Metabolic flux analysis of L-arginine of
Saccharomyces cerevisiae
Guohui Li, Dondguang Xiao
Guohui Li, Qiding Zhong, Zhenghe Xiong
Key Laboratory of Industrial Microbiology (Ministry of
Education)
School of Biological Engineering, Tianjin University of
Science & Technology
Tianjin, China
e-mail: xiao99@tust.edu.cn
National Standard Center of Food & Fermentation
China National Research Institute of Food & Fermentation
Industries
Beijing, China
e-mail: xzh57@263.net
Abstract—Metabolic network of the Saccharomyces cerevisiae was
established and modified by stoichiometry. The concentrations of
extra-cellular metabolites were determined under quasi-steady
state (18h~20h) of the batch culture. The metabolic flux
distribution maps of L-arginine was obtained and analyzed. This
study indicate that the flux of L-arginine account for 0.30% of
the total. Based on metabolic flux analysis, the main nodes are
pyruvic acid and glutamic acid. The analysis may play an
important role in helping fermentation industry to control ethyl
carbamate metabolism for further reduction of rice wine
fermentation process.
In the process of Chinese yellow rice winemaking, the
main parameters during fermentation period exactly fell in the
interval. It was reported that 90% of EC in Chinese yellow rice
wine was formed by the reaction between urea and ethanol [4].
However, there are no available studies on the formation of EC
in Chinese yellow rice wine. Metabolic flux of L-arginine of S.
cerevisiae from glucose in batch culture was analyzed
according to flux balance model which we established in this
paper.
II.
Keywords- Saccharomyces cerevisiae; L-arginine; metabolic flux
analysis; amino acid
I.
INTRODUCTION
Ethyl carbamate (EC, urethane) that widely exists
infermented food, occurs naturally in the process of
fermentation and storage. It has been reported that EC shows a
potential for carcinogenity as administered in high doses in
vivo[1]. Thus, the wine industry is interested in developing
suitable methods to reduce EC level in the final products. The
previous investigation indicates that the formation of EC in
alcoholic beverage is mainly related to its precursor, urea,
which was carried out by yeasts from arginine, reacts with
ethanol under specific conditions during winemaking and
storage[2].
L-Arginine is not only a conditionally essential basic
amino acid primarily involved in urea metabolism and
excretion as well as DNA synthesis, but also a semi-essential
amino acid of human and animal body. However, it would be a
harmful substance as a by-product of fermentation.
Flux balance analysis (FBA) has been used extensively for
predicting cellular growth and product secretion patterns in
microbial systems [3]. Extensions of classical FBA allow the
redesign of metabolic networks for the overproduction of
desired metabolites through gene deletions and insertions,
which are implemented by removing or adding intracellular
reactions to the network. These computational methods provide
metabolic engineering targets that are experimentally testable.
Natural Science Foundation of China (Project Number: 311101333)
MATERIALS AND METHODS
A. Microorganism
S. cerevisiae SCFF-Y-032 was provided by rice wine
factory (Shao Xing, China).
B. Fermentation Conditions
The mineral medium used for cultivation containing 20 g/L
glucose, 0.5 g/L (NH4)2HPO4, 1.0 g/L (NH4)2SO4, 0.05 g/L
MgSO4 •7 H2O, 0.025 g/L citric acid, 0.5 g/L KCl, 0.03 g/L
CaCl2•2 H2O, 3 mg/L FeCl3•6 H2O, 2.1 mg/L MnSO4•H2O, 1.8
mg/L ZnSO4•7 H2O, 0.5 mg/LCuSO4•5 H2O, 60.3 mg/L myoinositol, 30 mg/L Ca-panthotenate, 6 mg/L thiamin•HCl, 1.5
mg/L pyridoxine•HCl, 0.03 mg/L biotin and 50 mmol/L
phosphate buffer (pH 6.2). All chemicals were of analytical
grade and purchased from Sigma (St. Louis, USA).
Cultivations were performed in a shake flask with a culture
volume of 100 ml at 30°C and 150 rpm.
C.
Determination of extracellular concentration of
metabolites
Residual glucose was measured by SBA-Biosensor
(Shandong Academy of Sciences). Ethanol was analyzed by
automated biochemical analyzer (Konelab Arena20). Cell
concentration was measured with ultraviolet spectrophotometer
under the wavelength of 600 nm. Acetic acid, glycerol and
amino acids were determined by High Performance Liquid
Chromatography (WATERS).
D. The method of establishing flux balance model
A flux balance model based on a steady-state metabolic
network reconstruction is developed for in silico analysis of
Saccharomyces cerevisiae metabolism and arginine
metabolism in batch culture. To analysis the availability of
stoichiometric models of cellular metabolism, a popular
approach is flux balance analysis (FBA), where a linear
programming problem is posed to resolve the intracellular
fluxes in an underdetermined stoichiometric model under the
assumption that the cell utilizes available resources for growth
rate maximization [5].
