Enhanced poly-γ-glutamic acid production by a newly isolated Bacillus tequilensis BL01

Enhanced poly-γ-glutamic acid production by a newly isolated Bacillus tequilensis BL01 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced poly-γ-glutamic acid production by a newly isolated Bacillus tequilensis BL01 Dexin Wang, Dasen Zhou, Jiaqi Gao, Wenqin Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1695166/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Poly γ-glutamic acid (γ-PGA) is a promising biopolymer for various applications. In this study, we isolated a novel γ-PGA-producing strain, Bacillus tequilensis BL01. The one-factor-at-a-time method was used to investigate the influence of carbon and nitrogen sources and temperature on γ-PGA production. The optimal carbon and nitrogen sources were sucrose and (NH 4 ) 2 SO 4 , respectively. The optimal temperature for γ-PGA production was determined to be 37°C. Response surface methodology was used to determine the optimum medium components: 68.9 g/L sucrose, 47.7 g/L monosodium glutamate, and 2.5 g/L K 2 HPO 4 . γ-PGA titer increased significantly from 8.6 g/L to 21.1 g/L when strain BL01 was cultivated in the optimized medium. Furthermore, γ-PGA titer reached 54.7 g/L with a productivity of 1.37 g/L/h and a yield of 2.47 g of γ-PGA/g of L-glutamic acid with the optimized medium in fed-batch fermentation. It should be noted that the γ-PGA yield in this study was the highest of all reported studies, indicating a great potential for the industrial production of γ-PGA. poly-γ-glutamic acid Bacillus tequilensis BL01 response surface methodology fed-batch fermentation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Poly-γ-glutamic acid (γ-PGA), one of the most promising biopolymers, consists of D- and L-glutamic acid units linked by amide bonds between α-amino and γ-carboxylic acid groups (Luo et al. 2016 ). γ-PGA shows great potential for application in food (Yu et al. 2018 ), medicine (Khalil et al. 2017 ), cosmetics (Wang et al. 2019 ), wastewater treatment (Peng et al. 2020 ), and agriculture (Chen et al. 2021 ) due to its numerous properties, including water solubility, biodegradability, nontoxicity, and biocompatibility (Ajayeoba et al. 2019 ; Wang et al. 2022 ). Recently, γ-PGA has shown more and more promising applications. γ-PGA nanocomposite hydrogels are potentially applied for injectable tissue engineering hydrogels, tissue adhesives, and hemostatic materials (Kim et al. 2021 ). The SiOx electrode using the γ-PGA cross-linked by epichlorohydrin as the binder achieves high reversible capacity and outstanding cycle stability (Xiao et al. 2022 ). In agriculture, exogenous application of γ-PGA could significantly enhance the drought resistance of plants by improving photosynthesis, root development and enriching plant growth promoting bacteria (Ma et al. 2022 ). γ-PGA is primarily synthesized by various Bacillus species, including B. subtilis (Wu et al. 2010 ; Zhu et al. 2014 ), B. siamensis (Wang et al. 2020a ; Wang et al. 2020 b) licheniformis (Cai et al. 2018 ; Kongklom et al. 2015 ), B. methylotrophicus (Peng et al. 2015 ), B. amyloliquefaciens (Feng et al. 2014 ; Feng et al. 2015 )d velezensis (Liu et al. 2022 ) and others. Depending on the substrate used, strains can be divided into two types. The first type is glutamic acid-dependent strains that require L-glutamic acid as a direct precursor (Wu et al. 2010 ; Zhu et al. 2014 ; Wang et al. 2020a ; Wang et al. 2020 b). Supplying exogenous glutamic acid significantly increases γ-PGA production, but also increases production costs. The other type is glutamic acid-independent strains, which can synthesize γ-PGA from carbon sources such as glucose de novo (Cai et al. 2018 ; Kongklom et al. 2015 ; Peng et al. 2015 ; Feng et al. 2015 ). Glutamic acid-independent strains have attracted widespread attention due to their low production cost. Metabolic engineering strategies have been employed to reduce byproduct synthesis, increase precursor supply (Feng et al. 2014 ), and improve NADPH regeneration (Cai et al. 2017 ). However, low γ-PGA yields and productivity limit their industrial applications. Therefore, it is vital to obtain novel γ-PGA-producing strains with high titers, yields, and productivities that will be economically feasible for industrial production. To meet the growing demand for economical γ-PGA, there is an increasing number of studies focused on optimizing fermentation conditions for γ-PGA-producing strains. Nutrients and culture parameters are crucial for producing γ-PGA because they directly affect the titer, yield, and productivity of γ-PGA. Glucose, glycerol, and sucrose are the most successful carbon sources for γ-PGA production (Wu et al. 2010 ; Wang et al. 2020a ; Cai et al. 2018 ). A variety of inexpensive biomasses have been tested to reduce fermentation costs, including cane molasses (Wang et al. 2020 b), agro-industrial wastes (Tang et al. 2015 ), corncob fibers, and hydrolysates (Zhu et al. 2014 ). For B. methylotrophicus , yeast extract is an excellent nitrogen source for cell growth and γ-PGA production (Peng et al. 2015 ), but its high cost hinders its industrial application. Fortunately, several strains prefer to use inorganic nitrogen sources such as (NH 4 ) 2 SO 4 or NH 4 Cl (Cai et al. 2018 ; Kongklom et al. 2015 ). In addition, inorganic salts such as CaCl 2 (Huang et al. 2011 a), FeCl 3 (Feng et al. 2017 ), KCl (Zeng et al. 2016 ), and MnSO 4 (Kedia et al. 2010 ) can also affect the production, stereochemical composition, and quality of γ-PGA. Culture parameters such as temperature, pH, and dissolved oxygen also greatly affect γ-PGA production (Silva et al. 2014 ). Moreover, statistical methods have been employed to simultaneously study multiple variables and their interactions to optimize γ-PGA fermentation (Min et al. 2019 ; Xavier et al. 2020 ). This study attempted to screen an economical, high-yield γ-PGA-producing strain. Culture components and parameters affecting γ-PGA production were optimized using statistical predictions. Afterward, fermentation scale-up was performed in a 5 L fermenter under optimized nutrition and culture parameters. To the best of our knowledge, this study reports the highest γ-PGA yield obtained from the newly isolated strain B . tequilensis BL01. 2. Materials And Methods 2.1 Strain and medium The Bacillus tequilensis BL01 strain was isolated from soybean and stored at the China General Microbiological Culture Collection Center (CGMCC23661). γ-PGA-producing strains were preliminarily screened and cultured in a basal medium containing 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 20 g/L glucose, and 10 g/L monosodium glutamate. Agar (20 g/L) was added to basal medium agar plates. A strain with high γ-PGA yield was screened and the influencing factors were analyzed in the fermentation medium, which contained 30 g/L glucose, 20 g/L monosodium glutamate, 5 g/L NH 4 Cl, 0.5 g/L K 2 HPO 4 , 0.5 g/L MgSO 4 ·7H 2 O, 0.04 g/L FeCl 3 ·6H 2 O, 0.104 g/L MnSO 4 ·H 2 O, 0.15 g/L CaCl 2 , and 0.5 g/L NaCl. 2.2 Culture methods for γ-PGA production For shake-flask fermentation, a single colony from the agar plate was inoculated into a 250 mL flask containing 50 mL liquid basal medium and cultured at 37°C for 12 h at 200 rpm. Afterwards, 1% (v/v) of the precultures (OD 600 = 5.0 ± 0.1) were inoculated into 250 mL flasks containing 50 mL of fresh fermentation medium and cultivated at 37°C for 48 h at 200 rpm. Cell growth and γ-PGA yield were measured every 6 h. For fed-batch fermentation, cells were precultured in 300 mL basal medium at 37°C for 12 h at 200 rpm and then inoculated into a 5 L fermenter (BIOTECH-5JG; Shanghai Baoxing Biology Equipment Engineering Co., Ltd., China) containing 2.7 L optimized fermentation medium. The 5 L fermenter was operated at an aeration rate of 10 mL/min, and the dissolved oxygen (DO) was kept above 5% by adjusting the agitation rate to 400–700 rpm. The pH was not controlled to study the changes. When the sugar level dropped below 10 g/L, 150 mL of feed medium containing 700 g/L sucrose was fed into the fermenter. 2.3 Screening of a high γ-PGA- producing strain and phylogenetic analysis γ-PGA-producing strains were isolated from soybean purchased from farmers’ markets in Tianjin, China, and the screening method was performed as previously described (Wang et al. 2020a ). Soybeans (100 g) were boiled for 10 min in 500 mL of sterile water in a water bath to remove any non-spore strains. The solution was diluted to 10 − 1 , 10 − 2 , 10 − 3 , and 10 − 4 times after cooling, and 200 µL aliquots were placed on basal medium agar plates and incubated at 37°C for 24–48 h. Colonies with high viscosity and mucosity were selected and cultured in flasks to examine their γ-PGA production abilities. The strain with the highest γ-PGA production was subjected to 16S rDNA sequence analysis. The 16S rDNA gene sequence was amplified using universal 27F and 1492R primers. Sequencing was performed by GENEWIZ Inc. (Suzhou, China). The 16S rDNA gene sequence was compared with that of type strains reported in the EzBioCloud database ( https://www.ezbiocloud.net/ ). The phylogenetic tree was reconstructed using the neighbor-joining method in MEGA 7.0 (Jung et al. 2019 ). 2.4 One factor at a time (OFAT) experimentation design The main nutritional components, including carbon and nitrogen sources and culture parameters, were optimized using the OFAT experimental design. To optimize the carbon sources, 30 g/L (w/v) glucose, sucrose, fructose, glycerol, arabinose, and xylose were added to the fermentation medium. To determine the effect of different nitrogen sources, 5 g/L (w/v) NH 4 Cl, peptone, tryptone, yeast extract, and (NH 4 ) 2 SO 4 were added to the medium. After optimizing the carbon and nitrogen sources, the effect of temperature (28–42°C) on bacterial growth and γ-PGA production was studied. 2.5 Screening of significant effect parameters by Plackett-Burman design (PBD) The Plackett-Burman design (PBD) was employed to identify key fermentation parameters that could significantly influence γ-PGA production by B. tequilensis BL01. Using the PBD, nine independent variables (sucrose, monosodium glutamate, (NH 4 ) 2 SO 4 , K 2 HPO 4 , MgSO 4 ·7H 2 O, FeCl 3 ·6H 2 O, MnSO 4 ·H 2 O, CaCl 2 , and NaCl) were tested at high (+ 1) and low (-1) levels over twelve trials, as shown in Table 1 . The matrix was constructed, and analysis of variance (ANOVA) was performed using Design-Expert 10.0.4 software (Stat-Ease Inc., Minneapolis, MN, USA) (Table 2 ). 2.6 Statistical optimization of factors affecting γ-PGA production by Response surface methodology (RSM) A face-centered central composite design (FCCD) was adopted to identify the optimal levels of the three most significant variables: sucrose ( X 1 ), monosodium glutamate ( X 2 ), and K 2 HPO 4 ( X 4 ). The effect of each variable on γ-PGA production was studied at three different levels (-1, 0, and + 1), and the coded values, actual values, and 30 experimental setups were obtained (Table 3 ). ANOVA was applied to analyze the responses under different combinations (Table 4 ), and a second-order polynomial equation was formulated to analyze γ-PGA production, as follows: Y = β 0 +∑ β i X i +∑ β ij X i X j +∑ β ii X i 2 where Y is the concentration of γ-PGA, β 0 is the intercept term, β i is the linear coefficient, β ij is the quadratic coefficient, β ii is the squared term, and X i and X j are independent variables. 2.7 Analytical methods Cell biomass was determined by measuring the absorbance of the fermentation broth at 600 nm using a spectrophotometer. The concentration of γ-PGA was determined by CTAB-dependent spectrophotometric assay, as described earlier (Wang et al. 2020a ). Sucrose, glucose, and fructose levels were determined using an Agilent 1260 high-performance liquid chromatography (HPLC) system equipped with a refractive index detector (RID) and an Aminex HPX-87P column (300 × 78 mm; Bio–Rad, Hercules, CA, USA). An Aminex HPX-87H column (300 × 78 mm; Bio–Rad, Hercules, CA, USA) was used to analyze fermentation by-products (Wang et al. 2020a ). The γ-PGA molecular weight was measured using gel permeation chromatography (GPC) with an RID detector and an Ultrahydrogel™ linear column (10 µm, 7.8 mm×300 mm, Waters Corporation, USA). The mobile phase, flow rate, and injection volume were 0.1 N NaNO 3 , 0.5 mL/min, and 20 µL, respectively. Glutamic acid was measured using an Agilent ZORBAX Eclipse Plus C18 column (5 µm, 4.6 mm×150 mm, Agilent, USA) with a UV detector (338 nm) ( https://www.agilent.com /cs/library/applications/5990-4547EN.pdf). 