Top Banner
ORIGINAL PAPER Optimization and scale-up of 2,3-butanediol production by Bacillus amyloliquefaciens B10-127 Taowei Yang Xian Zhang Zhiming Rao Shenghui Gu Haifeng Xia Zhenghong Xu Received: 30 August 2011 / Accepted: 17 November 2011 / Published online: 26 November 2011 Ó Springer Science+Business Media B.V. 2011 Abstract The effects of culture conditions on 2,3- butanediol (2,3-BD) production and its possible scale-up have been studied. A newly isolated Bacillus amylolique- faciens B10-127, belonged to GRAS microorganisms and showed a remarkable 2,3-BD producing potency, was used for this experiment. Corn steep liquor, soybean meal and ammonium citrate were found to be the key factors in the fermentation according to the results obtained from the Plackett–Burman experimental design. The optimal con- centration range of the three factors was examined by the steepest ascent path, and their optimal concentration were further optimized via response surface methodological approach and determined to be 31.9, 22.0 and 5.58 g/l, respectively. The concentration of the obtained 2,3-BD increased significantly with optimized medium (62.7 g/l) when compared with unoptimized medium (45.7 g/l) and the 2,3-BD productivity was about 2.4-fold (The fermen- tation time was shorten from 72 to 42 h). To observe scale- up effects, batch fermentation was carried out at various working volumes. At a working volume of 20.0 l, the final 2,3-BD concentration and yield were 61.4 and 0.38 g/g at 36 h with a 2,3-BD productivity of 1.71 g/l h. This result shows similar amount of 2,3-BD obtained in lab-scale fermentation, and it is possible to scale up to larger fer- mentors without major problems. Keywords Optimization Scale-up 2,3-butanediol Bacillus amyloliquefaciens B10-127 Introduction The bio-based bulk chemicals production from renewable resources has recently attracted increasing attention as it is a green technology and environment-friendly compared with chemical processes (Hermann et al. 2007). Microbial production of 2,3-butanediol (2,3-BD) is one of the examples. Interest in this bioprocess has been increasing recently due to that 2,3-BD has large number of industrial applications and this course would alleviate the depen- dence on oil supply for the production of platform chem- icals. As an important starting material, 2,3-BD can be used to produce valuable derivatives such as methyl ethyl ketone and 1,3-butadiene (Haveren et al. 2008; Tran and Chambers 1987). Besides, 2,3-BD has wide application in transport fuels production, in the manufacturing of printing inks, perfumes, and fumigants, moistening and softening agents, explosives and plasticizes, pharmaceutical carriers (Celinska and Grajek 2009; Syu 2001). 2,3-BD could be produced from carbohydrates via the mixed acid fermentation pathway by many bacterial spe- cies such as Klebsiella pneumoniae (Yu and Saddler 1983), Enterobacter aerogenes (Zeng et al. 1990), Bacillus poly- myxa (De Mas et al. 1988) and Serratia marcescens (Neish et al. 1947; Zhang et al. 2010a, b). So far, the most efficient 2,3-BD producers reported are K. pneumoniae, Klebsiella oxytoca and E. aerogenes (Celinska and Grajek 2009). Among all these strains, Bacillus amyloliquefaciens was T. Yang X. Zhang Z. Rao (&) S. Gu H. Xia The Key Laboratory of Industrial Biotechnology, Ministry of Education, Laboratory of Applied Microorganisms and Metabolic Engineering, School of Biotechnology, Jiangnan University, Wuxi 214122, Jiangsu Province, People’s Republic of China e-mail: [email protected] Z. Xu Laboratory of Pharmaceutical Engineering, School of Medicine and Pharmaceutics, Jiangnan University, Wuxi 214122, Jiangsu Province, People’s Republic of China 123 World J Microbiol Biotechnol (2012) 28:1563–1574 DOI 10.1007/s11274-011-0960-7
12
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Yang 2012

ORIGINAL PAPER

Optimization and scale-up of 2,3-butanediol productionby Bacillus amyloliquefaciens B10-127

Taowei Yang • Xian Zhang • Zhiming Rao •

Shenghui Gu • Haifeng Xia • Zhenghong Xu

Received: 30 August 2011 / Accepted: 17 November 2011 / Published online: 26 November 2011

� Springer Science+Business Media B.V. 2011

Abstract The effects of culture conditions on 2,3-

butanediol (2,3-BD) production and its possible scale-up

have been studied. A newly isolated Bacillus amylolique-

faciens B10-127, belonged to GRAS microorganisms and

showed a remarkable 2,3-BD producing potency, was used

for this experiment. Corn steep liquor, soybean meal and

ammonium citrate were found to be the key factors in the

fermentation according to the results obtained from the

Plackett–Burman experimental design. The optimal con-

centration range of the three factors was examined by the

steepest ascent path, and their optimal concentration were

further optimized via response surface methodological

approach and determined to be 31.9, 22.0 and 5.58 g/l,

respectively. The concentration of the obtained 2,3-BD

increased significantly with optimized medium (62.7 g/l)

when compared with unoptimized medium (45.7 g/l) and

the 2,3-BD productivity was about 2.4-fold (The fermen-

tation time was shorten from 72 to 42 h). To observe scale-

up effects, batch fermentation was carried out at various

working volumes. At a working volume of 20.0 l, the final

2,3-BD concentration and yield were 61.4 and 0.38 g/g at

36 h with a 2,3-BD productivity of 1.71 g/l h. This result

shows similar amount of 2,3-BD obtained in lab-scale

fermentation, and it is possible to scale up to larger fer-

mentors without major problems.

Keywords Optimization � Scale-up � 2,3-butanediol �Bacillus amyloliquefaciens B10-127

Introduction

The bio-based bulk chemicals production from renewable

resources has recently attracted increasing attention as it is

a green technology and environment-friendly compared

with chemical processes (Hermann et al. 2007). Microbial

production of 2,3-butanediol (2,3-BD) is one of the

examples. Interest in this bioprocess has been increasing

recently due to that 2,3-BD has large number of industrial

applications and this course would alleviate the depen-

dence on oil supply for the production of platform chem-

icals. As an important starting material, 2,3-BD can be

used to produce valuable derivatives such as methyl ethyl

ketone and 1,3-butadiene (Haveren et al. 2008; Tran and

Chambers 1987). Besides, 2,3-BD has wide application in

transport fuels production, in the manufacturing of printing

inks, perfumes, and fumigants, moistening and softening

agents, explosives and plasticizes, pharmaceutical carriers

(Celinska and Grajek 2009; Syu 2001).

