optimization of medium composition for the production

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BIOTECHNOLOGICAL PRODUCTS AND PROCESS ENGINEERING

Optimization of medium composition for the production

of alkaline -mannanase by alkaliphilic Bacillus sp. N16-5

using response surface methodology

Shan-shan Lin &Wen-fang Dou &Hong-yu Xu &

Hua-zhong Li &Zheng-Hong Xu &Yan-he Ma

Received: 12 November 2006 /Revised: 22 February 2007 /Accepted: 24 February 2007 / Published online: 15 March 2007# Springer-Verlag 2007

Abstract In this work, a 22 factorial design was employed

combining with response surface methodology (RSM) tooptimize the medium compositions for the production of

alkaline -mannanase by alkaliphilic Bacillus sp. N16-5

isolated previously from sediment of Wudunur Soda Lake

in Inner Mongolia, China. The central composite design

(CCD) used for the analysis of treatment combinations

showed that a second-order polynomial regression model

was in good agreement with experimental results, with R2=

0.9829 (P


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Xanthomonas campestris, Aeromonas hydrophila, Tricho-

derma sp. made known to the public consecutively (Ma et

al.1991). In 1987, an alkaline -mannanase was found for

the first time by Akino from alkaliphilic Bacillus sp.

AM001 and the possibility of the application was proposed

(Akino et al.1987,1989). In our previous work, an alkaline

-mannanase produceralkaliphilic Bacillus sp.N16-5

has been isolated from the sediment of Wudunur Soda Lakein Inner Mongolia, China (Ma et al. 1991). This enzyme

was demonstrated to be a novel alkaline -mannanase

(optimum pH 9.5) and has a fine prospect of application

in the enzymatic production of mannooligosaccharide,

which is one of the growth factors of Bifidobacteriumsp.

(Ma et al. 2004). Recently, Hatada et al. cloned and

characterized a high-alkaline -mannanase (up to pH 10)

from an alkaliphilic Bacillus sp. strain JAMB-750 (Hatada

et al.2005).

It has become a bottleneck problem for industrial

application of extremozymes that the extremophiles directly

isolated from nature have some shortcoming for extrem-ozyme production, such as longer generation time, lower

biomass yield leading to the lower productivity of the

extremozymes, and higher production costs (Adams et al.

1995; Schiradi and De Rose 2002). To overcome these

problems, some approaches were proposed as follows: (1)

optimization of fermentation bioprocesses and media

design; (2) implementation of innovative bioreactors; and

(3) overproduction in mesophilic hosts. Some attempts

were made to increase the production from the wild/mutant

strains by optimization of the culture conditions; however,

up to now, the research in this field has not been so mature

(Heck et al. 2005a). As mentioned above, some genes of

alkaline -mannanase from alkaliphiles have been cloned,

sequenced, and expressed in the mesophiles host Escher-

ichia coli and B. substilis (Akino et al. 1989; Hatada et al.

2005). For their low expression level, they were hardly to

be successfully applied in the industries. In our previous

work, we have cloned the alkaline -mannanase gene of

strain N16-5 (Ma et al.2004), and tried to overexpress it in

Pichia pastoris and B. subtilis, but the expression levels

were also comparatively low (data not shown). Interesting-

ly, the wild alkaliphilic Bacillus sp.N16-5 showed high

ability to produce extracellular alkaline -mannanase (Ma

et al. 1991). Therefore, improvement of the fermentation

process and media design of this wild producer to enhance

the capacity of alkaline -mannanase production could be

another possible option.

Response surface methodology (RSM), which has been

extensively applied in optimization of medium composi-

tion, conditions of enzymatic hydrolysis, fermentation, and

food manufacturing processes (Cui et al. 2006), is the

collection of statistical techniques for experiment design,

model development, evaluation factors, and optimum

conditions search. To improve the alkaline -mannanase

production by alkaliphilic Bacillus sp.N16-5, Pluckett

Burman (PB) design, Central composite design (CCD),

and experimental factorial design can be employed to

optimize the medium compositions for enzyme production,

and perform a minimum number of experiments.

