Plant Physiology Preview. Published on September 22, 2015, as DOI:10.1104/pp.15.00981
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Running head: Metabolic architecture of the cereal grain
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To whom correspondence should be addressed:
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Ljudmilla Borisjuk, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK),
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Department of Molecular Genetics, Corrensstr. 3, D-06466 Gatersleben, Germany, Tel. +49
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(0)34982 5687; Fax +49 (0)39482 5500; E-mail: [email protected]
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Research area: Biochemistry and Metabolism
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This is a contribution to the Focus Issue on Metabolism (November 2015).
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Metabolic architecture of the cereal grain and its relevance to maximize
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carbon use efficiency
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Hardy Rolletschek1,#, Eva Grafahrend-Belau2,#, Eberhard Munz1,3, Volodymyr Radchuk1, Ralf
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Kartäusch4, Henning Tschiersch1, Gerd Melkus5, Falk Schreiber6, Peter M. Jakob3,4, and Ljudmilla
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Borisjuk1
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Genetics, Corrensstr. 3, 06466 Gatersleben, Germany
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Germany
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Research Center Magnetic Resonance Bavaria, Am Hubland, 97074 Würzburg, Germany
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Department of Medical Imaging, University of Ottawa, Canada
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Clayton School of IT, Monash University, Wellington Road, VIC 3800, Australia
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This indicates authors that have equally contributed to this study.
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular
Institut für Pharmazie, Martin-Luther-University of Halle, Hoher Weg 8, 06120, Halle, Germany
University of Würzburg, Institute of Experimental Physics 5, Am Hubland, 97074 Würzburg,
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One Sentence Summary
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This study applies noninvasive technology and metabolic modeling to demonstrate how the different
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grain organs (endosperm, embryo and pericarp) adjust their metabolism to fluctuating environments.
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FOOTNOTES
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The authors declare to have no conflicts of interest. Materials described in this manuscript are freely
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available to academic investigators for non-commercial research purposes.
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Corresponding author: Ljudmilla Borisjuk, e-mail: [email protected]
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Author Contributions
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H.R. and L.B. designed the research, performed research (metabolite analysis, histology)
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analyzed data, and wrote the paper. E.G.-B. performed FBA research, interpreted data and
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wrote the paper. E.M., G.M. and R.K. performed research (NMR/MRI) and analyzed data. V.R.
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performed research (transcriptomics). H.T. performed research (photosynthesis and gas
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exchange). F.S. and P.M.J. analyzed data.
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This work was supported by the German Plant Phenotyping Network.
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Abstract
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Here, we have characterized the spatial heterogeneity of the cereal grain's metabolism, and
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demonstrated how, by integrating a distinct set of metabolic strategies, the grain has evolved to
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become an almost perfect entity for carbon storage. In vivo imaging revealed light-induced
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cycles in assimilate supply toward the ear/grain of barley (Hordeum vulgare) and wheat
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(Triticum aestivum). In silico modelling predicted that in the two grain storage organs (the
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endosperm and embryo) the light-induced shift in solute influx does cause adjustment in
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metabolic flux without changes in pathway utilization patterns. The enveloping, leaf-like
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pericarp, in contrast, shows major shifts in flux distribution (starch metabolism, photosynthesis,
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remobilization, TCA cycle activity), allows to re-fix 79% of the CO2 released by the endosperm
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and embryo, allowing the grain to achieve an extraordinary high carbon conversion efficiency of
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95%. Shading experiments demonstrated that ears are autonomously able to raise the influx of
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solutes in response to light, but with little effect on the steady state levels of metabolites or
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transcripts, or on the pattern of sugar distribution within the grain. The finding suggests the
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presence of a mechanism(s) able to ensure metabolic homeostasis in the face of short-term
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environmental fluctuation. The proposed multi-component modeling approach is informative for
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predicting the metabolic effects of either an altered level of incident light or a momentary change
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in the supply of sucrose. It is therefore of potential value for assessing the impact of either
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breeding and/or biotechnological interventions aimed at increasing grain yield.
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Introduction
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Raising the productivity of a given crop genotype requires an increase in the plant's ability to
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efficiently respond to a changeable environment. At the physiological level, this involves
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adjustments to the photosynthetic activity of its leaves, to the control of assimilate movement
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from source to sink, and to the efficiency of assimilate storage. The manipulation of the plant's
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efficiency to fix carbon, to use energy and nutrients and to utilize water has been suggested as
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a route for improving crop productivity (Amthor 2010; de Block & van Lijsebettens, 2011).
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Particular attention has been devoted to the cereals, given their central role in the human diet.
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While the necessary integration of physiological and developmental processes in all plants
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relies on the transmission of hormonal, metabolic and other signals through the vascular system
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(van Bel et al., 2013), this is not the case in the cereal grain, since this network does not extend
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beyond the pericarp, a maternal structure which encloses the filial storage organs (embryo and
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endosperm) (Radchuk & Borisjuk, 2014). The isolation of the filial from the maternal tissues in
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the cereal grain creates a complex metabolic system, involving at the least a tripartite interaction
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between the pericarp, endosperm and embryo. These three grain components in effect form an
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interactive system of autonomous organs, each following own genetic programs (Sreenivasulu
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et al., 2006). The idea that the cereal grain is a metabolically homogeneous unit is an
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over-simplification, even if it is a useful assumption for metabolism and growth modelling
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(Grafahrend-Belau et al., 2013). There is a growing requirement to specify whole grain
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metabolism at the level of its component tissue parts.
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Given that metabolic activity varies qualitatively from tissue to tissue within the plant,
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approaches which can deliver a high level of spatial resolution are needed to gain a holistic
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picture of metabolism. Toward this end, various approaches have been successfully applied,
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e.g. mass spectrometry imaging (Peukert et al., 2014) and laser microdissection-coupled
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analytics (Schiebold et al., 2011; Belmonte et al., 2013). While such approaches can provide an
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excellent level of static chemical and/or spatial resolution, it is invasive and thus has only limited
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ability to generate dynamic measurements. A number of non-invasive imaging platforms,
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capable of monitoring growth and metabolism at different levels of spatial resolution, have been
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developed in recent years. The three leading ones are NMR (nuclear magnetic resonance), MRI
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(magnetic resonance imaging) and PET (positron emission tomography), all of which have been
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used to characterize sap flow in live material (Köckenberger et al., 2004; van As & Duynhoven,
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2013). The small size and complex structure of some seeds (and even of the cereal grain)
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present a technical challenge in applying such technologies (Borisjuk et al., 2012).
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A further challenge relates to the temporal variability in grain metabolism induced by
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environmental fluctuations. The supply of assimilate to the developing ear and grain is assumed
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to underlay significant diurnal variations (Grafahrend-Belau et al., 2013; Cheung et al., 2014)
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but their experimental validation has remained difficult to achieve. Diurnal variation in transcript
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abundance in barley grains has been found (Mangelsen et al., 2010), but it is unclear whether
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transcriptomic changes are mirrored by (proportional) changes to the metabolic activity. Where
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experimentation is difficult, it may be possible to exploit in silico metabolic modelling, an
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approach which has been validated in the form of flux balance analysis (FBA) (Sweetlove &
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Ratcliffe, 2011; Baghalian et al., 2014). FBA represents an established technique to generate
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quantitative models of optimal steady-state flux distributions at large-scale metabolic networks.
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Appropriate FBA models involving seed metabolism (Grafahrend-Belau et al., 2009; Hay &
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Schwender, 2011; Hay et al., 2014) and even individual seed organs/tissues (Rolletschek et al.,
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2011; Borisjuk et al., 2013) have been elaborated. As yet, however, these seed models can not
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account for the exchange of materials between the individual tissues/organs. The challenge
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now is to join up specific models to form a multi-layered representation of entire
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tissues/organs/organisms (Mintz-Oron et al., 2012; Grafahrend-Belau et al., 2013; Gomes de
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Oliveira Dal'Molin et al., 2015). Such an approach could represent a useful means of identifying
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the key drivers of metabolic behavior and the major bottle necks, which could then be viewed as
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viable targets for intervention (Sweetlove et al., 2014).