2.8 Statistical analysis For statistical analysis of cell growth and γ-PGA titers, the dataset from each experiment was treated individually with the Baranyi model fitted to each model. Data from each experiment under all studied conditions were pooled and statistically analyzed using SPSS software (version 11.5), employing a two-factor ANOVA, where the factors were biomass and γ-PGA titer, followed by a Tukey test, with significant differences at p < 0.05. 3. Results And Discussion 3.1 Screening of highly γ-PGA-producing strains To screen for a highly γ-PGA-producing strain, 24 isolates with mucoid colonies were selected from basal medium agar plates. Five strains produced γ-PGA during shake flask fermentation, and their 16S rDNA genes (1425 bp) were amplified and sequenced. BLAST analysis using EzBioCloud ( https://www.ezbiocloud.net/ ) showed that these strains belonged to different species (Table S1). BL01 produced 8.6 ± 0.3 g/L γ-PGA after 24 h of incubation, the γ-PGA titer normalized to the biomass of the individual cultures was the highest compared to other isolates, and BL01 was classified as the species B. tequilensis . Fermented soybean foods such as natto and cheonggukjang contain high levels of γ-PGA (Min et al. 2019 ; Araki et al. 2020 ). Therefore, soybean was selected as the screening material for γ-PGA-producing strains. Previous studies found that B. tequilensis can quickly and stably colonize plants, and has a high propagation rate and strong in proliferative capacity (Shultana et al. 2021 ). B. tequilensis has high efficiency and broad-spectrum plant pathogen resistance, has a combined treatment effect on plant diseases such as anthracnose (Kwon et al. 2022 ) and black spot disease (Xu et al. 2021 ), has a high prevention effect, and has good application prospects for the prevention and control of plant diseases (Guerrero-Barajas et al. 2020 ). However, it has never been reported for γ-PGA production; therefore, B. tequilensis BL01 was selected for subsequent experiments. BL01 colonies were creamy white, mucoid, translucent, and grown on solid culture medium. A phylogenetic tree was generated based on 16S rDNA gene sequences, as shown in Fig. 1 . 3.2 Optimization of nutritional and culture parameters for γ-PGA production All cells grew well, but after 36 h of incubation, different γ-PGA levels were obtained in culture media with different carbon sources (Figs. 2 a and 2 b). In glucose- and sucrose-based media, the γ-PGA titers gradually increased, peaking at 8.9 ± 0.2 g/L at 24 h. The γ-PGA production and cell growth rates of the two different carbon sources were approximately the same ( P > 0.05), but the γ-PGA titer normalized to the biomass of the sucrose-based medium was higher than that of the medium with glucose as the carbon source (Figure S1a). Furthermore, the highest biomass (6.0 ± 0.1 g/L) was obtained in the fructose medium, which showed an asymmetric relationship with the γ-PGA level (7.8 ± 0.4 g/L). Small amounts of γ-PGA were produced from the other carbon sources in the order of glycerol (5.6 ± 0.1 g/L) > arabinose (3.0 ± 0.2 g/L) > xylose (1.1 ± 0.1 g/L) ( P < 0.05) (Fig. 2 a). Carbon source metabolism at the γ-PGA level is related to several stress response proteins, such as catabolite control protein A (CcpA) (Halmschlag et al. 2020 ). Fructose bisphosphate and excess glucose activate CcpA (Jault et al. 2000 ). In B. licheniformis and B. subtilis , CcpA directly or indirectly regulates the expression of pgsB encoding PGA synthetase (Han et al. 2016 ). PGA synthetase is more likely to be strongly expressed in sucrose-, glucose-, and fructose-based media than media made with other carbon sources. Furthermore, the conversion of glutamic acid to 2-oxoglutarate is negatively controlled by CcpA (Halmschlag et al. 2020 ). This could explain the varied γ-PGA production in media with different carbon sources. In B. subtilis NX-2, glycerol improves γ-PGA production by reducing viscosity and increasing substrate uptake during fermentation (Wu et al. 2010 ). However, in this study, the highest γ-PGA titer was not obtained from the glycerol-based medium, which is consistent with B. siamensis (Wang et al. 2020a ). This may be because more glutamate produced from glycerol was used as a nitrogen source for increased biomass production. Experiments on the effect of nitrogen sources on cell growth and γ-PGA production were conducted in sucrose-based medium. As shown in Figs. 2 c and 2 d, all nitrogen sources used supported cell growth and γ-PGA production. The γ-PGA titer of the medium with yeast extract as the nitrogen source was the lowest (6.4 ± 0.5 g/L), and that of the medium with ammonium sulfate as the nitrogen source was the highest at 9.7 ± 0.3 g/L. In addition, the γ-PGA titer normalized to the biomass was highest in the medium with ammonium sulfate as the nitrogen source (Figure S1b). Additionally, there were no significant differences in cell growth rates among all tested nitrogen sources ( P > 0.05). In general, γ-PGA production was higher with inorganic nitrogen sources than with organic nitrogen sources, but organic nitrogen sources promoted cell growth, which is consistent with several previous reports (Luo et al. 2016 ). Under the action of NADPH-dependent glutamate dehydrogenase (GDH), free NH 4 + reacts with α-ketoglutarate in the tricarboxylic acid cycle to form glutamate, which contributes to γ-PGA production (Wu et al. 2010 ; Peng et al. 2015 ). Figures 2 e and 2 f show that γ-PGA production and cell growth were significantly affected by culture temperature. The highest γ-PGA titer (9.5 ± 0.2 g/L) with 5.4 ± 0.1 g/L biomass was obtained at 37°C. Although the highest biomass (5.6 ± 0.1 g/L) was obtained at 32°C, the γ-PGA titer was only 5.8 ± 0.2 g/L. Higher (42 ℃) and lower (28 ℃) temperatures were detrimental to cell growth. Although 8.9 ± 0.2 g/L γ-PGA was obtained at 42°C, the γ-PGA titer normalized to the biomass was the highest compared to other temperatures (Figure S1c). This may be due to the increased flux from iso-citrate to 2-oxoglutarate and from 2-oxoglutarate to glutamate at higher temperatures (Zeng et al. 2014 ). Temperature mainly affects the activity of enzymes, and for most isolated Bacillus strains, the optimal temperature for γ-PGA production is approximately 37°C (Wang et al. 2020a ; Huang et al. 2011 b). 3.3 Screening of significant impact factors using PBD for γ-PGA production Nine factors in the medium were studied using the PBD experiments. Table 1 shows the coded values for the selected variables at two levels and the responding γ-PGA titer. The maximum γ-PGA titer (15.0 ± 0.5 g/L) was obtained in Run 9. The response values ranged from 3.9 ± 0.1 to 15.0 ± 0.5 g/L ( P < 0.05) over twelve runs, and the large difference between the high and low values suggests that γ-PGA production is strongly affected by the medium components. Table 1 Coded PBD values for screening variables and responses Run No. X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 Y (g/L) 1 -1 1 1 -1 1 1 1 -1 -1 6.5 ± 0.4 2 1 -1 1 1 -1 1 1 1 -1 6.5 ± 0.2 3 -1 1 1 1 -1 -1 -1 1 -1 9.7 ± 0.4 4 1 1 1 -1 -1 -1 1 -1 1 8.3 ± 0.3 5 -1 -1 1 -1 1 1 -1 1 1 4.5 ± 0.2 6 -1 1 -1 1 1 -1 1 1 1 11.0 ± 0.4 7 -1 -1 -1 -1 -1 -1 -1 -1 -1 3.9 ± 0.1 8 1 -1 1 1 1 -1 -1 -1 1 7.8 ± 0.5 9 1 1 -1 1 1 1 -1 -1 -1 15.0 ± 0.5 10 1 -1 -1 -1 1 -1 1 1 -1 5.7 ± 0.2 11 1 1 -1 -1 -1 1 -1 1 1 12.6 ± 0.6 12 -1 -1 -1 1 -1 1 1 -1 1 5.4 ± 0.2 Note: X 1 denotes sucrose, X 2 denotes monosodium glutamate, X 3 denotes (NH 4 ) 2 SO 4 , X 4 denotes K 2 HPO 4 , X 5 denotes MgSO 4 ·7H 2 O, X 6 denotes FeCl 3 ·6H 2 O, X 7 denotes CaCl 2 ·2H 2 O, X 8 denotes MnSO 4 ·H 2 O, X 9 denotes NaCl and Y as the response, denotes γ-PGA. Samples were taken every 6 h and the values listed in the table are the maximum values during the 48 h of fermentation. Values represent mean ± SD. Table 2 presents the true values of each parameter at two levels and ANOVA. It gave a model P value of 0.0089, implying that the selected model is significant. The coefficient of determination ( R 2 = 0.9980) indicates a perfect fit; thus, it is a highly reliable (99.80%) model for γ-PGA production. The predicted R 2 value of 0.9286 is consistent with the adjusted R 2 value of 0.9891. A P value less than 0.05 indicates that the factors are significant. In this case, sucrose, monosodium glutamate, and K 2 HPO 4 (coefficient values of 1.23, 2.43, and 1.15, respectively) were found to have a positive effect on γ-PGA production. However, (NH 4 ) 2 SO 4 and CaCl 2 ·2H 2 O (coefficient values of -0.86) had negative effects on γ-PGA synthesis. The mathematical model for γ-PGA production in terms of coded factors was established and fitted to the first-order equation as follows: 𝑌=8.08 + 1.23𝑋 1 + 2.43𝑋 2 − 0.86𝑋 3 + 1.15𝑋 4 + 0.33𝑋 5 + 0.74𝑋 6 −0.86𝑋 7 + 0.27𝑋 8 +0.18𝑋 9 Table 2 True values of variables at two levels and the statistical analysis Factors Concentration (g/L) Mean square Coefficient estimate Standard error F value P value Lower (-1) Higher (+ 1) Model 14.10 8.08 0.049 111.85 0.0089 X 1 20 60 18.25 1.23 0.049 144.77 0.0068 X 2 10 30 71.05 2.43 0.049 563.54 0.0018 X 3 5 15 8.91 -0.86 0.049 70.66 0.0139 X 4 0.5 1.5 15.96 1.15 0.049 126.60 0.0078 X 5 0.5 1.5 1.33 0.33 0.049 10.58 0.0830 X 6 0.04 0.12 1.35 0.34 0.049 10.68 0.0822 X 7 0.1 0.3 8.81 -0.86 0.049 69.85 0.0140 X 8 0.1 0.3 0.89 0.27 0.049 7.02 0.1177 X 9 0.5 1.5 0.37 0.18 0.049 2.91 0.2299 R 2 = 0.9980 R 2 (adj) = 0.9891 R 2 (Pred) = 0.9286 Note: X 1 denotes sucrose, X 2 denotes monosodium glutamate, X 3 denotes (NH 4 ) 2 SO 4 , X 4 denotes K 2 HPO 4 , X 5 denotes MgSO 4 ·7H 2 O, X 6 denotes FeCl 3 ·6H 2 O, X 7 denotes CaCl 2 ·2H 2 O, X 8 denotes MnSO 4 ·H 2 O, and X 9 denotes NaCl. A P value < 0.05 indicates that the factors are significant. A positive coefficient value indicates a positive effect on γ-PGA production. In the PBD experiment, the significance of each factor was determined by comparing the difference between the two levels of each factor and the overall difference for screening (Vanaja & Rani 2007 ). Among the significant positive impact factors, sucrose provides carbon and energy for the normal growth and cell division of microorganisms and directly affects γ-PGA production. As a precursor, monosodium glutamate was used directly to synthesize γ-PGA. Potassium ions enhance the activity of glutamate dehydrogenase (GDH) and glutamate α-oxoglutarate aminotransferase (GOGAT), thereby increasing intracellular glutamate levels (Zeng et al. 2016 ). In addition, the expression of the γ-PGA synthetase gene was improved by the addition of K + or Fe 3+ , which helped improve γ-PGA production (Feng et al. 2017 ; Zeng et al. 2016 ). In B. subtilis CGMCC 2108, as CaCl 2 concentration increased from 0.01–0.03%, cell growth was inhibited and γ-PGA production decreased (Huang et al. 2011 a). The CaCl 2 concentration selected in this study exceeded the optimal level required for γ-PGA production. NH 4 + ions may have been incorporated into glutamine by glutamine synthetase due to the inhibition of GDH by excess glutamate in the medium (Mitsunaga et al. 2016 ). Thus, CaCl 2 and (NH 4 ) 2 SO 4 negatively affected γ-PGA synthesis. 