J. Microbiol. Biotechnol. (2013), 23(2), 211–217
http://dx.doi.org/10.4014/jmb.1206.06057
First published online November 21, 2012
pISSN 1017-7825 eISSN 1738-8872
Media Optimization of Corynebacterium glutamicum for Succinate Production
Under Oxygen-Deprived Condition S
Jeon, Jong-Min1, Rajesh Thangamani1, Eunjung Song2, Hyuk-Won Lee3, Hong-Weon Lee3, and
Yung-Hun Yang1*
1
Department of Microbial Engineering, Konkuk University, Seoul 143-701, Korea
School of Chemical and Biological Engineering, Seoul National University, Seoul 151-742, Korea
3
Biotechnology Process Engineering Center, Daejeon 305-806, Korea
2
Received: June 22, 2012 / Revised: September 19, 2012 / Accepted: October 16, 2012
Corynebacterium glutamicum is one of the well-studied
industrial strain that is used for the production of
nucleotides and amino acids. Recently, it has also been
studied as a possible producer of organic acids such as
succinic acid, based on its ability to produce organic acids
under an oxygen deprivation condition. In this study, we
conducted the optimization of medium components for
improved succinate production from C. glutamicum under
an oxygen deprivation condition by Plackett-Burman
design and applied a response surface methodology. A
Plackett-Burman design for ten factors such as glucose,
ammonium sulfate, magnesium sulfate, potassium phosphate
(K2HPO4 and KH2PO4), iron sulfate, manganese sulfate,
biotin, thiamine, and sodium bicarbonate was applied to
evaluate the effects on succinate production. Glucose,
ammonium sulfate, magnesium sulfate, and dipotassium
phosphate were found to have significant influence on
succinate production, and the optimal concentrations of
these four factors were sequentially investigated by the
response surface methodology using a Box-Behnken design.
The optimal medium components obtained for achieving
maximum concentration of succinic acid were as follows:
glucose 10 g/l, magnesium sulfate 0.5 g/l, dipotassium
phosphate (K2HPO4) 0.75 g/l, potassium dihydrogen phosphate
(KH2PO4) 0.5 g/l, iron sulfate 6 mg/l, manganese sulfate
4.2 mg/l, biotin 0.2 mg/l, thiamine 0.2 mg/l, and sodium
bicarbonate 100 mM. The parameters that differed from
a normal BT medium were glucose changed from 40 g/l to
10 g/l, dipotassium phosphate (K2HPO4) 0.5 g/l changed to
*Corresponding author
Phone: +82-2-450-3936; Fax: +82-2-3437-8360;
E-mail: [email protected]
S
Supplementary data for this paper are available on-line only at http://
jmb.or.kr.
0.75 g/l, and ammonium sulfate ((NH4)2SO4) 7 g/l changed to
0 g/l. Under these conditions, the final succinic acid
concentration was 16.3 mM, which is about 1.46 fold
higher than the original medium (11.1 mM) at 24 h. This
work showed the improvement of succinate production by
a simple change of media components deduced from
sequential optimization.
Key words: Succinic acid production, Plackett-Burman design,
response surface methodology, fermentation
Succinic acid is one of the major metabolites that plays a
biochemical role in the citric acid cycle, and is also utilized
as raw material for diverse specialty chemicals in foods,
pharmaceuticals, and biodegradable plastics [13]. Currently,
succinic acid is produced from butane through maleic
anhydride, reaching a market scale of up to 60,000 tons per
year [1, 2]. However, this route is economically undesirable
and leads to a limited supply, thereby limiting a further
expansion of the world market for succinic acid [1, 3]. As
an alternative, the microbial production of succinate
from renewable carbon sources is promising, and the
environment-friendly approach and fermentation process
for succinic acid production is expected to increase as oil
prices rise [12].
Currently, many biological scientists endeavor to
develop improved strains for succinate production, such as
Anaerobiospirillum succiniciproducens [5], Actinobacillus
succinogenes [6], genetically engineered Escherichia coli
[10], Corynebacterium glutamicum [11], and Mannheimia
succiniciproducens [14], by either aerobic or anaerobic
routes. However, despite the fact that a number of succinic
acid microbial producers have been isolated and genetically
212
Jeon et al.
modified for improved productivity, succinate fermentation
still has some problems, including low productivity, low
acid tolerance, and production of other organic acids [20].