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The aim of the present research was to gain a picture of the metabolic architecture of the cereal
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grain, in particular to recognize the separate contribution made to the grain's metabolism by
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each of the three grain components (embryo, endosperm, pericarp), and to explore how they
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respond to fluctuating environments (light/sucrose supply). By applying noninvasive NMR/MRI
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technology, we first monitored temporal variability in solute flow toward the living ear/grain, and
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could detect significant light-dependent (diurnal) cycles. Based on the experimental
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quantification of biomass composition, growth rate, metabolite abundance, gas exchange and
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photosynthetic activity we then generated component-specific stoichiometric models which
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were eventually combined into a multi-component grain model, accounting for the exchange of
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materials between the grain organs. We predicted component-specific metabolic fluxes under
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light (day) versus dark (night) conditions, and performed shading experiments with subsequent
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analysis of assimilate uptake/partitioning and metabolite/transcript abundance, allowing to
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illuminate the dynamic aspects of grain metabolism under fluctuating environmental conditions.
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Overall, our work provides a novel framework for metabolic modelling and perspectives for
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understanding the control of cereal grain metabolism.
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Results
Monitoring the sap flow to the individual ear using NMR demonstrates fast light responses and
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indicates ear-autonomous regulatory mechanisms
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In an attempt to determine whether sap flow towards the cereal ear responded to illumination in
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vivo, we used novel NMR technology (AC-gradient method for velocity profile measurement:
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Kartäusch et al., 2014). The AC-gradient method is feasible for fast and non-invasive in vivo
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detection of low fluxes under high endogenous water background. Additional advantage is that
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the low-field NMR instrument is transportable and allows monitoring plants in their natural
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environment. The NMR coil was attached to the base of a wheat ear (peduncle) in order to
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monitor solute flux during several days. Peduncle is the only way for sap flow toward ear, thus is
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appropriate to determine direction of net flux and quantify its velocity. The plant was exposed to
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a 12 h photoperiod (corresponding to the yellow and white areas shown in Figure 1A). The
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resulting NMR output revealed that the flux responded to illumination by a 3 to 4-fold change.
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Similarly, the switch to darkness resulted in an instant decrease in the flux, an effect which was
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reproducible over several days. A similar outcome was observed when the same set-up was
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applied to a barley plant (Figure 1B). This in vivo experiment indicates the existence of
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significant diurnal cycles in assimilate supply toward the growing ear/grain, probably induced by
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the photoperiod experienced by the plant.
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In the same experiment an individual ear was kept in the dark at the same time that the rest of
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the plant remained exposed to a diurnal variation in light. This is referred to henceforth as “ear
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shielding”. In this case, the light response of ear was abolished, and the flux remained at the low
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level characteristic of non-lit conditions (Figure 1A and Figure 1B). Once the ear shielding was
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removed, the light response recovered fully after 3-4 days. The conclusion was that both the
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wheat and barley ear are able to fast and autonomously regulate sap flow in response to
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ambient light conditions.
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MRI indicates localized changes in grain´s transport route
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Noninvasive magnetic resonance imaging (MRI) was used to visualize light responses of
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individual grains. The ear houses numerous grains, all at slightly different stages of
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development and so all following their own metabolic program. Three dimensional MRI-based
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models of the central part of an intact wheat (barley) ear are shown in Figure 1C and 1F and
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Supplementary Movie 1 and Movie 2. MRI could display the internal structure of a number of
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grains with a spatial resolution (102 x 204 x 204 µm) sufficient to enable the recognition of the
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pericarp, endosperm and embryo (Figure 1D, 1E and 1G, 1H; see also Supplementary Movie 3
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and 4). An intense 1H-NMR signal (signifying high water content) was observed in the crease
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region, and especially in the nucellar projection and the endosperm (apoplastic) cavity. The light
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response of individual wheat grains was monitored via MRI, tracking a number of grains over an
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18h period (6h dark, 6h light, 6h dark). The T2 value (which reflects a combination of water
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content and cell size in living tissue, see van der Weerd et al. (2002) and van As (2007)) rose
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statistically significantly in response to illumination (Figure 2A, t-test, p<0.05), implying an
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increase in the water content/cell size of the grain (Krishnan et al., 2004, 2014). Image analysis
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revealed that changes in T2 were confined to small regions of the wheat grains (from 0.63 to
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0.78 mm³) (Figure 2B), co-localizing with the crease (Figure 2C-E). Tissues within this region
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are known to be responsible for solute transfer into the grain (Fisher and Cash-Clark, 2000;
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Andriunas et al., 2013) and are structurally arranged to facilitate the flow and release of solute
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into the endosperm (Melkus et al., 2011; Radchuk and Borisjuk, 2014). The implication is that
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individual grains responded to illumination by increasing the influx of water/solutes, evident by
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local changes of tissue parameters along the main transport route.
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Defining the metabolic heterogeneity of the grain as the basis for a modelling approach
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An FBA-based metabolic modelling approach was applied to get insight into the metabolic
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responses of light stimulated assimilate uptake in the developing barley grain. The in silico study
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was based on a multi-component grain model to take into account that the grain represents a
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non-homogeneous metabolic system comprising the pericarp, the endosperm and the embryo,
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each endowed with its distinct metabolic features. A structural overview on the developing grain
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(~20 days after fertilization (DAF)) is given in Figure 3A. The vein is embedded in the pericarp
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and provides assimilates/water to the central endosperm region via the nucellar projection
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(Figure 3B). The endosperm specializes in starch accumulation (Figure 3D, Rolletschek et al.,
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2004), the embryo accumulates a mixture of lipids and proteins (Figure 3E, Neuberger et al.,
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2008)). Some lipids also accumulate in the aleurone layer of peripheral endosperm. The
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pericarp is photosynthetically active (Figure 3F, Tschiersch et al., 2011) and contributes to the
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alleviation of the hypoxic conditions prevailing within the central endosperm (Rolletschek et al.,
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2011), and mediates the delivery of assimilate into the embryo and endosperm (Melkus et al.,
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2011).
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Generation and parametrization of the multi-component FBA grain model
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As the first step, component-specific stoichiometric models for metabolism had to be
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constructed, based on a combination of literature-based biochemical, physiological, proteomic
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and genomic data and own unpublished experimental data. The second step combined the
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three individual models into a multi-component grain model, in which the pericarp mediated the
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transport of O2, CO2, C, N, and SO42- between the other components, thereby providing the link
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between the maternal and filial metabolism (Figure 3). Compound exchange between the
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embryo and endosperm was not considered given the lack of any experimental evidence for this
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process. The resulting network included 906 metabolites, 716 biochemical reactions and 201
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translocation processes.
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To parameterize the FBA model, it was necessary to experimentally quantify the biomass
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composition and growth rates of each component (pericarp, endosperm and embryo), the gas
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(O2 and CO2) exchange rate of the grain as a whole, the volume of each component and the
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pericarp's electron transport rate (all data are given in Supplementary Data Set 1). The focus
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was on the barley grains 18-22 DAF, a developmental stage both the endosperm and embryo
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are experiencing a rapid growth in biomass, while the biomass of the pericarp remains
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unchanged (Radchuk et al., 2011).
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The multi-component model was analyzed using FBA with an objective function to minimize the
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uptake of carbon sources per flux unit within the specified constraints. Flux ranges were
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calculated using flux variability analysis. The predicted fluxes were considered to significantly
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differ between conditions if their flux ranges did not overlap.
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Model simulation and constraining (based on biomass equations, growth rate and gas exchange
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rates used for parameter fitting) is detailed in section Methods. A fully detailed description of the
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multi-component FBA model, comprising the full set of reactions, the experimental basis for
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each reaction, the modelling constraints and the experimental data used for parameterization
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are given in Supplementary Methods File 1 and Supplementary Data Sets 1 and 2.
