전체메뉴
검색
Article Search

JMB Journal of Microbiolog and Biotechnology

QR Code QR Code

Research article

References

  1. Shih IL, Van YT. 2001. The production of poly(γ-glutamic acid) from microorganisms and its various applications. Bioresour. Technol. 79: 207-225.
    CrossRef
  2. Buescher JM, Margaritis AM. 2007. Microbial biosynthesis of polyglutamic acid biopolymer and applications in the biopharmaceutical, biomedical, and food industries. Crit. Rev. Biotechnol. 27: 1-19.
    Pubmed CrossRef
  3. Akagi T, Baba M, Akashi M. 2007. Preparation of nanoparticles by the self-organization of polymers consisting of hydrophobic and hydrophilic segments: potential applications. Polymer 48: 6729-6747.
    CrossRef
  4. Li C. 2002. Poly(L-glutamic acid)-anticancer drug conjugates. Adv. Drug Deliv. Rev. 54: 695-713.
    Pubmed CrossRef
  5. Matsuo K, Koizumi H, Akashi M, Nakagawa S, Fujita T, Yamamoto A, et al. 2011. Intranasal immunization with poly(γ-glutamic acid) nanoparticles entrapping antigenic proteins can induce potent tumor immunity. J. Control. Release 152: 310-316.
    Pubmed CrossRef
  6. Tanimoto H, Fox T, Eagles J, Satoh H, Nozava H, Okiyama A, et al. 2007. Acute effect of poly(γ-glutamic acid) on calcium absorption in post-menopausal women. J. Am. Coll. Nutr. 26: 645-649.
    Pubmed CrossRef
  7. Shih IL, Van YT, Sau YY. 2003. Antifreeze activities of poly(γ-glutamic acid) produced by Bacillus licheniformis. Biotechnol. Lett. 25: 1709-1712.
  8. Bhat AR, Irorere VU, Bartlett T, Hill D, Kedia G, Morris MR, et al. 2013. Bacillus subtilis natto: a non-toxic source of poly(γ-glutamic acid) that could be used as a cryoprotectant for probiotic bacteria. AMB Express 3: 36.
    Pubmed PMC CrossRef
  9. Lee CY, Kuo MI. 2011. Effect of γ-polyglutamate on the rheological properties and microstructure of tofu. Food Hydrocoll. 25: 1034-1040.
    CrossRef
  10. Zheng H, Gao Z, Yin J, Tang X, Ji X, Huang H. 2012. Harvesting of microalgae by flocculation with poly(γ-glutamic acid). Bioresour. Technol. 112: 212-220.
    Pubmed CrossRef
  11. Wang F, Zhao J, Wei X, Huo F, Li W, Hu Q, Liu H. 2014. Adsorption of rare earths (III) by calcium alginate-poly glutamic acid hybrid gels. J. Chem. Technol. Biotechnol. 89: 969-977.
    CrossRef
  12. Candela T, Fouet A. 2006. Poly-gamma-glutamate in bacteria. Mol. Microbiol. 60: 1091-1098.
    Pubmed CrossRef
  13. Ashiuchi M. 2010. Occurrence and biosynthetic mechanism of poly-gamma-glutamic acid, pp. 77-93. In: Hamano Y (ed), Amino-Acid Homopolymers Occurring in Nature. Springer, New York, N.Y.
    CrossRef
  14. Birrer GA, Cromwick AM, Gross RA. 1994. γ-Poly(glutamic acid) formation by Bacillus licheniformis 9945A: physiological and biochemical studies. Int. J. Biol. Macromol. 16: 265-275.
    CrossRef
  15. Kunioka M, Goto A. 1994. Biosynthesis of poly(γ-glutamic acid) from L-glutamic acid, citric acid, and ammonium sulfate in Bacillus subtilis IFO3335. Appl. Microbiol. Biotechnol. 40: 867-872.
    CrossRef
  16. Jeong JH, Kim JN, Wee YJ, Ryu HW. 2010. The statistically optimized production of poly(γ-giutamic acid) by batch fermentation of a newly isolated Bacillus subtilis RKY3. Bioresour. Technol. 101: 4533-4539.
    Pubmed CrossRef
  17. Cromwick AM, Birrer GA, Gross RA. 1996. Effects of pH and aeration on γ-poly(glutamic acid) formation by Bacillus licheniformis in controlled batch fermentor cultures. Biotechnol. Bioeng. 50: 222-227.
    CrossRef
  18. Jung DH, Jung S, Yun JS, Kim JN, Wee YJ, Jang HG, et al. 2005. Influences of cultural medium component on the production of poly(γ-glutamic acid) by Bacillus sp. RKY3. Biotechnol. Bioprocess Eng. 10: 289-295.
    CrossRef
  19. Chen X, Chen S, Sun M, Yu Z. 2005. Medium optimization by response surface methodology for poly-γ-glutamic acid production using dairy manure as the basis of a solid substrate. Appl. Microbiol. Biotechnol. 69: 390-396.
    Pubmed CrossRef
  20. Bajaj B, Lele SS, Singhal RS. 2009. A statistical approach to optimization of fermentative production of poly(γ-glutamic acid) from Bacillus licheniformis NCIM 2324. Bioresour. Technol. 100: 826-832.
    Pubmed CrossRef
  21. Reddy LVA, Wee YJ, Yun JS, Ryu HW. 2008. Optimization of alkaline protease production by batch culture of Bacillus sp. RKY3 through Plackett-Burman and response surface methodological approaches. Bioresour. Technol. 99: 2242-2249.
    Pubmed CrossRef
  22. Shi F, Xu Z, Cen P. 2006. Efficient production of poly-γ-glutamic acid by Bacillus subtilis ZJU-7. Appl. Biochem. Biotechnol. 133: 271-281.
    CrossRef
  23. Du G, Yang G, Qu Y, Chen J, Lun S. 2005. Effects of glycerol on the production of poly(γ-glutamic acid) by Bacillus licheniformis. Process Biochem. 40: 2143-2147.
    CrossRef
  24. Goto A, Kunioka M. 1992. Biosynthesis and hydrolysis of poly-(γ-glutamic acid) from Bacillus subtilis IFO3335. Biosci. Biotechnol. Biochem. 56: 1031-1035.
  25. Anju AJ, Binod P, Pandey A. 2017. Production and characterization of microbial poly-γ-glutamic acid from renewable resources. Indian J. Exp. Biol. 55: 405-410.
  26. Ashiuchi M, Tani K, Soda K, Misono H. 1998. Properties of glutamate racemase from Bacillus subtilis IFO 3336 producing poly-γ-glutamate. J. Biochem. 123: 1156-1163.
    Pubmed CrossRef
  27. Peng Y, Jiang B, Zhang T, Mu W, Miao M, Hua Y. 2015. High-level production of poly(γ-glutamic acid) by a newly isolated glutamate-independent strain, Bacillus methylotrophicus. Process Biochem. 50: 329-335.
    CrossRef
  28. Tork SE, Aly MM, Alakilli SY, Al-Seeni MN. 2015. Purification and characterization of gamma poly glutamic acid from newly Bacillus licheniformis NRC20. Int. J. Biol. Macromol. 74: 382-391.
    Pubmed CrossRef
  29. Bajaj IB, Singhal RS. 2009. Enhanced production of poly (γ-glutamic acid) from Bacillus licheniformis NCIM 2324 by using metabolic precursors. Appl. Biochem. Biotechnol. 159: 133-141.
    Pubmed CrossRef
  30. Soliman NA, Berekaa MM, Abdel-Fattah YR. 2005. Polyglutamic acid (PGA) production by Bacillus sp. SAB-26: application of Plackett-Burman experimental design to evaluate culture requirements. Appl. Microbiol. Biotechnol. 69: 259-267.
    Pubmed CrossRef
  31. Cao M, Geng W, Liu L, Song C, Xie H, Guo W, et al. 2011. Glutamic acid independent production of poly-γ-glutamic acid by Bacillus amyloliquefaciens LL3 and cloning of pgsBCA genes. Bioresour. Technol. 102: 4251-4257.
    Pubmed CrossRef
  32. Feng J, Shi Q, Zhou G, Wang L, Chen A, Xie X, et al. 2017. Improved production of poly-γ-glutamic acid with low molecular weight under high ferric ion concentration stress in Bacillus licheniformis ATCC 9945a. Process Biochem. 56: 30-36.
    CrossRef
  33. Cai D, Hu S, Chen Y, Liu L, Yang S, Ma X, et al. 2018. Enhanced production of poly-γ-glutamic acid by overexpression of the global anaerobic regulator Fnr in Bacillus licheniformis WX-02. Appl. Biochem. Biotechnol. 185: 959-970.
    Pubmed CrossRef
  34. Reddy LV, Kim YM, Yun JS, Ryu HW, Wee YJ. 2016. L-Lactic acid production by combined utilization of agricultural bioresources as renewable and economical substrates through batch and repeated-batch fermentation of Enterococcus faecalis RKY1. Bioresour. Technol. 209: 187-194.
    Pubmed CrossRef

