# Fluxomics links cellular functional analyses to whole-plant phenotyping.

Christophe Salon, Jean-Christophe Avice, Sophie Colombie, Martine Dieuaide Noubhani, Karine Gallardo-Guerrero, Christian Jeudy, Alain Ourry, Marion Prudent, Anne-Sophie Voisin, Dominique Rolin

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## To cite this version:

Christophe Salon, Jean-Christophe Avice, Sophie Colombie, Martine Dieuaide Noubhani, Karine Gallardo-Guerrero, et al.. Fluxomics links cellular functional analyses to whole-plant phenotyping.. Journal of Experimental Botany, 2017, 68 (9), pp.2083-2098. 10.1093/jxb/erx126. hal-01605024

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DARWIN REVIEW

# Fluxomics links cellular functional analyses to whole-plant phenotyping

Christophe Salon<fnref n="1" />*, Jean-Christophe Avice<fnref n="2" />, Sophie Colombié<fnref n="3" />, Martine Dieuaide-Noubhani<fnref n="3" />, Karine Gallardo<fnref n="1" />, Christian Jeudy<fnref n="1" />, Alain Ourry<fnref n="2" />, Marion Prudent<fnref n="1" />, Anne-Sophie Voisin<fnref n="1" /> and Dominique Rolin<fnref n="3" />

Received 5 January 2017; Editorial decision 23 March 2017; Accepted 28 March 2017

Editor: Christine Foyer, Leeds University

## Abstract

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

**Key words:** Fluxome, isotope labelling, metabolic flux analysis, metabolic models, metabolism, phenotyping.

## Introduction

During the past decade there has been both an increasing availability of genetic resources [plant resource collections arising worldwide, eco-genotype collections, recombinant inbred lines (RILs), and novel mutants] and the rapid development of wide-ranging analytical techniques for the identification and quantification of genes, transcripts, proteins, and metabolites. Thanks to the development of innovative methods

and tools, this has resulted in numerous plant genomes being completely sequenced and annotated (Pagani et al., 2012), massive datasets becoming available, and numerous experiments assessing gene function through the analysis of phenotypic and molecular changes associated with allelic variations or the disruption of specific genes. In parallel with the development of high-throughput DNA, RNA, or protein analysis,

<footnote n="1">¹ Agroécologie, AgroSup Dijon, INRA, Université Bourgogne Franche-Comté, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France</footnote>

<footnote n="2">² UNICAEN, UMR INRA 950 Ecophysiologie Végétale, Agronomie et nutritions N, C, S, Esplanade de la Paix, Université Caen Normandie, 14032 Caen Cedex 5, France</footnote>

<footnote n="3">³ UMR 1332 Biologie du Fruit et Pathologie, INRA, Université de Bordeaux, 33882 Villenave d'Ornon, France</footnote>

<footnote>* Correspondence: christophe.salon@inra.fr</footnote>
there was progress in metabolite characterization (Schauer and Fernie 2006). These revolutionary advances have allowed scientists to address many questions, including how the genome determines a plant's response to many environmental stimuli.

The development of new analytical technologies providing a comprehensive and unbiased glance at gene products was considered to be the solution for solving many biological problems. Today, the challenge is how this information can be integrated into a whole and lead to new insights into the functioning of cellular processes. Genomic analyses alone are insufficient to predict modulations of metabolism. Metabolite amounts and enzyme activities, key components of metabolic flux, often do not correlate with transcript or protein levels (Junker et al., 2007; Stitt and Gibon 2014), which themselves also do not correlate with fluxes (Hajduch et al., 2010; Marmagne et al., 2010; Fernie and Stitt, 2012; Hoppe 2012; Schwender et al., 2014). Regulation and post-translational modification, and subcellular and cellular metabolite compartmentalization are further sources of complexity in understanding the metabolic regulation that occurs at different levels in a plant.

Fluxomics aims to determine the rates of molecule exchange/transfer (fluxes) in a biological system, which can

be a cell, a tissue, an organ, or a whole plant (Fig. 1). A flux measurement does not reflect the same process when the architecture of the network being studied differs. At the cellular level, fluxomics consists of subcellular trafficking (transporters, vesicles) associated with a metabolic network of enzymatic reactions linking metabolites, which aims to determine rates of reactions and proposes a flux map that translates enzymatic activities and regulation (Fig. 1B). Such metabolic networks have been studied at the cell or tissue/organ levels, assuming that these are composed of almost identical cells. At the plant level (Fig. 1A), fluxomics could be used to determine the nutrient use efficiency (NUE) and concerns the rates of exchange between the organs, such as the reallocation of carbon (C) and/or minerals through xylem and phloem and changes in the content of a given element in the various organs. The fluxes of molecules are not carried by enzymes, but driven by physical force. In roots, water and minerals diffuse osmotically up to the endodermis, after which ions are actively pumped to the xylem apoplast (tracheids). The evaporative loss of water from shoot organs drives the upward flow of sap within xylem cells. In plant shoots, carbohydrates and amino compounds move from their sites of synthesis to the phloem, where the flow of photosynthesates and nutrients is

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** At the whole-plant level, fluxomics is considered as the flow of elements and molecules between various compartments, such as roots, leaves, and pods and seeds (A). Fluxomics data are used to determine the nutrient use efficiency (NUE), which is related to two main components corresponding to (i) uptake processes, so-called nutrient uptake efficiency (NUpE), mediated by various transporters (Salon et al., 2011), and (ii) assimilation, transfer, and storage [nutrient assimilation efficiency (NAE)], and nutrient remobilization efficiency (NRE) towards sink vegetative organs or reproductive tissues (Noquet et al., 2004; Larmure et al., 2005; Schiltz et al., 2005; Desclos et al., 2009; Masclaux-Daubresse et al., 2010; Hawkesford, 2011; Hirel and Lea, 2011; Xu et al., 2012; Avice and Etienne, 2014). At the cellular level (B), fluxomics may be seen as the flow of elements through subcellular trafficking (transporters, vesicle trafficking) and a metabolic pathway composed of enzymatic reactions occurring in various compartments. The illustration in B was designed with OMIX (Droste et al., 2011).</figcaption></figure>
driven by osmotic pressure differences from source organs to heterotrophic sinks, in which metabolites are then consumed for either growth, maintenance or storage.

In piloting fluxomics initiatives, a common strategy can be followed. First, a network has to be defined. As previously mentioned, this can consist of an association of metabolic pathways composed of the enzymes, their substrates, and their products, or can correspond to the plant's organization, with its organs, tissues, cells, organelles, and metabolites that are transferred in xylem or phloem saps. Then, experimental data will be required, such as metabolite contents, kinetic parameters of enzymes, gas exchange measurements for respiration and photosynthesis determinations, and isotopic labelling analysis. As a last step, mathematical modelling will allow estimation of fluxes.