Among these, C. glutamicum is still one of the attractive
strain that can scale up succinate production to an
industrial level, and it has been used for the industrial
production of various amino acids. Some of the desirable
properties of C. glutamicum are its acid resistance, an
extracellular transport system, and the productivity of
organic acids [11]. Besides, it was already discovered that
the wild type of C. glutamicum produced organic acids
such as succinate, lactate, and acetate from glucose in
mineral medium under oxygen deprivation when its cell
growth is arrested [8]. Although most previous works on
strain developments by applying genetic engineering and
fermentation techniques such as oxygen deprivation
condition and aerobic succinate production seem efficient
and valuable [11, 15, 18, 19], there is no report on the
systematic analysis of the media components for succinate
production, which is the most basic and simplest approach.
In this study, to improve succinate production from C.
glutamicum, we carried out an optimization of media
composition using the Plackett-Burman design and BoxBehnken design. As a result, we evaluated the effect of
each component in the media and the improved composition
of production media for succinate production from C.
glutamicum. The proposed medium can be directly applied
to other C. glutamicum mutants for increased production
of succinic acid.
MATERIALS AND METHODS
Microorganism, Media, and Reagents
The wild-type C. glutamicum strain (ATCC 13032) obtained from
the Biotechnology Process Engineering Center (Gwahangno, YuseongGu, Daejeon, Korea), used in this study, was maintained on LB agar
plate at 32oC. Analytical chemicals were obtained from BD (San
Jose, CA, USA) or Sigma-Aldrich (St. Louis, MO, USA). Liquid
cultures of C. glutamicum were cultivated at 32oC in 5 ml of A
medium (40 g glucose, 2 g yeast extract, 7 g casamino acids, 2 g
urea, 7 g ammonium sulfate, 0.5 g KH2PO4, 0.5 g K2HPO4, 0.5 g
MgSO4·7H2O, 6 mg FeSO4·7H2O, 4.2 mg MnSO4·H2O, 0.2 mg biotin,
and 0.2 mg thiamine, per liter) [9] with constant shaking at 200 rpm.
During the transition to the stationary phase, cells were harvested by
centrifugation (4,000 ×g at 4oC for 10 min), washed once with 1 ml
of BT medium, and resuspended to a final cell concentration of
10% (w/v) on wet weight in 1.4 ml of BT medium (OD600 of 1.9).
To test the effect of the bicarbonate addition, 100 mM sodium
bicarbonate was added to similar culture tubes. Cultures were incubated
at 32oC with constant agitation devoid of aeration.
Analytical Techniques
After the cells were cultured for 20 h, aliquots of the culture were
taken out and centrifuged at 12,000 rpm for 5 min, and the
supernatants were transferred to new Eppendorf tubes and heated at
94oC for 10 min. The samples were then centrifuged at 12,000 rpm
for 5 min and the supernatants were analyzed by HPLC (YL-9100,
Korea). Succinic acid, acetic acid, and lactic acid were determined
by HPLC with a UV detector. The analysis was performed using a
Bio-Rad Aminex-87H column (4.6 µm, 250 × 5 mm). The analysis
conditions were as follows: sample volume 20 µl, mobile phase
0.008N H2SO4; flow rate 0.6 ml/min; column temperature 60oC.
Plackett-Burman Design, Box-Behnken Design, and Response
Surface Methodology
All these designs were performed with Minitab 16. Based on previous
experiments, it was proposed that ten factors, namely glucose,
ammonium sulfate, magnesium sulfate, potassium phosphate (K2HPO4
and KH2PO4), iron sulfate, manganese sulfate, biotin, thiamine, and
sodium bicarbonate, were supposed to have effects on succinic acid
production [9]. Each factor was investigated at a high (+1) and a
low (-1) level (Table 1). As shown in Table 2, the design matrix
covered these ten factors to evaluate their effects on succinate and
other organic acids production and was given as response values.
