ORIGINAL PAPER

Alteration in expression of hormone-related genes in wildemmer wheat roots associated with droughtadaptation mechanisms

Tamar Krugman & Zvi Peleg & Lydia Quansah & Véronique Chagué &

Abraham B. Korol & Eviatar Nevo & Yehoshua Saranga & Aaron Fait &Boulos Chalhoub & Tzion Fahima

Received: 8 January 2011 /Revised: 3 May 2011 /Accepted: 5 May 2011 /Published online: 8 June 2011# Springer-Verlag 2011

Abstract Transcriptomic and metabolomic profiles wereused to unravel drought adaptation mechanisms in wildemmer wheat (Triticum turgidum ssp. dicoccoides), theprogenitor of cultivated wheat, by comparing the responseto drought stress in roots of genotypes contrasting indrought tolerance. The differences between the droughtresistant (R) and drought susceptible (S) genotypes werecharacterized mainly by shifts in expression of hormone-related genes (e.g., gibberellins, abscisic acid (ABA) and

auxin), including biosynthesis, signalling and response; RNAbinding; calcium (calmodulin, caleosin and annexin) andphosphatidylinositol signalling, in the R genotype. ABAcontent in the roots of the R genotype was higher in the well-watered treatment and increased in response to drought,while in the S genotype ABA was invariant. The metab-olomic profiling revealed in the R genotype a higheraccumulation of tricarboxylic acid cycle intermediates anddrought-related metabolites, including glucose, trehalose,proline and glycine. The integration of transcriptomics andmetabolomics results indicated that adaptation to droughtincluded efficient regulation and signalling pathways leadingto effective bio-energetic processes, carbon metabolism andcell homeostasis. In conclusion, mechanisms of droughttolerance were identified in roots of wild emmer wheat,supporting our previous studies on the potential of thisgenepool as a valuable source for novel candidate genes toimprove drought tolerance in cultivated wheat.

Keywords Transcriptome .Metabolome . Triticumturgidum ssp. dicoccoides . Roots .Water deficit . Hormonehomeostasis . Adaptation

Introduction

The ever-increasing human population, concomitantly withloss of agricultural land (due to urbanization processes),diminishing water availability and climate change, poseserious challenges to world agriculture (Mittler and Blumwald2010). Crop plants are often grown under unfavourableenvironmental conditions such as water deficit, heat stressand/or stress combination that prevent the full expression oftheir genetic yield potential (Cattivelli et al. 2008). Plant

Electronic supplementary material The online version of this article(doi:10.1007/s10142-011-0231-6) contains supplementary material,which is available to authorized users.

T. Krugman : Z. Peleg :A. B. Korol : E. Nevo : T. Fahima (*)Department of Evolutionary and Environmental Biology, Instituteof Evolution, Faculty of Natural Sciences, University of Haifa,Mt. Carmel,Haifa 31905, Israele-mail: fahima@research.haifa.ac.il

L. Quansah :A. FaitBlaustein Institute for Desert Research, Department of DrylandBiotechnology, Ben Gurion University of the Negev,Midreshet Ben Gurion 84990, Israel

V. Chagué :B. ChalhoubOrganisation and Evolution of Plant Genomes, Unité deRecherche en Génomique Végétale (URGV),91057 Evry, France

Y. SarangaThe Robert H. Smith Institute of Plant Science and Genetics inAgriculture, The Hebrew University of Jerusalem,Rehovot 76100, Israel

Present Address:Z. PelegDepartment of Plant Sciences, University of California,Davis, CA 95616, USA

Funct Integr Genomics (2011) 11:565–583DOI 10.1007/s10142-011-0231-6

development and productivity are determined by coordinatedactivities of roots and shoots; while the rate of photosynthe-sis in shoots is affected by the size and activity of the rootsystem, root growth and its maintenance depend onassimilates supplied by the shoot (Manschadi et al. 2008).Abiotic stress greatly affects the structure and developmentof the root systems (Malamy 2005; Nibau et al. 2008).