Measuring fluxes of nutrients into and out of various pathways within a given system (e.g. a set of metabolic pathways, a cell, or a tissue) relies on determination of the rates of substrate uptake and the rates of accumulation of storage or structural end-products. This uses a mass balance calculation of metabolites, and hence requires a thorough knowledge of the sequence of biochemical reactions and their stoichiometry within the metabolic pathways studied, which is often complex (Roscher et al., 2000). It does not require any knowledge of enzyme kinetic properties or regulatory processes determining the network operation. Based on a given metabolic network, kinetic modelling can provide detailed models with high predictive value, such as for microalgal metabolism (Salon et al., 1996a, 1996b; Salon and Canvin, 1997), leaf photosynthetic metabolism (Zhu et al., 2013), lignin biosynthesis (Wang et al., 2014), and sugar metabolism in sugarcane (Rohwer and Botha, 2001; Uys et al., 2007) and in tomato fruit (Beauvoit et al., 2014).

To decipher complex metabolic pathways, various approaches have been developed and are now applied to plant biology. Metabolic flux analysis (MFA) is used to calculate fluxes (reviewed by Wiechert, 2001; Wiechert et al., 2006; Sauer 2004; Schwender et al., 2004b; Allen et al., 2009a; Crown and Antoniewicz 2013). MFA often relies on supplying an isotopically labelled substrate, sometimes radioactive (Salon and Raymond, 1988a) but mostly stable (Dieuaide-Noubhani et al., 1995, 1997; Salon et al., 1996a, 1996b; Fernie et al., 2001; Rontein et al., 2002; Sriram et al., 2004; Allen et al., 2009b; Alonso et al., 2010a, 2011). The isotopomer composition of the metabolic intermediates is analysed during redistribution of the isotope-labelled C through a modelled metabolic network. Mathematical models and computation then allow quantification of metabolic fluxes by adjusting the best fit of computed (simulated) to experimental labelling data (Salon et al., 1988b; Antoniewicz et al., 2007a; Weitzel et al., 2013; Nargund and Sriram, 2014). As such, isotopic labelling provides a mechanistic, quantitative description of cellular phenotype (i.e. a flux map) based on stoichiometric balancing of isotopic labelling descriptions. MFA has been mostly applied to heterotrophic tissues when the system is in a metabolic and isotopic steady state. However, this is difficult or even impossible to achieve for a variety of systems with metabolic pools with slow turnover. In the past

decade, the isotopically non-stationary MFA (INST-¹³C-MFA) method has been developed. It dynamically quantifies variations of fluxes by monitoring labelling time-courses after supplying labelled substrate to a system in a metabolic steady state (Young et al., 2008; Wiechert and Nöh, 2013; Young, 2014). As well as MFA, flux balance analysis (FBA) has been developed to study large-scale models such as a genome-scale metabolic model, thus providing an important complement to MFA. Flux-balance modelling is a constraint-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bound solution space. The application of FBA to plants has been reviewed recently (Shi and Schwender, 2016). It has recently been applied to *Arabidopsis thaliana* (Poolman et al., 2009; de Oliveira Dal'Molin et al., 2010; Mintz-Oron et al., 2012), endosperm of barley seeds (Grafahrend-Belau et al., 2009, 2013), oilseed rape embryos (Hay and Schwender, 2011a), maize (Saha et al., 2011), and to rice leaf cells (Poolman et al., 2013). In FBA, the metabolic constraints such as mass balance boundaries (arising from substrate consumption, rate biomass composition, and growth rate) restrict the range of potential distributions. The likely flux distribution through the network is identified through optimization of a defined objective function by linear programming (Oberhardt et al., 2009). The permissible flux map produced by this approach indicates the potential of the network and allows the impact of changes in the network (or the biological objective of the system) to be studied. FBA does not require kinetic parameters, which simplifies analysis.

At the plant level, the main approaches involve establishing flux maps to assess their occurrence and then quantifying fluxes between plant compartments according to (i) plant physiology, (ii) the extent of various abiotic constraints, and (iii) the nature of plant and microorganism interactions. These studies rely on simple mass budget analysis involving measuring the dynamics of plant biomass and elemental content [e.g. C, nitrogen (N), and sulphur (S)] throughout the studied period. Relative contributions from xylem and phloem over the developmental period, net photosynthesis, respiration, and mobilization of elements from older to younger organs (pods to seeds) are calculated. The constraints of the models necessitate fixing boundaries and calculating all internal plant fluxes relative to the input and output fluxes, corresponding to net photosynthesis, respiration, and acquisition of minerals and water from the soil.

This review discusses sets of methodologies that have been developed to measure the fluxome from cellular scale to the plant level. It provides an overview of the latest developments in fluxomics based on both innovative models and tools/equipment. It is not intended to review the full range of fluxomic developments, as this has already been the subject of other excellent publications (Schwender et al., 2004b; Ratcliffe and Schacher-Hill 2006; Kruger and Ratcliffe 2015; Niedenführ et al., 2015). Rather, we focus here on how these methods have been historically employed to characterize flux maps and the regulation of central metabolism for a range of plant organizational levels (i.e. from cells to organs and plants). Challenges raised by the need to integrate 'omics'
data to arrive fully at functional phenotyping are highlighted, and the contribution of different omics data towards the reconstruction of plant metabolic models is discussed.

## Measuring fluxes in metabolic networks: from the cell to the organ

### Steady-state ¹³C-MFA in isolated cells: a model for identifying regulatory properties of metabolic networks

Historically, labelling with radioactive isotopes was used to elucidate metabolic flux. The fate of such isotopes was followed using labour-intensive enzymatic and chemical analysis and chromatographic fractionation of the labelled metabolites (Canvin and Beevers 1961; Calvin 1962; Graham and ap Rees 1965). In lettuce embryos, during the early steps of germination (Salon et al., 1988a), steady-state labelling experiments were carried out with a variety of ¹⁴C-labelled substrates, to determine flux entering the tricarboxylic acid (TCA) cycle. Amino acids and malate were extracted and purified, and then the total labelling, plus its distribution into the C of amino acids (glutamate and aspartate), were determined by combining enzymatic and chemical degradation. From this laborious approach, the authors were able to show that in germinating seeds, fatty acids played a major role, providing acetyl-CoA to enter directly into the TCA cycle and not by the classically favoured pathways of the glyoxylic acid cycle, gluconeogenesis, and glycolysis. The development of nuclear magnetic resonance (NMR) in the 1980s, and then mass spectrometry, providing intramolecular enrichments and positional and mass isotopomer data without purifying or degrading molecules, facilitated MFA. As the number of measurements greatly exceeded the number of fluxes to be determined, software was developed, such as ¹³C FLUX (Wiechert et al., 2001), allowing the optimization of fluxes by an iterative approach for finding the best flux set fitting the experimental data. The major difficulty associated with ¹³C-MFA in plants is probably how to supply a ¹³C-substrate to plant parts in order to reach isotopic steady state. Flux analyses were successfully performed in simplified systems, such as cell cultures, tissue slices, and isolated organs, which simplify isotope supply. However, if cells are used as a model, the validity of transferring the acquired knowledge to the tissue or organ level may be questioned. When metabolic interactions between cells are removed, the regulations themselves may differ. When a given tissue is removed from its *in vivo* metabolic context, connections to transport tissues, including phloem and xylem, are interrupted, and internal gas concentrations are changed. Nevertheless, these *in vitro* systems remain a first approach in our understanding of the regulation of metabolic networks. Metabolic maps have been established in tomato cell suspensions at different stages of their development and growth (Rontein et al., 2002), and in Arabidopsis cells in response to oxygen availability (Williams et al., 2008) or N source (Masakapalli et al., 2013). The metabolic network concerned glycolysis, the pentose phosphate pathway, the TCA cycle, and the pathways leading to biomass production. This included structural components such as cell walls, compounds such as starch, lipids, and proteins

