + + + – + + – + –
+ – – – + + + – + + – –
– + – – – + + + – + + –
0.82 0.72 2.62 1.53 0.64 1.71 2.64 2.45 4.54 2.66 2.51 2.18
± ± ± ± ± ± ± ± ± ± ± ±
0.06 0.03 0.13 0.11 0.09 0.11 0.12 0.10 0.13 0.18 0.13 0.14
regression coefficient
E (x1)
sum of squares /%
significant
intercept X1 X2 X3 X4 X5 X6 X7 X8
2.21 0.53 0.069 0.24 −0.11 −0.64 −0.62 0.23 −0.035
1.02 0.12 0.42 −0.25 −1.32 −1.7 0.49 −0.070
22.04 0.43 4.89 0.92 34.78 27.78 4.56 0.095
significant no no no significant significant no no
Sum of Squares
Mean Square
F Value
Probe (P) > F
Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total
4.48 3.32 0.22 0.084 4.900E-003 6.250E-004 0.051 0.79 0.016 3.042E-004 0.059 0.053 5.280E-003 4.54
0.50 3.32 0.22 0.084 4.900E-003 6.250E-004 0.051 0.79 0.016 3.042E-004 8.386E-003 0.018 1.320 E-003
59.31 395.32 26.37 10.02 0.58 0.075 6.04 94.57 1.87 0.036
﹤0.0001 ﹤0.0001 0.0013 0.0158 0.4696 0.7927 0.0437 < 0.0001 0.2140 0.8544
13.49
0.0147
(8:1) at a flow rate of 0.5 mL/min and detected at 220 nm. The purified γ-PGA was used as a standard [21].
The yield of
PGA = m(
PGA production)/m(Medium weight) (1)
Total Kjeldahl-N was determined by alkaline potassium persulphate digestion-UV spectrophotometric method [20]. The number of living bacteria was determined by the following paragraphs. At the end of the fermentation, fermented mixture was fully mixed with 10-fold of sterilized water. The mixture was aseptically diluted to suitable concentration by decuple dilution. The mixed liquid (0.1 mL) was spread on SM plates in triplicate. The number of colonies was counted after cultivating for 18 h at 37 ℃.
Table 4 Partial regression coefficients and analyses of their significance. Source
Source
3. Results and discussion 3.1. Effect of glutamic acid from different sources on γ-PGA production For glutamic acid-dependent bacteria, large amounts of glutamic acid should be added in the medium during the fermentation process. Whereas, its raw material cost is up to 50%, which is not suitable for mass production [17]. Therefore, glutamic acids from different sources were used instead of refined monosodium glutamate. Glutamic acid fermentation broth (GCFB), CEGA and crude monosodium glutamate after crystallization (CMG) were evaluated by the experiment. The amount of GCFB, CEGA and CMG were converted equally by the control group (L-glutamate, 20 g). As shown in Fig.1, CEGA, CMG and control group had the same
used for measurement of γ-PGA and total Kjeldahl-N. Quantitative analysis of γ-PGA was measured by high performance liquid chromatography (Waters, the United States) using TSK Gel G6000 PWXL gel permeation chromatogram column (7.8 mm·300 mm, Tosoh, Tokyo, Japan) equipped with an UV detector (Waters 2487). Samples were eluted with a mixture of 25 mM sodium sulfate solution:acetonitrile 119
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Fig. 2. Surface and contour plots of mutual-influence. (1) effect of chicken manure (A) and soybean cake (B); (2) effect of chicken manure (A) and CEGA (C); (3) effect of soybean cake (B) and CEGA (C).
effect on γ-PGA production (t-test, data not presented), which showed that CEGA and CMG were ideal alternatives to glutamate. However, the price of CEGA was only 70% of CMG. So CEGA was chosen in the following experiments as a substitute for glutamate for γ-PGA production. GCFB had a negative effect on the production of γ-PGA, which could be explainted by some inhibitors in the fermentation broth. This was consistent with the conclusion of Zhang et al. [22].
yields. From the results of Table 4, chicken manure, soybean cake and CEGA were selected for further optimization to achieve a maximum production of γ-PGA. 3.3. Box-Behnken design According to the results of Table 4, the optimal concentrations of the three significant factors (chicken manure, soybean cake and CEGA) were determined by BBD. The results are shown in Table 5. Variance for the quadratic design was analyzed to check the validity of the model (Table 6). The values of "Prob > F" was less than 0.0500, which indicates model terms were significant. In this case A, B, C, BC, A2 were
3.2. Plackett-Burman design In the 12 trials (Table 3), there was a wide variation in γ-PGA values, which reflected the importance of optimization to achieve higher 120
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Fig. 3. Kinetics of total nitrogen, γ-PGA and number of living bacteria.
significant model terms. Final equation in terms of coded factors was stated in the following equation:
was to use it directly as organic fertilizer or mixed it into organic fertilizer in a certain proportion. From the point of view of organic fertilizer, γ-PGA and total nitrogen were meaningful. So γ-PGA and the changes of total nitrogen content were measured. As shown in Fig. 3, the time courses of the number of living bacteria, the production of γPGA and the changes of total nitrogen content under optimal medium were monitored for 60 h by sampling every 4 h. After 6 h of cultivation, B. subtilis ZC-5 started to grow, and the concentration of total nitrogen began to decline. Meanwhile, the concentration of γ-PGA rapidly increased during exponential phase and reached a stable state in the stationary phase. The number of living bacteria reached the maximum of 9.01 ± 0.13 (10^10 cfu/g) at 48 h and then reduced gradually. This indicated that industrial wastes contained sufficient nutrient to support the cell growth. The γ-PGA yield reached the maximum (6.57 ± 0.13%, w/w) at 48 h and decreased slowly thereafter. The results suggested that γ-PGA produced by B. subtilis ZC-5 was associated partially with cell growth. The total nitrogen content decreased slowly at the lag growth phase and declined sharply at the exponential growth phase, and then remained unchanged until the end of the fermentation (Fig. 3). It seemed probable that nitrogen was converted into biomass and γ-PGA. Considering all these results, chicken manure, CEGA and soybean cake were cost-effective alternatives for γ-PGA production. Comparisons with various technologies were summarized in Table 7. At present, the main production technology was the production of γ-PGA by liquid fermentation. The components in the culture medium were all purified raw materials, and the fermentation broth needed to be purified later, resulting in high production costs [23–26]. Chen et al. [6] had shown that solid-state fermentations was carried out for high yield of γ-PGA by Bacillus subtilis CCTCC202048. The average γ-PGA yield (6.0%) was obtained under optimal medium consisted of 62.3% swine manure, 25.0% soybean cake, 5.0% wheat bran and 5.0% glutamic acid. However, purified glutamic acid and purified water were still needed in culture medium. Compared with the above technologies, this technology had many advantages in producing γ-PGA: (1) The cost of culture medium was lower. The compositions of culture medium were industrial and agricultural wastes. In addition, this technology solved the problem of waste disposal. (2) No purification process was required. The product was used directly or mixed with other organic fertilizers in a certain proportion. (3) Chicken manure and soybean cake were also common organic fertilizer raw materials. The fertilizer effect of γ-PGA mixture was very positive. (4)Monosodium glutamate waste liquor could be used instead of process water for γ-PGA production. (5) Crude extract of glutamic acid after isoelectric crystallization could be used instead of glutamate for γ-PGA production.