Related articles in JMB

More Related Articles

Article

Research article

J. Microbiol. Biotechnol. 2019; 29(7): 1061-1070

Published online July 28, 2019 https://doi.org/10.4014/jmb.1904.04013

Copyright © The Korean Society for Microbiology and Biotechnology.

Optimized Production of Poly(γ-Glutamic acid) By Bacillus sp. FBL-2 through Response Surface Methodology Using Central Composite Design

Ju-Hee Min1+, Lebaka Veeranjaneya Reddy 1, 2+, Charalampopoulos Dimitris 3, Young-Min Kim 4* and Young-Jung Wee 1*

1Department of Food Science and Technology, Yeungnam University, Gyeongsan 38541, Republic of Korea
2Department of Microbiology, Yogi Vemana University, Kadapa (A.P.) 516 003, India 3Department of Food and Nutritional Sciences, University of Reading, Whiteknights, PO Box 226, Reading RG6 6AP, UK
4Department of Food Science and Technology, Chonnam National University, Gwangju 61186, Republic of Korea

Correspondence to: Young-Min Kim   u9897854@jnu.ac.kr
Young-Jung Wee   yjwee@ynu.ac.kr

Received: April 9, 2019; Accepted: July 2, 2019

Abstract

In the present study, the optimization of poly(γ-glutamic acid) (γ-PGA) production by Bacillus sp. FBL-2 was studied using a statistical approach. One-factor-at-a-time method was used to investigate the effect of carbon sources and nitrogen sources on γ-PGA production and was utilized to select the most significant nutrients affecting the yield of γ-PGA. After identifying effective nutrients, response surface methodology with central composite design (CCD) was used to obtain a mathematical model to identify the optimum concentrations of the key nutrients (sucrose, L-glutamic acid, yeast extract, and citric acid) for improvement of γ-PGA production. The optimum amount of significant medium components appeared to be sucrose 51.73 g/l, L-glutamic acid 105.30 g/l, yeast extract 13.25 g/l, and citric acid 10.04 g/l. The optimized medium was validated experimentally, and γ-PGA production increased significantly from 3.59 g/l (0.33 g/l/h) to 44.04 g/l (3.67 g/l/h) when strain FBL-2 was cultivated under the optimal medium developed by the statistical approach, as compared to non-optimized medium.

Keywords: Poly(&gamma,-glutamic acid), L-glutamic acid, Bacillus sp. FBL-2, optimization, response surface methodology

Introduction

Poly(γ-glutamic acid) (γ-PGA) is an interesting biodegradable homopolyamide that is composed of D- and L-glutamic acid residues [1]. Although there are two types of PGA (α and γ forms), it is easier to produce γ-PGA than a-PGA by bacterial fermentation using Bacillus sp. [2]. γ-PGA is resistant to proteases as glutamate is usually polymerized into γ-PGA via the γ-amide linkages inside the bacterial cell [3]. The γ-PGA properties like water solubility, non-toxicity, edibility, thickening capacity, metal binding, good absorbability, and biodegradability provide for its usage as a drug carrier [4-6], an antifreeze agent, a food thickener [7-9], a flocculating agent for environmental protection, and as a humectant in cosmetics [10, 11]. All the above-mentioned properties and applications of γ-PGA are attracting present-day investigators to study high-yield production strategies.

γ-PGA can be produced through four different chemical and biological methods: chemical synthesis, peptide synthesis, biotransformation, and microbial fermentation [12]. However, γ-PGA production through microbial fermentation is considered economical and it has some advantages such as inexpensive raw materials, minimal environmental pollution, high natural product purity, and mild reaction conditions when compared with other methods. Ivanovics and Bruckner first discovered γ-PGA from a capsule of Bacillus anthracis [13]. Afterwards it was identified in archaea, bacteria, and eukaryotes. Apart from all other organisms and genera, Bacillus is very popular for use in γ-PGA production through fermentation. Bacillus strains can be classified into two major groups based on the requirement of L-glutamic acid as a carbon and nitrogen source for γ-PGA production and cell growth. L-Glutamate-dependent strains (such as B. licheniformis ATCC 9945a, B. subtilis IFO3335, B. subtilis F-2-01, Bacillus sp. RKY3) are known to produce high levels of γ-PGA by the addition of L-glutamate into the medium [14-16]. On the other hand, some other strains such as B. subtilis 5E, B. subtilis TAM-4, B. subtilis C10, B. licheiformis A35, and Bacillus sp. SAB-26 could produce γ-PGA through a de novo γ-PGA synthesis pathway [13]. γ-PGA is synthesized in a ribosome-independent manner, and then protein synthesis inhibitors like chloram-phenicol have no inhibition effect on its production. Depending on the strains, γ-PGA is produced in different molecular weights ranging from 100 to 10,000 kDa [1].