involved in the different cell machineries or reserves that could be accumulated, and soluble metabolites. Fluxes at the limit of the system considered in these studies were quantified from both the growth rate and the detailed biomass composition. Fluxes within the studied system were determined from steady-state labelling experiments using [1-¹³C]-glucose as tracer. Several interesting conclusions were drawn from these studies. Rontein et al. (2002) and Williams et al. (2008) both observed that absolute fluxes change with growth and C demand. For instance, in tomato cells, all the fluxes decreased with decreased glucose consumption during the growth cycle. Conversely, in Arabidopsis cells all absolute fluxes increased in response to a higher growth rate due to increased O₂ availability. However, when fluxes were expressed relative to the glucose influx, no variation was observed in Arabidopsis cells, and variation was observed only in anabolic reactions such as those involved in the synthesis of starch, cell wall polysaccharides, and synthesis of amino acids and organic acids in tomato cells. The authors concluded that fluxes of the central metabolic network remain stable, whereas the anabolic fluxes are more variable. These properties allow a rapid response to physiological perturbations with a minimum of rearrangements.

### ¹³C-MFA and FBA: two complementary approaches to quantitative analysis of the central metabolism in developing seeds

Seeds constitute the major product of agriculture and their metabolic engineering through genetic or abiotic environmental manipulation is of great economic interest. They have been the subject of extensive fluxomics studies aiming at better understanding of seed metabolism during storage product accumulation. One aim has been to reveal potential metabolic targets for increasing protein or lipid accumulation, for improved nutrition or biofuel production.

As seeds of heterotrophic organisms can be analysed via MFA, a number of studies have used the steady-state ¹³C-MFA strategy, labelling the isolated storage tissues (i.e. embryo or endosperm) *in vitro* and using tracer molecules such as ¹³C-sucrose, ¹³C-glucose, ¹³C-glutamine, or ¹³C-alanine, followed by the analysis of ¹³C partitioning into different compounds. This approach has yielded considerable insight into fluxes within the central metabolism of seeds from model and cultivated species, including Arabidopsis, soybean, pea, sunflower, oilseed rape, flax, barley, and maize. The isotopic labelling methods and the progress made towards understanding seed metabolism have been recently summarized by Allen et al. (2015).

One advantage of the ¹³C-metabolic flux strategy, which we outline here, is that it allows study of the impact of environmental or genetic variations on seed metabolic fluxes under controlled conditions *in vitro*. In this respect, Iyer et al. (2008) examined in developing soybean cotyledons the effect of different temperatures on metabolic fluxes. Significant variations in C fluxes were observed that influenced C partitioning into protein and oil. There were dramatic variations in C fluxes through the transporter that imports malate into the mitochondrion and through the anaplerotic reactions catalysed by
the plastid malic enzyme and cytosolic phosphoenolpyruvate carboxylase. These enzymes were proposed as possible targets for increasing (or stabilizing) protein and lipid production in soybean seeds (Iyer *et al.*, 2008). The plastid NADP-dependent malic enzyme has also been shown to play a key role in the developing maize embryo, where it provides one-third of the C and NADPH required for oil synthesis (Alonso *et al.*, 2010a).

Recently, ¹³C partitioning in the central metabolism of developing embryos from nine oilseed rape genotypes differing in seed reserve composition was determined (Schwender *et al.*, 2015). A metabolic flux map was built, revealing a high flow of C through glycolysis to provide phosphoenolpyruvate, which was further converted into pyruvate for the synthesis of acetyl-CoA, which is used for lipid production. Significantly, ~50% of the conversion of sugars into lipids involves Rubisco, which acts without the Calvin cycle under low light levels (i.e. through the Rubisco shunt) in order to enhance the recycling of CO₂, thus improving the efficiency of C utilization for the synthesis of oil (Schwender *et al.*, 2004a). Interestingly, a model of central metabolism in Brassica napus seeds was developed in a multilevel analysis involving correlation studies between metabolic fluxes and metabolome, enzyme, and proteome data obtained in parallel (Schwender *et al.*, 2015). This study pinpointed potential regulators of storage activities. For example, it has been suggested that plastid pyruvate kinase controls the metabolic fluxes towards lipid synthesis and that the lipid/starch trade-off is mediated by allosteric feedback regulation of phosphofructokinase and ADP-glucose pyrophosphorylase.

The validity of ¹³C-metabolic flux results obtained from seed tissues depends on how faithfully the *in vitro* system mimics the conditions in the mother plant, similar to the question of the fidelity of the cell and organ levels. In seeds, the three major tissues (seed coat, endosperm, and embryo) strongly interact, and metabolism is compartmentalized not only within each tissue (Borisjuk *et al.*, 2013; Rolletschek *et al.*, 2011) but also between these tissues (Gallardo *et al.*, 2007). Efforts have been made to develop improved culture media for isolated embryos with nutrient concentrations similar to those of the endosperm that surrounds the embryo. However, Borisjuk *et al.* (2013) outlined discrepancies in terms of compartmentation between *in vitro* and *in planta* metabolisms of green embryos from oilseed rape that needed light to grow. This might be explained by the varying amounts of light received by the different zones of the embryo *in planta*, which does not happen *in vitro*, where the different parts of the embryo receive similar amounts of light. In contrast, only minor differences in seed composition between *in planta* and *in vitro* conditions were reported in non-green seeds that can be cultured in the dark (Alonso *et al.*, 2010a, 2011).

