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JXB Advance Access originally published online on September 9, 2009
Journal of Experimental Botany 2009 60(15):4301-4314; doi:10.1093/jxb/erp271
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© 2009 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details)

RESEARCH PAPER

The ‘trade-off’ between synthesis of primary and secondary compounds in young tomato leaves is altered by nitrate nutrition: experimental evidence and model consistency

Jacques Le Bot1,*, Camille Bénard2, Christophe Robin2, Frédéric Bourgaud2 and Stéphane Adamowicz1

1INRA, UR 1115 Plantes et Systèmes de Culture Horticoles, F-84000 Avignon, France
2UMR Nancy-Université (INPL)-INRA Agronomie et Environnement Nancy-Colmar 1121, ENSAIA, 2 Av. Forêt de Haye, F-54500 Vandoeuvre, France

* To whom correspondence should be addressed. E-mail: Jacques.Lebot{at}avignon.inra.fr

Received 16 July 2009; Revised 16 July 2009 Accepted 12 August 2009


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 Conclusion
 References
 
Plants allocate internal resources to fulfil essential, yet possibly conflicting, demands such as defence or growth, as hypothesized by the ‘growth–differentiation balance theory’ (GDB). This trade-off was examined in young tomato plants grown for 25 d using the nutrient film technique with seven nitrate concentrations ([NO3]). The modification of primary (growth-related: organic acids, carbohydrates) and secondary (defence-related: phenolics) metabolite concentrations in leaves was assessed. Then a simple model was devised to simulate the trade-off between growth and secondary metabolism in response to N nutrition. N affected growth and metabolite concentrations in the leaves. Dry biomass, leaf area, and concentrations of nitrate and organic acid (malic, citric) increased with rising [NO3], up to a threshold, above which they remained constant. Starch, sucrose, and organic N concentrations were invariant with [NO3]. Glucose, fructose, and phenolic (chlorogenic acid, rutin, and kaempferol-rutinoside) concentrations were highest at lowest [NO3]. They declined progressively with rising [NO3] until a threshold, above which they remained constant. Model predictions are in phase with experimental phenolic concentration data although the simulated metabolic rates differ from the GDBH proposals depicted in the literature. From the model output it is shown that a careful definition of the C reserve compounds is required.

Key words: Growth–differentiation balance hypothesis (GDBH), leaf composition, major phenolics (chlorogenic acid, rutin and kaempferol-rutinoside), model, nitrate limitation, primary C compounds, Solanum lycopersicum L. (formerlyly Lycopersicon esculentum Mill., tomato)


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 Conclusion
 References
 
During evolution, plants have developed adaptive defensive responses against stresses such as grazing insects, microorganisms, or abiotic factors such as UV radiation. Among these, the mechanism of elicitation involves the stimulation of specific metabolic pathways that trigger syntheses and accumulation of a myriad of defence-related metabolites in plant tissues.

Growing plants are, therefore, continuously facing a dilemma regarding the partitioning of their available C resources. If priority is given to the plant growth processes, the availability of C resources (and other nutrients) may become limiting for plant defence-related processes, and vice versa. So far, four main plant defence hypotheses have been put forward to explain patterns and variations in the concentration of carbon-based secondary compounds in plant tissues, according to availability of resources: the carbon–nutrient balance hypothesis (CNBH; Bryant et al., 1983; Tuomi, 1992), the optimal defence theory (ODT; McKey, 1974, 1979), the protein competition model (PCM; Jones and Hartley, 1999), and the growth–differentiation balance hypothesis (GDBH; Loomis, 1932; Herms and Mattson, 1992; Herms, 2002).

These four hypotheses form a conceptual framework for the functional ecology of plant defence. They suggest that plants continuously make an effective use of costly versus beneficial investments towards defence versus growth processes, the trade-off being mainly conditioned by resource availability, such as N to which this paper is restricted. Many research papers (Herms and Mattson, 1992; Stamp, 2004; Mattson et al., 2005; Glynn et al., 2007) have focused on the GDBH. Herms and Mattson (1992) introduced a simple diagram (Fig. 1) to depict non-linear effects of nutrient availability on secondary metabolism. This representation is widely adopted (Stamp, 2004; Matyssek et al., 2005; Glynn et al., 2007). As the main determinant of this basic trade-off, the GDBH assumes that a shortage of nutrients produces differential effects on plant growth and photosynthetic rates, the former being more restricted than the latter. Considering growth as the product of leaf area by the rate of net CO2 assimilation, the main effect of N supply on plants is to increase leaf area duration (Gastal and Lemaire, 2002). Therefore, at a rate of N supply low enough to alter leaf area, but not so low as to lower the net CO2 assimilation rate, it follows that extra C accumulates in the plants and it may fuel the rate of secondary metabolism. Figure 1 sketches Herms and Mattson's views about the different effects of nutrient availability on the net CO2 assimilation rate (NAR), the plant relative growth rate (RGR), and the rate of secondary metabolism. The diagram explores two contrasted domains of ‘severe nutrient deficiency’ and ‘agricultural practices’, respectively. The former (zone 1) is rarely described in research papers on agricultural crops, but it is tackled by ecological studies on poor habitats (Lambers and Poorter, 1992; Tjoelker et al., 2005). Therein, nutrient availability limits NAR more than RGR and allows extra C substrates to flow into secondary metabolism. The latter domain, however, comprises most scenarios occurring with cropping systems. Therein, nutrient availability sustains maximum NAR due to regular fertilization practices, although these may not be sufficient to maintain maximum growth if there are still insufficient nutrients to give maximum leaf area. Therefore, depending on fertilization efficiency, this domain comprises two contrasted zones, in which nutrition is limiting (zone 2) or non-limiting (zone 3) for RGR. At the transition between zones 2 and 3, plant nutrient status is considered ‘optimal’, also defined elsewhere as critical (Lemaire, 1997) with respect to total plant dry biomass production. It follows that upon increase in nutrient availability, the rate of secondary metabolism is meant to increase over zone 1 and to decrease thereafter, as more of the assimilated C goes into growth processes.


