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Rohwer and Botha (2001), Sucrose accumulation in sugar cane

May 2009, model of the month by Nick Juty
Original model: BIOMD0000000023

Sucrose accumulation is accompanied by continuous synthesis and degradation processes in the developing sugar cane, Saccharum officinarum [1]. Sugar cane internode maturation coincides with increased sucrose storage, but is not dependent purely on time. In addition, cane varieties accumulate sucrose to quite divergent extents. The biochemical reasons for these differences cannot be evaluated effectively using a traditional approach; genetic manipulation of plants often results in unpredictable results [2]. The complexity of the system has made difficult the identification of suitable genetic manipulation targets. Elucidation of such targets has obvious implications in commercial sucrose production.


Mode Number Sequence of steps Net reaction
1 R3→R8→R9 -
2 R4→R6→R7→(-R8) -
3 R5→R6→R7→(-R8) -
4 R3→R4→R6→R7→R9 -
5 R3→R5→R6→R7→R9 -
6 R1→R4→R10 Fruex→glycolysis
7 R1→R5→R10 Fruex→glycolysis
8 R2→R3→R10 Glcex→glycolysis
9 (2×R1)→(2×R4)→R6→R7→R11 2 Fruex→Sucvac
10 (2×R1)→(2×R5)→R6→R7→R11 2 Fruex→Sucvac
11 (2×R2)→(2×R3)→R6→R7→R11 2 Glcex→Sucvac
12 (2×R1)→R4→R8→R11 2 Fruex→Sucvac
13 (2×R1)→R5→R8→R11 2 Fruex→Sucvac
14 R1→R2→R3→R8→R11 Fruex + Glcex→Sucvac

Table 1: Elementary flux modes in sucrose biosynthesis. Elementary modes were calculated by the program METATOOL, with stoichiometry as indicated in Figure 1 as input. All 14 elementary modes are irreversible and proceed only in the direction indicated. Fruex, extracellular fructose; Glcex, extracellular glucose; Sucvac, vacuolar sucrose. Table adapted from [3].

Sucrose accumulation in sugar cane culm tissue

Figure 1: Sucrose accumulation in sugar cane culm tissue. Reactions : 1, fructose (Fru) uptake; 2, glucose (Glc) uptake; 3, hexokinase(Glc); 4, hexokinase (fructose phosphorylating); 5, fructokinase; 6, sucrose phosphate synthase; 7, sucrose phosphate phosphatase; 8, sucrose synthase; 9, invertase; 10, glycolysis; 11, vacuolar sucrose import. Reaction 6 is defined forward in the direction of sucrose 6-phosphate (Suc6P) synthesis, and has a stoichiometry of 2 for HexP (fructose 6-phosphate and UDP-glucose). Reaction 8 is defined forward in the direction of sucrose (Suc) synthesis. The hexose phosphate pool was considered as an equilibrium block comprising UDP-glucose, glucose 1-phosphate, glucose 6-phosphate and fructose 6-phosphate. HexP, hexose phosphates; subscript "ex", extracellular; subscript "vac", vacuolar. Figure from [3].

In this study (BIOMD0000000023 [3]), the authors use a integrative approach, comprising experimentation, theoretical analysis and computational simulation, to identify potential genetic manipulation targets.

Sucrose is generated by photosynthesis, and transported through the phloem from the leaves to the culm. Following hydrolysis, a significant proportion is taken up as glucose and fructose. Sucrose is also synthesized in cytoplasmic culm cells, and transported into storage vacuoles where it accumulates (Figure 1).

Since the pathways around sucrose metabolism are highly branched, initial structural analysis was used to determine all elementary modes. This identification of all possible elementary steady-state flux distributions was calculated using METATOOL [4] (Table 1). This analysis identified 14 modes, of which five were futile cycles. These futile cycles hydrolyse ATP or UTP, and have been show to be responsible for the re-hydrolysis of around 30% of synthesized sucrose. These can siginficantly reduce agricultural sucrose yields, and in an optimised system would be processes targeted for minimisation.