In parallel to ¹³C-MFA, constraint-based modelling allows an estimation of metabolic fluxes with few experimental data. Large-scale computational metabolic models exist for cells of germinating rice seeds (Lakshmanan *et al.*, 2013), consisting of 326 reactions, and for the developing barley endosperm and oilseed rape embryo, representing 257 and 572 reactions, respectively (Grafahrend-Belau *et al.*, 2009; Hay and

Schwender, 2011b). These models were used to simulate the metabolism of seed tissues, or, in the case of rice, of cells from germinating seeds, in response to environmental perturbations. Some insights gained from these models include the following: (i) the predictions made using the barley model suggest a role for inorganic pyrophosphate metabolism in maintaining seed activities, including sucrose mobilization, under oxygen-deprived conditions (Grafahrend-Belau *et al.*, 2009); (ii) the flux simulations from rice seed-derived cells revealed the importance of ethanolic fermentation and glycolysis for ATP production under anaerobic growth conditions (Lakshmanan *et al.*, 2013); (iii) the model developed for oilseed rape embryos, named bna572, gave predictive insights into mechanisms behind the light-dependent C balance involving Rubisco under photoheterotrophy (Hay and Schwender, 2011a, 2011b).

Comparisons have been made between flux maps of seed metabolism derived from labelling and FBA studies. Importantly, the predicted flux patterns observed for oilseed rape embryos were highly correlated with results obtained using the ¹³C-metabolic flux strategy (Hay and Schwender, 2011a). Hence, the combination of labelled and computational approaches might help to reveal relevant metabolic pathways for genetic improvements of storage compound accumulation in oil-rich seeds. In this context, ¹³C-metabolic flux data have recently been integrated into a constraint-based metabolic model of developing seeds of oilseed rape (Hay *et al.*, 2014). For this model, an improved version of the predictive model bna572 (bna572+), which now contains 671 biochemical and transport reactions within the central metabolism, has been developed, along with a revised C-centric model for ¹³C-metabolic flux analysis, with all its reactions referenced to bna572+ based on linear projections. By combining isotope labelling and FBA, differences in metabolic fluxes within developing seeds of two *B. napus* genotypes contrasting in starch and oil content were observed (Hay *et al.*, 2014). Among the reactions predicted to be upregulated in the embryo of the high-starch genotype, oxidative phosphorylation, known to provide ATP for storage activities, and pyrophosphate metabolism, which participates in sugar metabolism in plants, were identified. A comparison of the net flux rates observed for these genotypes with transcript abundance derived from a genome-wide RNA-sequencing study was performed (Schwender *et al.*, 2014). Discrepancies between the two types of data were observed for many pathways, including glycolysis and amino acid synthesis, which highlighted the importance of post-transcriptional processes in the control of C fluxes within the central metabolism of the oilseed rape embryo.

Major differences between species in fluxomic data observed for developing seed tissues could be explained by large differences in seed composition, tissue function (storage and/or embryonic role), and/or seed shape and type (monocot or dicot). For instance, while the flux of malate oil biosynthesis is low in developing embryos of sunflower seeds (Alonso *et al.*, 2007), the plastid malic enzyme plays an important role in fatty acid synthesis in developing maize and soybean embryos (Iyer *et al.*, 2008; Alonso *et al.*, 2010a). As another
example, C flux distribution in immature pea seeds, whose developmental programme is oriented towards the accumulation of starch (40% on a dry weight basis) and proteins (23%), differs from that observed for *Medicago truncatula* seeds that store oil (12%) and substantial amounts of proteins (45%) (Rohn *et al.*, 2012). This highlights the complexity of the metabolic control of reserve accumulation in seeds and the difficulty of engineering compositional changes in seeds of one species based on flux data acquired for another species, even if the two species are closely related phylogenetically, as are pea and *M. truncatula* (Choi *et al.*, 2004). Case-by-case studies and/or comparative fluxomics are thus required to identify or validate potential targets for modifying seed composition.

## Coupling INST-¹³C-MFA and kinetic models for identifying control points in metabolic networks

Steady-state ¹³C-MFA represents a powerful method for estimating fluxes through a metabolic network. However, because it relies on modelling C rearrangements of metabolites, it is not suited for studies of end-products of metabolic networks, for instance, the reactions leading to the production of cell wall polysaccharides or secondary metabolites. Moreover, when cells use CO₂ as the sole C source, such as in leaves, steady-state ¹³C-MFA is unable to resolve fluxes because all metabolites become fully labelled. Over the past decade, dynamic MFA, also called isotopically non-stationary ¹³C-MFA (INST-MFA; Antoniewicz *et al.*, 2007b), has been developed. A major difference of this approach is that the positional labelling of intermediates can be analysed over time. The success of this approach has been made possible by (i) our capacity to monitor metabolic rearrangements, facilitated in recent years by the development of LC-MS (Arrivault *et al.*, 2009; Alonso *et al.*, 2010b; Choi and Antoniewicz, 2011), and (ii) the strategies developed to solve large-scale differential equation models (Nöh *et al.*, 2006; Young *et al.*, 2008) with tools dedicated to labelling optimization to determine the best-fit values of fluxes such as ¹³C-FLUX2 (Weitzel *et al.*, 2013) or INCA (Young, 2014). The strategies and major issues of INST-MFA have been reviewed by Wiechert and Nöh (2013).

Young *et al.* (2011) were the first to quantitatively map intracellular fluxes using isotopic tracers in photosynthetic cells. More recently, Szecowka *et al.* (2013) coupled transient ¹³CO₂ labelling experiments to non-aqueous fractionation. The subcellular repartition of the metabolites was used as a constraint to estimate fluxes fitting with the kinetics of metabolite labelling, thus permitting an estimate of fluxes in Arabidopsis leaves under illumination. The corresponding protocols have been detailed by Jazmin *et al.* (2014) and Heise *et al.* (2014).

Besides this direct use of INST-MFA for flux quantification, transient labelling experiments were also combined with kinetic modelling, with the aim of then applying metabolic control analysis. For instance, to decipher the metabolic pathways leading to the production of benzenoid compounds, petunia flowers were supplied with ²H₅-phenylalanine (Marshall-Colón *et al.*, 2010). In a first step, the unknown kinetic parameters were optimized by fitting the model to the

experimental data, that is, the pool sizes and labelling patterns obtained at two concentrations of phenylalanine. The model thus defined was then validated by simulating labelling time-course data in transgenic petunia flowers impaired in the production of benzenoid products. The outcome was a better understanding of the complex distribution of flux control in the metabolic network. A similar strategy was developed to study the regulation of cell wall synthesis in Arabidopsis cell suspensions (Chen *et al.*, 2013). Because of the importance of cell walls as feedstock for biofuel production, there is interest in developing fluxomic tools to understand the regulation of cell wall synthesis for improving plant biomass composition. This study highlighted the major role of UDP-glucose-4-epimerase in the synthesis of UDP-galactose, and reactions that exert control on cell wall xylose biosynthesis, especially the role of phosphoglucomutase.