2,3-BD could be produced from carbohydrates via the

mixed acid fermentation pathway by many bacterial spe-

cies such as Klebsiella pneumoniae (Yu and Saddler 1983),

Enterobacter aerogenes (Zeng et al. 1990), Bacillus poly-

myxa (De Mas et al. 1988) and Serratia marcescens (Neish

et al. 1947; Zhang et al. 2010a, b). So far, the most efficient

2,3-BD producers reported are K. pneumoniae, Klebsiella

oxytoca and E. aerogenes (Celinska and Grajek 2009).

Among all these strains, Bacillus amyloliquefaciens was

T. Yang � X. Zhang � Z. Rao (&) � S. Gu � H. Xia

The Key Laboratory of Industrial Biotechnology, Ministry

of Education, Laboratory of Applied Microorganisms and

Metabolic Engineering, School of Biotechnology, Jiangnan

University, Wuxi 214122, Jiangsu Province, People’s Republic

of China

e-mail: [email protected]

Z. Xu

Laboratory of Pharmaceutical Engineering, School of Medicine

and Pharmaceutics, Jiangnan University, Wuxi 214122, Jiangsu

Province, People’s Republic of China

123

World J Microbiol Biotechnol (2012) 28:1563–1574

DOI 10.1007/s11274-011-0960-7

Page 2: Yang 2012

rarely reported as a producing strain for 2,3-BD production

compared with other organisms. While the strain generally

recognized as safe (GRAS) microorganism used for

industrial production was much safer than 2,3-BD-pro-

ducing strain K. pneumoniae that cause disease.

Optimization of culture medium is a very important

aspect in the field of food microbiology and fermentation to

improve product yield and reduce process variability, as

well as reducing development time and overall costs

(Kennedy and Krouse 1999; Mu et al. 2009). Many

researchers are therefore studying processes for the pro-

duction of 2,3-BD. In the previous study, several strategies

have been widely used to enhance 2,3-BD production, such

as optimizing medium component, optimizing fermentation

operating conditions and establishing mathematical models

(Alam et al. 1990; Celinska and Grajek 2009; Ghosh and

Swaminathan 2003; Zeng et al. 1994). Nutritional and

physiological factors such as media composition, aeration,

pH, and temperature of the cell culture are essential for

operating fermentative processes. The optimization of

environmental and nutritional conditions for production of

2,3-BD by S. marcescens, K. pneumoniae and K. oxytoca

have been studied (Zhang et al. 2010a, b), respectively, and

the concentration of production showed a considerable

improvement under the optimal situation.

In our previous work, a GRAS strain B. amylolique-

faciens B10-127, capable of producing 2,3-BD effectively

and tolerating glucose up to 300 g/l was isolated. And we

also optimized fermentation operating conditions (Yang

et al. 2011). Compared with other known 2,3-BD produc-

ing strains, the 2,3-BD productivity of this microorganism

was not satisfactory, but B. amyloliquefaciens B10-127 is

superior for its GRAS status that meets safety regulations

for industrial-scale fermentation. In this report, for optimal

production of 2,3-BD, physiological and nutritional factors

have to be studied; however, scale-up for industrial appli-

cation production by Bacillus genus has rarely been stud-

ied. In the present investigation, the newly developed

GRAS strain B. amyloliquefaciens B10-127 was used.

Medium optimization was performed using Plackett-Bur-

man and central composite design to maximize the pro-

duction of 2,3-BD. In order to observe scale-up effects,

different sizes of fermentation culture have been tested.

Materials and methods

Bacterial identification

16S rRNA gene sequence was amplified according to

standard procedures (Sambrook and Russell 2001) and

compared to the sequences in the GenBank database

through BLAST sequence analysis (Yang et al. 2011).

Media and culture conditions

The strain B. amyloliquefaciens B10-127 was maintained

on agar slants containing the following medium: glucose

60 g/l, peptone 10 g/l, yeast extract 5 g/l, NaCl 5 g/l, and

2% agar at pH 6.5. The slants were incubated at 37�C for

14 h, maintained at 4�C and subcultured at 4-week

intervals.

The seed culture was prepared by inoculating a full loop

of cells from freshly prepared slants into 50 ml of the

following medium: glucose 60 g/l, K2HPO4 4 g/l, yeast

extract 5 g/l, corn steep liquor 10 g/l, pH 6.5. The culti-

vation was conducted in 250-ml shake flasks for 10 h with

agitation (160 rpm, reciprocal shaker) at 37�C.

Shaking flask effects of fermentation were investigated

in 250 ml Erlenmeyer flasks containing 50 ml of fer-

mentation medium. The size of inoculum was 4% (v/v).

The basic fermentation medium before optimization

contained (g/l): glucose 150 g/l, K2HPO4 6 g/l, corn

steep liquor 10 g/l, yeast extract 10 g/l, pH 6.5. The

cultures were incubated at 37�C on a rotary shaker at

160 rpm.

Effect of nitrogen sources on 2,3-BD production

The impact of various nitrogen sources on cell growth

and 2,3-BD production were firstly investigated by a tra-

ditional step-by-step replacing experimental procedure.

Sources of nitrogen include organic nitrogen and inor-

ganic nitrogen. The sources chosen for the study were

beef extract, yeast extract, peptone, soybean meal, corn

steep liquor, ammonium critrate, urea, (NH4)2SO4,

(NH4)2HPO4, NH4Cl.

Selection of significant variables by Plackett–Burman

design

The Plackett–Burman (PB) design, an efficient technique

for medium-component optimization (Plackett and Burman

1946), was used to select significantly variables for 2,3-BD

production, and insignificant ones were eliminated to

obtain a smaller, more manageable set of factors. The fitted

first-order model is:

Y ¼ b0 þX

biXi

Y is the predicted response, b0 and bi are constant coef-

ficients, and Xi is the coded independent factors. Each

variable is represented at two levels, high and low, which

are denoted by (?1) and (-1), respectively. A total of

eight parameters were included for selection, Table 2

illustrates the levels of each variable used in the experi-

mental design.