Figure 1
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  		Fig. 1. Hypothetical response of net assimilation rate (NAR), relative growth rate (RGR), and relative rate of secondary metabolism to N availability, after Herms and Mattson (1992).

 
In the case of nitrogen (N), results from the literature either corroborate or contradict this hypothesis. Slow growing N-limited plants generally exhibit elevated concentrations of C-based secondary metabolites (Herms and Mattson, 1992; Stout et al., 1998; Hoffland et al., 2000; Stewart et al., 2001). As a result of metabolic competition between growth-related and defence-related metabolism, reduced spring wheat growth was observed after activation of defence mechanisms (Heil et al., 2000). This series of results indicates some kind of trade-off that corroborates the GDBH. Results are not always in accordance with the GDBH, especially because secondary metabolites form a wide diversity of compounds. As an example, Koricheva (1998) carried out a meta-analysis on literature data of woody plants subjected to various environmental manipulations (N or P fertilization, shading, CO2 enrichment, drought stress, ozone exposure) and noticed that phenolics conformed to the GDBH predictions while terpenes and tannins did not. This may support the view that none of the current theories on resource limitations to secondary metabolism can account for all observed responses, emphasizing the need for a more detailed physiological, and biochemically informed, model (Lou and Baldwin, 2004).

Few attempts have yet tried to model the growth–defence trade-off in plants. Most trials concerned trees and herbs (Gayler et al., 2004; see Matyssek et al., 2005; Gayler et al., 2008) and, to our knowledge, annual crops were scarcely studied. At least three publications examined how resource availability (light, water, and N) influenced allocation to defence and growth in tomato (Wilkens et al., 1996a, b; Stamp et al., 2004). Their results were compatible with the GDBH predictions, although the authors estimated that the low number of treatments (only two levels of resource availability) hindered the rigorous testing of the theory. There is a need, therefore, to test experimentally the relationships between C acquisition, growth, and secondary metabolism in tomato along a wide-ranging gradient of resource availability, N for example. However, since the direct testing of the GDBH appears impossible (Stamp, 2004) there is also a need to test the theory through modelling and to confront the experimental patterns with output simulations. The issues for a more comprehensive understanding of these relationships are the control of plant N nutrition in relation to the promotion of natural plant defence and crop production.

The first objective of this work is to assess the patterns of the concentration of primary and secondary metabolites in leaves of young hydroponically grown tomatoes, in response to a wide range of nitrate concentrations in the nutrient solution. The second objective is to model these patterns and revisit the GDB hypothesis in the light of the experimental data, for a parameterization of the GDBH components (i.e. rates of C acquisition, growth, and secondary metabolism).

The present strategy was (i) to acquire experimental data on young tomato plants (Solanum lycopersicum L.) raised during their exponential growth phase, at contrasting levels of N availability, ranging from limiting to non-limiting for growth (i.e. zones 2–3 in Fig. 1); and (ii) to build up from these data a simple growth model based on the assumptions of Stamp (2004). The specific growth conditions are particularly important as young plants avoid self-shading and metabolic changes due to plant ontogeny. N limitation was assessed by dry biomass accumulation and composition of N resources (i.e. NO3 and organic N) in the leaves. Secondary metabolism was characterized by the quantification of phenolic compounds, particularly chlorogenic acid, rutin, and kaempferol-rutinoside, reported to be the main secondary metabolites in tomato leaves. Chlorogenic acid is a hydroxycinnamic derivative that is quantitatively important in the Solanaceae (Clifford, 1999), and rutin and kaempferol-rutinoside are two examples of plant defence-mediated glycosylated flavonoids (Treutter, 2006). Several primary C compounds were also quantified: organic acids and non-structural carbohydrates such as glucose, fructose, sucrose, and starch.

Model overview
The model comprises three pools of different biochemical compositions. To compare them, their sizes are expressed in equivalent C (i.e. mmol of C per plant), not in grams. These pools are the non-structural carbon resources (Wc, comprising soluble and insoluble carbohydrates such as sucrose, fructose, glucose, and starch) from which primary and secondary metabolites are built, the C-based secondary compounds (W2), and the remainder, named hereafter metabolic (Wm) as it contains all the metabolic machinery. Total plant mass (Wt, mmol of C per plant) aggregates these pools:

Formula 1 (1)

The respective concentration of these pools (i.e. Cc, Cm, and C2, mol ratios, dimensionless) is given by:

Formula 2 (2)

The rate at which Wc accumulates is the net balance between C gain, loss, and allocation to metabolic and secondary stores:

Formula 3 (3)
where P and R (both in mmol of C per plant d–1) are gross photosynthesis and respiration, respectively. P is the product of the gross assimilation rate (GAR, mmol C cm–2 d–1) and leaf area (A, cm2 per plant):

Formula 4 (4)

Sinclair (1991) reviewed many studies showing a good correlation between GAR and leaf N concentration ([N], mol N mol–1 C). The general pattern is a linear increase at lower [N], followed by a curvilinear response approaching a maximum rate (Pmax, mmol C cm–2 d–1) at higher [N] admittedly described by the threshold model of Sinclair and Horie (1989):

Formula 5 (5)
where S is the initial steepness and GAR=0 when [N] equals [N0].