In this paper, the objective was to optimize the medium

compositions for the cost-effective production of alkaline-mannanase by alkaliphilic Bacillus sp. N16-5 isolated

previously from sediment of Wudunur Soda Lake in Inner

Mongolia, China. We used the sodium glutamate and

soymeal, which is a cheap by-product from the soy protein

industry, to replace the expensive nitrogen resources such

as peptone and yeast extract used in the previously works,

in the hope of improving the economics for the production

of alkaline -mannanase.

Materials and methods

Microorganism

Alkaliphilic Bacillus sp. N16-5, a producer of extracellular

alkaline -mannanases, was isolated previously from

sediment of Wudunur Soda Lake in Inner Mongolia, China

(Ma et al. 1991). The bacterium was maintained as a spore

stock or, for short periods of time, on nutrient agar slant.

Media

The seed medium contained (g l1): soluble starch 10,

peptone 10, yeast extract 5, NaCl 80, K2HPO4 1.5, MgSO40.3. Initial pH of the culture was adjusted to 7.58.0 before

sterilization at 121 C for 20 min, then 10 g l1 Na2CO3was added to adjust the pH to 9.510.0.

The basal fermentation medium used for alkaline -

mannanase production is the modified alkaline Horikoshi-II

medium (Horikoshi1971), which contained (g l1): konjac

mannan 12; soymeal 15 (provided by Genencor (Wuxi)

International, Inc.), contents (%, w/w): water 12, crude

protein 4447, crude fat 67, carbohydrate 1821, ash 56),

NaCl 80, sodium glutamate 1, yeast extract 5, K2HPO41.5,

and MgSO4 0.3. Initial pH of the culture was adjusted to

7.58.0 before sterilization at 121C for 20 min, then 10 g l1

Na2CO3 was added to adjust the pH 9.510.0.

The single-factor experiments design: prepared according

to Table1

The fermentation medium for the PB design was pre-

pared according to Tables 2 and 3, and the fermentation

medium for the RSM design was prepared according to

Tables4 and 5.

1016 Appl Microbiol Biotechnol (2007) 75:10151022


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Inoculum preparation and Shake flask experiments

Inoculum was prepared by transferring colonies from a

24-h slant culture into a 30-ml seed medium in a 250-ml

Erlenmeyer flask, and incubated at 37C for 24 h at

230 rpm. Each flask was then inoculated with 2% (v/v) of

24-h liquid culture. The culture was incubated at 37C at

230 rpm for 34 h. All experiments were repeated in

triplicate.

Enzyme production and activity assay

Enzyme centrifugation was done at a temperature below

4C. The centrifugal supernatant of the culture broth was

used as the enzyme source for the enzyme analysis. With

locust bean gum as the substrate, alkaline -mannanase

activity was assayed at 70C and pH 9.6 (0.05 mol l1 Gly-

NaOH buffer) by measuring the reducing sugars liberatedduring the hydrolysis of locust bean mannan, as described

previously. One unit of activity was defined as the amount

of enzyme catalyzing the production of 1 mol of the

reducing sugar per minute, using mannose as the standard

(Ma et al. 2004).

Batch fermentation in 5-l fermenter

The batch fermentation was carried out in 5-l fermenter

(Biotech-2001) equipped with three six-bladed disc impel-

ler, oxygen and pH electrodes, under the following

conditions: medium volume 3 l, inoculation volume 2%(v/v), temperature 37C, initial pH 9.510.0, aeration rate

1.0 vvm, and agitation speed 500 rpm. The fermentation

continued until the alkaline -mannanase activity reached

the highest values.

Experimental design

PluckettBurman design

PB design is one special type of a two-level fractional

factorial design basing on incomplete equilibrium piece

principle. It can pick up the main factors with the least

number of experiments from a list of candidate factors

(Plackett and Burman1946; Kalil et al. 2000).

The possible factors that affected the alkaline -

mannanase yield included konjac mannan, soymeal, NaCl,

sodium glutamate, yeast extract, K2HPO4, MgSO4, and

initial pH of medium. The 12-run PB design was used to

study these eight factors and dummy variables were used to

estimate the standard error during analysis of data. The

different variables were prepared in two levels, 1 for low

level and +1 for high level, and the high level was 1.5 times

of the low level, as shown in Table 2.

