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m1-56 seeds

M1-56 seeds

Asymmetric seeds are more frequent in species of S. subg. Silene than in S. subg. Behenantha ( Table 2 ).

Model 1: The cardioid curve is described by the equation:

2.2. Structural Aspects

With Model 1 higher values of J index were obtained in S. conica than in S. gallica or S. otites, while the J index obtained with Model 3 was higher in S. gallica. From the value of 90 obtained with Model 1, the J index value increased in S. gallica to 90.4 with Model 3 ( Table 8 ). Thus, Model 3 represents an improvement for the description and quantification of seed shape in S. gallica. Not only because the values of J index increased for this species, also because they decreased with other species.

4.3. Surface Characteristics and other Structural Properties of Seeds

Box plot representing the values of J index with Model 1 for species of S. subg. Silene. Left to right: S. colpophylla, S. gallica, S. italica, S. mellifera, S. nutans Chk, S. nutans Pol, S. otites, S. saxifraga, S. schafta, S. tatarica, and S. wolgensis. Upper and lower limits of the discontinuous lines represent the maximum and minimum values not atypical (atypical values are outside, below the discontinuous lines). Lower and upper limits of the boxes represent respectively the first and third quartile. The thickened bar in the box is the median.

M1-56 seeds

Although no experimental data on PPi deficiency are available from oxygen-depleted barley seeds, low flux through the major source of PPi production (cytosolic AGPase) in conjunction with high flux through the major source of PPi consumption (UGPase) suggest low PPi availability in anoxic and hypoxic tissues. Although further experiments are required to evaluate the proposed hypothesis, the given examples show the potential of the model to verify or extend the understanding of controversially discussed biological processes by looking at systemic stoichiometric constraints only. Furthermore, it reveals the advantage of systems-oriented modeling by giving insight into complex biological processes provided by the integration of multiple data, which in this form cannot be obtained by studies restricted to the analysis of single enzymes.

With the aim of getting a systemic understanding of cereal seed storage metabolism, we constructed a stoichiometric model of primary metabolism in the developing endosperm of barley seeds during starch accumulation. The constructed model includes central metabolism (glycolysis, pentose phosphate pathway, citrate cycle), amino acid metabolism, starch synthesis, and some minor pathways. To our knowledge, this is the first detailed attempt at stoichiometric modeling of seed metabolism to date, although a few smaller models of storage metabolism in other plant species exist, including sugarcane (Saccharum spontaneum; Uys et al., 2007), potato (Solanum tuberosum; Poolman et al., 2004), and canola (Brassica napus; Schwender et al., 2003).

Carbon flux maps depicting the key fluxes within the cytosolic glycolysis and Suc-to-starch pathways of the anoxic phase, phase 1 (A), the hypoxic phase, phase 3 (B), and the aerobic phase, LO (C). Simulations were performed using the growth conditions outlined for simulation scenario 1. The representation of glycolysis is restricted to the ATP-utilizing reactions PFK and PK and the PPi-utilizing bypass PFP and PPDK to focus on differences between ATP- and PPi-dependent glycolysis in response to oxygen supply. Reactions are as follows: AGPase, ADP-Glc pyrophosphorylase; FK, fructokinase; PFK, ATP:Fru-6-P 1-phosphotransferase; PFP, pyrophosphate:Fru-6-P 1-phosphotransferase; PK, pyruvate kinase; PPDK, pyruvate orthophosphate dikinase; SuSy, Suc synthase; UGPase, UDP-Glc pyrophosphorylase. Metabolites are as follows: ADPglc, ADP-Glc; Frc, Fru; F6P, Fru-6-P; F1,6BP, Fru-1,6-bisP; G1P: Glc-1-P; PEP, phosphoenolpyruvate; Pyr, pyruvate; UDPglc, UDP-Glc.

Further increasing the oxygen supply results in the utilization of the complete citrate cycle, accompanied by a decrease of fermentative fluxes and an increase of flux through ATP-consuming processes such as starch synthesis ( Fig. 3B ). In contrast to phases 1 and 2, the futile cycle formed by PFK and PFP is no longer active and glycolytic flux is additionally carried by the PPi-utilizing bypass of PFP and pyruvate orthophosphate dikinase (PPDK; Fig. 4B ). The Suc availability decreases with increasing aerobioses ( Table II ), indicating that Suc has a growing importance as a limiting factor with increasing oxygen supply.

