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Plant Physiology Preview. Published on October 25, 2010, as DOI:10.1104/pp.110.166488
C4GEM - Genome-Scale Metabolic Model to study C4 plant
metabolism
Corresponding author
Lars Keld Nielsen
Australian Institute for Bioengineering and Nanotechnology, The University of Queensland,
Corner College and Cooper Roads (Bldg 75), Brisbane Qld 4072 Australia.
Phone: +617 3346 3986
Email address: [email protected]
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Copyright 2010 by the American Society of Plant Biologists
C4GEM – a Genome-Scale Metabolic Model to study C4 plant
metabolism
Cristiana Gomes de Oliveira Dal’Molin, Lake-Ee Quek, Robin William Palfreyman, Stevens
Michael Brumbley & Lars Keld Nielsen
Australian Institute for Bioengineering and Nanotechnology, The University of Queensland,
Brisbane Qld 4072 Australia.
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Financial source: CRC for Sugar Industry Innovation through Biotechnology;
Corresponding author: Lars Keld Nielsen, [email protected]
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Abstract
Leaves of C4 grasses (such as maize, sugarcane and sorghum) form a classical Kranz leaf
anatomy. Unlike C3 plants, where photosynthetic CO2 fixation proceeds in the mesophyll, the
fixation process in C4 plants is distributed between two cell types, the mesophyll (M) cell and
the bundle sheath (BS) cell. Here we develop a C4 genome-scale model (C4GEM) for the
investigation of flux distribution in M and BS cells during C4 photosynthesis. C4GEM is the first
large-scale metabolic model that encapsulates metabolic interactions between two different cell
types. C4GEM is based on the Arabidopsis model (AraGEM), but has been extended by adding
reactions and transporters responsible to represent 3 different C4 subtypes (NADP-ME, NADME and PCK. C4GEM has been validated for its ability to synthesize 47 biomass components
and consists of 1588 unique reactions, 1755 metabolites, 83 inter-organelle transporters and 29
external transporters (including transport through plasmodesmata). Reactions in the common C4
model have been associated with well annotated C4 species (NADP-ME subtypes): 3557 genes
in Sorghum bicolour, 11623 genes in Zea mays and 3881 genes in Saccharum officinarum. The
number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize and
sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and
BS cells during C4 photosynthesis. Our simulations were consistent with chloroplast proteomic
studies and C4GEM predicted the classical C4 photosynthesis pathway and its major effect in
organelle function in M and BS. The model also highlights differences in metabolic activities
around photosystem I and photosystem II for 3 different C4 subtypes. Effects of CO2 leakage
were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation
between M and BS cells during photosynthesis, and can be used to explore C4 plant metabolism.
Key words: metabolic reconstruction; genome-scale model, C4 plant; systems biology, flux
analyses.
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Introduction
Many of the most productive crops in agriculture, such as maize, sorghum and sugarcane,
possess the Kranz leaf anatomy directly and causally associated with the C4 photosynthetic
pathway (Laetsch, 1974; Hatch and Kagawa, 1976). Unlike C3 plants where photosynthetic CO2
fixation proceeds in a single tissue, the mesophyll, in C4 plants this process is distributed
between mesophyll (M) and bundle sheath (BS) (Moore, 1984). In the Kranz anatomy, a ring of
M, containing a few small chloroplasts concerned with the initial fixing of carbon dioxide,
surrounds a sheath of parenchyma cells (BS), which have large chloroplasts involved in the
Calvin-Benson cycle. BS cells have thick cell walls and contain centrifugally arranged
chloroplasts with large starch granules and unstacked thylakoid membranes, whereas the M cells
contain randomly arranged chloroplasts with stacked thylakoids and little or no starch (Kennedy
et al., 1977; Moore, 1984; Spilatro and Preiss, 1987). The fully differentiated BS and M
chloroplasts each accumulate a distinct set of photosynthetic enzymes and proteins that enable
them to cooperate in carbon fixation.
All C4 species operate on the same basic theme of pumping CO2 via C4 acids from mesophyll
tissue when PEPCase activity is enchanced to BS layer where Rubisco is localized and C4 acids
are decarboxilated. Other than this, the only common feature shared by C4 plants is a reduction
in the ratio of M to BS cells when compared to C3 plants. Because diffusion of organic acids
between the M and BS must be relatively rapid, M cells in the C4 plants are rarely more than one
cell distant from BS cells. As a result, M to BS ratios are between 1 and 2 in C4 plants, while
they are over 4 in most laminate C3 leaves. Beyond these common features, C4 plants exhibit
substantial variation in how they accomplish CO2 concentration. These variations results from
three distinct decarboxylation modes and multiple patterns of anatomical modification.
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Biochemically, the major distinguishing feature of C4 subtypes is the enzyme used for
decarboxylation step in BS (Hatch, 1974; Hatch and Kagawa, 1976; Hatch, 1992).The three
carboxilation modes are: NADP-malic enzyme (NADP-ME: present for example in maize,
sugarcane and sorghum), NAD-malic enzyme (NAD-ME: present in Panicum miliaceum and
Amaranthus spp) and PEP carboxykinase (PKC: present in Panicum maximum and Amaranthus
spp). These different carboxilation modes are represented in Figure 1. It is important to note that
these different mechanisms lead to different patterns of intercellular movements of C3 and C4
compounds. During C4 photosynthesis, atmospheric CO2 is initially fixed in M cells by
phosphoenolpyruvate carboxylase (PEPC) to form C4 dicarboxylic acids. The movement of
photosynthetic intermediates between mesophyll and sheath cells is restricted largely or entirely
to the plasmodesmata and transpirational water movement through the cell walls (Evert et al.,
1977). The C4 dicarboxylic acids are transferred to Kranz cells (BS cells), and decarboxylated
there. The released CO2 is refixed via ribulose 1,5 biphosphate carboxylase of Kranz cell
chloroplasts and incorporated into the Calvin-Benson cycle (Hatch and Kagawa, 1973, 1974,
1976) . The transfer of atmospheric CO2 to BS cells is unique to C4 photosynthesis since it leads
to concentration of CO2 at the site of the reaction of Rubisco (Furbank et al., 1989, 1990). This
CO2 concentrating mechanism, together with specific properties of photosynthetic enzymes,
configurations of intracellular and intercellular transporters and modifications in leaf anatomy,
allows C4 plants to achieve higher photosynthetic efficiency than C3 plants (Bull, 1969). C4
photosynthesis allows fast biomass accumulation with high nitrogen and water use efficiency
(Smith, 1976; Sage, 2004).
Attempts have been made to engineer C4 metabolism in C3 plant species, such as rice, by
overexpressing C4 enzymes (from maize) (Suzuki et al., 2000; Ku et al., 2001; Agarie et al.,
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2002; Suzuki et al., 2006). However, significant increase in photosynthetic efficiency in the C3
mutants is yet to be achieved.