Twelve experiments were conducted in duplicate to evaluate the
factors that had multiple effects on succinate production. The significant
variables identified from the experiment following a PlackettBurman design were optimized using the Box-Behnken design and
response surface methodology, while the other variables of nonsignificance were fixed at the initial medium level. In developing
the regression equation, the relation between the coded values and
actual values are described by the following equation: [16]
( Ai – A0 )
Xi = ------------------∆Ai
(1)
where Xi is the coded value of the ith variable, Ai the actual value
of the ith variable, Ao the actual value of the ith variable at the
center point, and ∆Ai is the step change value of the ith variable.
The correlation between the response and the four variables were
fitted to a predictive quadratic polynomial equation as follows:
Y = β0 + ∑ βiXi + ∑ βijXiXj + ∑ βiiX2i
i = 1,2,3,......k
(2)
Table 1. Coded and real values of the factors tested in the
Plackett-Burman experimental design.
Factor
Glucose (X1, g/l)
Ammonium sulfate (X2, g/l)
Potassium dihydrogen phosphate (KH2PO4) (X3, g/l)
Dipotassium phosphate(K2HPO4) (X4, g/l)
Magnesium sulfate (X5, g/l)
Manganese sulfate (X6, mg/l)
Iron sulfate (X7, mg/l)
Biotin (X8, mg/l)
Thiamine (X9, mg/l)
Sodium bicarbonate (X10, mM)
Dummy variable (X11)
Levels of
factor
-1
+1
10
0
0
0
0
0
0
0
0
100
-1
40
7
0.5
0.5
0.5
4.2
6
0.2
0.2
400
1
MEDIA OPTIMIZATION OF CORYNEBACTERIUM FOR SUCCINATE PRODUCTION
213
Table 2. Experimental design and results of the N = 12 Plackett-Burman design.
Runs
1
2
3
4
5
6
7
8
9
10
11
12
X1
1
-1
-1
-1
1
1
-1
-1
1
-1
1
1
X2
-1
1
-1
1
-1
-1
-1
-1
1
1
1
1
X3
1
-1
-1
1
1
-1
1
-1
-1
1
1
-1
X4
-1
-1
1
-1
1
-1
1
-1
1
1
-1
1
X5
-1
-1
1
1
-1
1
1
-1
1
-1
1
-1
X6
-1
1
1
-1
1
1
-1
-1
-1
1
1
-1
X7
1
1
-1
-1
-1
1
1
-1
1
1
-1
-1
where Y is the predicted response, β0 is the intercept term, βi is the
linear coefficient, βii is the squared coefficient, and βij is the
interaction coefficient. Xi, Xj represent the independent factors
(medium component) in the form of coded values. The accuracy
and general ability of the above polynomial model could be
evaluated by the coefficient of determination R2. Each experimental
design was carried out in duplicate, and the mean values are given.
The factors that were significant at 95% of confidence level
(p < 0.05) from the regression analysis were considered to have greater
effects on the succinate production and were further optimized by
response surface methodology using the Box-Behnken design [21].
RESULTS
Preliminary Screening of Key Components Using the
Plackett-Burman Design
As explained in the Materials and Methods section,
succinate production requires a two-stage fermentation; the
growth stage and the production stage [18]. Initially, C.
glutamicum was cultured in complex medium called A
medium (40 g glucose, 2 g yeast extract, 7 g casamino acids,
X8
1
1
1
-1
-1
-1
1
-1
-1
-1
1
1
X9
1
-1
1
1
-1
1
-1
-1
-1
1
-1
1
X10
-1
1
-1
1
1
1
1
-1
-1
-1
-1
1
X11
1
1
1
1
1
-1
-1
-1
1
-1
-1
-1
Succinic acid concentration (mM)
0.42 ± 0.17
2.44 ± 0.19
2.08 ± 0.06
0.51 ± 0.32
5.42 ± 0.09
2.90 ± 0.04
2.94 ± 0.02
0.65 ± 0.50
1.10 ± 1.03
1.37 ± 0.59
1.32 ± 0.11
0.12 ± 0.06
2 g urea, 7 g ammonium sulfate, 0.5 g KH2PO4, 0.5 g
K2HPO4, 0.5 g MgSO4·7H2O, 6 mg FeSO4·7H2O, 4.2 mg
MnSO4·H2O, 0.2 mg biotin, and 0.2 mg thiamine, per liter)
[9] and then cells were moved to a production medium
called BT medium (40 g glucose, 7 g ammonium sulfate,
0.5 g KH2PO4, 0.5 g K2HPO4, 0.5 g MgSO4·7H2O, 6 mg
FeSO4·7H2O, 4.2 mg MnSO4·H2O, 0.2 mg biotin, and
0.2 mg thiamine, per liter), which differed in nitrogen
sources such as yeast extract, casamino acids, and urea.