Y=5. 85＋0.64×A－0.17× B－0.10×C－0.035×A×B－0.013×A× C＋0. 11×B×C－0.43×A2＋0.061×B2＋8.500E-003× C2 (2) where Y was γ-PGA yield, A was chicken manure; B was soybean cake, and C was CEGA. The significance of the variables in the regression equation on the response value was judged by the F test. The smaller the probability P value, the higher the of the corresponding variables was. The first order term A (P < 0.000 1) of model (1) was very significant, and B, C (P < 0.05) had a significant effect. The effects of secondary order item A2 (P < 0.01) was very significant, and B2, C2 (P > 0.05) had no significant effects. The interaction item BC (P < 0.05) had a significant effect, while AB and AC (P > 0.05) had no significant effect. This results clearly showed that experimental values were distributed linearly with high correlation (R2 = 0.9704). Therefore, the model was applicable to the prediction of γ-PGA values within the range of variables. The maximum γ-PGA yield of 6.61% was obtained at 62.35 g chicken manure, 25.15 g soybean cake, and 15.09 g CEGA. To validate this prediction, three independent fermentations were carried out, and a γPGA yield of 6.47 ± 0.18% was obtained, which was 20.5% higher than the maximum value in the single factor tests (Fig. 1). The effects of the three variables (chicken manure, soybean cake and CEGA) on γ-PGA yield were analyzed using RSM. As shown in Fig. 2, 3D response surface plots and contour plots were generated to study the interactive effects of any two variables on the response. If the response surface is relatively gentle, it means that the change of factors has little effect on the response value; On the contrary, if the response surface is very steep, it means that the influence of factors on the response value is significant. The shape of contour plot indicates the strength of interaction, the shape of circle indicates the weakness of interaction, and the shape of ellipse indicates the strength of interaction. Comparing the highest points and contours of the response surface in Fig.2, we can see that there are extremes in the selected range, that is, the highest point of the response surface and the center point of the smallest ellipse of the contour. 3.4. Time course of fermentation The potential for utilizing industrial wastes in γ-PGA fermentation was investigated in this section. Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for agricultural γ-PGA production. The main function of this product 121
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[23–26] [6]
4. Conclusions
Prize(kg/dollar)
200-300 30-40 20-26 23.00-49.75 g/L 6.0% 6.57 ± 0.13%
Liquid fermentation Solid fermentation Solid fermentation
Industrial wastes, such as CEGA, chicken manure and soybean cake, were used for γ-PGA production by B. subtilis ZC-5. Effects of glutamic acids from different sources on γ-PGA production were evaluated by the experiment. Crude extract of glutamic acid after isoelectric crystallization could be used instead of glutamate for γ-PGA production. The optimum composition of medium was obtained by RSM. Firstly, the PB design was undertaken to assess the effects of eight factors. With statistic regression analysis, the significant factors affecting γ-PGA production were determined as follows: chicken manure, soybean cake and CEGA. Then the BBD was adopted for further optimization. The optimal addition amounts and the relationships among these factors were found out by quadratic regression model equation with Design-Expert software, the optimal addition amounts of factors were determined as: 62.35 g chicken manure, 25.15 g soybean cake and 15.09 g CEGA. Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%, which was 20. 6% higher than the maximum value in the single factor test. Compared to former investigations, solid-state fermentation using chicken manure and soybean cake provided numerous advantages for the production of γ-PGA, such as low cost substrates, environmental friendly process, a reduced energy requirement and reduced wastewater production. Moreover, the solid-state fermented mixture was used directly as fertilizer synergist or seed manure, eliminating the downstream process. These investigations would lay a foundation for reducing the pollution of industrial wastes, exploring a late-model for seed manure production. Acknowledgment This research was financially supported by State Key Laboratory of Microbial Technology (M2012-14), Shandong University.
Purified carbon source, purified nitrogen source, purified glutamate, purified water Swine manure, soybean cake, purified glutamic acid, purified water Chicken manure, soybean cake, unpurified glutamic acid, effluent Traditional technologies 1 Traditional technology 2 This study
Yes No No
Medium Forms
Table 7 Comparisons with various technologies.