The steepest ascent experiment

The steepest ascent experiment can approach the largest

response area rapidly, determine the center point of the

central composite design (CCD) described further, and

ensure the validity and correctness of the response surface

analysis results. The first-order model equation obtained

from the PB test determined the ascent direction (or

descent, as required) and the length of ascent pace.

Table 1 Effect of different carbon and nitrogen sources on the

production of alkaline-mannanase from N16-5

Factors Concentration (g l1) Enzyme activity (U/ml)

Glucose 10 2.70.1

Mannose 10 2.20.1

Soluble starch 10 2.70.1

Konjac powder 10 122.84.2

12 167.36.1

15 153.25.2

Locust bean 10 105.33.7

12 143.64.6

15 151.24.9

Peptone 5 101.23.6

10 132.74.3

15 104.23.7

20 75.22.6

Yeast powder 10 64.52.1

15 93.03.1

20 117.14.3

30 87.42.6

Soymeal 10 89.72.715 144.74.3

20 112.73.7

30 93.22.9

Table 2 The factors, levels, and the regression analysis of the PB

design

Factors (g l1) Levels ttest P> |t|

1 +1

X1 pH 9.0 11.0 0.597373 0.592342

X2 NaCl 60.0 90.0 3.78336 0.032369*

X3 konjac mannan 10.0 15.0 0.0181 0.986694

X4 soybean meal 12.0 18.0 0.41635 0.705147

X5 yeast extract 4.0 6.0 0.19912 0.854897

X6 sodium glutamate 2.4 3.6 6.933144 0.006153**

X7 MgSO4 0.2 0.3 1.43007 0.248056

X8 K2HPO4 1.2 1.8 0.16292 0.880938

*Statistically significant at 95% of confidence level

**Statistically significant at 99% of confidence level

Appl Microbiol Biotechnol (2007) 75:10151022 1017


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Central composite design

Based on the results obtained from the PB design and the

steepest ascent experiment, the CCD was conducted to gain

the optimized levels of the main factors (Oh et al. 1995;

Wang et al. 2004). We can get a second-order empirical

model from the experimental data about the relationship

between the response value (alkaline -mannanase yield)

and the variables through polynomial regression analysis.

The form of the second-order polynomial model is:

Yb0X

biiXX

bijijX

bii2ii;

where Y is the predicted response, bi is the regression

coefficient, and xi is the coded value of the independent

variables. The relationship betweenxi and the natural value

of independent variable is:

xi Xi X0 =Xi;

whereXistands for the natural value of independent variable,X0 for the natural value of the independent variable at the

center point, and Xi is the step change value.

Software for experimental design and statistical analysis

Statistical Analysis System (SAS) Version 9.0 was used for

the experimental design and statistical analysis of the

experimental data. All of the optimization fermentation

experiments were conducted in triplicate, and the average

data of alkaline -mannanase yields were further analyzed

by the SAS 9.0. The quality of fit for the regression model

equation was expressed by the coefficient of determinationR2 and its statistical significance was determined by an F

test. The significance of the regression coefficients was

tested by a ttest (Heck et al. 2005b).

Results

The single-factor experiments

In our previous works, we found that the production of

alkaline -mannanase by strain N16-5 was induced by

addition of some substrates containing -mannan into the

medium (Ma et al.1991). The most suitable carbon source

was locust bean gum and the nitrogen sources were peptone

and yeast extract. But in the hope of improving the

economics for the production of alkaline -mannanase, we

used the cheaper substrates, such as konjac powder, soymeal,

and sodium glutamate to replace the locust bean gum,

peptone, and yeast extract. The results showed that all tested

concentrations of the konjac powder could induce the

alkaline-mannanase production, while at the concentration

Table 3 The experiments and results of the PB design

Run number Factor-coded levels Y alkaline -mannanase

yield (U/ml)X1 X2 X3 X4 X5 X6 X7 X8pH NaCl Konjac

mannan

Soybean

meal

Yeast extract Sodium

glutamate

MgSO4 K2HPO4

1 1 1 1 1 1 1 1 1 175.18.1

2 1 1 1 1 1 1 1 1 160.26.3

3 1 1 1 1 1 1 1 1 159.16.1

4 1 1 1 1 1 1 1 1 256.515.2

5 1 1 1 1 1 1 1 1 128.24.3

6 1 1 1 1 1 1 1 1 206.58.2

7 1 1 1 1 1 1 1 1 189.67.4

8 1 1 1 1 1 1 1 1 169.37.1

9 1 1 1 1 1 1 1 1 241.79.1

10 1 1 1 1 1 1 1 1 246.59.7

11 1 1 1 1 1 1 1 1 208.410.1

12 1 1 1 1 1 1 1 1 172.49.2

Table 4 The design and results of the steepest ascent experiment

Run NaCl

(g l1)