With the genome sequence available for sorghum (Sorghum bicolour) (Paterson et al., 2009) and
maize (Zea mays) (Maize Genome Sequencing Project, 2009; Schnable et al., 2009), it is
possible to perform a whole-genome-level exploration of C4 pathway evolution by comparing
key photosynthetic genes in sorghum and maize (Wang et al., 2009; Yilmaz et al., 2009). These
genome projects also facilitate the annotation of other important C4 crops for which incomplete
genome sequences are available, such as sugarcane (also a NADP-ME type) (Dufour et al., 1997;
Guimaraes et al., 1997). Along with the increase in genomic information, diverse datasets,
including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily
available for plants (Morgenthal et al., 2006; Thompson and Goggin, 2006; Dai et al., 2007;
Yonekura-Sakakibara et al., 2008; Minic et al., 2009; Sawada et al., 2009), including C4 plant
species (Majeran et al., 2005; Papini-Terzi et al., 2005; Majeran et al., 2008; Vicentini and
Menossi, 2009).
The annotated genomes, along with literature data, define the known biological components of
organisms. From these components, biological networks can be reconstructed and the properties
of the system explored. In silico genome-scale metabolic reconstruction remains the most
tangible and applicable systems biology approach for metabolic engineering (Blazeck and
Alpert, 2010; Dietz and Panke, 2010; Tyo et al., 2010). A well curated genome-scale network
reconstruction is a common denominator for those studying systems biology of an organism. For
these metabolic networks, a stoichiometric formalism (Edwards and Palsson, 2000; Thiele and
Palsson, 2010) is natural and underpins a powerful set of analytical tools for exploring
relationship between genotype and phenotype, predicting product yields and growth rates under
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different environment, and discovering global effects of genetic manipulations (Edwards and
Palsson, 2000; Famili et al., 2003; Price et al., 2003; Quek and Nielsen, 2008; Oberhardt et al.,
2009; Thiele and Palsson, 2010).
The most represented domain for genome-scale models is bacteria, with 25 species reconstructed
(Oberhardt et al., 2009). Genome scale metabolic networks have also been reconstructed for
mouse (Sheikh et al., 2005; Quek and Nielsen, 2008), human (Mo et al., 2007; Sigurdsson et al.,
2009) and more recently for Arabidopsis (Poolman et al., 2009; de Oliveira Dal'Molin et al.,
2010). These reconstructions vary both in detail and in scope. For example, Poolman et al.’s
Arabidopsis reconstruction (Poolman et al., 2009) considered two organelles (cytosol and
mitochondria) to represent the plant metabolic network and hence was limited to describing
heterotrophic cell culture. In contrast, our model (AraGEM) (de Oliveira Dal'Molin et al., 2010)
describes metabolism across cytosol, plastids, mitochondria, peroxisomes and vacuole, which
enabled exploration of metabolic differences in photosynthetic and non-photosynthetic cells, and
to examine in greater metabolic detail cells undergoing photosynthesis, photorespiration, betaoxidation or respiration. While both models covered the full range of known Arabidopsis
reactions, validation was limited to primary metabolism, i.e., the synthesis of major biomass
components (amino acids, nucleotides, lipid, starch, cellulose, some vitamins). Nevertheless both
models are important contributions redressing the conspicuous lack of plant metabolic
reconstructions (Oberhardt et al., 2009).
Genome-scale model reconstruction and validation of eukaryotes is certainly more challenging
than prokaryotic organisms given the multiple organelles. However such approach applied for
complex, multi-tissue plant systems would be of potentially even greater value, since direct
measurement of fluxes using pulse chase studies is practically impossible in plants due to signal
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dispersion in the large network as well as metabolite and pathway replication in multiple
organelles. In this work we attempted to use this systems-based framework to understand the
interplay of metabolic network in complex systems, like C4 plant metabolism. This is the first
reconstruction used to formulate an in silico genome scale model describing the interactions
between the two tissue types, M and BS, involved in C4 metabolism. The predicted differences
in flux distribution between M and BS cells were in agreement with observed proteomic studies
and C4 metabolism features reported in the literature, suggesting that the model can be used to
further explore C4 plant metabolism.
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Results and Discussion
Characteristics of the Genome-scale Model
The metabolic network reconstruction includes associations between genes, enzymes and
reactions to represent C4 plant metabolism based on best available online resources (Table I).
Draft models were constructed from all reactions supported in KEGG for the three C4 plants
sorghum, maize and sugarcane. Inconsistencies in the KEGG database (e.g., multiple IDs for
single metabolites and unbalanced reactions) were resolved as described previously (Quek and
Nielsen, 2008; de Oliveira Dal'Molin et al., 2010).
Subcellular compartmentalization plays an important role in plant metabolism. An initial attempt
to use WoLF PSORT (http://wolfpsort.org/), a protein subcellular localization predictive tool
(Horton et al., 2007), to assign compartments for each gene failed as most calls were ambiguous
or inconsistent across the three species. Instead, AraGEM (de Oliveira Dal'Molin et al., 2010)
was used to make the compartmentalization calls for each reaction. AraGEM is manually curated
for subcellular localisation (cytosol, mitochondrion, plastid, peroxisome or vacuole) and we have
not identified any inconsistencies between localisation in Arabidopsis and the limited number of
proteins in C4 plants, for which experimental localisation data are available. The
compartmentalised draft model covers 3557 genes and 13,114 gene-reaction-association entries
for Sorghum, 11,623 genes and 38,829 associations for maize, and 3,881 genes and 13,593
associations for sugarcane (Table II). The large number of gene-reaction associations reflects
that – in absence of localisation information – genes associated with a reaction found in multiple
compartments must be associated with each compartment.
A functional model of primary metabolism, C4GEM, was derived from the draft model through
manual curation. The model describes 1588 unique reactions involving 1755 metabolites (Table
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II). Forty seven (47) biomass drain equations describe the accumulation of carbohydrates, amino
acids, fatty acid (palmitic acid only), cellulose and hemi-cellulose, representing the major
biomass drains for a plant cell, as well as some vitamins and co-factors (Table III). Eighteen (18)
inter-cellular exchange reactions (cytoplasm–extracellular) have been included to describe the
uptake of light (photons), assimilation/secretion of inorganic compounds (CO2, H2O, O2, NO3,
NH3, H2S, SO42–, PO43–), translocation of sugars (sucrose, glucose, fructose, and maltose), and
amino acids (glutamine, glutamate, aspartate, alanine and serine). Together with biomass drains,
the intercellular exchangers define the broad physiological domain of the model, that is, the
curated aspects of primary C4 plant metabolism captured by C4GEM. A total of 83 interorganelle transporters were introduced in the model to achieve the desired functionality. The
active, validated scope of C4GEM includes glycolysis (plastidic and cytosolic), pentose
phosphate pathway (PPP) (plastidic and cytosolic), tricarboxylic acid cycle (TCA cycle), light
and dark reactions (Calvin cycle), NADH/ NADPH redox shuttle between the subcelular
compartments, fatty acid synthesis, beta-oxidation and glyoxylate cycle.