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The model predicts differential flux pattern and light responses within the individual grain organs
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The multi-component FBA enabled to predict the dynamic, metabolic architecture of grains; the
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log-ratio flux maps in Figure 4 illustrate the response of each organ to illumination. More
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detailed flux maps are given in Supplementary Figure 1 and 2, and the full set of flux values
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(including flux ranges) is listed in Supplementary Data Set 3.
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In general, metabolic capabilities (defined by the underlying networks), biomass composition,
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growth rates, etc.) differed markedly among grain components, which reflected in the
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component-specific sub-models. The endosperm as the principal storage organ displayed the
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greatest substrate uptake, channeling the incoming sucrose mostly into starch; the embryo
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receives <15% of assimilate passing through the pericarp, and uses it to produce starch,
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proteins and lipids; finally, the pericarp is photosynthetically active, and also experiences
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photorespiration and a daily starch turnover. A detailed description of the predicted
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component-specific flux patterns in response to illumination is given below.
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The flux pattern in pericarp
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The light response of the pericarp is dominated by unloading and redirection of sucrose,
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photosynthetic CO2 fixation, assimilate synthesis, photorespiration and transient starch
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synthesis. The O2 and assimilate are either recycled within the pericarp or exported to the
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endosperm and embryo. The TCA cycle acts in non-cyclic mode and provides carbon skeletons
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for
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photosynthesis/photorespiration is abolished and transient starch degradation sets in. The
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remobilization of starch results in the accumulation of glucose, which serves either as a
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precursor for sucrose re-synthesis or as a cyclic TCA substrate. Of the sucrose fed into the filial
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tissues, 17.4% is derived from starch degradation (see below). However, this contribution is
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insufficient to compensate for the decreased supply of photosynthetically derived sucrose (from
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the mother plant).
nitrogen
assimilation
and
reducing
power.
In
the
absence
of
light,
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The flux pattern in endosperm
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The endosperm metabolism revolves largely around the storage of imported assimilate,
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primarily in the form of starch. The processing of sucrose is catalyzed by sucrose synthase and
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the cytosolic isoform of ADP-glucose pyrophosphorylase (Figure 4). Note that plastidial
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AGPase (pAGPase) in the endosperm is predicted to catalyse ADP glucose. Glycolytic flux is
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largely restricted to the pyrophosphate (PPi)-dependent phosphofructophosphatase and
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pyruvate phosphate dikinase (PPDK). The TCA cycle acts in cyclic mode to provide energy,
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reducing equivalents and carbon skeletons for nitrogen assimilation. Under lit conditions, the
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increased sucrose supply raises absolute flux values including biomass accumulation (i.e. the
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metabolic fluxes differ greatly with respect to their flux value). However, the pathway utilization
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pattern remains stable (in clear contrast to the situation in the pericarp).
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The flux pattern in embryo
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Embryo's pathway utilization pattern is unresponsive to the diurnal cycle (Figure 4), thus
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fluctuations in absolute fluxes are largely restricted to the effects of the light-induced variation in
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sucrose supply. The pathway utilization pattern resembles that in the endosperm, with some
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notable differences (Figure 5A): first, absolute flux values are much lower than in the
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endosperm, a feature related primarily to differences in sucrose supply; second, in line with its
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biomass composition, the flux into starch and cell wall materials is less, while that into
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lipid/protein is greater; third, ADP glucose synthesis is confined to the plastids (because the
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embryo lacks both cytosolic AGPase and an ADP glucose transporter); fourth, the enzymes
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plastidial
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phosphofructokinase and aldolase are all active, whereas they are essentially inactive in the
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endosperm; finally, respiratory activity is higher in the embryo.
pyrophosphatase
(PPase),
phosphoglucomutase
and
the
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glycolysis-related
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We tested some of the predictions in view of experimental data. Planar oxygen sensors
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designed to image oxygen consumption over time generated an output considered to mirror
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respiratory activity (Tschiersch et al., 2012). A respiration map of the barley grain generated in
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this way revealed that respiratory activity was much higher in the embryo than in the endosperm
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(Figure 5B). (Almost identical respiration maps were acquired for wheat grain; data not shown).
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When calculated O2 consumption rates were compared to the model's predictions (taking
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cytochrome C oxidase as the major O2 consuming flux), the experimental and simulated values
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proved to be well aligned (Figure 5C). We further measured the maximum catalytic activity
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(Vmax) of PPase, phosphofructokinase and phosphoglucomutase in crude extracts of dissected
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barley embryo and endosperm tissue. For all three enzymes we found much higher activities in
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embryo versus endosperm tissues (Figure 5C). Similar ratios in enzyme activity have been
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derived from model predictions (following conversion of absolute flux values (µmol h-1;
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Supplemental Table S3) in relative values (µmol g-1 min-1)). Please note that the measured
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values provide maximum catalytic activities, which does not necessarily reflect the actual (in
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vivo) flux predicted by the model.
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Pericarp photosynthesis supports high grain carbon conversion efficiency (CCE)
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The predicted CCEs of the three grain components varied (Table I). The enclosure of the
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non-photosynthetic endosperm and embryo by the pericarp supports the re-fixation of ~79% of
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the endogenously released CO2, ultimately allowing the grain's CCE to reach ~95% under lit
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conditions. RuBisCO contributes most significantly to this high CCE (Supplementary Data Set
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3). Pericarp photosynthesis relies exclusively on the respiration of the filial tissues for its CO2
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supply. Photosynthetic O2 production generates a net export of O2 under lit conditions and
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provides sufficient O2 to meet the metabolic demand of both the endosperm and embryo. CO2
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fixation in both the embryo and endosperm is mediated largely by phoshoenolpyruvate
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carboxykinase, although at a much lower rate than occurs in the pericarp (Supplementary Data
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Set 3). The CCEs of the non-green embryo (67.1%) and endosperm (85.3%) were not
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responsive to illumination, and the differences between these two organs reflected the
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differences in their biomass composition (the higher lipid content of the embryo induces a lower
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CCE). Sucrose allocation within the grain was strongly affected by light – it was 0.05 µmol h-1 in
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the dark and 0.23 µmol h-1 in the light (Table II). While most of these light-dependent differences
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can be related to changes in sucrose flux from the maternal plant to the pericarp, some 9.4% of
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the sucrose in the pericarp was endogenously synthesized.
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Light-enhanced solute flux towards the ear is coupled with metabolic and molecular
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adjustments in the grain
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While the molecular and metabolic responses of barley grains to diurnal cycles have been
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analysed in detail (Mangelsen et al., 2010), here, we focused on shorter time scales (6h) using
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the shielding approach (as demonstrated in Figures 1A and 1B) combined with isotope feeding.
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Cut stems were provided with
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within the grain were monitored. The provision of light prompted a significant increase (25 ± 9%)
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in the uptake of
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14
C-labelled sucrose, and the
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C content of and its partitioning
C sucrose (Figure 6A). About 20.0% of the label was associated with starch,
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about 7.5% with protein, about 1.5% with the cell wall and about 0.9% with the lipid fraction (the
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remaining label was found in the soluble fraction). The only fraction which was statistically
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significantly altered by the light conditions was the cell wall component, which was reduced in
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the absence of light. In a similar experiment based on 15N-labelled glutamine, the stimulation in
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uptake generated by light was 39 ± 9% (Figure 6A).
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At the level of metabolites, the LC/MS analysis suggested that despite the light-stimulated
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uptake of sucrose, its steady state level in the grain remained unchanged irrespective of the
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lighting regime (Figure 6B). Meanwhile, the provision of light reduced the abundance of both the
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glycolysis entry substrates (glucose- and fructose-6-P) and end products (pyruvate) and the
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intermediates of the tricarboxylic acid (TCA) cycle. There was also a reduction in the levels of
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AMP, lactate (fermentation product), most free amino acids, ADP glucose (starch precursor)
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and trehalose-6-phosphate (sugar signalling molecule). Only three of the 36 metabolites
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assayed were markedly affected (>50% change in concentration) by the provision of light.