Purification process
γ-PGA yield
Remarks
Reference
C. Zhang, et al.
References [1] T. Bao, L. Peng, Z. Xu, Y. Jiang, X. Zheng, J. Liang, X. Feng, X. Hong, Highly efficient rice straw utilization for poly-(γ-glutamic acid) production by Bacillus subtilis NX-2, Bioresour. Technol. 193 (2015) 370–376. [2] Q. Wu, H. Xu, H. Ying, P. Ouyang, Kinetic analysis and pH-shift control strategy for poly(γ-glutamic acid) production with Bacillus subtilis CGMCC 0833, Biochem. Eng. J. 50 (2010) 24–28. [3] F. Hellmers, P. Ferguson, J. Koropatnick, R. Krull, A. Margaritis, Characterization and in vitro cytotoxicity of doxorubicin-loaded γ-polyglutamic acid-chitosan composite nanoparticles, Biochem. Eng. J. 75 (2013) 72–78. [4] B.S. Inbaraj, J.T. Chien, G.H. Ho, J. Yang, B.H. Chen, Equilibrium and kinetic studies on sorption of basic dyes by a natural biopolymer poly(gamma-glutamic acid), Biochem. Eng. J. 31 (2006) 204–215. [5] R. Andrew, M. Argyrios, Kinetics of molecular weight reduction of poly(glutamic acid) by in situ depolymerization in cell-free broth of Bacillus subtilis, Biochem. Eng. J. 30 (2006) 303–307. [6] X. Chen, S. Chen, M. Sun, Z. Yu, High yield of poly-gamma-glutamic acid from Bacillus subtilis by solid-state fermentation using swine manure as the basis of a solid substrate, Bioresour. Technol. 96 (2005) 1872–1879. [7] S. Karmaker, T.K. Saha, Chelation of vanadium(IV) by a natural and edible biopolymer poly(γ‐glutamic acid) in aqueous solution: structure and binding constant of complex, Macromol. Biosci. 8 (2008) 171–176. [8] M. Kunioka, Biosynthesis of poly (γ-glutamic acid) from l -glutamine, citric acid and ammonium sulfate in Bacillus subtilis IFO3335, Appl. Microbiol. Biotechnol. 44 (1995) 501–506. [9] Z. Luo, G. Yuan, J. Liu, Q. Hua, M. Zhao, Z. Wei, S. Li, Microbial synthesis of poly-γglutamic acid: current progress, challenges, and future perspectives, Biotechnol. Biofuels 9 (2016) 134. [10] J. Feng, Y. Gu, Y. Quan, M. Cao, W. Gao, W. Zhang, S. Wang, C. Yang, C. Song, Improved poly-γ-glutamic acid production in Bacillus amyloliquefaciens by modular pathway engineering, Metab. Eng. 32 (2015) 106–115. [11] A. Hoppensack, F.B. Oppermannsanio, A. Steinbüchel, Conversion of the nitrogen content in liquid manure into biomass and polyglutamic acid by a newly isolated strain of Bacillus licheniformis, FEMS Microbiol. Lett. 218 (2003) 39–45. [12] M. Pötter, F.B. Oppermannsanio, A. Steinbüchel, Cultivation of bacteria producing polyamino acids with liquid manure as carbon and nitrogen source, Appl. Environ. Microbiol. 67 (2001) 617–622. [13] N. Kongklom, H. Luo, Z. Shi, C. Pechyen, Y. Chisti, S. Sirisansaneeyakul, Production of poly-γ-glutamic acid by glutamic acid-independent Bacillus licheniformis TISTR 1010 using different feeding strategies, Biochem. Eng. J. 100 (2015) 67–75. [14] B.P. Kelleher, J.J. Leahy, A.M. Henihan, T.F. O’Dwyer, D. Sutton, M.J. Leahy,
122
Biochemical Engineering Journal 146 (2019) 117–123
C. Zhang, et al.
[15] [16] [17] [18] [19] [20]
Advances in poultry litter disposal technology – a review, Bioresour. Technol. 83 (2002) 27–36. Y. Ren, B. Huang, Y. Meng, L. Wei, C. Zhang, Metabolic and phylogenetic analyses based on nitrogen in a new poly-纬-glutamic acid-producing strain of Bacillus subtilis, Biotechnol. Lett. 37 (2016) 1–6. X. Wei, G. Tian, Z. Ji, S. Chen, A new strategy for enhancement of poly‐γ‐glutamic acid production by multiple physicochemical stresses in Bacillus licheniformis, J. Chem. Technol. Biotechnol. 90 (2015) 709–713. Z. Xu, X. Feng, D. Zhang, B. Tang, P. Lei, J. Liang, H. Xu, Enhanced poly(γ-glutamic acid) fermentation by Bacillus subtilis NX-2 immobilized in an aerobic plant fibrous-bed bioreactor, Bioresour. Technol. 155 (2014) 8. Z. Wei, G. Chen, Q. Wang, S. Zheng, S. Lin, Z. Liang, Metabolic studies of temperature control strategy on poly(γ-glutamic acid) production in a thermophilic strain Bacillus subtilis GXA-28, Bioresour. Technol. 155 (2014) 104. W. Zeng, Z. Liang, Z. Li, Y. Bian, Z. Li, Z. Tang, G. Chen, Regulation of poly-γglutamic acid production in Bacillus subtilis GXA-28 by potassium, J. Taiwan Inst. Chem. Eng. 61 (2016) 83–89. L. Clesceri, A. Greenberg, A.D. Pato, L. Clesceri, A.S. Greenberg, Standard Methods for Examination of Water and Wastewater, APHA, 1995.
[21] Q. Wu, H. Xu, L. Xu, P. Ouyang, Biosynthesis of poly(γ-glutamic acid) in Bacillus subtilis NX-2: regulation of stereochemical composition of poly(γ-glutamic acid), Process. Biochem. 41 (2006) 1650–1655. [22] D. Zhang, X. Feng, Z. Zhou, Y. Zhang, H. Xu, Economical production of poly(γglutamic acid) using untreated cane molasses and monosodium glutamate waste liquor by Bacillus subtilis NX-2, Bioresour. Technol. 114 (2012) 583. [23] Y. Qiu, Y. Sha, Y. Zhang, Z. Xu, S. Li, P. Lei, Z. Xu, X. Feng, H. Xu, Development of Jerusalem artichoke resource for efficient one-step fermentation of poly-(γ-glutamic acid) using a novel strain Bacillus amyloliquefaciens NX-2S, Bioresour. Technol. 239 (2017) 197–203. [24] R. Zhu, X. Ma, J. Liu, Optimization of γ-polyglutamic acid synthesis using response surface methodology of a newly isolated glutamate dependent Bacillus velezensis Z3, Int. Microbiol. 21 (2018) 143–152. [25] M. Cao, J. Feng, S. Sirisansaneeyakul, C. Song, Y. Chisti, Genetic and metabolic engineering for microbial production of poly-γ-glutamic acid, Biotechnol. Adv. 36 (2018) 1424–1433. [26] D. Cai, S. Hu, Y. Chen, L. Liu, S. Yang, X. Ma, S. Chen, Enhanced production of Polyγ-glutamic acid by overexpression of the global anaerobic regulator fnr in Bacillus licheniformis WX-02, Appl. Biochem. Biotechnol. 185 (2018) 958–970.
