Metabolic and Phenotypic Responses of Greenhouse-Grown Maize Hybrids to Experimentally Controlled Drought Stress

Molecular Plant • Pages 1–18, 2011 RESEARCH ARTICLE

Metabolic and Phenotypic Responses ofGreenhouse-Grown Maize Hybrids toExperimentally Controlled Drought Stress

Sandra Witta, Luis Galiciab, Jan Liseca, Jill Cairnsc, Axel Tiessend, Jose Luis Arause, Natalia Palacios-Rojasb

and Alisdair R. Ferniea,1

a Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germanyb CIMMYT, Apdo. Postal 6–641, 06600 Mexico, DF, Mexicoc CIMMYT Int., PO Box MP 163, Mount Pleasant, Harare, Zimbabwed Departamento de Ingenierıa CINVESTAV Unidad Irapuato, KM 9.8, Libramiento Norte, CP 36821 Irapuato, Guanajuato, Mexicoe University of Barcelona, Department of Vegetal Biology, Faculty of Biology, Av. Diagonal 645, 08028 Barcelona, Spain

ABSTRACT Adaptation to abiotic stresses like drought is an important acquirement of agriculturally relevant crops like

maize. Development of enhanced drought tolerance in crops grown in climatic zones where drought is a very dominant

stress factor therefore plays an essential role in plant breeding. Previous studies demonstrated that corn yield potential

and enhanced stress tolerance are associated traits. In this study,we analyzed six differentmaize hybrids for their ability to

deal with drought stress in a greenhouse experiment. We were able to combine data from morphophysiological param-

eters measured under well-watered conditions and under water restriction with metabolic data from different organs.

These different organs possessed distinct metabolite compositions, with the leaf blade displaying the most considerable

metabolome changes following water deficiency. Whilst we could show a general increase in metabolite levels under

drought stress, including changes in amino acids, sugars, sugar alcohols, and intermediates of the TCA cycle, these changes

were not differential between maize hybrids that had previously been designated based on field trial data as either

drought-tolerant or susceptible. The fact that data described here resulted from a greenhouse experiment with rather

different growth conditions compared to natural ones in the field may explain why tolerance groups could not be con-

firmed in this study. We were, however, able to highlight several metabolites that displayed conserved responses to

drought as well as metabolites whose levels correlated well with certain physiological traits.

Key words: carbon metabolism; metabolomics; water relations.

INTRODUCTION

Although improved adaptation to abiotic stress has long been

a pursuit of breeders, it has been difficult to achieve, partially

due to the fact that it is a quantitative trait controlled by many

different genes (Lopes et al., 2011). Currently, one of the most

critical abiotic stresses is water deficiency and it can be antic-

ipated that climate change will exacerbate this problem in the

future. C4 plants such as maize are often considered to have

mastered the art of drought control, particularly since they

can maintain photosynthesis when their stomata are closed

(Lopes et al., 2011). Despite this fact, drought is currently es-

timated to cause average annual yield losses in the C4 plant

maize the region of 17% in the tropics (Edmeades et al.,

1989) whilst, for some regions of southern Africa, yield losses

within a season can approach as much as 60% (Rosen and

Scott, 1992). That said, this problem is by no means confined

to tropical maizes, with breeding programs of considerable

scale being carried out to enhance grain potential and yield

stability in stress-prone environments for temperate maizes

grown in the USA (Bruce et al., 2002). Another attempt to pro-

duce more drought-tolerant maize is the development of

drought-tolerant germplasm sources such as those created

by CIMMYT’s breeding programs (Edmeades et al., 1996a,

1996b, 1999), which can then be crossed with maize germ-

plasm that is well adapted to the target environment.

1 To whom correspondence should be addressed. E-mail fernie@mpimp-

golm.mpg.de, tel. +49 (0)331 5678211.

ª The Author 2011. Published by the Molecular Plant Shanghai Editorial

Office in association with Oxford University Press on behalf of CSPB and

IPPE, SIBS, CAS.

doi: 10.1093/mp/ssr102

Received 28 September 2011; accepted 13 November 2011

Molecular Plant Advance Access published December 15, 2011 at * on Septem

ber 13, 2014http://m

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Importantly, increased yield and yield stability of recently devel-

oped tropical and temperate maize genotypes have been re-

lated to increased abiotic stress tolerance and, in particular,

to drought stress (Tollenaar and Wu, 1999; Banziger et al.,

2002, Araus et al., 2002). Under drought, maize ovule abortion

appears to be related to the flux of carbohydrates to the young

ear around flowering and concurrent photosynthesis is re-

quired to maintain this above threshold levels (Zinselmeier

et al., 1995). Drought has additionally been shown to reduce

invertase activities in the ovaries, which would also likely result

in a reduced flux of hexose sugars, altered hormone balance,

and ovary abortion (Zinselmeier et al., 2000; Chourey et al.,

2010).