Although genome annotation can be used to derive a majority of plant metabolic reactions, there
are missing reactions where the genome has not been fully elucidated. During the reconstruction,
gaps were found in the primary metabolism for the three C4 models: 131, 135 and 156 gaps for
Sorghum bicolour, Zea mays and Saccharum officiarum, respectively. Some of these gaps could
be filled by combining all of the C4 plant models. The remaining 100 essential reactions were all
found in AraGEM [See list of covered reactions in additional file 1]. The reconstruction allowed
a functional annotation of the C4 plant models to ensure that reaction gaps are filled in the
context of metabolic functions of nucleotide, carbohydrates, amino acids and nitrogen
metabolism, citrate cycle (TCA), carbon fixation and pentose phosphate pathway (PPP).
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The final model has 446 singleton or dead-end metabolites (i.e. internal metabolites only used in
a single reaction; supplemental data Table S2). A total of 512 reactions are linked to the use or
production of dead-end metabolites. These reactions will by definition have zero flux in flux
balance analysis. There are several causes of singletons. Most are linked to vitamins, cofactors,
and secondary metabolites not currently included in the model (i.e. not described by biomass
drains or intercellular transporters). Some result from true gaps in the network, where one or
more essential reactions have not yet been assigned. Finally, some result from KEGG’s
automatic annotation, in which every reaction known to be catalyzed by a particular EC enzyme
in some organism will be included by default, whether or not other reactions required for
functionality of the particular reaction are present. Continued curation efforts will focus on
resolving these singletons.
In its current form, the model has 183 degrees of freedom. Of these, 47 are associated with the
production of biomass components and reduce to a single degree of freedom (growth rate) when
a fixed biomass composition is assumed. Another 18 degrees of freedom are associated with
intercellular transport. The remaining 118 degrees of freedom represent the maximum cellular
scope for using alternative pathways to achieve identical outcomes in terms of growth rate and
net transport (e.g. the use of cytosolic or chloroplastic glycolysis). The real scope is further
limited by irreversibility constraints as well as regulatory constraints. C4GEM is available in
System Biology Markup Language (SBML) format for maize, sorghum and sugarcane
(Supplemental Data Files S3, S4, and S5).
Two-tissue model
The active scope of C4GEM is similar to AraGEM (de Oliveira Dal'Molin et al., 2010). We have
previously demonstrated how AraGEM can be used to explore metabolism in a single tissue, e.g.,
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photorespiration in C3 mesophyll. The purpose of this study, however, is to explore the
interactions between two distinct tissues, M and BS. Significantly, it represents the first step to
represent a plant multi-tissue model.
C4GEM encapsulates the full complement of primary metabolic reactions available to any cell in
a C4 plant. Tissue specificity is realised through differential gene expression, i.e., by
constraining what part of the network is available to the individual tissues. Thus, M and BS
tissue will be described as two instances of C4GEM differing only in terms of the constraints
applied to the models (see later). The Kranz anatomy is modelled by connecting these two
instances, M and BS, through transporters representing plasmodesmata. The movement of
photosynthetic intermediates between mesophyll and sheath cells (Figure 1) is largely or entirely
limited to the plasmodesmata (Evert et al., 1977). While plasmodesmata are capable of
transporting a broad spectrum of low molecular weight (<800-900 Da) metabolites, significant
transport requires high concentration and transporters were only added for major substrate
carriers involved in the C4 photosynthetic pathway (subtypes NADP-ME, NAD-ME and PCK),
i.e., malate, pyruvate, phosphoglycerate, triose phosphates, aspartate, alanine and phosphates
(pyrophosphate and orthophosphate) (Furbank et al., 1989, 1990) (Figure 1).
The final C4 model is described pictorially in Figure 2A. The model based on the annotated
genome for sorghum, but uses AraGEM for compartmentalization and the 131 missing, essential
reactions in primary metabolism. The model has been extended with 11 intercellular transporters
(plasmodesmata), including sucrose translocation and gases efflux.
Mathematically, the large network is broken down into a stoichiometric matrix, S, with a row for
each intracellular metabolite and a column for each reaction. Component balances governing a
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metabolic network of N = {1,.., n} chemical transformations and M = {1,.., m} chemical entities
are
೔
∑ , (1)
where Ci denotes the concentration of chemical entity i, Si is the stoichiometric coefficient of
chemical entity i in transformation j, and rj the corresponding flux of transformation j. The time
scale of metabolic reactions is short compared to regulation and growth dynamics allowing us to
invoke the steady-state assumption
∑ 0, (2)
In addition, some reactions are irreversible, so
0, (3)
With two tissues communicating, the model is duplicated and the metabolites in plasmodesmata,
which are external (unbalanced) for the individual tissues, become internal and balanceable in
the two-tissue model. In matrix form, the two-tissue model reads
(4)
where PM and PBS have a row for each of the 11 metabolites transported via plasmodesmata and a
“1” entry in the column for the corresponding transporter. Thus, the third row dictates that the
sum of metabolites exported into the plasmodesmata is 0, or in other words, whatever is exported
from M must be taken up by BS (negative export).
The two-tissue model describes the network topology connecting inputs (e.g., photons, inorganic
compounds) to outputs (e.g., biomass). The network is redundant, i.e., there are several paths
through the network connecting any given input to any given output. This redundancy is inherent
in all biological networks and confers flexibility and robustness to biological systems. The actual
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path chosen is defined by enzyme kinetics and regulation and the current model does not
consider these.
Equations (3) and (4) define the feasible flux space, i.e., the combinations of fluxes in M and BS
that satisfy flux balance and irreversibility constraints. By careful formulation of additional
assumptions and – in some instances – an optimality criterion is possible to explore the feasible
space and formulate testable biological hypotheses, e.g.,
•
Given a certain photon supply, what is the maximum rate of photosynthate production?
•
What is the minimum number of photons required to support plant metabolism and what
is the optimal flux distribution?
•
Is a particular gene deletion expected to affect growth and/or photosynthate production?
It is also possible to incorporate expression data from transcriptomics or proteomics to reduce the
feasible solution space by deleting non-expressed reactions. In the following sections, we will
illustrate the use of constraint-based analysis.
