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Metabolic Engineering
Department of Bioinformatic Engineering,Graduate School of Information Science and Technology,
Osaka University
Dr. Hiroshi Shimizu Professor
SubjectsIntroduction of Metabolic Engineering (4/14)Metabolic Pathway (MP) Modeling and Observability of MP (4/21)Metabolic Flux Analysis (5/12)Experimental Determination Method of Flux Distribution with Isotope Labeling(5/19)Metabolic Control Analysis (5/26)Metabolic Control Analysis 2 Metabolic Engineering with Bioinformatics (6/2)
April 14th – June 2nd, 2004
Connection TheoremX0 S1 X3
e1 e2
v1 v2
Large FCCs: Small Elasticity(Rigid Reaction)
11
21
2
1vS
vS
Je
Je
CC
εε
−=
Small FCCs: Large Elasticity(Flexible Reaction)
v1
v2
S1
Inhibition
Time
v1
v2
S1
Inhibition
Time
DetailedReactionKineticsor Model
GroupPerturbationExperiments
Measurementof Flux Changes
GroupFlux ControlCoefficients
Elasticities
IndividualFlux ControlCoefficients
GroupFlux ControlCoefficients
IndividualFlux ControlCoefficients
Individual Reaction PerturbationExperiments
Map of Control Distribution in Metabolic Network
Top-DownApproach
Bottom-UpApproach
Two Methods in MCA
Determination of Flux Control CoefficientsDetermination of Flux Control CoefficientsBottom-up Approach
Detailed reaction kinetic modelElasticity coefficients
Critical LimitationsRequirements of each and every enzyme reaction kineticsIn vitro experimental data<->in vivo metabolic reaction control?
Top-down ApproachPerturbation experiments: use of well defined changes in enzyme
activities Measurement of flux changes: application of MFA
Bottom-up Approach
Summation and Connection Theorem
⎥⎦
⎤⎢⎣
⎡=⎥
⎦
⎤⎢⎣
⎡⎥⎦
⎤⎢⎣
⎡0111
2
121
11
Je
Je
vS
vS C
Cεε
: knownvjSiε J
eiC : to be solved
Every and all kinetics: known FCCs: to be solved
Bottom Up Approach for MCA
deleted based on copyright concern.Prof. Jens Nielson GroupTechnical Univ. Denmark
2
Fig. 11.3 The penicillin biosynthetic pathway in Penicillium chrysogenumPissala et al., Biotechnol Bioeng, 51, 168-176 (1996)
Fig. 11.3 The penicillin biosynthetic pathway in Penicillium chrysogenumPissala et al., Biotechnol Bioeng, 51, 168-176 (1996)
Fig. 11.4 Elasticity coefficients for the two enzymes ACVS and IPNS with respect to LLD-ACV during fed-batch cultivation. Note that the elasticity coefficient for ACVS is negative.
Fig. 11.4 Elasticity coefficients for the two enzymes ACVS and IPNS with respect to LLD-ACV during fed-batch cultivation. Note that the elasticity coefficient for ACVS is negative.
Fig. 11.5 Flux control coefficients for the two enzymes ACVS and IPNS during fed-batch cultivation.
Fig. 11.5 Flux control coefficients for the two enzymes ACVS and IPNS during fed-batch cultivation.
Metabolic Control Analysis in Metabolic Control Analysis in Amino Acid ProductionAmino Acid Production
Hiroshi ShimizuDepartment of Bioinformatic Engineering
Graduate School of Information Science and TechnologyOsaka University, Japan
GIM2002, Gyeongju, Korea
Top down Approach
Metabolic Control Analysis in Glu Production1. Control Mechanism of Glutamate Production2. MCA around at A Key Branch Point
deleted based on copyright concern. Nampoothiri.et.al., AMB,58,89-96(2002)Eggering and Sahm.JBB,92,201-231(2002)
deleted based on copyright concern.
deleted based on copyright concern.
deleted based on copyright concern.