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To check for possible changes to the pattern of sucrose distribution across the grain, we applied
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an MRI-based imaging approach to ear-shielded grains. It revealed that irrespective of the
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lighting regime the sucrose concentration was high throughout the crease region, including the
354
endosperm cavity (Figure 6C). The largest concentrations were noted within the vascular tissue.
355
Thus, the distribution pattern of sucrose was not influenced by the light conditions.
356
A transcriptomic analysis of the grains (using 12K cDNA arrays) from the ear-shielding
357
experiment revealed that exposure to light up-regulated 53 and down-regulated 44 genes
358
(Supplementary Data Set 4). In parallel, a set of 16 genes (Table III) was subjected to qRT-PCR
359
analysis; the combined data revealed that the transcript abundance of only a rather small
360
number of genes involved in primary metabolism was changed. Genes up-regulated by light
361
included one encoding the plasma membrane localized sucrose transporter SUT1, one
362
encoding starch branching enzyme 1, one encoding a plastidial triosephosphate/phosphate
363
translocator, one encoding the pericarp-specific RuBisCo small subunit and a few sugar/energy
364
metabolism-related genes (trehalose-6-P phosphatase); the down-regulated ones comprised
365
genes involved in sucrose cleavage (cell wall-bound invertase), energy metabolism (alcohol
366
dehydrogenase, aldolase, glyceraldehyde 3-phosphate dehydrogenase) and lipid storage
367
(oleosin).
368
369
19
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370
Discussion
371
Maximizing carbon and energy use efficiency is key to crop improvement, but these rather
372
straight-forward physiological concepts are underlain by a complexity of molecular and
373
metabolic processes, including their compartmentation within the living plant. The present study
374
aims to characterize the spatial heterogeneity of the grain's metabolism, and especially to
375
assess the metabolic capacity and efficiency of individual grain organs in the face of a
376
fluctuating environment. Our data suggest that the grain employs a set of distinct metabolic
377
strategies to deal with both diurnal and environmental variation in incident light, and the
378
consequential variation in the supply of assimilate to the ear.
379
380
The ear and caryopsis respond to illumination by adjusting their solute flux
381
As the grain is a sink organ throughout its development, a consistent supply of assimilate is a
382
high priority. The source of this assimilate (Sanchez-Bragado et al., 2014), its routing (Melkus et
383
al., 2011) and the variability in its supply rate (Matsuhashia et al., 2006) have all been
384
investigated in some detail, but are still under debate. To explore the dynamics of solute flow
385
toward cereal grains, this study harnessed novel NMR-based technology (Kartäusch et al.,
386
2014). Method enabled to monitor flux in the peduncle of wheat and barley during many days in
387
vivo. Results demonstrate net solute flow directed toward the ear/grain and flow velocities
388
altering according to the photoperiodic (diurnal) cycle (Figure 1). Analysis of assimilate
389
transport in wheat using 11C positron emitting tracer likewise indicated an important role that the
390
photo-condition of the ear plays in the transportation of photoassimilates (Matsuhashia et al.,
391
2006). We conclude that the diurnal cycle has a profound effect on the metabolic behavior of the
392
cereal grain.
393
An unexpected rapid response to shielding the ear was the abolition of solute flow (Figure 1),
394
the implication of which is that the ear/grain cannot be merely a passive receiver of assimilate,
395
but rather must have developed its own means of regulating solute flow. The relevant underlying
396
mechanism(s) remain obscure, but their effect is to provide the individual ear/grain a measure of
397
autonomy in controlling its import of assimilate, and thereby its metabolism.
398
399
The light-dependent velocity of solute flow within the peduncle was accompanied by changes to
400
the MRI-parameter T2 in the grain (Figure 2A). Relaxation time (T1, T2) is a highly reliable
401
surrogate for water distribution/state in both plant (van As and van Duynhoven, 2013) and
402
animal (Wu et al., 2014; Hueper et al., 2013) tissue. In living plant tissue containing vacuolated
403
cells, the majority of the water present is contained in the vacuoles, and as a result, T2 images
20
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
404
are dominated by vacuolar water (van As et al., 2009). Thus, increases over time in T2 probably
405
reflect increases in vacuolar size (Metzner et al., 2014). Here, light-induced changes in T2 were
406
shown to occur within the crease region (Figure 2B), which is not only the primary entry point
407
into the grain for solutes (Melkus et al., 2011), but also for water (Jenner et al., 1988). Given this,
408
it becomes reasonable to suggest that the response to light includes an increased flow of water
409
into the grain. The simultaneous influx of both water and solutes towards/within the grain is
410
biologically meaningful because assimilate is transported in aqueous form, and thus changes in
411
solute flow imply changes in water flow. The proposition is therefore that water and assimilate
412
uptake into the grain are tightly coupled with one another via a mechanism(s) as yet unknown.
413
Future studies may help to clarify whether the source of this water is from the xylem or from the
414
phloem, and/or whether light-controlled aquaporins (Prado et al., 2013) are involved in
415
regulating the process.
416
417
Orchestration of organ-specific metabolic responses
418
Sucrose and light are the orchestrators of the metabolic activity of the grain. Assimilate flux in
419
the pericarp was highly sensitive to incident light, while both the endosperm and the embryo
420
responded more proportionately, at the same time leaving pathway utilization patterns largely
421
unaltered (Figure 4). What is the primary signal for these metabolic responses? Light signals
422
seem to have an “infinite spectrum of possibilities” (Chory 2010), including numerous links to
423
phytohormones and sugars (Lau and Deng, 2010; Sairanen et al., 2012). A plausible hypothesis
424
is represented by the idea that enzyme capacity in central metabolism is in large excess and
425
that flux regulation propagates rapidly via simple mass action across the network, controlled by
426
the ratio between the various enzymes' product and substrate concentrations. Allosteric and
427
redox control of enzyme proteins is another possibility to link light/sucrose availability with
428
metabolic activities (Michalska et al., 2009; Schwender et al., 2015a). These hypotheses chime
429
with the recent finding that flux regulation in central metabolism of seeds is unlikely to reflect
430
transcriptional activity (Schwender et al., 2015b). In principle, trehalose-6-P could act as the
431
instantaneous signal of sugar availability, but the response observed in the shading experiment
432
(a light-induced decrease in trehalose-6-P level and a corresponding stimulation of
433
trehalose-6-P phosphatase transcript) is the opposite to what is generally expected when the
434
availability of sugar rises (Smeekens et al., 2010).
435
The metabolic and molecular response of grains in ears from which light had been excluded
436
(Figure 6, Table III) differed significantly from those observed in the course of a normal night
437
(Mangelsen et al., 2010). While during night time numerous changes in transcript abundance
21
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
438
occur, only a limited number of transcripts (including, notably, the major sucrose transporter
439
SUT1) were affected by ear shielding, and there was little perturbation either in the steady state
440
levels of major metabolites or to the pattern of sugar distribution within the grain. Clearly the
441
decline in the supply of sucrose imposed by a 6h period of ear shielding can be readily
442
compensated for, establishing a strong homeostasis with respect to both the level and
443
distribution of sugar. The aspect of homeostasis could be very relevant because fluctuating
444
sugar levels could lead to unwanted effects on sucrose-mediated control circuits (Rhamani et
445
al., 2009), causing energy-costly transcriptional/translational re-programming. The proposition
446
is that short-term changes in the supply of sucrose imposed by environmental fluctuation are
447
evened out via (post-translational) metabolic control. Network stability mechanisms have been
448
demonstrated in a number of diverse plant systems: some examples are provided by the
449
response to alterations in the supply of glucose (Rontein et al., 2002), oxygen (Williams et al.,
450
2008) and light (Borisjuk et al., 2013). These may represent the key to ensuring a robust and
451
energy-efficient physiological homeostasis.
452
453
The grain grows preferentially during the lit period
454
When growth is considered to be an increase in biomass over time, the indication is that the
455
developing grain grows preferentially in the light, coinciding with the period when its sugar
456
uptake is at its maximum. The intake of sugar in turn induces a high level of metabolic activity,
457
much of which is directed to converting sugar into biomass (preferentially starch). The light
458
response also included a presumably higher water uptake and a higher partitioning of sugar into
459
the cell wall (Figure 6A), which may reflect the light-induced transcriptional activation of both cell
460
wall synthesis and expansion (Mangelsen et al., 2010). Thus, grain growth appear to be favored
461
during the daytime, in agreement with the finding that fruits grow more rapidly during the day
462
than during the night (Guichard et al., 2005).