1564 World J Microbiol Biotechnol (2012) 28:1563–1574

123

Page 3: Yang 2012

Path of steepest ascent

The method of steepest ascent is a procedure for moving

sequentially along the direction of the maximum increase

in the response (Box et al. 1978). The direction of steepest

ascent is the direction in which 2,3-BD increased rapidly

by increasing or decreasing the condition of the significant

factors. Based on the results of the PB experimental design,

the optimal level scope of each selected factor was

examined by means of this well-known path of steepest

ascent method. The path of steepest ascent was initiated

from the center of the PB design. Experiments were per-

formed along the steepest ascent path until the response did

not increase any more.

Central composite designs and response surface

analysis

The next step in the formulation of the medium was to

determine the optimum levels of significant variables for

2,3-BD production. For this purpose, response surface

methodology (RSM) based on central composite design

(CCD) with five coded levels was employed to determine

the most significant factors screened by PB design for

enhancing 2,3-BD production (Abdul Rahman et al. 2011;

Kennedy and Krouse 1999; Malinowska et al. 2009). The

three independent factors were investigated at five different

levels (-1.682, -1, 0, ?1, ?1.682) and the experimental

design used for study are all shown in Table 6. The

response values (Y) in each trial were the average of the

duplicates. The data obtained from RSM on 2,3-BD pro-

duction were subjected analysis of variance (ANOVA).

The experimental results of RSM were fitted via the

response surface regression procedure, using the following

second-order polynomial equation:

Y ¼ b0 þX

bixi þX

biix2i þ

Xbijxixj

in which Y is the predicted response, xi and xj are inde-

pendent variables, b0 is the intercept, bi is the linear

coefficient, bii is the ith quadratic coefficient, and bij is the

ijth interaction coefficient.

Design-Expert, Version 7.0 (STAT-EASE Inc., Minne-

apolis, USA) was used for the experimental designs and

statistical analysis of the experimental data. The analysis of

variance (ANOVA) was used to estimate the statistical

parameters.

Scale-up production of 2,3-BD

In order to obtain scale-up factors, batch fermentation

experiments were performed at various working volumes

using the optimal culture conditions obtained from the

labscale tests. The working volumes were 3.0, 7.0 and 20.0

in 5, 10 and 30 l vessels, respectively (manufactured by

Biotron). Optimization of nutritional conditions determined

by flask experiments were used for scale-up tests. Agitation

speed, aeration rate and temperature were maintained at

350 rpm, 0.66 vvm and 37�C which detected in our pre-

liminary tests.

Analytical methods

The cell mass concentration was determined by measuring

the OD at 600 nm in a UV-visible spectroscopy system

(UV-2000, UNICO, China). The cell dry weight (DCW)

was calculated from the optical density using calibration

curve for the strain. The composition of fermentation broth

was analyzed using a Agilent 1200 high performance liquid

chromatograph (HPLC) system (Agilent Corp., USA) with

a RID-10A refractive index detector. The stationary and

mobile phases were a Waters SugarPak1 (6.5 mmid 9

300 mm; Waters, USA) and ddH2O at 0.5 ml/min,

respectively. The column temperature was controlled at

30�C. All experiments were repeated at least three times.

Results

Bacterial identification

The bacterium used in this study was isolated from our

preliminary work. After purified several times, the isolate

B10-127 was identified through BLAST analysis of the

partial sequences of 16S rRNA gene. It was 99% identical

with some sequence of B. amyloliquefaciens according to

its 16S rDNA sequence. The sequence was deposited in the

GenBank database with accession no. HQ005359. Based

on these results, strain B10-127 was identified as a strain of

B. amyloliquefaciens and designated as B. amyloliquefac-

iens B10-127 (Yang et al. 2011).

Effect of nitrogen sources on 2,3-BD production

The influences of organic and inorganic nitrogen sources

on cell growth and 2,3-BD production were tested. The

results (Fig. 1; Table 1) had clearly shown that cell mass

and 2,3-BD production were markedly affected by the

addition of nitrogen sources. Among the organic nitrogen

sources investigated, soybean meal resulted in the maxi-

mum 2,3-BD, followed by corn steep liquor (Fig. 1),

whereas ammonium critrate proved to be the best among

the inorganic compounds tested (Table 1). So soybean

meal, corn steep liquor and ammonium critrate were cho-

sen as nitrogen sources for further optimization.

World J Microbiol Biotechnol (2012) 28:1563–1574 1565

123

Page 4: Yang 2012

Selection of significant variables by Plackett–Burman

design

The PB design is a powerful method for screening signif-

icant factors. The statistical technique is widely used as a

tool for checking the efficiency of several processes. To

optimize the culture medium in the fermentation of glucose

by B. amyloliquefaciens B10-127 to yield 2,3-BD, the

constituents of the medium were firstly examined. PB

design for a total of eight variables was used to identify

which variables have significant effects on 2,3-BD pro-

duction (Table 2). The medium includes phosphate, suc-

cinic acid, Fe2?, Mn2?, and Mg2?, which significantly

affect 2,3-BD production (Garg and Jain 1995; Syu 2001).

In addition to these factors, the medium also contained

glucose as carbon source and corn steep liquor, soybean

meal and ammonium citrate as nitrogen source according

to the pre-experiments. The upper and lower limits of each

variable were chosen according to the preliminary inves-

tigation of the limits of the variables. The PB experimental

design for 14 trials with two levels for each variable and

the effect of 8 variables on 2,3-BD production are dem-

onstrated in Table 3.

