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Systems Modeling of C4 and CAM Photosynthesis
Xinguang Zhu Plant Systems Biology Group
CAS-MPG Partner Institute for Computational Biology
C4-CAM Meeting, Aug 9th 2013, Urbana IL
Roadmap
• Rationale of dynamic systems modeling and new options to improve light and water use efficiency
• Systems model of NADP-ME type C4 photosynthesis and blueprint for engineering an NADP-ME type C4 photosynthesis into a C3 crop
• Physiological significance of co-existing decarboxylases?
• What is the critical step for C4 evolution?
Wh =
Harvested
yield
S
Total solar
energy
i
Interception
efficiency
c
Conversion
efficiency
Partitioning
efficiency
Monteith (1977) Philosophical Transactions of the Royal Society of London, 281 277-294
90%
60%
Zhu et al (2008) Current Opinion in Biotechnology
4.6% 6%
What c is achieved in the field?
• The highest c over a whole growing
season:
–C3: 2.4%
–C4: 3.7%
• Common c over a whole growing season:
– < 0.5%
Reviewed in: Zhu et al (2008) Current Opinion in Biotechnology
How to engineer a higher efficiency?
A systems approach!
Sink
KG
O 2
PGA
PGCA
GCA
GCA
GOA
GLY SER
HPR
GLU
O 2
H 2 O 2
GCEA
GCEA
111
112
121
122
123
113
124
NAD
NADH
Pi
ATP
ADP
GLY + NAD + CO 2 + NADH
131
101
GOA
GLY
KG
O 2
PGA
PGCA
GCA
GCA
GOA
GLY SER
HPR
GLU
O 2
H 2 O 2
GCEA
GCEA
111
112
121
122
123
113
124
+
NA
Pi
ATP
ADP
GLY + NAD + CO 2 + NADH
131
Stroma
Cytosol, mitochondria, and peroxisome
GOA
GLY
RUBP
CO 2
PGA + PGA
1
DPGA
ATP
ADP
GAP
NADPH +H NADP+Pi 2
GAP GAP GAP DHAP
DHAP
FBP
Pi F6P
3 4
5
6
7 Xu5P E4P
8
SBP
S7P
9
Xu5P Ri5P
10
Ru5P Ru5P Ru5P
G6P 21
G1P
ADPG
22
ATP
PPi
23
Pi
Starch
25
11 12
12
ATP ADP
Pi Pi
Pi
PGA
Pi
31 32
GAP
33
Pi
Pi
DHAP
Pi
DHAP
RUBP
CO 2
PGA + PGA
1
DPGA
ATP
ADP
GAP
NADPH +H NADP+Pi 2
GAP GAP GAP DHAP
DHAP
FBP
Pi F6P
3 4
5
6
7 Xu5P E4P
8
SBP
S7P
9
Xu5P Ri5P
10
Ru5P Ru5P Ru5P
G6P 21
G1P
ADPG
22
ATP
PPi
23
Pi
Starch
25
11 12
12
13
ATP ADP
Pi Pi
Pi
PGA
Pi
31
GAP
Pi
Pi
DHAP
Pi
DHAP
OP
UTP OPOP 2OP
ATP
ADP
OP
FBP F6P G6P G1P UDPGlu
SUCP SUC
53 54
55
56
57
58 59
52
F26BP
F6P
UDP
60
UDP
61
Sink
62
55
61
101
Model of carbon metabolism
Drawn based on Zhu et al (2007) Plant Physiology 145: 513-526
Evolutionary algorithm
Zhu et al (2007) Plant Physiol.