Sodium glutamate

(g l1)

Alkaline-mannanase

yield (U/ml)

1 100.0 2.4 128.15.4

2 95.0 2.6 189.38.1

3 90.0 2.8 263.211.5

4 85.0 3.0 307.215.3

5 80.0 3.2 254.410.3

6 75.0 3.4 237.29.5

7 70.0 3.6 190.56.3

8 65.0 3.8 204.18.6

9 60.0 4.0 220.39.5



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of 12 g l1, the enzyme activity (EA) reached the highest. In

the range of 1020 g l1, the soymeal could enhance the

enzyme production. In the concentration of 15 g l1, the EA

is the highest. Above this concentration, the EA decreased

(Table1).

Our previous experiments indicated that in addition to

soymeal, sodium glutamate was another nitrogen source for

effective production of alkaline -mannanase. We investi-gated the effects of various sodium glutamate concentra-

tions on the enzyme production; the results are shown in

Fig.1:the sodium glutamate really played an important role

in the production of the alkaline -mannanase. In the

concentration of 3 g l1

, the EA was 216.2 U/ml, which was

40% higher than that of the medium without sodium

glutamate.

NaCl plays a very important role in the alkaliphiles

physiology (Ma 1999). We also found that NaCl played a

very important role in the production of alkaline -

mannanase by alkaliphilic Bacillus sp.N16-5. The tolerant

concentration of NaCl is 150 g l1 for this strain (Ma et al.1991), while in the range of 80100 g l

1of NaCl, the EA

were comparatively high (Fig. 1). When the concentration

of NaCl was too high or too low, the enzyme production

sharply decreased.

Main factors for the production of alkaline -mannanase

The levels of the variables for the PB design were selected

according to the previous experiments. As shown in Table2,

the 12-run PB design was chosen to pick up the main

factors. The experiments and results of the PB design are

shown in Table3.

We can get the first-order regression equation by

regression analysis of the experimental data obtained from

the PB design.

Y192:41672:75X1 17:41667X2

0:08333X3 1:916667X4 0:916667

X5

31:91667X66:583333X7 0:75X8 1

This fit of the model was checked by R2, which was

calculated to be 95.59%, that is to say the regression equation

regressed well. According to the t-test results in Table2, we

can see NaCl and sodium glutamate are the two major factors

affecting the performance of the culture in terms of alkaline

-mannanase yields; the reliability values were 96.76 and

99.38%, respectively, at 95 and 99% confidence levels.

Optimization of the medium components for alkaline

-mannanase production

Validation of the central point for CCD

According to the results of the PB experiment, we can see

NaCl and sodium glutamate were the essential factors.

From the data shown in Table4, we can get the center point

of the subsequent experiment, which was NaCl 85.0 g l1,

sodium glutamate 3.0 g l1.

Optimization and analysis for the CCD design for alkaline

-mannanase production

The optimal concentration of medium components was

determined by CCD with the two variable NaCl and sodium

glutamate using the central point obtained from the steepest

ascent experiment. The level of konjac mannan, soymeal,

yeast extract, K2HPO4, MgSO4, and pH still remained the

original (konjac mannan 12, soymeal 15, yeast extract 5,

K2HPO4 1.5, MgSO4 0.3. pH 9.510.0). To make the

regression model accurate, repeat the center point five

times. The asterisk arm length =1.414. The experimental

design and results are in Table 5 and the statistical analysis

of data are shown in Table 6.

Regressing the data through SAS 9.0, we can get the

following quadratic empirical model:

Y2873:651611X19:510448X2

19:81252X1X14X1X2 18:06252

X2X2: 2

The analysis of variance was employed for the determi-

nation of significant parameters and to estimate the alkaline

-mannanase as a function of NaCl and sodium glutamate.