In the absence of a specific model, it is considered that leaf area develops in proportion ({alpha}, cm2 mmol–1 C) to the size of the metabolic pool during the exponential growth phase:

Formula 6 (6)

Respiration (R) comprises two components, growth (Rg) and maintenance (Rm), respectively (Thornley, 1970; McCree, 1983; Thornley and Johnson, 1990):

Formula 7 (7)

Growth respiration (Rg) is the main component arising from the new synthesis of plant material from Wc. Therefore, it is proportional to the growth rate of both metabolic and secondary pools according to a conversion efficiency or yield (Yg, dimensionless) of the growth process (Thornley and Johnson, 1990). To keep the model simple, Yg was considered equal for both pools:

Formula 8 (8)

Maintenance respiration (Rm) powers the re-synthesis of degraded material (mainly organic N turnover) and maintenance of concentration gradients in the plants. McCree (1983) proposed to relate Rm linearly to plant N content via a maintenance coefficient (m, mol C mol–1 N per day):

Formula 9 (9)

The rate of C allocation (mmol of C per plant d–1) to the metabolic pool depends on the size of this pool (i.e. allowing for exponential growth) and on the availability of non-structural carbon resources according to a saturable function (Thornley and Johnson, 1990). Instead, its response to [N] is usually considered linear (Ingestad and Ågren, 1992) as shown for tomato (Martinez et al., 2005), thus:

Formula 10 (10)
with Vk in mol C mol–1 N d–1 and K1 dimensionless.

The rate of C allocation (mmol C per plant d–1) to the C-based secondary compounds pool depends on the availability of non-structural carbon resources and on the size of the metabolic pool since this store encompasses all the metabolic machinery required in the pathways. This rate may thus be modelled as a saturable function such as:

Formula 11 (11)
with K2 dimensionless and V2max (maximum rate of synthesis) in d–1.

The overall plant RGR (d–1) and the RGRs of the metabolic (Mm, d–1) and secondary (M2, d–1) pools are calculated as:

Formula 12 (12)

Numerical integration of the differential equations (i.e. GoGoGoEquations 3, 10, and 11GoGo) relates biomass accumulation in the three pools to [N]. In order to relate it to the concentration of N in the nutrient medium, as proposed in Fig. 1, it is necessary to introduce an N uptake submodel.

For this purpose, the model of Cárdenas-Navarro et al. (1999), developed and parameterized on young tomato plants, was used. The uptake rate (I, mol N mol C–1 d–1) responds to NO3 concentration (ns, mol m–3) in the nutrient solution following saturable Michaelis–Menten kinetics. Furthermore, it responds negatively to plant NO3 concentration, but their model was simplified by replacing plant NO3 by [N] as follows:

Formula 13 (13)
with Ks in mol m–3, Imax being the maximum influx rate when [N] tends to zero and ns tends to infinity. I=0 when [N]=[N]max.

Assuming the N uptake rate is proportional to the importance of the metabolic machinery, the plant nitrogen content (QN, mol of N per plant) increases with time at the following rate:

Formula 14 (14)

As QN=[N]xWt, it follows that:

Formula 15 (15)
thus, combining GoGoEquations 14 and 15Go yields :

Formula 16 (16)
which, according to GoGoEquations 2 and 12Go, can be written:

Formula 17 (17)

Numerical solving of this latter differential allows the plant N status to be related to N nutrition and to growth.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 Conclusion
 References
 
Experimental set-up
The plants were grown in Avignon (France) on seven independent recycling NFT (nutrient film technique) systems randomly arranged in a heated greenhouse equipped with mist spray (see a comprehensive description in Adamowicz and Le Bot, 2008). On each system, 42 plants were raised with a nutrient solution of constant nitrate concentration ([NO3]) flowing at the rate of ~0.6 l min–1.

[NO3] in treatments were 0.050, 0.125, 0.2, 0.3, 3.0, 7.0, and 15.0 mol m–3, and their volumes of solution were, respectively, 1, 1, 0.7, 0.7, 0.3, 0.3, and 0.3 m3 per treatment. Higher volumes were used with lower concentrations to help limit [NO3] drifts due to plant ion uptake. The drifts in solution ionic composition were monitored from manual sampling and [NO3] and pH measurements, with an increasing frequency as growth proceeded, up to twice a day. Based upon analyses, NO3 salts and H2SO4 were added to compensate for drifts. At the end of the experiment it was verified that the cationic concentrations did not deviate significantly from pre-set values.

The greenhouse was whitewashed to reduce incident light intensity on the crops and to make air temperature control easier. Routine climatic measurements included plant photosynthetic flux density (PPFD) at crop level (QS sensors, Delta-T Devices, Cambridge, UK) and temperature (thermistance probes ±0.1 °C, TJI18-44043-1/4-2, Newport Omega, Stamford, CA, USA) of air under cover and of each of the nutrient solutions.

Plant material and growth conditions
The tomato plants (Solanum lycopersicum L. cv Rondello F1, De Ruiter seeds, Bleiswijk-Holland) were sown directly in the NFT system (9 September 2000). The greenhouse was heated at 18 °C and aerated at 25 °C during germination and emergence (9 d), then aerated at 20 °C. Relative humidity was regulated at ~55%. To avoid any mechanical stress, plants were never handled until harvest.

All nutrient solutions, except the 15 mol m–3 nitrate solution, were devised to make the sum (NO3– + SO42–)=12 eq m–3, inferring constant concentrations of other ionic species in all treatments. Nutrient solutions were prepared from deionized water and pure salts at the following concentrations (eq m–3): K 3; Ca 7; Mg 3; H2PO4 1; trace elements were provided as Kanieltra (Hydro Azote, France) formula 6 Fe (0.1 l m–3) and EDTA-Fe (43 µM). Inevitably the treatment at 15 mol m–3 NO3 contained more salts (eq m–3): SO4 5; K 4; Ca 12.2; Mg 4.8; H2PO4 1. Nonetheless, all recipes shared analogous chemical activity ratios for major cationic species. Solution pH was 5.5 and electrical conductivity (EC, mS cm–1) ranged from 1.2–1.4 to 2.1 depending on treatment.