## FBA and kinetics models to study heterotrophic tissues and metabolic regulation in planta: the example of fruit development

¹³C-MFA is limited by the labour-intensive processes required to generate flux maps. These limitations can be circumvented by using modelling approaches such as FBA and kinetic modelling to estimate metabolic fluxes. FBA and, more widely, constraint-based modelling (CBM) of metabolism is a systems biology methodology that allows the gap between genomics-predicted pathways and phenotypic whole-cell flux states to be bridged (Shi and Schwender, 2016). FBA has been used to simulate interactions between different cell types (de Oliveira Dal'Molin *et al.*, 2010) or primary CO₂ assimilation and aspects of diurnal metabolic changes on a whole-plant scale (de Oliveira Dal'Molin *et al.*, 2015).

Modelling of metabolic networks is also a powerful tool for analysing the development and growth of fruits (Colombié *et al.*, 2015). A medium-scale stoichiometric model describing heterotrophic metabolism including the balance of co-factors and energy was used for FBA in order to describe metabolic shifts that occur during tomato fruit development. This computational approach allowed the generation of relevant flux maps describing tomato fruit development. The partitioning of C among the main pathways, the import of C and N by the fruit, CO₂ release, and the overall energy balance were in good agreement with results reported for other plant systems. Moreover, the increase of both respiration and energy dissipation just before ripening highlighted a climacteric behaviour as an emergent property of the metabolic system (Colombié *et al.*, 2017). Finally, the comparison of 30 flux rates with their respective enzyme capacities (Vmax) showed that no enzyme capacity is essential for running the model, and suggested a complex reprogramming of the metabolic machinery during fruit development. This work demonstrated that systems modelling can be a valuable tool for yielding insight into the physiology of plants when provided with an extensive set of experimental data and an accurate definition of the metabolic system.

In parallel to constraint-based modelling, a kinetic model of sugar metabolism was assembled in tomato. It was used to estimate flux changes during the growing phase of fruit
development by using kinetic parameters measured in fruits, subcellular volumes, and pH determination. Unknown kinetic parameters and fluxes were optimized to fit the curves of sugar contents (Beauvoit et al., 2014). Interestingly, the fluxes estimated by the two modelling approaches were similar and allowed a cross-validation of the results, such as sucrose import by the fruit.

## Measuring fluxes at the plant level

### Application of fluxomics to the assessment of C and N economy and their components

Analysis of C and N fluxes at the plant level was applied to a range of species (e.g. legumes, *B. napus*, *Ricinus communis*, etc.). Originally conducted on legume plants (Pate et al., 1979a, 1979b; Pate and Layzell 1981), such studies were based on models for exchanges of C, N, and H₂O in phloem and xylem among plant organs, allowing calculation of the economy of C, N, and H₂O in plant compartments. Data were obtained by gas exchange measurements and isotopic labelling of either CO₂, soil, or atmospheric N, transpirational losses, and the solute composition of transport fluids (phloem and xylem) collected at selected sites on the plant. These were measured for different age groups of leaflets and stem and petiole segments of the shoot.

Flow charts provide flux values for explaining not only the growth of plant compartments (roots, nodules, apex, fruit) but also their determinants. In legumes, the economy of below-ground organs in assimilating NO₃ and N₂ was studied via the use of empirically based models for C and N partitioning within plants, which revealed the high C cost of root and nodule functioning (Layzell et al., 1981; Avice et al., 1996; Voisin et al., 2003). N acquisition occurring through soil mineral uptake by roots (Atkins et al., 1979) or symbiotic fixation of atmospheric N by root nodules (Pate, 1980) and modulation of atmospheric N availability (Pate et al., 1984) were assessed, demonstrating that legumes can quickly restore the flow of translocate towards developing organs after nutrient deprivation.

In these studies, the nature of organic and inorganic solutes moving in xylem and phloem could be determined (McNeil et al., 1979; Pate et al., 1979a). The high specificity of solutes in xylem sap in different species was revealed (Layzell et al., 1981), as well as the dependence of the solute spectrum upon the N nutrition regime. The flow charts also demonstrated the occurrence of xylem-to-phloem exchanges en route to meristematic tissues (Layzell et al., 1981; Pate and Layzell, 1981) and in transporting N towards the apices. The models for N and H₂O flow indicated that a progressive increase in N concentration occurred within stem xylem elements from the base to the top of the shoot, generated via xylem transfer cells, which dispense C-rich assimilate streams to roots and translocate N-enriched assimilates to shoot apices.

This flux characterization at the plant level provided evidence of a mass flow hypothesis for phloem transport in which all solutes are flowing together as a common stream from source to sink. Similarly, in studies on remobilization (Schiltz et al., 2005), the N pool flowing from xylem to seeds

during their filling stage was found to be common to all seeds at the plant level and the role of vegetative parts in the synthesis and transport of assimilates supplied to fruits and seeds was quantified (Fig. 2).

At the level of fruits, studies in legumes have concentrated on the uptake of translocate through phloem and fixation by the fruit of either atmospheric CO₂ or CO₂ accumulated in the gas space from the respiring seeds. The high efficiency (80%) of N mobilization from pod to seed during fruit ripening should be noted. These studies provided important findings concerning the water economy of legume fruits, their high efficiency of water use, and their conservation of respiratory products, useful under drought conditions. The studies highlighted the significance of the legume pod as a temporary reservoir of seed-bound solutes and as a site of reutilization

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Flow chart obtained from measurements of the allocation of exogenous N (left, blue arrows) and ¹⁵N enrichment and distribution of endogenous remobilized N (right, green arrows) from vegetative parts to seeds during the filling period of the first two reproductive nodes of pea plants cultivated in the greenhouse. N fluxes are expressed in mg plant⁻¹. The width of arrows is proportional to the N fluxes (an indication of width is given in the lower right of the figure). Fluxes of exogenous N are delivered to the plant compartments, where part of this N can be accumulated (as is the case for stems). More often, additional N is being remobilized from the roots, vegetative leaves (VL1, VL2, and VL3), reproductive leaves (RL1, RL2, and RL3), and pods (P1, P2 and P3) to feed the flow of N reaching seeds of the various plant strata (S1, S2 and S3). This figure has been adapted and simplified from Schiltz et al. (2005); www.plantphysiol.org. Copyright American Society of Plant Biologists.</figcaption></figure>
of respiratory products of the seeds (Atkins et al., 1977; Flinn et al., 1977; Pate et al., 1977). All of these findings are crucial in the context of selecting legume cultivars whose fruits remain green and adequately illuminated for as long as possible during development.

Using ¹⁵N tracing methods to decipher flow chart components, it was demonstrated that during the reproductive stage root N assimilation usually decreases while N remobilized from senescing vegetative parts becomes the source used to ensure optimum flowering and grain filling; this was the case for many species, such as pea (Pate et al., 1977; Salon et al., 2001; Schiltz et al., 2005), oilseed rape (Malagoli et al., 2005), wheat (Gallais et al., 2006; Kichey et al., 2007), maize (Masclaux-Daubresse et al., 2001), and rice (Tabuchi et al., 2007). In non-legumes such as oilseed rape, the physiological events and proteomic changes during the remobilization of N associated with sequential senescence in leaves were characterized (Desclos et al., 2009). This work revealed that four proteases were specifically involved in N remobilization; these proteases varied in their induction during N remobilization and leaf senescence.