E. Analysis of accumulation rates of metabolites
The accumulation rates of metabolites were calculated by
the following equation (1):
V 
(C 20  C 18)  1000
2MW
(1)
In which V is accumulation rate of metabolite (mmol•L-1h), C20 and C18 is the metabolite concentration at 20h and 18h,
respectively (g•L-1), MW is the molecular weight of
metabolite.
1
III.
RESULTS AND DISCUSSION
The central metabolic routes, biosynthetic pathways
towards the different amino acids were implemented. In most
researches amino acid formation was carried out by a single
pathway in either one of the two compartments. Exceptions are
the biosynthesis of alanine and glycine. For alanine
biosynthesis a cytosolic and a mitochondrial pathway was
implemented in the model. In addition to the known
mitochondrial route, a cytoplasmic alanine amino transferase,
generating alanine from cytosolic pyruvate, was recently
identified in S. cerevisiae. For glycine the alternative route via
threonine aldolase [6] was taken into account in addition to the
synthesis from serine.Finally, complete content and
organizational editing before formatting.
A. Establish model of L-arginine
The analysis shows that the carbon skeleton of L-arginine,
energy and reducing power are mainly from glucose. In
addition to the central metabolic pathway of glycolysis,
pentose phosphate cycle and tricarboxylic acid cycle, the
synthesis pathway of 18 amino acids also are considered in this
model. Meanwhile, ethanol and acetic acid are the main
metabolites of S. cerevisiae, as well as glycerol. According to
above-mentioned theory and the research results of Nissen [7],
by using flux balance analysis, the metabolic network of Larginine and equations of equilibrium were constructed (Fig I).
Metabolite pools were established according to metabolic
network of L-arginine synthesis:
Glucose 6-phosphate: r1-r2-r8=0
(1)
Ribulose 5- phosphate: r8-r9-r10=0
(2)
Xylulose 5- phosphate: r9-r11-r12=0
(3)
Ribose 5- phosphate: r10-r11-r22=0
(4)
Erythrose 4-phosphate: r13-r12-r18=0
(5)
Sedoheptulose 7-phosphate: r11-r13=0
(6)
Fructose 6-phosphate: r2-2r3+r12+r13=0
(7)
Gylceraldehyde-3-phosphate: r3-r4+r11+r12-r13+r39+r46=0
(8)
3-Phosphoglycerate: r4-r5-r19 =0
(9)
Phosphoenolpyruvate: r5-r6-r18-r17=0
(10)
Pyruvate: r6-r7-r23-r25-r32-r41=0
(11)
Acetyl Coenzyme A: r7-r14-r21-r27-r33+r44=0
(12)
α-ketoglutarate: r14-r15-r16+r19+r38+r23+r26+r27+r29+r32+
r33+r35+r36+r37=0
(13)
Glutamate: r15-r19-r23-r24-r26-r27-r28-r29-r32-2r33-r35-r36r37+r39=0
(14)
Glutamine: r24 -r38-r39=0 (15)
Oxaloacetic acid: r16 -r14-r29+r33+r17=0
(16)
Serine: r19-r20-r21-r39=0
(17)
Chorismate: r18-r34-r39=0
(18)
α-ketoisovalerate: r25-r26-r27=0
(19)
Threonine: r30-r32=0 (20)
Aspartic acid: r29-r30-r31-r33=0
(21)
NADPH: 2r8+r14-r15-r18-r25-2r28-2r29-2r30-r31-r32-r33
+r36-2r37+1/2 r43=0
(22)
PrePhA: r34-r35-r36=0 (23)
Phosphoribosylpyrophosphate: r22-r38-r39=0
(24)
Acetaldehyde: r41-r42-r43=0
(25)
Acetate: r43-r44=0
(26)
Glycerol-3-phosphate: r45-r40=0
(27)
Dihydroxyacetone- phosphate: r3-r45-r46=0
(28)
There were 28 equations and 46 unknown numbers in this
matrix of metabolic flux. The degrees of freedom in this
system were 18, it means that we must measure the formation
and consumption rats of 18 kinds of metabolites to determine
the flux distribution in metabolic network. The extracellular
concentration of glucose, ethanol, acetic acid, glycerol and
some amino acids was measured in this research at different
fermentation times.
Figure I. Simplified metabolic network of L-arginine synthesis for
S. cerevisiae.
B. Metabolic flux analysis of L- arginine
In the fermentation process sample and analysis on time,
then draw out the curves of yeast fermentation process (Figure
II).
synthetic flux of other 17 amino acids, such as Ala, His, Pro,
Lys, etc, which would set the stage for the researches on amino
acid metabolism during rice wine production in
future.
TABLE II.