Harvest and sample preparation
The experiment lasted for 28 d, during which five harvests were taken 14, 18, 21, 25, and 28 days after sowing (das). Plants were harvested at the end of the night period (6 am). Light has pronounced effects on plant metabolite concentrations (Fritz et al., 2006) but it is observed that these changes over the diel cycle are of least influence at the end of the night period. Thus, under natural climate, harvesting plants at the end of the night period minimizes the variability due to PPFD changes during the previous day. Plants were sampled in organ classes (leaves, stems, roots), which were analysed separately for non-structural compounds. All leaf samples collected 14, 18, 21, and 25 das were analysed for phenolics but, due to low biomass, only a few stem and root fractions sampled 25 das were examined. In this paper, only leaf data (≥60% of total plant dry biomass) obtained at the fourth harvest (25 das) from eight plants per treatment are presented and used for the model. The plants were sampled at random and at least one plant was selected per growing tray. The other plants remained on the set-up in isolated growth conditions with respect to light interception.

All plants were stored in a dark cold room (15 °C, 1–3 h) before their measurements. Leaf area was measured on an area meter (LI-3000A with LI-3050A belt conveyor, LI-COR, Lincoln, NE, USA) and fresh biomass was weighed on a precision balance (model AE 260, sensitivity 0.1 mg, Mettler, Greifensee, Switzerland). All samples were frozen in liquid nitrogen and kept at –20 °C. For the harvest reported herein, the entire process concerned 56 plants and lasted for ~3 h.

Samples were freeze-dried (Genesis 25 ES, Virtis Company, Gardiner, NY, USA), weighed (model AE 163, sensitivity 0.01 mg, Mettler), and ground to a fine powder (model MM200, Retsch, Haan, Germany) in grinding jars, which were pre-cooled in liquid nitrogen. Dry powders were kept at –20 °C until analysed.

Chemical analyses
Solution nitrate concentration was measured by UV spectrometry using the method developed by Vercambre and Adamowicz (1996). Plant tissue nitrate was determined on water extracts of dried powders using an autoanalyser (AQUATEC 5500, Tecator, Höganäs, Sweden) by colorimetry of nitrite after reduction by cadmium. Total sample N and C were determined according to the Dumas method (elemental analyser NA 1500, Carlo Erba, Milano, Italy). Organic N was computed as the difference between total and nitrate N. Starch was extracted and determined according to the procedure of Gomez et al. (2003). Soluble sugars and carboxylic acids were extracted and measured by HPLC following the method described by Gomez et al. (2002) and Wu et al. (2002). In the spectrum of acids, only malic and citric are reported here, but low concentrations of fumaric (<0.2% DW) and shikimic (<0.05% DW) were also detected in the extracts. Phenolics were extracted using the method of Fleuriet (1976) modified as follows. All steps were carried out at 4 °C either in a cold chamber or on ice. Dry powder (50 mg) was extracted three times with 2 ml of 70% aqueous ethanol. Taxifolin solution (50 µl of a stock at 2 mg ml–1 methanol) was added as an internal standard. Each time the mixture was blended for 1 min and homogenized for 15 min. After centrifugation, supernatants were pooled to constitute the raw extract. The extract (total volume: 6 ml) was evaporated to dryness under vacuum. The residue was dissolved in 0.4 ml of methanol and filtered through a 0.45 µm filter (Minisart RC 4, Sartorius) prior to injection (20 µl) into the HPLC. The analytical conditions were those described by Gautier et al. (2008). All compounds were correctly separated (not shown), allowing the quantification of rutin, chlorogenic acid, and kaempferol-rutinoside from peak area calibrated against a standard curve. Rutin and chlorogenic acid standards were purchased from Sigma (Saint Quentin-Fallavier, France), and kaempferol-rutinoside and taxifolin from Extrasynthèse (Lyon, France).

Data processing
Changes in leaf growth or composition (y) in response to nitrogen nutrition (x) were analysed using a broken stick procedure whereby data were fitted with two successive linear regressions y=a1xx+b1 and y=a2xx+b2, joining at dose (xbp) called the ‘breakpoint’. At this point y=a1xxbp+b1=a2xxbp+b2 and, as a result, it follows that:

Formula

Formula 18 (18)

In order to determine the parameters of the regressions, a statistical analysis was carried out with the software SYSTAT (v. 5.1 for the Macintosh, SYSTAT Inc., Evanston, IL, USA) using the piecewise linear regression procedure with an unknown breakpoint, with the general formula:

Formula 19 (19)

where y is the explained variable (either growth or leaf concentration), x is solution [NO3], b1 the constant, and a1 and a2 the slopes before and after the breakpoint (xbp), respectively. Parameters were found by optimization. The statistical significance of the slopes was assessed, especially above xbp where the asymptotic nature of the response is supposed to intervene. Moreover, if a1 and a2 did not prove significantly different, a single linear regression was calculated over the entire range of [NO3] treatments.

In order to represent correctly the 300-fold changes in [NO3] on a linear scale, the data were plotted in a broken axis chart (DeltaGraph v 5.7.5 for the Macintosh, SPSS Inc. & Red Rock Software, Inc., Chicago, IL, USA), which skipped the value axis portion where 0.4 < [NO3] <3 mol m–3 so as to scale specifically the two regions that varied clearly from each other.

Numerical solving of the model was performed with ExtendSim software (v.7, Imagine That, Inc., San Jose, CA, USA).


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 Conclusion
 References
 
Experimental data
At 25 das, leaves, stems, and roots accounted for 60, 30, and 10% of plant DW, respectively. It was found (not shown) that the chemical composition of both shoot pools shared the same response patterns to N. Leaf polyphenol concentrations were higher than in stems and roots, making leaves a suitable indicator of the whole plant. Leaf dry biomass (DW, g per plant) and leaf area (LA, cm2 per plant) responded to NO3 availability and, according to the piecewise regression analysis, the breakpoint occurred in the region of 0.3 mol m–3 NO3 in solution (Table 1, Fig. 2). At the most, low NO3 treatment decreased DW and LA by ~30%. Above the breakpoint, DW was not significantly enhanced by NO3 availability, although LA was slightly augmented by increasing N nutrition. This clear-cut phase transition segregates the limiting from the adequate domain of N availability for growth. In the former, growth was ~60 times more responsive to NO3 than in the latter, as shown by the calculation of the slope ratio (Table 1). Leaf NO3 concentration was markedly affected by the treatment (Table 1, Fig. 3 circles, dotted line). Under low N, it increased sharply with plant N nutrition and above the breakpoint it remained constant. Reduced N concentration was not significantly affected by the treatment (Table 1, Fig. 3 squares, thin line). Overall, it resulted that increasing NO3 availability in the nutrient solution increased total N leaf concentration slightly, but significantly (Table 1, Fig. 3, triangles, thick line). In the adequate N treatments, leaf N NO3 accounted for ~10% of the total N store.