To approach the neighborhood of the optimum response,

the fitted first-order model equation for 2,3-BD production

was obtained from the PB design experiments:

Y ¼ 53:88þ 3:63 X1 þ 2:74 X2 þ 5:56 X3 � 0:17 X4

þ 2:01 X5 þ 0:46 X6 � 0:56 X7 þ 2:09 X8

Statistical testing was carried out using Fisher’s test for

ANOVA according to the experimental data. The

coefficient R2 of the first-order model was 0.964,

indicating that nearly 97% of the variability in the

response could be explained by the model. The value of

the adjusted determination coefficient (Adj R2 = 89.3%)

was also very high to advocate for a high significance of

the model. R2 value of this model higher than 0.9 was

considered as having a very high correlation. So it was

reasonable to use the regression model to analyze the

Fig. 1 Effect of organic

nitrogen sources on 2,3-BD

production. Beef extract (filledsquare), Corn steep liquor (filledcircle), Soybean meal (filledtriangle), Peptone (filledinverted triangle), Yeast extract

(filled star)

Table 1 Effect of Inorganic

nitrogen sources on 2,3-BD

production

Time (h) DCW (g/l) Acetoin (g/l) 2,3-BD

(g/l)

Yield

(g/g Glucose)

Productivity

(g/l h)

Control 84 16.1 7.8 52.1 0.35 0.62

Urea 78 17.9 6.9 54.3 0.36 0.70

Ammonium citrate 66 19.1 4.6 57.2 0.38 0.87

(NH4)2SO4 78 20.4 6.3 53.9 0.36 0.69

(NH4)2HPO4 72 19.6 5.4 55.3 0.37 0.77

NH4Cl 84 15.7 8.9 50.7 0.34 0.60

Table 2 The Plackett–Burman design for screening variables in 2,3-

BD production

Factors (g/l) Variables Low level (-1) High level (?1)

Corn steep liquor X1 10 20

Soybean meal X2 4 10

Ammonium Citrate X3 1 4

K2HPO4 X4 1 4

FeSO4�7H2O X5 0 0.1

MnSO4�7H2O X6 0 0.1

MgSO4�7H2O X7 0.2 0.6

Succinic acid X8 0.1 0.5

1566 World J Microbiol Biotechnol (2012) 28:1563–1574

123

Page 5: Yang 2012

trends in the responses. In this model, the F test and

P values were used to identify the effect of each factor on

2,3-BD production. P value below 0.05 indicates that the

model terms are significant. Table 4 shows the effects of

the variables on the response and the significant levels.

Based on the statistical analysis, the factors (P \ 0.05)

such as X1 (corn steep liquor), X2 (soybean meal), X3

(ammonium citrate) and X8 (succinic acid) had the greatest

positive impacts on the production of 2,3-BD, but

compared with X1, X2 and X3, X8 had less positive

effect. X5 (FeSO4) and X6 (MnSO4) were set at their high

levels according to the positive effects although they were

nonsignificant to 2,3-BD production. Factors such as X4

(K2HPO4) and X6 (MgSO4) had negative effects and were

set at their low levels. And then, corn steep liquor, soybean

meal and ammonium citrate were selected for further

optimization to obtain a maximum response.

Path of steepest ascent

The path of steepest ascent was determined to find the

proper direction of changing variables by increasing or

decreasing the value of the main factors. The above results

indicated that corn steep liquor, soybean meal and

ammonium citrate can significantly influenced the 2,3-BD

production compared with other factors. To search the

proper direction of these three factors with the other factors

fixed at zero level, the path of the steepest ascent was

employed. The design and responses of the steepest ascent

experiment are shown in Table 5. It is shown that the

highest response was 61.43 g/l when the concentration of

corn steep liquor, soybean meal and ammonium citrate was

selected to be 30, 20 and 5 g/l, respectively. It suggested

that this point was near the optimal point and this combi-

nation was used as the middle point for the second-order

experiment, i.e., CCD.

Table 3 Plackett–Burman

design for screening of

significant factors affecting

2,3-BD production

Run Variable levels 2,3-BD (g/l)

X1 X2 X3 X4 X5 X6 X7 X8

1 1 -1 1 1 -1 1 1 1 58.3

2 1 1 1 -1 -1 -1 1 -1 60.2

3 -1 -1 -1 -1 -1 -1 -1 -1 36.3

4 1 1 -1 1 1 1 -1 -1 53.2

5 1 -1 -1 -1 1 -1 1 1 52.7

6 -1 1 1 1 -1 -1 -1 1 58.8

7 -1 1 1 -1 1 1 1 -1 59.2

8 1 1 -1 -1 -1 1 -1 1 58.2

9 0 0 0 0 0 0 0 0 60.4

10 -1 -1 -1 1 -1 1 1 -1 39.4

11 -1 -1 1 -1 1 1 -1 1 57.7

12 0 0 0 0 0 0 0 0 59.6

13 -1 1 -1 1 1 -1 1 1 50.1

14 1 -1 1 1 1 -1 -1 -1 62.4

Table 4 Effects and statistical analysis of variables

Variable Coefficient Standard error F value P value

Intercept 53.88 0.75 13.52 0.0118*

X1 3.63 0.75 23.48 0.0084*

X2 2.74 0.75 13.43 0.0215*

X3 5.56 0.75 55.21 0.0018*

X4 -0.17 0.75 0.055 0.8265

X5 2.01 0.75 7.21 0.0550

X6 0.46 0.75 0.38 0.5732

X7 -0.56 0.75 0.56 0.4969

X8 2.09 0.75 7.82 0.0498

R2 = 0.9643, R2 (Adj) = 0.8930

* Significant at 99% confidence degree (P \ 0.05)

Table 5 Experiment design and results of the steepest ascent path

Run Corn steep

liquor (g/l)

Soybean

meal (g/l)

Ammonium

Citrate (g/l)

2,3-BD

(g/l)

Origin 15 8 2 35.5

1 20 12 3 47.4

2 25 16 4 53.2

3 30 20 5 61.4

4 35 24 6 58.5

5 40 28 7 50.9

World J Microbiol Biotechnol (2012) 28:1563–1574 1567

123

Page 6: Yang 2012

Central composite designs and response surface

analysis

Response surface optimization is more advantageous than

the traditional single parameter optimization in that it saves

time, space and raw materials (Ryad et al. 2010). Based on

the PB design and the path of steepest ascent, RSM using

CCD was applied to determine the optimal levels of the

three selected variables (corn steep liquor, soybean meal

and ammonium citrate) which significantly influenced the

2,3-BD production. A total of 20 runs were needed for

optimizing the three individual parameters in the current

CCD (Table 6). By applying multiple regression analysis

on the experimental data, the response variable and the test

variables were related by the following quadratic equation:

Y ¼ 61:93þ 1:62 X1 þ 2:32 X2 þ 2:46 X3 � 0:66 X1X2

þ 0:31 X1X3 þ 0:29 X2X3 � 1:92 X21 � 2:19 X2

2

� 2:35 X23

where Y is the predicted 2,3-BD production (g/l); X1, X2

and X3 are the coded values of corn steep liquor, soybean

meal and ammonium citrate, respectively.