Although the microbial production of γ-PGA is well established, the production cost still remains high because of the cost of both raw materials and the recovery process. Generally, in the fermentation process, the substrate cost is very high, i.e. nearly 60% of the total production cost. Therefore, it is essential to focus on the development of a cost-effective alternative medium. It requires selection of carbon source, nitrogen source, and inorganic salts. Recently, many research groups have investigated the optimization of growth conditions to increase yield, manipulation of enantiomeric composition, and alteration of the molecular mass of the γ-PGA [17-20]. Medium components and fermentation conditions can be manipulated by conventional or statistical methods. The conventional method is a very tedious and time-consuming process where we can only study one variable at a time while keeping others at a fixed level. However, advanced statistical methods provide possibility of studying many variables at one time and of studying the interactions among the medium and fermentation factors, and it is also fast and reliable. This in turn reduces the total number of experiments and saves the cost of the nutrients [20, 21]. Therefore, the aim of the present research is enhancement of γ-PGA production through the optimization of medium components by central composite design (CCD) for response surface methodology (RSM) using a newly isolated Bacillus sp. FBL-2.

Materials and Methods

Bacterial Strain and Culture Medium

Bacillus sp. FBL-2 KCTC 12962BP isolated from cheonggukjang, a fermented soybean paste, was utilized for γ-PGA production. The 16S rRNA gene sequence of strain FBL-2 was submitted to the GenBank database (https://www.ncbi.nlm.nih.gov/genbank), under accession number LY513821. To preserve the culture, 50% (v/v) glycerol as a cryoprotectant was added to the culture and then kept at -70°C in a deep freezer until usage. The stock culture was activated by inoculation into the culture medium, followed by cultivation at 37°C on a shaking incubator (Vision Scientific Co., Korea) at 200 rpm.

The medium for growth and maintenance consisted of glucose 10.0 g/l, yeast extract 3.0 g/l, L-glutamic acid 20.0 g/l, KH2PO4 1.0 g/l, and MgSO4•7H2O 1.0 g/l (pH 7.0). The production medium was composed of glucose 10.0 g/l, yeast extract 4.0 g/l, L-glutamic acid 30.0 g/l, KH2PO41.0 g/l, and MgSO4•7H2O 1.0 g/l (pH 7.0). The composition and amount of production medium used were different depending on the experimental conditions. A detailed experimental condition of the production medium was given later.

Fermentation

The bacterial strain was cultured at 37°C for 24 h in the shaking incubator (Vision Scientific Co.) at 200 rpm by inoculating 2 ml of the stock culture into 100 ml growth medium in a 250-ml Erlenmeyer flask. The fermentation for γ-PGA production was carried out at 37°C for 12 h in the shaking incubator at 200 rpm by inoculating 3.0% (v/v) growth culture into 100 ml production medium in a 250-ml Erlenmeyer flask.

To investigate the effect of carbon sources on γ-PGA production by Bacillus sp. FBL-2, the medium components except glucose in the production medium were kept the same as above. The carbon source, including glucose, lactose, fructose, sucrose, galactose, maltose, glycerol, and xylose was added to a 250 ml-Erlenmeyer flask at a concentration of 10 g/l, and the cells were inoculated and aerobically cultivated at 37°C for 12 h in the rotary shaker at 200 rpm. To investigate the effect of nitrogen sources on γ-PGA production by Bacillus sp. FBL-2, the medium components except yeast extract in the production medium were kept the same as above. The nitrogen source, including yeast extract, beef extract, malt extract, tryptone, peptone, urea, ammonium sulfate, ammonium chloride, and corn steep liquor was added to a 250 ml-Erlenmeyer flask at a concentration of 4 g/l, and the cells were inoculated and aerobically cultured at 37°C for 12 h in the rotary shaker at 200 rpm.

Statistical Experimental Design for Response Surface Methodology

Reaction surface methodology (RSM) was carried out through the central composite design (CCD) to investigate which combination of key interactions and independent variables of γ-PGA production could lead to the maximum response value. RSM is a combination of statistical methods for selecting the optimum experimental conditions that require the minimum number of experiments.

xi=XiX0ΔXi

The experimental variables were coded by using the above equation, where Xi is the actual value of the independent variable, X0 is the independent variable value at the center point, ΔXi is the step change value, and xi is the coded value of each independent variable.

In this study, the concentration of KH2PO4 and MgSO4•7H2O in the production medium was kept at 1.0 g/l, and four important components such as sucrose, L-glutamic acid, yeast extract, and citric acid were used as the independent variables for the designed sets of experiment. As shown in Table 1, to investigate the nature of the response surface in the optimum region, a 24 factorial CCD with eight axial points and six replicates of center points was used at five levels, resulting in the total number of 30 experiments.

Table 1 . Central composite design matrix of four independent variables..

Run numberLevels of independent variables

Sucrose (x1, g/l)L-Glutamic acid (x2, g/l)Yeast extract (x3, g/l)Citric acid (x4, g/l)
130751015
270751015
3301251015
4701251015
530752015
670752015
7301252015
8701252015
930751025
1070751025
11301251025
12701251025
1330752025
1470752025
15301252025
16701252025
17901001520
18101001520
1950501520
20501501520
2150100520
22501002520
23501001510
24501001530
25501001520
26501001520
27501001520
28501001520
29501001520
30501001520


To optimize the production of γ-PGA, the following second-order polynomial equation was used for statistical analysis.

y=b0+Σbixi+Σbijxixj+Σbiixi2

where y is a predicted value, b0 is a constant, bi and bii, and bij are first-order coefficients, second-order coefficients, and interaction coefficients, respectively. xi is the independent variable of i, xixj is the interaction between independent variables, and xi2 is the second order coefficient. The quality of fit of the model equation was described by the coefficient of determination, R2, and the significance of statistics was determined by an F-test. The significance of the regression coefficients was investigated by a t-test. The computer software used was Design-Expert, version 9.0.0 by Stat-Ease, Inc. (USA).