Despite the above hints towards mechanisms that may be

responsible for yield loss under drought and despite the fact

that the molecular responses of a wide range of plants to

drought stress have been well characterized (for reviews,

see Bartels and Sunkar, 2005; Bohnert et al., 2006; Tardieu

et al., 2011), the multigenic nature of drought tolerance has

rendered application of this knowledge to crop improvement

a considerable challenge. That said, considerable improve-

ments in yield under drought selection have been made in

both tropical and temperate maizes (for reviews, see Bruce

et al., 2002; Lopes et al., 2011). Furthermore, the potential

of marker-assisted breeding approaches is highly recognized

and efforts have been made in the development of newer

germplasm with improved stress tolerance (see Hoisington

et al., 1996; Quarrie et al., 1999; Xu et al., 2009; Crossa

et al., 2010) as are transgenic approaches exploiting modifica-

tions of target genes (Bartels and Nelson, 1994; Leung and

Giraudat, 1998; Rontein et al., 2002). More recently, high

throughput methods such as genome map-based and tran-

scriptomic and metabolomic approaches that additionally

highlight less obvious target genes have been employed as

screening tools for target gene selection (Seki et al., 2001;

Sun et al., 2001; Schauer et al., 2006; Harrigan et al., 2007a,

2007b; Semel et al., 2007). The combination of such methods

will likely rapidly reduced the tens of thousands of possible

genes to a handful of candidate genes for direct testing

and will thus likely accelerate the target selection process

(Fernie and Schauer, 2009).

In this paper, we determined a range of morphophysiolog-

ical parameters including plant height, ear height, leaf tem-

perature, leaf stomatal conductance, and leaf chlorophyll

content, whilst the metabolite composition was determined

by gas-chromatography mass spectrometry (GC–MS) for leaf

blades, ears, husks, sheath, and silks of six hybrids of the CIM-

MYT maize physiology program, in response to drought

imposed under a controlled experimental greenhouse envi-

ronment. The six CIMMYT hybrids selected have previously

been designated as drought-susceptible, moderately tolerant,

and tolerant to drought under field conditions (Edmeades

et al., 1996a, 1996b; Elings et al., 1996; Monneveux et al.,

2005, 2008; Cabrera-Bosquet et al., 2011; Pandey and Gardner

1992). This represents a highly comprehensive initial survey,

providing a more detailed inter-tissue analysis of a broad

range of metabolites than carried out to date and providing

important insight into the utility of these various tissues for

biomarker identification. The obtained data were additionally

carefully statistically evaluated with respect to whether plants

could be discriminated on their genotype or the environmen-

tal conditions that they were subjected to. Finally, correlation

analysis between metabolite and physiological traits was car-

ried out in an attempt to identify relationships between these

traits that may be of interest from a breeding perspective. The

combined results are discussed in the context of the use of

metabolomics as a tool to aid strategies for crop improvement.

RESULTS

Description of the Experimental Set-Up

In order to characterize the impact of water deficit on plant

development, six different maize traits were tested related

to their response after application of drought. The hybrid ma-

terial used in this experiment was selected on the basis on field

data collected under well-watered and drought-stress condi-

tions in Tlaltizapan (Morelos, MX) in 2009. Grain yield, the an-

thesis-to-silking interval (ASI), ear aspect, and phenology were

the parameters chosen to discriminate the genotypes. With re-

spect to this dataset, three hybrids tolerant to drought and

three hybrids displaying varying susceptibility to drought were

used (Table 1). For this particular study, we were interested in

analyzing the metabolic composition of five different tissues

and identify the one with more metabolic information regard-

ing drought stress. Plant height, leaf temperature, stomatal

conductance, ear height, and chlorophyll content were taken

in order to monitor the stress under greenhouse conditions. Six

individuals per genotype were planted in the greenhouse (one

plant per pot) and the water was controlled by drip irrigation.

Two weeks before flowering, stress was applied by stopping

irrigation and, following 2 weeks of stress (or, in the case of

the control, continued watering), tissues were harvested

and snap-frozen in liquid nitrogen for metabolite analysis.

Influence of Drought Stress on Morphophysiological

Parameters in the Different Maize Hybrids

The intensity of drought stress and its related influence on the

development of the tested hybrids was investigated by mea-

suring the abovementioned parameters. Prior to the applica-

tion of stress, plant height was invariant both between the

pre-stress and control samples of each genotype and between

genotypes (Figure 1A). A second measurement after 13 d of

stress application, however, revealed significantly lower plant

height under drought for all tested genotypes (Figure 1B) to-

gether with genotypic differences in the response to drought

(Figure 1C and 1D). However, there was surprisingly no effect

of tolerance type on this parameter (Table 2).

We next took measurements of leaf temperature in order to

monitor the plant during stress. Prior to the stress application,

no changes were observed (Figure 2A and 2C). However,

2 | Witt et al. d Drought-Stressed Maize

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Table 1. Genotypes Used in the Greenhouse Experiment.

Pedigree Origin Tolerance to drought

NPE1 CML-486-B-B/CML-312 SR Mexico, subtropical Moderate

NPE2 CML311/MBR C2 Bc F41-2-BBBBB-B-B-B-B/CML-312 SR Mexico, insect resistance Susceptible

NPE3 [GQL5/[GQL5/[MSRXPOOL9]C1F2-205-1(OSU23i)-5–3-X-X-1-BB]F2-4sx]-11–3–1–1-B*5-B-B/CML-312 SR

Zimbabwe, subtropical Tolerant

NPE4 DTPWC9-F31-1–3–1–1-B-B-B-B-B/CML-312 SR Mexico, subtropical Tolerant

NPE5 CML311/MBR C3 Bc F95-2–2–1-B-B-B-B-B-B-B/CML-312 SR Mexico, insect resistance Moderate

NPE6 La Posta Seq C7-F96-1–2–1–3-B-B-B-B/CML-312 SR Mexico, tropical Tolerant

Source material is described in Elings et al. (1996), Edmeades et al. (1996a, 1996b), Cabrera-Bosquet et al. (2011), and Monneveux et al. (2005).