463
464
The grain as a synergistic metabolic and architectural system
465
During early phase of grain development, the pericarp functions as an assimilate sink (Radchuk
466
et al., 2009). It largely degenerates toward maturation while chlorenchyma layer and crease
467
region remain alive till desiccation (Tran et al., 2014). At the mid stage (as sampled here), it
468
somehow behaves as if it were an autotrophic leaf enveloping the heterotrophic storage organs.
469
Three characteristic processes are involved: first, the starch - accumulated largely in
470
chlorenchyma tissue of pericarp during the lit period - is degraded during the night. This pattern
471
echoes the diurnal regulation of beta-amylases (Mangelsen et al., 2010) and is characteristic for
22
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472
source leaves; second, peak photosystem II electron transport rates are equivalent to those
473
prevailing in the flag leaf (Tschiersch et al., 2011), a level which is much higher than has been
474
recorded in the green seeds of an number of dicotyledonous species (Borisjuk et al., 2005;
475
2013); and finally, as in the leaf, the lit period features non-cyclic rather cyclic TCA activity.
476
Non-cyclic TCA fluxes have been noted as being characteristic for the illuminated leaf
477
(Sweetlove et al., 2010; Cheung et al., 2014), while in contrast, cyclic flux is important in
478
heterotrophic tissues such as the endosperm and embryo. The pericarp differs anatomically
479
from the leaf in its substantially lower frequency of stomata in the epidermal layer (Cochrane
480
and Duffus, 1979). This feature ties in well with the model's prediction that photosynthesis in the
481
pericarp relies exclusively on endogenously released CO2, having no requirement for the uptake
482
of any atmospheric CO2 through the stomata.
483
The FBA model predicted that pericarp photosynthesis is of high relevance in terms of carbon
484
re-fixation. The estimated CCE of the barley grain (~95%) exceeds that of maize endosperm
485
(76-92%; Alonso et al., 2011); although there is little compositional difference between the
486
barley and the maize grain, the latter does not benefit from photosynthesis-related CO2
487
re-fixation. The estimated CCE of other non-green seeds (Alonso et al., 2007) and green seeds
488
(Goffmann et al., 2005; Allen et al., 2009; Borisjuk et al., 2013) is lower still. The conclusion is
489
that the architecture of the developing barley grain – and by inference, also that of wheat, rice
490
and oat - is one important key to its high CCE and thus to the crop's yield potential.
491
492
Implications for crop improvement
493
The small grain cereal caryopsis appears to be an almost perfect entity for carbon storage; with
494
a CCE of ~95%, there seems to be little room for any improvement in efficiency. Thus, the
495
emphasis needs to be put on stimulating the supply of assimilates to the developing grain,
496
rather than on attempting to manipulate grain metabolism.
497
The pericarp's photosynthetic activity makes a contribution to the energy/carbon balance of the
498
grain. It might therefore be advantageous to stimulate its capacity which is already high but
499
spatially confined to a narrow band within pericarp.
500
Illumination does not only stimulate photosynthetic activity, but also prompts solute/water fluxes
501
in general. Thus, fast acclimation to changing light supply could be key to high energy and
502
carbon efficiency. The developing grain benefits from the capacity to anticipate a burst of sugar
503
arriving via the phloem, since this facilitates the grain’s rapid adjustment to its synthesis of
504
storage products. Improvement of the adaptive abilities should be considered in the context of
505
both structural and functional organization of the grain, ear and/or whole plant.
23
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
506
Flux maps have provided several striking predictions, e.g. pAGPase in the endosperm catalyses
507
the
508
endosperm-specific down regulation of pAGPase may have the effect of increasing the grain's
509
starch content. This contradicts the commonly held view that carbon entering the plastid in the
510
form of ADP-glucose is committed to starch synthesis and cannot be diverted into other
511
metabolic pathways (James et al., 2003). The present prediction is supported by the
512
demonstration that pAGPase in the barley endosperm catalyses a readily reversible reaction in
513
vivo (Tiessen et al., 2012). A likely scenario is therefore that endosperm pAGPase represents a
514
metabolic valve which, depending on metabolite concentrations, can divert carbon away from
515
starch synthesis. While experimental validation is needed, we conclude that the application of
516
modelling tools can provide useful predictions for further experimental work.
degradation
of
ADP-glucose.
The
unexpected
conclusion
would be
that
the
517
518
Potential limitations of our modeling approach
519
The predictive power of FBA models highly depends on the accuracy of data used for
520
reconstruction and parameterization (i.e., constraints). With respect to model reconstruction,
521
the main factors potentially affecting the precision of flux estimation comprise the simplification
522
of not taking into account (i) organ-specific genome-scale models, and (ii) different cell types in
523
the modeled grain components. With respect to model parameterization, uncertainties are due
524
to the lack of experimental data, and result in the need for parameter estimation. In this study,
525
maintenance energy/reductant costs were estimated by fitting the model to experimentally
526
measured gas exchange rates. Maintenance costs were assumed to be the same under
527
lit/non-lit conditions and in the different grain tissues. As energy demand can have substantial
528
effect on predicted flux distribution (Cheung et al., 2013, Poolman et al., 2009), these
529
simplifications can be assumed to have the strongest effect on prediction quality compared to
530
the remaining constraints applied in this study. However, neglecting maintenance cost (as done
531
by many plant metabolic models) would have resulted into an even greater potential uncertainty.
532
Parameter assumptions having a lesser effect on the accuracy of model predictions are related
533
to (i) biomass composition (lit = non-lit), (ii) starch degradation/biosynthesis in the pericarp (rate
534
of biosynthesis = rate of degradation), and (iii) Rubisco: ratio oxygenase/carboxylase (taken
535
from literature).
536
The accuracy of FBA models also depends on the choice of the objective function, although to a
537
lesser extent than the constraints (Cheung et al., 2013). In line with other studies, in this study
538
the efficient use of carbon sources was assumed to be the main objective of metabolism. The
539
objective
was
computed
as
a
two-step
24
optimization
process,
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
(first:
minimize
540
sucrose/asparagine/glutamine uptake; second: minimize the overall intracellular flux). The
541
biological assumption underlying this is that the barley caryopsis grows while (1) using the
542
minimal amount of carbon, resulting in carbon efficiency, and (2) achieving an efficient
543
channeling of metabolites. Good agreement has been reported between the predicted and
544
measured fluxes for heterotrophic plant metabolism using the first (Hay & Schwender, 2011)
545
and second objective function (Cheung et al., 2013), respectively. In comparison to other
546
objective functions applied in non-plant FBA studies (maximization of biomass or minimization
547
of energy requirements) the chosen objective function seems to best represent the overall
548
objective of the mixotrophic nature of the barley caryopsis.
549
Despite the uncertainties in modeling constraints discussed above, the predicted flux
550
distributions reflect the success of the objective function and modeling constraints in capturing a
551
realistic picture of the metabolic architecture of cereal grains. We believe that exploring the
552
metabolic architecture of cereal grains could open new avenues for crop improvement.
553
554
Materials and Methods
555
Plant growth
556
Barley (cv. Barke) and wheat (cv. Certo) plants were cultivated in growth chambers under a light/dark
557
regime of 16/8 h at 20/14°C. Supplemental light was set to an intensity of approx. 600 µmol quanta m-2
558
s-1 (at ~1m above ears). Flowering ears were tagged for determination of days after flowering (DAF).