Analytical Methods

The cell growth was monitored by measurement of optical density using a UV-1600 spectrophotometer (Shimadzu Co., Tokyo, Japan) at 660 nm, which was then converted to dry cell weight (DCW, g/l) based on the liner relation of DCW and optical density. The viscosity of the culture broth containing γ-PGA was measured by DV2T digital rheometer equipped with a spindle CP-42 (Brookfield, Middleboro, MA, USA) at 25°C and 10 rpm for 30 sec. The measured viscosity was corrected by silicon oil standards (44.8 cP and 496 cP at 25°C).

γ-PGA was determined by alcohol precipitation according to the modified method reported by Kunioka and Goto [15]. The fermentation broth was diluted and centrifuged at 32,000 ×g for 30 min. The resulting supernatant was poured into 4 volumes of cold ethanol. The precipitate was collected and washed with ethanol, then dissolved and dialyzed against deionized water overnight. The dialyzed solution was centrifuged and the supernatant was lyophilized to prepare pure γ-PGA.

Results and Discussion

The eight different carbon sources and nine nitrogen sources were selected for screening and evaluating their potentiality in producing high quantity γ-PGA using the shake flask culture method. Among the various carbon sources evaluated, sucrose showed the highest levels of production of γ-PGA and DCW followed by glucose (Table 2). These results are in accordance with Shi et al. [22] where they examined 7 different carbon sources and found sucrose as the best with similar γ-PGA yield and DCW.

Table 2 . Effect of carbon sources on γ-PGA fermentation by Bacillus sp. FBL-2..

Carbon sources (10.0 g/l)γ-PGA (g/l)Dry cell weight (g/l)Viscosity (cP)Productivity (g/l/h)
None0.460.991.700.038
Lactose0.631.042.330.053
Fructose3.631.4917.130.303
Sucrose4.601.8120.940.383
Galactose1.251.094.450.104
Maltose2.861.1712.790.238
Glucose3.961.7419.110.330
Glycerol0.860.633.910.072
Xylose0.630.862.140.053


However, some of the previous reports concluded that citric acid and glycerol were better carbon sources for the production of γ-PGA than glucose or sucrose [20, 23, 24]. In our present work, the results showed that sucrose was the best carbon source for γ-PGA accumulation and cell growth, which suggests that the metabolic pathways in Bacillus sp. FBL-2 might be different from other glutamic acid-dependent strains for γ-PGA production. In general, glucose is the preferable carbon source for the production of γ-PGA which mostly utilizes the TCA cycle. It can be also concluded that Bacillus sp. FBL-2 utilizes the general TCA cycle for γ-PGA production after hydrolyzing the sucrose to glucose and fructose. Anju et al. [25] studied the utilization of lignocellulosic renewable resources for the production of γ-PGA and reported that the highest γ-PGA yield was from rice straw hydrolyzates, where glucose was the main carbon source.

In nitrogen sources investigated, yeast extract showed the highest production of γ-PGA and peptone resulted in the highest cell growth (Table 3). The results indicated that inorganic nitrogen sources such as ammonium sulfate and ammonium chloride could not support γ-PGA synthesis and cell growth. Shi et al. [22] have screened 7 different nitrogen sources and reported that incorporation of organic nitrogen sources increased the yields of γ-PGA and no inorganic nitrogen sources had a significant effect. At the same time, many of the researchers used inorganic nitrogen sources for γ-PGA production [23, 24]. Goto and Kunioka [24] have observed the enhancement of γ-PGA by the addition of ammonium sulfate (5 g/l) and suggested that the free amino group was necessary for γ-PGA production and was readily available from inorganic nitrogen salts such as ammonium sulfate. According to the above reports, it can be concluded that the selection of nitrogen source is purely dependent on the microbial strain and there is no universal/single suitable source.

Table 3 . Effect of nitrogen sources on γ-PGA fermentation by Bacillus sp. FBL-2..

Nitrogen sources (4.0 g/l)γ-PGA (g/l)Dry cell weight (g/l)Viscosity (cP)Productivity (g/l/h)
None1.630.761.830.136
Beef extract3.981.7217.170.332
Yeast extract4.501.8519.640.375
Malt extract1.740.832.230.145
Tryptone4.391.5119.330.366
Peptone3.831.3323.380.319
Urea0.000.221.230.000
Ammonium sulfate2.630.775.930.219
Ammonium chloride2.290.805.670.191
Corn steep liquor1.670.823.290.139


Based on the primary screening process, sucrose (x1), L-glutamic acid (x2), yeast extract (x3), and citric acid (x4) were selected as the independent variables for further optimization by RSM. A central composite factorial design of 30 experiments was conducted to examine the combined effect of these medium components on γ-PGA production. The P-values were used as the tool to check the significance of each variable, which, in turn, are necessary to understand the pattern of the mutual interactions between the selected variables. The ANOVA (analysis of variance) result of the optimization study indicated that the model terms, x1, x2, x 4, x2 x3, x2x4, x3x4, x12, and x22, were significant (p < 0.05) (Table 4). The lower the P-value is, the more significant the corresponding variable. The variables and the corresponding P-values suggest that among the variables tested in the present study, sucrose (x1), L-glutamic acid (x2), and citric acid (x4) independently show their significance. The interaction effects between L-glutamic acid (x2) and yeast extract (x3), L-glutamic acid (x2) and citric acid (x4), and yeast extract (x3) and citric acid (x4) also showed good significance. Other interactions were found to be insignificant. The model F-value was 122.44, and the F-value for lack of fit was 5.52. The high F-value and non-significant lack of fit indicate that the model is a good fit. The p-values for the model (<0.0001) and for lack of fit (0.0389) suggested that the obtained experimental data was a good fit with the model. The regression equation coefficients were calculated and the data were fitted to a second-order polynomial equation. The response, γ-PGA production (y) by Bacillus sp. FBL-2, can be expressed in terms of the following regression equation:

Table 4 . Analysis of variance (ANOVA) for the response surface quadratic model..