Figure 1. A Comparison of Drought Effect on Plant Height.

(A) Black bars represent drought-stressed plants, gray bars well-watered plants. Plant height was measured before drought-stress appli-cation.(B) A second measurement of plant height was taken after 19 d of stress (or lack thereof). Each value represents the mean of six individualbiological replicates. Asterisks represent values that were determined to be significantly different from the control using the Student’s t-test, P , 0.01.(C, D)Different treatments, genotypes, and drought tolerance are color-coded. Blue colors represent well-watered control plants; red colorsshow drought-stress-treated plants (from the left to the right: light blue/orange: susceptible genotypes, orange/blue: moderate genotypes,dark blue/orange: tolerant genotypes). Box plots of individual measurements are shown. (C) corresponds to data presented in (A); (D)corresponds to data presented in (B).

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following 7 d of stress, clear changes as strong and highly sig-

nificant increases in leaf temperature were observed in all

hybrids in comparison to their corresponding control plants

(Figure 2B and 2D). We next determined leaf stomatal conduc-

tance. The influence of drought stress on this trait was very

rapid. Following 1 d of stress application, the stomatal conduc-

tion in stressed plants decreases rapidly in all hybrids (Figure 3A

and 3C). However, following 7 d of stress, only NPE1 exhibits

a slight but significant decrease in the stomatal conduction

(Figure 3B and 3D) but no genotypic differences were recorded

(Table 2). As a consequence, there is no statistical evidence

for differences in the behavior of this trait between hybrids

(Table 2). Ear height was significantly different following stress

in NPE1, NPE2, NPE3, and NPE5, but not for NPE4 or NPE6

(Figure 4A and 4C), while genotypic differences were also pres-

ent (Table 2). Chlorophyll content was decreased following

drought stress for NPE1, NPE2, NPE3, NPE5, and NPE6, but

not for NPE4 (Figure 4B and 4D), and genotypic differences

were also significant (Table 2). Ear height was also significantly

affected by both treatment and genotype (Table 2). In spite of

genotypic differences existing for plant and ear height and

chlorophyll content, differences between the subset of toler-

ant and susceptible genotypes were only present for the chlo-

rophyll content (Table 2).

Metabolite Abundance Differs in Tested Tissues

One of the aims in this study was to investigate relative metab-

olite abundance in different tissues of maize. The tested

organs of leaf blade, leaf sheath, ear, husk, and silks differed

very strongly in their metabolite abundance, with blade tissue

being the most diverse in terms of metabolites while containing

considerably lower metabolite levels than the other tissues

(Figure 5). A similar, although less strong, pattern was also seen

in leaf sheath and silks; however, ears and husks differed

greatly. In the ear, there was a considerable accumulation of nic-

otinamide, a water-soluble vitamin and part of the vitamin B

complex and the organic nitrogen-containing compound urea.

Metabolic Profiling Allows Clear Separation of Tissue Type

Subjecting the metabolite data to a principal component anal-

ysis (PCA) separates all tested tissue regarding to metabolite

composition, independently from genotype or treatment sit-

uation (Figure 6A), reflecting that major differences exist in

metabolite composition within the different tissues. PC1

reveals that blade tissue shows the most contrasting metabolic

profile in comparison to other tissues. PC2 separates different

treatments. A tendency for a treatment effect on the meta-

bolic level is in fact visible for all tested tissues but, for blade

tissue, this effect is most highly significant (Figure 6B). It is

worth mentioning that, under well-watered conditions, dif-

ferent tested hybrids show a very similar metabolic pattern

whereas, under drought, these hybrids show a more diverse

metabolic composition with regard to their genetic back-

ground. Furthermore, the PCA also fails to resolve hybrids that

were predicted to be stress-susceptible or apparently drought-

stress-tolerant. Using the statistical tool ANOVA (Figure 6B), it

is clear that the factor that harbors the most influence is tissue,

followed by treatment, then genotype.

We next assessed the metabolites in a point-by-point man-

ner (the full dataset is available as Supplemental Figure 1),

whereas selected metabolites associated with tissue type,

drought treatment, or tolerance class are presented in Fig-

ures 7–9, respectively. Starting with those that were strongly

related to tissue type, the metabolites xylose—a minor cell wall

sugar, vanillic acid, and methionine were most closely associ-

ated with this feature—in all instances being at relatively low

levels in the leaf blade (Figure 7). Looking at those metabolites

varying with the drought treatment, perhaps not surprisingly,

putrescine and proline, but also histidine, were amongst those

prominently associated (Figure 8). By contrast, very few metab-

olites were closely associated with the tolerance class; how-

ever, beta-alanine and phosphoric acid were exceptional in

this manner, particularly in the instance of the drought-

stress-susceptible genotype NPE2 (Figure 9).

Several Drought-Stress-Related Changes Can Be Observed

in the Metabolic Constitution of the Blade Tissue

The PCA analysis described above suggested that blade tissue

was the best to visualize metabolomic changes following

drought stress. We next attempted to perform a point-by-

point analysis across all tissues. For this purpose, we present

the data in a heatmap (Figure 10), in which values are normal-

ized to their respective well-watered controls. The data of the

heatmap reflect that of the PCA analysis in demonstrating that

Table 2. Two-Way ANOVA of the Physiological Data.

