PEP fixes carbon dioxide into a four-carbon molecule, called malate, that is transported to the deeper bundle sheath cells that contain Rubisco. The malate is then broken down into a compound that is recycled back into PEP and carbon dioxide that Rubisco fixes into sugars—without having to deal with the oxygen molecules that are abundant in the mesophyll cells.
C3 plants do not have the anatomic structure no bundle sheath cells nor the abundance of PEP carboxylase to avoid photorespiration like C4 plants. One focus of the RIPE project is to create a more efficient pathway for photorespiration to improve the productivity of C3 crops.
The RIPE project is also working to improve photosynthesis in C3 crops to ensure greater food security under future climate scenarios. C3 plants are limited by carbon dioxide and may benefit from increasing levels of atmospheric carbon dioxide resulting from the climate crisis. However, this benefit may be offset by a simultaneous increase in temperature that may cause stomatal stress. C3 plants include some of the most important sources of calories all over the world: cowpea, cassava, soybean, and rice.
The regions where these crops are grown in are often hot and dry, meaning they could benefit from the energy-saving mechanisms of C4 photosynthesis. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
C3 photosynthesis is the major of the three metabolic pathways for carbon fixation by plants. This process uses the enzyme RuBisCO in relatively inefficient conditions, to fix CO2 from the air and obtain the 3-carbon organic intermediate molecule 3-phosphoglycerate.
C3 photosynthetic plants possess a specific leaf structure, and are not adapted to non-optimal conditions. Research 15 April Open Access. Research 12 April Open Access. Yunke Peng et al. These results challenge the assumption that leaf-level photosynthetic capacity depends on soil N supply yet supports the relationship between photosynthesis and soil P supply.
Research 26 June Open Access. So we complemented the xylose pathway in C4GEM, thus the biased results can be avoided. Next we investigated the effects of particular key enzymes on photosynthesis and biomass synthesis in C3 and C4 plants. Table 5 illustrated these enzymes, their functions and the ratio of objective flux after deletion. Knockouts of enzymes in Calvin cycle have lethal effects on both C3 and C4 networks.
For example, the central enzyme of Calvin cycle, Rubisco EC: 4. Its deletion results in zero flux of CO 2 fixation and biomass, which accords with the fact that photosynthesis and plant growth is positively correlated with Rubisco activity [ 31 , 32 ].
Aconitases EC: 4. Its deletion has no influence, because sucrose synthesis locates in cytosol and has no direct connection with photosynthesis. Amylase isomerase EC: 2. Therefore, knockout of PEPC resulted in zero flux of biomass, which validates its crucial role in C4 photosynthesis. Its deletion reduced the flux of CO 2 fixation and biomass, which is consistent with experiment results that inhibition of PPDK significantly hinders C4 plant growth [ 40 ].
In comparison, these two enzymes have no effect on CO 2 fixation and biomass in C3 network. There are some reactions co-utilized in precise stoichiometric ratios and exhibit correlated flux in the metabolic network, which called correlated reaction sets.
We used the uniform random sampling method to determine dependencies between reactions which can be further used to define modules of reactions [See Methods section]. The simplified model of the C3 network has reactions, metabolites and narrow range on constraints, which can be separated into 65 modules and the largest module consists of 92 reactions.
The simplified model of the C4 network has reactions, metabolites and narrow range on constraints, which can be separated into modules and the largest module consists of reactions. There are more correlated reaction sets in C4 than C3 network.
The fluxes of reactions in the same module exhibit linear correlation. We found the reactions in Calvin cycle are correlated in both C3 and C4 network, as illustrated in Figure 3 and 4 respectively. However, there are some reactions from different pathways also exhibit linear correlation in C4 network, but they are not correlated in C3 model. For example, the reactions from Sugar metabolism, Stibene, counarine and lignin biosynthesis, and Coumarine and phenylpropanoid biosynthesis pathways are significantly correlated in C4 shown in Figure 5 , but no correlation among them in C3 shown in Figure 6.
It demonstrated that C4 plants have better modularity with complex mechanism coordinates the reactions and pathways than that of C3 plants. The biomass and CO 2 fixation of C3 and C4 models were simulated under different light intensity, as shown in Figure 7 and 8.
The C3 model red in Figure 7 and C4 model blue in Figure 7 presented linear relationship between biomass and light intensity when light intensity is less than Then with the light intensity increasing, the biomass would be unchanged in C4 model and still increased in C3 model. The C3 model red in Figure 8 and C4 model blue in Figure 8 also presented linear relationship between CO 2 fixation and light intensity when light intensity is less than Then the CO 2 fixation was almost keeping unchanged.
The increase of both biomass and CO 2 fixation with light intensity in C4 are faster than that in C3, which reflect more efficient use of solar energy in C4 plants [ 41 ]. In addition, we simulated the flux of biomass synthesis and CO 2 fixation under different CO 2 concentration, as shown in Figure 9 and The more CO 2 concentration increases, the more flux of biomass and CO 2 fixation, and the increase gradually change slowly until to steady state.
The simulated curve was consistent with experiment A-Ci curve [ 42 ]. We found that the increase of both biomass and CO 2 fixation with CO 2 concentration in C4 are faster than that in C3, which reflect more efficient use of CO 2 in C4 plants. The effect of light intensity on CO 2 fixation in C3 and C4 model. The effect of CO 2 concentration on biomass synthesis in C3 and C4 model.