SourceSum of squaresDegree of freedomMean squareF-valuep-value Prob. > F
Model2,233.0814159.51122.44< 0.0001
x1285.941285.94219.49< 0.0001
x269.56169.5653.4< 0.0001
x38.3218.326.390.0232
x4779.531779.53598.38< 0.0001
x1x20.6110.610.470.5034
x1x31.4511.451.110.3088
x1x40.310.30.230.6399
x2x317.98117.9813.80.0021
x2x4330.421330.42253.64< 0.0001
x3x458.26158.2644.72< 0.0001
x12382.781382.78293.83< 0.0001
x22326.491326.49250.62< 0.0001
x328.9718.976.890.0191
x428.0518.056.180.0252
Residual19.54151.3
Lack of fit17.87101.795.360.0389
Pure error1.6750.33
Corrected total2,252.6229

R2= 0.9913; adjusted R2= 0.9832; adequately precise = 43.877; CV = 4.94%..


y=28.87+3.45x1+1.7x2-0.59x3-5.70x4-0.20x1x2-0.30x1x3-0.14x1x4+1.06x2x3-4.54x2x4-1.91x3x4-3.74x12-3.45x22-0.57x32+0.54x42

The regression equation obtained from the ANOVA showed that the R2 (multiple correlation coefficient) was 0.9913, where R2>0.75 usually indicates fitness of the model. This is an estimate of the fraction of overall variation in the data accounted by the model, and thus the model is capable of explaining 99.13% of the variation in response. The ‘adjusted R2’ is 0.9832, and this result indicates that the model should be acceptable. For a good statistical model, the R2value should be in the range of 0-1.0, and the nearer to 1.0 the value is, the more fit the model is deemed to be. The ‘adequate precision value’ of the present model was 29.50, and this suggests that the model can be used to navigate the design space. The ‘adequate precision value’ is an index of the signal-to-noise ratio, and values of higher than 4 are essential prerequisites for a model to be a good fit. As shown in Fig. 1, the correlation between predicted and observed values showed very good linearity.

Figure 1. Correlation of predicted values versus experimental values of the response surface methodological model developed by central composite design.

In order to determine the optimal levels of each variable for maximum γ-PGA production, 3-dimensional response surface plots were constructed by plotting the response on the z-axis against any two independent variables, while maintaining other variables at their optimal levels. As shown in Figs. 2A-2F, γ-PGA production could not increase further with increasing sucrose or L-glutamic acid and showed very good center point (optimum levels). From the central point of the contour plot, the optimal process parameters were identified. A linear increase in γ-PGA secretion was observed when the citric acid and yeast extract concentrations were increased, and no concomitant decline in γ-PGA production was observed. A similar profile was observed in Fig. 2E with citric acid and L-glutamic acid concentration. The experimental data were fitted into the aforementioned equation, and the optimum levels of each variable were determined to be as follows: sucrose 51.73 g/l, L-glutamic acid 105.30 g/l, yeast extract 13.25 g/l, and citric acid 10.04 g/l.

Figure 2. Response surface and contour plots of poly(γ-glutamic acid) production by Bacillus sp. FBL-2 showing the mutual interactions of independent parameters. Other variables except for two variables in each figure were maintained at center point level. (A), sucrose and L-glutamic acid; (B), sucrose and yeast extract; (C), sucrose and citric acid; (D), L-glutamic acid and yeast extract; (E), L-glutamic acid and citric acid; (F), yeast extract and citric acid.

Fig. 3 shows the profiles of cell growth, γ-PGA production, viscosity, and productivity when Bacillus sp. FBL-2 was cultured under the optimized medium factors, and it was observed the cells were very quickly grown within 12 h of incubation period with maximum DCW of 1.28 g/l. There was no decline in the growth after 12 h, which suggests the optimized medium with sufficient amounts of L-glutamic acid and sucrose. The experiment was conducted for only 12 h and maximum production of 44.04 g/l γ-PGA was obtained with 19.59 cP viscosity. This amount of γ-PGA production under the optimized condition was almost similar to the predicted γ-PGA production (43.52 g/l). γ-PGA production was not considerably increased after 12 h probably because this fermentation was performed under the flask cultivation (data not shown). On the other hand, the maximum γ-PGA production using the non-optimized medium was only 3.96 g/l with 19.11 cP viscosity. From the above results, it can be concluded that a significant improvement (11.12-fold) in the production of γ-PGA was achieved using sucrose and yeast extract as carbon and nitrogen sources within a lesser incubation period (Table 5). The close relationship between the predicted and experimental response values from the investigation demonstrated the validity and acceptability of the statistical model for the optimization of the medium nutrients, allowing for maximum yields.

Table 5 . Poly(γ-glutamic acid) fermentation results of Bacillus sp. FBL-2 cultured by using non-optimized and optimized medium components..

γ-PGA (g/l)DCW (g/l)Viscosity (cP)Productivity (g·l-1·h-1)Fold production
Non-optimized3.96 ± 0.671.74 ± 0.0619.11 ± 1.140.331.0
Optimized44.04 ± 1.541.28 ± 0.0419.59 ± 0.953.6711.12

Figure 3. Profiles of poly(γ-glutamic acid) production, viscosity, cell growth, and volumetric productivity in shake flask experiments using the optimized media. Symbols: - ●-, γ-PGA; - ○-, viscosity; - ■-, DCW (dry cell weight); - □-, productivity.

The present investigation shows that the amount of sucrose and L-glutamic acid in the culture medium are important for high γ-PGA productivity in Bacillus sp. FBL-2. γ-PGA synthesis in B. subtilis is an ATP-consuming bioprocess. The rate of conversion from L-glutamic acid to γ-PGA should be considered very important for reducing the cost of large-scale fermentation-based production of γ-PGA [26]. It is very important to note that the nitrogen source, yeast extract concentration (10 g/l) used in the present study is low when compared to previous studies [22, 27, 28]. It is very essential to use less nitrogen source because it is very expensive and it will be 20-30% of the total medium cost. γ-PGA has recently been produced on a large scale using several bacterial strains. Table 6 shows the comparison of γ-PGA fermentation by Bacillus sp. FBL-2 with other literature reported previously. The highest γ-PGA productivity reported by Shi et al. [22] was 2.267 g/l/h (54.4 g/l γ-PGA) using the B. subtilis ZJU-7 cultivated at 37 C for 24 h. Bajaj et al. [29] reported a maximum γ-PGA production of 35.75 g/l using B. licheniformis NCIM2324. Soliman et al. [30] reported a maximum γ-PGA production of 33.5 g/l using Bacillus sp. SAB-26, and Cao et al. [31] reported a much lower amount of γ-PGA (4.36 g/l). B. licheniformis ATCC 9945a produced 34.93 g/l of γ-PGA but the volumetric productivity was only 0.64 g/l/h [32]. Cai et al. [33] reported 1.11 g/l/h of γ-PGA productivity by flask fermentation of B. licheniformis WX-02. As shown in Table 6, Bacillus sp. FBL-2 could produce a high amount of γ-PGA with the highest volumetric productivity even though the compositions of production medium were simple compared with the other investigations. Therefore, compared with the strains in Table 6, Bacillus sp. FBL-2 has some advantages such as high productivity (3.67 g•l-1•h-1), short incubation period (12 h), and utilization of less nitrogen source (10 g/l). To the best of our knowledge, the γ-PGA productivity reported in the present study is highest in the literature. The findings of this study indicate that media optimization and the use of Bacillus sp. FBL-2 organisms have significant scope for use in the industrial production of γ-PGA. In addition, the results presented here could be efficiently used in the area of γ-PGA production from inexpensive bioresources such as agricultural residues or food wastes [34].