Treatment Tolerance Interaction

Plant height_1 6.09E-01 3.49E-01 8.17E-01

Plant height_2 7.12E-11 5.89E-01 4.82E-01

Ear height 1.49E-02 7.89E-01 6.19E-01

Stomatal conductance_1 5.88E-15 6.62E-01 6.51E-01


Leaf temperature_1 9.57E-01 7.18E-01 8.48E-01


Chlorophyll content 1.46E-07 2.40E-03 5.13E-01

Treatment Genotype Interaction

Plant height_1 5.48E-01 1.45E-04 3.70E-01

Plant height_2 2.09E-18 9.33E-13 2.14E-01

Ear height 3.13E-05 1.09E-15 6.80E-01





Chlorophyll content 1.09E-08 6.78E-05 2.07E-01

The upper table represents a tolerance by treatment comparison; thelower table represents a treatment by genotype comparison. For both,all physiological data were used. Gray squares represent significantassociation (P , 0.01).




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leaf blade material definitely exhibits large metabolic changes

following drought stress. For example, there were dramatic

increases in tryptophan, phenylalanine, and histidine as well

as in proline. By contrast, the levels of pyruvic acid decreased

(in four of the six hybrids), as did the levels of quinic acid (in

five of six hybrids) following drought stress. Shown heat map

data also make clear that leaf sheath shows similar changes

on metabolic level compared to leaf blade tissue due to the

drought-stress response, but not so strong by far. Nevertheless,

an increase in several amino acids like proline, isoleucine, and

phenylalanine is visible and this is seen also for silks tissue. Slight

increases directing especially sugar substances could be ob-

served in ear tissue whereas, for husk tissue, hardly any consis-

tent changes through all tested genotypes were found. These

data strongly support the contention from the PCA that leaf

blade is the best tissue to study metabolic responses to drought

stress, followed by leaf sheath and silk, but that ear and husk are

relatively metabolically inert in response to water deficit.

Evaluation of this entire dataset by ANOVA analysis revealed

an impressive number of metabolites responding to drought

across all genotypes with far fewer genotype-specific effects

and fewer still tolerance group-specific effects on metabolite

levels (Table 3).

Correlation of Specific Metabolites with Leaf Temperature

and Stomatal Conductance

An interesting aspect of this study was to combine knowledge

from physiological response of maize following drought stress

Figure 2. A Comparison of Drought Effect on Maize Leaf Temperature.

Black bars represent drought-stressed plants, gray bars well-watered plants.(A) Maize leaf temperature was measured before drought-stress application.(B) A second measurement of maize leaf temperature was taken 7 d following stress application. Each value represents the mean of sixindividual biological replicates. Asterisks represent values that were determined to be significantly different from the control using theStudent’s t-test, P , 0.01.(C, D)Different treatments, genotypes, and drought tolerance are color-coded. Blue colors represent well-watered control plants; red colorsshow drought-stress-treated plants (from the left to the right: light blue/orange: susceptible genotypes, orange/blue: moderate genotypes,dark blue/orange: tolerant genotypes). Box plots of individual measurements are shown. (C) corresponds to data presented in (A);(D) corresponds to data presented in (B).




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with metabolic data. For this purpose, a correlation analysis of

the physiological parameters leaf temperature and leaf stoma-

tal conductance and metabolic response was performed and

results are presented in Figure 11. A strong negative correla-

tion for the TCA cycle intermediates pyruvic acid and succinic

acid with leaf temperature (and, by inference, water stress)

was observed. Interestingly, the opposite is seen for the amino

acids proline and isoleucine (Figure 11A). A positive correlation

between stomatal conductance and pyruvic acid and succinic

acid was also observed using the stomatal conductance data

taken 1 d after application of drought stress (Figure 11B). Pyr-

uvic acid positively correlated with stomatal conductance 1 d

after application of drought stress, whilst the levels of phe-

nylalanine, adenine, and putrescine negatively correlated with

these data (Figure 11C). It is important to note that this is only

a subset of the analyses we performed and data for all signif-

icant correlations in all tested tissues are available in Supple-

mental Figures 2–5.

DISCUSSION

The work presented here provides an initial controlled envi-

ronment assessment of tropical maize hybrid response to

drought stress at the level of the metabolome. Whilst consider-

able work has been carried out in other crop species, most no-

tably tomato (for reviews, see Fernie et al., 2006; Fernie and

Klee, 2011), only a handful of studies have been carried out

using metabolomics in maize to date (Harrigan et al., 2007a,

Figure 3. A Comparison of Drought Effect on Stomatal Conductance.

Black bars represent drought-stressed plants, gray bars well-watered plants.(A) Stomatal conductance was measured 1 d following drought-stress application.(B) A second measurement of stomatal conductance after 7 d following drought-stress application. Each value represents the mean of sixindividual biological replicates. Asterisks represent values that were determined to be significantly different from the control using theStudent’s t-test, P , 0.01.(C, D)Different treatments, genotypes, and drought tolerance are color-coded. Blue colors represent well-watered control plants; red colorsshow drought-stress-treated plants (from the left to the right: light blue/orange: susceptible genotypes, orange/blue: moderate genotypes,dark blue/orange: tolerant genotypes). Box plots of individual measurements are shown. (C) corresponds to data presented in (A); (D)corresponds to data presented in (B).