Raines (2003) Photosynthesis Research 75:1-10
Evolution selects for fecundity, not productivity
High yield Defense (e.g. insects)
Preparation for rare disaster
Wild plants
Desired crops
• Elevated [CO2 ]
• Increased temperature
• Increased O3
• Altered precipitation pattern
Global Climatic Change
1. Photosynthetic processes
a) Photosynthetic light reactions
b) Photosynthetic carbon metabolism
c) Whole photosynthetic process
2. Leaf primary metabolism
a) The dynamic systems model of plant primary metabolism
b) Modeling the partitioning of photosynthate for building metabolic machinery, cellular compounds and export etc
3. Reaction diffusion models of leaf photosynthesis
a) Reconstruction of 3D leaf anatomy
b) Ray tracing algorithm inside a leaf
c) Modeling CO2 , humidity and temperature distributions inside a canopy with realistic 3d architecture
d) Modeling reaction diffusion and related physical processes inside a leaf
4. Canopy microenvironments
a) Ray tracing algorithm inside a canopy
b) Modeling CO2 , humidity and temperature distributions inside a canopy with realistic 3d architecture
5. Photosynthate partitioning
The ePlant Project
The Mission and Major Activities of the ePlant Project
Mission • To quantitatively study photosynthesis and plant
primary metabolism and its regulation • To systematically identify new targets and
strategies to optimize photosynthesis
Mechanistic model of mesophyll conductance
Tholen et al (2012) Plant Physiology; Tholen et al (2012) Plant Cell and Environment
Potential Targets to Improve Water Use Efficiency
• Decrease cell wall thickness
• Increase stromal CA concentration
• Increase the permeability of chloroplast envelop to CO2
• Decrease the permeability of chloroplast envelop to HCO3
-
Light inside a canopy is highly heterogeneous both temporarily and spatially
See poster P12
Overall C4 systems modeling and design
• Questions to address: – Define key anatomical
and biochemical features required for high efficiencies of C4 photosynthesis
– Identify viable and optimal steps to engineer a C4 rice
• Models to develop – Kinetic systems model
of C4 photosynthesis – Reaction diffusion
models for C4 photosynthesis
– Dynamic systems model of C4 canopy photosynthesis
A dynamic systems model of C4 photosynthesis
Novel features of the C4 systems model
• Detailed and updated description of the BSC and MC metabolism, i.e. incorporation of the Calvin-Benson cycle, starch, sucrose, mitochondria respiratory and complete photorespiratory metabolism in a cell specific manner, but also incorporates detailed diffusion of metabolites between these two cell types;
• The metabolite transport between BSCs and MCs was described as a diffusional process through plasmodesmata, and metabolite transport across chloroplast envelope was assumed to follow Michaelis-Menten kinetics;
• Starch synthesis and breakdown occur at the same time; • The electron transfer rate, directly linked to ATP and NADPH
synthesis were explicitly modeled.
A dynamic systems model
of C4 photosynthesis predicts Aci and
AQ curves.
Responses of metabolite levels under different Ci
Responses of metabolite levels under PPFD levels
Key enzymes controlling A-Ci responses of C4 photosynthesis
Enzyme Abbreviation
EC Number Vmax (μmol m-2 s-1)
Flux Control Coefficient
High light
Low CO2 Low light
CA 4.2.1.1 200000 0.001 0.203 0.000 PEPC 4.1.1.31 170 0.011 0.431 0.000 NADP-ME 1.1.1.40 90 0.008 0.037 0.000 Rubisco_CO2 4.1.1.39 65 0.349 0.119 0.041 PGAK &GAPDH 2.7.2.3 &1.2.1.13 225 0.034 0.002 0.024 SBPase 3.1.3.37 29.18 0.052 0.020 0.050 PRK 2.7.1.19 1170 0.043 0.019 0.061 PGAK_M &GAPDH_M 2.7.2.3M &1.2.1.13M 300 0.018 -0.150 0.020 Rubisco_O2 4.1.1.39 7.15 -0.017 0.032 -0.036 Jmax 500 0.637 -0.017 0.082 I 2000 or 200 0.148 -0.007 1.091
Diffusion parameter Value
Flux Control Coefficient
High light Low CO2 Low light
gm 0.7mol m-2 s-1bar-1 0.003 0.533 0.000 Pmal 42.14μm/s 0.001 0.047 0.000 Pco2 113.92μm/s -0.044 -0.058 -0.023 φ 0.03 -0.037 -0.032 -0.018 Lpd 400 nm 0.041 0.035 0.018
Control coefficients for parameters related to C4 photosynthesis
Enzymes Comparison (C3/C4) PEPC NADP-MDH PPDK NADP-ME Rubisco
CO2 uptake ratio
Ci=50 mbar
0.34 1.00 1.00 1.00 1.03
Ci=200 mbar
0.59 1.00 1.00 1.00 1.03
Nitrogen cost ratio 1.10 5.14 1.20 1.39 1.24
Necessity of using C4 isoforms in C4 engineering
• Cleome gynandra displays age-dependent plasticity of C4 decarboxylation biochemistry (Sommer et al. 2012)
• Maize leaf gradient (Pick et al. 2011)
Having the mixed pathway does not increase CO2 uptake rate
Having mixed pathway decrease malate levels in BSC and MC
The decreased photosynthetic efficiency in a mixture pathway is related to the increased leakage in the system
Different combinations of transported four carbon compounds and decarboxylation mechanisms
A compromise between efficiency and capacity
Assuming only cyclic electron transport occurs in BSC.
Leakiness increases by additional C4 pathways
Assuming only cyclic electron transport occurs in BSC.
Photorespiration rate decreases by additional C4 pathways
Can PCK pathway exist alone?
If there is only cyclic ETR in BSC, PCK can not exists alone
Increase linear electron transport in BSC increases CO2 assimilation rate
u=v=0 Assuming only cyclic electron transport occurs in BSC u=v=1 Assuming only linear electron transport occurs in BSC
The limited access to light by BSC limit the photosynthetic efficiency of the PCK pathway
PPFD = 2000 μmol m-2 s-1 PPFD = 300 μmol m-2 s-1
Assuming linear electron transport occurring in BSC X: proportion of light partitioned into mesophyll cells
Physiological significance of co-existing decarboxylases
• A mixture of PEPCK and NADP-ME decrease the quantum yield but increase the capacity of CO2 uptake.