3.4 Optimizing fermentation medium for γ-PGA production by RSM Response surface methodology (RSM) has been widely applied to optimize γ-PGA production, especially from newly isolated γ-PGA-producing strains (Wang et al. 2020a ; Silva et al. 2014 ). The effects of important positive influencing factors such as sucrose, monosodium glutamate, and K 2 HPO 4 , on γ-PGA titer, yield, and productivity were examined by RSM using FCCD. Table 3 Range of independent factors used in FCCD and responses Run No. X 1 X 2 X 4 Y : PGA (g/L) Yield (g/g) Productivity (g/L/h) 1 0(60) -1(20) 0(2) 15.9 ± 0.5 1.03 ± 0.05 0.43 ± 0.02 2 0(60) 0(40) 0(2) 18.9 ± 0.2 0.76 ± 0.04 0.47 ± 0.02 3 0(60) 0(40) 0(2) 19.6 ± 0.8 0.75 ± 0.03 0.53 ± 0.03 4 0(60) 1(60) 0(2) 20.9 ± 0.8 0.69 ± 0.02 0.38 ± 0.01 5 0(60) 0(40) 0(2) 20.2 ± 0.5 0.70 ± 0.03 0.32 ± 0.01 6 0(60) 0(40) 0(2) 19.7 ± 0.8 0.84 ± 0.02 0.42 ± 0.03 7 0(60) 0(40) 1(3) 19.8 ± 0.9 0.92 ± 0.04 0.39 ± 0.02 8 0(60) 0(40) -1(1) 16.4 ± 0.4 0.65 ± 0.02 0.48 ± 0.04 9 -1(30) 1(60) 1(3) 14.9 ± 0.3 0.72 ± 0.03 0.49 ± 0.03 10 0(60) 0(40) 0(2) 20.5 ± 0.9 1.09 ± 0.05 0.47 ± 0.02 11 -1(30) 1(60) -1(1) 13.6 ± 0.7 0.70 ± 0.04 0.55 ± 0.03 12 0(60) 0(40) 0(2) 20.5 ± 0.9 0.73 ± 0.02 0.60 ± 0.03 13 -1(30) -1(20) 1(3) 9.7 ± 0.2 0.94 ± 0.04 0.60 ± 0.02 14 -1(30) -1(20) -1(1) 10.6 ± 0.4 0.89 ± 0.02 0.52 ± 0.04 15 1(90) 0(40) 0(2) 19.5 ± 0.7 0.87 ± 0.03 0.37 ± 0.01 16 -1(30) 0(40) 0(2) 15.3 ± 0.5 0.79 ± 0.02 0.57 ± 0.02 17 1(90) 1(60) -1(1) 15.6 ± 0.4 0.75 ± 0.02 0.53 ± 0.01 18 1(90) -1(20) 1(3) 15.4 ± 0.4 0.93 ± 0.03 0.49 ± 0.02 19 1(90) 1(60) 1(3) 19.5 ± 0.7 0.91 ± 0.04 0.53 ± 0.04 20 1(90) -1(20) -1(1) 12.4 ± 0.4 0.78 ± 0.02 0.51 ± 0.03 Note: X 1 denotes sucrose, X 2 denotes monosodium glutamate, and X 4 denotes K 2 HPO 4 . (-1, 0, 1) denotes the coded values, and numeric values in parentheses represent actual values (unit: g/L). Samples were taken every 6 h and γ-PGA titers listed in the table are the maximum value during the 48 h of fermentation. The yield and productivity are consistent with the time corresponding to the maximum γ-PGA values. Values represent the mean ± SD. Table 3 shows the FCCD experimental design and response values. γ-PGA yield and productivity were not affected by these three factors after multiple regression analysis. The ANOVA results for the γ-PGA titer are presented in Table 4 . The determination coefficient ( R 2 ) was 0.9823, indicating that 98.23% of the variability in the response could be explained by this model. The predicted R 2 and adjusted R 2 values were 0.9037 and 0.9664, respectively, indicating greater model reliability and significance. The lack-of-fit P value was 0.4951 ( P > 0.05, non-significant), implying that the proposed model fit the experimental data and the independent variables had significant effects on the response. Table 4 Analysis of variance (ANOVA) for γ-PGA production by B. tequilensis BL01 Factor Coefficient estimate Standard error F value P value Model 19.90 0.22 61.77 < 0.0001 X 1 1.83 0.20 84.03 < 0.0001 X 2 2.04 0.20 104.27 < 0.0001 X 4 1.07 0.20 28.57 0.0003 X 1 X 2 -0.11 0.22 0.23 0.6413 X 1 X 4 0.81 0.22 13.02 0.0048 X 2 X 4 0.38 0.22 2.92 0.1182 X 1 2 -2.51 0.38 43.33 < 0.0001 X 2 2 -1.55 0.38 16.44 0.0023 X 4 2 -1.86 0.38 23.69 0.0007 Lack-of-Fit 1.02 0.4951 R 2 = 0.9823 R 2 (adj) = 0.9664 R 2 (Pred) = 0.9037 Note: X 1 denotes sucrose, X 2 denotes monosodium glutamate, and X 4 denotes K 2 HPO 4 . A P value < 0.05 indicates that the factors are significant. A positive coefficient value indicates a positive effect on γ-PGA production. A “model F value” of 61.77 corresponds to a “model P value” of < 0.0001 in Table 4 , implying that the model is significant. There is less than 0.01% chance that a large “model F value” of 61.77 could occur due to noise. ANOVA indicated that the model terms linear sucrose ( X 1 : P < 0.0001), monosodium glutamate ( X 2 : P < 0.0001), K 2 HPO 4 ( X 4 : P = 0.0003), the interaction terms of sucrose and K 2 HPO 4 ( X 1 X 4 : P = 0.0048), quadratic sucrose ( X 1 2 : P < 0.0001), quadratic monosodium glutamate ( X 2 2 : P = 0.0023), and quadratic K 2 HPO 4 ( X 4 2 : P = 0.0007) were significant. The regression equation in terms of γ-PGA production ( Y ) as a function of sucrose ( X 1 ), monosodium glutamate ( X 2 ), and K 2 HPO 4 ( X 4 ) yields the following equation: 𝑌=19.90 + 1.83𝑋 1 + 2.04𝑋 2 + 1.07𝑋 4 − 0.11𝑋 1 𝑋 2 + 0.81𝑋 1 𝑋 4 + 0.38𝑋 2 𝑋 4 − 2.51𝑋 1 2 − 1.55𝑋 2 2 −1.86𝑋 4 2 Three-dimensional (3D) response surface graphs were used to investigate γ-PGA production and the interactions between individual variables (Fig. 3 ). Figure 3 a shows γ-PGA production with respect to sucrose and monosodium glutamate. Based on the interaction responses of sucrose and monosodium glutamate, the γ-PGA titers increased with increasing sucrose and monosodium glutamate concentrations up to 68.9 g/L and 47.7 g/L, respectively. γ-PGA titers decreased as sucrose and monosodium glutamate concentrations continued to increase, indicating that substrate inhibition occurred. Figure 3 b shows the effect of the interaction between sucrose and K 2 HPO 4 on γ-PGA production. The γ-PGA titer increased with increasing sucrose (30.0–68.9 g/L) and K 2 HPO 4 (1.0–2.5 g/L) concentrations. Therefore, the optimal values for sucrose and K 2 HPO 4 were 68.9 and 2.5 g/L, respectively. The K 2 HPO 4 curve was smoother than that of sucrose, indicating that sucrose had a stronger influence on γ-PGA production than K 2 HPO 4 . Figure 3 c also shows the same results. The optimal concentrations of each component were determined from the 3D response surface plots, which were 68.9 g/L sucrose, 47.7 g/L monosodium glutamate, and 2.5 g/L K 2 HPO 4 . The highest predicted γ-PGA was 21.0 g/L. Experiments were conducted in triplicate under optimum conditions to validate the optimization model. The γ-PGA titer was 21.2 ± 0.7 g/L, which was consistent with the predicted value. This demonstrated the predictability and precision of the quadratic equation. 3.5 Fed-batch fermentation for γ-PGA production To further increase the γ-PGA level using strain BL01, a scale-up experiment was carried out in a 5 L fermenter with the optimized medium. As shown in Figure S2a, the amount of dissolved oxygen clearly decreased within the first 6 h, and then, when the stirring speed was increased to 500 rpm, the amount of dissolved oxygen increased, which was beneficial for cell growth and γ-PGA fermentation. γ-PGA level increased as the agitation rate increased to 600 and 700 rpm at 12 and 24 h, respectively, but the dissolved oxygen did not significantly increase and eventually stabilized at approximately 10%. The pH decreased from 6.88 to 5.72 due to increasing organic acid concentration. In addition, it was found that if no alkali was added to the broth, the pH increased to 6.5 after the growth period due to the consumption of organic acids during fermentation (Figure S2a). As shown in Fig. 4 , the biomass continuously increased and 14.3 ± 0.6 g/L was achieved after 48 h of cultivation. As cell density increased, sucrose was consumed, and the γ-PGA titer increased. Interestingly, the concentration of L-glutamic acid increased from 46.4 ± 1.1 to 54.8 ± 2.1 g/L after 6 h of incubation and then decreased (Fig. 4 ). After 48 h, 22.1 ± 0.8 g/L of L-glutamic acid was consumed, and the maximum γ-PGA titer reached 54.7 ± 1.5 g/L. The γ-PGA yield was 2.47 g/g L-glutamic acid, and the highest productivity of γ-PGA reached 1.37 g/L/h after 36 h of cultivation. 2,3-BD was the main by-product, and the titer during the fermentation process is shown in Figure S2b. The maximum 2,3-BD titer achieved was 37.8 ± 1.2 g/L at 48 h. Additionally, the R, R-2,3-BD level was lower than that of meso-2,3-BD before 18 h, but its level increased rapidly thereafter. The amount of R, R-2,3-BD was 74.6% of the total 2,3-BD at the end of fermentation (Figure S2b). Dissolved oxygen directly affects cell growth, γ-PGA titer, and productivity, and is an important parameter of fermentation control. Aerobic respiration occurs with appropriate amounts of dissolved oxygen, allowing B. tequilensis BL01 to produce more energy, which accelerates growth and γ-PGA synthesis. During fed-batch fermentation, the production of γ-PGA increases the medium viscosity and thus reduces the dissolved oxygen and mass transport. Though remained high dissolved oxygen (≥ 20%) was beneficial to improve γ-PGA production (Kongklom et al. 2015 ), further increase agitation speed with the highest aeration only maintained the dissolved oxygen above 5% (Fig. S2a), which is the minimum dissolved oxygen value for γ-PGA production (Kongklom et al. 2015 ). It is not feasible to remain high dissolved oxygen by fermentation alone, future research will employ metabolic engineering strategies to improve dissolved oxygen, such as expression of Vitreoscilla haemoglobin (VHb) in B. tequilensis BL01 (Taymaz-Nikerel & Lara 2022). pH affects enzyme activity and changes the charge of the cell membrane, thus affecting nutrient absorption (Zhu et al. 2013 ). In this study, the pH changed from 6.88 to 5.72 during the initial cultivation period and then stabilized at approximately 6.5. Other researchers have reported that the optimum pH range for γ-PGA production is weakly acidic conditions (pH 6.0-7.0) (Zhu et al. 2013 ; Cromwick et al. 1996 ). Thus, it was not necessary to control pH during fermentation. B. tequilensis BL01 is a glutamic acid-dependent strain for γ-PGA production, and γ-PGA was not produced in the absence of L-glutamic acid (data not shown). Interestingly, the γ-PGA yield reached 2.47 g/g of L-glutamic acid because glutamic acid can be converted from sucrose through the tricarboxylic acid cycle (Li et al. 2021 ). This was confirmed by the increase in L-glutamic acid concentration before 6 h. With the increase in γ-PGA level, L-glutamic acid concentration decreased after 6 h (Fig. 4 ). NH4 + acts as an aminotransferase substrate, which transforms α-ketoglutaric acid into glutamic acid (Wu et al. 2010 ; Peng et al. 2015 ). Glutamic acid performs two functions: one part of glutamic acid is degraded by GDH for nitrogen metabolism (Gunka & Commichau 2012 ), and the other is converted to γ-PGA by γ-PGA synthetases ( pgsBCA ) (Li et al. 2021 ). To date, the maximum yield reported for B. licheniformis NCIM 2324 is 1.30 g γ−PGA /g L−glutamic acid (Bajaj et al. 2009 ). In this study, the yield was 2.47 g γ−PGA /g L−glutamic acid , which is far greater than that previously reported (Table 5 ). Supplying exogenous a large amount of glutamic acid significantly increases production costs, a high yield not only increases the titer of γ-PGA, but also reduces the production cost, which will be economically feasible for industrial production of γ-PGA. 2,3-BD is the major by-product of γ-PGA production by Bacillus species (Wang et al. 2020 c). Further optimization strategies, such as mutagenesis (Wang et al. 2020 b; Zhu et al. 2014 ) and metabolic engineering (Feng et al. 2015 ), can be used to further enhance γ-PGA production and reduce the amounts of by-products. Overall, B. tequilensis BL01 shows great promise as a γ-PGA producer. Table 5 Production of γ-PGA by B. tequilensis BL01 and other microorganisms reported previously. Bacillus strain Major nutrients Titer (g/L) Productivity (g/L/h) Yield (g/g) Ref. B. licheniformis NCIM 2324 glycerol, L-glutamic acid, citric acid 26.12 0.24 1.30 (Bajaj et al. 2009 ) B. licheniformis ATCC 9945a glycerol, citric acid, L-glutamate acid, 34.9 0.36 1.94 (Feng et al. 2017 ) B. subtilis CGMCC1250 glucose, glutamate, yeast extract 101 2.19 0.57 (Huang et al. 2011 b) B. subtilis NX-2 glucose, glutamic, yeast extract 71.21 1.24 0.63 (Xu et al. 2014 ) B. licheniformis A14 glucose, glycerol, monosodium glutamate, citric acid 37.8 0.78 0.94 (Ali et al. 2020 ) B. amyloliquefaciens NX-2S inulin, glutamate, (NH 4 ) 2 SO 4 , 39.4 0.43 0.84 (Qiu et al. 2017 ) B. subtilis MJ80 glutamic acid, starch, citric acid, glycerol 68.7 0.95 1.08 (Ju et al. 2014 ) B. licheniformis P-104 Glucose, sodium glutamate, sodium citrate 41.6 1.07 1.19 (Zhao et al. 2013 ) B. tequilensis BL01 sucrose, monosodium glutamate, (NH 4 ) 2 SO 4 , 54.7 1.37 2.47 This study 3.6 Properties of γ-PGA produced by B. tequilensis BL01 The molecular weight of γ-PGA produced by B. tequilensis BL01 was 2.06×10 6 Da, as determined by GPC (Figure S3). The polymer was identified by 1 H NMR spectroscopy. Figure S4 shows the β-CH 2 chemical shifts at 1.91 and 2.13 ppm. Furthermore, the γ-CH 2 , α-CH, and amide chemical shifts were observed at 2.35 ppm, 4.02 ppm, and 8.20 ppm, respectively, in the 1 H NMR spectrum (Figure S4). This is in agreement with other published NMR data for γ-PGA (Wang et al. 2020a ). The molecular weight of γ-PGA is typically 1×10 4 –10×10 6 Da (Zhao et al. 2013 ). Molecular weight is affected by fermentation time, temperature, pH, and metal ions (Huang et al. 2011 a; Feng et al. 2017 ; Huang et al. 2011 b). γ-PGA with a high molecular weight (1–2×10 6 Da) is useful for mediating antitumor immunity and may be a promising candidate for cancer immunotherapy (Poo et al. 2010 ). Composite materials containing high-molecular-weight γ-PGA (1×10 6 Da) have important applications as bioadhesive hydrogels (Kim et al. 2021 ). However, high molecular weight causes an increase in broth viscosity and, as a result, reduces dissolved oxygen, limiting γ-PGA production during fermentation. At the end of the fed-batch fermentation, the molecular weight of γ-PGA was 2.06×10 6 Da with a titer of 54.7 ± 1.5 g/L, showing promising potential in biomedical materials. 