On the basis of the experimental values, statistical

testing was carried out using Fisher’s test for ANOVA

(Table 7). The Student’s F test and P values were used as a

tool to check the significance of each coefficient, which

also indicated the interaction strength between each inde-

pendent variable. For any of the terms in the model, a large

regression coefficient and a small P value would indicate a

more significant effect on the respective response variables

(Elibol 2004). Thus, the smaller was the values of P, the

more significant was the corresponding coefficient. As

Table 6 The results of the central composition experiment

Run Coded variable level Real variable level 2,3-BD

(g/l)X1 X2 X3 Corn steep

liquor (g/l)

Soybean

meal (g/l)

Ammonium

Citrate (g/l)

1 -1 -1 -1 25 16 4.0 49.1

2 1 -1 -1 35 16 4.0 52.3

3 -1 1 -1 25 24 4.0 54.2

4 1 1 -1 35 24 4.0 55.5

5 -1 -1 1 25 16 6.0 52.7

6 1 -1 1 35 16 6.0 57.9

7 -1 1 1 25 24 6.0 59.7

8 1 1 1 35 24 6.0 61.5

9 -1.68 0 0 21.6 20 5.0 53.5

10 1.68 0 0 38.4 20 5.0 59.8

11 0 -1.68 0 30 13.3 5.0 52.1

12 0 1.68 0 30 26.7 5.0 59.7

13 0 0 -1.68 30 20 3.32 51.6

14 0 0 1.68 30 20 6.68 59.3

15 0 0 0 30 20 5.0 61.7

16 0 0 0 30 20 5.0 62.3

17 0 0 0 30 20 5.0 61.9

18 0 0 0 30 20 5.0 62.0

19 0 0 0 30 20 5.0 61.5

20 0 0 0 30 20 5.0 62.1

Table 7 Significance test of regression coefficient

Variable Coefficient Standard error F value P value

Intercept 61.93 0.18

X1 1.62 0.12 188.55 \0.0001

X2 2.32 0.12 387.65 \0.0001

X3 2.46 0.12 437.31 \0.0001

X1X2 -0.66 0.15 18.52 0.0016

X1X3 0.31 0.15 4.12 0.0698

X2X3 0.29 0.15 3.49 0.0914

X12 -1.92 0.11 280.46 \0.0001

X22 -2.19 0.11 363.23 \0.0001

X32 -2.35 0.11 418.03 \0.0001

Model 214.15 \0.0001

Lack of fit 3.64 0.0911

R2 = 0.9948, R2 (Adj) = 0.9902

1568 World J Microbiol Biotechnol (2012) 28:1563–1574

123

Page 7: Yang 2012

shown in Table 7, the F and P values were 214.15

and \0.0001, respectively. So the test model was statisti-

cally significant at the 99% level of significance.

The regression equation obtained from the ANOVA

showed that the multiple correlation coefficient R2 was

0.9984 (a value [0.75 indicates fitness of the model). This

is an estimate of the fraction of overall variation in the

data accounted by the model, and thus the model is

capable of explaining 99.84% of the variation in response.

The adjusted R2 is 0.9902 and the predicted R2 is 0.9665,

which indicates that the model is good (for a good sta-

tistical model, The closer the R2 value is to 1.00, the

stronger the model is and the better it predicts the

response). ‘‘Adeq Precision’’ measures the signal to noise

ratio. The adequate precision value of the present model

was 41.950, and this also suggests that the model can be

used to navigate the design space. The adequate precision

value is an index of the signal-to-noise ratio, and values of

higher than 4 are essential prerequisites for a model to be

a good fit.

The response surface curves are plotted to explain the

interaction of the variables and to determine the optimum

level of each variable for maximum response. The

response surface curves are shown in Figs. 2, 3 and 4.

Each figure demonstrates the effect of two factors while

the other factors were fixed at zero level. The model

predicted the optimal values of the three most significant

variables were X1 = 0.38, X2 = 0.51 and X3 = 0.58.

Correspondingly, the values of corn steep liquor, soybean

meal and ammonium citrate were 31.9, 22.0 and 5.58 g/l,

respectively. The maximum predicted concentration of

2,3-BD was 63.5 g/l. By optimization of culture condi-

tions, 2,3-BD production was enhanced from 45.7 to

63.5 g/l, and the fermentation time was shorten from 72 to

42 h.

Validation of the second-order polynomial equation

Based on the results of medium optimization, the optimum

composition for 2,3-BD production by B. amyloliquefac-

iens B10-127 is as follows (g/l): glucose 150, corn steep

liquor 31.9, soybean meal 22.0, ammonium citrate 5.58,

K2HPO4 2.5, MgSO4�7H2O 0.3, MnSO4�7H2O 0.05, FeS-

O4�7H2O 0.05, Succinic acid 0.3. To validate the adequacy

of the model equation for predicting maximum 2,3-BD

production, three additional experiments in shake flasks

were performed using the predicted culture conditions.

Under the optimized condition, the 2,3-BD average yield of

62.7 g/l was obtained at 42 h, which was obviously in good

agreement with the model predicted maximum value of

63.5 g/l. Therefore, this result indicated that the optimized

medium favored the production of 2,3-BD.

Scale-up production of 2,3-BD

The maximum 2,3-BD concentration of 62.7 g/l at 42 h

with a 2,3-BD productivity of 1.49 g/l h was obtained by

batch culture in shake flasks, although these results were

new records on 2,3-BD fermentation by GRAS microor-

ganism to our knowledge, it was less efficient than that of

reported ‘‘high-producers’’ (such as K. pneumoniae,

K. oxytoca and E. aerogenes). In order to determine the

scale-up capacity, fermentation was carried out at 5-, 10-

and 30-l fermenter with working volumes at 3.0, 7.0 and

20.0 l, respectively. Optimal media and culture conditions

determined by flask experiments were used for scale-up

tests. The fermentation results are shown in Fig. 5, when

the working volume was increased from 3.0 to 20 l, 2,3-BD

production was slightly reduced and the fermentation time

was extended by about 4 h. However, the cellular growth

Fig. 2 Response surface figure (a) and corresponding contour (b) of

the mutual effects of corn steep liquor and soybean meal on 2,3-BD

production

World J Microbiol Biotechnol (2012) 28:1563–1574 1569

123

Page 8: Yang 2012

and 2,3-BD secretion were similar at the different working

volumes. It was also found that 2,3-BD production rapidly

reduced after reaching the maximum value and a con-

comitant increase of acetoin production appeared after the

glucose was depleted. The similar phenomenon was also

found by Zhang et al. (2011).