3
Dr. Kimura et al. at Ajinomoto co., Inc.
For enhancement of L-glutamate production,Depetion condition of biotin which is required for cell growth.Addition of a detergent such as polyoxyethylene sorbitan
monopalmitate (Tween 40).Addition of a lactum antibiotic such as penicillin.
Back ground of Metabolic Control Analysis of L-Glutamate Production
Decrease in 2-oxoglutarate dehydrogenase complex (ODHC)activity.
Enhancement of production of L-Glutamate.
Metabolic Control Analysisaround at a Key Branch Point of 2-Oxoglutarate(αKG)
Quantification of Magnitude of Impact of (1) Enhancement of isocitrate dehydrogenase(ICDH),(2) Enhancement of glutamate dehydrogenase (GDH),
or,(3)Attenuation of 2-oxoglutarate dehydrogenase complex
(ODHC)on
Targeted Flux of L-Glutamate
Flux Control Coefficients (FCC)
Bacterial Strains and Plasmids Used in This Study
a:Bormann et al., Molecular Microbiology, 6, 317 (1992)b: Eikmanns et al., J. Bacteriol., 177, 774 (1995)
Cultivation conditionCultivation conditionpH pH 7.207.20Temperature Temperature 31.531.5℃℃DO DO > 3.0> 3.0 ppmppmAir flow rate Air flow rate 1 1 vvmvvm
Fermentation
Carbon Source: GlucoseNitrogen Source: AmmoniaTrigger of Glutamate Production : Biotin depletion
Exhaust gas CO2 analyzer
O2 analyzer
NH3
PH sensor
Thermo sensor
DO sensor
Data logger RS232C
AirFlow meter
Experimental Apparatus
RS232C
balance Laser turbidimeter
Metabolic Map of Glutamate Synthesis
IsoIso--citratecitrate
SuccinylSuccinyl--CoACoA
αα--ketoglutarateketoglutarate
LL--GlutamateGlutamate
rr77
rr88rr99
ICDHICDH
ATP
ADP
NAD+
+ATP
O2
NADH2
LacNADH2
6NADPH2
3CO2
Cell
NH3
PEP
PYR
G-6-P
G-A-P
Glucose
F-6-P
NADH2CO2
NADH2ATP
ATP
PEPPYR
CO2
NADH2
GTPGTP
CO2
5/3NADH2
NADP+
NADPH2OXA
ICIT
SucCoA
NADPH2 NADP+
NH3
L-Gluα-KG
CO2
GDHODHC
Modeling of metabolic pathwayChecking ConsistencyMetabolic Flux AnalysisMetabolic Control Analysis
deleted based on copyright concern.
4
0=Er
[ ]iC
mC
rr
rAr
=
=− 0
Material Balance Equation in the Pathway
(16 Calculated reaction rates (ri))12 Intermediate metabolites(constraints))7 Extracellular measured reaction rates rm))
Where, A: stoichiometric coefficients matrix (19x16))rc: calculated reaction rates vector (16x1)rm: measured reaction rates vector (19x1)
Degree of information redundancy = (12+7)-16=3Mole flux distribution: determined by the least squares method
(i=1..16)
rm=[rgluc,rcell,rglu,rCO2,rO2,rNH3,rlac,rG6P,rF6P,rGAP,rPEP,rpyr,ricit,rKG,rSucCoA,rOxA,rATP,rNADH,rNADPH]
[ ] mTT
c rAAAr ˆˆ1−
=
Time course of Consistency IndexTime course of Consistency Index
ΧΧ22 valuevalue(=(=11.311.3))(degree of freedom :3)(degree of freedom :3)
0 10 20 300
10
20
30
Time(h)
Con
sist
ency
inde
x (Method 1)
(Method 2)
Method1: addition rate of NH3 = ammonia uptake rateMethod2: addition rate of NH3 = ammonia uptake rate + addition rate for neutralization of