Simulations
In this study, we are interested in exploring, if (a) the observed differences between M and BS
metabolism in C4 plants are consistent with an assumption that the plant has evolved to use the
network in an optimal manner and (b) if the different C4 subtypes differs in their potential
efficiency.
For a photosynthetic system, a logical optimality criterion is photon efficiency, i.e., a plant
network will operate with the feasible flux distribution that minimizes photon uptake for fixed
rates of biomass synthesis and export of carbon assimilates to other tissues. While no proof exists
for the validity of this criterion, we observed for AraGEM that this criterion accurately predicted
the classical photorespiration pathway as the most photon efficient way to handle the effect of
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the RuBisCo oxygenation reaction and that the predicted cost of photorespiration compared to
pure photosynthesis was a 40% increase in photon usage for a 3:1 carboxylation:oxygenation
ratio, which is consistent with experimental data (de Oliveira Dal'Molin et al., 2010).
Assumptions and constraints
As a minimum definition of tissue specificity, RuBisCO and phosphoenolpyruvate carboxylase
activity is constrained to BS and M tissues, respectively, based on gene expression, enzyme
activity and proteome studies (Ghosh et al., 1994; Gutteridge and Gatenby, 1995; Dong et al.,
1998; Patel and Berry, 2008) (Table IV). These constraints are common to all the 3 C4 subtypes.
Mathematically, the tissue specific activities are captured by equality constraints:
0; 0; మ
0;
(5)
In order to represent each C4 subtype, the enzyme used to release CO2 from the C4 acids is
specified (NADPH-ME, NAD-ME or PCK) (Table IV). Similarly, the active C3-C4 transport
systems have been specified based on subtypes. Finally, the drain of biomass components, starch
accumulation and export of carbon assimilates (sucrose) were fixed based on average measured
values.
It should be stressed that this is a minimum definition of C4 subtype metabolism used in this
study because we seek to explore questions around optimality. More detailed constraints, e.g.,
based on transcriptome analysis, may be used when exploring other questions.
Preferential starch accumulation in BS cannot be explained by photon efficiency
In C4 plants, starch is principally synthesized in BS, though the amount of mesophyllic starch
biosynthesis is species dependent (Black and Mollenha, 1971; Spilatro and Preiss, 1987). In
maize, for example, starch biosynthesis occurs only in the BS during normal photosynthesis, but
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plants growing in continuous light do accumulate starch in the M, suggesting that photosynthate
availability is the limiting factor (Downton and Hawker, 1973).
One explanation for preferential starch biosynthesis in BS could be that it is “less expensive”
(i.e., require less photons) to accumulate starch in BS than in M, and hence BS stores are filled
before M stores of starch. We tested this hypothesis by comparing the minimum photon
requirement predicted with all starch produced in M to the minimum photon requirement for the
standard model with all starch produced in BS. We did not observe an energy efficiency
advantage for producing starch in BS rather than in M, thus preferential starch production in BS
cannot be argued to be the result of an evolutionary pressure for higher energy efficiency. Starch
accumulation in BS only was added as an experimentally-based constraint for subsequent
analysis.
Sucrose translocation
In order to sustain other plant tissues, fixed carbon must move out of the photosynthetic cells and
into phloem (Braun and Slewinski, 2009). For most plants, this occur by loading sucrose into the
phloem and transporting it from source tissues (net exporters) to sink tissues (net importers),
where sucrose in unloaded. Sucrose drain from BS to vascular parenchyma has been added to
represent the translocation of carbon for the sustenance of the sink tissues. It unclear if sucrose
produced in mesophyll or BS is transported through plasmosdesmata during C4 photosynthesis
or if malate and pyruvate (for NADP-ME subtypes, like maize) are the main carbon flow during
the carbon assimilation. We tested this hypothesis in silico by allowing sucrose exchange
between both cells during C4 photosynthesis. According to the flux variability analysis (see
Methods) sucrose can be exchanged during this cooperative process but malate and pyruvate
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show a much greater flux in magnitude (about 100 folds). Sucrose exchange between both cells
did not affect the main contrasts in metabolic activity by comparing BS and M.
In silico
flux profile correlates with maize chloroplast proteome studies
The optimal flux distribution predicted by the two tissue model shows a number of reactions
through which there is a significant difference in the flux observed in BS and M (Figure 3).
While the magnitude of flux is not directly linked to the amount of enzyme present, one might
reasonably expect that a significant increase in flux in a tissue in most cases is matched by a
significant increase in the expression of the corresponding enzyme. We compared metabolic flux
predictions to a large dataset comparing BS and M chloroplast proteomes in maize (Majeran et
al., 2005; Majeran et al., 2008; Friso et al., 2010). Since the optimality criterion does not
necessarily yield a unique flux distribution, flux variability analysis (see Methods) was used to
establish the range of values for each flux for which optimal photon usage could be achieved. All
but four fluxes – ATP/ADP and OAA/Malate transporters, Glyceraldehyde 3-phosphate
dehydrogenase and triose phosphate isomerise – were uniquely defined by the optimality
criterion.
For 50 of the 66 proteins or protein complexes annotated in the proteome assigned to enzymatic
reactions, differential expression (or absence hereof) was consistent with predicted flux
differences (Table V). Of these 50, two predictions were a direct consequence of the model
assumptions for RuBisCO and starch synthesis (Table IV). Twenty of the reactions were
assigned to fatty acid biosynthesis and the average and median BS-M ratios for proteins involved
in fatty acids synthesis were 0.91 and 0.94, respectively, supporting the assumption that the
demand for fatty acids is similar in M and BS.
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The simulations show that flux through enzymes involved in carbon fixation, PPP, and starch
synthesis are upregulated in BS, while flux through enzymes involved in purine, pyruvate and
nitrogen metabolism and nitrogen assimilation are upregulated in M. The model also highlights
contrasts in flux distribution through isoenzymes distributed in multiple organelles, such as
higher flux through malate dehydrogenase (MDH) in M plastids, and higher flux through malic
enzyme in BS plastids. Interestingly, in silico analysis shows no difference in flux through
chloroplastic pyruvate dehydrogenase during photoshynthesis, indicating an essential need to
maintain malate and pyruvate pools required for operation of the C4 cycle. These flux
predictions agree with the proteome studies.
Of the 16 reactions not accurately predicted, six are involved in chlorophyll and isoprenoid
synthesis. While C4GEM has been validated for its ability to synthesize chlorophyll, the biomass
content of chlorophyll was not specified in the model. Since BS chloroplasts have lower
chlorophyll b levels, most of the predictions would agree with the proteome data if the content
was accurately specified in the biomass equation. The reason for higher protoporphyrinogen
oxidase activity in BS is unclear given that enzyme catalysis an early step in chlorophyll
biosynthesis. Another seven of the reactions not predicted in the simulated fluxes relate to the
expected overexpression of oxidative stress response activities in M tissues. The model currently
does not describe the generation of reactive oxygen species or their downstream products, and
hence it does not predict the need for these activities. Similarly, the overexpression of three
enzymes involved in degradation pathways was not predicted. The photon optimal solution for
making biomass clearly will not predict degradation, which is a wasteful process. The flux
through maintenance and turn-over processes need to be expressed explicitly to be accounted for
in the model.