Table 6 . Poly(γ-glutamic acid) fermentation results of Bacillus sp. FBL-2 and other microorganisms reported previously..

StrainsFermentation medium componentsCulture conditionsγ-PGA (g/l)Productivity (g·l-1·h-1)Ref.
B. subtilis ZJU-7Sucrose 60 g/l, tryptone 60 g/l, L-glutamic acid 80 g/l, NaCl 10 g/l500-ml flask, 200 rpm, 37°C, initial pH 7.054.42.26722
B. methylotrophicus SK19.001Glycerol 30 g/l, peptone 50 g/l, sodium citrate 15 g/l250-ml flask, 200 rpm, 30°C, initial pH 7.233.840.94027
B. licheniformis NCIM 2324Glycerol 62.4 g/l, L-glutamic acid 20 g/l, citric acid 15.2 g/l, (NH4)2SO4 8 g/l, K2HPO4 1 g/l, MgSO4•7H2O 0.5 g/l, MnSO4•7H2O 0.05 g/l, CaCl2•2H2O 0.2 g/l250-ml flask, 200 rpm, 37°C, initial pH 6.535.750.33129
B. licheniformis SAB-26Glucose 20 g/l, casein hydrolysate 8 g/l, (NH4)2SO4 2 g/l, K2HPO4 5 g/l, KH2PO4 5 g/l250-ml flask, 200 rpm, 37°C, initial pH 7.033.52.79230
B. licheniformis ATCC 9945aGlycerol 80 g/l, citric acid 12 g/l, L-glutamate acid 20 g/l, NH4Cl 7 g/l, K2HPO4 0.5 g/l, MgSO4•7H2O 0.5 g/l, MnSO4•H2O 0.104 g/l, CaCl2•2H2O 0.15 g/l, FeCl3•6H2O 0.04 g/l300-ml flask, 110 rpm, 37°C, initial pH 6.534.930.36432
B. licheniformis WX-02Glucose 80 g/l, L-glutamic acid 30 g/l, sodium citrate, sodium nitrate 10 g/l, NH4Cl 8 g/l, CaCl2 1 g/l, K2HPO4•6H2O 1 g/l, MgSO4•7H2O 1 g/l, ZnSO4•7H2O 1 g/l, MnSO4•7H2O 0.15 g/l250-ml flask, 180 rpm, 37°C, initial pH 7.239.961.1133
Bacillus sp. FBL-2Sucrose 51.73 g/l, L-glutamic acid 105.23 g/l, yeast extract 13.25 g/l, citric acid 10.04 g/l, K2HPO4 1.0 g/l, MgSO4•7H2O 1.0 g/l250-ml flask, 200 rpm, 37°C, initial pH 7.044.043.67This study


This study was conducted in an attempt to optimize medium composition for maximum γ-PGA production. The eight carbon sources and seven nitrogen sources were screened, and sucrose and yeast extract were selected as carbon and nitrogen sources, respectively, based on the preliminary experiments. In order to optimize low-cost fermentation medium with precursors, such as L-glutamic acid and citric acid for γ-PGA production, RSM study was carried out using the screened variables. The most significant medium components appear to be sucrose 51.73 g/l, L-glutamic acid 105.30 g/l, yeast extract 13.25 g/l, and citric acid 10.04 g/l. The maximum γ-PGA concentration (44.04 g/l) was obtained from this study with a much shorter incubation period (12 h) at 37°C. As a result, the γ-PGA productivity could reach to 3.67 g•l-1•h-1. This represents an 11.12-fold enhancement over the productivity observed with non-optimized medium. Thus, the statistical methods (RSM) may be effective for optimizing bioprocessing conditions for developing low-cost, large-scale methods of producing this important γ-PGA biomaterial in the future.

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03032906). This work was also supported by the 2017 Yeungnam University Research Grant (217A061045).

Conflict of Interest

The authors have no financial conflicts of interest to declare.

Fig 1.

Figure 1.Correlation of predicted values versus experimental values of the response surface methodological model developed by central composite design.
Journal of Microbiology and Biotechnology 2019; 29: 1061-1070https://doi.org/10.4014/jmb.1904.04013

Fig 2.

Figure 2.Response surface and contour plots of poly(γ-glutamic acid) production by Bacillus sp. FBL-2 showing the mutual interactions of independent parameters. Other variables except for two variables in each figure were maintained at center point level. (A), sucrose and L-glutamic acid; (B), sucrose and yeast extract; (C), sucrose and citric acid; (D), L-glutamic acid and yeast extract; (E), L-glutamic acid and citric acid; (F), yeast extract and citric acid.
Journal of Microbiology and Biotechnology 2019; 29: 1061-1070https://doi.org/10.4014/jmb.1904.04013

Fig 3.

Figure 3.Profiles of poly(γ-glutamic acid) production, viscosity, cell growth, and volumetric productivity in shake flask experiments using the optimized media. Symbols: - ●-, γ-PGA; - ○-, viscosity; - ■-, DCW (dry cell weight); - □-, productivity.
Journal of Microbiology and Biotechnology 2019; 29: 1061-1070https://doi.org/10.4014/jmb.1904.04013

Table 1 . Central composite design matrix of four independent variables..

Run numberLevels of independent variables

Sucrose (x1, g/l)L-Glutamic acid (x2, g/l)Yeast extract (x3, g/l)Citric acid (x4, g/l)
130751015
270751015
3301251015
4701251015
530752015
670752015
7301252015
8701252015
930751025
1070751025
11301251025
12701251025
1330752025
1470752025
15301252025
16701252025
17901001520
18101001520
1950501520
20501501520
2150100520
22501002520
23501001510
24501001530
25501001520
26501001520
27501001520
28501001520
29501001520
30501001520

Table 2 . Effect of carbon sources on γ-PGA fermentation by Bacillus sp. FBL-2..

Carbon sources (10.0 g/l)γ-PGA (g/l)Dry cell weight (g/l)Viscosity (cP)Productivity (g/l/h)
None0.460.991.700.038
Lactose0.631.042.330.053
Fructose3.631.4917.130.303
Sucrose4.601.8120.940.383
Galactose1.251.094.450.104
Maltose2.861.1712.790.238
Glucose3.961.7419.110.330
Glycerol0.860.633.910.072
Xylose0.630.862.140.053

Table 3 . Effect of nitrogen sources on γ-PGA fermentation by Bacillus sp. FBL-2..