Data are shown in Table 6. The determining coefficientR2

0 20 40 60 80 100 1200

50

100

150

200

250-1 0 1 2 3 4 5

concentration of glutamine (g L-1)

EA(UmL-1)

concentration of NaCl (g L-1

)

Fig. 1 Effect of sodium glutamate and NaCl concentration on alkaline

-mannanase production. Sodium glutamate, NaCl



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was 98.29%, which indicated that this model congresses

well with the practice and can be used to analyze and

estimate alkaline -mannanase fermentation. Significance

of coefficients has been reported to be directly proportional

to t test and inversely to Pvalue (Heck et al. 2005b). The

smaller the P values, the bigger the significance of the

corresponding coefficient (Liu et al. 2003). In our work,

sodium glutamate, second-order NaCl, and second-order

sodium glutamate were highly significant, statistically

significant at 99% confidence level. The NaCl and the

interaction of the NaCl and sodium glutamate were also

very significant, and the reliability values were 97.51 and93.65% at 95% confidence level.

We can find the influences on the yield of the alkaline-

mannanase imposed by the factors and the reciprocity

between them directly in Fig. 2. The shapes of contour

curves showed that there were great effects on each other.

Maximal enzyme extraction was obtained at lower NaCl

and higher sodium glutamate concentration. From the

quadratic empirical model, we can get the models extreme

coordinate (0.120, 0.276), the corresponding NaCl

(84.40 g l1), sodium glutamate (3.11 g l1), and now

forecast the largest yield of the alkaline -mannanase for288.5 U/ml.

Validation of the models

The availability of the regression model (Eq. 2) of the

alkaline -mannanase by alkaliphilic Bacillus sp. N16-5

was tested using the calculated optimal culture composition

(g l1), viz. konjac mannan 12, soymeal 15, yeast extract 5,

NaCl 84.4, sodium glutamate 3.11, K2HPO4 1.5, MgSO40.3, Na2CO3 10, pH 9.510.0, with triplicate experiments.

The mean value of the alkaline -mannanase was

295.0 U/ml, which agreed well with the predicted value(288.5 U/ml). As a result, the models developed were

considered to be accurate and reliable for predicting the

production of alkaline -mannanase by alkaliphilic Bacil-

lus sp. N16-5.

Batch fermentation results

The feasibility of the regression models in a 5-l scaled

fermenter was also tested using the predicted optimal medium

composition (Fig. 3). With the optimal, maximum production

of alkaline -mannanase could reach 310.1 U/ml, which is

nearly twice compared with the original (167.1 U/ml). From

this, we can see this model can estimate the practical ferment

conditions correctly and improve the yield of alkaline -

mannanase effectively.

Discussion

Although previous research regarding alkaline -manna-

nase production has been reported, little information on the

Table 5 The experiment design and results of the CCD

Run number Coded factor values Y

X1 X2 Alkaline -

mannanase

yield (U/ml)

NaCl

(g l1)

Sodium glutamate

(g l1)

1 1 (80) 1 (2.6) 239.1 10.2

2 1 (80) 1 (3.4) 261.2 12.3

3 1 (90) 1 (2.6) 243.4 7.1

4 1 (90) 1 (3.4) 249.2 8.4

5 1.414 (78) 0 (3.0) 256.3 10.4

6 1.414 (92) 0 (3.0) 241.1 9.2

7 0 (85) 1.414 (2.4) 235.3 9.2

8 0 (85) 1.414 (3.6) 269.5 12.2

9 0 (85) 0 (3.0) 287.4 13.1

10 0 (85) 0 (3.0) 286.3 12.1

11 0 (85) 0 (3.0) 289.3 11.1

12 0 (85) 0 (3.0) 285.5 10.6

13 0 (85) 0 (3.0) 288.1 11.3

Table 6 Coefficient estimates by the regression model

Term SE t P>F

X1 1.284285 2.8433 0.024926*

X2 1.284285 7.405247 0.000149**

X1X1 1.377244 14.3856 0.0001**

X1X2 1.816252 2.20234 0.063512*

X2X2 1.377244 13.115 0.0001**

*Statistically significant at 95% of confidence level

** Statistically significant at 99% of confidence level

Fig. 2 Response surface plot for the effects of NaCl and sodium

glutamate on alkaline -mannanase production



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optimization of its production is available (Heck et al.