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The model predicts cyclic electron flow in bundle chloroplast for NADPH-ME subtype
Whereas linear electron transport (LET) in photosynthesis produces both ATP and NADPH, the
cyclic electron transport (CET) around photosystem I has been shown to produce only ATP
(Shikanai, 2007; Endo et al., 2008). Two alternative routes have been shown for CET; NAD(P)H
dehydrogenase (NDH)-and ferredoxin:plastoquinone oxidoreductase (FQR)-dependent flows,
but their physiological relevance has not been elucidated in detail. Meanwhile, because C4
photosynthesis requires more ATP than does C3 photosynthesis to concentrate CO2, it has not
been clear how the extra ATP is produced. LET alone cannot produce enough ATP to satisfy the
stoichiometry required for CO2 fixation. The shortage of ATP may be compensated for by the
function of CET. Interestingly, two subtypes of C4 photosynthesis, NAD-ME and NADP-ME,
have been shown to have different cell-specific ATP requirements. The ATP/NADPH ratio
required in NAD-ME species is higher in M cells than in BS cells, but the opposite is true for
NADP-ME species (Moore, 1984; Takabayashi et al., 2005). This cell-type-specific ATP
requirement suggests that the activity of CET should be higher in M cells of NAD-ME species
and in BS cells of NADP-ME species, if CET plays a critical role in supplying the additional
ATP needed in C4 photosynthesis.
C4GEM predicts conventional linear electron transfer (LET) in M chloroplasts and cyclic
electron transfer (CET) in BS chloroplast for NADP-ME species (Figure 4). One explanation for
preferential CET in BS could be that it requires less photons to get the extra ATP in BS
chloroplast than in M chloroplast. We tested this hypothesis by comparing the minimum photon
requirement predicted with CET in M chloroplast to the minimum photon requirement for the
model with the CET in BS chloroplast. C4GEM highlights that CET in BS is energetically more
efficient (about 25% less photons required) in NADP-malic enzyme type metabolism than if
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CET was active in M. Our simulations are consistent with the proteome data showing high NDH
activity in BS (Table V), which is backed by previous studies showing that ndh genes
expression are upregulated in BS for subtypes NADP-ME (Dariel et al., 2006; Shikanai, 2007).
The model predicts little difference in theoretical quanta requirement for the C4
subtypes
The costs of concentrating CO2 in BS in NADP-ME and NAD-ME type plants consist of 2 ATP
per CO2 fixed for regeneration of PEP, plus the amount of ATP required to pump extra CO2
(overcycle CO2) to compensate for the CO2 that leaks out of the BS. In PEP-CK type, one extra
ATP and 0.5 extra NADPH are required in addition to the amount required for overcycling.
Our flux simulations show little difference in theoretical quantum yield of photosynthesis among
the three C4 decarboxylation types (Figure 4), also discussed in the literature [60]. Such result
indicates that these distinct biochemical subtypes amongst C4 plants represent different, equally
optimal biochemical solutions to the same problem.
Although the theoretical yield is similar for all the C4 subtypes, in practice differences in
quantum yield is observed (Hatch et al., 1995). The reasons for the differences in quantum yield
between decarboxylation types have proved elusive to identify. Our results suggest that these
differences are not due the distinct C4 decarboxylation pathways. It has been suggested that if
CO2 leakage (from BS back into M) occurs, this would change quantum yield in between C4
plants (Furbank et al., 1989). However, estimates of leak rates show little correlation between C4
subtype and quantum yield. Our flux simulations show little change in total quanta requirement
under the assumption that leakage occurs (Figure 4), but the simulation show considerable
differences in activity of linear and cyclic electron transport around PSI and PSII for each of the
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C4 species. These results are consistent with the differences in photochemical activity in C4
subtypes observed in the literature (Moore, 1984; Takabayashi et al., 2005; Romanowska and
Drozak, 2006). As discussed previously, photosynthetic electron transport can involve either a
linear flow from water to NADP, via photosystem II (PSII) and photosystem I (PSI) or a cyclic
flow just involving PSI. Because C4 subtypes have different cell-specific ATP requirements [3,
63], there are differences in activity of PSI and PSII (Romanowska and Drozak, 2006). It is
found for example that NAD-ME and PCK species have substantial PSII activity in the BS
tissues, while NADP-ME species have little PSII activity.
Conclusion
C4GEM is the first genome scale metabolic reconstruction of C4 plants. The two tissue model
based on C4GEM is the first attempt of constructing and integrating context-specific metabolic
networks in multi-cellular interactions. The use of this model for in silico flux analysis illustrates
the potential of using genome scale models to explore complex and compartmentalized networks
to derive non-trivial hypotheses. The in silico predicted differences in the metabolic activity in
BS and M during C4 photosynthesis agreed with observed differences in proteome data for many
plastidic enzymatic reactions. The fact that in silico fluxes predicted assuming optimal photon
use agree with observed proteome differences and C4 metabolic features reported in the
literature lend support to the assumption that gene expression in BS and M has evolved to realise
photon optimality.
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Material and Methods
Metabolic reconstruction
The reconstruction process employed here is the same as the ones used for Mus musculus (Quek
and Nielsen, 2008) and Arabidopsis GEMs (de Oliveira Dal'Molin et al., 2010). The workflow
for the development and refinement of the C4 genome-scale model (C4GEM) is illustrated in
Figure 5. The process of the metabolic reconstruction naturally lends itself to an iterative
approach and subsequent rounds of model refinement facilitate the testing of hypothesis.
The network reconstruction followed the steps:
1) Extraction of the metabolic information: A gene-centric organization of metabolic information
was adopted, in which each known metabolic gene is mapped to one or several reactions.
The metabolic elements contained in C4GEM (genomic metabolic reactions) were collected
from Kyoto Encyclopedia of Genes and Genomes (KEGG, Release 49.0, August 3, 2009)
(Kanehisa et al., 2008) for three C4 plant models: maize, sorghum and sugarcane (stored in
excel spread sheet)
2) Manual curation: The metabolic information was merged into a single generic plant cell
model, and subsequently curated using literature information. AraGEM was used as a core model
to cover gaps found in primary metabolism of the C4 plant models. Curation was performed in
Excel, accounting for cellular enzyme localization.