*Use of fluxomics to decipher the components of nutrient use efficiency at the plant level*

In the context of environmentally friendly farming, reducing fertilizer inputs has become imperative. A better understanding of the traits/mechanisms involved in NUE is required to optimize the agri-environmental balance as well as to identify new plant varieties with enhanced NUE produced through breeding programmes. Moreover, for a given nutrient, the regulation of flux and partitioning within the plant (Fig. 1A) and its consequences for NUE in response to (i) environmental fluctuations, (ii) interactions with other elements, and (iii) interactions with other plant partners and/or soil microbial communities remain largely unknown. Although considerable data are already available concerning NUE and its components at the molecular, cellular, or organ level in various species (see Hawkesford, 2011 for a review), to date little is known at the whole-plant level.

To address these agronomic and physiological questions, the use of a ¹⁵N/¹³C/³⁴S labelling-based strategy at the scale of the whole plant allowed the determination of the endogenous and exogenous N and S fluxes between source and sink organs during the different stages of development of various species (Salon et al., 2013). More recently, pulse-chase labelling methods applied at the whole-plant level also allowed the accurate calculation of various indexes related to N and S management, such as the N or S harvest index (¹⁵NHI, ³⁴SHI), as well as the different components of NUE (see Fig. 1A).

In a large panel of Arabidopsis genotypes (Masclaux-Daubresse and Chardon, 2011; Chardon et al., 2012) and a few genotypes of oilseed rape (B. napus; Girondé et al., 2015), ¹⁵N pulse-chase labelling methods were applied to whole plants to explore the natural variation in NUE and N management-related traits [¹⁵NHI, N uptake efficiency, N remobilization efficiency], and to define different genotype groups according to their responses to mineral N availability. These ‘whole-plant fluxomics’ approaches were also

performed to demonstrate that variations in N and S fluxes, N and S use efficiency, and S remobilization efficiency observed in oilseed rape were strongly modulated by mineral N and S availability and by the stage of development when sulfate deficiency occurs (Dubousset et al. 2010; Girondé et al., 2014). Furthermore, by using a whole-plant ¹⁵N labelling-based strategy at the whole-plant scale to determine ¹⁵NHI and N remobilization efficiency in Arabidopsis (Guiboileau et al., 2012; Masclaux-Daubresse et al., 2014) and maize (Li et al., 2015a), a crucial role for autophagy in N remobilization efficiency was demonstrated. Plants genetically defective in autophagy remobilized less stored N from vegetative tissues to developing seeds. Thus, this ‘whole-plant fluxomics’ approach revealed a possible strategy for enhancing the yield of seed-bearing food and biofuel crops, especially under conditions where fertilizing soil is prohibitively expensive or ecologically unsound (Li et al., 2015a).

Environmental perturbations cause significant variations in nutrient fluxes at the plant level, which in turn may affect both physiological plant responses and nutritional seed quality. Interestingly, comparing nutrient allocation patterns between plant parts and seed quality traits can provide insights into adaptive strategies used by the mother plant to maintain seed quality under environmental constraints. Such a comparison has been done in the context of response to S deficiency in M. truncatula (Zuber et al., 2013) and oilseed rape (Dubousset et al., 2010; D'Hooghe et al., 2014) via the analysis of total S or ³⁴S-sulfate allocation and remobilization from the source organs towards the reproductive sink tissues. The efficiency of source organs in remobilizing S may be explained at least in part by vacuolar sulfate efflux via group 4 sulfate transporters to maintain metabolism (i.e. sulfate reduction, methionine and glutathione synthesis) throughout reproductive growth, as suggested by transcript changes, metabolite profiling (Zuber et al., 2013), and analysis of ³⁴S flux between sulfate and reduced S compounds (Girondé et al., 2014).

*Besides ¹³C, ¹⁵N, and ³⁴S flux analysis, new opportunities are emerging for other neglected but important elements involved in plant physiology*

Whereas many studies using stable isotopes such as ¹⁵N and ¹³C or, to a much smaller extent, ³⁴S have been published, much less attention has been given to other mineral nutrients required for optimal plant growth. This could be the result of:

1) The lack of stable isotopes for elements such as P, Na, Mn, and Co, necessitating the use of radioactive isotopes. When available, their use can be further limited by their short half-lives (e.g. half-lives of ~24 and 14 days for ³³P and ³²P, respectively), which are not always compatible with the length of the biological process studied. Even when the radioactive isotope half-life is sufficiently long for the planned number of experiments, these isotopes have not been widely used: few publications have used ⁵⁴Mn (Ducic et al., 2006; Riesen and Feller, 2005), ²²Na (Minder and Feller, 2003), ⁵⁷Co (Riesen and Feller, 2005; Page et al., 2006), ⁶⁵Zn (Haslett et al., 2001; Impa et al., 2013), or ⁴⁵Ca (Reid and Smith, 1992)
2) The high cost of some enriched stable isotopes (e.g. <sup>41</sup>K, <sup>25</sup>Mg, and <sup>44</sup>Ca)

3) The availability of equipment for stable isotope ratio measurements other than isotope ratio mass spectrometers (IRMS) with dedicated interfaces (elemental analysers, gas or liquid chromatography) that are currently used for light elements (H/deuterium, C, N, O, and S), although this limitation is progressively being overcome.

Isotope labelling of 'minor' elements (i.e. those other than N, C, and S) allows distinction between several sources of nutrients available to higher plants. Foliar uptake of <sup>10</sup>B has been quantified and compared with <sup>11</sup>B root uptake (Mattiello et al., 2009; Will et al., 2011), while Matoh and Ochiai (2005) used labelling to discriminate B uptake and allocation to leaves from remobilization of B from older leaves. Augusto et al. (2011) and Van der Heijden et al. (2013) used Mg and Ca labelling to assess the contribution of atmospheric deposition in forest systems versus soil uptake. Given the nutritional importance of Zn, several studies (Wang et al., 2011; Hegelund et al., 2012) have focused on the origin of Zn in cereal grains derived from uptake and translocation or from leaf remobilization using stable isotopes (<sup>70</sup>Zn and <sup>67</sup>Zn, respectively), while its tissue localization was assessed by laser ablation ICP-MS. Similarly, discrimination between current root uptake and remobilization from bark to leaves in trees was studied using pulse-chase labelling with <sup>41</sup>K, <sup>26</sup>Mg, and <sup>15</sup>N (Proe et al., 2000; Weatherall et al., 2006).

