GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach...
25
GE nome-scale M etabolic RE construction and analysis of Cyanobacteria: A systems biology approach towards full exploitation of their biotechnological applications Juan Nogales Enrique Departament of Environmental Biology Centro de Investigaciones Biológicas (CSIC), Madrid, Spain [email protected]
-
Upload
antony-hutchinson -
Category
Documents
-
view
217 -
download
1
Transcript of GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria: A systems biology approach...
GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria:
A systems biology approach towards full exploitation of their biotechnological applications
Juan Nogales Enrique
Departament of Environmental BiologyCentro de Investigaciones Biológicas (CSIC), Madrid, Spain
What is a GEnome-scale Metabolic REconstruction?
“A GEMRE is a stequiometric representation of the metabolic capabilities of a given organism at genome-scale, which can be further translated to a mathematical format allowing the computation of its phenotype from its genotype”
Biochemical Representation
HEX1
HEX1
PGI
PFK
TPI
GADP
PGK
ENO
Stequiometric Representationgene
transcript
protein
reaction
Mathematical Representation
Reed JL et al, Nature Reviews Genetics: 2006
Phylogeny of COnstraints Based Reconstruction and Analysis Methods
McCloskey et al, MSB: 2013
Applications of GEMs
Addressing the 1,2 propanediol overproduction in Synechocystis
Eric KnightIceland University
Synechocystis sp. PCC6803
Multiple problems found during the engineering and fermentation processes
• Low genetic stability of the synthetic pathway• The fermentation process lost efficiency at long term• Very low yield (≈ µg/L)
mgsA dkgB gldA
mgsA dkgB gldA
Synechocystis sp. PCC6803 Genome-Scale Model Reconstruction and Analysis
kan mgsA dkgB gldA
DHAP Methylglyoxal Acetol R-1,2-PD
MgsA DkgB GldA
Genome-Scale Metabolic Reconstruction of Synechocytis sp. PCC 6803
Genes Reactions Metabolites BOFLevela
PhotosynthesisModeling
LipidsModeling
Mass and Charge
Balancing
Compartments Reference
678 863 795 Advance Complete Complete Yes [e],[p],[c],[u] (Nogales et al., 2012)
Nd 93 Nd Basic Lumped No No [e],[c] (Shastri and Morgan, 2005)
78 56 72 Basic Lumped No No [e],[c] (Hong and Lee, 2007)
505 652 701 Basic Lumped No No [e],[c] (Fu, 2009)
Nd 46 29 Basic Lumped No No [e],[c] (Navarro et al., 2009)
343 380 291 Intermediate Lumped Partial No [e],[c] (Knoop et al., 2010)
669 882 790 Intermediate Complete Partial No [e],[c] (Montagud et all, 2010)
GrowthCondition
µ (h-1) qglc
(mmol/g/h)
qO2
(mmol/g/h)
qCO2
(mmol/g/h)
Heterotrophic In vivo 0.076 0.85 Nd 1.99 iJN678 0.063 0.85 (-)1.18 2.53 Mixotrophic In vivo 0.059 0.38 Nd 0.0 iJN678 0.056 0.38 1.19 0.0 Autotrophic In vivo 0.085 - 4.82 (-) 3.7 iJN678 0.088 - 5.58 (-) 3.7
54.5 mmol.gDW-1.h-1
Cell mass = 0.5 pgCell diameter = 1.75 µmPhotosynthesis efficiency = 4.6-6 %
13.14 - 17.14 µE.m2.s-1
15 µE.m2.s-1
Model Validation
0 100 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)
In v
ivo
flu
x [m
mo
l/gD
W/h
]
-50 0 50 100 150 200-50
0
50
100
150
200
In silico flux [mmol/gDW/h] (Shastri)
In v
ivo
flux
[mm
ol/g
DW
/h]
0 100 200 300 400-50
0
50
100
150
200
250
300
350
400
In silico flux [mmol/gDW/h] (iSyn669)
In v
ivo
flux
[mm
ol/g
DW
/h]
Carbon Flux Distribution Validation
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)
In v
ivo
flu
x [m
mo
l/gD
W/h
]
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (Shastri)
In v
ivo
flux
[mm
ol/g
DW
/h]
(ho=0.96)
0 100 200 300 4000
50
100
150
200
250
300
350
400
In silico flux [mmol/gDW/h] (iSyn669)
In v
ivo
flux
[mm
ol/g
DW
/h]
(ho=0.67)Photoautotrophic τ=0.96Mixotrophic τ=0.92
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)
In v
ivo
flu
x [m
mo
l/gD
W/h
]
0 50 100 150 2000
20
40
60
80
100
120
140
160
180
200
In silico flux [mmol/gDW/h] (Shastri)
In v
ivo
flux
[mm
ol/g
DW
/h]
(ho=0.88)
0 50 100 150 2000
20
40
60
80
100
120
140
160
180
200
In silico flux [mmol/gDW/h] (iSyn669)
In v
ivo
flux
[mm
ol/g
DW
/h]
(ho=0.59)Heterotrophic τ=0.89
The photosynthetic metabolism... so simple?