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2007b; Lisec et al., 2011). This is, at first sight, surprising, given

that an incredible research effort has been expended on im-

proving metabolic traits. In maize, examples of this include

the Illinois long-term selection program for oil and protein

content (Moose et al., 2004) and the more recent association

mapping approaches that revealed the enzymes responsible

for kernel oil content and composition (Zheng et al., 2008)

and pro-vitamin A content (Harjes et al., 2008). In conjunction

with drought stress, many studies have been carried out aimed

at understanding, for example, the genetic factors underlying

abscisic acid levels (Setter et al., 2011), osmolyte accumulation

(Yancey, 2005), and the relationships between yield, ash con-

tent, and isotope distributions (Cabrera-Bosquet et al., 2009,

2011). Four previous studies have looked at a broader range

of metabolites in maize breeding material (Harrigan et al.,

2007a, 2007b; Romisch-Margl et al., 2010; Lisec et al., 2011).

The first of those of Harrigan and co-workers studied sugar,

oil, amino acid, and organic acid content across a broad range

of hybrids derived from 48 inbreds crossed against two differ-

ent testers and grown under multiple environments and

revealed that the variance in these compounds was consider-

able. Indeed, it exceeded that observed for analytes tested by

Figure 4. Influence of Drought on Ear Height and Chlorophyll Content.

Black bars represent drought-stressed plants; gray bars well-watered plants.(A) Ear height under drought-stress conditions. Each value represents the mean of six individual biological replicates. Asterisks representvalues that were determined to be significantly different from the control using the Student’s t-test, P , 0.01.(B) SPAD measurement of chlorophyll content. Each value represents the mean of six individual biological replicates. Asterisks representvalues that were determined to be significantly different from the control using the Student’s t-test, P , 0.01.(C, D) Different treatments, genotypes, and drought tolerance are color-coded. Blue colors represent well-watered control plants; red colorsshow drought-stress-treated plants (from the left to the right: light blue/orange: susceptible genotypes, orange/blue: moderate genotypes,dark blue/orange: tolerant genotypes). Box plots of individual measurements are shown. (C) corresponds to data presented in (A); (D) cor-responds to data presented in (B).




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Figure 5. Heatmap of Metabolite Levels in Different Tissues.

Red colors represent high metabolite levels; blue colors highlight low levels using a false-color scale. Values represent the mean of allgenotypes and conditions (n = 72).




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the Organisation for Economic Co-operation and Development

(OCED) for the introduction of new biotech crops (Harrigan

et al., 2007b). Given the high variability inherent in the primary

metabolome, these authors next evaluated the mean levels of

the same metabolites as well as glycine, betaine, and absicic acid

(Harrigan et al., 2007a). The other two studies were more con-

cerned with heterosis—the superior performance of hybrids

over their parents. The study of Romisch-Margl et al. studies

sugar and amino acid components in developing maize kernels

(Romisch-Margl et al., 2010), whilst that of Lisec and co-workers

determined the levels of 112 metabolites in roots of corn

hybrids. In contrast to previous studies in tomato fruits (Schauer

et al., 2008), both groups found a considerable proportion of

heterosis in these maize populations; however, neither study

was repeated under adverse growth conditions.

In contrast to the Harrigan et al. (2007a, 2007b) studies, we

performed our experiments under controlled-environment

greenhouse conditions and we evaluated metabolite leaf

blades, ears, husks, sheath, and silks rather than merely the

grains. In addition, it is important to note that the genotypes

were tropical or subtropical as opposed to temperate. The se-

lected genotypes in this study were defined as either tolerant,

moderate, or susceptible on the basis of multiple field trials.

That said, in the greenhouse, they did not display consistent dif-

ference from one another on the basis of these classifications,

making it difficult to associate phenotypic characteristics of the

plants with their supposed response to drought stress (see

Figures 1–4). This conclusion was supported by the performance

of two-way ANOVA tests across the physiological parameters

that revealed that, whilst the genotypes were significantly dif-

ferent on an individual basis, they did not follow similar behav-

ior in the greenhouse on the basis of their classifications. This

was initially somewhat disappointing. But, the fact that a green-

house trial differs in several aspects to a field trial may explain

this phenomenon. Important environmental factors like light

intensity, length of photoperiod per day, water status, or tem-

perature differ in a natural setting compared to a strongly con-

trolled greenhouse environment. Therefore, previous field trials

were performed in the winter season whereas the greenhouse

trial was carried out in the main season. In fact, this enlarges

differences in growth conditions even more. However, it is clear

from the dense dataset collected that several important obser-

vations could be made on the metabolic response to drought

stress and as to which tissue would be the best to harvest diag-

nostic markers from. Such information is likely to be of high

value for maize breeders, since it may aid in the generation

of material that is more tolerant to drought. Worthy of mention

in this vein is the proposal that has been muted that selection

under drought may not be the most efficient approach for

obtaining drought-tolerant crops (Bolanos and Edmeades, 1996).

To address the question regarding the best tissue to use for

understanding drought stress from a metabolic perspective,

we performed both a PCA analysis (Figure 6) and a point-

by-point metabolite analysis (Figure 10). These data revealed

that the leaf blade clearly displayed the greatest metabolic re-

sponse to drought stress and thus that, despite containing

Tissue

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Blade Ear Husk Sheet Silks

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Figure 6. Principal Component Analysis (PCA) of Metabolite Profiles in Different Tissues.