Plant adaptations to environmental cues such as temper-ature fluctuations, water and nutrients imbalance andresponse(s) to pathogens are mediated by phytohormonesthat can act either at the site of synthesis or following theirtransport, elsewhere in the plant (reviewed by Peleg andBlumwald 2011). Genomics and physiological data indicatethat plant hormones act within a complex network withextensive cross talk (Nemhauser et al. 2006). One of thefaster responses of plants to soil water stress is theaccumulation of abscisic acid (ABA) in the roots (Thompsonet al. 2007), which is transported through the xylem to theshoot (Wilkinson and Davies 2010) causing stomatal closure,reducing water loss via transpiration (Schroeder et al. 2001)and eventually restricting cellular growth. Recent evidenceshow that other plant hormones, such as gibberellin (GA)and auxin (indole-3-acetic acid, IAA), may also play a role inplant adaptation to stress. GA promotes plant growththrough cell elongation via the disappearance of nuclearDELLA proteins (Murase et al. 2008). IAA has animportant role in the fine tuning of hormone signallingin the roots (Iglesias et al. 2010) and in hydrotropism,which is the response of roots to a moisture gradient in thesoil (Miyazawa et al. 2009). Most information onmolecular processes activated in roots comes from studiesof the model plant Arabidopsis thaliana, which ischaracterized by simple anatomy. The major differencesin roots anatomy of dicots vs. monocots are also mirroredby gene expression patterns (Jiao et al. 2009). Further-more, the current available information on the role ofphytohormones in roots adaptation to abiotic stress incereal crops is limited (Brady et al. 2007; Levin et al.2009; Benfey et al. 2010 and references therein).

Wheat (Triticum spp.), with ~620 million tons producedannually worldwide (FAOstat 2009, http://faostat.fao.org/site/339/default.aspx), provides about one fifth of thecalories consumed by humans. Wheat production isseriously affected in many growing areas of the world byreduction of water availability. The genetic diversity ofcrop species, including wheat, has been severely erodedthroughout the processes of domestication and modernbreeding. Wild relatives of crop plants offer a promisinggenetic resource for crop improvement (Tanksley andMcCouch 1997). Wild emmer wheat [Triticum turgidumssp. dicoccoides (Körn.) Thell] is the allo-tetraploid (2n=4x=28; genome BBAA) progenitor of both domesticated tetra-ploid durum wheat [T. turgidum ssp. durum (Desf.) MacKey]

and hexaploid (2n=6x=42; BBAADD) bread wheat(Triticum aestivum L.) (Feldman 2001). Wild emmergenepool harbour wide allelic repertoire for variousagronomically important traits (Nevo et al. 2002 andreferences therein) including for drought tolerance (Peleget al. 2005, 2007, 2009). Recently, we have used tran-scriptome analysis for identification of drought adaptationmechanisms in wild emmer (Krugman et al. 2010).

In the current study, we have investigated droughtadaptation mechanisms in the roots of wild emmer wheatusing transcriptomic and metabolomic profiling. Thecombination of gene expression and metabolites profilingrevealed differential expression of genes encoding enzymesassociated with GA and ABA biosynthesis and signallingand with response to IAA, in addition to hormone-regulatedmetabolic changes. These results shed new light on droughtadaptation mechanism in wheat roots that can serve as abasis for improvement of drought tolerance in cultivatedwheat and other cereal crops.

Material and methods

Plant materials

In previous studies, 110 accessions of wild emmer collectedacross a water availability gradient were characterized fortheir responses to drought conditions in the field (Peleg etal. 2005, 2007). Based on these multiple-year studies, twogenotypes were selected for transcriptome and metabolomeanalyses of roots: (1) a drought resistant (R) genotype(Y12-3) originated from Yehudiyya (35°42′ N; 32°56′ E)and (2) a drought susceptible (S) genotype (A24-39)originated from Amirim (35°27′ N; 32°55′ E). The R andS genotypes exhibited contrasting productivity under water-limited conditions (e.g. spike and total dry matter) and yieldstability across environments (Peleg et al. 2005, 2007; Avneri2009), as well as considerable differences in leaf tran-scriptome and its response to drought (Krugman et al. 2010).