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Biochemical Engineering Journal journal homepage: www.elsevier.com/locate/bej
Regular article
Economical production of agricultural γ-polyglutamic acid using industrial wastes by Bacillus subtilis
T
Chao Zhanga,b, , Daoji Wua,b, Huixue Rena,b ⁎
a b
School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan, 250101, China Co-Innovation Center of Green Building, Jinan, 250101, China
HIGHLIGHTS
wastes, such as CEGA and chicken manure, were used for γ-PGA production. • Industrial glutamate waste liquor could be used instead of process water for γ-PGA production. • Monosodium could be used instead of glutamate for γ-PGA production. • CEGA • Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%. ARTICLE INFO
ABSTRACT
Keywords: Monosodium glutamate waste liquor Crude extract of glutamic acid γ-Polyglutamic acid
Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for agricultural γ-polyglutamic acid (γ-PGA) production by Bacillus subtilis ZC-5. Crude extract of glutamic acid after isoelectric crystallization (CEGA) could be used instead of glutamate for γ-PGA production. The optimal composition of medium was obtained by response surface methodology. Firstly, Plackett-Burman design was undertaken to assess the effects of eight factors. With statistic regression analysis, the significant factors affecting γ-PGA production were determined as follows: chicken manure, soybean cake and CEGA. Then the Box-Behnken design was adopted for further optimization. The optimal addition amounts of factors were determined as: 62.35 g chicken manure, 25.15 g soybean cake and 15.09 g CEGA. Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%, which was 20.6% higher than the maximum value of the single factor tests. These investigations would lay a foundation for reducing the pollution of industrial wastes, exploring a late-model for agricultural γ-PGA production.
1. Introduction The γ-polyglutamic acid is a natural polymer composed of D- and Lglutamic acid units connected by γ-amide linkages [1–5]. This unusual anionic polymer is water soluble, water-absorbent, metal binding, biodegradable, edible and non-toxic to both human and environments [6]. These unique properties make γ-PGA has wide application prospects in the fields of pharmaceutical, food, cosmetic industries and agriculture [7–9]. Especially in agriculture, γ-PGA has attracted wide attention as an environment-friendly fertilizer synergist [10,11]. Up to now, the high cost of γ-PGA production is the bottleneck of the business in agriculture, which is directly related to the cost of medium. Thus, many studies have been focused on looking for cheaper medium to produce γ-PGA. And waste is one of the most important choices. However, only a few studies have been reported about the use of waste to produce γ-PGA. For example, Potter et al. [12] and Hoppensack et al. ⁎
[11] have shown that Bacillus licheniformis could produce 0.16−0.85 g/L γ-PGA from swine manure liquid in the presence of sodium gluconate, or glycerol and citrate. These investigations reported that the expensive refined supplements were still necessary for fermentation, and the waste in the medium improved the difficulty of downstream [13]. Because of the rapid development of poultry in China, it has caused serious environmental problems [14]. However, chicken manure has a high nutrient value, such as nitrogen, phosphorous and potassium. Soybean cake, a byproduct obtained during the processing of soybean oil, has been shown to be a good medium composition. The utilization of chicken manure and soybean cake provides carbon source and nitrogen source for γ-PGA production, which cuts down the cost of medium. In addition, this process can offset the disposal costs of agricultural wastes [15,16]. PGA producing bacteria are divided into two categories: L-glutamic acid-dependent bacteria and L-glutamic acid-independent bacteria.
Corresponding author at: School of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan, 250101, China. E-mail address: [email protected] (C. Zhang).
https://doi.org/10.1016/j.bej.2019.03.013 Received 19 November 2018; Received in revised form 24 February 2019; Accepted 14 March 2019 Available online 15 March 2019 1369-703X/ © 2019 Published by Elsevier B.V.
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Currently, γ-PGA is mainly generated by glutamic acid-dependent Bacillus subtilis. B. subtilis can produce γ-PGA with lower nutrition requirements, and so it is very economical to be applied in industrial fermentors [17–19]. We therefore chose L-glutamic acid-dependent B. subtilis ZC5 for the production of γ-PGA in this study. For glutamic aciddependent bacterium, L-glutamic acid plays an important role in the production of γ-PGA. Whereas, its raw material cost is up to 50%, which is not suitable for mass production [17]. In China, the industry of monosodium glutamate is the most important pillar industries of the national economy. The industry produces a large amount of monosodium glutamate waste liquor (MGWL). Most of MGWL is disposed of by landfilling, which causes serious environmental problems. Zhang et al. (2012) had shown that B. subtilis NX-2 could produce 29.8 ± 0.26 g/L γ-PGA by adding 20 g/L glutamate to the fermentation medium dissolved in MGWL. But it could not solve this problem of high price of culture medium completely because it still needed 20 g/L exogenous glutamate in the medium. In this study, crude extract of glutamic acid after isoelectric crystallization could be a replacement for glutamate, and MGWL could be used instead of process water for γ-PGA production. Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for γ-PGA production by solid-state fermentation, which was never investigated previously. Compared with former investigations, solid-state fermentation provided numerous advantages for γ-PGA production, such as low cost substrates, a reduced energy requirement, environmental friendly process and reduced wastewater production. Moreover, the solid-state fermented mixture was used directly as fertilizer synergist or seed manure, and can eliminate the downstream process [20]. In this study, the goal was to increase γ-PGA production by B. subtilis ZC-5 through the optimization of medium, and evaluating the effect of solid-state fermented mixture as seed manure.