the produced L-glu (pKa=4.25)
G-6-P
G-A-P
Glucose
F-6-P
PEP
PYR LacNADH2
NADP+
NADPH2
CO2
ICIT
6NADPH2
OXAGTPGTP
Cell
L-Glu
SucCoA CO2
CO2
CO2
NAD
5/3NADH2
NADPH2
3CO2NADH2ATP
ATP NADH2
NH3ATP
PEPPYR
NADP+
NADH2
NAD+
+ATP
ATP
ADP
O2
NH3
α-KG
r1r1: 100100r2r2:42.4/42.4/105105
r3r3:47.1/47.1/101101
r4r4:96.6/96.6/201201r14r14:72.3/72.3/1.11.1
r5r5: 0/0/00
r12r12:2.4/2.4/00
r7r7:72.9/72.9/107107
rr99:11.1/11.1/93.593.5rr1010:61.8/61.8/13.313.3
rr1111:11.1/11.1/93.593.5
rr88:61.8/61.8/13.313.3
rr1313:0/0/00
rr1515:167/167/172172
rr1616:0/0/237237r6r6:72.9/72.9/107107
Flux Distribution Analysis
ri (growth phase/production phase)
rgluc=100
α-KG
0.267(×2.41)
0.660(×1.06)
0.9280.928((××1.27)1.27)
ICIT
L-GluSucCoA
0.7290.729
0.6180.618 0.1110.111
ICIT
L-Glu
α-KG
SucCoA
ICDHICDH
enhancedenhanced
riri=3.0=3.0
Flux Distribution Analysis at the Flux Distribution Analysis at the ααKG KG Branch PointBranch Point
((Case 1: ICDH enhancedCase 1: ICDH enhanced))
0kJ
)( ki
rk
f
J
WT->AJ13679
:
:0
ri
i
e
eEnzyme Activity Amplification factor ri
0i
ri
i eer =
Flux Amplification factor kif
0,
k
rikk
i JJ
f =Enzyme activity at the original state
Enzyme activity at the perturbed state :
:
,
0
rik
k
J
J Flux at the original stateFlux at the perturbed state
0.7290.729
0.6180.618 0.1110.111
SucCoA
ICIT
L-Glu
α-KG
0.8260.826((××1.13)1.13)
0.7130.713((××1.15)1.15)
0.1130.113((××1.02)1.02)
α-KG
SucCoA L-Glu
ICIT
GDHGDH
enhancedenhanced
riri=3.2=3.2
Flux Distribution Analysis at theFlux Distribution Analysis at the ααKGKG Branch PointBranch Point
((Case 2: GDH enhancedCase 2: GDH enhanced))
)( ki
rk
f
J0kJ
WT->AJ13678
Enzyme Activity Amplification factor ri
0i
ri
i eer =
Flux Amplification factor kif
0,
k
rikk
i JJ
f =Enzyme activity at the original state
Enzyme activity at the perturbed state :
:
,
0
rik
k
J
J Flux at the original stateFlux at the perturbed state
α-KG
L-Glu
ICIT
SucCoA
0.7290.729
0.6180.618 0.1110.111
SucCoA
α-KG
L-Glu
ICIT
0.6770.677((××0.93)0.93)
0.1470.147(×(×0.240.24))
0.5300.530((××4.77)4.77)
ODHCODHC
attenuatedattenuated
riri=0.52=0.52
Flux Distribution Analysis at theFlux Distribution Analysis at the ααKGKG Branch PointBranch Point
((Case 3: ODHC attenuatedCase 3: ODHC attenuated))
)( ki
rk
f
J0kJ
By depletion of biotin
Enzyme Activity Amplification factor ri
0i
ri
i eer =
Flux Amplification factor kif
0,
k
rikk
i JJ
f =Enzyme activity at the original state
Enzyme activity at the perturbed state :
:
,
0
rik
k
J
J Flux at the original stateFlux at the perturbed state
5
Estimation of Impact of Changes in EnzymeActivities at αKG
Aim of Metabolic Control AnalysisRealization of over production of targeting metabolites
Identification of enzyme reaction(s) with the greatest impact on the overall flux
Methodology of MCA
Quantification of the magnitude of the impact of each enzyme activity on the overall flux