Experimental conditions

Seeds were germinated on moist germination paper (Hof-man Manufacturing, Inc, Albany). Seedlings were trans-planted into 0.5-L pots containing a mixture of pure quartzsand (80%) and peat (20%), supplemented with a slow-release fertilizer (2 g/L, Osmocote® Standard 14–14–14,Scotts-Sierra Horticulture, Marysville, OH, USA) and aweekly application of 100 mL of 0.5× Murashige andSkoog (MS) growth solution (Sigma Chemical Co., StLouis, MO, USA). Plants were grown in a controlledenvironment greenhouse (23/18°C; 12:12- h day/nightcycle). Drought stress was applied 4 weeks after germina-

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tion when plants were at a five to six leaf stage. Droughtstress was applied by withholding water for 7 days until stresssymptoms (i.e. leaf rolling and wilting) were visible in leavesof both genotypes. Five biological replicates were grown foreach genotype/treatment combination; root tissues of three ofthem were used for transcriptome analysis and quantitativePCR while five replicates were used for metabolomicanalysis. Roots were gently cleaned from sand remains,excised, immediately frozen in liquid nitrogen and storedat −80°C for RNA extraction and for metabolic profiling.

RNA extraction, Affymetrix GeneChip® hybridizationand data analyses

RNA extraction, cRNA preparation and AffymetrixGeneChip® hybridization were applied as described inKrugman et al. (2010). Altogether, 12 RNA samples wereused for transcriptome analyses, by hybridization to 12Affymetrix GeneChip® Wheat Genome Arrays (Affymetrix,Santa Clara, CA, USA; i.e. 2 genotypes×2 environments×3biological replicates). The CELL data files of the 12Affymetrix GeneChip® results were deposited in the publicmicroarray database NCBI GEO (https://www.ncbi.nlm.nih.gov/projects/geo), accession GSE25940.

Statistical analysis Transformation, normalisation and qualitycontrol procedures were performed using JMP Genomics®(ver. 3.2) statistical package (SAS, Cary, NC, USA) asdescribed by Krugman et al. (2010). Briefly, analysis ofvariance (ANOVA) was employed to test the effects of twofactors: genotype (G; A24-39 vs. Y12-3) and environment (E;drought stress vs. well-watered control) and their interactions(G × E) on log2 of expression values. In the ANOVA model,E, G and ‘probe’ were considered as fixed effects and ‘Array’as random effect. A threshold of significance was based onthe Bonferroni correction of P=0.05 (Hochberg 1988).

Gene filtering In order to identify transcripts having largedifferences between the two genotypes in response to droughtstress, the full list of differentially expressed transcripts(DETs) obtained by ANOVA, which included thousands oftranscripts, was narrowed down by further gene filtering. Thefiltered DETs in the R or S genotypes (further designated asDETR or DETS) were selected based on the followingstatistical requirements: significant effect of E (i.e. waterregimes) for the resistant or susceptible genotype, effect of Gunder the drought conditions and G × E interaction effect,a FC>1.4 (log2 FC>0.4) was selected as the threshold in thecomparisons between R and S genotypes under drought andbetween treatments in the R or S genotypes.

Bioinformatics Transcripts were annotated using HarvESTAffymetrix Wheat1 Chip (http://harvest.ucr.edu/; ver. 1.55).

Plant MetGenMAP visualization and analysis package(http://bioinfo.bti.cornell.edu/cgi-bin/MetGenMAP/home.cgi) was used to identify ‘significantly changed pathways’and ‘enriched GO terms’ (described by Joung et al. 2009).The Plant MetGenMAP system does not support wheat;therefore, the Affymetrix probe-set numbers were firsttranslated into rice loci by HarvEST Affymetrix Wheat1Chip and were then up-loaded to MetGenMAP for furtheranalysis (Krugman et al. 2010).

Quantitative PCR analysis

The RNA samples used for microarray analysis were alsoserved as templates for qPCR (as described in Krugman et al.2010), with three technical replicates for each biologicalsample. Ubiquitin was selected as an endogenous gene tonormalise well to well within and across plates based on thestudy of Paolacci et al. (2009) and additional indications ofminimal change in expression level between genotypes andtreatments in the current microarray analysis. Primer sequen-ces of the selected transcripts used for validation aredescribed in Table S1. Quantification of gene expressionwas carried out by the relative quantification method (Skernet al. 2005).