Blue color shows well-watered traits; red color represents drought-stressed traits. Bright colors highlight stress-susceptible traits; dark colorsaccent tolerant traits. Mean values and standard deviation are shown.(A) PCA shows clearly separation of metabolite composition within different tissues. Blade tissue can be pointed out here as the tissue that isseparating the most from the other tissues. (B) Bonferroni-corrected ANOVA displays number of metabolites in which highly significantchanges were observed regarding tissue, treatment, and genotype.




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a fairly specific metabolome complement under non-stressed

conditions (Figure 5), it is likely the best tissue to use for diag-

nostic studies of improving drought tolerance by metabolic

mechanisms. Surveying the other tissues suggests that, whilst

leaf sheath, leaf blade, and silks may be of some use in this re-

spect, the relative metabolic inertness of ears and husks renders

them inappropriate for this purpose. Among the plant parts an-

alyzed, leaf blades are the most adequate to record changes in

growing water conditions experienced by the plant. While

husks and leaf sheaths are organs that, like the blades, remain

active in the plant for a long time, their anatomical (xeromor-

phic) adaptations as well as their placement as a shell coating

other parts of the plant expose them less to water stress than

the blades (Ristic and Cass, 1981; Araus et al., 1993; Tambussi

et al., 2007). Concerning the ears and silks, these are short-

duration organs (which remain active for one or a few weeks)

that are only active if fully hydrated and so do not register the

changes in growing conditions. Thus, drought may dramatically

shorten the duration of ears and silks but not their relative wa-

ter content while these organs remain active.

A second major aim was to identify whether there were

metabolic features that rendered certain hybrids susceptible

and others resistant. Whilst we were not able to find strong

statistical support for many such features, some interesting

Figure 7. Box Plots of Selected Metabolite Levels that Showed Highest Differences Due to Tested Tissues.

Blue color shows well-watered traits; red color represents drought-stressed traits. Bright colors highlight stress-susceptible traits; dark colorsaccent tolerant traits. Each value represents the mean and SD of six individual biological replicates.




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observations were apparent in our study. For example, in the

leaf blades following drought stress, it is notable that amino

acids increase in all but one of the hybrids—NPE1, which is the

only fully susceptible genotype. Moreover, correlation analysis

revealed strong positive correlation with proline with temper-

ature and negative with stomatal conductance. It has been

long documented that proline accumulates in response to

drought and, for that, proline has been frequently argued

to play a role in drought tolerance (Verbruggen and Hermans,

2008; Yang et al., 2010; Sharma et al., 2011) as well as a number

of other amino acids such as isoleucine (see also Nunes-Nesi

et al., 2010; Bowne et al., 2012; Kochevenko et al., 2012). In

addition, increases in putrescine, pyruvic, and succinic acids

in response to drought were highly consistent and, since poly-

amines have previously been implicated in drought stress

(Alcazar et al., 2011), they may prove to be interesting targets

in the future. Intrudingly, histidine was one of the amino acids

that were elevated following drought stress in the study of

Harrigan and co-workers (2007a). That these changes were

consistent across both tropical and temperate maizes grown

under dramatically different environmental conditions sug-

gests that genetic manipulation of the enzymes responsible

for their accumulation may represent an important strategy

for future breeding of drought-tolerant maize.

Figure 8. Box Plots of Selected Metabolite Levels that Showed Highest Differences Due to the Drought Treatment in Tested Tissues.

Blue color shows well-watered traits; red color represents drought-stressed traits. Bright colors highlight stress-susceptible traits; dark col-ors accent tolerant traits. Each value represents the mean and SD of six individual biological replicates.




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CONCLUSIONS

The greenhouse drought-stress experiment conducted in Ira-

puato (Guanajuato, MX) in 2009 give new possibilities into in-

vestigating drought stress in maize combining physiological

and metabolic datasets. Six different hybrids were analyzed

and, in all traits, differences in plant height, transpiration,

ear height, leaf temperature, and chlorophyll content due

to the drought-stress treatment was monitored. Plant height

was affected from drought in all hybrids. Furthermore, leaf

temperature increased due to the water limitation in all

hybrids. An increase in ear height could be observed in hybrids

NPE1, NPE2, NPE3, and NPE5. A decrease in chlorophyll content

was detected for all lines except NPE4. However, significant

differences regarding the predicted drought tolerance could

not be verified. Thus, the metabolic profile analyses also did

not show differences in between these three tolerance groups

(susceptible, moderate, and tolerant). Selection of described

genotypes and its predicted drought tolerance were based

on field trial data that could not be confirmed in this green-

house experiment. The fact that a natural setting cannot be

fully transferred to a greenhouse may be a possible reason

for this. Nevertheless, the metabolic pattern of all tested traits

showed a large number of metabolites that were significantly

changed due to the applied treatment. Analyses of different

maize tissues like leaf blade, leaf sheath, ear, husk, and silks

showed that the most contrasting metabolic pattern due to

drought-stress treatment was observed in leaf blade. At the

top with highest p-values regarding the treatment effect

are amino acids like tryptophan, proline, and histidine, sugars

like fucose, and several intermediates from the TCA cycle. Fo-

cusing on leaf blade tissue, we found several compounds that

showed significant differences between tested hybrids. Sum-

marizing evaluated data, we could verify a strong correlation

between phenotypical changes in maize during drought-stress

response or/and adaptation and changes in plant metabolism.

When compared with the data from wheat presented in this

issue (Bowne et al., 2012), similar findings were found, sug-

gesting that this information will likely be useful to find more

tolerant crop genotypes in the future and will improve plant

breeding.