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  		Table 1. Parameters of piecewise linear regression analyses with an unknown breakpoint (xbp) for leaf growth (DW, dry weight; LA, leaf area) and chemical concentrations ([NO3], nitrate; [Nt], total N; [Nr], reduced N; OA, organic acids; KR, kaempferol-rutinoside)

 

Figure 2
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  		Fig. 2. Leaf dry biomass (A, g per plant) and area (B, cm2 per plant) as a function of NO3 concentration in the nutrient solution. Symbols and bars are means and standard errors calculated on eight replicate plants per N treatment. The dashed lines are piecewise linear regressions (parameters in Table 1). Note the x-axis break between 0.4 and 3 mol NO3 m–3.

 

Figure 3
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  		Fig. 3. Leaf nitrogen concentration ([N], g per 100 g DW) as a function of NO3 concentration in the nutrient solution. The graph displays NO3 N (circles, dotted line), organic N (squares, thin line), and total N (triangles, thick line) concentrations. The lines are piecewise linear regressions (parameters in Table 1). Symbols and bars are means and standard errors.

 
Leaf starch concentration was not significantly affected by N supply, although starch tended to accumulate at low N nutrition (Table 1, Fig. 4a). Similarly, leaf sucrose concentration was insensitive to N supply (Table 1, Fig. 4b). Hexose concentration, however, responded to N nutrition (Table 1, Fig. 4b) as both fructose and glucose decreased sharply in the limiting N domain before they increased slightly (but significantly, Table 1) in the non-limiting zone. The responses were far more intensive under N limitation, as assessed by the slope ratio of 350–400 calculated between the two regressions (Table 1). Under non-limiting N, the leaf accumulated three times more fructose than glucose, making total hexose account for 2.4% of leaf dry biomass. In comparison, leaf starch represented up to 4.7% of the leaf dry biomass.


Figure 4
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  		Fig. 4. Leaf starch (A, g per 100 g DW) and soluble carbohydrate (B, g per 100 g DW) concentrations as a function of NO3 concentration in the nutrient solution. Symbols in (B) denote sucrose (open circles), glucose (filled circles), and fructose (filled squares). The lines are piecewise linear regressions (parameters in Table 1). Symbols and bars are means and standard errors.

 
Non-structural leaf organic acid concentration increased markedly with N nutrition (Fig. 5a). Under N limitation, the rate of organic acid accumulation in the leaves was 50 times higher than in the adequate N range (Table 1). Amongst carboxylic acids, malic accumulated almost twice more than citric. Under non-limiting N, free organic acids accounted for 7.3% of the leaf dry biomass, rendering this pool almost as important as non-structural sugars (8.3%) in the leaf C reserve store.


Figure 5
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  		Fig. 5. Leaf organic acid (A, g per 100 g DW) and phenolic (B, g per 1000 g DW) concentration as a function of NO3 concentration in the nutrient solution. Symbols in (A) denote citric acid (circle, dotted line), malic acid (squares, thin line), and total carboxylic acids (triangles, thick line). Symbols in (B) denote chlorogenic acid (circles, dotted line), rutin (squares, thin line), and kaempferol-rutinoside (triangles, thick line). The lines are piecewise linear regressions (parameters in Table 1). Symbols and bars are means and standard errors.

 
The phenolic molecules analysed herein were carefully selected amongst dozens of already identified tomato compounds (Moco et al., 2006). The main substances found in the leaf extract of the tomato cv Rondello were chlorogenic acid, rutin, and kaempferol-rutinoside, which are reported to be important secondary compounds in plants (Clifford, 1999; Treutter, 2006). Leaf phenolic concentration responded to N supply (Table 1, Fig. 5b). The pattern was similar for chlorogenic acid, rutin, and kaempferol-rutinoside. Chlorogenic acid was the major leaf phenolic compound. The phenolic concentration decreased rapidly with increasing N availability in the domain of N limitation, but it did not vary significantly under adequate N nutrition. The slope ratio between these regressions ranged from 170 to 270 according to phenolic compounds (Table 1), indicating a more intensive plant response under N limitation.

Model simulations
Parameterization:
The parameters used in the simulations are summarized in Table 2. Their values were either taken from the literature (or derived assuming 40% C in the dry matter, Broadley et al., 2004) or optimized on the present data.


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  		Table 2. Model parameters used in the simulations

 
[N0] and S were calculated by applying GoEquation 5 to the tomato data of Chapin et al. (1988). The maintenance coefficient (m) was derived from the data of Gary (1988) at 21 °C (the mean growth temperature in the present experiment). The model of Cárdenas-Navarro et al. (1999) describes triphasic NO3 influx into tomato with increasing ns, meaning that there are three Ks and Imax values depending on the ns domain. Cárdenas-Navarro et al. (1999) also showed that two values of Imax can be inferred from the knowledge of the three Ks and one Imax. Thus, the values of Ks and the ns domains published by these authors were used, and one Imax was determined by optimization.

Optimization followed the procedure described by Wallach et al. (2001) yielding the parameter values that minimized the following goodness-of-fit criterion:

Formula 20 (20)

Over the seven treatments, (Wt), (A) and ([Cc]) are, respectively, the measured plant weight, leaf area, and non-structural (i.e. carbohydrates) C concentration of treatment (i) while (Wtc), (Ac), and ([Cc]c) are the corresponding value calculated with the model; {sigma}W, {sigma}A and {sigma}Cc are the respective standard deviations.