In other studies, isotopic labelling was used with the aim of finely characterizing uptake and transport mechanisms. Zheng et al. (2012) used <sup>65</sup>Cu labelling to evaluate the effect of the Yellow stripe-like 16 mutation on Cu transport, while Cornu et al. (2015) identified an organic acid complex involved in the transport of Zn by <sup>67</sup>Zn labelling. Jentschke et al. (2000) and Bücking et al. (2002) estimated the contribution of ectomycorrhizal fungi for Mg transport to the host tree by <sup>25</sup>Mg labelling. Carneiro et al. (2010) and Kato et al. (1997) used <sup>30</sup>Si labelling to assess the accumulation and translocation of Si in rice and bean and to evaluate Si adsorption in soils amended by slag fertilizer application, respectively. In order to study the lateral exchange of nutrients between xylem and surrounding tissues, Metzner et al. (2010) used stable isotope tracers (<sup>26</sup>Mg, <sup>41</sup>K, <sup>44</sup>Ca) delivered for various periods via a cut stem to the transpiration stream of bean shoots. In *Picea abies* trees grown with a split root system, Kuhn et al. (1995) used multiple stable isotope labelling (<sup>25</sup>Mg or <sup>26</sup>Mg, <sup>41</sup>K, <sup>42</sup>Ca or <sup>44</sup>Ca) to analyse the combined effect of Al and high proton concentration on the long-distance transport of these minerals.

Stable isotopes also offer the possibility of using multi-isotope labelling for simultaneous measurements of many mineral nutrients in plants, frequently referred to as ionomic analyses (Baxter et al., 2007, 2008). Such mineral element profiling in vegetative or reproductive tissues has been successfully used in genetically diverse plant populations, providing insights into the control of elemental accumulation and the effects of plant-environment interactions (Baxter and Dilkes, 2012; Baxter et al., 2014), previous breeding (Kusano et al.,

2015), or mutations allowing identification of the underlying genes. In recent papers (Parent et al., 2013; Baxter 2015), ionomic analysis has been considered as a multivariable system to detect plant physiological status and more particularly to assess the interactions between mineral nutrients in plants. Ionomic analysis of plant tissues conducted kinetically was used to quantify the net remobilization of nutrients during irreversible processes such as leaf senescence or during periods of individual mineral nutrient deficiency (Maillard et al., 2015). In this way, the net remobilization of P or Mn from mature leaves and roots, respectively, to young growing leaves could be quantified without isotopic labelling (Fig. 3).

Integrating high-throughput data from omics

Recent advancements in high-throughput large-scale analytical methods to the genomes of organisms and to quantify gene expression, proteins, and metabolites have changed the paradigm of metabolic modelling used to estimate fluxes (Rai and Saito, 2016). Compared with the progress made in microbial metabolic modelling, plant metabolic modelling still remains limited due to genomic complexity and the compartmentalization of metabolic reactions. Rai and Saito (2016) reviewed and discussed different omics datasets with potential application in functional genomics towards the construction and reconstruction of plant metabolic models. Genome-scale reconstructions are advanced functional annotations, combining standard annotations of metabolic genes—gene–protein reaction mapping—with a network topology captured in a stoichiometric matrix. As a model accounting for only mass balance is largely undefined, further constraints are required to enrich the range of feasible solutions with biologically realistic ones. In this context, omics data have been used both to constrain calculated flux distributions and to compare and validate model predictions, thus enabling context-specific studies of the metabolism of an organism.

Concerning genomics, the computation tool PlantSEED streamlines the plant genome annotation process and constructs metabolic models based on genome annotation. PlantSEED allows the detection of gaps and errors in gene annotations, and predicts new functions for unknown genes (Seaver et al., 2014). A high-quality *A. thaliana* annotation is critical for plant genome-scale modelling, and compartmentation makes plant genome-scale reconstruction challenging. While reconstruction has already moved from *A. thaliana* to crop species such as maize (*Zea mays*), sorghum (*Sorghum bicolour*), and sugarcane (*Saccharum officinarum*), any plant reconstruction in the foreseeable future will depend directly or indirectly on the reconstruction of Arabidopsis for functional annotation and compartmentation. Although genomics data provide a broad overview of the metabolic potential of a species, they do not convey information regarding the specificity of a metabolic process for a particular developmental stage, tissue type, or set of environmental conditions.

Transcriptome data, on the other hand, take into account the dynamic state of mRNA levels and serve as a constraint for the reconstruction and prediction of metabolic phenotype. For instance, PlaNet, a tool developed by Mutwil et al.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Net fluxes of nutrients for which no stable isotope is available, such as P (mg plant⁻¹) or Mn (µg plant⁻¹), calculated from mass balance during 30 days of either P or Mn deficiency in hydroponically grown *Brassica napus* L. Remobilization is given as the percentage of initial content of a given tissue (roots, mature leaves, or petioles that emerged before deficiency, or young leaves or petioles emerging during deficiency). For each nutrient, uptake by control plants (provided with an unlimited supply of nutrients) is given as the mean±SE for *n*=4 replicates. Green and white boxes indicate the nutrient content for each organ before and after nutrient deficiency, respectively, and are given as the mean±SE for *n*=4 replicates. Apparent nutrient remobilization from an organ (negative value, calculated from mass balance) and allocation (positive value) of nutrient taken up by roots are given as the mean±SE for *n*=16 replicates. Level of significance: \*P<0.05, \*\*P<0.01, \*\*\*P<0.001, between nutrient-deficient plants and t=0 control plants. NS, no significant uptake. Redrawn from Maillard et al. (2015).</figcaption></figure>

(2011), provides integration of transcriptomics, genomics, phenomics, and ontology terms for seven plant species, with the aim of providing a common platform for transferring knowledge and assisting in functional genomics across these

plant species. These authors showed that co-expression networks are at least partially conserved across different models and crop species within a consensus network.

In the area of proteomics, comprehensive proteomics profiling also provides a unique possibility for verifying and correcting the genome annotation in terms of assignments of open reading frames and splicing patterns, and for confirming the presence of a particular protein at a specific location. For instance, using genomics and proteomics approaches in differentiated bundle sheath and mesophyll cells of maize, Friso et al. (2010) provided an integrated overview of both primary and especially secondary metabolisms. They used an integrated genomics and proteomics approach to provide an in-depth overview of the quantitative distribution of metabolic pathways, protein expression, and homeostasis functions in differentiated cells with minimal assumptions about their tissue specificity. Using efficient resource utilization as the optimization criterion, the predicted relative fluxes between bundle sheath and mesophyll cells correlated strongly with the relative abundance of 50 of the 66 proteins or protein complexes annotated in the proteome studies of maize. Although enzyme accumulation does not relate directly to metabolic flux, this correlation suggests that (i) changes in flux to support C4 metabolism are reflected in proteome-wide adjustments in enzyme amounts in bundle sheath and mesophyll, and (ii) energy efficiency could be a multi-tissue objective and evolutionary driver.