PQ PQH2
H+
PCCytC
FdrdFdox
PSI
FNR
NADPH
O2 + H+H2O
H+
ATPase
Pi + ADP ATP
CYTBF
PSII
NADP + H+
E. coli
NDH-1
H+
CydBD
Succ
Fum
O2
CYO
H2O
H+
O2
H2O
SDH
MEHLER
H2O
O2
H2ase
H2
H+
NADH
H+ NDH-13
NADP NADPH
PQ PQH2
H+
CO2 + H2O HCO3 + H+NDH-2
NAD
Extracellular
Periplasm
Cytoplasm
Thylakoid
OM
CM
TM
FQR
Pi + ADP
PQ PQH2PC
CytC
FdrdFdox
PSI
FNR
NADPH
O2 + H+H2O
H+
ATPase
ATP
CYTBF
NADP + H+
PSII
PQH2PQ
CydBD
NDH-14
NADP NADPH
PQPQH2
H+
CO2 + H2OHCO3 + H+
O2H2O
NDH-1
NADP
H2O
H+
PCCytC
CYTBF
CYO
O2
H+
ATPase
Pi + ADP ATPNADPH
H+
SDH
Fum Succ
NDH-2
NAD NADH
CydBD
PQ PQH2
H+
PCCytC
FdrdFdox
SDH CydBD PSI
FNR
NADPH
O2 + H+H2O
Succ
Fum
NDH-1
H+
CYO
O2
H2O
H+
H+
ATPase
Pi + ADP ATP
CYTBF
PSII
O2
H2O
NDH-13
NADP NADPH
PQ PQH2
H+
CO2 + H2O HCO3 + H+
NDH-14
NADP NADPH
PQPQH2
H+
CO2 + H2OHCO3 + H+
O2H2O
NDH-1
NADP
H2O
H+
PCCytC
CYTBF
CYO
O2
H+
ATPase
Pi + ADP ATP
MEHLER
H2O
O2
H2ase
H2
H+
NADPH
H+
NDH-2
NADHNAD
FQR
NADP + H+
SDH
Fum Succ
NDH-2
PQH2PQ
NAD NADH
The photosynthetic metabolism... so simple? so complex !!!.
CydBD
PQ PQH2
H+
PCCytC
FdrdFdox
SDH CydBD PSI
FNR
NADPH
O2 + H+H2O
Succ
Fum
NDH-1
H+
CYO
O2
H2O
H+
H+
ATPase
Pi + ADP ATP
CYTBF
PSII
O2
H2O
NDH-13
NADP NADPH
PQ PQH2
H+
CO2 + H2O HCO3 + H+
NDH-14
NADP NADPH
PQPQH2
H+
CO2 + H2OHCO3 + H+
O2H2O
NDH-1
NADP
H2O
H+
PCCytC
CYTBF
CYO
O2
H+
ATPase
Pi + ADP ATP
MEHLER
H2O
O2
H2ase
H2
H+
NADPH
H+
NDH-2
NADHNAD
FQR
NADP + H+
SDH
Fum Succ
NDH-2
PQH2PQ
NAD NADH
iJN678 as computational tool for studying the photoautotrophic metabolism
iJN678 as computational tool for studying the photoautotrophic metabolism
Defining a key photosynthetic parameter:
• Optimal photosynthesis operates at ATP/NADPH ≈ 1.5• LEF provides a ATP/NADPH = 1.28
-100 -80 -60 -40 -20 0-5
0
5
10
15
20
25
30
35
40
Flu
x (m
mo
l.gD
W-1
.h-1
)
-100 -80 -60 -40 -20 00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Gro
wth
ra
te
Growth rate
CLS LLS
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0-5
0
5
10
15
20
25
30
35
40
Flux (m
mol.gD
W-1.h-1)
Ci O2 NDH13 NDH1 NDH2
syn PSI CYTBF PSII FNR RBPC H2 MEHLER FQR ATPSu CydBD CYO RBPOx NOR PHOTORE
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0-5
0
5
10
15
20
25
30
35
40
Flux (m
mol.gD
W-1.h-1)
Ci O2 NDH13 NDH1 NDH2
syn PSI CYTBF PSII FNR RBPC H2 MEHLER FQR ATPSu CydBD CYO RBPOx NOR PHOTORE
Photon flux (mmol.gDW-1.h-1)
Autotrophic growth as a function of Ci and light availability
* 9 AEF pathways - 5 CEF pathways - 2 PCEF pathways - 2 NADPH consuming pathways * 2 Metabolic pathways - Photorespiration - NO3 reduction
-0.2 -0.15 -0.1 -0.05 0-0.02
0
0.02
0.04
0.06
-0.2 -0.15 -0.1 -0.05 00
1
2
3
4x 10-5
NH4
10-3
Light input (mmol.gDW-1.h-1)Fl
ux (m
mol
.gDW
-1.h
-1)
Quantification and classification of alternative photosynthetic pathways.
