research paper initial water deﬁcit effects on lupinus ...pinheiro/cp_pdfs/2011_jxb...tration (ca)...

Journal of Experimental Botany, Vol. 62, No. 14, pp. 4965–4974, 2011doi:10.1093/jxb/err194 Advance Access publication 19 July, 2011

RESEARCH PAPER

Initial water deficit effects on Lupinus albus photosyntheticperformance, carbon metabolism, and hormonal balance:metabolic reorganization prior to early stress responses

Carla Pinheiro1,*, Carla Antonio2,†, Maria Fernanda Ortuno1, Petre I. Dobrev3, Wolfram Hartung4,

Jane Thomas-Oates2, Candido Pinto Ricardo1, Radomira Vankova3, M. Manuela Chaves1 and Julie C. Wilson2

1 Instituto de Tecnologia Quımica e Biologica, Universidade Nova de Lisboa, Av. da Republica-EAN, 2780-157 Oeiras, Portugal2 Department of Chemistry, University of York, Heslington, York YO10 5DD, UK3 Institute of Experimental Botany, Academy of Sciences of Czech Republic, Rozvojova 263, 165 02 Prague 6- Lysolaje, Czech Republic4 Universitat Wurzburg, Biozentrum, Julius-von-Sachs-Institut fur Biowissenschaften, Julius-von-Sachs-Platz 2, D-97082 Wurzburg, Germany

y Present address: Max-Planck-Institut fur Molekulare Pflanzenphysiologie, Wissenschaftspark Golm, Am Muhlenberg 1, D-14476Potsdam, Golm, Germany.* To whom correspondence should be addressed. E-mail: [email protected]

Received 29 November 2010; Revised 29 April 2011; Accepted 23 May 2011

Abstract

The early (2–4 d) effects of slowly imposed soil water deficit on Lupinus albus photosynthetic performance, carbon

metabolism, and hormonal balance in different organs (leaf blade, stem stele, stem cortex, and root) were evaluated on

23-d-old plants (growth chamber assay). Our work shows that several metabolic adjustments occurred prior to

alteration of the plant water status, implying that water deficit is perceived before the change in plant water status. Theslow, progressive decline in soil water content started to be visible 3 d after withholding water (3 DAW). The earliest

plant changes were associated with organ-specific metabolic responses (particularly in the leaves) and with leaf

conductance and only later with plant water status and photosynthetic rate (4 DAW) or photosynthetic capacity

(according to the Farquhar model; 6 DAW). Principal component analysis (PCA) of the physiological parameters, the

carbohydrate and the hormone levels and their relative values, as well as leaf water-soluble metabolites full scan data

(LC-MS/MS), showed separation of the different sampling dates. At 6 DAW classically described stress responses are

observed, with plant water status, ABA level, and root hormonal balance contributing to the separation of these

samples. Discrimination of earlier stress stages (3 and 4 DAW) is only achieved when the relative levels of indole-3-acetic acid (IAA), cytokinins (Cks), and carbon metabolism (glucose, sucrose, raffinose, and starch levels) are taken into

account. Our working hypothesis is that, in addition to single responses (e.g. ABA increase), the combined alterations in

hormone and carbohydrate levels play an important role in the stress response mechanism. Response to more

advanced stress appears to be associated with a combination of cumulative changes, occurring in several plant organs.

The carbohydrate and hormonal balance in the leaf (IAA to bioactive-Cks; soluble sugars to IAA and starch to IAA;

relative abundances of the different soluble sugars) flag the initial responses to the slight decrease in soil water

availability (10–15% decrease). Further alterations in sucrose to ABA and in raffinose to ABA relative values (in all organs)

indicate that soil water availability continues to decrease. Such alterations when associated with changes in the roothormone balance indicate that the stress response is initiated. It is concluded that metabolic balance (e.g. IAA/bioactive

Cks, carbohydrates/IAA, sucrose/ABA, raffinose/ABA, ABA/IAA) is relevant in triggering adjustment mechanisms.

Key words: Carbon metabolism, hormone balance, LC-MS, metabolic reorganization, organ relative water content, plant

performance, plant water status, soil water deficit.

Abbreviations: ABA, absicic acid; Cks, cytokinins; DAW, days after withholding water; DHZ, dihydrozeatin; DHZ9G, dihydrozeatin 9-glucoside; DHZ9R, dihydrozeatin9-riboside; DHZOG, dihydrozeatin-O-glucoside; IAA, indole-3-acetic acid; iP, N6-(D2-isopentenyl)adenine; iP7G, N6-(D2-isopentenyl)adenine 7-glucoside; iP9G, N6-(D2-isopentenyl)adenine 9-glucoside; iP9R, N6-(D2-isopentenyl)adeninosine; MS, mass spectrometry; MVA, multivariate analysis; PAR, photosynthetically active radiation;PCA, principal components analysis; RW, rewatering; WD, water deficit; WW, well watered; Z, trans-zeatin; Z7G, trans-zeatin-7-glucoside; Z9G, trans-zeatin-9-glucoside; Z9R, trans- zeatin 9-riboside; Z9ROG, trans-zeatin 9-riboside O-glucoside; ZOG, trans-zeatin O-b-glucoside.ª The Author [2011]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved.For Permissions, please e-mail: [email protected]

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Introduction

The effects of water deficit on photosynthesis, plant growth,

and source–sink relationships have been studied widely in the

last decade (Chaves et al., 2002; Neumann, 2008), with the

emphasis on short-term, severely imposed water deficit (WD).

Much less information is available on the early stages (2–4 d)

of a slowly imposed soil WD and the underlying metabolic

events. Moreover, an integrated picture involving several

plant organs in relation to water status and photosynthetic

performance is usually not available. Specifically, it is not

known how the earliest effects on metabolism are related to

the decline in water status (both soil water content and plant

water status), but some reports show that soil water deficit

affects plant metabolism even in the absence of changes in

the plant water status (Pustovoitova et al., 2003; Pinheiro

et al., 2005; Antonio et al., 2008). The metabolic status

provides a link between environmental signals (soil water

content), growth regulation, and plant performance. Optimal

utilization of the available carbohydrate for growth and

development has been suggested (Hanson and Smeekens,

2009; Robertson et al., 2009), with WD leading to sugar

accumulation in an organ- and stress-intensity dependent

manner (Pinheiro et al., 2001; Pinheiro et al., 2004; Antonio

et al., 2008; Wingler and Roitsch, 2008). The cycle of starch

synthesis and breakdown provides another way to modulate

stress responses (Usadel et al., 2008; Sulpice et al., 2009), with

the circadian clock proposed to be involved in such

modulation during the early stress response, in order to

maximize carbon uptake and growth (Robertson et al., 2009).

Moreover, responses to the environment and the regulation

of the growth patterns involve extensive interactions between

carbohydrate status, reserve utilization, and the plant hor-

mone network, such as abscisic acid (ABA), indole-3-acetic

acid (IAA), and cytokinins (Cks) (Gibson, 2004; Gonzali

et al., 2006; Sakakibara, 2006; Wingler and Roitsch, 2008). It

is still unknown how such regulation occurs at the whole

plant level, when several organs that react differently to the

same stress are considered (Pinheiro et al., 2001; Pinheiro

et al., 2004). It can be hypothesized that there must be

a mechanism to discriminate between an environmental

fluctuation and a steady, progressive (although minor) soil

water deficit, which implies plant adaptation to different

degrees of stress and the need to make adjustments on a daily

basis (Boyer, 2010). Since water deficit can lead to starvation

of photosynthetic products (Boyer, 2010; Pinheiro and

Chaves, 2011), the hormonal and carbohydrate balance at

the whole plant level are good candidates for the water deficit

decision-making mechanism(s).

An experiment has been designed that aims to establish

a timeline of events at the organ level, detecting the initial

metabolic alterations in a range of different plant organs and

relating them to changes in photosynthetic performance, soil

water content, and plant water status. Our working hypoth-

esis is that ABA levels are increased only under de facto stress

conditions, and that initial stress responses are triggered by

alterations in hormone balance and carbohydrate availability.