Discussion

In order to make the production of 2,3-BD economical on

industrial scale, high concentration and productivity and

low cost of fermentation are essential (Garg and Jain 1995;

Ma et al. 2009). To achieve this aim several strategies have

been used such as screening a productive strain, optimizing

fermentation operating conditions and establishing mathe-

matical models, beside the mentioned methods, designing

an appropriate fermentation medium is also crucial

important to improve the efficiency and productivity of the

fermentation process because product concentration, yield,

and cell growth conditions are strongly influenced by

medium composition such as the carbon source, nitrogen

source, inorganic salts, and so on (Garg and Jain 1995; Ma

et al. 2009). However, there is no general defined medium

for 2,3-BD production by different microbial strains

because every microorganism has its own special nutri-

tional requirements depending on its environment. So it is

Fig. 3 Response surface figure (a) and corresponding contour (b) of

the mutual effects of corn steep liquor and ammonium citrate on 2,3-

BD production

Fig. 4 Response surface figure (a) and corresponding contour (b) of

the mutual effects of soybean meal and ammonium citrate on 2,3-BD

production

1570 World J Microbiol Biotechnol (2012) 28:1563–1574

123

Page 9: Yang 2012

Fig. 5 Batch fermentation

profiles of Bacillusamyloliquefaciens B10-127 for

the scale-up experiment,

determined using various

working volumes: a 3.0 l,

b 7.0 l, c 20.0 l. Acetoin (filledsquare), 2,3-BD (filled circle),

acetoin ?2,3-BD (filledtriangle), DCW (filled invertedtriangle), Glucose (filled star)

World J Microbiol Biotechnol (2012) 28:1563–1574 1571

123

Page 10: Yang 2012

not an easy task to explore a medium that contains all the

main nutritional factors and obtain their optimum levels.

This work primarily aimed at optimizing the process

variables for production of 2,3-BD in using statistical

optimization technique for multivariable effect. Culture

conditions and media composition optimization by a con-

ventional one-at-the-approach led to a substantial increase

in 2,3-butanediol yield. However, this approach not only is

generally time consuming and requires a large number of

experiments to be carried out but also has the limitation of

ignoring the importance of interaction of various parame-

ters (Anvari and Safari Motlagh 2011). More efficient

analytical techniques are based on RSM (Bezerra et al.

2008). This was first proposed by Box and his collaborators

in 1951 as a method to determine the optimal conditions

which maximize or minimize a response (Box and Wilson

1951). It enables a large amount of data to be obtained

from a reduced number of experiments, including the

potential interactions between the studied factors (Bas and

BoyacI 2007). The RSM can be defined as a group of

statistical and technical tools used to study the relationship

between a response of interest and several input variables.

The model has to describe the behavior of a group of data

with a view to making statistical predictions. The aim is the

simultaneous optimization of several factors to lead to the

best performance of a particular system (Bezerra et al.

2008; Ryad et al. 2010).

Production of 2,3-BD has been shown to be sensitive to

repression by different nitrogen sources (Ma et al. 2009;

Zhang et al. 2010a, b). So nitrogen sources were firstly

tested on the growth and 2,3-BD production of B. amylo-

liquefaciens B10-127 by the conventional one-at-the-

approach. In the present investigation, results obtained

showed that soybean meal, corn steep liquor and ammo-

nium critrate resulted in a positive effect on 2,3-BD pro-

duction compared to other nitrogen sources. Then

statistically based experimental designs proved to be a

valuable tool in optimizing the medium for 2,3-BD pro-

duction by the isolated strain B. amyloliquefaciens B10-

127. Among the eight variables tested by PB experiments,

soybean meal, corn steep liquor and ammonium critrate

were identified as the most important components for 2,3-

BD production. Their optimal concentrations were

obtained by using statistical analysis of RSM.

In microbial fermentations, the production costs are

mainly dependent on the nitrogen source cost (Karin and

Barbel 2000), as well as the carbon source cost. The

maximum 2,3-BD yield was usually obtained when cells

were grown in glucose media containing peptone and beef

extract (Alam et al. 1990; Nilegaonkar et al. 1992) or yeast

extract (Perego et al. 2000; Zhang et al. 2010a, b). Soybean

meal, the by-product after extracting most of the oil from

soybeans, which is rich in protein and energy, is an

inexpensive valuable nutrient source available on a large

scale. So soybean meal is an inexpensive valuable and

stimulating media component for 2,3-BD fermentation. It

has an obvious economic advantage as compared to other

organic nitrogen sources including yeast extract and pep-

tone. So soybean meal was demonstrated as a good nitro-

gen source for large-scale 2,3-BD fermentation. Corn steep

liquor is a major byproduct of the corn wet-milling

industry, contains approximately 47% crude protein and is

a low-cost nutrient source available on a large scale

(Parekh et al. 1999). Corn steep liquor is widely used in the

fermentation industry to produce a variety of substances,

such as lactic acid by Streptomyces sp. (Rivas et al. 2004)

and ethanol by Zymomonas mobilis (Silveira et al. 2001).

The preliminary experiment results have clearly shown that

2,3-BD production was markedly enhanced by corn steep

liquor compared with other nitrogen sources culture

(Tables 1, 4). This result is in agreement with Ma (Ma

et al. 2009). What’s more, soybean meal and corn steep

liquor are alternative, low-cost, and high-yield media

component for 2,3-BD fermentation and has an obvious

economic advantage. Ammonium critrate is also a most

important factor that influenced the 2,3-BD accumulation

in this study (Table 4). It is reported that 2,3-butanediol

production can be increased by addition of different

organic acids, because they are intermediate metabolites

for 2,3-BD production (Anvari and Safari Motlagh 2011;

Yu and Saddler 1982). So ammonium critrate offers the

nitrogen source for cell growth and is also very important

to product formation.