Even though this two stage culture process is laborious, it
is necessary to apply two-stage culture processes because
C .glutamicum does not produce succinate in the growth
media (data not shown).
To find out the optimal medium components for the
production medium (BT), we determined that the experiments
should be set using a Plackett-Burman design. Ten
different components such as glucose, ammonium sulfate,
magnesium sulfate, potassium phosphate (K2HPO4 and
KH2PO4), iron sulfate, manganese sulfate, biotin, thiamine,
and sodium bicarbonate were used for the organic
production of C. glutamicum medium (Table 1) and all
Table 3. Regression results of the Plackett-Burman design.
Model term
Intercept
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
a
Effect
0.8064
2.1538
-0.3765
0.8071
0.9601
-0.1655
0.4963
-0.6007
-0.0961
-0.7552
Coefficient
1.7711
0.4032
1.0769
-0.1882
0.4035
0.4801
-0.0828
0.2481
-0.3003
-0.0481
-0.3776
Statistically significant at 95% of confidence level (p < 0.05).
SE coefficient
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
0.1092
t-Value
16.21
3.69
9.86
-1.72
3.69
4.39
-0.76
2.27
-2.75
-0.44
-3.46
p-Value
0.000a
0.003a
0.000a
0.109
0.003a
0.001a
0.462
0.041a
0.017a
0.667
0.004a
214
Jeon et al.
Table 4. Coded and real values of factors in the Box-Behnken
experimental design.
Level of factor
Factor
Glucose (X1, g/l)
Ammonium sulfate (X2, g/l)
Dipotassium phosphate (X4, g/l)
Magnesium sulfate (X5, g/l)
-1
10
0
0
0
0
40
7
0.5
0.5
1
70
14
1
1
Table 5. Box-Behnken experimental design matrix with
experimental values of organic acids production by C. glutamicum.
Runs
Fig. 1. Pareto chart of ten-factor standard effects on succinate
production.
The important terms were ammonium sulfate, magnesium sulfate, dipotassium
phosphate, and glucose. Sodium bicarbonate and biotin were excluded by
effect value (Table 3).
factors were set up by two levels (-1 or +1). Although
sodium bicarbonate was not a component of the BT
medium, it has been used to decrease the ratio of
intracellular NADH/NAD+(18) and also as a positive
control for the Plackett–Burman design. The cells were
prepared as explained in the Materials and Methods
section, with 12 different media conditions (Table 2), and
the supernatants were isolated and analyzed by HPLC. The
result of the succinate concentrations is presented in Table 2.
Based on these values, we defined four major factors based
on effect value (Table 3). As shown in the pareto chart
(Fig. 1), the major factors were identified as ammonium
sulfate, magnesium sulfate, potassium phosphate, and
glucose. Sodium bicarbonate and biotin were excluded
because their effects were relatively weak but well-known
factors at the given concentration (100 mM -400 mM)
[18]. In the case of FeSO4, its effects value was lower than
biotin, therefore it was excluded too.