We explored the influence of each subtype on biomass synthesis and CO2 fixation, by blocking the flux of other two enzymes and giving enough supply of water and nitrogen. As shown in Table 6 , for each specific subtype, only the corresponding enzyme has flux and the other two enzymes have zero flux.
There are little differences on biomass in the three subtypes. Moreover, when all the three subtypes are assumed to be active in one metabolism system, the PCK subtype is superior to be used for CO2 decarboxylation. These results are consistent with Fravolini's experiments that photosynthetic performance and above-ground biomass production of B. However, the photosynthesis and biomass of different subtypes also depend on environment conditions, including water and nitrogen supply [ 44 , 45 ].
For example, some species of NADP-ME type show higher rates of photosynthetic and biomass production under low nitrogen availability [ 46 ]. Therefore, to clearly elucidate the superiority of C4 subtypes, further design and analysis under multi-factorial combination of environment conditions are required. There is possibility to engineer C4 photosynthesis into C3 plants, because all C4 key enzymes are also present in C3 plants, although the expression levels are much lower than that in C4 species [ 1 ].
However it is an enormous challenge. To realize the transition from C3 to C4, systems biology will play a critical role in many aspects, including identification of key regulatory elements controlling development of C4 features and viable routine towards C4 using constraint-based modeling approach [ 47 ].
In this study, we improved the current metabolism models AraGEM and C4GEM by setting the ratio of carboxylation and oxygenation by Rubisco, and then systematically compared the constraint-based metabolic networks of C3 and C4 plants for the first time. We found C4 plants have less dense topology, higher robustness, better modularity, and higher CO 2 and radiation use efficiency, which provide important basis for engineering C4 photosynthesis into C3 plants.
All results are consistent with the actual situation, which indicate that Flux Balance Analysis is a useful method to analyze and compare large-scale metabolism systems of plants. For C3 plants, the ratio r between carboxylation and oxygenation under specific CO 2 and O 2 concentration can be calculated by the following Unlike C3 plants, C4 photosynthesis requires the coordinated functioning of mesophyll and bundle sheath cells by CO 2 concentrating mechanism.
The ratio r of carboxylation to oxygenation can be expressed as equation 7 [ 48 ]:. In C4 plants, CO 2 concentration in mesophyll cell is only 37 percent of CO 2 in air [ 49 ] and the other parameters can be obtained in [ 48 ]. The topological properties of metabolic network can be analyzed based on graph theory, which can reflect the structure and robustness of large-scale network.
In this study, the reactions are represented as nodes, if the product of reaction A is the substrate of a reaction B, there will be an edge from A to B.
We consider some important parameters including degree, clustering coefficient, betweenness centrality and distance path length. The degree of a node is the number of edges connected with other reactions. Degree centralization of a network is the variation in the degrees of vertices divided by the maximum degree variation which is possible in a network of the same size. Clustering coefficient is used to compute different inherent tendency coefficients in undirected network.
Betweenness centralization is the variation in the betweenness centrality of vertices divided by the maximum variation in betweenness centrality possible in a network of the same size. The distance between two nodes is the shortest path length from one to the other. The diameter of network is the maximal distance among all pairs of nodes.
All the topology analysis was conducted using the visual software Pajek [ 26 ]. In any realistic large-scale metabolic model, there are more reactions than compounds, so there is no unique solution to this system of equations. Flux Balance Analysis FBA can solve the flux distribution by setting a set of upper and lower bounds on v and optimizing some objective function with linear programming, as following:.
Where c is a vector of weights indicating how much each reaction contributes to the objective function. In this study, we choose CO 2 fixation and biomass synthesis as two objective functions. The fluxes that are identified at various perturbations can be compared with each other and with experimental data. Uniform random sampling of the solution space in any environmental condition is a rapid and scalable way to characterize the structure of the allowed space of metabolic fluxes.
Before the sampling was performed, the effective constraints for each reaction were calculated using the method of Flux Balance Analysis in COBRA toolbox [ 50 ]. Specifically in sampling, COBRA toolbox uses an implementation of the artificial centered hit-and-run ACHR sampler algorithm with slight modifications to generate such a set of flux distributions that uniformly sample the space of all feasible fluxes.
Initially, a set of non-uniform pseudo-random points, called warm-up points, was generated. In a series of iterations, each point was randomly moved while keeping it within the feasible flux space. This was accomplished by choosing a random direction, computing the limits on how far a point could travel in that direction positive or negative , and then choosing a new point randomly along that line.
After numerous iterations, the set of points was mixed and approached a uniform sample of the solution space [ 51 ] and points was loaded for analysis. The sampling procedure can be achieved with the function 'sampleCbModel' and the correlated reaction sets can be identified by 'identifyCorrelSets' in the COBRA toolbox.
Correlated reaction sets are mathematically defined as modules in biochemical reaction network which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. This sampling approach is used to fully determine the range of possible distributions of steady-state fluxes allowed in the network under defined physicochemical constraints and used to analyze the general properties of networks by testing their robustness to parameter variation [ 50 ].
J Exp Bot. Plant Cell. CurrOpin Plant Biol. CAS Google Scholar. Evans JR: Enhancing C3 photosynthesis. Plant Physiol.
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