3) Enzyme localization: enzymes were assigned to different compartments according to literature
evidence or enzyme localization databases (e.g., PPDB - a Plant Proteome DataBase for
Arabidopsis thaliana and Zea mays);
4) Gene association: in many cases there are many genes for a particular enzyme (i.e.,
isoenzymes) found in multiple compartments. Attempts to assign localisation based on in silico
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predictions failed (see Results & Discussion) and generally all genes for a particular reaction had
to be assigned to all organelles to which the reaction had been assigned in 3).
5) Stoichiometric matrix: The set of unique reactions ID were extracted and stored as a
stoichiometric matrix (Java application). In this step, multiple entries for a reaction in a particular
compartment appearing in the Excel gene-enzyme-reaction table are collapsed to a single
reaction entry. The stoichiometric matrix is used for flux balance analysis.
Inter-cellular and extracellular transport elements
A multi-tissue model capturing the interactions between BS and M was formulated by combining
two instances of the C4GEM and adding the movement of photosynthetic intermediates between
mesophyll and sheath cells (Figure 1), which are restricted largely or entirely to the
plasmodesmata (Evert et al., 1977). Transport through the bridge channel plasmodesmata was
restricted to the main metabolites that give the characteristic of the C4 photosynthetic pathway,
inferred from literature (Furbank et al., 1989, 1990), i.e., malate, pyruvate, phosphoglycerate,
triose phosphates, and phosphates (pyrophosphate and orthophosphate) (Figure 1). The extra
cellular transporters were included for water and gases exchange flux through the cell wall (Evert
et al., 1977), biomass drains for M and BS and Sucrose drain from BS to vascular parenchyma to
represent the translocation of carbon for the sustenance of the sink tissues(Braun and Slewinski,
2009). We have also considered gases efflux (CO2 and O2) to be exchanged through
plasmodesmata to test the hypothesis, for example, that some percentage of CO2 that is released
from the C4 acids in BS leaks back to M, as reported in the literature (Farguhar and Hatch, 1983;
Furbank et al., 1990; Hatch et al., 1995) (Please, see Results and Discussion session).
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Optimality criterion
For the C4 metabolic model, the corresponding photon minimisation solution can be expressed
as
Min
ಾ ಳೄ
Subject to
The last constraint can be use to define the previously mentioned irreversibility constraints as
well as tissue specificity (e.g., 0 flux for RuBisCO in M). It is also used to define the fixed
output of the system, in the form of biomass drains. In order to simplify comparisons, biomass
synthesis rates were assumed to be the same for the two tissue types (except for starch, see
below) and estimated based on literature values (Poorter and Bergkotte, 1992, 1992). Inorganic
carbon was assumed to be limited in BS (no CO2 uptake) and free in M, where CO2 is initially
fixed by (PEPC) to form C4 dicarboxylic acids. It was further assumed that the two tissues can
freely exchange other inorganic compounds with the environment while the exchange rate for
organic compounds is assumed zero except for exchange via plasmodesmata.
Flux simulations
Following manual curation, flux balance analysis was applied to confirm that each biomass
component could be synthesized and in the subsequent model validation. C4GEM was compiled
and curated as described for the compartmentalised C3 plant model (de Oliveira Dal'Molin et al.,
2010). From the data collection, a 2D reaction centric SBML (System Biology Markup
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Language, www.sbml.org) database was generated. The stoichiometric matrix, S, and
reversibility constraints (defining vmin), were extracted from the SBML database in MATLAB
(Version 9, The MathWorks) and the linear programming problems were solved using the
MOSEK Optimization Toolbox for MATLAB (Version 6). Constraints were applied in Matlab
(C4Flux tool box) to represent cell type interactions (Figure 2B). Reactions were activated or
deactivated based on gene and enzyme activity studies (Westhoff et al., 1991; Bassi et al., 1995)
for the specified cells (e.g., RuBisCO is not present is Mesophyll, but present in BS
chloroplasts). Biomass drains (see the following section for more details) were also specified
based on literature values (Poorter and Bergkotte, 1992, 1992). Optimum flux distribution were
simulated by linear programming and visualized on a metabolic flux map (which represents the
central metabolism of a compartmentalised plant cell) drawn in Excel (Figure 3).
Biomass synthesis and network functionality
In the final step of the manual curation process, network gaps were identified based on the
models ability to produce biomass components from substrates. Biomass drain reactions are
incorporated into C4GEM as the accumulation terms of the biomass precursors (e.g., “Starch =
Starch_biomass”), in order to simplify the task of uncovering the pathway gaps in the each of the
biosynthetic routes separately. The list of biomass components considered are shown in Table 2
and includes major structural and storage components as well as trace elements such as vitamins.
For each biomass component, the following linear programming problem was formulated and
solved
maximise vi
subject to Sv = 0
v min ≤ v ≤ v max
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where vi is the corresponding biomass drain reaction. In this study, the problem was solved for
leaf tissue (photons as energy source, CO2 as carbon source and nitrate as nitrogen source) to
represent biomass synthesis during C4 photosynthesis. The model can be also be used for nonphotosynthetic tissues, when the minimization criterion is applied for carbon source usage (e.g.,
sucrose).
Flux variability analysis
Due to the lack of experimental constraints, flux solutions generated by linear programming for
large-scale models are often not unique. Despite having identical network topology and
maintaining the same set of constraints and objective function, it is possible to have different flux
distributions that give rise to the same objective function value, with each flux distribution
representing a different metabolic state (e.g., different reaction utilization and/or directionality)
(Lee et al., 2000; Papin et al., 2002). Instead of a single flux distribution, it is therefore important
to characterize the flux space encompassing all alternate optimal solutions before making
reasonable interpretation of whether a metabolic phenotype is sufficiently different between two
systems (e.g., BS versus M). An efficient linear programming-based strategy was used to
quantify the range and variability of flux values, which involves minimizing and subsequently
maximizing the flux value of all reactions in the network while keeping the same optimum
objective function value (Mahadevan and Schilling, 2003). This method identifies the range of
fluxes that is possible within all the possible alternate optimal solutions. The following linear
programming problem was formulated and solved
maximise vi
subject to Sv = 0
cT v = Z obj
v min ≤ v ≤ v max
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and
minimize vi
subject to Sv = 0
cT v = Z obj
vmin ≤ v ≤ v max
where vi represent each reaction in the network, c is the objective function vector and Zobj is the
objective function value calculated from the first optimization instance.
This method was used to identify and interpret the main contrasts between the two systems, M
and BS, as shown in Table V (See Results and Discussion session).
Supplemental Material
Table S1: Reactions with no ORFs in all C4 models, covered by AraGEM.
Table S2: List of singletons.
Supplemental data S3: Maize C4GEM_vs1.0; SBML format.