• Having additional 4-C shuttle and decarxylases decreases the cellular malate concentrations and avoid potential osmotic toxicity.
• The PCK pathway is limited by the amount of light accessible by BSC.
See poster P31
Developing a Reaction Diffusion Model of C4 Leaf Photosynthesis to Explore Anatomical Requirement for C4 Photosynthesis
Predicted CO2 distribution in cells affiliated with a Kranz Structure
Red: Reactions implemented in the model
0 200 400 600 800 1000 1200 1400
24
30
36
42
48
A
(m
ol m
-2s
-1)
Ci (bar)
coverage=80%,mesophyll chloroplast thickness=1.5[m]
coverage=95%,mesophyll chloroplast thickness=1.5[m]
coverage=80%,mesophyll chloroplast thickness=0.75[m]
coverage=95%,mesophyll chloroplast thickness=0.75[m]
Rice chloroplast number needs to be decreased for C4 engineering
0 500 1000 1500
35
42
49
A
(u
mo
l/(m
^2
*s))
Ci (ubar)
CA concentration=0.5[mol/m^3]
CA concentration=0.27[mol/m^3]
CA concentration=0.16[mol/m^3]
Increase of carbonic anhydrase concentration can enhance CO2 assimilation rate in C4
What is the critical step for C4 emergence?
Sage and Zhu (2011) Journal of Experimental Botany; Zhu et al (2010) Journal of Integrative Biology
Christin et al (2008) Current Biology
Zhu et al (2008) Current Opinion in Biotechnology
Single Cell C4 system is more efficient than C3 system only under low CO2 levels
Salvucci and Bowes Plant Physiol. 67, 335-340 (1981)
Single-cell C4 photosynthesis is a survival strategy under low CO2
The Critical Step for Kranz Type C4 photosynthesis
MC MC BSC BSC
ME ？
ME
Predictions if cellular compartmentation of ME is a critical step during C4 emergence
• C4 type NADP-ME should appear much later than C4 type PEPC in evolution;
• After establishment of the C4 cycle, there should be dramatic changes in the redox property and correspondingly the expression of genes related to light reactions;
• The emergence of C4 species needs anatomical preconditioning to decrease the leakiness to CO2.
PEPC V
MDH V
ME/PEPCK V
PPDK V
SSU
PEPC V
MDH V
ME/PEPCK V
PPDK V
SSU
The dS values of the C4 shuttle genes
0 0.2 0.4 0.6 0.8 1
PEPC
MDH
ME
PEPCK
PPDK
SSU
dS
Optimization of the light reaction occurred at a late stage of C4 evolution
Red: C3 Yellow: C3-C4 Orange: C4-like Blue: C4
ATC G 00590(b6f−com plex)
rpm
05
10
15
20
25
30
35
AT1G 70760(N A D H )
rpm
01
00
200
30
04
00
50
0
AT1G 74880(N A D H −O )
rpm
05
01
00
15
02
00
25
03
00
35
0
AT2G 39470(P N S L1)
rpm
02
00
40
06
00
80
0
AT1G 14150(P N S L2)
rpm
05
01
00
15
02
00
25
03
00
AT3G 01440(P N S L3)
rpm
02
040
60
80
ATC G 00700(P S II)
rpm
02
04
06
080
AT4G 37230(P S II)
rpm
02
04
060
80
10
01
40
AT1G 60950(Ferredo xin1)
rpm
05
00
10
00
15
00
AT1G 45474(LH C −P S I)
rpm
02
00
400
60
08
00
AT5G 64040(P S I)
rpm
02
000
40
00
60
00
AT2G 46820(P S I)
rpm
05
00
10
00
15
00
20
00
AT3G 62410(C P−12)
rpm
010
020
030
040
050
0
Conclusions
• A systems model of C4 photosynthesis with detailed description of the involved biochemical and biophysical processes is developed and there is much space to increase C4 photosynthetic energy conversion efficiency through manipulation of C4 related parameters.
• Having mixtures of C4 subtypes can increase the photosynthetic capacity but decrease the light use efficiency.
• Incorporation of aspartate as a C4-shuttle compound can decrease the malate concentration in the system.
• PCK pathway can exists alone if there is linear electron transfer in the bundle sheath cells.
• Proper cellular positioning of decarboxylases might be a critical step during emergence of C4 photosynthesis.
Funding: MOST, NSFC, CAS, MPG, Pujiang Plan, SIBS
Collaborators: C4 Rice Consortium, 3to4 consortium, Global Wheat Yield Consortium, Grassmargin consortium, RIPE consortium. Stephen Long, Donald Ort, Andreas Weber, Peter Westhoff, Mark Stitt, Yan Li, Hui Zhang
Acknowledgements
Yu Wang Danny Tholen Qingfeng Song Yimin Tao Mingzhu Lv