4. Conclusions A statistical approach was used to optimize γ-PGA production from B. tequilensis BL01. With nutritional (sucrose 68.9 g/L, monosodium glutamate 47.7 g/L, and K 2 HPO 4 2.5 g/L) and cultural (37°C, 200 rpm) conditions optimized by one-factor-at-a-time and RSM, γ-PGA titer increased 2.5-fold (from 8.6 ± 0.2 to 21.2 ± 0.7 g/L). The fed-batch bioreactor increased γ-PGA production to 54.7 ± 1.5 g/L with a productivity of 1.37 g/L/h and the maximal γ-PGA yield of 2.47 g/g L-glutamic acid after 36 h cultivation. This is the first report of a Bacillus tequilensis γ-PGA-producing strain. Declarations Acknowledgements We thank Prof. Sang Yup Lee in Korea Advanced Institute of Science and Technology (KAIST) for providing great suggestions on this work. We also thank Dr. Lingmin Jiang in Korea Research Institute of Bioscience and Biotechnology for phylogenetic tree analysis. Author’s Contributions WB and DW designed the project. DW and DZ performed the screening of the strain, optimization of nutritional and culture parameters by PDB experiment and RSM, and Fed-batch fermentation. WB, DW, and JG participated in the data analysis and the discussion of results. WB and DW wrote and edit the paper. All authors read and approved the final manuscript. Funding This work was financially supported by the National Key Research and Development Program of China (2021YFC2100900), the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project (TSBICIP-CXRC-010 and TSBICIP-CXRC-049). 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J Biotechnol 164:67–74 Zhu P, Chen X, Li S, Xu H, Dong S, Xu Z, Zhang Y (2014) Screening and characterization of Sphingomonas sp . mutant for welan gum biosynthesis at an elevated temperature. Bioprocess Biosyst Eng 37:1849–1858 Supplementary Files SupplementaryInformation.docx graphicalabstract.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1695166","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":110349267,"identity":"be2ad5f2-a150-4cd6-9ec5-af715e725715","order_by":0,"name":"Dexin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYLACHgYbBjYJErWkSZCs5bAEA9FaDI6fPfzibdv5Oj7pBsYPP/4wyJsT1HImL81yzpnbEmwyB5gle9sYDHc2ENBidiDHzJinAqhFIoGNgbeBIcHgACEt598AtRicA2th/POHGC03cowf81QcAGth5mEjQov9jTdmjHPOJEu2yRxslpZtkzDcQEiLZH+O8Ye3bXb88rObD35888dGnqAtQACLRMYGIEFc7DB/IErZKBgFo2AUjFwAAA/kOv9DFsH+AAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dexin","middleName":"","lastName":"Wang","suffix":""},{"id":110349268,"identity":"2d6c731e-56e2-4e8d-98ae-646432b5a073","order_by":1,"name":"Dasen Zhou","email":"","orcid":"","institution":"Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dasen","middleName":"","lastName":"Zhou","suffix":""},{"id":110349269,"identity":"38532315-0a13-445f-9f9a-02561b6d43c6","order_by":2,"name":"Jiaqi Gao","email":"","orcid":"","institution":"Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Gao","suffix":""},{"id":110349270,"identity":"fe411a4d-30e8-4741-9d6b-a4621024b83d","order_by":3,"name":"Wenqin Bai","email":"","orcid":"","institution":"Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenqin","middleName":"","lastName":"Bai","suffix":""}],"badges":[],"createdAt":"2022-05-26 07:02:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1695166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1695166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22411963,"identity":"370d372a-0ef4-4323-879c-ef9e9bfb58ab","added_by":"auto","created_at":"2022-06-08 14:59:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92995,"visible":true,"origin":"","legend":"\u003cp\u003eNeighbor-joining tree based on the 16S rDNA gene sequence showing relationships between BL01 and \u003cem\u003eBacillus\u003c/em\u003e species. Bootstrap values greater than 50 are indicated at the branch nodes. Bar, 0.005 substitutions per nucleotide position. T, representative type strain. GenBank accession numbers of 16S rDNA appear in brackets.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/9ca6d6f7e6a360fdb0d8650a.png"},{"id":22411606,"identity":"ae1c1cd4-6d4e-43ae-9f60-50a0b8f58342","added_by":"auto","created_at":"2022-06-08 14:54:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51319,"visible":true,"origin":"","legend":"\u003cp\u003eOne-factor-at-a-time method for γ-PGA production by \u003cem\u003eB.\u003c/em\u003e \u003cem\u003etequilensis\u003c/em\u003e BL01. (a) Effect of carbon sources on cell growth; (b), Time-course profile of γ-PGA production in different carbon sources. (c) Effect of nitrogen sources on cell growth; (d), Time-course profile of γ-PGA production in different nitrogen sources. (e) Effect of temperature on cell growth; (f), Time-course profile of γ-PGA production at different temperatures. Values represent the mean ± SD, n=3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/e7bc9de2cc2d4d41c1c7f488.png"},{"id":22411965,"identity":"efc14b9b-25cc-49d3-8763-c92877433f83","added_by":"auto","created_at":"2022-06-08 14:59:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103135,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional graphs showing the effects of K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, monosodium glutamate and sucrose for γ-PGA production by\u003cem\u003e B.\u003c/em\u003e \u003cem\u003etequilensis\u003c/em\u003e BL01. (a) Effects of monosodium glutamate and sucrose, (b) Effects of K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4 \u003c/sub\u003eand sucrose, (c) Effects of K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4 \u003c/sub\u003eand monosodium glutamate.\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/f4775569554dce4616d4052d.png"},{"id":22411608,"identity":"0bf5bddd-e210-4089-9718-ba2a1649d3a1","added_by":"auto","created_at":"2022-06-08 14:54:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22661,"visible":true,"origin":"","legend":"\u003cp\u003eFed-batch fermentation for\u003cstrong\u003e \u003c/strong\u003eγ-PGA production by\u003cem\u003e B.\u003c/em\u003e \u003cem\u003etequilensis\u003c/em\u003e BL01. Time course profile of sucrose utilization, L-glutamic acid utilization, γ-PGA production, and cell growth in a 5 L bioreactor. Data are given as the mean ± SD, n=3. Aeration rate: 10 mL/min; agitation rate: 400 rpm (0–6 h), 500 rpm (6–12 h), 600 rpm (12–24 h), 700 rpm (24–48 h); the pH was not controlled; 150 mL of feed medium containing 700 g/L of sucrose was fed at 24 h.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/ab77b54fb58ade315bc8c7a9.png"},{"id":23084746,"identity":"92fe1373-eb51-4ad6-8495-4557cfd58c35","added_by":"auto","created_at":"2022-06-25 19:59:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":975607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/e524ce22-3255-4a3d-ae81-a0c6617e9436.pdf"},{"id":22411610,"identity":"a74bd621-16a8-496b-a4f7-e5dfe7d014e6","added_by":"auto","created_at":"2022-06-08 14:54:05","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":204197,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/7469f149c37d0dfcd672ff5b.docx"},{"id":22411968,"identity":"6cc3dc73-ea20-4f4c-b15c-01c2e8e51d9d","added_by":"auto","created_at":"2022-06-08 14:59:05","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":588734,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-1695166/v1/80b14b45830739c4be2724d5.docx"}],"financialInterests":"","formattedTitle":"Enhanced poly-γ-glutamic acid production by a newly isolated Bacillus tequilensis BL01","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePoly-γ-glutamic acid (γ-PGA), one of the most promising biopolymers, consists of D- and L-glutamic acid units linked by amide bonds between α-amino and γ-carboxylic acid groups (Luo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). γ-PGA shows great potential for application in food (Yu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), medicine (Khalil et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), cosmetics (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), wastewater treatment (Peng et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and agriculture (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) due to its numerous properties, including water solubility, biodegradability, nontoxicity, and biocompatibility (Ajayeoba et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, γ-PGA has shown more and more promising applications. γ-PGA nanocomposite hydrogels are potentially applied for injectable tissue engineering hydrogels, tissue adhesives, and hemostatic materials (Kim et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SiOx electrode using the γ-PGA cross-linked by epichlorohydrin as the binder achieves high reversible capacity and outstanding cycle stability (Xiao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In agriculture, exogenous application of γ-PGA could significantly enhance the drought resistance of plants by improving photosynthesis, root development and enriching plant growth promoting bacteria (Ma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eγ-PGA is primarily synthesized by various \u003cem\u003eBacillus\u003c/em\u003e species, including \u003cem\u003eB. subtilis\u003c/em\u003e (Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), B. \u003cem\u003esiamensis\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb) \u003cem\u003elicheniformis\u003c/em\u003e (Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kongklom et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), B. \u003cem\u003emethylotrophicus\u003c/em\u003e (Peng et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), B. \u003cem\u003eamyloliquefaciens\u003c/em\u003e (Feng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)d \u003cem\u003evelezensis\u003c/em\u003e (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and others. Depending on the substrate used, strains can be divided into two types. The first type is glutamic acid-dependent strains that require L-glutamic acid as a direct precursor (Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb). Supplying exogenous glutamic acid significantly increases γ-PGA production, but also increases production costs. The other type is glutamic acid-independent strains, which can synthesize γ-PGA from carbon sources such as glucose \u003cem\u003ede novo\u003c/em\u003e (Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kongklom et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Glutamic acid-independent strains have attracted widespread attention due to their low production cost. Metabolic engineering strategies have been employed to reduce byproduct synthesis, increase precursor supply (Feng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and improve NADPH regeneration (Cai et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, low γ-PGA yields and productivity limit their industrial applications. Therefore, it is vital to obtain novel γ-PGA-producing strains with high titers, yields, and productivities that will be economically feasible for industrial production.\u003c/p\u003e \u003cp\u003eTo meet the growing demand for economical γ-PGA, there is an increasing number of studies focused on optimizing fermentation conditions for γ-PGA-producing strains. Nutrients and culture parameters are crucial for producing γ-PGA because they directly affect the titer, yield, and productivity of γ-PGA. Glucose, glycerol, and sucrose are the most successful carbon sources for γ-PGA production (Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A variety of inexpensive biomasses have been tested to reduce fermentation costs, including cane molasses (Wang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb), agro-industrial wastes (Tang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), corncob fibers, and hydrolysates (Zhu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For \u003cem\u003eB. methylotrophicus\u003c/em\u003e, yeast extract is an excellent nitrogen source for cell growth and γ-PGA production (Peng et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but its high cost hinders its industrial application. Fortunately, several strains prefer to use inorganic nitrogen sources such as (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e or NH\u003csub\u003e4\u003c/sub\u003eCl (Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kongklom et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, inorganic salts such as CaCl\u003csub\u003e2\u003c/sub\u003e (Huang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003ea), FeCl\u003csub\u003e3\u003c/sub\u003e (Feng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), KCl (Zeng et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and MnSO\u003csub\u003e4\u003c/sub\u003e (Kedia et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) can also affect the production, stereochemical composition, and quality of γ-PGA. Culture parameters such as temperature, pH, and dissolved oxygen also greatly affect γ-PGA production (Silva et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, statistical methods have been employed to simultaneously study multiple variables and their interactions to optimize γ-PGA fermentation (Min et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xavier et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study attempted to screen an economical, high-yield γ-PGA-producing strain. Culture components and parameters affecting γ-PGA production were optimized using statistical predictions. Afterward, fermentation scale-up was performed in a 5 L fermenter under optimized nutrition and culture parameters. To the best of our knowledge, this study reports the highest γ-PGA yield obtained from the newly isolated strain \u003cem\u003eB\u003c/em\u003e. \u003cem\u003etequilensis\u003c/em\u003e BL01.