Flux Control Coefficients (FCC)
eJ
rJreD
k
iJki ∆
∆=
Determination of FCC by large perturbation experiments at the Branch Point
( eir , Jkr )(ei0 , Jk0 )
△J
ei
△e
J
eiJk
JeC
k
iJki ∂
∂=
)( ikDFC kJi
kJi
kJi ≠×=
ririD
ririC
FkJ
i
iJi
kJi 11
11
−−
−−
= 0/ eieirri =
Definition of FCC: infinitesimal perturbation
Real experiment: large perturbation(Deviation index)
)( ikDC iJi
iJi ==
ICD
αKG
GluSucCoA
J7
J8J9
eICDH
eODHCeGDH
k
CikICDH ODHC GDH
J7 0.32 0.33 0.19
J8 0.08 3.43 0.22
J9 1.67 -16.9 0.025
Table. FCCs around at the branch point of αKG
J7: flux through ICDHJ8: flux through ODHCJ9: flux through GDH (Glu production)
Cik : Impact of change in i-th enzyme activity
on k-th flux
ICDH(×5)
ODHC(×1/5)
GDH(×5)
f 7 1.4 0.91 1.2f 8 1.1 0.06 1.2f 9 2.8 5.6 1.0
Table. Prediction of flux change by enzyme activity enhancement and attenuation
J7: flux through ICDHJ8: flux through ODHCJ9: flux through GDH (L-Glu production)
fik : k-th flux amplification by i-th enzyme activity change
*Cki
ICDH ODHC GDH
J7 0.92 0.07 0.01
J8 0.23 0.76 0.01
J9 4.74 -3.74 0.001
Table. gFCCs around at the branch point of αKG calculated by flux amplification factor only
J7: flux through ICDHJ8: flux through ODHCJ9: flux through GDH (Glu production)
*Cik : Impact of change in i-th group enzyme activity
on k-th group flux
Corynebacterium efficiens:
・Glutamate Producing Strain
・Ability to grow at higher temperature (37oC)
than C. glutamicum (31oC)
*Mechanism of Glutamate Production*Effect of Temperature on Glutamate Production
in C. glutamicum and C. efficieins
6
0 10 20 30 40 500102030405060
020406080
Time(h) Time(h)
Time(h) Time(h)
OD
660
glut
amat
e(g/
L)O
D66
0gl
utam
ate(
g/L)
OD
660
glut
amat
e(g/
L)
OD
660
glut
amat
e(g/
L)
gluc
ose(
g/L)
gluc
ose(
g/L)
gluc
ose(
g/L)
gluc
ose(
g/L)
31.5℃
0 10 20 30 40 500102030405060
020406080
37.0℃
0 10 20 30 400102030405060
020406080
0 10 20 30 40 500102030405060
020406080
Time courses of OD660, gluc, and glu of C. glutamicum (a), (b) and C. efficiens (c), (d). Temperature: 31.5℃ (a), (c) and 37.0℃ (b), (d).Symbols: ●, OD660; △, glucose concentration; ○, glutamate concentration
(a)
(d)(c)
(b)
C.glutamicum
C.efficiens
Glu
Glucose 31.5℃Flux Distribiution at αKG
TCA cycle
Time(h) Time(h)0 10 20 30 40
00.0010.0020.003
Flux
/ dr
y ce
ll (m
ol /
h / g
-cel
l) C. glutamicum C. efficiens
0 10 20 30 400
0.0010.0020.003
α-KG
Isocitrate
GlutamateSuccinyl-CoA
r7 (ICDH)
r9 (GDH) r8 (ODHC)
Glu
Glucose 37.0℃
TCA cycle
Time(h) Time(h)Flux
/ dr
y ce
ll (m
ol /
h / g
-cel
l) C. glutamicum C. efficiens
0 10 20 30 400
0.0010.0020.003
0 10 20 30 400
0.0010.0020.003
α-KG
Isocitrate
GlutamateSuccinyl-CoA
r7 (ICDH)
r9 (GDH) r8 (ODHC)
Flux Distribiution at αKG
31.5℃ 37.0℃
C. g
luta
mic
umC
. effi
cien
s
ICDH and GDH Activities
Activities of ICDH and GDH do not change significantly.