The drawback of omics methodologies is that they rely on an inaccurate assumption, considering gene transcription to be linearly correlated with the corresponding flux of the reactions encoded (Saha et al., 2014). Indeed, the integration of transcriptomics and proteomics datasets in several studies has revealed that for several enzymes, mRNA levels do not correlate with changes in the abundance of the corresponding enzymes, or rather, that there is a time-delayed correlation between mRNA and protein levels, possibly due to several factors that affect translational efficacy, such as the average half-life of RNA, physical properties of the transcript, ribosome density, or post-translational machinery (Stitt and Gibon, 2014).

Finally, metabolome data describe the ultimate response of cellular processes. Integrated metabolomics with other omics databases are particularly useful for functional characterization of unknown genes in Arabidopsis and other plants. Bioinformatics portals capable of mining and integrating such data are being developed (for review of the available tools, see Fukushima et al., 2014) and are promising tools for dealing with our knowledge of complex metabolisms in depth. For example, the MetNet (Metabolic Network exchange) systems biology tool is designed to analyse genome-wide mRNA, protein, and metabolite profiling data from Arabidopsis and soybean. Another bioinformatics platform, the Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR), is available for analyses of correlations, with associated statistics, between metabolomics and transcriptomics data obtained in several plant species, including maize and soybean (Hur et al., 2013). These tools have recently been used to combine ¹³C-based fluxome, metabolome, and transcriptome
data from developing soybean seeds to provide insights into the regulation of seed composition (Li et al., 2015b). In contrast to what has been observed for oilseed rape (Schwender et al., 2014), the conclusions from the metabolic flux data obtained in soybean (e.g. GABA shunt, TCA cycle, and glycolysis) were, in most cases, congruent with the transcriptome data, and correlation studies allowed the identification of regulatory genes and their potential targets within the network.

Zivy et al. (2015) concisely review the problems encountered when working on a crop and outline possible workflows for initiating cellular phenotyping via omics techniques (transcriptomics, proteomics, and metabolomics). The exploration of crop biodiversity and the correlation of cellular responses to stress tolerance at the plant level is currently a challenge. Data integration, as a tool to enable in-depth insights into processes to be gained, can be performed by integrating homogeneous data derived from measurements on the same entity made in the same experimental set-up to get a clearer view on plant (dys)function. Statistical multivariate methods have been refined to uncover those links and are now being used to integrate two or even more datasets based on pre-defined models of one-by-one dataset relationships. High-throughput sequencing technologies are now being routinely applied to crop species, and important biological questions are being addressed through genome-wide transcriptome and/or proteome studies. The main task for the future is the integration of high-throughput data obtained at different levels (fluxome, metabolome, proteome, and transcriptome) to develop metabolic models linked to regulatory networks for crops. To achieve such an objective, an exhaustive metabolic network model (i.e. including gene to protein to reaction) is necessary, which can be built using various databases, enriched by cellular localization of the various components (i.e. proteins and metabolites), when available. Although successful integrations have been achieved for specific tissues or organs, it remains a challenge to bridge the gap between genotype and phenotype at the whole-plant level.

In the end, integration of omics data by mathematical models will offer the opportunity on the one hand to decipher the interactions between plants and their environment (biotic and abiotic) through a better knowledge of the processes involved, and on the other hand to identify novel biological processes (Fukushima et al., 2014).

## Conclusions

Today, there is an urgent need to generate data flows for characterizing at high throughput the role played by a given gene in determining the phenotype, and the associated variation at its locus (Furbank and Tester, 2011; Fiorani and Schurr, 2013), and to explore all the genetic biodiversity. Towards this goal, progress in high-throughput phenotyping nowadays mostly concerns ‘morphological characterization’ (e.g. measurement of leaf or root architectural traits) of gene expression in a variety of phenotyping platforms. This can be done for plant shoots and roots either in the field or in a greenhouse (Berger et al., 2012; Tisné et al., 2013; Araus

and Cairns 2014; Jeudy et al., 2016), where more precise control and characterization of environmental conditions can be achieved (Cabrera-Bosquet et al., 2016).

An understanding of complex metabolic pathways and their regulation and control points is essential for better characterization of plant phenotypes. Flux analysis methods may be combined or used alone according to experimental objectives. Schwender and Hay (2012) elegantly demonstrated how integrative traits such as biomass and its component changes may be linked to those in intracellular fluxes, and how to tackle such links. From a computational point of view, FBA is a powerful tool for studying metabolic networks by permitting analysis at the genome scale. It is also promising for studying fluxes at the whole-plant scale, as was recently shown for barley (Grafahrend-Belau et al., 2013). In addition to providing a better understanding of seed fluxome plasticity in response to these variations, the ¹³C-metabolic flux strategy promises to reveal potential metabolic targets for stabilizing seed product accumulation under fluctuating environmental conditions. The discrepancies observed between *in vitro* and *in planta* metabolisms of embryos outline the difficulties for accurately measuring flux interactions between the intimately associated but distinct tissues of the seed or organisms; fluxomics could also help to decipher fluxes involved in symbiotic partner interactions as well as in seeds.

It is hoped that, with improved labelling techniques and with associated mathematical analyses, technical bottlenecks will be alleviated thanks to the increasing capabilities of *in vivo* NMR (Ratcliffe and Shachar-Hill, 2001) and mass spectrometry. Furthermore, there are broad opportunities to go beyond the ‘classical’ C, N, and even S flux maps by using stable isotopes of other elements (²H, ¹⁷O, ¹⁸O, etc.), because high-resolution mass spectrometry now permits the characterization of differential label incorporations within molecules. Extending ionomic studies with metabolic analysis (Kusano et al., 2015) and, in the future, with stable isotope labelling will be a breakthrough technology for plant phenotyping, in order to decipher genetic diversity and the involvement of metabolic pathways on a larger scale. Moreover, the set-up of high-performance metabolomics platforms (Gibon et al., 2004), regrouped in large projects (http://www.metabohub.fr/), will allow major advances on methods, magnifying the success of fluxomic developments.

Metabolite, transcript, and proteomic data will help to characterize model networks used in the various methods of flux analysis. Developing bioinformatics and advanced computer models will supply new methods for the integration of data combining metabolic and regulatory network information with omics. This is probably the way to assess the genetic contribution to plant phenotype and ultimately guide breeding strategies, and may eventually result in the ability to describe cellular functions *in silico*. However, this field is still in its infancy, and time will be needed before a real integration of this huge amount of data can be used to solve biological questions.

## Acknowledgements

The research leading to some of the results presented herein was partly funded by the European Community's Seventh Framework Programme
(FP7/2007–2013) under the grant agreement FP7-613551, LEGATO pro- ject, and the grant agreement FP7-289562, ABSTRESS project. We are grateful to R. Thompson for his critical reading of the manuscript and for improving the English. We thank the reviewers for their helpful suggestions and sound guidance.

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