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Strain and medium\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eBacillus tequilensis\u003c/em\u003e BL01 strain was isolated from soybean and stored at the China General Microbiological Culture Collection Center (CGMCC23661). γ-PGA-producing strains were preliminarily screened and cultured in a basal medium containing 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 20 g/L glucose, and 10 g/L monosodium glutamate. Agar (20 g/L) was added to basal medium agar plates. A strain with high γ-PGA yield was screened and the influencing factors were analyzed in the fermentation medium, which contained 30 g/L glucose, 20 g/L monosodium glutamate, 5 g/L NH\u003csub\u003e4\u003c/sub\u003eCl, 0.5 g/L K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 0.5 g/L MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, 0.04 g/L FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO, 0.104 g/L MnSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO, 0.15 g/L CaCl\u003csub\u003e2\u003c/sub\u003e, and 0.5 g/L NaCl.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Culture methods for γ-PGA production\u003c/h2\u003e \u003cp\u003eFor shake-flask fermentation, a single colony from the agar plate was inoculated into a 250 mL flask containing 50 mL liquid basal medium and cultured at 37\u0026deg;C for 12 h at 200 rpm. Afterwards, 1% (v/v) of the precultures (OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) were inoculated into 250 mL flasks containing 50 mL of fresh fermentation medium and cultivated at 37\u0026deg;C for 48 h at 200 rpm. Cell growth and γ-PGA yield were measured every 6 h.\u003c/p\u003e \u003cp\u003eFor fed-batch fermentation, cells were precultured in 300 mL basal medium at 37\u0026deg;C for 12 h at 200 rpm and then inoculated into a 5 L fermenter (BIOTECH-5JG; Shanghai Baoxing Biology Equipment Engineering Co., Ltd., China) containing 2.7 L optimized fermentation medium. The 5 L fermenter was operated at an aeration rate of 10 mL/min, and the dissolved oxygen (DO) was kept above 5% by adjusting the agitation rate to 400\u0026ndash;700 rpm. The pH was not controlled to study the changes. When the sugar level dropped below 10 g/L, 150 mL of feed medium containing 700 g/L sucrose was fed into the fermenter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Screening of a high γ-PGA- producing strain and phylogenetic analysis\u003c/h2\u003e \u003cp\u003eγ-PGA-producing strains were isolated from soybean purchased from farmers\u0026rsquo; markets in Tianjin, China, and the screening method was performed as previously described (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Soybeans (100 g) were boiled for 10 min in 500 mL of sterile water in a water bath to remove any non-spore strains. The solution was diluted to 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e times after cooling, and 200 \u0026micro;L aliquots were placed on basal medium agar plates and incubated at 37\u0026deg;C for 24\u0026ndash;48 h. Colonies with high viscosity and mucosity were selected and cultured in flasks to examine their γ-PGA production abilities.\u003c/p\u003e \u003cp\u003eThe strain with the highest γ-PGA production was subjected to 16S rDNA sequence analysis. The 16S rDNA gene sequence was amplified using universal 27F and 1492R primers. Sequencing was performed by GENEWIZ Inc. (Suzhou, China). The 16S rDNA gene sequence was compared with that of type strains reported in the EzBioCloud database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ezbiocloud.net/\u003c/span\u003e\u003cspan address=\"https://www.ezbiocloud.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The phylogenetic tree was reconstructed using the neighbor-joining method in MEGA 7.0 (Jung et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 One factor at a time (OFAT) experimentation design\u003c/h2\u003e \u003cp\u003eThe main nutritional components, including carbon and nitrogen sources and culture parameters, were optimized using the OFAT experimental design. To optimize the carbon sources, 30 g/L (w/v) glucose, sucrose, fructose, glycerol, arabinose, and xylose were added to the fermentation medium. To determine the effect of different nitrogen sources, 5 g/L (w/v) NH\u003csub\u003e4\u003c/sub\u003eCl, peptone, tryptone, yeast extract, and (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e were added to the medium. After optimizing the carbon and nitrogen sources, the effect of temperature (28\u0026ndash;42\u0026deg;C) on bacterial growth and γ-PGA production was studied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Screening of significant effect parameters by Plackett-Burman design (PBD)\u003c/h2\u003e \u003cp\u003eThe Plackett-Burman design (PBD) was employed to identify key fermentation parameters that could significantly influence γ-PGA production by \u003cem\u003eB. tequilensis\u003c/em\u003e BL01. Using the PBD, nine independent variables (sucrose, monosodium glutamate, (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO, MnSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO, CaCl\u003csub\u003e2\u003c/sub\u003e, and NaCl) were tested at high (+\u0026thinsp;1) and low (-1) levels over twelve trials, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The matrix was constructed, and analysis of variance (ANOVA) was performed using Design-Expert 10.0.4 software (Stat-Ease Inc., Minneapolis, MN, USA) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical optimization of factors affecting γ-PGA production by Response surface methodology (RSM)\u003c/h2\u003e \u003cp\u003eA face-centered central composite design (FCCD) was adopted to identify the optimal levels of the three most significant variables: sucrose (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e), monosodium glutamate (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e), and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e). The effect of each variable on γ-PGA production was studied at three different levels (-1, 0, and +\u0026thinsp;1), and the coded values, actual values, and 30 experimental setups were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). ANOVA was applied to analyze the responses under different combinations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and a second-order polynomial equation was formulated to analyze γ-PGA production, as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e+\u0026sum;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e+\u0026sum;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003eij\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e+\u0026sum;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003eii\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e is the concentration of γ-PGA, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the intercept term, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the linear coefficient, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the quadratic coefficient, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eii\u003c/em\u003e\u003c/sub\u003e is the squared term, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are independent variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analytical methods\u003c/h2\u003e \u003cp\u003eCell biomass was determined by measuring the absorbance of the fermentation broth at 600 nm using a spectrophotometer. The concentration of γ-PGA was determined by CTAB-dependent spectrophotometric assay, as described earlier (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Sucrose, glucose, and fructose levels were determined using an Agilent 1260 high-performance liquid chromatography (HPLC) system equipped with a refractive index detector (RID) and an Aminex HPX-87P column (300 \u0026times; 78 mm; Bio\u0026ndash;Rad, Hercules, CA, USA). An Aminex HPX-87H column (300 \u0026times; 78 mm; Bio\u0026ndash;Rad, Hercules, CA, USA) was used to analyze fermentation by-products (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). The γ-PGA molecular weight was measured using gel permeation chromatography (GPC) with an RID detector and an Ultrahydrogel\u0026trade; linear column (10 \u0026micro;m, 7.8 mm\u0026times;300 mm, Waters Corporation, USA). The mobile phase, flow rate, and injection volume were 0.1 N NaNO\u003csub\u003e3\u003c/sub\u003e, 0.5 mL/min, and 20 \u0026micro;L, respectively. Glutamic acid was measured using an Agilent ZORBAX Eclipse Plus C18 column (5 \u0026micro;m, 4.6 mm\u0026times;150 mm, Agilent, USA) with a UV detector (338 nm) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.agilent.com\u003c/span\u003e\u003cspan address=\"https://www.agilent.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e /cs/library/applications/5990-4547EN.pdf).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eFor statistical analysis of cell growth and γ-PGA titers, the dataset from each experiment was treated individually with the Baranyi model fitted to each model. Data from each experiment under all studied conditions were pooled and statistically analyzed using SPSS software (version 11.5), employing a two-factor ANOVA, where the factors were biomass and γ-PGA titer, followed by a Tukey test, with significant differences at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 Screening of highly \u0026gamma;-PGA-producing strains\u003c/h2\u003e\n \u003cp\u003eTo screen for a highly \u0026gamma;-PGA-producing strain, 24 isolates with mucoid colonies were selected from basal medium agar plates. Five strains produced \u0026gamma;-PGA during shake flask fermentation, and their 16S rDNA genes (1425 bp) were amplified and sequenced. BLAST analysis using EzBioCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ezbiocloud.net/\u003c/span\u003e\u003c/span\u003e) showed that these strains belonged to different species (Table S1). BL01 produced 8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 g/L \u0026gamma;-PGA after 24 h of incubation, the \u0026gamma;-PGA titer normalized to the biomass of the individual cultures was the highest compared to other isolates, and BL01 was classified as the species \u003cem\u003eB. tequilensis\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eFermented soybean foods such as natto and cheonggukjang contain high levels of \u0026gamma;-PGA (Min et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Araki et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, soybean was selected as the screening material for \u0026gamma;-PGA-producing strains. Previous studies found that \u003cem\u003eB. tequilensis\u003c/em\u003e can quickly and stably colonize plants, and has a high propagation rate and strong in proliferative capacity (Shultana et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eB. tequilensis\u003c/em\u003e has high efficiency and broad-spectrum plant pathogen resistance, has a combined treatment effect on plant diseases such as anthracnose (Kwon et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and black spot disease (Xu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), has a high prevention effect, and has good application prospects for the prevention and control of plant diseases (Guerrero-Barajas et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it has never been reported for \u0026gamma;-PGA production; therefore, \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 was selected for subsequent experiments. BL01 colonies were creamy white, mucoid, translucent, and grown on solid culture medium. A phylogenetic tree was generated based on 16S rDNA gene sequences, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 Optimization of nutritional and culture parameters for \u0026gamma;-PGA production\u003c/h2\u003e\n \u003cp\u003eAll cells grew well, but after 36 h of