559
560
NMR-Tools und methods
561
Flux measurements using low field NMR-instruments
562
Long term measurements of flux toward the spike were performed using a home-built mobile MRI
563
scanner with 0.5 T and a conventional driveL console (Pure Devices GmbH, Würzburg, Germany). The
564
light level used during NMR experiments corresponds to that in the greenhouse. Velocity profiles were
565
acquired using the AC-gradient method described by Walton and Conradi (1987). A solenoid coil with a
566
height of 2 mm was used for transmit/receive. Diameter of solenoid coil was fitted to the size of stem.
567
The number of echoes was set to 372 and 1 ms gradients with a magnitude of 0.2 T/m were applied. The
568
measurement was performed as a single shot measurement. However, due to the very low
569
signal-to-noise ratio (SNR) the measurement was averaged ten times and the calculated mean flux
570
change was averaged over ten complete measurements. Therefore, the total measurement duration for
571
a single point of the flow trend lasts approximately three minutes. In conclusion, the monitored net flow
572
is the difference between solute flow and the negligible perpendicular flow within the measured volume.
573
The measured signal is created in large part by water protons but also by the protons attached to the
25
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
574
sugar molecules and further constituents. All details are explained in Kartäusch et al. (2014).
575
576
Imaging experiments using high field NMR-instruments
577
Imaging experiments on entire spike were performed on a Bruker Avance 17.6 T wide-bore (89 mm
578
inner diameter) superconducting magnet (747.6 MHz, Bruker BioSpin) equipped with a 1000 mT m-1
579
gradient system and a custom-built birdcage-coil with 20 mm inner diameter. A glass-fiber connected to
580
the custom-built light source and operated automatically by timer clock was used for setting a fixed
581
light-dark interval during experiments in the 17.6 T NMR instrument. The image acquisition was
582
performed by the following settings: the longtime monitoring of the spike during dark-light switches was
583
performed based on the repeatable 3D-imaging using a Multi-Slice Multi-Echo Sequence for acquisition
584
of 3D datasets. Repetition time is 800 ms, echo time is 5.886 ms, number of echoes per dataset 12. For
585
each echo a 192 x 96 x 96 matrix was acquired. The total scan time of a 3D dataset was 2 h 3 min. The
586
sucrose imaging experiments were performed using the slice selective geHMQC sequence (details
587
given in Melkus et al., 2011).
588
589
Data processing
590
The acquired raw datasets were processed in MATLAB R2013a (MathWorks). Using an in-house
591
written computer program, the k-space data was Fast Fourier-transformed to image space and T2 maps
592
were calculated by fitting the multi-echo images pixel wise (i.e. for each pixel of the image the data was
593
processed individually) to a mono-exponential function. The T2-values shown in Figure 2A are
594
averaged over three time steps belonging to one darkness/light period. For statistical analysis in Figure
595
2A, the number of voxels used for calculation of the T2 values was 141 (white), 160 (yellow) and 174
596
(blue, green). A mask (Figure 2B) was used on the resulting 3D datasets containing the calculated T2
597
parameters. Commercial software Amira 4.1 (Mercury Inc.) was used for the overview of seed internal
598
structure as well as for segmentation and preparation of figures.
599
600
Data acquisition for FBA model parameterization and fitting
601
Intact grains were taken at distinct developmental stages for manual dissection of endosperm and
602
embryo, followed by weighing (fresh and dry weight) to determine growth rates. We also analyzed
603
soluble sugars/free amino acids (Rolletschek et al., 2005), fatty acid composition (Borisjuk et al., 2005),
604
and total content of starch, lipid and protein (Borisjuk et al., 2013). Gas exchange measurements were
605
done by incubating freshly harvested, intact grains in buffered solution (50 mM Gln, 50 mM sucrose, 10
606
mM MES buffer, pH 6.2) in closed flasks for up to 2h at 20 °C. During this time the oxygen and carbon
607
dioxide concentration inside the flasks was continuously monitored using sensor spots (O2: PSt3; CO2:
608
SP-CD1-D5-rMy-US; PreSens GmbH; Regensburg/Germany) inside the flasks connected via fiber optic
26
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
609
cables to transmitters (Fibox-3; pCO2 mini; PreSens GmbH). Incubation started with non-lit conditions,
610
followed by step-wise increases in light supply using the lightning system of a microscope (Schott,
611
Germany) (each time period 5-10 min). Rates for O2/CO2 uptake (release) were determined from the
612
change in sensor signal over time using linear regression models. Data on photosynthetic electron
613
transport (photosystem II) in the pericarp were taken from Pulse Amplitude Modulation fluorescence
614
analysis published earlier (Tschiersch et al., 2011).
615
616
Flux balance analysis
617
To predict component-specific metabolic fluxes under lit and non-lit conditions, the
618
multi-component model was analyzed using FBA with the objective function and constraints
619
specified below.
620
621
Objective function - Assuming that developing barley grains make the most efficient use of their
622
carbon sources, the minimization of sucrose/asparagine/glutamine uptake per flux unit was
623
used as the objective function for all FBA simulations. The objective was computed as a
624
two-step optimization process, where the first step is to minimize pericarp´s uptake of sucrose,
625
asparagine and glutamine, and the second step is to minimize the overall intracellular flux.
626
627
Constraints - Model simulation under lit conditions was performed by fixing the endosperm- and
628
embryo-specific growth rate according to experimentally determined rates (endosperm: 0.06 mg
629
hr-1; embryo: 0.007 mg hr-1). Tissue-specific maintenance energy/reductant costs (endosperm:
630
0.3152/0.0004; embryo: 0.0925/0.0002; pericarp: 6.4415/0.0266 [ATP/NADPH drain in µmol
631
hr-1]) were estimated and constrained by fitting the model to experimentally determined gas
632
exchange rates under lit conditions. In the pericarp, the uptake of light was constrained to 16
633
µmol hr-1 according to experimentally determined rates of photosynthetic electron transport. The
634
flux ratio of RuBisCO (oxygenase to carboxylase reaction) was set to a ratio of 30:70 according
635
to barley-specific data reported in the literature (Skillmam 2008). In addition, the ratio of starch
636
biosynthesis to sucrose biosynthesis was set to a ratio of 50:50.
637
Model simulation under non-lit conditions was performed by fixing the tissue-specific growth
638
rates (endosperm: 0.014 mg hr-1; embryo: 0.0017 mg hr-1) according to estimates resulting from
639
fitting the model to gas exchange rates under non-lit conditions. Assuming that maintenance
640
costs do not vary between lit/non-lit conditions, tissue-specific maintenance energy/reductant
641
costs were fixed according to 60% of total ATP consumption and 1% of NADPH consumption
642
(endosperm: 0.0757/0.00009; embryo: 0.0222/0.00005; pericarp: 0.0187/0.0 [ATP/NADPH
27
Downloaded from on June 17, 2017 - Published by www.plantphysiol.org
Copyright © 2015 American Society of Plant Biologists. All rights reserved.
643
drain in µmol hr-1]). Due to the lack of experimental data, the rate of starch degradation was fixed
644
to 0.0213 according to the rate of starch synthesis predicted by the model under lit conditions.
645
646
Flux ranges - Flux ranges were calculated using flux variability analysis (Mahadevan & Schilling,
647
2003). The predicted fluxes were considered to significantly differ between conditions if their
648
flux ranges did not overlap (See Supplemental Methods File 1 for details).
649
A fully detailed explanation of the process of model reconstruction, parameterization and
650
analysis is given in Supplemental Methods File 1 and Supplemental Data Sets 1 & 2.
651
652
Oxygen imaging
653
For analyzing the local respiration rate in embryo versus endosperm, we used a fluorescence
654
ratiometric-based device, consisting of an oxygen-sensitive foil and a USB microscope (for details, see
655
Tschiersch et al., 2012). Briefly, the grain was cut in two halves and the sensor foil was placed on the
656
sample surface; the sample/sensor foil is kept in darkness. Based on the localized change in
657
fluorescence signal (= oxygen concentration) over time (5 min), the local oxygen consumption can be
658
estimated.