Simulation results:
Simulations were run with different ns ranging from 0 (low N) to 30 mol NO3 m–3. Plant growth and composition were simulated between 14 (first harvest) and 25 das, and the ultimate simulation data (25 das) were plotted either against ns (Fig. 6a, c) or [N] (Fig. 6b, d). In each run the initial growth values were those of the real experiment. At 14 das, no significant difference was observed between ns treatments; thus the mean Wt (1.32 mmol of C per plant) was used as the initial condition for all treatments. Initially, Wt was arbitrarily allocated 20, 40, and 40% to Wc, Wm, and W2, respectively (GoEquation 1).


Figure 6
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  		Fig. 6. Simulated effects of either solution [NO3] (A, C) or plant [N] (B, D) on metabolic rates and C store concentrations. (A, B) Relative rates of growth (RGR, thin line, left axis), secondary metabolism (M2, thick line, left axis), and gross assimilation rate (GAR, dotted line, right axis). (C, D) Concentration of the C stores, where lines are [Cm] (thin), [Cc] (thick), and [C2] (dashed). Simulations were performed from 0 to 30 mol NO3 m–3, but for clarity the plots (A, C) were restricted to 0–1 mol NO3 m–3 as traces were stable beyond this range.

 
As stated in the hypotheses, the rate of C fixation (GAR in the model) is responsive to plant [N] (Fig. 6b), with a sharp increase at low N followed by a low or no increase at high [N], while RGR is nearly linear with [N]. M2 increases non-linearly with [N]. When plotted against ns (Fig. 6a), all the rates (GAR, RGR, and M2) saturate at high NO3 availability. The bumpy patterns are a consequence of the multiphasic uptake model and thus appear solely in Fig 6a.

Changes in the concentration of the different plant C pools with N status are plotted in Fig. 6d. [Cc] increases from low to 0.09 mol N mol–1 C and decreases thereafter. [Cm] declines very slightly from low to 0.04 mol N mol–1 C and thereafter increases steadily with [N]. [C2] decreases almost linearly with [N]. When plotted against NO3 availability in the nutrient solution (Fig. 6c), [Cc] shows little response to ns except at very low availability (maximum [Cc] at 7x10–3 mol NO3 m–3). The minimum [Cm] at 8x10–4 mol NO3 m–3 is barely visible but, thereafter, [Cm] responds positively to ns following a multiphasic saturable function. Conversely, [C2] is maximal at low ns and decreases thereafter. All the concentrations ([Cc], [Cm], and [C2]) stabilize at >1 mol NO3 m–3.

[C2] relates to [Cc] following a complex pattern (Fig. 7). In the region of strong N limitation (i.e. 0.020 < [N] ≤ 0.089) the relationship is negative. Conversely, at higher N status it becomes positive.


Figure 7
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  		Fig. 7. Model simulation of [C2] as a function of [Cc] for various N supplies. Tags indicate plant N concentration.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
Conclusion
 References
 
This paper is not a comprehensive test of the GDBH, whose scope is far larger than the particular focus made here on N. The GDB theory has previously been tested on trees (Stamp, 2004; Mattson et al., 2005; Glynn et al., 2007), and dynamic models complementary to GDB have been proposed (Gayler et al., 2004; Matyssek et al., 2005). Similar attempts are lacking for agronomic/annual plants and, due to different plant growth dynamics from perennial species, it is debatable that the GDB theory would properly describe the dynamics of carbon partitioning in the former plants.

Growth response to N supply under continuous monitoring
Solution [NO3] was tightly controlled and the experiment segregated N limitation (zone 2 of Fig. 1) from adequate N nutrition (zone 3 of Fig. 1) for plant growth (Fig. 2), supporting published statements that large N treatment numbers are required to measure this characteristic (Justes et al., 1994, 1997). The critical solution [NO3] under which growth restriction occurred was low (300 µM) compared with other experimental systems or plant species (range =2.75–12 mM, see Gomez-Lepe and Ulrich, 1974; Gunes et al., 1998; Siddiqi et al., 1998; Tocquin et al., 2003) and with efficient flowing solution culture (FSC), in which solution [NO3] in the range of 10–14 µM can support maximum growth (Parker and Norvell, 1999). This implies that its value depends essentially on the capacity of the growing systems to renew nutrients at the root level.

Due to its large number (seven) of N treatments, the present study compares with an experiment on willow trees made by Glynn et al. (2007). It contrasts, however, with many published studies comprising a limited set (two or three) of N treatments (Scheible et al., 1997; Guidi et al., 1998; Stout et al., 1998; Gayler et al., 2004; Urbanczyk-Wochniak and Fernie, 2005). The tomato growth response to N obeys Liebig's law of the minimum and follows a generalized response curve relating plant production to N supply (see Lawlor, 2002). Plant response was analysed according to a broken stick procedure (as in Justes et al., 1994), which fitted the data well, the model accounting for >95% of the observed growth variation. In the leaves, dry biomass accumulation was N limited from 50 µM to 300 µM NO3 in solution (Fig. 2a). The drop in biomass accumulation attained 30% at the most between treatments. This was matched by a similar decrease (34%) of leaf area expansion (Fig. 2b), which inevitably reduced, in proportion, light interception and crop production, inferring that NAR was constant in all treatments. These points and consistent data are thoroughly discussed elsewhere (see Adamowicz and Le Bot, 2008). It must be emphasized that the experimental data did not cover the entire range of N supplies described by Fig. 1 and by the simulation model (Fig. 6). The avoidance of N deficiency per se and the unaltered NAR are corroborated by the lack of variation in leaf organic N concentration, inferring steadiness in operation of the photosynthetic apparatus. Mean leaf N concentration (1.01 g N m–2) appears low in comparison with data on other plants (Sinclair and Horie, 1989), but the present growth data support the general findings that leaf area development is tightly controlled by N availability (Palmer et al., 1996; Lawlor, 2002; Dreccer, 2005).