Such responses can range from: (i) metabolic reorganisation

(that are defined here as a response to a changing environ-

ment, without affecting the plant water status; (ii) initial

stress responses when plant water status are affected; and (iii)

established water deficit when the photosynthetic capacity is

affected and ABA levels are dramatically increased in allorgans. With this multilevel and comprehensive approach,

the aim was to determine the onset of stress (i.e. when it

begins affecting plant metabolism) and to detect critical

events for the activation of the stress response mechanism(s).

Materials and methods

Plant material

Lupinus albus L. plants (cv. Rio Maior) were cultivated ona sterilized soil, peat, and sand mixture (1:1:1, by vol.) incontrolled-environment growth chambers: photon flux density290–320 lmol m�2 s�1 photosynthetically active radiation (PAR),photoperiod (12 h), temperature (19/25 �C, night/day), and relativehumidity (65–70%) as previously described by Pinheiro et al.(2005). Under these conditions, and without fertilization, lupinplants developed rhizobia-containing nodules. Twenty-three daysafter sowing, WD was slowly induced by withholding watering.Plants were collected 3, 4, 5, 6, 9, and 13 d after withholding water(DAW), and 1 d and 2 d after re-watering (RW). Control plantswere watered throughout the whole period. Sample collection tookplace 3–4 h after the beginning of the photoperiod, and the stemwas always separated into vascular (stele) and cortical (cortex)tissue (Pinheiro et al., 2004; Antonio et al., 2008). The leaf blade,fleshy root, stem stele, and stem cortex were immediately frozen inliquid nitrogen, lyophilized, and stored at –80 �C until extraction.

Soil water content and plant water status

Soil water content (%) was measured with a ThetaProbe soilmoisture sensor (ML2x ThetaProbe coupled to a ThetaMeter typeHH2 from Delta-T Devices Ltd, Cambridge, UK). Leaf waterpotential was measured with a Scholander pressure chamber (PMSinstrument Co, Corvallis, Oregon, USA) at predawn (Wleaf pd).The relative water content (RWC) for leaf blade, stem cortex, stemstele, and root was determined as previously described byRodrigues et al. (1995).

Gas-exchange measurements

Net CO2 assimilation rate (A, lmol m�2 s�1) and stomatalconductance (gs, mmol m�2 s�1) were measured 2–3 h after thebeginning of the photoperiod, in six plants per treatment, usinga portable open gas exchange photosynthesis system LI-6400 (Li-Cor Inc., Lincoln, NE, USA). The most recently expanded leavesat the onset of the stress period were marked and used throughoutthe entire assay. A and gs values were used to calculate theinstantaneous intrinsic water use efficiency (WUE, A/gs).For the sampling dates 0, 2, 4, 6, and 13 DAW and 1 RW,

curves of CO2 assimilation versus intercellular CO2 concentration(A/Ci) were also measured in three plants per treatment using theopen gas exchange system LI-6400 with an integrated fluorescencechamber head (LI-6400-40) following the protocol of Long andBernacchi (2003). Cuvette conditions were maintained at a photo-synthetic photon flux density (PPFD) of 250 or 1000 lmol m�2 s�1,relative humidity of 60%, and a leaf temperature of 25 �C.Measurements were carried out by changing ambient CO2 concen-tration (Ca) in the cuvette, controlled with a CO2 mixer, in thefollowing order: 400, 300, 200 100, 50, 400, 400, 600, 800, 1000, and1200 lmol CO2 mol�1 air. Non-linear regression techniques, based

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on the Farquhar model (Farquhar et al., 1980) and latermodifications (Sharkey, 1985; Harley and Sharkey, 1991), wereused to estimate the maximum ratio of Rubisco carboxylation(Vcmax), the maximum electron transport capacity at saturatinglight (Jmax) and the velocity for triose phosphate utilization(VTPU) for each A/Ci curve. Our analysis was developed in Excelfollowing the specifications of Long and Bernacchi (2003).

Plant hormone extraction and purification

IAA and Cks were extracted and purified as previously describedby Dobrev and Kaminek (2002). Briefly, leaf and root sampleswere ground in liquid nitrogen and extracted overnight withmethanol:water:formic acid (15:4:1, vol., pH 2.5, –20 �C). For theanalysis of endogenous Cks, 50 pmol of 12 deuterium-labelled Ckstandards were added while, for IAA, a tritiated internal standardwas used (Dobrev and Kaminek, 2002). The extracts were purifiedusing Sep-Pak Plus Si-C18 columns and Oasis MCX mixed modecolumns (Waters, MA, USA) and evaporated. Dried extracts ofleaf blades and fleshy roots were re-dissolved in 10% acetonitrile inwater and filtered. Cks were quantified by HPLC-MS, using a TSQQuantum Ultra AM triple-quadrupole mass spectrometer (ThermoElectron, San Jose, USA). The mass spectrometer was operated inthe positive single reaction monitoring (SRM) MS/MS mode withmonitoring of between two and four transitions for each com-pound (Dobrev et al., 2002). The quantification was made usingcalibration curves with [2H]-labelled Cks as internal standards (6–10 concentration points). Detection limits of different Cks variedfrom 0.05– 0.1 pmol sample�1. Results represent averages ofanalyses of three independent samples and of two HPLC-MS/MSinjections for each sample.Levels of IAA were determined using two-dimensional HPLC

with fluorescence detection (Perkin Elmer), as previously describedby Dobrev et al. (2005).ABA was extracted and quantified as described by Jiang et al.

(2004). Freeze-dried tissue samples were homogenized andextracted in 80% aqueous methanol. After partial purification ona Sep-Pak C18 cartridge (Waters, MA, USA), methanol wasremoved and the aqueous residue was partitioned against ethylacetate at pH 3.0. The ethyl acetate of the combined organicfractions was removed under reduced pressure. The newlyobtained residue was taken up in TBS-buffer (TRIS bufferedsaline; 150 mmol l�1 NaCl, 1 mmol l�1 MgCl2, and 50 mmol l�1

TRIS at pH 7.8) and subjected to an immunological ABA assay(ELISA) as described by Peuke et al. (1994). The accuracy of theELISA has been verified, and recoveries of ABA during thepurification procedures were checked using radioactive ABA andfound to be higher than 95%.

Extraction of water-soluble carbohydrates

Water-soluble carbohydrates were extracted from the differentL. albus organs following the addition of chloroform/methanol asdescribed previously (Antonio et al., 2008). Briefly, lyophilizedplant material was finely ground in liquid nitrogen and extractedwith 250 ll ice-cold chloroform:methanol (3:7, v/v), vortex-mixedand incubated for 2 h at –20 �C. After incubation, samples weretwice-extracted with ice-cold water and, after centrifugation, theupper phases were collected and pooled. The combined super-natants containing the water-soluble carbohydrates were evapo-rated to dryness (Savant SpeedVac system, Thermo ElectronCorporation, Runcorn, UK). Samples were reconstituted in 100 llwater and centrifuged at 6800 g at 20 �C for 30 min followed byliquid chromatography ion trap mass spectrometry (LC-MS)analysis.

Quantification of water-soluble carbohydrates

LC-MS analyses were performed on a Thermo Finnigan SurveyorHPLC system coupled to an ion trap mass spectrometer (LCQ

DECA XP Plus, Thermo Electron, San Jose, CA, USA), equippedwith a Thermo Finnigan orthogonal electrospray interface (Antonioet al., 2008). Neutral carbohydrates (glucose, sucrose, raffinose,fructose, and trehalose), sugar alcohols (mannitol, sorbitol, galac-tinol, and maltitol), and the polyol myo-inositol were detected in thenegative ion mode. Mass spectra were acquired over the scan rangem/z 50–1000, and data were processed using Xcalibur 1.3 software(Thermo Finnigan, San Jose, CA, USA). Precursor ions wereselected with an isolation width of 2 m/z units and activated for 30ms. Chromatographic separation was carried out using a PGCHypercarb� column (5 lm, 10034.6 mm; Thermo Electron,Runcorn, Cheshire, UK) at a flow rate of 600 ll min�1. The sampleinjection volume was 20 ll and the PGC column was used atambient temperature (25 �C). The binary mobile phase wascomposed of (A) water modified with 0.1% (v/v) of formic acid(FA) and (B) acetonitrile modified with 0.1% FA. The gradientelution was as follows: 0–5 min, 96% A+4% B to 92% A+8% B; 5–7min, 92% A+8% B to 75% A+25% B, and maintained for 3 min,followed by column re-equilibration: 10–11 min, 75% A+25% B to50% A+50% B, and maintained for 8 min; 19–20 min, 50% A+50%B to 96% A+4% B and maintained for 10 min.