autoclaving at 121 ℃ for 20 min. 2.4. Solid-state fermentation A loopful of cells from the slant was transferred into a 250 ml conical flask containing 30 ml SM and cultivated at 37 ℃ for 24 h in a rotary shaker at 180 r/min. The culture in the conical flask was used as seed culture. Twenty grams of fermentation medium in 250 ml conical flasks were inoculated with 2 ml of the seed culture previously prepared, mixed thoroughly to achieve uniformity, sealed with eight layers of gauze and then cultivated in a static condition with relative humidity of more than 80% at 37 ℃ for 48 h. 2.5. Plackett-Burman design The Plackett-Burman design (PB) was the first step in optimization. It could determine which factors significantly affect the production of γPGA. Significance analyses of eight factors, such as chicken manure, soybean cake, wheat bran, CEGA, MGWL, MgSO4•7H2O, K2HPO4 and MnSO4•H2O, were evaluated by the PB (Table 1). According to the previous single-factor optimization experiments, they were used as the basis for selecting the level of PB test (Data not presented). Then, a total of eight factors were screened in 12 experimental runs. The experiment was designed and analyzed using Design-Expert software (Version 8.0.6, Stat-Ease, Inc, USA). All experiments were performed in triplicate. 2.6. Box-Behnken design Box-Behnken design (BBD) was used to determine optimal concentrations of factors using Design-Expert software (Version 8.0.6, StatEase, Inc, USA), and to understand the relationship among various factors. The three factors were studied at three levels (Table 2) and twenty sets of experiments were carried out (Table 6).
2. Materials and methods 2.1. Strain
2.7. Time course of fermentation
B. subtilis ZC-5 (CICC 20646) was stored in our laboratory. The strain was maintained on slant medium at 5℃.
Fermentation experiment was carried out in a 2-L conical flask, in which 1 kg of FM media was initially used for fermentation. The flask was inoculated with 4% inoculum, mixed thoroughly. In addition, the humidity and temperature of the flask were maintained at 65% and 37 ℃, respectively. The fermentation process lasted for 60 h. Samples were taken every four hours for measuring the yield of γ-PGA and the number of living bacteria (10^10cfu/g).
2.2. Industrial wastes The chicken manure was collected from chicken farms around JiNan, China. It contained 54% moisture. The characteristic parameters of dry chicken manure were as follow: 37% total carbon, 3.0% total nitrogen, 2.1% phosphorus, 1.6% potassium and 1.1% magnesium. The MGWL, glutamic acid fermentation broth (glutamic acid, 8 g/ L), crude extract of glutamic acid after isoelectric crystallization (glutamate mass fraction, 80%) and crude monosodium glutamate after crystallization (glutamate mass fraction, 90%) were collected from Bioengineering Experiment Center of Shandong Jianzhu University, Jinan, China, and stored at 4 ℃.
2.8. Analytical methods At the end of the fermentation, fermented mixture (10 g) was transferred to a 500 ml conical flask. Then 10-fold volume of water was added in the conical flask and the mixture was mixed on a rotary shaker at 150 r/min for 1 h (room temperature). The mixed liquid was centrifuged for 20 min at 10,000 r/min and the resulting supernatant was
2.3. Media
Table 1 Range of different factors investigated with PB.
Slant medium, in g/L: L-glutamate, 20; glucose, 20; (NH4) 2SO4, 10; NaCl, 5; agar 18. Seed medium (SM), in g/L: L-glutamate, 20; glucose, 20; (NH4) 2SO4, 10; MgSO4•7H2O, 0.1; MnSO4•H2O, 0.03; K2HPO4, 2. The pH was adjusted to 7.0 by HCl or NaOH. The fermentation medium without crude extract of glutamic acid for B. subtilis ZC-5, in g: L-glutamate, 20; chicken manure, 50; soybean cake, 30; wheat bran, 10; MGWL, 15; MgSO4•7H2O, 0.4; MnSO4•H2O, 0.2; K2HPO4, 0.4. Fermentation medium (FM), in g: CEGA, 15.09; chicken manure, 62.35; soybean cake, 25.15; wheat bran, 10; MGWL, 15; MgSO4•7H2O, 0.4; MnSO4•H2O, 0.2; K2HPO4, 0.4. These media were sterilized by 118
Code
Factors (g)
Low level (-1)
High level (+1)
X1 X2 X3 X4 X5 X6 X7 X8 X9, X10, X11
Chicken manure MgSO4•7H2O Wheat bran K2HPO4 Soybean cake CEGA MGWL MnSO4•H2O Dummy variable
30 0.3 8 0.2 20 10 12 0.1 –
70 0.6 16 0.8 30 20 18 0.3 +
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Table 2 Levels of factors used in the BBD.
Table 5 BBD experiments design matrix and results of γ-PGA production.
Factors
Level (g)
(A) Chicken manure (B) Soybean cake (C) CEGA
−1
0
1
55 20 10
60 25 15
65 30 20
Code
Chicken manure (A)
Soybean cake (B)
CEGA (C)
γ-PGA (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
−1 1 −1 1 −1 1 −1 1 0 0 0 0 0 0 0 0 0
−1 −1 1 1 0 0 0 0 −1 1 −1 1 0 0 0 0 0
0 0 0 0 −1 −1 1 1 −1 −1 1 1 0 0 0 0 0
5.05 6.25 4.77 5.83 4.77 6.24 4.63 6.05 6.31 5.77 5.84 5.75 5.82 5.83 5.85 5.83 5.91
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.23 0.14 0.13 0.20 0.10 0.13 0.17 0.16 0.13 0.15 0.13 0.14 0.16 0.13 0.15 0.17 0.15
Table 6 ANOVA of RSM.