Plant composition in primary and secondary metabolites
Many differences in plant composition concerned both primary and secondary metabolites. Under N limitation, leaf [NO3] responded strongly to N nutrition (Fig. 3), but was nearly constant at maximum value under non-limiting N, which fully matches with previous data (Gomez-Lepe and Ulrich, 1974; Scheible et al., 1997; Chen et al., 2004). In contrast, organic N concentration (Fig. 3) was not significantly changed by the treatments, supporting the view that, in tomato, NO3 is the main labile N store responding to environmental changes in N availability (Le Bot et al., 2001). Consistently, this has been used to elaborate practical tools to diagnose plant N status and devise fertilization strategies (Lawlor, 2002; Lemaire et al., 2008).

Dissimilar responses occurred in carbohydrate concentrations. Starch, the main leaf C store, did not change concentration over the range of solution [NO3] (Fig. 4a). At first sight this is at odds with literature data reporting on a remarkable negative relationship between N availability and starch level in leaves (Radin and Eidenbock, 1986; Rufty et al., 1988; Paul and Driscoll, 1997; Ball et al., 1998). However, (i) the plants were never N deprived but limited in their supply (Fig. 2a) and (ii) the observation is prone to be influenced by the harvest time (end of the night period). Because starch is strongly depleted overnight (Rufty et al., 1983; Paul and Driscoll, 1997; Ball et al., 1998) to sustain structural growth, all treatments were likely to exhibit comparable low starch concentration at dawn. Similar to starch, the concentration of sucrose (Fig. 4b, circles, solid line) remained constant between N treatments in accordance with other results for tomato (Guidi et al., 1998; Urbanczyk-Wochniak and Fernie, 2005), although the latter reported a different behaviour at low light intensity. Sucrose is a transport sugar whose concentration results from synthesis and export processes. The former is largely controlled by the enzyme SPS (sucrose phosphate synthase), whose activity is perturbed when N deficiency impairs RGR (Foyer et al., 1995). In the present experiment, however, given the unaltered NAR and starch concentration between treatments, unchanged sucrose concentration is likely to reflect similar export activities to sink organs.

In contrast, fructose and glucose concentrations were altered at low N nutrition. The pattern was a sharp decrease between 50 µM and ~200 µM NO3 followed by a slight concentration increase above this value (Fig. 4b). The hexose accumulation rate was 350–400 times higher at low N than with adequate N supplies (Table 1). It did not proceed from a dilution effect, as fructose and glucose concentrations increased by 84% and 65%, respectively, at 50 µM NO3 although growth was only reduced by 30%. Increased hexose concentration in N-deficient tobacco and tomato plants has already been reported (Paul and Driscoll, 1997; Urbanczyk-Wochniak and Fernie, 2005), and this appears coherent with an overall slowing down of plant metabolism (including respiration) following N shortage.

The response of organic acid concentration to N supply diverged from that of hexoses. In this trial, acid concentration was under the detection limit (<0.03% DW) in the lowest N treatments but increased sharply with N supply up to ~0.6 mol NO3 m–3 in solution (Fig. 5a, Table 1). Thereafter, the concentrations continued to rise but the slope was 50 times slower. This pattern mimicked that described for leaf [NO3] (Fig. 3). Organic acid concentrations, and in particular that of malic acid, are known to evolve in response to the cation–anion plant uptake imbalances, in particular those provoked by altered NO3 supplies, and to the control of cytoplasmic pH. Textbooks (such as Mengel and Kirkby, 1987) described how the pH-stat model of Davies enables malate synthesis to counter the internal pH rise associated with NO3– assimilation. This behaviour was described for tomato plants fed with increasing solution [NO3] (Kirkby and Knight, 1977). Furthermore, nitrate acts as a signal to initiate a coordinated increase in the expression of different genes involved in organic acid synthesis, leading to accumulation in N-sufficient tobacco plants (Scheible et al., 1997). Recently, leaf organic acid profile analysis of N-deficient tomato plants confirmed this behaviour for the major acids of the tricarboxylic acid (TCA) cycle such as malate, citrate, iso-citrate, fumarate, succinate, and 2-oxoglutarate (Urbanczyk-Wochniak and Fernie, 2005). In the present trial, it is noteworthy that the amount of organic acid C reserves is similar to that of hexoses, but their responses to N limitation are opposite. Therefore, it may be remarked that carboxylic acid accumulation with rising N supply nearly compensates for depletion of the hexose store. This is important in growth models because the C reserves are solely ascribed to the pool of non-structural sugars, although they should also rely on other C sources.

Leaf phenolic concentrations responded strongly to N nutrition, as hexoses did. In the N-limiting growth domain (i.e. <300 µM), phenolic concentration increased with nitrate limitation, up to 2-fold for chlorogenic acid and rutin in the 50 µM NO3 treatment compared with adequate nutrition (Fig. 5b). There is a general agreement in the literature that N deficiency stimulates phenolic synthesis in tomato leaves (Wilkens et al., 1996b; Stout et al., 1998; Hoffland et al., 2000; Stewart et al., 2001) and in several crops (Haukioja et al., 1998; Lux-Endrich et al., 2000; Kovácik and Backor, 2007), although the shape of this relationship is unknown. Based on this ubiquitous relationship, Cartelat et al. (2005) proposed a quick test to diagnose wheat N status from the rapid optical measurement of their leaf content of polyphenolics. Hence, the authors found a highly significant negative correlation between leaf polyphenolic and N contents, independent of the growth stage. This allowed the rapid estimation of the crop nitrogen nutrition index (NNI; see Lemaire et al., 2008). Indeed they observed that when NNI was ≥1 (i.e. N status was adequate for growth), leaf polyphenolics remained constant, but when NNI was <1 (N-limiting growth) they increased as leaf N decreased. This pattern is similar to what is reported on the tomato plants here.