659
660
Enzyme activity assays
661
All enzyme assays were done in grain extracts using the UVIKON XL spectrophotometer (GOEBEL
662
Instrumentelle Analytik GMBH/Germany). Activity of the enzyme pyrophosphatase (PPase) was
663
determined according to Jonik et al. (2012). Assay conditions for the determination of
664
phosphofructokinase and phosphoglucomutase were taken from Gibon et al. (2004). All assays were
665
checked to be dependent on each substrate and linear with respect to time and amount of extract.
666
667
Experimental design to study the light response of grains
668
Intact ears were kept in either light (600 µE) or darkness (enclosed with black paper), while the
669
remaining part of the plant was fully illuminated (600 µE). The experiment started during light phase at
670
8 am. After a 6 h period, grains (~18 DAF) were quickly removed, immediately frozen in liquid N2 and
671
stored at -80°C until used for further metabolite and transcript analysis.
672
673
Metabolite analysis
674
Metabolic intermediates were extracted and analyzed exactly as in Rolletschek et al. (2005).
675
676
Transcript analysis
28
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
677
Total RNA was extracted from treated and control grains using the Trizol reagent (Invitrogen) as
678
described early (Radchuk et al., 2011) and treated with DNase. For cDNA array analysis, RNA was
679
reverse-transcribed and used for the synthesis of
680
hybridization and processing of 12K barley seed cDNA array were done essentially as described
681
(Sreenivasulu et al., 2006). Images of hybridized nylon membranes were subjected to automatic spot
682
detection using MATLAB program. Signal intensities of 11,787 genes were scored from the double
683
spots, enabling us for assessing two replications. Additionally, two biological repetitions were
684
performed using RNA from independently grown and treated grains. Quantile normalization (Bolstad
685
et al., 2003) was carried out on the complete data set. Fold changes between wild type and treated
686
probes were calculated from the replicates. P-values were calculated based on moderated t-test to
687
detect false positives. Genes that showed statistically significant differences in expression under light
688
conditions in comparison to wild type grains at the level 1.5-fold and higher in both biological
689
replications were selected for further analyses. For quantitative RT-PCR (qRT-PCR), 5 µg of the total
690
RNA were reverse-transcribed by SuperScript III reverse transriptase (Invitrogen) with the oligo(dT)
691
primer. The resulting cDNAs were used as template for qRT-PCR analyses which were performed as
692
described (Radchuk et al., 2011). Primer sets for each gene have been selected as recommended by
693
Udvardi et al. (2008) and are listed in Supplemental Data Set 6. Experiments were run using three
694
biological replications with at least three technical repetitions per replication. Dissociation curves
695
confirmed the presence of a single amplicon in each PCR. The Ct of each gene of interest (GOI) from
696
each sample was normalized against endogenous reference gene actin and calculated as an
697
arithmetic mean of the replicates. The relative gene expression levels in treated versus control grains,
698
the fold changes of gene expression values were presented as 2-ΔΔCt according to Livak and
699
Schmittgen (2001), where ΔΔCt is the difference between ΔCtco and ΔCttreated.
33
P-dCTP labeled probes. Probe preparation,
700
701
Isotope labelling experiments
702
For monitoring sucrose uptake and partitioning of grains, stems were cut approx. 20 cm below the ear,
703
and placed for 6 h in 200 ml nutrient solution containing 20 mM KH2PO4, 25 mM sucrose, 5 mM
704
glutamine, 5 mM asparagine, 10 mM MES buffer, 1 mM CaCl2, pH 5.5 and 500 µl of [U-14C]-sucrose
705
(7.4 MBq ml-1, Amersham-Buchler, Braunschweig, Germany). During this period, the ears were held
706
under lit (600 µE) or non-lit conditions. Subsequently, seeds were rapidly removed from the ear and
707
immediately frozen in liquid N2. A subset of seeds was extracted as in Bligh and Dyer (1959) giving a
708
chloroform-soluble fraction containing the lipids. A second set of seeds was grounded and extracted
709
three-times with ethanol (100%). This soluble phase contained
710
metabolic intermediates. The remaining pellet was dried, and subsequently incubated for 24 h at 30°C
29
14
C-sucrose as well as
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
14
C-labelled
711
in 1.5 mL of 50 mM Tris-HCl (pH 7.4) containing 0.08% (w/v) pronase (Sigma, Germany). After
712
centrifugation (5 min, 14000 x g), the supernatant was collected, giving the protein fraction. The pellet
713
was washed twice in 1 mL distilled water. To hydrolyse starch, the remaining insoluble material was
714
incubated with 14U amyloglucosidase in 1 ml 50 mM sodium acetate (pH 4.8) for 24 h at 55°C. After
715
centrifugation (10 min, 14,000 x g), the supernatant was collected, giving the starch fraction. The
716
residue (cell wall) was re-suspended in 1 ml distilled water. Radioactivity in the different fractions was
717
determined by liquid scintillation counting. Counts were corrected for background and quenching by
718
external standards. By combining the different fractions and relating to the weighted sample the total
719
label uptake was calculated (given in dpm per mg fresh weight of grain). The partitioning to either lipid,
720
protein, cell wall or starch is given as percentage of total label. For monitoring Gln uptake of grains, we
721
applied the identical setup but used 30 mM 15N-labeled Gln (CDN Isotopes, Quebec, Canada) (without
722
14
723
using elemental analysis (Vario EL3, Elementaranalysesysteme, Hanau, Germany) coupled to
724
isotope ratio mass spectrometry (ESD 100, IPI, Bremen, Germany).
C-sucrose in media). The amount of 14/15N-label was determined in the dry powdered grain samples
725
726
ACKNOWLEDGEMENTS
727
We are grateful to K. Blaschek and A. Gündel for assistance in enzyme analysis, M. Hajirezaei for help
728
with
14
C-isotope studies and N. Sreenivasulu for help with array analysis.
729
730
30
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
731
Figure legends
732
733
Figure 1. NMR-based in vivo analysis of solute flux toward the ear of wheat/barley and
734
structural modelling.
735
(A,B) Monitoring of the solute flux (ΔVavg) of a wheat (A) and barley (B) plant exposed to a 12 h
736
photoperiod. The grey region indicates shielding of the ear above the NMR coil while the
737
remainder of the plant still experienced the light/dark cycle.
738
(C-H) Ear modelling in wheat (C-E) and barley (F-H). The three dimensional ear models (C,F)
739
were derived from MRI data and are shown in more detail as Supplementary Movie 1 and 2;
740
(D,G) longitudinal and (E,H) transverse sections (see also Supplementary Movie 3 and 4); Bars:
741
5 mm.
742
Abbreviations: cr - crease; em - embryo; np - nucellar projection; p - pericarp; se - starchy
743
endosperm
744
745
746
Figure 2. In vivo analysis of light responses in developing wheat grains based on MRI.
747
(A) T2 times measured for four individual grains of an intact wheat ear under dark versus light
748
conditions. Mean values +/- SD are presented.
749
(B) Localization of the responsive region in grain (fragments of 3D datasets are shown where T2
750
value significantly changed): colored region in longitudinal (above) and cross-section (below).
751
(C-E) Light microscopic image of grain (C) showing the corresponding section level in (D,E);
752
Bars: 1mm.
753
754
Figure 3. Structural and metabolic heterogeneity of the developing barley grain and the
755
corresponding FBA model.
756
(A) Longitudinal and (B) transverse sections.
757
(C) The cellular structure of crease region; red arrows indicate route of assimilate allocation.
758
(D) Starch distribution as shown by iodine staining.
759
(E) lipid distribution (in red color) as shown by Sudan staining.
760
(F) Pulse amplitude modulation fluorescence image showing quantum efficiency in photosystem
761
II (light adapted state).
762
(G) The topological arrangement of the grain components and the corresponding FBA model.
763
Abbreviations: AA - amino acids; em – embryo; np - nucellar projection; p – pericarp; S –
764
sulphate; se -starchy endosperm; Suc – sucrose; v - vein
31
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
765
766
Figure 4. Predicted log-ratio flux maps (lit versus non-lit conditions) of the endosperm,
767
embryo and pericarp during the grain's main storage phase.