Since elementary mode analysis considers only stoichiometric information, it is not able to predict which factors are influential in the flux distribution changes that cause a shift from one elementary mode to another. Kinetic and thermodynamic information of the enzymes involved were combined to generate a kinetic model to enable a more complete understanding of sucrose accumulation. The resulting model predicted 22% futile cycling, consistent with experimental results. In addition, model metabolite fluxes and steady state concentrations agreed well with experimentally determined values with experimental results differing at most by a factor of two (Table 2).

Having generated a validated model, the authors then performed control coefficient analysis (Metabolic Control Analysis), where the objective was to determine which reactions had the greatest effect on futile cycling of sucrose (Figure 2).

Fructose and glucose uptake, hexokinase, invertase and vaculoar sucrose accumulation are suggested to have the greatest effect on futile cycling of sucrose. The negative control coefficients (e.g. glucose uptake) imply decreased futile cycling upon increased activity of that respective step. Positive control coefficients (e.g. invertase) indicate increased futile cycling upon increased step activity.

Flux (mM/min)
Step
Model Experiment
Glucose uptake
0.127 0.195
Hexokinase(Glc)
0.155 0.197
Glycolysis
0.046 0.041
Net sucrose accumulation
0.094 0.127
Concentration (mM)
Metabolite
Model Experiment
Glucose
30 29
Fructose
41 30
UDP-glucose
2.46 2.67
Glucose 6-phosphate
0.34 0.21
Fructose 6-phosphate
0.17 0.12

Table 2: Kinetic model validation: comparison of calculated and experimentally determined fluxes and metabolite concentrations. All values refer to cytosolic metabolite concentrations. Table modifed from [4].

Futile cycle control coefficients

Since control coefficients are defined using miniscule changes around a specific steady state, the results are not directly extrapolatable to distant steady state values. Here, the authors chose to use kinetic modeling to modify steps identified from coefficient control analysis. In simulations, step activity was increased 5-fold (for negative control coefficient steps), or decreased 5-fold (for positive control coefficient steps). Sucrose accumulation flux, percentage futile cycling and conversion efficiency from hexose to sucrose were calculated. In all cases, significantly diminished futile cycling of sucrose was demonstrated (Figure 3). In all cases except the hexokinase step, enzyme activity changes resulted in increased flux to sucrose accumulation.

Overall, the overexpression of glucose or fructose transporters, vacuolar sucrose transporter and potentially decreasing the activity of invertase seem the most promising targets to increase sucrose yields. The authors themselves seek to further this work by using antisense technology to knockdown invertase activity in sugar cane.

The final model encompasses current knowledge of sucrose accumulation, as well as providing a starting point for further extensions. These could include the simulation of metabolism in other internodes, or in other plant tissues such as leaves or roots, if appropriate enzyme activity levels are known.

Figure 2: Futile cycling control coefficients. For each step, the value of the futile cycling control coefficient CiJ9/J11 was calculated. The value, as calculated by the model, is indicated alongside the arrow for the respective reaction.The control coefficient describes to what extent that step controls the flux partitioning between neutral invertase (reaction 9) and sucrose accumulation (reaction 11). Abbreviations defined in Figure 1. Figure adapted from [3]

Effect of manipulating enzyme activities on sucrose accumulation and futile cycling

Figure 3: Effect of manipulating enzyme activities on sucrose accumulation and futile cycling. From [3].

Bibliographic References

  1. P.H. Moore. Temporal and spatial regulation of sucrose accumulation in the sugarcane stem. Aust J Plant Physiol, 22:661-679, 1995.
  2. M. Stitt and U. Sonnewald. Regulation of metabolism in transgenic plants. Annu Rev Plant Physiol Plant Mol Biol, 46:341-368, 1995.
  3. J.M. Rowher and F.C. Botha. Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data. Biochem J, 358:437-445, 2001. [SRS@EBI]
  4. T. Pfeiffer, I. Sánchez-Valdenebro, J.C. Nuño, F. Montero and S. Schuster. METATOOL: for studying metabolic networks. Bioinformatics, 15:25-257, 1999. [SRS@EBI]
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