Optimization of Significant Variables on Succinate
Production Using Box-Behnken Design and its Validation
The four significant variables (ammonium sulfate, glucose,
potassium phosphate, and magnesium sulfate) were explored
using Box-Behnken design (Table 4), while the other
variables that had been tested as nonsignificance were
fixed at the standard level (same as in BT medium). The
experimental design and results obtained are displayed in
Table 5. The regression equation obtained after the analysis
of variance gave the response (succinate concentration) as
a function of four significant variables. To obtain a
polynomial equation, a quadratic model was attempted to
fit the data by least squares, and all terms regardless of
their significance were included in the following equation:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Glucose (NH4)2SO4
(g/l)
(g/l)
70
70
10
70
10
10
10
10
40
40
10
10
40
40
40
40
40
40
40
40
40
40
40
70
10
40
40
0
14
7
7
7
7
0
7
7
7
14
0
7
0
7
7
0
7
7
14
0
0
7
7
14
14
0
MgSO4
(g/l)
K2HPO4
(g/l)
0.5
0.5
0.5
0.5
1
0.5
0.5
0
1
0
0.5
0.5
0.5
0.5
0.5
0
1
0.5
0.5
0.5
0.5
0.5
0
0.5
0.5
1
0.5
0.5
0.5
1
0
0.5
0
0.5
0.5
1
1
0.5
0.5
0.5
0
0.5
1
0.5
0.5
0.5
1
1
1
0
1
0.5
0.5
0
Succinic
acid
(mM)
9.04
7.64
6.88
7.98
8.39
7.50
10.54
6.33
6.81
6.42
7.40
9.83
7.04
9.44
7.79
6.40
10.55
7.86
8.09
6.66
11.23
10.75
6.62
7.28
6.72
7.16
9.44
Succinate (mM) = 8.61 + 0.0466 A − 0.194 B + 1.06 C
− 0.163 D
(3)
(A: glucose; B: ammonium sulfate; C: magnesium
sulfate; D: dipotassium phosphate)
Fig. 2 shows the contour plots and surface plots
obtained using the model equation, which clearly shows
the relative effects of the concentrations of glucose and
(NH4)2SO4 while K2HPO4 and MgSO4 have little effect,
MEDIA OPTIMIZATION OF CORYNEBACTERIUM FOR SUCCINATE PRODUCTION
215
Fig. 2. Contour plots of succinic acid production by C. glutamicum ATCC 13032.
(A: glucose (%), ammonium sulfate (g/l); B: glucose (%), magnesium sulfate (g/l); C: glucose (%), dipotassium phosphate (g/l); D: ammonium sulfate (g/l),
magnesium sulfate (g/l); E: ammonium sulfate (g/l), dipotassium phosphate (g/l); F: magnesium sulfate (g/l), dipotassium phosphate (g/l). The white square
box represents the mmol of succinic acid). The arrows represent the increase of succinate production.
respectively. The whole data revealed that ammonium
sulfate is a critical factor to determine succinic acid
productivity (Fig. 1). Comparing media containing the
ammonium sulfate data group (Fig. 2A, 2D, and 2E)
with media devoid of the ammonium sulfate data group
(Fig. 2B, 2C, and 2F), it is clear that ammonium sulfate has
a negative effect in the production media. Three other
factors were also defined that showed an effect on succinic
acid productivity; however, they are not major key factors
because of a limitation of an absolute quantity of extracellular
succinic acid concentration by existence of ammonium
sulfate in the production media.
The expected maximum succinate concentration is
11.6 mM at 12 h with glucose 10 g/l, non-(NH4)2SO4,
magnesium sulfate 0.5 g/l, dipotassium phosphate (K2HPO4)
0.75 g/l, with original potassium dihydrogen phosphate
(KH2PO4) 0.5 g/l, iron sulfate 6 mg/l, manganese sulfate
4.2 mg/l, biotin 0.2 mg/l, thiamine 0.2 mg/l, and sodium
bicarbonate 100 mM. The determination coefficient R2
value of 90.5% (Table 6) indicates the good agreement
between the predicted and experimental values of response
and suggests the mathematical model is quite reliable for
depicting succinate production from C. glutamicum. To
validate our systematic approaches, the cultivation of C.
Table 6. Analysisof variance (ANOVA) for the selected model.
Source
Model
Error
Total
DF
10
43
53
SS
MS
1,199,556 119,956
126,487
2,942
1,326,044
Correlation coefficient (R2) = 0.905.
F-value
40.78
Prob>F
<0.0001
Fig. 3. Comparison of succinate production using orginal BT
medium and modified BT medium (MBT) by suggested methods
(closed circle: BT; open circle: MBT).
216
Jeon et al.
glutamicum under both original production medium (BT)
and modified production medium (MBT) was conducted
in duplicate. Under these conditions, the average succinic
acid concentration was up to 16.3 mM (Fig. 3) at 24 h, and
it was 1.46-fold higher than using the original medium
(11.1 mM). Again, this showed evidence of adequacy and
the accuracy of this model, and the validation experiments
showed that the predicted value agreed with the experimental
values accurately.