768
Red dashed arrows indicate metabolic routes only active under lit conditions, and blue dashed
769
ones those exclusive to non-lit conditions. The direction of the metabolic flux indicated by
770
arrowheads refers to flux under lit conditions. Changes in flux direction induced by the switch
771
from lit to non-lit conditions are indicated by black diamonds. Numerical flux values
772
(color-coded) indicate x-fold changes in flux value, given as where and correspond to the
773
simulated flux values of a given network reaction under lit and non-lit conditions, respectively.
774
Fluxes marked with an asterix indicate fluxes with non-overlapping flux ranges. The grain
775
components (top panels) were derived from MRI-based models of grains imaged ~20 days after
776
fertilization.
777
778
Figure 5. Contrasting metabolic processes in the barley endosperm and embryo.
779
(A) A log-ratio flux map depicts central metabolic processes operating under lit conditions. Red
780
and blue dashed arrows indicate metabolic routes exclusively active in, respectively, the
781
endosperm and the embryo. Flux direction as indicated by arrowheads refers to
782
endosperm-specific flux. Changes in flux direction are indicated by black diamonds. Numerical
783
flux values (color-coded) indicate x-fold changes in flux value, given as where and correspond
784
to the simulated flux values of a given network reaction
785
respectively. Fluxes marked with an asterix indicate fluxes with non-overlapping flux ranges. (B)
786
Respiration mapping across the grain using a planar oxygen sensor. The left panel shows a
787
grain cross-section and the right panel the derived respiration map based on the fluorescence
788
signal emitted by the sensor (red indicates peak respiratory activity). (C) Enzyme activities
789
predicted from the FBA model (under lit conditions) compared to experimentally determined
790
Vmax values in extracts of (em) embryo, (se) endosperm and as derived from the respiration map
791
shown in (B).
in the endosperm and embryo,
792
793
Figure 6. The light responses of the barley grain during ear-shielding.
794
(A) The uptake of 15N glutamine and 14C sucrose into the intact grain, and the partitioning of 14C
32
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
795
label into the three grain components.
796
(B) Steady state metabolite levels as measured by LC/MS. Data given in the form mean ± SE
797
(n=6). Asterisks indicate statistically significant (p<0.05) differences according to a t-test.
798
Changes in transcript abundance are indicated in red (up-regulated under lit conditions) and
799
blue (up-regulated under non-lit conditions).
800
(C) Steady state distribution of sucrose under lit conditions, as measured by MRI.
801
33
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
802
Supplementary Material (online only)
803
804
Supplementary Methods file 1. Construction and parameterization of the FBA model.
805
Supplementary Movie 1. Three dimensional model of the wheat ear derived from MRI data.
806
Supplementary Movie 2. Three dimensional model of the barley ear derived from MRI data.
807
Supplementary Movie 3. Internal structures of the wheat ear in transversal view; data derived
808
from 3D MRI.
809
Supplementary Movie 4. Internal structures of the barley ear in longitudinal view; data derived
810
from 3D MRI.
811
Supplementary Figure 1. Model-predicted flux maps depicting the central metabolic processes
812
occurring in the embryo, endosperm and pericarp under lit conditions. The arrow color
813
corresponds to the flux magnitude. A complete list of reactions and fluxes is given in
814
Supplementary Data Set 3.
815
Supplementary Figure 2. Model-predicted flux maps depicting the central metabolic processes
816
occurring in the embryo, endosperm and pericarp under non-lit conditions. The arrow color
817
corresponds to the flux magnitude. A complete list of reactions and fluxes is given in
818
Supplementary Data Set 3.
819
Supplementary Data Set 1. Biomass composition, experimental data, biomass equations and
820
growth rates used for model parametrization.
821
Supplementary Data Set 2. Set of reactions included in the reconstructed network of barley
822
seed metabolism and the respective references supporting each network reaction. Separate
823
worksheets give information for pericarp (A), endosperm (B), embryo (C), reference ID´s (D)
824
and list of reactions (E).
825
Supplementary Data Set 3. Model-predicted flux values and ranges. (A) Model predicted flux
826
values under lit and non-lit conditions. (B) Flux ranges calculated using flux variability analysis.
827
(C) Non-overlapping flux ranges with respect to the comparison of lit versus non-lit conditions
828
(D) Non-overlapping flux ranges with respect to the comparison of embryo versus endosperm
829
under lit conditions.
830
Supplementary Data Set 4. Complete list of differentially expressed genes from developing
831
grains incubated under lit versus non-lit conditions. Normalized gene expression values,
832
differential fold levels and functional annotations are provided. Only those genes with
833
expression differences at the level 1.5-fold are regarded as statistically significant.
834
Supplemental Data Set 5. List of primers used in qRT-PCRs.
835
34
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
836
35
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837
838
839
Table I: Model-predicted effect of light availability on carbon conversion efficiency (CCE) in
barley seed organs
C Uptake2
dark
light
µmol h-1
embryo
dark
light
µmol h-1
CCE
dark
light
[%]
0.115
0.481
0.038
0.158
67.1
67.1
endosperm 0.583
2.428
0.086
0.358
85.3
85.3
pericarp
0.708
3.169
0.137
0.132
80.6
95.8
0.584
2.653
0.137
0.132
76.5
95
seed
840
841
842
843
844
845
CO2 Efflux
1
1
multi-component seed model composed of embryo, endosperm, and pericarp; 2uptake of Asn,
Gln, Suc, and CO2 (CO2 is only taken up by the pericarp).
36
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
846
847
848
849
Table II: Model-predicted effect of light availability on sucrose allocation within the grain.
Sucrose allocation is defined as the sum of pericarp-specific Suc import (Suc allocation from the
vascular tissue to the pericarp) and pericarp-specific Suc synthesis (catalyzed via the enzyme
sucrose-P-synthase).
dark
light
µmol h-1
%
µmol h-1
%
Suc import
0.05
82.57
0.21
90.64
Suc synthesis
0.01
17.43
0.02
9.36
sum
0.05
100
0.23
100
850
851
852
37
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Copyright © 2015 American Society of Plant Biologists. All rights reserved.
853
854
Table III. List of genes analyzed by qRT-PCRs for their expression under light conditions compared to the dark
during mid to late grain filling phase (18 days after flowering)
Clone ID
a
Gene Identification
Fold Change
Preferential tissue
in expression
localisation of gene
under light
expressionb
Genes, up-regulated under light conditions
AJ272309
Sucrose transporter 1 (SUT1), plasma membrane
1.74***
endosperm
localised
1.46***
endosperm
FN179382
Starch branching enzyme (SBE1)
1.45***
pericarp
BQ472366
Small subunit of ribulose-bisphosphate carboxylase
1.29***
pericarp
JX878491
Glutamine synthetase (GS1), cytosolic
1.72**
ubiquitous
CA022884
Pyruvate dehydrogenase E1, α subunit
1.50*
endosperm
X91347
UDP-glucose pyrophosphorylase
1.27*
ubiquitous
BQ469324
Pyruvate dehydrogenase E1, β subunit
Genes without changes in expression levels between light and dark conditions
X69931
Sucrose synthase 2
1.66
endosperm
AJ272308
Sucrose transporter 2 (SUT2), vacuolar
1.20
endosperm
AY483150
Cellulose synthase A1 (CesA1)
1.20
ubiquitous
AY483152
Cellulose synthase A2 (CesA2)
1.20
pericarp, late endosperm
AL507830
Glycerol-3-phosphate dehydrogenase, NAD+
1.01
embryo
Z26322
Alanine aminotransferase
0.88
endosperm
-2.68***-1.48
endosperm
*
embryo
Genes, down-regulated under light conditions
855
856
857
858
859
860
BQ468368F
Cell wall invertase
J403592
Fructose 1-,6-biphosphate aldolase
a
EMBL/GenBank accession numbers. (n = 3 biological x ≥ 3 technical repetitions; *t significant at P < 0.05; **t
significant at P < 0.01 and ***t significant at P < 0.001).
b
The data about tissue-specific localisation of the respective gene expression were extracted from Sreenivasulu et
al. (2006).
38
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861
39
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