METHODS

Plant Material, Growth Conditions, and Experimental

Design

Seeds from six different maize hybrids were planted under

well-watered and drought-stress conditions in the greenhouse

in Irapuato (Guanajuato, Mexico) in 2009 (Table 1). Selected

hybrids were part of the germplasm drought breeding pro-

gram at CIMMYT. A prediction of drought tolerance of the

tested traits was made based on nine independent field experi-

ments. For experimental conditions of the field experiments,

see Monneveux et al. (2008). The presented data represent

results of six biological replicates of six different hybrids for

every measured parameter. In the greenhouse, one plant per

10-L pot was planted in 1:1 peatmoss and vermiculite mixed soil

and optimal fertilized. Water was controlled by drip irrigation

with 2 L water per hour per pot. Two weeks before flowering,

drought stress was applied by stopping irrigation for 12 d.

Stomatal conductance, chlorophyll content, plant height, ear

height, and leaf temperature were monitored to assess the

stress level. In vivo chlorophyll content of leaves was measured

using a portable chlorophyll meter (SPAD-502, Minolta, Tokyo,

Japan). Plant water status was estimated by measuring leaf tem-

perature and stomatal conductance on the abaxial surface of

sun-exposed leaves from the upper part of the plant with

a Decagon Leaf Porometer SC-1 (Decagon Device Inc., Pullman,

WA, USA). Eight weeks after planting, the leaf blade, ear, and

husks were harvested. All samples were snap-frozen in liquid

nitrogen, lyophilized, and sent to Potsdam-Golm for metabolic

profiling. Two plants per genotype were grown into physiolog-

ical maturity.

GC–TOF–MS Analysis of Metabolite Abundance

Metabolites for GC–TOF–MS were extracted using a protocol

adapted from Lisec et al. (2006) and Roessner et al. (2001).

Figure 9. Box Plots of Selected Metabolite Levels that ShowedHighest Differences Due to Predicted Drought Tolerance in TestedTissues.

Blue color shows well-watered traits; red color represents drought-stressed traits. Bright colors highlight stress-susceptible traits; darkcolors accent tolerant traits. Each value represents the mean and SDof six individual biological replicates.




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Figure 10. Heatmap of Metabolite Levels in Different Tissues and Genotypes Normalized to Well-Watered Conditions.

Red colors represent increase in metabolite levels; blue colors highlight decreases using a false-color scale. Values represent the mean ofeach genotype (n = 6).




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Table 3. Two-Way ANOVA of the Metabolic Data.