In conclusion, this research is the first systematic
optimization for a succinate production medium and it
showed the effect of each component. From our results, we
could decrease the amount of substrates like glucose from
40 g/l to 10 g/l and ammonium sulfate from 7 g/l to 0 g/l
and obtain increased production of succinate by 1.46-fold.
Although some scale-up experiments are yet to be studied,
the optimum culture medium obtained in this experiment
gave evidence that further study with large-scale fermentation
in a fermentor to produce succinic acid from this strain is
warranted.
DISCUSSION
In this study, to find the optimal production medium for
succinate production from C. glutamicum, we applied the
Plackett-Burman design and response surface methodology
using the Box-Behnken design and proposed a new
medium composition designated modified BT (MBT)
medium. The final MBT optimized medium composition
contained glucose 10 g, MgSO4 0.5 g, KH2PO4 0.5 g,
K2HPO4 0.75 g, FeSO4 6 mg, MnSO4 4.2 mg, biotin
0.2 mg, thiamine 0.2 mg, and NaHCO3 100 mM, per liter,
and the differences are glucose from 40 g/l to 10 g/l,
dipotassium phosphate (K2HPO4) from 0.5 g/l to 0.75 g/l,
and ammonium sulfate ((NH4) 2SO4) from 7 g/l to 0 g/l.
Employing this medium, an overall 1.46-fold increase in
succinate production was obtained when compared with
that using the original medium. As for lactate production, a
decrease from 72.6 mM to 58.3 mM by 1.25-fold was
obtained (Supplementary Fig. S1), and this could mainly
have been due to the decreased amount of glucose, as
the glucose consumption rate is proportional to lactate
production [18].
Among important factors to affect succinate production,
the dipotassium phosphate (K2HPO4) seems to exert a pH
effect up to certain point [17]. As initial pH increases from
6.0 to 7.5 below 30oC, the succinate production is increased
in accordance with the increase of pH, although they gave
approximately the same final pH of between 5.0 and 5.3
(data not shown). As for ammonium sulfate, we found that
its addition increased the extracellular concentrations of
lactic acid and acetic acid, whereas the concentration of
succinic acid had decreased (data not shown). Although
the principle effect of ammonium ions on the organic acid
synthetic pathway is unclear, considering the ammonium
ion is broadly related in the synthetic pathway especially in
amino acid synthesis [4, 7], it is estimated that the nitrogen
source can have an effect on the organic acid synthetic
pathway and succinic acid is more involved in amino acid
synthesis in the TCA cycle. As a result, succinate was
expected to be more easily consumed with ammonium ion
than lactate and it might decrease the amount of succinate
with ammonium ion.
Acknowledgments
This work was partially supported by the Basic Science
Research Program (2010-0009942) funded by the Korea
Government (MEST). This Work is also partially supported
by the Korea Ministry of Environment as ‘Converging
Technology Project (2012-000620001)’ and “Eco-Innovation
Project (405-112-038)”.
REFERENCES
1. Chae, Y. K. and J. L. Markley. 2000. Functional recombinant
rabbit muscle phosphoglucomutase from Escherichia coli.
Protein Expr. Purif. 20: 124-127.
2. Corma, A., S. Iborra, and A. Velty. 2007. Chemical routes for
the transformation of biomass into chemicals. Chem. Rev. 107:
2411-2502.
3. Fink, D., N. Weissschuh, J. Reuther, W. Wohlleben, and A.
Engels. 2002. Two transcriptional regulators GlnR and GlnRII
are involved in regulation of nitrogen metabolism in Streptomyces
coelicolor A3(2). Mol. Microbiol. 46: 331-347.
4. Goodchild, J. A. and C. V. Givan. 1990. Influence of
ammonium and extracellular pH on the amino and organic acid
contents of suspension culture cells of Acer pseudoplatanus.
Physiol. Plant 78: 29-37.
5. Guettler, M. V. and M. Jain. 1996. Method for making succinic
acid, Anaerobiospirillum succiniciproducens variants for use
in process and methods for obtaining variants. US patent
5,521,075.
6. Guettler, M. V., D. Rumler, and M. K. Jain. 1999. Actinobacillus
succinogenes sp. nov., a novel succinic-acid-producing strain
from the bovine rumen. Int. J. Syst. Bacteriol. 49: 207-216.