Fig. 1. Effect of glutamic acids from different sources on γ-PGA production. Table 3 PB experiment design and response values. Trials
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
γ-PGA /%
1 2 3 4 5 6 7 8 9 10 11 12
+ – + – – – + + + – + –
+ + – + – – – + + + – –
– + + – + – – – + + + –
+ – + + – + – – – + + –
+ + – + + – + – – – + –
+ + + – + + – + – – – –
– + + + – + + – + – – –
– – + + + – + + – + – –
– – – + + + – + + – + –
+ – – – + + + – + + – –
– + – – – + + + – + + –
0.82 0.72 2.62 1.53 0.64 1.71 2.64 2.45 4.54 2.66 2.51 2.18
± ± ± ± ± ± ± ± ± ± ± ±
0.06 0.03 0.13 0.11 0.09 0.11 0.12 0.10 0.13 0.18 0.13 0.14
regression coefficient
E (x1)
sum of squares /%
significant
intercept X1 X2 X3 X4 X5 X6 X7 X8
2.21 0.53 0.069 0.24 −0.11 −0.64 −0.62 0.23 −0.035
1.02 0.12 0.42 −0.25 −1.32 −1.7 0.49 −0.070
22.04 0.43 4.89 0.92 34.78 27.78 4.56 0.095
significant no no no significant significant no no
Sum of Squares
Mean Square
F Value
Probe (P) > F
Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total
4.48 3.32 0.22 0.084 4.900E-003 6.250E-004 0.051 0.79 0.016 3.042E-004 0.059 0.053 5.280E-003 4.54
0.50 3.32 0.22 0.084 4.900E-003 6.250E-004 0.051 0.79 0.016 3.042E-004 8.386E-003 0.018 1.320 E-003
59.31 395.32 26.37 10.02 0.58 0.075 6.04 94.57 1.87 0.036
﹤0.0001 ﹤0.0001 0.0013 0.0158 0.4696 0.7927 0.0437 < 0.0001 0.2140 0.8544
13.49
0.0147
(8:1) at a flow rate of 0.5 mL/min and detected at 220 nm. The purified γ-PGA was used as a standard [21].
The yield of
PGA = m(
PGA production)/m(Medium weight) (1)
Total Kjeldahl-N was determined by alkaline potassium persulphate digestion-UV spectrophotometric method [20]. The number of living bacteria was determined by the following paragraphs. At the end of the fermentation, fermented mixture was fully mixed with 10-fold of sterilized water. The mixture was aseptically diluted to suitable concentration by decuple dilution. The mixed liquid (0.1 mL) was spread on SM plates in triplicate. The number of colonies was counted after cultivating for 18 h at 37 ℃.
Table 4 Partial regression coefficients and analyses of their significance. Source
Source
3. Results and discussion 3.1. Effect of glutamic acid from different sources on γ-PGA production For glutamic acid-dependent bacteria, large amounts of glutamic acid should be added in the medium during the fermentation process. Whereas, its raw material cost is up to 50%, which is not suitable for mass production [17]. Therefore, glutamic acids from different sources were used instead of refined monosodium glutamate. Glutamic acid fermentation broth (GCFB), CEGA and crude monosodium glutamate after crystallization (CMG) were evaluated by the experiment. The amount of GCFB, CEGA and CMG were converted equally by the control group (L-glutamate, 20 g). As shown in Fig.1, CEGA, CMG and control group had the same
used for measurement of γ-PGA and total Kjeldahl-N. Quantitative analysis of γ-PGA was measured by high performance liquid chromatography (Waters, the United States) using TSK Gel G6000 PWXL gel permeation chromatogram column (7.8 mm·300 mm, Tosoh, Tokyo, Japan) equipped with an UV detector (Waters 2487). Samples were eluted with a mixture of 25 mM sodium sulfate solution:acetonitrile 119
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Fig. 2. Surface and contour plots of mutual-influence. (1) effect of chicken manure (A) and soybean cake (B); (2) effect of chicken manure (A) and CEGA (C); (3) effect of soybean cake (B) and CEGA (C).
effect on γ-PGA production (t-test, data not presented), which showed that CEGA and CMG were ideal alternatives to glutamate. However, the price of CEGA was only 70% of CMG. So CEGA was chosen in the following experiments as a substitute for glutamate for γ-PGA production. GCFB had a negative effect on the production of γ-PGA, which could be explainted by some inhibitors in the fermentation broth. This was consistent with the conclusion of Zhang et al. [22].
yields. From the results of Table 4, chicken manure, soybean cake and CEGA were selected for further optimization to achieve a maximum production of γ-PGA. 3.3. Box-Behnken design According to the results of Table 4, the optimal concentrations of the three significant factors (chicken manure, soybean cake and CEGA) were determined by BBD. The results are shown in Table 5. Variance for the quadratic design was analyzed to check the validity of the model (Table 6). The values of "Prob > F" was less than 0.0500, which indicates model terms were significant. In this case A, B, C, BC, A2 were
3.2. Plackett-Burman design In the 12 trials (Table 3), there was a wide variation in γ-PGA values, which reflected the importance of optimization to achieve higher 120
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Fig. 3. Kinetics of total nitrogen, γ-PGA and number of living bacteria.
significant model terms. Final equation in terms of coded factors was stated in the following equation:
was to use it directly as organic fertilizer or mixed it into organic fertilizer in a certain proportion. From the point of view of organic fertilizer, γ-PGA and total nitrogen were meaningful. So γ-PGA and the changes of total nitrogen content were measured. As shown in Fig. 3, the time courses of the number of living bacteria, the production of γPGA and the changes of total nitrogen content under optimal medium were monitored for 60 h by sampling every 4 h. After 6 h of cultivation, B. subtilis ZC-5 started to grow, and the concentration of total nitrogen began to decline. Meanwhile, the concentration of γ-PGA rapidly increased during exponential phase and reached a stable state in the stationary phase. The number of living bacteria reached the maximum of 9.01 ± 0.13 (10^10 cfu/g) at 48 h and then reduced gradually. This indicated that industrial wastes contained sufficient nutrient to support the cell growth. The γ-PGA yield reached the maximum (6.57 ± 0.13%, w/w) at 48 h and decreased slowly thereafter. The results suggested that γ-PGA produced by B. subtilis ZC-5 was associated partially with cell growth. The total nitrogen content decreased slowly at the lag growth phase and declined sharply at the exponential growth phase, and then remained unchanged until the end of the fermentation (Fig. 3). It seemed probable that nitrogen was converted into biomass and γ-PGA. Considering all these results, chicken manure, CEGA and soybean cake were cost-effective alternatives for γ-PGA production. Comparisons with various technologies were summarized in Table 7. At present, the main production technology was the production of γ-PGA by liquid fermentation. The components in the culture medium were all purified raw materials, and the fermentation broth needed to be purified later, resulting in high production costs [23–26]. Chen et al. [6] had shown that solid-state fermentations was carried out for high yield of γ-PGA by Bacillus subtilis CCTCC202048. The average γ-PGA yield (6.0%) was obtained under optimal medium consisted of 62.3% swine manure, 25.0% soybean cake, 5.0% wheat bran and 5.0% glutamic acid. However, purified glutamic acid and purified water were still needed in culture medium. Compared with the above technologies, this technology had many advantages in producing γ-PGA: (1) The cost of culture medium was lower. The compositions of culture medium were industrial and agricultural wastes. In addition, this technology solved the problem of waste disposal. (2) No purification process was required. The product was used directly or mixed with other organic fertilizers in a certain proportion. (3) Chicken manure and soybean cake were also common organic fertilizer raw materials. The fertilizer effect of γ-PGA mixture was very positive. (4)Monosodium glutamate waste liquor could be used instead of process water for γ-PGA production. (5) Crude extract of glutamic acid after isoelectric crystallization could be used instead of glutamate for γ-PGA production.