Figure II. Fermentation time courses of Saccharomyces cerevisiae
From the analysis of this figure, the yeast entered stable
phase after 18h. We could suppose this state was pseudo
steady state. It meant that intracellular metabolic intermediates
accumulation rate was zero. Meanwhile, accumulation rate of
L-arginine was faster between 18h~20h. Consequently, the
concentrations of extracellular metabolites were determined at
fermentation 18h and 20h (Table I). Then the accumulation
rates of metabolites were calculated by the following equation
(1). Arginine metabolic flux was simulated by software of
MATLAB 2009a (table II)
TABLE I.
ACCUMULATION RATE OF METABOLITES
Metabolite
Accumulation rate（mmol/h·L）
glucose
ethanol
1.972
1.8598
acetate
glycerol
0.03845
1.598
arginine
0.002939
cysteine
0.0007371
phenylalanine
arginine
isoleucine
leucine
proline
methionine
0.0004541
0.02695
0.002930
0.001143
0.001789
0.001913
valine
0.0004473
glycine
0.006097
lysine
tyrosine
tryptophan
histidine
0.006476
0.0002257
0.001300
0.004930
In the whole metabolism network, glutamate and ethanol
account for the proportion of total flux which is 3.64 and 47.26,
respectively. That results has no obvious difference with the
data of Nissen [7] and Oliver [8]. Furthermore, L-arginine took
up 0.30 of the total flux. In this study we also analyzed the
METABOLIC FLUX DISTRIBUTION
Reaction
Rate
r1
100*
r2
93.88
r3
46.94
r4
86.78
r5
86.45
r6
85.60
r7
6.90
r8
6.12
r9
2.88
r10
3.24
r11
2.93
r12
-0.05
r13
0.05
r14
7.45
r15
3.64
r16
7.03
r17
0.75
r18
0.10
r19
0.34
r20
0.31
r21
0.13
r22
0.32
r23
1.37
*Glucose was considered 100.
Reaction
r24
r25
r26
r27
r28
r29
r30
r31
r32
r33
r34
r35
r36
r37
r38
r39
r40
r41
r42
r43
r44
r45
r46
Rate
0.63
0.08
0.02
0.06
0.09
0.39
0.15
0.10
0.15
0.30
0.03
0.02
0.01
0.33
0.25
0.07
8.01
47.61
47.26
0.35
0.31
8.01
38.84
 
C. Main nodes analysis
Synthetic rate of metabolites were controlled by enzyme
activities in metabolic pathway. However, the accumulation of
metabolites was the synthetic result of various nodes flux
distribution. Flux of the mainly nodes which effected Larginine synthesis, including pyruvate and glutamate, was
analyzed, according to metabolic network of L-arginine
synthesis. These provided theoretical basis for flux distribution
of amino acid metabolism.
As shown in table II, the flux of pyruvate nodes was more
complex than others. There is 85.60 flux from
phosphoenolpyruvate into pyruvate node. And then the flux
would be distributed into different pathway, including AcCoA
6.90, Lys 0.33, Ile 0.15, Ala 1.37 and α-ketoisovalerate
0.08(0.02(Val)+0.06(Leu)). The most flux was Ala in all the
amino acid, and L-lysine was the largest product outflow
branch at node of L-aspartic acid. The synthesis of L-leucine
consumed 0.15 AcCoA in the next way.
Figure III. Effective metabolic flux distribution at the glutamate node

L-glutamate was the precursor and the first reactants of 8step reaction during the L-arginine biosynthesis. The
accumulation of L-arginine was directly related to flux
distribution in this node. Table III indicates that the flux
distribution at the glutamate node.
As the L-glutamate was the amine donor in amino acid
biosynthesis, syntheses of other amino acids led to plenty of
ineffective metabolic circulation during α-ketoglutarate and Lglutamate. This result was consistent to previous researches.
Arginine was the final metabolites of branched metabolic
pathway and its synthesis was regulated by itself.
Acetylglutamate kinase which was inhibited by L-arginine was
the key enzyme. It was feedback inhibited and repressed by Larginine. Thus, accumulation of L-arginine was determined by
the degree of relieving the feedback inhibition and repression
of acetylglutamate kinase. It was important to reduce effective
metabolic flux of L-arginine and improve the synthesis of other
amino acids through by decreasing the resistant level of
Saccharomyces cerevisiae L-arginine analogue. L-arginine was
precursor of EC. Current study would provide more evidences
for illustrating EC distribution and formation during Chinese
yellow rice winemaking.
IV.
ACKNOWLEDGMENT
Our research was supported by Natural Science Foundation
of China (Project Number: 311101333), express cordial
acknowledgment here!
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In this research, a metabolic model of Saccharomyces
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control synthesis of L-arginine. We also analyzed the synthetic
flux of other 17 amino acids, such as Ala, His, Pro, Lys, etc,
which would set the stage for the researches on amino acid
metabolism during rice wine production in future.
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