7. Hachiya, T., C. K. Watanabe, M. Fujimoto, T. Ishikawa, K.
Takahara, M. Kawai-Yamada, et al. 2012. Nitrate addition
alleviates ammonium toxicity without lessening ammonium
accumulation, organic acid depletion and inorganic cation
depletion in Arabidopsis thaliana shoots. Plant Cell Physiol.
53: 577-591.
8. Inui, M., S. Murakami, S. Okino, H. Kawaguchi, A. A. Vertes,
and H. Yukawa. 2004. Metabolic analysis of Corynebacterium
glutamicum during lactate and succinate productions under
MEDIA OPTIMIZATION OF CORYNEBACTERIUM FOR SUCCINATE PRODUCTION
9.
10.
11.
12.
13.
14.
15.
oxygen deprivation conditions. J. Mol. Microbiol. Biotechnol. 7:
182-196.
Inui, M., M. Suda, S. Kimura, K. Yasuda, H. Suzuki, H. Toda,
et al. 2008. Expression of Clostridium acetobutylicum butanol
synthetic genes in Escherichia coli. Appl. Microbiol. Biotechnol.
77: 1305-1316.
Jantama, K., X. Zhang, J. C. Moore, K. T. Shanmugam, S. A.
Svoronos, and L. O. Ingram. 2008. Eliminating side products
and increasing succinate yields in engineered strains of
Escherichia coli C. Biotechnol. Bioeng. 101: 881-893.
Krause, J. P., T. Polen, J. W. Youn, D. Emer, B. J. Eikmanns,
and V. F. Wendisch. 2012. Regulation of the malic enzyme gene
malE by the transcriptional regulator MalR in Corynebacterium
glutamicum. J. Biotechnol. 159: 204-215.
Kurzrock, T. and D. Weuster-Botz. 2010. Recovery of succinic
acid from fermentation broth. Biotechnol. Lett. 32: 331-339.
Lee, K., H. S. Joo, Y. H. Yang, E. Song, and B. G. Kim. 2006.
Proteomics for Streptomyces: “Industrial proteomics” for
antibiotics. J. Microbiol. Biotechnol. 16: 331-348.
Lee, P. C., S. Y. Lee, S. H. Hong, and H. N. Chang. 2002.
Isolation and characterization of a new succinic acid-producing
bacterium, Mannheimia succiniciproducens MBEL55E, from
bovine rumen. Appl. Microbiol. Biotechnol. 58: 663-668.
Litsanov, B., M. Brocker, and M. Bott. 2012. Toward homosuccinate
fermentation: Metabolic engineering of Corynebacterium
glutamicum for anaerobic production of succinate from glucose
and formate. Appl. Environ. Microbiol. 78: 3325-3337.
217
16. Ma, F. X., J. H. Kim, S. B. Kim, Y. G. Seo, Y. K. Chang, S. K.
Hong, and C. J. Kim. 2008. Medium optimization for enhanced
production of rifamycin B by Amycolatopsis mediterranei S699:
Combining a full factorial design and a statistical approach.
Process Biochem. 43: 954-960.
17. Oh, I. J., D. H. Kim, E. K. Oh, S. Y. Lee, and J. Lee. 2009.
Optimization and scale-up of succinic acid production by
Mannheimia succiniciproducens LPK7. J. Microbiol. Biotechnol.
19: 167-171.
18. Okino, S., M. Inui, and H. Yukawa. 2005. Production of organic
acids by Corynebacterium glutamicum under oxygen deprivation.
Appl. Microbiol. Biotechnol. 68: 475-480.
19. Okino, S., R. Noburyu, M. Suda, T. Jojima, M. Inui, and H.
Yukawa. 2008. An efficient succinic acid production process in
a metabolically engineered Corynebacterium glutamicum strain.
Appl. Microbiol. Biotechnol. 81: 459-464.
20. Song, H., Y. S. Huh, S. Y. Lee, W. H. Hong, and Y. K. Hong.
2007. Recovery of succinic acid produced by fermentation of a
metabolically engineered Mannheimia succiniciproducens strain.
J. Biotechnol. 132: 445-452.
21. Yu, P., Y. A. Yan, and Y. P. Tang. 2011. Medium optimization
for endochitinase production by recombinant Pichia pastoris
ZJGSU02 using response surface methodology. Afr. J. Biotechnol.
10: 75-84.