Starch extraction and quantification

The pellet resulting from the chloroform:methanol extraction ofthe different L. albus organs was washed twice with water. Tenvolumes of water were added to the pellet that was boiled for3 min and incubated at 130 �C for 1 h. After cooling, samples wereincubated for 2 h at pH 4.8 and 60 �C with amyloglucosidase(Roche Applied Science, Amadora, Portugal), starch being quan-tified in the supernatant as previously described by Pinheiro et al.(2001).

Statistical analysis

Two distinct approaches were used to assess for significantalterations induced by soil water deficit. Univariate analysis wasused to provide significance levels for the individual parameters(SWC, Wpd, RWC, gs, A, WUE, Vcmax, Jmax, TPU, ABA, IAA,Cks, starch, glucose, sucrose, raffinose). The non-parametricMann–Whitney U test was used in STATISTICA� version 5.0(StatSoft Inc., Oklahoma, USA). Data for each DAW that differsignificantly with respect to day zero were assigned * (P <0.05),** (P <0.01) or *** (P <0.001).Unsupervised multivariate analysis was used to explore the

relationships between the physiological and metabolic alterationsin the early WD stages. In addition, the relative values (log ratios;see Supplementary Tables S15 and S16 at JXB online) were used inorder to evaluate the relevance of metabolic co-occurrences.Principal Components Analysis (PCA) was carried out usingsoftware written in-house in the Matlab software environment(Matlab, 2002). The full LC-MS datasets were also analysed toprovide a detailed and untargeted analysis of the water-solublemetabolites in the extracts of the different plant organs (root, stemstele, stem cortex and blade). As the samples differed in dryweight, the raw data were normalized to the total ion count toaccount for any differences in concentration. The adaptive binningmethod of Davis et al. (2007) was used to obtain bins for m/zvalues by identifying the minima in a smoothed referencespectrum. The data were averaged over time and the median valueover all observations at each point in the time-averaged spectrataken as the reference spectrum, which was then smoothed usinga two level non-decimated wavelet transform. The bin endsidentified as the minima in this reference spectrum were thenapplied to the original spectra and a time series for each binobtained. A similar procedure was then used to bin the time seriesfor each m/z bin separately. The resulting binned values were thenused as variables in PCA. To prevent large variables dominatingthe analysis, the variables were scaled to unit variance.

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Results

Soil water deficit imposition and plant water status

Daily monitoring of the soil water content (SWC; see

Supplementary Table S1 at JXB online) made it possible to

detect a significant difference in relation to day 0 at 3 DAW

(from 22.3% down to 19.7%, i.e. a 12% decrease), the wilting

point (6.4%) being achieved at 13 DAW (a 75% decrease in

the SWC) as previously described by Pinheiro et al. (2005).The plant water status was evaluated through the determina-

tion of the leaf water potential at predawn and the relative

water content of the different plant organs (see Supplemen-

tary Table S2 at JXB online). At 4 DAW (a 30% reduction in

the SWC), the leaf Wpd decreased from –0.36 to –0.48 MPa

(;25% decrease) but only in the stem cortex was the RWC

affected (from 73% to 67%, i.e. a 10% decrease). Further

alterations in the RWC were recorded at 5 DAW (root), 6DAW (blade), and 9 DAW (stem stele). On re-watering, the

plant water status returned to that prior to withholding water

(see Supplementary Table S2 at JXB online).

Leaf gas-exchange measurements

With a small decrease in the SWC (;12%, i.e. 3 DAW), leaf

conductance was affected (from 369 to 189 mmol m�2 s�1;

Fig. 1A) while the photosynthetic rate (Fig. 1B), the plant

water status (see Supplementary Table S2 at JXB online), the

transpiration rate, and the vapour pressure deficit (see

Supplementary Table S3 at JXB online) were not. As a result

of stomatal closure, WUE was increased at 3 DAW (Fig. 1C)reaching a maximum at 6 DAW and steadily decreasing until

re-watering took place. Considering the soil WD effects on

net photosynthesis (Fig. 1B), at 4 DAW an early WD was

imposed (15% reduction), 5 DAW and 6 DAW represented

a mild WD (25% and 35% reduction) and 9 DAW and 13

DAW represented a severe and late WD (90–95% decrease).

On re-watering, the CO2 assimilation rate recovers to ;70%

of the control (Fig. 1B) and leaf conductance was quantifiedas ;30% of the control (Fig. 1A). In order to determine and

compare the limitations of photosynthesis under water deficit,

the Farquhar parameters were considered (Fig. 2). Soil water

deficit effects on the carboxylation rate (Vcmax) and the PSII

electron transport rate (Jmax) were detected 4 DAW (PAR

1000, maximum potential) or 6 DAW (PAR 250 that reflect

the growing conditions). After 6 DAW, when the plant water

status was severely affected by the soil WD, the photosyn-thetic rate was also limited to the triose-phosphate utilisation

(TPU) (9 or 13 DAW, for PAR 1000 or 250, respectively).

The gradual decrease in soil water availability not only leads

to larger stomatal limitation, but also decreases maximum

rubisco carboxylation activity and electron transport, and

therefore RuBP regeneration (Flexas et al., 1999). On re-

watering, the estimated rates were calculated as 80–110%

(PAR 250) or 40–60% (PAR 1000) of the controls.

Phytohormone levels

The amount of free ABA was quantified in the different plant

organs throughout the whole experiment (see Supplementary

Table S4 at JXB online). A dramatic increase in ABA levels

was observed in the four organs, and the maximum ABA

content was determined 6 DAW (stem) and 9 DAW (leaf

blade and root; see Supplementary Fig. S1 at JXB online).

Although differences can be found 3 DAW (a minor ABA

increase in blade and root and a decrease in the stem stele),

the difference is only statistically significant in the case ofstem stele at this early stage (from 283 to 149 pmol g�1 DW).

Significant ABA increases were later detected in the blade

and root (4 DAW) and in the stem cortex (5 DAW). By

5 DAW, ABA levels were also significantly increased in the

stem stele in relation to day zero. The initial ABA decrease in

Fig. 1. Leaf conductance (A), net photosynthesis (B), and water use

efficiency (C) of Lupinus albus leaves during the period of water-

deficit imposition and on re-watering (shaded area). Mean values and

standard errors (n¼6) are presented. The significance levels were

calculated using the Mann–Whitney U test, data differing significantly

from day zero being labelled with * (P <0.05), ** (P <0.01). i) DAW

(days after withholding water); ii) WW (well watered); iii) WD (water

deficit).


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the stem stele coincided with small reductions in the soil

water content and leaf conductance (10–15%), while more

general ABA alterations were associated with a 30% decrease

in SWC, a 40% decrease in leaf conductance and Wpd, and

a 25% decrease in CO2 assimilation rate. The sharp ABAincrease (6 DAW) was detected simultaneously with a re-

duction in the blade and root RWC and with biochemical

limitations in CO2 assimilation (Vcmax and Jmax reduction).

A relationship between ABA biosynthesis and reduced RWC

(leaf in addition to root) has already been described

(Wilkinson and Davies, 2010).

The effects of the soil WD imposition on IAA and Ck

content were evaluated in the leaf blade and root during theearly WD stages, i.e. until the sharp ABA increase 6 DAW in

all organs (see Supplementary Tables S5–S10 at JXB online).

Cks were grouped according to their structure and func-

tion: bioactive Cks, cis-derivatives, O-glucosides (storage

forms), N-glucosides (deactivation forms), and Ck phosphates(biosynthesis intermediates). Figure 3 (presented as the log

ratio between WD and WW) shows that the earliest changes

were detected for IAA and Cks (3 DAW) but not for ABA (4

DAW). It was found that the response of the different Ck

classes was similar in both blade and root, with a reduction

for bioactive-Ck and an increase in the other forms (Fig. 3).