ICDH GDH
0 10 20 30 4000.5
11.5
2
Time(h)
Time(h)
Time(h)
Time(h)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
0 10 20 30 4000.5
11.5
2
0 10 20 30 4000.5
11.5
2
0 10 20 30 4000.5
11.5
2
31.5℃ 37.0℃
C. g
luta
mic
umC
. effi
cien
s
Activity of ODHC
ODHC of C. glutamicum at 31.5 ℃ decreases to 30 %
ODHC of C. efficiens decreases 75% only.
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Spec
ific
enzy
me
activ
ity(μ
mol
/min
/mg-
prot
ein)
Time(h)Time(h)
Time(h) Time(h)
0 10 20 30 400
0.01
0.02
0 10 20 30 400
0.01
0.02
0 10 20 30 400
0.01
0.02
0 10 20 30 400
0.01
0.02
Glu
Glucose
Summary of Results of MFAAnd Enzyme Activity Measurement
TCAcycle
Flux Change Specific Activityr8 (ODHC)
r9 (GDH)
Decrease at Glu production phase
Increase at Glu production phase
Constant
α-KG
Isocitrate
GlutamateSuccinyl-CoA
r7 (ICDH)
r9 (GDH) r8 (ODHC)
Decrease at Glu production phase
7
Glu
Glucose
Km values of enzymes
TCAcycle
ICDH
ODHC
GDH
0.03 mM
0.03 mM
5 mM
0.03 mM
0.08 mM
2 mM
C. glutamicum C. efficiens
α-KG
Isocitrate
GlutamateSuccinyl-CoA
r7 (ICDH)
r9 (GDH) r8 (ODHC)
C.glutamicum C.efficiens
Temp(℃) 31.5 37.0 31.5 37.0
∆J8/∆ODHCa 0.11 0.39 0.22 0.34
∆J9/∆ODHCa -0.082 -0.086 -0.096 -0.123
Sensitivity of J8 and J9(Glu) against ODHC Change
a: (mol/h/g-cell)/(µmol/min/mg-protein)
GDH
α-KG
Before Biotin DepletionBefore Biotin Depletion
α−KG conc is not high enough to produce Glu and Flux r8 in TCA is large, because Km of GDH is large.
ODHC α-KG
Succinyl-CoA
ICDH
Low Km
r 8r 9
High Km
Succinyl-CoAGlutamate
Decrease in activity
After Biotin DepletionAfter Biotin DepletionDecrease in ODHC activity happens. α−KG conc is increased and flux r9 to Glu production is enhanced.
GDH
ODHCα-KG
ICDH
r 9
r 8
High Km
Targeting ProductProductivityProduction Yield
Specific rates ofgrowthsubstrate consumptionproduct formationCO2 formationO2 consumptionby-product formation
Metabolic fluxesenzyme reaction energy formprecursor formpolymerizationtransport
Metabolic Control
Optimal profile of Specific ratesMacroscopic modeling
Metabolic reaction modelMetabolic flux analysisMetabolic control analysis
Micro-array2-D electrophoresisLC-massData mining
Bio-informaticsgenome transcriptomeproteomemetabolome
10-103 103-105<10No. of variables
Methodology&
Tools
Cell activityinfo Metabolic pathway info Molecular level infoObjectives
SummarySummaryMetabolic flux distribution analysis and large perturbation
experiments was applied in order to analyze metabolic control.
ODHC attenuation has the greatest impact on glutamate flux.
Flux amplification of glutamate can be predicted quantitatively.
Bioinformatic data will be integrated in Metabolic Engineering.
Recommended