Nitrogen sources (4.0 g/l)γ-PGA (g/l)Dry cell weight (g/l)Viscosity (cP)Productivity (g/l/h)
None1.630.761.830.136
Beef extract3.981.7217.170.332
Yeast extract4.501.8519.640.375
Malt extract1.740.832.230.145
Tryptone4.391.5119.330.366
Peptone3.831.3323.380.319
Urea0.000.221.230.000
Ammonium sulfate2.630.775.930.219
Ammonium chloride2.290.805.670.191
Corn steep liquor1.670.823.290.139

Table 4 . Analysis of variance (ANOVA) for the response surface quadratic model..

SourceSum of squaresDegree of freedomMean squareF-valuep-value Prob. > F
Model2,233.0814159.51122.44< 0.0001
x1285.941285.94219.49< 0.0001
x269.56169.5653.4< 0.0001
x38.3218.326.390.0232
x4779.531779.53598.38< 0.0001
x1x20.6110.610.470.5034
x1x31.4511.451.110.3088
x1x40.310.30.230.6399
x2x317.98117.9813.80.0021
x2x4330.421330.42253.64< 0.0001
x3x458.26158.2644.72< 0.0001
x12382.781382.78293.83< 0.0001
x22326.491326.49250.62< 0.0001
x328.9718.976.890.0191
x428.0518.056.180.0252
Residual19.54151.3
Lack of fit17.87101.795.360.0389
Pure error1.6750.33
Corrected total2,252.6229

R2= 0.9913; adjusted R2= 0.9832; adequately precise = 43.877; CV = 4.94%..


Table 5 . Poly(γ-glutamic acid) fermentation results of Bacillus sp. FBL-2 cultured by using non-optimized and optimized medium components..

γ-PGA (g/l)DCW (g/l)Viscosity (cP)Productivity (g·l-1·h-1)Fold production
Non-optimized3.96 ± 0.671.74 ± 0.0619.11 ± 1.140.331.0
Optimized44.04 ± 1.541.28 ± 0.0419.59 ± 0.953.6711.12

Table 6 . Poly(γ-glutamic acid) fermentation results of Bacillus sp. FBL-2 and other microorganisms reported previously..

StrainsFermentation medium componentsCulture conditionsγ-PGA (g/l)Productivity (g·l-1·h-1)Ref.
B. subtilis ZJU-7Sucrose 60 g/l, tryptone 60 g/l, L-glutamic acid 80 g/l, NaCl 10 g/l500-ml flask, 200 rpm, 37°C, initial pH 7.054.42.26722
B. methylotrophicus SK19.001Glycerol 30 g/l, peptone 50 g/l, sodium citrate 15 g/l250-ml flask, 200 rpm, 30°C, initial pH 7.233.840.94027
B. licheniformis NCIM 2324Glycerol 62.4 g/l, L-glutamic acid 20 g/l, citric acid 15.2 g/l, (NH4)2SO4 8 g/l, K2HPO4 1 g/l, MgSO4•7H2O 0.5 g/l, MnSO4•7H2O 0.05 g/l, CaCl2•2H2O 0.2 g/l250-ml flask, 200 rpm, 37°C, initial pH 6.535.750.33129
B. licheniformis SAB-26Glucose 20 g/l, casein hydrolysate 8 g/l, (NH4)2SO4 2 g/l, K2HPO4 5 g/l, KH2PO4 5 g/l250-ml flask, 200 rpm, 37°C, initial pH 7.033.52.79230
B. licheniformis ATCC 9945aGlycerol 80 g/l, citric acid 12 g/l, L-glutamate acid 20 g/l, NH4Cl 7 g/l, K2HPO4 0.5 g/l, MgSO4•7H2O 0.5 g/l, MnSO4•H2O 0.104 g/l, CaCl2•2H2O 0.15 g/l, FeCl3•6H2O 0.04 g/l300-ml flask, 110 rpm, 37°C, initial pH 6.534.930.36432
B. licheniformis WX-02Glucose 80 g/l, L-glutamic acid 30 g/l, sodium citrate, sodium nitrate 10 g/l, NH4Cl 8 g/l, CaCl2 1 g/l, K2HPO4•6H2O 1 g/l, MgSO4•7H2O 1 g/l, ZnSO4•7H2O 1 g/l, MnSO4•7H2O 0.15 g/l250-ml flask, 180 rpm, 37°C, initial pH 7.239.961.1133
Bacillus sp. FBL-2Sucrose 51.73 g/l, L-glutamic acid 105.23 g/l, yeast extract 13.25 g/l, citric acid 10.04 g/l, K2HPO4 1.0 g/l, MgSO4•7H2O 1.0 g/l250-ml flask, 200 rpm, 37°C, initial pH 7.044.043.67This study