Supplemental data S4: Sorghum C4GEM_vs1.0; SBML format.
Supplemental data S5: Sugarcane C4GEM_vs1.0; SBML format.
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Figure legends
Figure 1. Schematic representation of the photosynthetic metabolism of three C4 subtypes
distinguished according to the decarboxylating enzyme. NADP-ME, NADP requiring malic
enzyme; NAD-ME, NAD requiring malic enzyme; PCK, PEP carboxykinase. Numbers refer to
enzymes: (1) PEP carboxylase, (2) NADP-malate dehydrogenase, (3) NADP-malic enzyme, (4)
Pyruvate-Pi dikinase, (5) Rubisco, (6) PEP carboxykinase, (7) Alanine amino trasnferase, (8)
Aspartate aminotrasferase, (9) NAD-malate dehydrogenase, (10) NAD-malic enzyme. Some
steps were occulted for the sake of simplicity.
Figure 2. Elements of the reconstructed C4GEM network and the two tissue model.
Figure 3. Flux map of the central metabolism.
Relative flux distributions (BS/M) between bundle sheath (BS) and Mesophyll (M) during C4
photosynthesis. Green and red lines represent increased or decreased flux in BS, respectively.
Gray lines represent flux values that do not differ significantly between cells.
Figure 4. Flux simulation for each C4 subtype with no leakage of CO2 and then considering
50% of CO2 leakage out of bundle sheath. A. Effect on linear electron flow (LEF) and cyclic
electron flow (CEF). Flux ratios:(i) NADP-ME mode:NAD-ME mode;(ii) NAD-ME
mode:NADP-mode; (iii) NAD-ME mode:NADP-mode (iv) CO2 leakage mode:NADP-ME
mode. B. Effect on total absorbed quanta; Flux ratio: related to CO2 leakage mode.
Photosysthem I (PSI); photosystem II (PSII); Mesophyll (M); Bundle sheath (BS). NADP-malic
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enzyme subtype (NADP-ME); NAD-malic enzyme subtype (NAD-ME); PEP carboxykinase
subtype (PCK).
Figure 5. C4 genome scale-model reconstruction process.
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Tables
Table I. Online resources for the reconstruction of the metabolic network of C4 plants.
Database
Link
Genome Database
Sorghum bicolor Genome (SorghumDB)
http://www.plantgdb.org/SbGDB/
Zea mays Genome (MaizeGDB)
http://www.maizegdb.org/
Resource for Comparative Grass Genomics
http://www.gramene.org/
(GrameneDB)
Grass Regulatory Information Server
http://grassius.org/
(GrassiusDB)
Pathway Databases
Kyoto Encyclopedia of Genes and
http://www.genome.jp/kegg/pathway.html
Genomes (KEGG)
PlantCyc
http://www.plantcyc.org/
ExPASy Biochemical Pathways
http://www.expasy.ch/cgi-bin/searchbiochem-index
Enzymes Databases
ExPASy Enzyme Database
http://ca.expasy.org/enzyme/
BRENDA
http://www.brenda-enzymes.info/
Enzyme/Protein Localization DataBases
AraPerox (Arabidopsis Protein from Plant
http://www.araperox.uni-goettingen.de/
Peroxisomes)
SUBA (Arabidopsis subcellular database)
http://www.plantenergy.uwa.edu.au/appli
cations/suba2/index.php
PPDB (Plant proteome database)
http://ppdb.tc.cornell.edu/default.aspx
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Table II. Characteristics of C4GEM based on the genomic information* of Sorghum, Sugarcane
and Maize.
Elements
Gene-reaction-association entries
Genes (unique)
C4 plant model
Sorghum
bicolor
13114
Zea mays
38892
Saccharum
officinarum
13593
3557
11623
3881
Metabolites
1755
Unique reactions
1588
Extra cellular transporters
18
Transporters (Intercellular11
plasmodesmata)
Transporters (Inter-organelle)
83
Biomass drains
47
Metabolic Reactions from primary
metabolism not assigned to any
131
135
particular gene (covered by AraGEM
* Extracted from Kyoto Encyclopedia of Genes and Genomes.
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156
Table III. List of biomass components – major drains*
Carbohydrates and Sugars
Starch, sucrose, fructose, glucose, maltose
Cell wall
Lignin (4-coumaryl alcohol, coniferyl alcohol, sinapyl
alcohol), cellulose, hemi-cellulose (xylose)
Amino acids
Alanine, arginine, aspartate, asparagine, cystein, lysine,
leucine, isoleucine, glutamate, glutamine, histidine,
methionine, phenylalanine, proline, serine, tyrosine,
tryptophan, valine
Nucleotides
ATP, GTP, CTP, UTP, dATP, dGTP, dCTP, dTTP
Fatty acids
C16:0 (Palmitic acid)
Vitamins and cofactors
Biotin, coenzyme A, riboflavin, folate, chlorophyll,
nicotinamide, thiamine, ubiquinone,
*Coefficients were estimated based on literature data (Guinn, 1966; Poorter and Bergkotte, 1992). Although
individual drains for vitamins and cofactors were tested, it was not added in the stoichiometric biomass equation as
it accounts for minor compounds.
Coefficients for biomass drain: 0.001 Maltose + 0.096 Sucrose + 0.053 Glucose + 0.053 beta-D-Fructose + 0.265
Starch + 0.305 Cellulose + 0.282 D-Xylose + 0.04 4-Coumaryl alcohol + 0.048 Coniferyl alcohol + 0.056 Sinapyl
alcohol + 0.026 Hexadecanoic acid + 0.107 L-Alanine + 0.39 L-Asparagine + 0.162 L-Aspartate + 0.024 L-Cysteine
+ 0.39 L-Glutamate + 0.35 L-Glutamine + 0.14 Glycine + 0.06 L-Isoleucine + 0.14 L-Leucine + 0.002 LMethionine + 0.08 L-Phenylalanine + 0.01 L-Proline + 0.21 L-Serine + 0.086 L-Threonine + 0.04 L-Tryptophan +
0.03 L-Tyrosine + 0.1 L-Valine + 0.001 L-Lysine + 0.0027 dATP + 0.0017 dGTP + 0.0019 dCTP + 0.0028 dTTP +
0.0029 ATP + 0.0039 GTP + 0.0033 CTP + 0.0029 UTP + 30 ATP = Biomass + 30 ADP + 30 Orthophosphate.
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Table IV. Set of constraints used for C4 subtypes based on literature.