2005a). In this paper, we used the method of experimental

factorial design, and RSM design demonstrated that the

CCD and regression analysis methods were effective to find

the optimized NaCl and sodium glutamate concentrations

for the production of alkaline -mannanase from alkali-

philic Bacillus sp. N16-5, a newly isolated strain growing

on some economical and abundant substrate as konjac

mannan and soymeal replace the expensive carbon and

nitrogen resource, locust gum, peptone, and yeast extract.

As we know, NaCl plays a very important role in the

alkaliphiles physiology (Ma 1999). It can function as the

driving force of some endergonic processes in the cell

(Detkova and Pusheva 2006), so it can balance the

extracellular and intracellular pH of the cell, help the cell

produce energy, and is also very important in the substrate

transports (Ma 1999). The enzymes of such organisms

display maximum activity in the presence of salts and,

moreover, are inactivated in their absence. Our previous

experiments indicated that alkaliphilic Bacillus sp. N16-5

was also a salt-tolerant bacterium, but not so much

halophilic. When under high pH condition, around 9.510.0, the cell growth and the production of alkaline -

mannanase were comparatively desirable. But for the

effects of various NaCl concentrations on production of

alkaline -mannanase by strain N16-5, it was not so much

associated with the cell growth (data not shown). Around

the concentration of 80 g l1 NaCl, the production of

alkaline -mannanase is comparatively high. The lower

concentration of NaCl benefited cell growth, but not

enzyme production. While at higher concentration, NaCl

sharply decreased both cell growth and production of

alkaline -mannanase.

In our works, we added different amino acids to thesoymeal substrate and the sodium glutamate, and the alka-

line -mannanase activity increased greatly. With the RSM

analysis, we found that the maximum alkaline -manna-

nase activity was obtained at sodium glutamate concentra-

tion (3.11 g l1), and the activity was increased nearly twice

compared with the original medium. The amino acids and

their analogues have been known for their stimulatory

effect on the production of a number of enzymes such as -

amylase, glutamate oxaloacetate transaminase and gluta-

mate pyruvate transaminase, -galactosidase, and xylanases

(Ikura and Horikoshi1987). The effect of sodium glutamate

on the production of mannanase has been reported before

(Ma et al.1991), but the mechanism as to the -mannanase

stimulation has not been reported. It is possible that a

certain amino acid plays an important role at the start of the

regulative gene of the hydrolase (Levin and Forschiassin

1998). Gupta et al. (1999) demonstrated that the carbon

chain length and the position of CH3 and NH2 groups

have a significant role in the stimulation of production of

xylanase.

Statistical optimization method for fermentation process

could overcome the limitations of classic empirical methods

and was proved to be a powerful tool for the optimization

of the production of alkaline-mannanase from alkaliphilic

Bacillussp. N16-5. In this study, RSM model was proposed

to study the combined effects of culture media composi-

tions. Under optimal condition, the predicted production of

alkaline -mannanase was 288.5 U/ml by canonical

analysis. Validation experiments were also carried out to

verify the availability and the accuracy of the models, and

the results showed that the predict values agreed well with

the experimental values. The batch fermentation results in a

5-l fermenter also showed that optimized culture medium

Fig. 3 Time profile of fermentation in 5-l fermenter using the basal

medium (a) and the optimized medium (b) on the -mannanase

production from N16-5. pH, cell biomass (g l1), enzyme

activity (EA, U/ml)



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could improve the production of alkaline -mannanase

from alkaliphilic Bacillus sp. N16-5 in a large-scale

fermentation process. It strongly suggested that the produc-

tion of alkaline -mannanase by wild-strain alkaliphilic

Bacillus sp. N16-5 may be interesting for industrial

applications. The results of this study also provided useful

information for the optimization of medium composition

for the other extremozymes fermentation processes.

Acknowledgment This work was supported by grants of National

Basic Research Program of China (No.2007CB707804), National

High-Tech Program (No. 2006AA020104), Jiangsu Planned Projects

for Postdoctoral Research Funds (No.0601004A), and Program for

Changjiang Scholars and Innovative Research Team in University

(No. IRT0532). The authors would thank Dr. Luhong Tang for his

critical reading of this manuscript.

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