References

  1. Shih IL, Van YT. 2001. The production of poly(γ-glutamic acid) from microorganisms and its various applications. Bioresour. Technol. 79: 207-225.
    CrossRef
  2. Buescher JM, Margaritis AM. 2007. Microbial biosynthesis of polyglutamic acid biopolymer and applications in the biopharmaceutical, biomedical, and food industries. Crit. Rev. Biotechnol. 27: 1-19.
    Pubmed CrossRef
  3. Akagi T, Baba M, Akashi M. 2007. Preparation of nanoparticles by the self-organization of polymers consisting of hydrophobic and hydrophilic segments: potential applications. Polymer 48: 6729-6747.
    CrossRef
  4. Li C. 2002. Poly(L-glutamic acid)-anticancer drug conjugates. Adv. Drug Deliv. Rev. 54: 695-713.
    Pubmed CrossRef
  5. Matsuo K, Koizumi H, Akashi M, Nakagawa S, Fujita T, Yamamoto A, et al. 2011. Intranasal immunization with poly(γ-glutamic acid) nanoparticles entrapping antigenic proteins can induce potent tumor immunity. J. Control. Release 152: 310-316.
    Pubmed CrossRef
  6. Tanimoto H, Fox T, Eagles J, Satoh H, Nozava H, Okiyama A, et al. 2007. Acute effect of poly(γ-glutamic acid) on calcium absorption in post-menopausal women. J. Am. Coll. Nutr. 26: 645-649.
    Pubmed CrossRef
  7. Shih IL, Van YT, Sau YY. 2003. Antifreeze activities of poly(γ-glutamic acid) produced by Bacillus licheniformis. Biotechnol. Lett. 25: 1709-1712.
  8. Bhat AR, Irorere VU, Bartlett T, Hill D, Kedia G, Morris MR, et al. 2013. Bacillus subtilis natto: a non-toxic source of poly(γ-glutamic acid) that could be used as a cryoprotectant for probiotic bacteria. AMB Express 3: 36.
    Pubmed KoreaMed CrossRef
  9. Lee CY, Kuo MI. 2011. Effect of γ-polyglutamate on the rheological properties and microstructure of tofu. Food Hydrocoll. 25: 1034-1040.
    CrossRef
  10. Zheng H, Gao Z, Yin J, Tang X, Ji X, Huang H. 2012. Harvesting of microalgae by flocculation with poly(γ-glutamic acid). Bioresour. Technol. 112: 212-220.
    Pubmed CrossRef
  11. Wang F, Zhao J, Wei X, Huo F, Li W, Hu Q, Liu H. 2014. Adsorption of rare earths (III) by calcium alginate-poly glutamic acid hybrid gels. J. Chem. Technol. Biotechnol. 89: 969-977.
    CrossRef
  12. Candela T, Fouet A. 2006. Poly-gamma-glutamate in bacteria. Mol. Microbiol. 60: 1091-1098.
    Pubmed CrossRef
  13. Ashiuchi M. 2010. Occurrence and biosynthetic mechanism of poly-gamma-glutamic acid, pp. 77-93. In: Hamano Y (ed), Amino-Acid Homopolymers Occurring in Nature. Springer, New York, N.Y.
    CrossRef
  14. Birrer GA, Cromwick AM, Gross RA. 1994. γ-Poly(glutamic acid) formation by Bacillus licheniformis 9945A: physiological and biochemical studies. Int. J. Biol. Macromol. 16: 265-275.
    CrossRef
  15. Kunioka M, Goto A. 1994. Biosynthesis of poly(γ-glutamic acid) from L-glutamic acid, citric acid, and ammonium sulfate in Bacillus subtilis IFO3335. Appl. Microbiol. Biotechnol. 40: 867-872.
    CrossRef
  16. Jeong JH, Kim JN, Wee YJ, Ryu HW. 2010. The statistically optimized production of poly(γ-giutamic acid) by batch fermentation of a newly isolated Bacillus subtilis RKY3. Bioresour. Technol. 101: 4533-4539.
    Pubmed CrossRef
  17. Cromwick AM, Birrer GA, Gross RA. 1996. Effects of pH and aeration on γ-poly(glutamic acid) formation by Bacillus licheniformis in controlled batch fermentor cultures. Biotechnol. Bioeng. 50: 222-227.
    CrossRef
  18. Jung DH, Jung S, Yun JS, Kim JN, Wee YJ, Jang HG, et al. 2005. Influences of cultural medium component on the production of poly(γ-glutamic acid) by Bacillus sp. RKY3. Biotechnol. Bioprocess Eng. 10: 289-295.
    CrossRef
  19. Chen X, Chen S, Sun M, Yu Z. 2005. Medium optimization by response surface methodology for poly-γ-glutamic acid production using dairy manure as the basis of a solid substrate. Appl. Microbiol. Biotechnol. 69: 390-396.
    Pubmed CrossRef
  20. Bajaj B, Lele SS, Singhal RS. 2009. A statistical approach to optimization of fermentative production of poly(γ-glutamic acid) from Bacillus licheniformis NCIM 2324. Bioresour. Technol. 100: 826-832.
    Pubmed CrossRef
  21. Reddy LVA, Wee YJ, Yun JS, Ryu HW. 2008. Optimization of alkaline protease production by batch culture of Bacillus sp. RKY3 through Plackett-Burman and response surface methodological approaches. Bioresour. Technol. 99: 2242-2249.
    Pubmed CrossRef
  22. Shi F, Xu Z, Cen P. 2006. Efficient production of poly-γ-glutamic acid by Bacillus subtilis ZJU-7. Appl. Biochem. Biotechnol. 133: 271-281.
    CrossRef
  23. Du G, Yang G, Qu Y, Chen J, Lun S. 2005. Effects of glycerol on the production of poly(γ-glutamic acid) by Bacillus licheniformis. Process Biochem. 40: 2143-2147.
    CrossRef
  24. Goto A, Kunioka M. 1992. Biosynthesis and hydrolysis of poly-(γ-glutamic acid) from Bacillus subtilis IFO3335. Biosci. Biotechnol. Biochem. 56: 1031-1035.
  25. Anju AJ, Binod P, Pandey A. 2017. Production and characterization of microbial poly-γ-glutamic acid from renewable resources. Indian J. Exp. Biol. 55: 405-410.
  26. Ashiuchi M, Tani K, Soda K, Misono H. 1998. Properties of glutamate racemase from Bacillus subtilis IFO 3336 producing poly-γ-glutamate. J. Biochem. 123: 1156-1163.
    Pubmed CrossRef
  27. Peng Y, Jiang B, Zhang T, Mu W, Miao M, Hua Y. 2015. High-level production of poly(γ-glutamic acid) by a newly isolated glutamate-independent strain, Bacillus methylotrophicus. Process Biochem. 50: 329-335.
    CrossRef
  28. Tork SE, Aly MM, Alakilli SY, Al-Seeni MN. 2015. Purification and characterization of gamma poly glutamic acid from newly Bacillus licheniformis NRC20. Int. J. Biol. Macromol. 74: 382-391.
    Pubmed CrossRef
  29. Bajaj IB, Singhal RS. 2009. Enhanced production of poly (γ-glutamic acid) from Bacillus licheniformis NCIM 2324 by using metabolic precursors. Appl. Biochem. Biotechnol. 159: 133-141.
    Pubmed CrossRef
  30. Soliman NA, Berekaa MM, Abdel-Fattah YR. 2005. Polyglutamic acid (PGA) production by Bacillus sp. SAB-26: application of Plackett-Burman experimental design to evaluate culture requirements. Appl. Microbiol. Biotechnol. 69: 259-267.
    Pubmed CrossRef
  31. Cao M, Geng W, Liu L, Song C, Xie H, Guo W, et al. 2011. Glutamic acid independent production of poly-γ-glutamic acid by Bacillus amyloliquefaciens LL3 and cloning of pgsBCA genes. Bioresour. Technol. 102: 4251-4257.
    Pubmed CrossRef
  32. Feng J, Shi Q, Zhou G, Wang L, Chen A, Xie X, et al. 2017. Improved production of poly-γ-glutamic acid with low molecular weight under high ferric ion concentration stress in Bacillus licheniformis ATCC 9945a. Process Biochem. 56: 30-36.
    CrossRef
  33. Cai D, Hu S, Chen Y, Liu L, Yang S, Ma X, et al. 2018. Enhanced production of poly-γ-glutamic acid by overexpression of the global anaerobic regulator Fnr in Bacillus licheniformis WX-02. Appl. Biochem. Biotechnol. 185: 959-970.
    Pubmed CrossRef
  34. Reddy LV, Kim YM, Yun JS, Ryu HW, Wee YJ. 2016. L-Lactic acid production by combined utilization of agricultural bioresources as renewable and economical substrates through batch and repeated-batch fermentation of Enterococcus faecalis RKY1. Bioresour. Technol. 209: 187-194.
    Pubmed CrossRef