incubation, different \u0026gamma;-PGA levels were obtained in culture media with different carbon sources (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). In glucose- and sucrose-based media, the \u0026gamma;-PGA titers gradually increased, peaking at 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/L at 24 h. The \u0026gamma;-PGA production and cell growth rates of the two different carbon sources were approximately the same (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but the \u0026gamma;-PGA titer normalized to the biomass of the sucrose-based medium was higher than that of the medium with glucose as the carbon source (Figure S1a). Furthermore, the highest biomass (6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/L) was obtained in the fructose medium, which showed an asymmetric relationship with the \u0026gamma;-PGA level (7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 g/L). Small amounts of \u0026gamma;-PGA were produced from the other carbon sources in the order of glycerol (5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/L)\u0026thinsp;\u0026gt;\u0026thinsp;arabinose (3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/L)\u0026thinsp;\u0026gt;\u0026thinsp;xylose (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/L) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Carbon source metabolism at the \u0026gamma;-PGA level is related to several stress response proteins, such as catabolite control protein A (CcpA) (Halmschlag et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fructose bisphosphate and excess glucose activate CcpA (Jault et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). In \u003cem\u003eB. licheniformis\u003c/em\u003e and \u003cem\u003eB. subtilis\u003c/em\u003e, CcpA directly or indirectly regulates the expression of \u003cem\u003epgsB\u003c/em\u003e encoding PGA synthetase (Han et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). PGA synthetase is more likely to be strongly expressed in sucrose-, glucose-, and fructose-based media than media made with other carbon sources. Furthermore, the conversion of glutamic acid to 2-oxoglutarate is negatively controlled by CcpA (Halmschlag et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). This could explain the varied \u0026gamma;-PGA production in media with different carbon sources. In \u003cem\u003eB. subtilis\u003c/em\u003e NX-2, glycerol improves \u0026gamma;-PGA production by reducing viscosity and increasing substrate uptake during fermentation (Wu et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, in this study, the highest \u0026gamma;-PGA titer was not obtained from the glycerol-based medium, which is consistent with \u003cem\u003eB. siamensis\u003c/em\u003e (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e). This may be because more glutamate produced from glycerol was used as a nitrogen source for increased biomass production.\u003c/p\u003e\n \u003cp\u003eExperiments on the effect of nitrogen sources on cell growth and \u0026gamma;-PGA production were conducted in sucrose-based medium. As shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed, all nitrogen sources used supported cell growth and \u0026gamma;-PGA production. The \u0026gamma;-PGA titer of the medium with yeast extract as the nitrogen source was the lowest (6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 g/L), and that of the medium with ammonium sulfate as the nitrogen source was the highest at 9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 g/L. In addition, the \u0026gamma;-PGA titer normalized to the biomass was highest in the medium with ammonium sulfate as the nitrogen source (Figure S1b). Additionally, there were no significant differences in cell growth rates among all tested nitrogen sources (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In general, \u0026gamma;-PGA production was higher with inorganic nitrogen sources than with organic nitrogen sources, but organic nitrogen sources promoted cell growth, which is consistent with several previous reports (Luo et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Under the action of NADPH-dependent glutamate dehydrogenase (GDH), free NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e reacts with \u0026alpha;-ketoglutarate in the tricarboxylic acid cycle to form glutamate, which contributes to \u0026gamma;-PGA production (Wu et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peng et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef show that \u0026gamma;-PGA production and cell growth were significantly affected by culture temperature. The highest \u0026gamma;-PGA titer (9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/L) with 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/L biomass was obtained at 37\u0026deg;C. Although the highest biomass (5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 g/L) was obtained at 32\u0026deg;C, the \u0026gamma;-PGA titer was only 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/L. Higher (42 ℃) and lower (28 ℃) temperatures were detrimental to cell growth. Although 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g/L \u0026gamma;-PGA was obtained at 42\u0026deg;C, the \u0026gamma;-PGA titer normalized to the biomass was the highest compared to other temperatures (Figure S1c). This may be due to the increased flux from iso-citrate to 2-oxoglutarate and from 2-oxoglutarate to glutamate at higher temperatures (Zeng et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Temperature mainly affects the activity of enzymes, and for most isolated \u003cem\u003eBacillus\u003c/em\u003e strains, the optimal temperature for \u0026gamma;-PGA production is approximately 37\u0026deg;C (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Huang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 Screening of significant impact factors using PBD for \u0026gamma;-PGA production\u003c/h2\u003e\n \u003cp\u003eNine factors in the medium were studied using the PBD experiments. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the coded values for the selected variables at two levels and the responding \u0026gamma;-PGA titer. The maximum \u0026gamma;-PGA titer (15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 g/L) was obtained in Run 9. The response values ranged from 3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 to 15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 g/L (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) over twelve runs, and the large difference between the high and low values suggests that \u0026gamma;-PGA production is strongly affected by the medium components.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCoded PBD values for screening variables and responses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRun No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eY\u003c/em\u003e(g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eNote: \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e denotes sucrose, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e denotes monosodium glutamate, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e denotes (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e denotes K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e denotes MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e denotes FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e denotes CaCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e denotes MnSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e denotes NaCl and \u003cem\u003eY\u003c/em\u003e as the response, denotes \u0026gamma;-PGA. Samples were taken every 6 h and the values listed in the table are the maximum values during the 48 h of fermentation. Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the true values of each parameter at two levels and ANOVA. It gave a model \u003cem\u003eP\u003c/em\u003e value of 0.0089, implying that the selected model is significant. The coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9980) indicates a perfect fit; thus, it is a highly reliable (99.80%) model for \u0026gamma;-PGA production. The predicted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value of 0.9286 is consistent with the adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e value of 0.9891. A \u003cem\u003eP\u003c/em\u003e value less than 0.05 indicates that the factors are significant. In this case, sucrose, monosodium glutamate, and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (coefficient values of 1.23, 2.43, and 1.15, respectively) were found to have a positive effect on \u0026gamma;-PGA production. However, (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and CaCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO (coefficient values of -0.86) had negative effects on \u0026gamma;-PGA synthesis. The mathematical model for \u0026gamma;-PGA production in terms of coded factors was established and fitted to the first-order equation as follows:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e𝑌=8.08\u0026thinsp;+\u0026thinsp;1.23𝑋\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;2.43𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u0026thinsp;0.86𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;1.15𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;0.33𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;0.74𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e6\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026minus;0.86𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;0.27𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e8\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+0.18𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrue values of variables at two levels and the statistical analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eConcentration (g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003cp\u003esquare\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003cp\u003eestimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003eerror\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower (-1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigher (+\u0026thinsp;1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9980 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (adj)\u0026thinsp;=\u0026thinsp;0.9891 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (Pred)\u0026thinsp;=\u0026thinsp;0.9286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e denotes sucrose, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e denotes monosodium glutamate, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e denotes (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e denotes K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e denotes MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e6\u003c/sub\u003e denotes FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e7\u003c/sub\u003e denotes CaCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e denotes MnSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;H\u003csub\u003e2\u003c/sub\u003eO, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e9\u003c/sub\u003e denotes NaCl. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates that the factors are significant. A positive coefficient value indicates a positive effect on \u0026gamma;-PGA production.