The scale-up experiment was performed to observe scale-

up effects for application in industrial processes. As shown

in Fig. 5 there was a slight reduction on 2,3-BD production

at a larger working volume. This phenomenon was also

finely explained by Oh (Oh et al. 2009). When scale-up

experiments were performed, parameters such as tempera-

ture and pH were difficult to maintain at the same value,

especially at all locations within the fermentor. As the

absolute quantity of carbon and nitrogen sources increased,

the production of organic acids and endotoxins was

increased. The surrounding conditions were thus harmful to

cell growth. Therefore, when working volumes increased,

cell and production concentration were decreased. And the

maximum 2,3-BD concentration was about 67.6 g/l at sta-

tionary phase. This result shows similar amount of 2,3-BD

obtained in lab-scale fermentation, and it is possible to scale

up to larger fermentors without major problems.

In conclusion, we conducted a sequential statistical

experimental design to optimize the medium for 2,3-BD

production by B. amyloliquefaciens B10-127. Corn steep

liquor, soybean meal and ammonium citrate had a signifi-

cantly effect on 2,3-BD production and the optimal values

of the three key factors were 31.9, 22.0 and 5.58 g/l,

1572 World J Microbiol Biotechnol (2012) 28:1563–1574

123

Page 11: Yang 2012

respectively. After the optimization, the 2,3-BD concen-

tration increased to 66.5 g/l over a fermentation period of

30 h for the fed-batch culture when using the optimized

culture medium and the 2,3-BD productivity reached

2.22 g/l h. It was shown that statistical experimental design

offered an effect and feasible approach for 2,3-BD fer-

mentation medium optimization. This newly isolated

GRAS strain B. amyloliquefaciens B10-127 showed a

higher 2,3-BD production potency than that of K. pneu-

moniae, which had been demonstrated for its potential on

an industrial scale. Furthermore, the optimum medium for

2,3-BD production by this strain mainly composed of

inexpensive nutrient sources (common and cheap inorganic

salts, a small quantity of corn steep liquor, soybean meal,

and ammonium critrate) that are available for efficient and

economical production of 2,3-BD on a large scale.

Although cell concentration and 2,3-BD concentration

were decreased slightly, the scale-up experiment from flask

to fermentor showed the possibility of extended application

to commercial production processes. So B. amylolique-

faciens B10-127 should be an excellent candidate for the

microbial fermentation of 2,3-BD on an industrial scale.

Acknowledgments This work was supported by the Program for

New Century Excellent Talents in University (NCET-10-0459), the

National Natural Science Foundation of China (30970056), the High-

tech Research and Development Programs of China (2007AA

02Z207), the Fundamental Research Funds for the Central Universi-

ties (JUSRP31001), the Program of Introducing Talents of Discipline

to Universities (111-2-06) and a Project Funded by the Priority

Academic Program Development of Jiangsu Higher Education

Institutions.

References

Abdul Rahman MB, Jarmi NI, Chaibakhsh N, Basri M (2011)

Modeling and optimization of lipase-catalyzed production of

succinic acid ester using central composite design analysis. J Ind

Microbiol Biotechnol 38:229–234. doi:10.1007/s10295-010-

0817-3

Alam S, Capit F, Weigandg WA, Hong J (1990) Kinetics of 2,

3-butanediol fermentation by Bacillus amyloliquefaciens: effect

of initial substrate concentration and aeration. J Chem Technol

Biotechnol 41:71–84. doi:10.1002/jctb.280470109

Anvari M, Safari Motlagh MR (2011) Enhancement of 2, 3-butane-

diol production by Klebsiella oxytoca PTCC 1402. J Biomed

Biotechnol 2011:1–7. doi:10.1155/2011/636170

Bas D, BoyacI IH (2007) Modeling and optimization I: usability of

response surface methodology. J Food Eng 78:836–845. doi:

10.1016/j.jfoodeng.2005.11.024

Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008)

Response surface methodology (RSM) as a tool for optimization

in analytical chemistry. Talanta 76:965–977. doi:10.1016/j.

talanta.2008.05.019

Box GEP, Wilson KB (1951) On the experimental attainment of

optimum conditions. J Roy Stat Soc Ser B Method 13:1–45

Box GEP, Hunter JS, Hunter WG (1978) Statistics for experimenters.

Wiley, New York, pp 291–334

Celinska E, Grajek W (2009) Biotechnological production of 2,

3-butanediol—current state and prospects. Biotechnol Adv

27:715–725. doi:10.1016/j.biotechadv.2009.05.002

De Mas C, Jansen NB, Tsao GT (1988) Production of optically active

2, 3-butanediol by Bacillus polymyxa. Biotechnol Bioeng

31:366–377. doi:10.1002/bit.260310413

Elibol M (2004) Optimization of medium composition for actinorho-

din production by Streptomyces coelicolor A3(2) with response

surface methodology. Process Biochem 39:1057–1062. doi:

10.1016/s0032-9592(03)00232-2

Garg SK, Jain A (1995) Fermentative production of 2, 3-butanediol: a

review. Bioresour Technol 51:103–109. doi:10.1016/0960-

8524(94)00136-O

Ghosh S, Swaminathan T (2003) Optimization of process variables

for the extractive fermentation of 2, 3-butanediol by Klebsiellaoxytoca in aqueous two-phase system using response surface

methodology. Chem Biochem Eng Q 17:319–325

Haveren Jv, Scott EL, Sanders J (2008) Bulk chemicals from biomass.

Biofuels Bioprod Bioref 2:41–57. doi:10.1002/bbb.43

Hermann BG, Blok K, Patel MK (2007) Producing bio-based bulk

chemicals using industrial biotechnology saves energy and

combats climate change. Environ Sci Technol 41:7915–7921.

doi:10.1021/es062559q

Karin H, Barbel HH (2000) Factors affecting the fermentative lactic

acid production from renewable resources. Enzyme Microb

Technol 26:87–107. doi:10.1016/S0141-0229(99)00155-6

Kennedy M, Krouse D (1999) Strategies for improving fermentation

medium performance: a review. J Ind Microbiol Biotechnol

23:456–475. doi:10.1038/sj.jim.2900755

Ma C, Wang A, Qin J, Li L, Ai X, Jiang T, Tang H, Xu P (2009)

Enhanced 2, 3-butanediol production by Klebsiella pneumoniae

SDM. Appl Microbiol Biotechnol 82:49–57. doi:10.1007/

s00253-008-1732-7

Malinowska E, Krzyczkowski W, Lapienis G, Herold F (2009)

Improved simultaneous production of mycelial biomass and

polysaccharides by submerged culture of Hericium erinaceum:

optimization using a central composite rotatable design (CCRD).