Y=5. 85＋0.64×A－0.17× B－0.10×C－0.035×A×B－0.013×A× C＋0. 11×B×C－0.43×A2＋0.061×B2＋8.500E-003× C2 (2) where Y was γ-PGA yield, A was chicken manure; B was soybean cake, and C was CEGA. The significance of the variables in the regression equation on the response value was judged by the F test. The smaller the probability P value, the higher the of the corresponding variables was. The first order term A (P < 0.000 1) of model (1) was very significant, and B, C (P < 0.05) had a significant effect. The effects of secondary order item A2 (P < 0.01) was very significant, and B2, C2 (P > 0.05) had no significant effects. The interaction item BC (P < 0.05) had a significant effect, while AB and AC (P > 0.05) had no significant effect. This results clearly showed that experimental values were distributed linearly with high correlation (R2 = 0.9704). Therefore, the model was applicable to the prediction of γ-PGA values within the range of variables. The maximum γ-PGA yield of 6.61% was obtained at 62.35 g chicken manure, 25.15 g soybean cake, and 15.09 g CEGA. To validate this prediction, three independent fermentations were carried out, and a γPGA yield of 6.47 ± 0.18% was obtained, which was 20.5% higher than the maximum value in the single factor tests (Fig. 1). The effects of the three variables (chicken manure, soybean cake and CEGA) on γ-PGA yield were analyzed using RSM. As shown in Fig. 2, 3D response surface plots and contour plots were generated to study the interactive effects of any two variables on the response. If the response surface is relatively gentle, it means that the change of factors has little effect on the response value; On the contrary, if the response surface is very steep, it means that the influence of factors on the response value is significant. The shape of contour plot indicates the strength of interaction, the shape of circle indicates the weakness of interaction, and the shape of ellipse indicates the strength of interaction. Comparing the highest points and contours of the response surface in Fig.2, we can see that there are extremes in the selected range, that is, the highest point of the response surface and the center point of the smallest ellipse of the contour. 3.4. Time course of fermentation The potential for utilizing industrial wastes in γ-PGA fermentation was investigated in this section. Industrial wastes, such as monosodium glutamate waste liquor, chicken manure and soybean cake, were used for agricultural γ-PGA production. The main function of this product 121
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[23–26] [6]
4. Conclusions
Prize(kg/dollar)
200-300 30-40 20-26 23.00-49.75 g/L 6.0% 6.57 ± 0.13%
Liquid fermentation Solid fermentation Solid fermentation
Industrial wastes, such as CEGA, chicken manure and soybean cake, were used for γ-PGA production by B. subtilis ZC-5. Effects of glutamic acids from different sources on γ-PGA production were evaluated by the experiment. Crude extract of glutamic acid after isoelectric crystallization could be used instead of glutamate for γ-PGA production. The optimum composition of medium was obtained by RSM. Firstly, the PB design was undertaken to assess the effects of eight factors. With statistic regression analysis, the significant factors affecting γ-PGA production were determined as follows: chicken manure, soybean cake and CEGA. Then the BBD was adopted for further optimization. The optimal addition amounts and the relationships among these factors were found out by quadratic regression model equation with Design-Expert software, the optimal addition amounts of factors were determined as: 62.35 g chicken manure, 25.15 g soybean cake and 15.09 g CEGA. Under such condition, the γ-PGA yield was increased to 6.57 ± 0.13%, which was 20. 6% higher than the maximum value in the single factor test. Compared to former investigations, solid-state fermentation using chicken manure and soybean cake provided numerous advantages for the production of γ-PGA, such as low cost substrates, environmental friendly process, a reduced energy requirement and reduced wastewater production. Moreover, the solid-state fermented mixture was used directly as fertilizer synergist or seed manure, eliminating the downstream process. These investigations would lay a foundation for reducing the pollution of industrial wastes, exploring a late-model for seed manure production. Acknowledgment This research was financially supported by State Key Laboratory of Microbial Technology (M2012-14), Shandong University.
Purified carbon source, purified nitrogen source, purified glutamate, purified water Swine manure, soybean cake, purified glutamic acid, purified water Chicken manure, soybean cake, unpurified glutamic acid, effluent Traditional technologies 1 Traditional technology 2 This study
Yes No No
Medium Forms
Table 7 Comparisons with various technologies.