However, in these two organs, opposing effects on IAA were

observed with a strong decrease in the blade and increase inthe root (Fig. 3; see Supplementary Table S5 at JXB online).

Taken together, the data show that soil water deficit reduced

Ck physiological activity (decrease in the bioactive forms and

increase in the deactivated forms, O and N-glucosylated). The

pattern of soil water content reflected in the hormone level

changes in the different plant organs is similar to that

described in tomato for the early stages of exposure to salinity

(Albacete et al., 2008). When considering the hormonalbalance (calculated as the log ratios for ABA/IAA, ABA/

bioactive Cks, and IAA/bioactive Cks; see Supplementary Fig.

S2 at JXB online) it becomes clear that the hormone balance

(ABA/IAA) is dramatically reversed in the leaf blade as early

as 3 DAW, while a clear change is not detected in the root

until 6 DAW.

Fig. 2. Estimated Rubisco carboxylation (Vcmax) (A), maximum

electron transport capacity at saturating light (Jmax) (B), and the

velocity for triose phosphate utilization (TPU) (C) at PAR 250 (solid

line) and PAR 1000 (dotted line) of Lupinus albus leaves during the

period of water-deficit imposition and on re-watering (shaded area).

Data are the means 6standard error of three measurements. The

significance levels were calculated using the Mann–Whitney U test,

data differing significantly from day zero (P <0.05) being labelled

with * (PAR 250) or # (PAR 1000). i) DAW (days after withholding

water); ii) WW (well watered); iii) WD (water deficit).

Fig. 3. Log ratio of ABA, IAA, and several Ck levels in Lupinus albus

leaf blade (A) and root (B) during the period of the soil-water-deficit

imposition. Values are the log ratio (water deficit/control), day

0 being considered the control. Mean values and standard errors

(3%n%6) were considered. The significance levels were calculated

using the Mann–Whitney U test, data differing significantly from day

zero being labelled with * (P <0.05) or ** (P <0.01).


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Carbohydrate analysis (targeted study)

The effects of slowly imposed WD on carbon metabolism

(levels of starch, neutral sugars, and sugar alcohols) were

investigated in the different L. albus organs (see Supplemen-

tary Tables S11–S14 at JXB online). It was possible to

detect and quantify starch, glucose, sucrose, and raffinose

but not fructose, trehalose, myo-inositol, mannitol, sorbitol,galactinol or maltitol.

Carbon metabolism was readily responsive to the earliest

and smallest changes in the SWC (;12% decrease; Figs 4, 5),

showing distinct patterns in the four organs, Soil water

deficit led to transient alterations in starch (Fig. 4), with the

leaf blade and the root showing opposite trends: initial

accumulation in the blade (3 and 4 DAW) and remobiliza-

tion on mild and severe stress. Transient effects were alsoobserved for glucose, sucrose, and raffinose (Fig. 5) and,

with the sole exception of blade sucrose, these components

accumulated at 6 DAW.

Multivariate data analysis

In order to gain insight into the events occurring at the

whole plant level as a consequence of progressive soil water

deficit, principal component analysis (PCA) of the data was

performed, using as variables: (i) all the physiological

parameters and the quantities of hormones, starch, glucose,sucrose, and raffinose from the different organs (Fig. 6A);

(ii) the log ratios for the different carbohydrates and

hormones, from all organs (Fig. 6B; see Supplementary

Fig. S3A at JXB online), from the root (Fig. 6C), the stem

stele (Fig. 6D), the leaf blade (Fig. 6E), the leaf and the root

(see Supplementary Fig. S3B at JXB online), and the stem

cortex (see Supplementary Fig. S3C at JXB online); and

(iii) the full LC-MS/MS datasets of the leaf water-solublemetabolite fractions (Fig. 6F). PCA plots showed clear

separation of the 6 DAW sampling date from the other

dates along PC1, accounting for most of the variance in the

data (Fig. 6A–F). The plots show a progression of in-

terrelated events defining three major groups: control (day 0),

onset of WD (3 and 4 DAW), and established WD

(6 DAW). Loadings show that the leaf Wpd and ABA (in

all organs but particularly in the root) are responsible for

the differences in the physiological and metabolic changes

in the early WD stages (Fig. 6A) along the first principalcomponent (PC1), while IAA and Cks contribute most to

the second principal component (PC2). For the carbohy-

drate and hormone ratios (Fig. 6B), the important variables

for PC1 are the relative values of sucrose to ABA and

raffinose to ABA in all organs and the hormone balance in

the roots. When considering the organs individually (Fig.

6C–E), the variables that relate hormone balance to ABA

(leaf), soluble sugars to ABA (stele, leaf), and soluble sugars

Fig. 4. Starch log ratio in Lupinus albus leaf blade and root during

the period of the soil-water-deficit imposition and recovery (RW).

Values are the log ratio (water deficit/control), day 0 being considered

the control. Mean values and standard errors (3%n%6) were

considered. The significance levels were calculated using the Mann–

Whitney U test, data differing significantly from day zero being labelled

with * (P <0.05).

Fig. 5. Log ratio of glucose, sucrose, and raffinose levels in Lupinus

albus leaf blade (A), fleshy root (B), stem stele (C), and stem cortex (D)

during the period of the soil-water-deficit imposition. Values are the

log ratio (water deficit/control), day 0 being considered the control.

Mean values and standard errors (3%n%6) were considered. The

significance levels were calculated using the Mann–Whitney U test,

data differing significantly from day zero being labelled with

* (P <0.05).


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to starch (root, stele) contribute most to PC1, accounting

for 36–50% of the total variation. The relevance of soluble

sugars in the leaf is reinforced when considering the

LC-MS/MS full scans (Fig. 6F); several compounds con-

tribute to the separation [with m/z values ;103, 149, 387

(sucrose), 475, 549 (raffinose), and 728]. The identification

of the unknown species is currently under investigation inorder to characterize these components in detail.

PCA analysis also shows that the various sampling

dates can be separated using carbohydrate and hormone

log ratios from all organs (Fig. 6B). The separation is due

mostly to values from the leaf (Fig. 6E) and, to a lesser

extent, from the root and stem stele (Fig. 6C, D). Figure

6B shows that differentiation between 3 DAW and 4

DAW can be achieved along PC2 where the separation is

due to the relative values of: carbohydrates to IAA (leaf),

IAA to bioactive Cks (leaf and root), carbohydrate

balance (leaf, root, stele), and carbohydrate to ABA (root,stele). Clear separation of the different organs is seen

when using log ratios as variables (see Supplementary Fig.

S3 at JXB online). The relative levels of soluble sugars

together with the variables that relate soluble sugars to

starch are responsible for the clear separation of leaf from

the other organs (see Supplementary Fig. S3A at JXB

online); the leaf and the root are discriminated by the

relative levels of soluble sugar to starch and carbohydrateto IAA (see Supplementary Fig. S3B at JXB online), and

the stem components are separated by the level of soluble

sugars in relation to starch and starch to ABA (see

Supplementary Fig. S3C at JXB online).