Constraints
NADP-ME
NAD-ME
PCK
subtype
subtype
subtype
M
BS
M
BS
M
BS
RuBisCO (plastid)
-
+
-
+
-
+
PEPCase (cytosol)
-
+
-
+
-
+
CO2 uptake
+
-
+
-
+
-
NADP-ME (plastid)
+
+
-
-
-
-
NAD-ME (mitochondrion)
-
-
+
+
-
-
PCK (cytosol)
-
-
-
-
+
+
Malate transport (plasmodesmata)
+
-
+
Pyruvate transport (plasmodesmata)
+
-
+
Aspartate transport (plasmodesmata)
-
+
+
Alanine transport (plasmodesmata)
-
+
+
Fixed growth
Fixed growth
Fixed growth
Biomass s
M: Mesophyll; BS: bundle sheath; RuBisCO: Ribulose-1,5-bisphosphate carboxylase oxygenase; PEPcase:
Phosphoenolpyruvate carboxylase; PCK: PEP carboxykinase; NADP-ME: NADP malic enzyme; NAD-ME: NAD
malic enzyme. (-) flux limited: constraint to zero. (+) free flux: estimated through optimization. s Starch synthesis
(biomass drain) was limited in M, and the remaining biomass components were assumed to be evenly distributed in
both cells.
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Table V. Regulation of key enzymes/transporters during C4 photosynthesis in mesophyll (M)
and bundle sheath (BS) predicted by C4GEM and correlated/no correlated to Proteomics studies.
Pp
0.5
Ff
0
Carbon fixation
1.5
37
5.3.1.1
Glycolysis
0.5
-1
Phosphoglycolate phosphatase
3.1.3.18
Photorespiration
Fructose-bisphosphate aldolase
4.1.2.13
Glycolysis/PPP
1.5
39
Pyruvate Dehydrogenase (PDH E1)
1.2.4.1
Glycolysis/TCA
1
1
Glutamine synthetase
6.3.1.2
Nitrogen metabolism
0.5
0
Aspartate aminotransferase
2.6.1.1
Carbon fixation
0.5
-0
Phosphate/Triose-phosphate (IEP30)
translocator
Chloroplast membrane
Phosphate/Phosphoenolpyruvate (IEP33)
translocator
Chloroplast membrane
ATP/ADP translocator
translocator
Chloroplast membrane
0.5
-1
2-oxoglutarate/malate (DiT1)
translocator
Chloroplast membrane
0.5
0
2-oxoglutarate/malate (DiT2)
translocator
Chloroplast membrane
1.5
1.4
GAPDH (GAP-A)
1.2.1.12; 1.2.1.13
Glycolysis
1.5
4.1
Phosphoglycerate kinase
2.7.2.3
Glycolysis
Phosphoribulokinase
2.7.1.19
Carbon fixation
1.5
37
Ribulose-5-phosphate-3-epimerase
5.1.3.1
PPP
1.5
24
Transketolase
2.2.1.1
PPP
1.5
12
Ribose-5-phosphate isomerase
5.3.1.6
PPP
1.5
12
ADP-glucose pyrophosphorylase
2.7.7.27
Starch synthesis
Starch synthase
2.4.1.21
Starch synthesis
Carbonic anhydrase
4.2.1.1
Carbon fixation
1.5
24
Enzymes
Ferredoxin-NADP(+) reductase 1 (FNR-1)
EC
1.18.1.2
Metabolism
Photosynthesis/LET
NAD(P)H dehydrogenase (NDH)
1.6.5.2
Photosynthesis/CET
RuBisCO
4.1.1.39
Triosephosphate isomerase
Fatty acid biosynthesis (20 enzyme average)
Fatty Acids
Malic enzyme
1.1.1.40
Carbon fixation/TCA
Malate dehydrogenase (MDH)
1.1.1.37
Carbon fixation/TCA
Pyruvate orthophosphate dikinase
2.7.9.1
Pyruvate metabolism
0.2
0
Ferredoxin-dependent Glu synthase
1.4.7.1
Nitrogen metabolism
0.5
0
Ferredoxin-nitrite reductase
1.7.7.1
Nitrogen metabolism
Adenylate kinase
2.7.4.3
Purine metabolism
p
0.5
0
Proteomics studies (BS/M): Maize chloroplast BS:M protein accumulation (Majeran et al., 2005; Majeran et al.,
2008; Friso et al., 2010). f Flux analyses (BS/M): Relative flux distribution (in this study). Green: Increased protein
accumulation (P) or increased flux (F); Red: Decreased protein accumulation (P) or decreased flux (F); Grey: no
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change, White: not predicted. PPP: Pentose phosphate pathway. GAPDH: Glyceraldehyde 3-phosphate
dehydrogenase. CET: cyclic electron flow. LET: linear electron flow. PEP: phosphoenolpyryvate.
Table V. Continue. Regulation of key enzymes/transporters during C4 photosynthesis in
mesophyll (M) and bundle sheath (BS) predicted by C4GEM and correlated/no correlated to
Proteomics studies.
p
Pp
0.5
Enzymes
EC
Geranylgeranyl reductase
1.3.1.83
Metabolism
Chlorophyll and Isoprenoids
Zeta-carotene desaturase
1.14.99.30
Chlorophyll and Isoprenoids
Phytoene dehydrogenase
1.14.99.-
Chlorophyll and Isoprenoids
1.5
Protochlorophyllide oxidoreductase
1.3.1.33
Chlorophyll and Isoprenoids
0.5
Chlorophyll synthase
2.5.1.62
Chlorophyll and Isoprenoids
Protoporphyrinogen oxidase
1.3.3.4
Chlorophyll and Isoprenoids
Flavin reductase
1.5.1.30
Oxidative stress
1
Glutathione peroxidase
1.11.1.9
Oxidative stress
0.5
Ascorbate peroxidase
1.11.1.11
Oxidative stress
0.5
Aldo/keto reductase
1.1.1.21; 1.1.1.2
Oxidative stress
Class 1 DAD1-like acylhydrolase
4.2.1.60
Oxidative stress
Lysophospholipase
3.1.1.5
Oxidative stress
0.5
Lipoxygenase 2
1.13.11.12
Oxidative stress
0.5
Aldose-1-epimerase
5.1.3.3
Degradation
1.5
beta-D-glucosidase
3.2.1.21
Degradation
1.5
Pheophorbide a oxygenase
1.14.12.20
Degradation
Ff
1.5
Proteomics studies (BS/M): Maize chloroplast BS:M protein accumulation (Majeran et al., 2005; Majeran et al.,
2008; Friso et al., 2010). f Flux analyses (BS/M): Relative flux distribution (in this study). Green: Increased protein
accumulation (P) or increased flux (F); Red: Decreased protein accumulation (P) or decreased flux (F); Grey: no
change, White: not predicted. PPP: Pentose phosphate pathway. GAPDH: Glyceraldehyde 3-phosphate
dehydrogenase. CET: cyclic electron flow. LET: linear electron flow. PEP: phosphoenolpyryvate.
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