How do results and simulations compare?
The proposed model adopts the very same principles used by Herms and Mattson (1992), i.e. primary and secondary metabolites compete for the C resources produced by photosynthesis, but the sink strength of the pool of primary metabolites (i.e. the maximum rate in GoEquation 10) depends on N availability. As the response of secondary metabolism to N and C resources is not known, it is inferred from the respective responses of photosynthesis and growth to N and C. Inevitably, the pattern of the former depends on the patterns of the latter. Herms and Mattson (1992) imposed arbitrary sigmoid responses of NAR and RGR to soil resource availability (i.e. ns in this paper). Favouring instead a rational approach, here photosynthesis and growth were related to plant [N] on the basis of existing models with experimental backing, the link with N availability in the soil solution also being made from a published model. It is clear that output simulations (Fig. 6a) differ greatly from Fig. 1. Indeed, simulated photosynthetic rates and RGR are saturable, but not sigmoid, and this is clearly driven by the N uptake submodel. Nonetheless, simulations conform to the predictions of Herms and Mattson that ‘at low to moderate level of resource availability ... rates of net assimilation, growth and secondary metabolism are positively correlated’. In contrast, at a moderate to high level of resource availability, the simulations (Fig. 6a) contradict previous assumptions (Fig. 1), that ‘... relative growth rate and secondary metabolism are inversely correlated ...’, although in this region photosynthesis and growth show similar patterns in Figs 1 and 6a. This discrepancy is easily explained. Indeed, C-based secondary metabolites do not contain N, but the metabolic machinery necessary for their synthesis does need and does contain N. Thus, as shown by Fig. 6b, any [N] increase benefits both primary and secondary metabolism. Unfortunately, there is no way to measure the overall rate of secondary metabolism and thus to assess the likelihood of Fig. 6a versus Fig. 1. Instead, it may be tempting to infer secondary metabolism from the measurement of metabolite concentrations. For instance, Stamp (2004) interpreted the bell-shaped curve of secondary metabolism in Fig. 1 as reflecting the concentration of secondary metabolites expressed as a percentage of plant dry biomass (Fig. 2 in the cited paper). The present experimental results are restricted to the domain of agricultural practices, i.e. zones 2 and 3 in Fig. 1. In this range, a decreasing concentration of phenolics with increasing ns up to 0.3 mol NO3 m–3 was observed, followed by a constant concentration thereafter. Such an observation conforms to Stamp's prediction and other experimental results (Stout et al., 1998; Schmelz et al., 2003; Glynn et al., 2007). However, this does not validate the initial hypothesis, because the present model predicts a decrease of [C2] with increasing ns (Fig. 6c), although the rate of secondary metabolism increases (Fig. 6a, b). Indeed, according to Table 2, the primary metabolism has much higher affinity for C resources than the secondary metabolism (i.e. K1 << K2). Thus, Wm accumulates more C than W2 when [N] increases (Fig. 6b). It results that [C2] declines only because the accumulation of primary metabolites dilutes secondary metabolites (Fig. 6d).

Herms and Mattson (1992) emphasized the idea that C-based secondary compound synthesis is favoured when C resources increase in the plant stores. Thus, it seems logical to expect [C2] to be positively related to [Cc]. Figure 7 is an outcome of the model, which predicts a more complex relationship, because the correlation gets inversed from low to high [N]. It was not possible to find in the literature the data required to test this prediction. However, the present data address a mere question about the nature of these C resources. Figure 8a shows a positive relationship between tomato leaf phenolic and non-structural carbohydrate concentrations, supporting the model prediction pattern (Fig. 7) in the ‘agronomic’ domain. However, non-structural carboxylic acids are C substrates for various biosynthetic pathways and they might also be accounted for in the mean store of C resources. This would be relevant considering their strong response to N supply (Fig. 5a). Leaf phenolic concentration was also plotted against the sum of the concentrations of non-structural carbohydrates and carboxylic acids (Fig. 8b). The course of the relationship was reversed, which poses question about the nature of the C resources that should be integrated in this C pool.


Figure 8
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  		Fig. 8. Measured leaf phenolic concentration (g per 1000 g DW) as a function of measured C resource concentration (g per 100 g DW) taken as (A) non-structural carbohydrates or (B) the sum of non-structural carbohydrates and carboxylic acids. Lines are quadratic smoothing.

 

   Conclusion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 Conclusion
References
 
The experiment confirms literature data about accumulation of primary and secondary compounds in relation to wide-ranging N nutrition. The C allocation to both pools is predictable from a simple model merely based on trophic hypotheses. It suggests that secondary compound concentration declines at high N availability following dilution by primary metabolites and not necessarily a lesser rate of secondary metabolism. Thus, it follows that plants of low N status are likely to produce a high concentration of ‘defensive’ compounds, giving them an advantage against aggressors. Conversely, this model predicts that high rates of N fertilization are likely to produce larger amounts of secondary compounds, which may represent a better strategy for production purposes. It is a challenging issue to infer the likelihood of this prediction, but more experimental work is obviously required.


   Acknowledgements
 
We thank J Fabre, J Hostalery, and V Serra for conducting the experiments, D Vailhen and P Orlando for help with harvests, and E Rubio and D Bancel for chemical analyses. We are grateful to Dr DJ Pilbeam (University of Leeds, UK) for English revision and valuable comments.


   Abbreviations
 
A, total leaf area; [C2], concentration of C-based secondary compounds; [Cc], concentration of non-structural carbon resources; [Cm], concentration of metabolic compounds; das, days after sowing; DW, leaf dry biomass; GAR, gross CO2 assimilation rate; GDB, growth–differentiation balance; GDBH, growth–differentiation balance hypothesis; [N], total nitrogen concentration; NAR, net CO2 assimilation rate; NFT, nutrient film technique; NNI, nitrogen nutrition index; [NO3], nitrate concentration; PPFD, photosynthetic photon flux density; RGR, plant relative growth rate.


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