Discussion

In order to detect and differentiate initial soil water deficit

effects in the lupin plant (a reduction in the net CO2

assimilation rate lower than 20%) metabolic analysis have

been combined with the standard procedures for plant

performance evaluation, i.e. plant water status and photo-synthetic rate (Verslues et al., 2006; Pinheiro and Chaves,

2011). In particular, in a new approach, the full scan LC-

MS/MS data recorded for the targeted sugar quantitation

have been exploited and those data have been analysed

further in an unbiased approach using PCA, in order to

take advantage of any additional information hidden in

those datasets. Small decreases in soil water content (;12%)

observed at 3 DAW are shown (i) not to affect the plantwater status; (ii) to reduce stomatal aperture while not

affecting photosynthesis; and (iii) to alter the phytophor-

mone and carbohydrate balance in an organ specific

manner. In contrast to IAA and Cks, at 3 DAW, ABA only

slightly changes in the leaf blade and root. Strikingly, ABA

content decreases in the vascular tissue. To our knowledge,

just two other publications report a decrease in ABA

content as a result of water deficit (Zholkevich andPustovoitova, 1993; Pustovoitova et al., 2003). This effect

of water deficit on ABA levels may result from the stress

imposition rate and mode (slowly imposed soil water

deficit). This ABA decrease that was observed in the stem

vascular tissue may be related to ABA remobilization

through the guard cells via the xylem, which would lead

to ABA-dependent stomatal closure (Seki et al., 2007;

Wilkinson and Davies, 2010). However, it is well establishedthat stomatal aperture does not rely exclusively on the

absolute ABA level (Hartung and Witt, 1968; Munns and

King, 1988; Trejo and Davies, 1991; Zhang et al., 1997), but

is also controlled by other hormones like auxins, ethylene,

and Cks (Hartung and Witt, 1968; Tanaka et al., 2006;

Fig. 6. Principal components analysis with variables: (A) the

physiological and metabolic alterations on the early WD stages

(SWC, Wpd, RWC, gs, A, WUE, ABA, IAA, Cks, starch, glucose,

sucrose, and raffinose); (B) the carbohydrate and hormone log

ratios for the leaf blade, root, stem stele, and stem cortex; (C) the

carbohydrate and hormone log ratios for the root; (D) the

carbohydrate and hormone log ratios for the stem stele; (E) the

carbohydrate and hormone log ratios for the leaf blade; (F) the full

scan LC-MS data for the aqueous extracts of the different lupin

organs. (Dark triangles) Control at day 0; (open circles) 3 d after

withholding water; (open triangles) 4 d after withholding water;

(open squares) 6 d after withholding water.


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Wilkinson and Davies, 2010). So, the regulation of stomatal

closure can be achieved through signal repression via the

hormone balance between IAA, Cks, and ABA. This

implies a tightly regulated process that comprises incoming

signals and in which local synthesis and action (on the

different plant organs) as well as long-distance signals are

involved (Wolf et al., 1990; Sakakibara, 2006; Neumann,

2008; Robert and Friml, 2009). The effect of the initial soilwater deficit on the hormone balance does not seem to be

explained merely through long-distance transport effects,

since the transpiration rate is not significantly affected until

6 DAW. Local effects on hormone synthesis/metabolism

can be postulated, based on the observed increase in levels

of the Ck deactivated forms (O- and N-glucosides), which

accumulate under stress (Havlova et al., 2008).

Altered hormone balance will impact on the carbonstatus, dependent on stomatal regulation, CO2 assimilation

rate, and carbohydrate use and storage. Interactions

between carbohydrate metabolism and ABA, auxin, Ck,

and ethylene signalling networks are known and can act as

connecting nodes for multiple pathways (Leon and Sheen,

2003; Gibson, 2004; Robertson et al., 2009; Cutler et al.,

2010; Stitt et al., 2010), co-ordinating nutritional and

environmental inputs. Our results show that metabolicalterations occur prior to changes in the plant water status

(that is defined here as metabolic reorganization), implying

that a mechanism for the distinction between short-term

fluctuations and steadier alterations must be active.

Metabolic reorganization also implies that the existing

metabolic machinery responds and determines the adjust-

ments of the metabolite status to the changing environ-

ments (Stitt et al., 2010), namely the use and storage ofcarbohydrates (Sakakibara, 2006; Albacete et al., 2008;

Smeekens et al., 2010). The transient metabolic alterations

found in L.albus organs, for example, starch accumulation

in the leaf blade, can be related to short-term responses to

a small decrease in the soil water content. Transient

responses can act as a switching node, the achievement of

several thresholds being necessary to activate the mecha-

nisms that lead to a larger and long-standing soil waterdeficit. The nature of the sensor(s) and signalling pathways

has been the subject of intense research (Pinheiro and

Chaves, 2011), the most direct candidates for signal

metabolites being sugars, such as sucrose, glucose, and

trehalose (Gibson, 2004; Smith and Stitt, 2007; Bolouri-

Moghaddam et al., 2010; Stitt et al., 2010). Our analysis

demonstrates that the carbohydrate balance and relation

to hormone levels are relevant to the earliest responses tosoil water deficit, that is, in the leaf. The relative values of

the different carbohydrates analysed (glucose, sucrose,

raffinose, and starch), their ratio to IAA, and the IAA

and bioactive Ck balance can discriminate 3 DAW from

the 4 DAW sampling dates, providing the initial responses

to a slight decrease in soil water availability (10–15%

decrease). Further alterations in the sucrose to ABA and in

raffinose to ABA relative values (in all organs) indicatethat soil water availability continues to decrease. It is

concluded that the balances for sucrose, raffinose, ABA,

and IAA seem to be metabolically relevant in triggering

the adjustment of stress response mechanisms. The role of

raffinose does not seem to be compatible with osmopro-

tection. Although readily responsive to small variations in

the soil water content, raffinose is detected in very small

amounts (100-fold lower than sucrose and 1000-fold lower

than glucose). Further, raffinose accumulation in lupin is

higher on initial stress and early recovery than undersevere stress (Antonio et al., 2008), which also seems to

exclude osmolyte functions. Raffinose accumulation can be

related to reactive oxygen species (ROS) signalling and the

antioxidant response (Nishizawa-Yokoi et al., 2008;

Bolouri-Moghaddam et al., 2010), which occurs indepen-

dently of the ABA signalling pathways (Urano et al.,

2009). Such a signalling role and the interaction between

hormone, sugar, and ROS pathways in the responses toenvironmental conditions require further investigation

(Wingler and Roitsch, 2008; Pinheiro and Chaves, 2011).

Further studies are also necessary in order to determine

the eventual physiological relevance of the detected co-

occurrences, to understand how progressive water deficit

modulates carbon sensing and signalling (Muller et al.,

2011) and to understand the impact of metabolic re-

organization under changing environments on plantperformance.

Supplementary data

Supplementary data can be found at JXB online.

Supplementary Table S1. The characterization of the soil

water content.

Supplementary Table S2. The characterization of the

plant water status.Supplementary Table S3. The characterization of the

transpiration rate and vapour pressure deficit.

Supplementary Tables S4–S10. The characterization of

the hormone levels.

Supplementary Tables S11–S14. The characterization of

the starch, glucose, sucrose, and raffinose levels.

Supplementary Tables S15 and S16. The characterization

of the carbohydrate and hormone log ratios.Supplementary Fig. S1. The characterization of the ABA

log ratios.

Supplementary Fig. S2. The characterization of the

hormone balance.

Supplementary Fig. S3. PCA plots using carbohydrate

and hormone log ratios.

Acknowledgements

The authors wish to acknowledge Margarida Oliveira (ITQB)for helpful discussions. CP acknowledges the fellowship

SFRH/BPD/14535/2003 from Fundacxao para a Ciencia e

Tecnologia (Portugal); CA acknowledges the CHEMCELL

Marie Curie Early Stage Research Training Fellowship of the

European Community’s Sixth Framework Programme


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(MEST-CT-2004-504345); MF Ortuno acknowledges a Post-

doctoral research fellowship from Ministerio de Educacion y

Ciencia (Spain). This work was partially supported by the

Treaty of Windsor (Anglo-Portuguese Joint Research

Programme, B-18/08).