Purification process
γ-PGA yield
Remarks
Reference
C. Zhang, et al.
References [1] T. Bao, L. Peng, Z. Xu, Y. Jiang, X. Zheng, J. Liang, X. Feng, X. Hong, Highly efficient rice straw utilization for poly-(γ-glutamic acid) production by Bacillus subtilis NX-2, Bioresour. Technol. 193 (2015) 370–376. [2] Q. Wu, H. Xu, H. Ying, P. Ouyang, Kinetic analysis and pH-shift control strategy for poly(γ-glutamic acid) production with Bacillus subtilis CGMCC 0833, Biochem. Eng. J. 50 (2010) 24–28. [3] F. Hellmers, P. Ferguson, J. Koropatnick, R. Krull, A. Margaritis, Characterization and in vitro cytotoxicity of doxorubicin-loaded γ-polyglutamic acid-chitosan composite nanoparticles, Biochem. Eng. J. 75 (2013) 72–78. [4] B.S. Inbaraj, J.T. Chien, G.H. Ho, J. Yang, B.H. Chen, Equilibrium and kinetic studies on sorption of basic dyes by a natural biopolymer poly(gamma-glutamic acid), Biochem. Eng. J. 31 (2006) 204–215. [5] R. Andrew, M. Argyrios, Kinetics of molecular weight reduction of poly(glutamic acid) by in situ depolymerization in cell-free broth of Bacillus subtilis, Biochem. Eng. J. 30 (2006) 303–307. [6] X. Chen, S. Chen, M. Sun, Z. Yu, High yield of poly-gamma-glutamic acid from Bacillus subtilis by solid-state fermentation using swine manure as the basis of a solid substrate, Bioresour. Technol. 96 (2005) 1872–1879. [7] S. Karmaker, T.K. Saha, Chelation of vanadium(IV) by a natural and edible biopolymer poly(γ‐glutamic acid) in aqueous solution: structure and binding constant of complex, Macromol. Biosci. 8 (2008) 171–176. [8] M. Kunioka, Biosynthesis of poly (γ-glutamic acid) from l -glutamine, citric acid and ammonium sulfate in Bacillus subtilis IFO3335, Appl. Microbiol. Biotechnol. 44 (1995) 501–506. [9] Z. Luo, G. Yuan, J. Liu, Q. Hua, M. Zhao, Z. Wei, S. Li, Microbial synthesis of poly-γglutamic acid: current progress, challenges, and future perspectives, Biotechnol. Biofuels 9 (2016) 134. [10] J. Feng, Y. Gu, Y. Quan, M. Cao, W. Gao, W. Zhang, S. Wang, C. Yang, C. Song, Improved poly-γ-glutamic acid production in Bacillus amyloliquefaciens by modular pathway engineering, Metab. Eng. 32 (2015) 106–115. [11] A. Hoppensack, F.B. Oppermannsanio, A. Steinbüchel, Conversion of the nitrogen content in liquid manure into biomass and polyglutamic acid by a newly isolated strain of Bacillus licheniformis, FEMS Microbiol. Lett. 218 (2003) 39–45. [12] M. Pötter, F.B. Oppermannsanio, A. Steinbüchel, Cultivation of bacteria producing polyamino acids with liquid manure as carbon and nitrogen source, Appl. Environ. Microbiol. 67 (2001) 617–622. [13] N. Kongklom, H. Luo, Z. Shi, C. Pechyen, Y. Chisti, S. Sirisansaneeyakul, Production of poly-γ-glutamic acid by glutamic acid-independent Bacillus licheniformis TISTR 1010 using different feeding strategies, Biochem. Eng. J. 100 (2015) 67–75. [14] B.P. Kelleher, J.J. Leahy, A.M. Henihan, T.F. O’Dwyer, D. Sutton, M.J. Leahy,
122
Biochemical Engineering Journal 146 (2019) 117–123
C. Zhang, et al.
[15] [16] [17] [18] [19] [20]
Advances in poultry litter disposal technology – a review, Bioresour. Technol. 83 (2002) 27–36. Y. Ren, B. Huang, Y. Meng, L. Wei, C. Zhang, Metabolic and phylogenetic analyses based on nitrogen in a new poly-纬-glutamic acid-producing strain of Bacillus subtilis, Biotechnol. Lett. 37 (2016) 1–6. X. Wei, G. Tian, Z. Ji, S. Chen, A new strategy for enhancement of poly‐γ‐glutamic acid production by multiple physicochemical stresses in Bacillus licheniformis, J. Chem. Technol. Biotechnol. 90 (2015) 709–713. Z. Xu, X. Feng, D. Zhang, B. Tang, P. Lei, J. Liang, H. Xu, Enhanced poly(γ-glutamic acid) fermentation by Bacillus subtilis NX-2 immobilized in an aerobic plant fibrous-bed bioreactor, Bioresour. Technol. 155 (2014) 8. Z. Wei, G. Chen, Q. Wang, S. Zheng, S. Lin, Z. Liang, Metabolic studies of temperature control strategy on poly(γ-glutamic acid) production in a thermophilic strain Bacillus subtilis GXA-28, Bioresour. Technol. 155 (2014) 104. W. Zeng, Z. Liang, Z. Li, Y. Bian, Z. Li, Z. Tang, G. Chen, Regulation of poly-γglutamic acid production in Bacillus subtilis GXA-28 by potassium, J. Taiwan Inst. Chem. Eng. 61 (2016) 83–89. L. Clesceri, A. Greenberg, A.D. Pato, L. Clesceri, A.S. Greenberg, Standard Methods for Examination of Water and Wastewater, APHA, 1995.
[21] Q. Wu, H. Xu, L. Xu, P. Ouyang, Biosynthesis of poly(γ-glutamic acid) in Bacillus subtilis NX-2: regulation of stereochemical composition of poly(γ-glutamic acid), Process. Biochem. 41 (2006) 1650–1655. [22] D. Zhang, X. Feng, Z. Zhou, Y. Zhang, H. Xu, Economical production of poly(γglutamic acid) using untreated cane molasses and monosodium glutamate waste liquor by Bacillus subtilis NX-2, Bioresour. Technol. 114 (2012) 583. [23] Y. Qiu, Y. Sha, Y. Zhang, Z. Xu, S. Li, P. Lei, Z. Xu, X. Feng, H. Xu, Development of Jerusalem artichoke resource for efficient one-step fermentation of poly-(γ-glutamic acid) using a novel strain Bacillus amyloliquefaciens NX-2S, Bioresour. Technol. 239 (2017) 197–203. [24] R. Zhu, X. Ma, J. Liu, Optimization of γ-polyglutamic acid synthesis using response surface methodology of a newly isolated glutamate dependent Bacillus velezensis Z3, Int. Microbiol. 21 (2018) 143–152. [25] M. Cao, J. Feng, S. Sirisansaneeyakul, C. Song, Y. Chisti, Genetic and metabolic engineering for microbial production of poly-γ-glutamic acid, Biotechnol. Adv. 36 (2018) 1424–1433. [26] D. Cai, S. Hu, Y. Chen, L. Liu, S. Yang, X. Ma, S. Chen, Enhanced production of Polyγ-glutamic acid by overexpression of the global anaerobic regulator fnr in Bacillus licheniformis WX-02, Appl. Biochem. Biotechnol. 185 (2018) 958–970.
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