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\u003cbr\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn the PBD experiment, the significance of each factor was determined by comparing the difference between the two levels of each factor and the overall difference for screening (Vanaja \u0026amp; Rani \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Among the significant positive impact factors, sucrose provides carbon and energy for the normal growth and cell division of microorganisms and directly affects \u0026gamma;-PGA production. As a precursor, monosodium glutamate was used directly to synthesize \u0026gamma;-PGA. Potassium ions enhance the activity of glutamate dehydrogenase (GDH) and glutamate \u0026alpha;-oxoglutarate aminotransferase (GOGAT), thereby increasing intracellular glutamate levels (Zeng et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, the expression of the \u0026gamma;-PGA synthetase gene was improved by the addition of K\u003csup\u003e+\u003c/sup\u003e or Fe\u003csup\u003e3+\u003c/sup\u003e, which helped improve \u0026gamma;-PGA production (Feng et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zeng et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In \u003cem\u003eB. subtilis\u003c/em\u003e CGMCC 2108, as CaCl\u003csub\u003e2\u003c/sub\u003e concentration increased from 0.01\u0026ndash;0.03%, cell growth was inhibited and \u0026gamma;-PGA production decreased (Huang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003ea). The CaCl\u003csub\u003e2\u003c/sub\u003e concentration selected in this study exceeded the optimal level required for \u0026gamma;-PGA production. NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e ions may have been incorporated into glutamine by glutamine synthetase due to the inhibition of GDH by excess glutamate in the medium (Mitsunaga et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, CaCl\u003csub\u003e2\u003c/sub\u003e and (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e negatively affected \u0026gamma;-PGA synthesis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.4 Optimizing fermentation medium for \u0026gamma;-PGA production by RSM\u003c/h2\u003e\n \u003cp\u003eResponse surface methodology (RSM) has been widely applied to optimize \u0026gamma;-PGA production, especially from newly isolated \u0026gamma;-PGA-producing strains (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Silva et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The effects of important positive influencing factors such as sucrose, monosodium glutamate, and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, on \u0026gamma;-PGA titer, yield, and productivity were examined by RSM using FCCD.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRange of independent factors used in FCCD and responses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRun No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eY\u003c/em\u003e: PGA (g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYield (g/g)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProductivity (g/L/h)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e denotes sucrose, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e denotes monosodium glutamate, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e denotes K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e. (-1, 0, 1) denotes the coded values, and numeric values in parentheses represent actual values (unit: g/L). Samples were taken every 6 h and \u0026gamma;-PGA titers listed in the table are the maximum value during the 48 h of fermentation. The yield and productivity are consistent with the time corresponding to the maximum \u0026gamma;-PGA values. Values represent the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the FCCD experimental design and response values. \u0026gamma;-PGA yield and productivity were not affected by these three factors after multiple regression analysis. The ANOVA results for the \u0026gamma;-PGA titer are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The determination coefficient (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was 0.9823, indicating that 98.23% of the variability in the response could be explained by this model. The predicted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e and adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values were 0.9037 and 0.9664, respectively, indicating greater model reliability and significance. The lack-of-fit \u003cem\u003eP\u003c/em\u003e value was 0.4951 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, non-significant), implying that the proposed model fit the experimental data and the independent variables had significant effects on the response.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of variance (ANOVA) for \u0026gamma;-PGA production by \u003cem\u003eB. tequilensis\u003c/em\u003e BL01\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient estimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLack-of-Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9823 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (adj)\u0026thinsp;=\u0026thinsp;0.9664 \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (Pred)\u0026thinsp;=\u0026thinsp;0.9037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: \u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e denotes sucrose, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e denotes monosodium glutamate, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e denotes K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates that the factors are significant. A positive coefficient value indicates a positive effect on \u0026gamma;-PGA production.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eA \u0026ldquo;model \u003cem\u003eF\u003c/em\u003e value\u0026rdquo; of 61.77 corresponds to a \u0026ldquo;model \u003cem\u003eP\u003c/em\u003e value\u0026rdquo; of \u0026lt;\u0026thinsp;0.0001 in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, implying that the model is significant. There is less than 0.01% chance that a large \u0026ldquo;model \u003cem\u003eF\u003c/em\u003e value\u0026rdquo; of 61.77 could occur due to noise. ANOVA indicated that the model terms linear sucrose (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), monosodium glutamate (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), the interaction terms of sucrose and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0048), quadratic sucrose (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), quadratic monosodium glutamate (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0023), and quadratic K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007) were significant. The regression equation in terms of \u0026gamma;-PGA production (\u003cem\u003eY\u003c/em\u003e) as a function of sucrose (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e), monosodium glutamate (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e), and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (\u003cem\u003eX\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e) yields the following equation:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e𝑌=19.90\u0026thinsp;+\u0026thinsp;1.83𝑋\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;2.04𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;1.07𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u0026thinsp;0.11𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;0.81𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e+\u0026thinsp;0.38𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u0026thinsp;2.51𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u0026thinsp;1.55𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026minus;1.86𝑋\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eThree-dimensional (3D) response surface graphs were used to investigate \u0026gamma;-PGA production and the interactions between individual variables (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea shows \u0026gamma;-PGA production with respect to sucrose and monosodium glutamate. Based on the interaction responses of sucrose and monosodium glutamate, the \u0026gamma;-PGA titers increased with increasing sucrose and monosodium glutamate concentrations up to 68.9 g/L and 47.7 g/L, respectively. \u0026gamma;-PGA titers decreased as sucrose and monosodium glutamate concentrations continued to increase, indicating that substrate inhibition occurred. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the effect of the interaction between sucrose and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e on \u0026gamma;-PGA production. The \u0026gamma;-PGA titer increased with increasing sucrose (30.0\u0026ndash;68.9 g/L) and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e (1.0\u0026ndash;2.5 g/L) concentrations. Therefore, the optimal values for sucrose and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e were 68.9 and 2.5 g/L, respectively. The K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e curve was smoother than that of sucrose, indicating that sucrose had a stronger influence on \u0026gamma;-PGA production than K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec also shows the same results. The optimal concentrations of each component were determined from the 3D response surface plots, which were 68.9 g/L sucrose, 47.7 g/L monosodium glutamate, and 2.5 g/L K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e. The highest predicted \u0026gamma;-PGA was 21.0 g/L.\u003c/p\u003e\n \u003cp\u003eExperiments were conducted in triplicate under optimum conditions to validate the optimization model. The \u0026gamma;-PGA titer was 21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 g/L, which was consistent with the predicted value. This demonstrated the predictability and precision of the quadratic equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e3.5 Fed-batch fermentation for \u0026gamma;-PGA production\u003c/h2\u003e\n \u003cp\u003eTo further increase the \u0026gamma;-PGA level using strain BL01, a scale-up experiment was carried out in a 5 L fermenter with the optimized medium. As shown in Figure S2a, the amount of dissolved oxygen clearly decreased within the first 6 h, and then, when the stirring speed was increased to 500 rpm, the amount of dissolved oxygen increased, which was beneficial for cell growth and \u0026gamma;-PGA fermentation. \u0026gamma;-PGA level increased as the agitation rate increased to 600 and 700 rpm at 12 and 24 h, respectively, but the dissolved oxygen did not significantly increase and eventually stabilized at approximately 10%. The pH decreased from 6.88 to 5.72 due to increasing organic acid concentration. In addition, it was found that if no alkali was added to the broth, the pH increased to 6.5 after the growth period due to the consumption of organic acids during fermentation (Figure S2a). As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the biomass continuously increased and 14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 g/L was achieved after 48 h of cultivation. As cell density increased, sucrose was consumed, and the \u0026gamma;-PGA titer increased. Interestingly, the concentration of L-glutamic acid increased from 46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 to 54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 g/L after 6 h of incubation and then decreased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). After 48 h, 22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 g/L of L-glutamic acid was consumed, and the maximum \u0026gamma;-PGA titer reached 54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 g/L. The \u0026gamma;-PGA yield was 2.47 g/g L-glutamic acid, and the highest productivity of \u0026gamma;-PGA reached 1.37 g/L/h after 36 h of cultivation. 2,3-BD was the main by-product, and the titer during the fermentation process is shown in Figure S2b. The maximum 2,3-BD titer achieved was 37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 g/L at 48 h. Additionally, the R, R-2,3-BD level was lower than that of meso-2,3-BD before 18 h, but its level increased rapidly thereafter. The amount of R, R-2,3-BD was 74.6% of the total 2,3-BD at the end of fermentation (Figure S2b).\u003c/p\u003e\n \u003cp\u003eDissolved oxygen directly affects cell growth, \u0026gamma;-PGA titer, and productivity, and is an important parameter of fermentation control. Aerobic respiration occurs with appropriate amounts of dissolved oxygen, allowing \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 to produce more energy, which accelerates growth and \u0026gamma;-PGA synthesis. During fed-batch fermentation, the production of \u0026gamma;-PGA increases the medium viscosity and thus reduces the dissolved oxygen and mass transport. Though remained high dissolved oxygen (\u0026ge;\u0026thinsp;20%) was beneficial to improve \u0026gamma;-PGA production (Kongklom et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), further increase agitation speed with the highest aeration only maintained the dissolved oxygen above 5% (Fig. S2a), which is the minimum dissolved oxygen value for \u0026gamma;-PGA production (Kongklom et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). It is not feasible to remain high dissolved oxygen by fermentation alone, future research will employ metabolic engineering strategies to improve dissolved oxygen, such as expression of \u003cem\u003eVitreoscilla\u003c/em\u003e haemoglobin (VHb) in \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 (Taymaz-Nikerel \u0026amp; Lara 2022).