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Initial water deficit effects on Lupinus albus photosynthetic performance, carbon metabolism and hormonal balance: metabolic reorganisation prior to early stress responses

Carla Pinheiro, Carla António, Maria Fernanda Ortuño, Petre I. Dobrev, Wolfram Hartung, Jane Thomas-Oates, Cândido Pinto Ricardo, Radomira Vanková, M. Manuela Chaves, Julie C. Wilson

DAW

0 22.3 ± 0.8

1 22.5 ± 1.3 22.8 ± 1.4

2 21.8 ± 0.4 21.8 ± 1.1

3 23.8 ± 0.6 19.7* ± 0.6

4 24.7 ± 0.4 16.1** ± 0.5

5 25.3 ± 0.2 12.9** ± 0.5

6 24.6 ± 0.6 10.2** ± 0.3

8 22.3 ± 1.2 8.9** ± 0.3

9 24.6 ± 1.2 7.8** ± 0.3

10 24.2 ± 0.9 6.8** ± 0.4

13 24.7 ± 1.1 6.4** ± 0.4

1 RW 23.7 ± 1.0 22.1 ± 0.9

2 RW 24.0 ± 1.0 25.0 ± 2.2

DAW

0 -0.36 ± 0.01 88 ± 1 95 ± 1 87 ± 2 73 ± 1

1 90 ± 1 88 ± 3 87 ± 0 87 ± 2 74 ± 1 75 ± 0

2 -0.34 ± 0.01 -0.38 ± 0.01 88 ± 0 89 ± 0 95 ± 2 95 ± 3 89 ± 1 89 ± 2 75 ± 1 72 ± 2

3 -0.37 ± 0.03 -0.41 ± 0.02 88 ± 1 87 ± 1 95 ± 1 94 ± 1 88 ± 1 87 ± 1 71 ± 1 71 ± 1

4 -0.37 ± 0.01 -0.48* ± 0.01 89 ± 1 87 ± 2 94 ± 0 91 ± 2 89 ± 1 88 ± 2 72 ± 2 67* ± 2

5 -0.35 ± 0.02 -0.52** ± 0.01 90 ± 1 86 ± 2 95 ± 2 90* ± 0 86 ± 2 83 ± 3 69 ± 1 63** ± 1

6 -0.33 ± 0.02 -0.59** ± 0.01 90 ± 1 82** ± 1 96 ± 1 88* ± 2 87 ± 1 83 ± 2 72 ± 1 65** ± 1

9 -0.41 ± 0.03 -1.15** ± 0.04 90 ± 0 66*** ± 2 97 ± 1 79** ± 4 89 ± 1 82* ± 1 75 ± 1 64*** ± 1

13 -0.42 ± 0.02 -1.87** ± 0.04 90 ± 1 51*** ± 2 95 ± 1 63** ± 3 91 ± 1 77*** ± 1 73 ± 2 57*** ± 1

1 RW -0.43 ± 0.02 -0.42 ± 0.04 90 ± 1 86* ± 1 96 ± 1 91 ± 2 91 ± 1 88 ± 1 72 ± 2 73 ± 1

2 RW -0.42 ± 0.02 -0.39 ± 0.01 92 ± 1 87 ± 1 93 ± 0 93 ± 0 91 ± 1 88 ± 1 74 ± 1 77* ± 1

plant water status

soil water content (%)

control WD

RWC (%)

Table S2. Leaf water potential (Ypd) and leaf, stem cortex and stele and fleshy root relative water content (RWC) of Lupinus albus during the period of water deficit imposition and on

rewatering (RW). Data are the means ± standard error (6≤n≤9). Significance levels were calculated with the Mann–Whitney U test, data differing significantly from day zero being labelled

with * (P <0.05), ** (P <0.01) or *** (P <0.001).

Table S1. Soil water content during the period of

water deficit imposition and on rewatering (RW).

Data are the means ± standard error of 6 pots.

Significance levels were calculated with the

Mann–Whitney U test, data differing significantly

from day zero being labelled with * (P <0.05) or ** (P

<0.01).

stem cortex

control WD control WDWD

leaf blade root stem steleleaf Ypd (MPa)

control WD control WD control

DAW

0 2.7 ± 0.3 ± 1.0 ± 0.02 ±

2 2.7 ± 0.3 2.9 ± 0.1 1.4 ± 0.05 1.3* ± 0.02

3 2.8 ± 0.5 2.4 ± 0.3 1.1 ± 0.05 1.1 ± 0.10

4 2.4 ± 0.3 2.5 ± 0.3 1.1 ± 0.13 1.1 ± 0.11

5 3.8 ± 0.0 2.3 ± 0.2 1.0 ± 0.00 1.3* ± 0.02

6 4.1 ± 0.6 0.8* ± 0.1 1.0 ± 0.03 1.5* ± 0.12

9 3.5 ± 0.5 0.2* ± 0.1 1.2 ± 0.04 1.7* ± 0.06

13 3.1 ± 0.5 0.1* ± 0.0 1.2 ± 0.09 1.7* ± 0.04

1 RW 3.7 ± 0.2 1.3* ± 0.2 1.1 ± 0.01 1.4* ± 0.01

2 RW 2.9 ± 0.1 1.1* ± 0.2 1.2 ± 0.01 1.5* ± 0.01

Vapor pressure deficit (kPa)

control WD

Table S3. Transpirate rate (mmoles m-2s

-1) and vapor pressure deficit (kPa) during the imposed WD and early

rewatering (RW). Data are the means ± standard error of 3 measurements. Significance levels were calculated with

the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).

control WD

Transpirate rate (mmoles m-2

s-1

)

DAW

ABA 0 579 ± 43 149 ± 20 283 ± 39 390 ± 77

(pmol g-1DW) 3 594 ± 70 624 ± 105 176 ± 18 209 ± 39 222 ± 45 149* ± 11 450 ± 33 345 ± 26

4 500 ± 75 836* ± 44 214 ± 20 500* ± 178 283 ± 83 405 ± 52 305 ± 8 379 ± 17

5 624 ± 51 833* ± 83 207 ± 19 820* ± 27 174 ± 38 577* ± 49 452 ± 114 1979* ± 201

6 624 ± 51 3009** ± 401 207 ± 19 4747** ± 556 174 ± 38 5156** ± 819 452 ± 114 5164** ± 517

9 755 ± 92 12801** ± 2623 127 ± 53 8973** ± 2043 50 ± 0 5365** ± 679 189 ± 3 5589** ± 954

13 417 ± 75 12303** ± 581 129 ± 9 2658** ± 17 83 ± 6 4717** ± 776 206 ± 48 5233** ± 1151

1 RW 514 ± 52 1699* ± 469 118 ± 10 153 ± 5 69 ± 3 114* ± 34 189 ± 32 285 ± 86

2 RW 612 ± 29 1093 ± 495 106 ± 12 136 ± 42 54 ± 1 154* ± 16 173 ± 17 305 ± 124

DAW

IAA 0 1408 ± 197 3655 ± 745

(pmol g-1DW) 3 1050 ± 189 322* ± 146 6769 ± 682 7131* ± 901

4 1157 ± 117 229* ± 94 6119 ± 277 10631* ± 748

5 1479 ± 117 299* ± 161 5735 ± 277 6766* ± 601

6 1800 ± 579 275* ± 28 5351 ± 601 4864 ± 556

Cytokinins DAW

bioactive Cks 0 5.9 ± 0.9 10.9 ± 0.7

(pmol g-1DW) 3 4.6 ± 0.2 1.8* ± 0.4 12.9 ± 1.8 6.1* ± 1.7

4 2.6* ± 0.6 4.1* ± 0.7

5 1.9* ± 0.1 6.1* ± 2.4

6 5.8 ± 0.5 1.6* ± 0.5 13.0 ± 1.4 7.8 ± 1.5

DAW

0 12.6 ± 1.3 11.6 ± 1.1

3 12.7 ± 5.1 17.9 ± 3.2 8.8 ± 1.6 31.4* ± 10.3

(pmol g-1DW) 4 33.3* ± 3.3 34.1* ± 6.2

5 20.0* ± 2.4 18.3 ± 5.5

6 15.0 ± 2.8 25.6* ± 1.8 12.4 ± 3.8 21.1 ± 6.6

DAW

N-glucosides 0 2.1 ± 0.3 8.7 ± 1.8

(pmol g-1DW) 3 2.5 ± 0.0 19.7* ± 1.6 11.3 ± 1.9 24.8* ± 1.2

4 17.89* ± 0.5 24.1* ± 0.6

5 19.8* ± 1.8 19.2* ± 1.9

6 4.1 ± 0.4 14.6* ± 2.9 12.3 ± 1.2 19.6* ± 2.0

DAW

O-glucosides 0 7.7 ± 1.5 10.0 ± 1.3

(pmol g-1DW) 3 10.4 ± 0.9 7.5 ± 3.1 11.8 ± 1.0 12.3 ± 3.3

4 14.6* ± 1.7 17.0* ± 0.6

5 22.8* ± 2.4 22.6* ± 1.5

6 12.7 ± 1.8 19.0 ± 2.1 13.1 ± 2.1 16.6* ± 1.7

DAW

P-Cks 0 4.2 ± 1.5 9.3 ± 0.5

(pmol g-1DW) 3 4.0 ± 0.2 4.2 ± 0.7 11.9 ± 0.6 10.1 ± 2.2

4 4.4 ± 1.1 16.7* ± 3.5

5 3.7 ± 0.5 11.2 ± 0.6

6 5.3 ± 1.1 4.4 ± 0.7 13.0 ± 3.5 9.2 ± 0.8

leaf blade root stem stele stem cortex

control WD control WD control WD WD

control WD control WD

leaf blade root


WD control WD

leaf blade root


leaf blade root


Table S9. Cytokinins (O-glucosides forms) content (pmol g-1 DW) on the several Lupinus albus organs until

6DAW (mild stress). Data are the means ± standard error of 3 measurements. The significance levels were

calculated with the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P

<0.05).