Nogales J., et al, PNAS: 2012
Wild type Mehler KO PHOTOR KO Double KOHackenberg et al. Planta. 2009 Sep;230(4):625-37
Photosynthetic robustness at work
0 20 40 60 80 100-5
0
5
10
15
20
0 20 40 60 80 1000
0.02
0.04
0.06
0.08
-5
0
5
10
15
20
25
0
0.02
0.04
0.06
0.08
0.1
-5
0
5
10
15
20
25
30
0
0.02
0.04
0.06
0.08
-5
0
5
10
15
20
25
30
0
0.02
0.04
0.06
0.08
0.1
1
1.5
2
2.5
3
3.5
0 20 40 60 80 100
Wild typePHOTORMEHLERDOUBLETheoretical
3.5
3
2.5
2
1.5
1
Light input (mmol.gDW-1.h-1)
0 20 40 60 80 100Light input (mmol.gDW-1.h-1)
Gro
wth
rate
Gro
wth
rate
Gro
wth
rate
Gro
wth
rate
Flux
(mm
ol.g
DW
-1.h
-1)
Flux
(mm
ol.g
DW
-1.h
-1)
Flux
(mm
ol.g
DW
-1.h
-1)
Flux
(mm
ol.g
DW
-1.h
-1)
ATP/
NAD
PH ra
tio
A
B
C
D
ELLS LLSsub TS CLS
0 20 40 60 80 100Light input (mmol.gDW-1.h-1)
Wild type Mehler KO
PHOTOR KO
Double KO
Photorespiratory 2-phosphoglycolate metabolism and photoreduction of O2 cooperate in high-light acclimation of Synechocystis sp. strain PCC 6803.
Defining additional emergent properties of photosynthetic networks
Essential Synthetic Lethal
Essential Synthetic Lethal Non-essential
Autotrophic 350 158 170
Mixotrophic 259 274 145
Heterotrophic 261 234 183
0 100 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)
In vivo flu
x [mmol/
gDW/h]
-50 0 50 100 150 200-50
0
50
100
150
200
In silico flux [mmol/gDW/h] (Shastri)
In vivo fl
ux [mmol/
gDW/h]
0 100 200 300 400-50
0
50
100
150
200
250
300
350
400
In silico flux [mmol/gDW/h] (iSyn669)
In vivo fl
ux [mmol/
gDW/h]
Carbon Flux Distribution Validation
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)In viv
o flux [m
mol/gDW
/h]
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (Shastri)
In vivo fl
ux [mmol/
gDW/h]
(ho=0.96)
0 100 200 300 4000
50
100
150
200
250
300
350
400
In silico flux [mmol/gDW/h] (iSyn669)
In vivo fl
ux [mmol/
gDW/h]
(ho=0.67)Photoautotrophic τ=0.96Mixotrophic τ=0.92
0 50 100 150 2000
50
100
150
200
In silico flux [mmol/gDW/h] (iJN678)
In vivo flu
x [mmol/
gDW/h]
0 50 100 150 2000
20
40
60
80
100
120
140
160
180
200
In silico flux [mmol/gDW/h] (Shastri)
In vivo fl
ux [mmol/
gDW/h]
(ho=0.88)
0 50 100 150 2000
20
40
60
80
100
120
140
160
180
200
In silico flux [mmol/gDW/h] (iSyn669)
In vivo fl
ux [mmol/
gDW/h]
(ho=0.59)Heterotrophic τ=0.89
Reduced metabolic robustn
ess
Impact of the photosynthetic systems properties on biotechnology
High Photosynthetic Robustness
Low Metabolic Robustness
Eric KnightIceland University
Multiple problems found during the engineering and fermentation processes
• Low genetic stability of the synthetic pathway• The fermentation process lost efficiency at long term• Very low yield (≈ µg/L)
kan mgsA dkgB gldA
DHAP Methylglyoxal Acetol R-1,2-PD
MgsA DkgB GldA
Mutant AMutant BMutant C
Wild type
Multiple problems found during the engineering and fermentation processes
• Low genetic stability of the synthetic pathway• The fermentation process lost efficiency at long term• Very low yield (≈ µg/L)
Computational design of growth-coupled overproducer strains
Nogales et al., Bioengineered 4:3, 1–6; May/June 2013
Computational design of growth-coupled overproducer strains: Autotrophic conditions
Computational design of growth-coupled overproducer strains: Heterotrophic conditions
Computational design of growth-coupled overproducer strains: Mixotrophic conditions
Summary
Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production.Vu et al, Biotechnol J. May 2013, 8(5):619-30
Multiple problems found during the engineering and fermentation processes
• Low genetic stability of the synthetic pathway• The fermentation process lost efficiency at long term• Very low yield (≈ µg/L)
GEnome-scale Metabolic REconstruction and analysis of Cyanobacteria:
A systems biology approach towards full exploitation of their biotechnological applications
Juan Nogales Enrique
Departament of Environmental BiologyCentro de Investigaciones Biológicas (CSIC), Madrid, Spain
Prof. Bernhard O. PalssonDr. Ines Thiele Dr. Stein Gudmundsson
Thanks