\u003c/p\u003e\n \u003cp\u003epH affects enzyme activity and changes the charge of the cell membrane, thus affecting nutrient absorption (Zhu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this study, the pH changed from 6.88 to 5.72 during the initial cultivation period and then stabilized at approximately 6.5. Other researchers have reported that the optimum pH range for \u0026gamma;-PGA production is weakly acidic conditions (pH 6.0-7.0) (Zhu et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cromwick et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus, it was not necessary to control pH during fermentation.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eB. tequilensis\u003c/em\u003e BL01 is a glutamic acid-dependent strain for \u0026gamma;-PGA production, and \u0026gamma;-PGA was not produced in the absence of L-glutamic acid (data not shown). Interestingly, the \u0026gamma;-PGA yield reached 2.47 g/g of L-glutamic acid because glutamic acid can be converted from sucrose through the tricarboxylic acid cycle (Li et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This was confirmed by the increase in L-glutamic acid concentration before 6 h. With the increase in \u0026gamma;-PGA level, L-glutamic acid concentration decreased after 6 h (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). NH4\u003csup\u003e+\u003c/sup\u003e acts as an aminotransferase substrate, which transforms \u0026alpha;-ketoglutaric acid into glutamic acid (Wu et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peng et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Glutamic acid performs two functions: one part of glutamic acid is degraded by GDH for nitrogen metabolism (Gunka \u0026amp; Commichau \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the other is converted to \u0026gamma;-PGA by \u0026gamma;-PGA synthetases (\u003cem\u003epgsBCA\u003c/em\u003e) (Li et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). To date, the maximum yield reported for \u003cem\u003eB. licheniformis\u003c/em\u003e NCIM 2324 is 1.30 g \u003csub\u003e\u0026gamma;\u0026minus;PGA\u003c/sub\u003e /g \u003csub\u003eL\u0026minus;glutamic acid\u003c/sub\u003e (Bajaj et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study, the yield was 2.47 g \u003csub\u003e\u0026gamma;\u0026minus;PGA\u003c/sub\u003e /g \u003csub\u003eL\u0026minus;glutamic acid\u003c/sub\u003e, which is far greater than that previously reported (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Supplying exogenous a large amount of glutamic acid significantly increases production costs, a high yield not only increases the titer of \u0026gamma;-PGA, but also reduces the production cost, which will be economically feasible for industrial production of \u0026gamma;-PGA.\u003c/p\u003e\n \u003cp\u003e2,3-BD is the major by-product of \u0026gamma;-PGA production by \u003cem\u003eBacillus\u003c/em\u003e species (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003ec). Further optimization strategies, such as mutagenesis (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003eb; Zhu et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and metabolic engineering (Feng et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), can be used to further enhance \u0026gamma;-PGA production and reduce the amounts of by-products. Overall, \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 shows great promise as a \u0026gamma;-PGA producer.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProduction of \u0026gamma;-PGA by \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 and other microorganisms reported previously.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e strain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMajor nutrients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTiter\u003c/p\u003e\n \u003cp\u003e(g/L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProductivity\u003c/p\u003e\n \u003cp\u003e(g/L/h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003cp\u003e(g/g)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. licheniformis\u003c/em\u003e NCIM 2324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglycerol, L-glutamic acid, citric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Bajaj et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. licheniformis\u003c/em\u003e ATCC 9945a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglycerol, citric acid, L-glutamate acid,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Feng et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e CGMCC1250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglucose, glutamate, yeast extract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Huang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003eb)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e NX-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglucose, glutamic,\u003c/p\u003e\n \u003cp\u003eyeast extract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Xu et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. licheniformis\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eA14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglucose, glycerol, monosodium glutamate, citric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ali et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. amyloliquefaciens\u003c/em\u003e NX-2S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einulin, glutamate, (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Qiu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e MJ80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglutamic acid, starch, citric acid, glycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ju et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. licheniformis\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eP-104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose, sodium glutamate, sodium citrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Zhao et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB. tequilensis\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eBL01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esucrose, monosodium glutamate, (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e3.6 Properties of \u0026gamma;-PGA produced by B. tequilensis BL01\u003c/h2\u003e\n \u003cp\u003eThe molecular weight of \u0026gamma;-PGA produced by \u003cem\u003eB. tequilensis\u003c/em\u003e BL01 was 2.06\u0026times;10\u003csup\u003e6\u003c/sup\u003e Da, as determined by GPC (Figure S3). The polymer was identified by \u003csup\u003e1\u003c/sup\u003eH NMR spectroscopy. Figure S4 shows the \u0026beta;-CH\u003csub\u003e2\u003c/sub\u003e chemical shifts at 1.91 and 2.13 ppm. Furthermore, the \u0026gamma;-CH\u003csub\u003e2\u003c/sub\u003e, \u0026alpha;-CH, and amide chemical shifts were observed at 2.35 ppm, 4.02 ppm, and 8.20 ppm, respectively, in the \u003csup\u003e1\u003c/sup\u003eH NMR spectrum (Figure S4). This is in agreement with other published NMR data for \u0026gamma;-PGA (Wang et al. \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe molecular weight of \u0026gamma;-PGA is typically 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u0026ndash;10\u0026times;10\u003csup\u003e6\u003c/sup\u003e Da (Zhao et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Molecular weight is affected by fermentation time, temperature, pH, and metal ions (Huang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003ea; Feng et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003eb). \u0026gamma;-PGA with a high molecular weight (1\u0026ndash;2\u0026times;10\u003csup\u003e6\u003c/sup\u003e Da) is useful for mediating antitumor immunity and may be a promising candidate for cancer immunotherapy (Poo et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Composite materials containing high-molecular-weight \u0026gamma;-PGA (1\u0026times;10\u003csup\u003e6\u003c/sup\u003e Da) have important applications as bioadhesive hydrogels (Kim et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, high molecular weight causes an increase in broth viscosity and, as a result, reduces dissolved oxygen, limiting \u0026gamma;-PGA production during fermentation. At the end of the fed-batch fermentation, the molecular weight of \u0026gamma;-PGA was 2.06\u0026times;10\u003csup\u003e6\u003c/sup\u003e Da with a titer of 54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 g/L, showing promising potential in biomedical materials.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eA statistical approach was used to optimize γ-PGA production from \u003cem\u003eB. tequilensis\u003c/em\u003e BL01. With nutritional (sucrose 68.9 g/L, monosodium glutamate 47.7 g/L, and K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e 2.5 g/L) and cultural (37\u0026deg;C, 200 rpm) conditions optimized by one-factor-at-a-time and RSM, γ-PGA titer increased 2.5-fold (from 8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 to 21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 g/L). The fed-batch bioreactor increased γ-PGA production to 54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 g/L with a productivity of 1.37 g/L/h and the maximal γ-PGA yield of 2.47 g/g L-glutamic acid after 36 h cultivation. This is the first report of a \u003cem\u003eBacillus tequilensis\u003c/em\u003e γ-PGA-producing strain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Sang Yup Lee in Korea Advanced Institute of Science and Technology (KAIST) for providing great suggestions on this work. We also thank Dr. Lingmin Jiang in Korea Research Institute of Bioscience and Biotechnology for phylogenetic tree analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWB and DW designed the project. DW and DZ performed the screening of the strain, optimization of nutritional and culture parameters by PDB experiment and RSM, and Fed-batch fermentation. WB, DW, and JG participated in the data analysis and the discussion of results. WB and DW wrote and edit the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Key Research and Development Program of China (2021YFC2100900), the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project (TSBICIP-CXRC-010 and TSBICIP-CXRC-049).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli AAM, Momin B, Ghogare P (2020) Isolation of a novel poly-γ-glutamic acid-producing \u003cem\u003eBacillus licheniformis\u003c/em\u003e A14 strain and optimization of fermentation conditions for high-level production. 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Bioprocess Biosyst Eng 37:1849\u0026ndash;1858\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"poly-γ-glutamic acid, Bacillus tequilensis BL01, response surface methodology, fed-batch fermentation","lastPublishedDoi":"10.21203/rs.3.rs-1695166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1695166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePoly γ-glutamic acid (γ-PGA) is a promising biopolymer for various applications. In this study, we isolated a novel γ-PGA-producing strain, \u003cem\u003eBacillus tequilensis\u003c/em\u003e BL01. The one-factor-at-a-time method was used to investigate the influence of carbon and nitrogen sources and temperature on γ-PGA production. The optimal carbon and nitrogen sources were sucrose and (NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, respectively. The optimal temperature for γ-PGA production was determined to be 37\u0026deg;C. Response surface methodology was used to determine the optimum medium components: 68.9 g/L sucrose, 47.7 g/L monosodium glutamate, and 2.5 g/L K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e. γ-PGA titer increased significantly from 8.6 g/L to 21.1 g/L when strain BL01 was cultivated in the optimized medium. Furthermore, γ-PGA titer reached 54.7 g/L with a productivity of 1.37 g/L/h and a yield of 2.47 g of γ-PGA/g of L-glutamic acid with the optimized medium in fed-batch fermentation. It should be noted that the γ-PGA yield in this study was the highest of all reported studies, indicating a great potential for the industrial production of γ-PGA.\u003c/p\u003e","manuscriptTitle":"Enhanced poly-γ-glutamic acid production by a newly isolated Bacillus tequilensis BL01","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-08 14:54:02","doi":"10.21203/rs.3.rs-1695166/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de728944-f0a7-43d0-8731-83c9e551d818","owner":[],"postedDate":"June 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-25T19:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-08 14:54:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1695166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1695166","identity":"rs-1695166","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}