Analyteblade Treatment p–value

Tryptophan 1.09E-17

Histidine 2.01E-16

Butanoic acid, 2-amino- 6.71E-16

Phenylalanine 2.07E-15

Putrescine 5.23E-14

Glycine 1.19E-13

Methionine 1.50E-12

Asparagine 6.11E-12

Tyrosine 7.44E-12

Galactinol 2.82E-11

Serine 2.96E-11

Alanine, beta- 3.26E-11

Glutamine 3.55E-11

Isoleucine 5.78E-11

Pyroglutamic acid 5.94E-11

Quinic acid, 3-caffeoyl-, trans- 1.97E-10

Dihydrosphingosine 2.91E-09

Quinic acid, 3-caffeoyl-, cis- 3.50E-09

Proline 1.21E-08

Adenine 2.55E-08

Pyruvic acid 9.33E-08

2-Piperidinecarboxylic acid 2.40E-07

Succinic acid 3.23E-07


Valine 1.92E-06

Fucose 3.37E-06

Turanose 6.24E-06

Ornithine 1.25E-05

Glutaric acid, 2-oxo- 1.67E-05

Kestose, 1- 4.80E-05

Cytidine-2’,3’-cyclic-monophosphate 5.07E-05

Homoserine 5.32E-05

Jasmonic acid methyl ester, 2-trans- 8.28E-05

Xylose 8.61E-05

Glucosamine, N-acetyl- 1.13E-04

Propylamine-2,3-diol 1.15E-04

Nicotinamide 4.57E-04


Urea 6.75E-04

Lactulose 1.39E-03

Glucopyranose 1.43E-03

Leucrose 3.05E-03

Idose 3.91E-03

Sorbose 5.62E-03

Aconitic acid, trans- 7.25E-03

Glutamic acid 8.74E-03

Erythritol 9.03E-03

Table 3. Continued

Analyteblade Treatment p–value

Tyramine 9.06E-03


2-Piperidinecarboxylic acid 7.14E-10

Spermidine 1.58E-07

Galactinol 2.14E-05

Fucose 2.24E-05

Ferulic acid, trans- 4.07E-05

Caffeic acid, trans- 7.06E-05


Propylamine-2,3-diol 1.52E-04

Androst-5-en-17-one, 3beta-hydroxy- 1.61E-04

Itaconic acid 2.12E-04

Alanine 2.97E-04

Serine, O-acetyl- 4.71E-04

Caffeic acid, cis- 4.76E-04

Kestose, 1- 4.80E-04


Phosphoric acid 8.18E-04

Quinic acid, 3-caffeoyl-, cis- 9.17E-04

Fumaric acid, 2-methyl- 1.26E-03

Glycerol 1.96E-03

Rhamnose 1.97E-03

myo-Inositol-1-phosphate 2.25E-03

Glycerol-3-phosphate 2.27E-03

Erythritol 3.03E-03

Dihydrosphingosine 3.16E-03

Nicotinamide 4.32E-03

Turanose 5.23E-03

Cinnamic acid, 4-hydroxy-, trans- 7.12E-03

Itaconic acid 1.39E-05

Caffeic acid, trans- 1.45E-05

Fumaric acid, 2-methyl- 5.81E-05

Serine, O-acetyl- 7.39E-05

Caffeic acid, cis- 1.35E-04


Galactinol 1.89E-04

Phosphoric acid 5.11E-04

Spermidine 1.04E-03

Cinnamic acid, 4-hydroxy-, trans- 1.49E-03

Hexadecanoic acid, 3-hydroxy- 1.64E-03

Fucose 4.57E-03

myo-Inositol-1-phosphate 5.07E-03



Lists for treatment, genotype, and tolerance classification are provided.Gray squares represent significant association (P , 0.01).




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After sampling, the material was freeze-dried for 1 week in

a desiccator. For the extraction, 100 mg of lyophilized flour

from different tissue was used and mixed with 60 ll Ribitol

(0.2 mg ml�1 stock in water) as an internal quantitative stan-

dard for the polar phase. After shaking the mix for 15 min at

70�C, the extract was centrifuged for 10 min at 14 000 rpm.

For separation of polar and non-polar metabolites, the super-

natant was very carefully mixed with 750 ll Chloroform and

1500 ll water. After centrifugation for 15 min at 4000 rpm,

polar and non-polar fraction was separated and a 150-ll

aliquot from the upper polar phase was taken off and dried

in vacuo. Samples were then shipped to Golm, Potsdam, for

metabolic profile.

The pellet was re-suspended in 60 ll methoxyaminhydro-

chloride (30 mg ml�1 in pyridine) and shaken for 2 h at 37�C.

After this, 1 ml of MSTFA (N-methyl-N-[trimethylsilyl] trifluoroa-

cetamide) containing 20 ll retention timestandard mixture of

fatty acid methylesters (methylcaprylate, methyl pelargonate,

methylcaprate, methyllaurate, methylmyristate, methylpalmi-

tate, methylstearate, methyleicosanoate, methyldocosanoate,

Figure 11. Bonferroni-Corrected Correlation Analysis.

Blue color represents well-watered samples; red color represents drought-stressed samples. Bright colors highlight stress-susceptible gen-otypes; dark colors accent tolerant genotypes. Mean values are shown.(A) Relation of leaf temperature after 7 d of water restriction and abundance of pyruvic acid and succinic acid under well-watered andwater-restricted conditions is shown as well as for the amino acids isoleucine and proline.(B) Relation of leaf stomatal conduction after 1 d of water restriction and abundance of TCA cycle intermediates pyruvic acid and succinicacid under well-watered and water-restricted conditions is shown as well as for the amino acids isoleucine and proline.(C) Relation of leaf stomatal conductance after 7 d of water restriction and abundance of TCA cycle intermediate pyruvic acid, amino acidsphenylalanine, alanine and the organic compound putrescine.




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lignoceric acid methylester, methylhexacosanoate, methylocta-

cosanoate, triacontanoic acid methylester, d6-cholesterol) was

added. This mix was incubated for 30 min at 37�C. 1 ll of each

sample were used to inject into a GC–TOF–MS system (Pegasus

III, Leco, St Joseph, USA). Gas chromatography was performed

using a 30-m MDN-35 column. The injection temperature was

230�C whereas the transfer line and ion source were set at

250�C. The initial oven temperature of 85�C was constantly in-

creased to a final temperature of 360�C by increments of 15�Cper minute. Mass spectra were recorded at 20 scans per second

with an m/z 70–600 scanning range. Chromatograms and mass

spectra were evaluated and metabolite levels determined in

a targeted fashion using a library derived from the Golm-

Metabolome-Database (GMD; Kopka et al., 2005). Each metab-

olite is represented by the observed ion intensity of a selected

unique ion that allows for a relative quantification between

groups. Metabolite data were log10-transformed to improve

normality and normalized to show identical medium peak sizes

per sample group (Steinfath et al., 2008).

Statistical Analysis

Statistical analyses and graphical representations (Student’s

t-test, Analysis of Variance (ANOVA), Bonferroni-correction,

principal component analysis (PCA), boxplots) were performed

using the R-software environment 2.13.0 (http://cran.r-project.

org/). The PCA was conducted using the ‘bpca’ algorithm of the

pcaMethods package (Stacklies et al., 2007). ANOVA was con-

ducted using Genotype, Treatment, and Tolerance as factors.

Resulting P-values were corrected for multiple testing using

the stringent Bonferroni method.

SUPPLEMENTARY DATA

Supplementary Data are available at Molecular Plant Online.

FUNDING

This work was supported in part by the ‘Precision phenotyping

for improving drought-stress tolerant maize in southern Asia

and eastern Africa’ project, funded by the Bundesministerium

fur Wirtschaftliche Zusammenarbeit und Entwicklung (BMZ),

Germany, and in part by the OPTIMAS project of the Bundesminis-

terium fur Bildung und Forschung (BMBF) Germany.

ACKNOWLEDGMENTS

We are additionally grateful to Ciro Sanchez and the personnel of

the maize quality lab at CIMMYT for their technical support. No

conflict of interest declared.

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Metabolic and Phenotypic Responses of Greenhouse-Grown Maize Hybrids to Experimentally Controlled Drought Stress