Seed Metabolism under Aerobic Conditions

In this futile phase, oxygen excess is inhibitory toward obtaining maximal biomass production ( Table II ). Phase 5 is excluded from further analysis, as the physiological condition of oxygen excess does not occur under in vivo conditions.

With respect to glycolysis, the model predicts a PFK-PFP substrate cycle (Stitt, 1990) in which energy is directed from ATP to PPi and, thus, to PPi-consuming reactions. PFP catalyzes a near equilibrium reaction (Stitt, 1990) and is usually characterized by high activity in young developing tissues and starch-storing tissues (Xu et al., 1989). The precise metabolic function of PFP is still unclear and controversially discussed (Stitt, 1990; Hajirezaei et al., 1994). Several metabolic functions have been proposed, including a role in glycolysis (Duff et al., 1989; Hatzfeld et al., 1989), gluconeogenesis (Fahrendorf et al., 1987; Paul et al., 1995), and operation in a cycle with PFK to produce PPi required for PPi-consuming reactions, such as Suc mobilization via Suc synthase and UGPase (Stitt, 1990). Several investigations support the existence of such a cycle in cereal seed metabolism. Studies about the role of PFP and PFK in the endosperm of developing seeds of wheat (Triticum aestivum; Mahajan and Singh, 1990, 1992) indicate that PFP is primarily involved in the generation of PPi. A similar mechanism was suggested in anoxic rice (Oryza sativa) coleoptiles (Gibbs et al., 2000). By providing evidence for the gluconeogenetic direction of PFP in anoxic tissues, the authors support the view of PFK being a significant control point for glycolytic flux under anoxia. Studying the role of PPi-dependent glycolytic enzymes during anoxia in anoxia-tolerant rice and anoxia-intolerant Arabidopsis (Arabidopsis thaliana), Huang et al. (2008) proposed PFP in rice coleoptiles to function (1) in the glycolytic direction in the early stage of anoxia in order to accelerate glycolysis in response to the anoxic energy crises and (2) in the gluconeogenetic direction during long-term anoxia in order to slow net glycolysis to conserve carbohydrates. In agreement with the model predictions, the authors suggest that PFP operates in a cycle with PFK to produce PPi required for PPi-consuming processes in anoxia-intolerant Arabidopsis. Nevertheless, as there is still no clear evidence about the precise metabolic function of PFP under anoxia, more studies are needed to provide further evidence for the model predictions.

Further information about the definition of the metabolic model can be found in “Materials and Methods.” A metabolic map of the network (Supplemental Fig. S1), the set of reactions included in the network (Supplemental Data Set S1), and the terminology used for the network reactions and metabolites (Supplemental Data Set S3) are given in the online supplemental material.

Table III.

Despite the known limitations of FBA, including the constraints of not incorporating regulatory events and of predicting optimal behavior only, which may not reflect suboptimal growth in vivo (Edwards and Palsson, 2000), the results presented here indicate that the constructed model has the potential to simulate cereal seed metabolism. Thus, by providing an initial framework for studying cereal seed storage metabolism in silico, in future applications the model can be used to verify and extend the understanding of complex processes, to generate and test hypotheses, and to explore in silico scenarios, which eventually should allow us to find suitable targets for the improvement of grain and yield quality.

Barley seeds are known to develop under hypoxic conditions during intermediate and storage phases (Rolletschek et al., 2004). To elucidate the possible role of oxygen and Suc supply for storage patterning in developing barley seeds, a phenotypic phase plane (PhPP; Edwards et al., 2002) was computed that depicts the metabolic behavior of seed metabolism at various levels of oxygen and Suc availability ( Fig. 1 ). PhPP analysis is a method used to obtain the range of optimal flux distribution (i.e. optimal phenotypic behavior) in response to changes in two environmental parameters such as substrate and oxygen uptake rates. By defining the environmental parameters under investigation as two axes on an (x,y) plane, the optimal flux distribution is computed for all points in the plane and lines are drawn to demarcate regions of constant flux distributions (i.e. phenotypic phases). Based on this procedure, the optimal flux distributions in the phase plane are classified into a finite number of regions, each with a distinct metabolic pathway utilization pattern, corresponding to a different optimal phenotypic behavior (Edwards et al., 2001, 2002). Thus, PhPP allows a quick and comprehensive overview of the optimal use of metabolism in the growth environment studied (see “Materials and Methods” for details).