Table S10. Cytokinins (phosphorilated forms) content (pmol g-1 DW) on the several Lupinus albus organs until



<0.05).

Table S4. ABA content (pmol g-1

DW) on the several Lupinus albus organs during the imposed WD and early rewatering (RW). Data are the means ± standard error (3≤n≤6). The significance

levels were calculated with the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).

Table S5. IAA content (pmol g-1

DW) on the several Lupinus albus organs until 6DAW (mild stress). Data are

the means ± standard error of 3 measurements. The significance levels were calculated with the Mann–Whitney

U test, data differing significantly from day zero being labelled with * (P <0.05).

Table S6. Bioactive Cytokinins content (pmol g-1 DW) on the several Lupinus albus organs until 6DAW (mild

stress). Data are the means ± standard error of 3 measurements. The significance levels were calculated with the

Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).

Table S7. Cytokinins cis-derivatives content (pmol g-1 DW) on the several Lupinus albus organs until 6DAW

(mild stress). Data are the means ± standard error of 3 measurements. The significance levels were calculated

with the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).


Table S8. Cytokinins (N-glucosides forms) content (pmol g-1 DW) on the several Lupinus albus organs until



<0.05).

cis derivatives

leaf blade root

control

control

DAW

starch 0 58 ± 9 4 ± 1 1 ± 0 19 ± 2

3 57 ± 7 120* ± 15 2 ± 0 3 ± 1 1 ± 0 1 ± 0 14 ± 2 15 ± 3

4 42 ± 12 93* ± 10 2 ± 0 3 ± 0 2 ± 0 3* ± 1 16 ± 1 13* ± 1

6 45 ± 4 30* ± 6 8 ± 4 5 ± 3 2 ± 1 3* ± 2 22 ± 3 27* ± 3

9 48 ± 3 22* ± 8 24 ± 3 14* ± 2 1 ± 0 6* ± 3 20 ± 2 15 ± 4

13 50 ± 4 2* ± 1 29 ± 2 18* ± 4 2 ± 1 9* ± 5 24 ± 5 9* ± 1

1 RW 43 ± 5 15* ± 3 29 ± 3 2 ± 0 2 ± 1 3* ± 2 24 ± 6 14 ± 3

2 RW 35 ± 3 13* ± 1 30 ± 4 1* ± 0 4 ± 2 5* ± 3 28 ± 2 12 ± 4

DAW

glucose 0 3.4 ± 0.5 1.3 ± 0.2 4.0 ± 0.1 3.6 ± 0.2

3 4.3 ± 0.4 2.7 ± 0.8 0.8 ± 0.1 0.71* ± 0.1 3.4 ± 0.3 3.5 ± 0.2 2.7 ± 0.3 9.3* ± 2.7

4 2.6 ± 0.4 1.6 ± 0.2 4.4 ± 0.4 4.1 ± 0.8

6 4.6 ± 0.3 4.6* ± 0.2 1.9 ± 0.2 3.4* ± 0.5 3.5 ± 0.9 6.1* ± 0.4 3.9 ± 0.1 6.3* ± 0.2

DAW

sucrose 0 138 ± 7 282 ± 48 679 ± 71 327 ± 52

3 276 ± 33 187 ± 30 363 ± 16 333 ± 62 579 ± 35 577 ± 27 420 ± 77 679* ± 134

4 288* ± 13 408 ± 20 633 ± 192 699* ± 75

6 190 ± 10 102* ± 5 403 ± 11 354 ± 44 506 ± 122 866* ± 27 606 ± 92 604* ± 16

DAW

raffinose 0 4.7 ± 0.7 2.5 ± 0.3 2.0 ± 0.5 2.4 ± 0.4

3 3.3 ± 0.4 8.1* ± 0.6 2.2 ± 0.3 2.6 ± 0.3 1.4 ± 0.3 2.6 ± 0.4 1.6 ± 0.4 4.0 ± 1.4

4 2.3* ± 0.6 1.6* ± 0.2 4.2* ± 0.7 9.1* ± 1.1

6 3.0 ± 0.3 9.8* ± 1.2 1.7 ± 0.2 3.6* ± 0.4 2.3 ± 0.4 3.7 ± 1.1 3.6 ± 1.2 8.8* ± 1.0

stem stele stem cortex


control WD

control WD

(mmol g-1 DW)

WD control WD

control WD control WD control WD

leaf blade root

(nmol g-1 DW)


Table S11. Starch content (mmol equiv. glucose g-1

DW) on the several Lupinus albus organs during the imposed WD and early rewatering (RW). Data are the means ± standard error (3≤n≤6). The

significance levels were calculated with the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).

Table S12. Glucose content (mmol g-1

DW) on the several Lupinus albus organs until 6DAW (mild stress). Data are the means ± standard error (3≤n≤6). The significance levels were calculated with the


Table S13. Sucrose content (mmol g-1

DW) on the several Lupinus albus organs until 6DAW (mild stress). Data are the means ± standard error (3≤n≤6). The significance levels were calculated with the


Table S14. Raffinose content (nmol g-1

DW) on the several Lupinus albus organs until 6DAW (mild stress). Data are the means ± standard error (3≤n≤6). The significance levels were calculated with

the Mann–Whitney U test, data differing significantly from day zero being labelled with * (P <0.05).

control WDcontrol WD

(nmol g-1 DW)


control WD control

(mmol equiv. glucose g-

1 DW)


control WD control WD control WD control WD

log (glucose/raffinose) -0.13 ± 0.08 -0.51 ± 0.09 0.08 ± 0.13 -0.32 ± 0.07 0.18 ± 0.03 -0.55 ± 0.04 -0.01 ± 0.09 -0.03 ± 0.04

log (glucose/sucrose) -1.61 ± 0.04 -1.86 ± 0.09 -2.05 ± 0.05 -1.37 ± 0.03 -1.86 ± 0.02 -2.66 ± 0.05 -2.42 ± 0.05 -2.02 ± 0.04

log (sucrose/starch) 0.37 ± 0.02 0.18 ± 0.08 0.49 ± 0.02 0.57 ± 0.00 1.87 ± 0.07 2.07 ± 0.08 2.21 ± 0.02 1.85 ± 0.05

log (sucrose/raffinose) 1.48 ± 0.08 1.36 ± 0.05 2.13 ± 0.14 1.05 ± 0.08 2.04 ± 0.03 2.11 ± 0.03 2.41 ± 0.06 1.99 ± 0.01

log (raffinose/starch) -1.11 ± 0.07 -1.18 ± 0.03 -1.64 ± 0.13 -0.48 ± 0.07 -0.17 ± 0.05 -0.04 ± 0.06 -0.20 ± 0.04 -0.14 ± 0.05

log (sucrose/ABA) -0.58 ± 0.05 -0.61 ± 0.01 -0.46 ± 0.03 -1.47 ± 0.02 0.16 ± 0.06 0.19 ± 0.06 -0.04 ± 0.16 -1.16 ± 0.06

log (glucose/ABA) -2.19 ± 0.09 -2.47 ± 0.09 -2.51 ± 0.04 -2.83 ± 0.03 -1.71 ± 0.05 -2.47 ± 0.11 -2.46 ± 0.20 -3.18 ± 0.10

log (starch/ABA) -0.95 ± 0.04 -0.79 ± 0.07 -0.95 ± 0.02 -2.04 ± 0.02 -1.71 ± 0.01 -1.88 ± 0.13 -2.25 ± 0.14 -3.01 ± 0.06