J Ind Microbiol Biotechnol 36:1513–1527. doi:10.1007/s10295-

009-0640-x

Mu W, Chen C, Li X, Zhang T, Jiang B (2009) Optimization of

culture medium for the production of phenyllactic acid by

Lactobacillus sp. SK007. Bioresour Technol 100:1366–1370.

doi:10.1016/j.biortech.2008.08.010

Neish AC, Blackwood AC et al (1947) Production and properties of 2,

3-butanediol; dissimilation of glucose by Serratia marcescens.

Can J Res 25:65–69

Nilegaonkar S, Bhosale SB, Kshirsagar DC, Kapadi AH (1992)

Production of 2, 3-butanediol from glucose by Bacillus lichen-iformis. World J Microbiol Biotechnol 8:378–381. doi:

10.1007/BF01198748

Oh IJ, Kim DH, Oh EK, Lee SY, Lee J (2009) Optimization and

scale-up of succinic acid production by Mannheimia succinic-iproducens LPK7. J Microbiol Biotechnol 19:167–171. doi:10.4014/jmb.0807.447

Parekh M, Formanek J, Blaschek HP (1999) Pilot-scale production of

butanol by Clostridium beijerinckii BA101 using a low-cost

fermentation medium based on corn steep water. Appl Microbiol

Biotechnol 51:152–157. doi:10.1007/s002530051375

Perego P, Converti A, Borghi AD, Canepa P (2000) 2, 3-Butanediol

production by Enterobacter aerogenes: selection of the optimal

conditions and application to food industry residues. Bioprocess

Eng 23:613–620. doi:10.1007/s004490000210

Plackett RL, Burman JP (1946) The design of optimum multifactorial

experiments. Biometrika 33:305–325. doi:10.1093/biomet/33.

4.305

World J Microbiol Biotechnol (2012) 28:1563–1574 1573

123

Page 12: Yang 2012

Rivas B, Moldes AB, Domı́nguez JM JCP (2004) Development of

culture media containing spent yeast cells of Debaryomyceshansenii and corn steep liquor for lactic acid production with

Lactobacillus rhamnosus. Int J Food Microbiol 97:93–98. doi:

10.1016/j.ijfoodmicro.2004.05.006

Ryad A, Lakhdar K, Majda KS, Samia A, Mark A, Corinne AD, Eric

G (2010) Optimization of the culture medium composition to

improve the production of hyoscyamine in elicited Daturastramonium L. Hairy roots using the response surface method-

ology (RSM). Int J Mol Sci 11:4726–4740. doi:10.3390/ijms11

114726

Sambrook J, Russell DW (2001) Molecular cloning: a laboratory

manual, 3rd edn. Cold Spring Harbor Laboratory, New York

Silveira MM, Wisbeck E, Hoch I, Jonas R (2001) Production of

glucose-fructose oxidoreductase and ethanol by Zymomonasmobilis ATCC 29191 in medium containing corn steep liquor as

a source of vitamins. Appl Microbiol Biotechnol 55:442–445.

doi:10.1007/s002530000569

Syu MJ (2001) Biological production of 2, 3-butanediol. Appl

Microbiol Biotechnol 55:10–18

Tran AV, Chambers RP (1987) The dehydration of fermentative 2,

3-butanediol into methyl ethyl ketone. Biotechnol Bioeng

29:343–351. doi:10.1002/bit.260290308

Yang TW, Rao ZM, Zhang X, Lin Q, Xia HF, Xu ZH, Yang ST

(2011) Production of 2,3-butanediol from glucose by GRAS

microorganism Bacillus amyloliquefaciens. J Basic Microbiol.

doi:10.1002/jobm.201100033

Yu EK, Saddler JN (1982) Enhanced production of 2, 3-butanediol by

Klebsiella pneumoniae grown on high sugar concentrations in

the presence of acetic acid. Appl Environ Microbiol 44:777–784

Yu EK, Saddler JN (1983) Fed-batch approach to production of 2,

3-butanediol by Klebsiella pneumoniae grown on high substrate

concentrations. Appl Environ Microbiol 46:630–635

Zeng A, Biebl H, Deckwer W (1990) Effect of pH and acetic acid on

growth and 2, 3-butanediol production of Enterobacter aerog-enes in continuous culture. Appl Microbiol Biotechnol

33:485–489. doi:10.1007/BF00172538

Zeng AP, Byun TG, Posten C, Deckwer WD (1994) Use of

respiratory quotient as a control parameter for optimum oxygen

supply and scale-up of 2, 3-butanediol production under

microaerobic conditions. Biotechnol Bioeng 44:1107–1114. doi:

10.1002/bit.260440912

Zhang L, Sun J, Hao Y, Zhu J, Chu J, Wei D, Shen Y (2010a) Microbial

production of 2, 3-butanediol by a surfactant (serrawettin)-

deficient mutant of Serratia marcescens H30. J Ind Microbiol

Biotechnol 37:857–862. doi:10.1007/s10295-010-0733-6

Zhang L, Yang Y, Sun J, Shen Y, Wei D, Zhu J, Chu J (2010b)

Microbial production of 2, 3-butanediol by a mutagenized strain

of Serratia marcescens H30. Bioresour Technol 101:1961–1967.

doi:10.1016/j.biortech.2009.10.052

Zhang X, Yang T, Lin Q, Xu M, Xia H, Xu Z, Li H, Rao Z (2011)

Isolation and identification of an acetoin high production bacte-

rium that can reverse transform 2,3-butanediol to acetoin at the

decline phase of fermentation. World J Microbiol Biotechnol. doi:

10.1007/s11274-011-0754-y

1574 World J Microbiol Biotechnol (2012) 28:1563–1574

123