log (raffinose/ABA) -2.06 ± 0.10 -1.97 ± 0.04 -2.59 ± 0.11 -2.52 ± 0.09 -1.88 ± 0.04 -1.92 ± 0.08 -2.45 ± 0.11 -3.15 ± 0.06

log (glucose/starch) -1.24 ± 0.06 -1.68 ± 0.12 -1.56 ± 0.06 -0.80 ± 0.03 0.00 ± 0.06 -0.59 ± 0.05 -0.22 ± 0.06 -0.17 ± 0.06

log (ABA/IAA) -0.42 ± 0.06 0.46 ± 0.27 1.07 ± 0.20 1.08 ± 0.02 -1.42 ± 0.10 -1.52 ± 0.14 -1.36 ± 0.06 0.02 ± 0.08

log (ABA/bioactive Cks) 1.96 ± 0.10 2.64 ± 0.11 2.52 ± 0.11 3.36 ± 0.15 1.25 ± 0.02 1.59 ± 0.28 2.05 ± 0.09 2.83 ± 0.13

log (sucrose/bioactive Cks) 1.38 ± 0.09 2.03 ± 0.11 2.06 ± 0.09 1.90 ± 0.14 1.92 ± 0.12 2.02 ± 0.17 2.16 ± 0.22 2.06 ± 0.08

log (raffinose/bioactive Cks) -0.10 ± 0.02 0.67 ± 0.08 -0.07 ± 0.21 0.84 ± 0.08 -0.20 ± 0.08 -0.33 ± 0.22 0.01 ± 0.15 -0.35 ± 0.19

log (starch/bioactive Cks) 1.01 ± 0.08 1.85 ± 0.09 1.57 ± 0.11 1.33 ± 0.14 -1.00 ± 0.03 -0.66 ± 0.15 -0.06 ± 0.08 -0.40 ± 0.08

log (glucose/bioactive Cks) -0.23 ± 0.10 0.17 ± 0.06 0.01 ± 0.09 0.53 ± 0.13 -0.32 ± 0.08 -0.19 ± 0.15 0.04 ± 0.11 -0.10 ± 0.06

log (starch/IAA) -1.37 ± 0.08 -0.34 ± 0.20 0.11 ± 0.18 -0.95 ± 0.04 -3.68 ± 0.09 -3.77 ± 0.06 -3.46 ± 0.09 -3.20 ± 0.05

log (glucose/IAA) -2.61 ± 0.14 -2.02 ± 0.28 -1.45 ± 0.24 -1.75 ± 0.05 -3.00 ± 0.03 -3.30 ± 0.08 -3.36 ± 0.11 -2.90 ± 0.07

log (raffinose/IAA) -2.48 ± 0.12 -1.51 ± 0.23 -1.53 ± 0.25 -1.44 ± 0.11 -2.88 ± 0.07 -3.44 ± 0.09 -3.39 ± 0.16 -3.15 ± 0.08

log (sucrose/IAA) -1.00 ± 0.10 -0.15 ± 0.28 0.61 ± 0.19 -0.38 ± 0.03 -0.75 ± 0.07 -1.09 ± 0.03 -1.25 ± 0.22 -0.74 ± 0.04

log (IAA/bioactive Cks) 2.38 ± 0.11 2.18 ± 0.22 1.46 ± 0.25 2.28 ± 0.17 2.67 ± 0.09 3.11 ± 0.18 3.40 ± 0.08 2.80 ± 0.12

log (glucose/raffinose) -0.12 ± 0.09 0.15 ± 0.06 0.03 ± 0.06 0.25 ± 0.14 0.10 ± 0.07 0.38 ± 0.10 -0.35 ± 0.02 -0.02 ± 0.11

log (glucose/sucrose) -2.24 ± 0.05 -2.21 ± 0.05 -2.12 ± 0.11 -2.15 ± 0.02 -2.02 ± 0.08 -1.88 ± 0.04 -2.24 ± 0.09 -1.98 ± 0.02

log (sucrose/starch) 2.93 ± 0.13 2.68 ± 0.03 2.22 ± 0.14 2.46 ± 0.01 1.70 ± 0.09 1.65 ± 0.09 1.72 ± 0.04 1.45 ± 0.10

log (sucrose/raffinose) 2.12 ± 0.14 2.35 ± 0.06 2.15 ± 0.07 2.41 ± 0.12 2.12 ± 0.06 2.26 ± 0.13 1.88 ± 0.07 1.97 ± 0.13

log (raffinose/starch) 0.80 ± 0.05 0.33 ± 0.06 0.07 ± 0.07 0.05 ± 0.12 -0.43 ± 0.03 -0.61 ± 0.15 -0.16 ± 0.03 -0.52 ± 0.03

log (sucrose/ABA) 0.18 ± 0.17 0.18 ± 0.01 0.18 ± 0.16 -0.74 ± 0.04 0.52 ± 0.08 0.62 ± 0.07 0.24 ± 0.05 -0.92 ± 0.09

log (glucose/ABA) -2.06 ± 0.13 -2.02 ± 0.05 -1.94 ± 0.05 -2.89 ± 0.02 -1.50 ± 0.09 -1.26 ± 0.11 -1.99 ± 0.06 -2.90 ± 0.09

log (starch/ABA) -2.74 ± 0.04 -2.49 ± 0.03 -2.04 ± 0.02 -3.20 ± 0.04 -1.18 ± 0.03 -1.03 ± 0.04 -1.48 ± 0.06 -2.36 ± 0.03

log (raffinose/ABA) -1.94 ± 0.06 -2.17 ± 0.07 -1.97 ± 0.09 -3.14 ± 0.16 -1.61 ± 0.03 -1.64 ± 0.17 -1.64 ± 0.06 -2.88 ± 0.06

log (glucose/starch) 0.68 ± 0.09 0.47 ± 0.02 0.10 ± 0.04 0.31 ± 0.03 -0.33 ± 0.08 -0.23 ± 0.12 -0.52 ± 0.05 -0.54 ± 0.09

Table S15. Calculated leaf and root log ratios based on carbohydrates levels (glucose, sucrose, raffinose and starch) and hormone levels (ABA, IAA, bioactive-Cks) at the early stages of the

soil water deficit.

Table S16. Calculated stem stele and cortex log ratios based on carbohydrates levels (glucose, sucrose, raffinose and starch) and hormone levels (ABA, IAA, bioactive-Cks) at the early

stages of the soil water deficit.

root4WD root6WD

stem cortex 3WD stem cortex 4WD stem cortex 6WD

leaf blade0 leaf blade 3WD leaf blade 4WD leaf blade 6WD

stem stele 0 stem stele 3WD stem stele 4WD stem stele 6WD stem cortex 0

root0 root3WD

-0.5

0.0

0.5

1.0

1.5

2.0

blade root stele cortex

log r

ati

o

day 3

day 4

day 5

day 6

day 9

day 13

day 1 RW

day 2 RW

*

**

*

*

**

*

**

*

**

**

**

** **** ********

***

Figure S1. ABA log ratio (WD/WW) in Lupinus albus leaf blade, fleshy root, stem stele and stem cortex during the period of soil water

deficit imposition and recovery (RW). Values are the log ratio (water deficit/control), day 0 being considered the control. Mean values

and standard errors (3≤n≤6) were considered. The significance levels were calculated with the Mann–Whitney U test, data differing

significantly from day zero being labelled with * (P <0.05) or ** (P <0.01).

-2

-1

0

1

2

3

4

ABA/IAA ABA/bioactive

Cks

IAA/bioactive Cks

root

log

ra

tio

-1

0

1

2

3

4blade control 3DAW 4DAW 5DAW 6DAW

Figure S2. Hormonal balance (calculated as the ratios between ABA/IAA,

ABA/bioactive Cks and IAA/bioactive Cks) in Lupinus albus leaf blade and

root during the period of the soil water deficit imposition. Values are the log

ratio (water deficit/control) and mean values were considered (n=3).

research paper initial water deﬁcit effects on lupinus ...pinheiro/cp_pdfs/2011_jxb...tration (ca)...

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