Metabolic profiling

Root tissues of five biological replicates of each genotype/treatment were analysed by GC-MS using an establishedprotocol (Lisec et al. 2006). Relative metabolite content wascalculated as described in Roessner et al. (2001) followingpeak identification using TagFinder (Luedemann et al. 2008).Substances were identified by comparison to mass spectraltags library (Schauer et al. 2005; Erban et al. 2006). Thedata were analysed using Student t test embedded in the Rsoftware environment (http://www.R-project.org). Thedata was subjected to principal component analysis(PCA), performed with the software package TMEV(Saeed et al. 2004, 2006), and graphs were created usingSigma Plot (Sigma Plot 2000, Systat Software Inc., SanJose, CA, USA).

Abscisic acid determination

Determination of ABA was conducted by UPLC-QTOF-MS/MS (Waters Ltd.) in a similar manner to the methoddescribed by López-Carbonell and Jáuregui (2005) withmodification as pertaining to our tissue. Tissue (100 mg)was extracted using an extraction solution of methanol andwater (80:20). During sample running, the mobile phaseconsisted of 95% water, 5% acetonitrile, 0.1% formic acid(phase A) and 0.1% formic acid in acetonitrile (phase B).

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The solvent gradient programme was conditioned asfollows: 100–60% solvent A over the first 8 min, 60–0%solvent A over 1 min and return to the initial 100% A in3.5 min and conditioning at 100% A. Masses of the elutedcompounds was detected by the QTOF equipped withelectrospray ionization in negative ion mode. ABA wasidentified and quantified by comparing with authenticstandard calibration curves.

Results

Alteration in gene regulation in response to drought

The prolonged drought stress of 7 days resulted inalteration of growth and development in both genotypes,including roots elongation and growth inhibition of theaerial part of the plants. Stress symptoms that wereobserved in both genotypes after withholding water weremore visible in the leaves of the S genotype; however, nosignificant difference was observed in root length betweenthe two genotypes. The whole transcriptome comparison ofdrought response in roots of the R and S genotypesrevealed 2,962 DETs at least in one genotype, R or S. Atotal of 745 DETs were up- and 558 were downregulated(altogether 1,303 DETs) in the R genotype, while only 314DETs were differentially expressed in the S genotype, ofwhich 138 were up- and 176 were downregulated. Inaddition, 1,345 DETs showed similar expression patterns inresponse to drought stress in both genotypes, of which 648were up- and 697 were downregulated. The trend ofdifferential gene regulation in response to drought in thetwo genotypes is illustrated by a Venn diagram (Fig. 1a). Ahigher number of DETs in the R genotype was observedpreviously in the leaves of the same genotype underterminal drought (Krugman et al. 2010). A similar trendof gene expression was obtained also in drought resistantand susceptible genotypes of potato clones (Legay et al.2011). As described in the M&M, further gene filteringnarrowed down the list of DETs to 185 DETRs and DETSsshowing the largest difference of gene regulation betweenthe genotypes. Of these 185 transcripts, 125 were highly oruniquely altered in the R genotype (DETRs hereafter),while only 60 transcripts had altered expression in the Sgenotype (DETSs hereafter). Notably, the majority of thesetranscripts were upregulated (117 DETRs and 48 DETSs).Of the 185 transcripts, 31 showed a significant droughteffect in both genotypes, of which 16 showed higherexpression in the R genotype and 15 showed higherexpression in the S genotype; thus, they were regarded asDETRs and DETSs, respectively. The results of genefiltering in the two genotypes is illustrated by a Venndiagram (Fig. 1b).

Gene enrichment analysis and functional classificationof DETs

Gene enrichment analysis The very large and complexgenome of wheat is not fully sequenced yet, and it is poorlyannotated (Choulet et al. 2010). Therefore, gene annotationand further enrichment analysis were based on sequencecomparisons with the fully sequenced rice genome (Oryzasativa L.). As a result, the list of DETs that was used forGO term enrichment analyses was reduced twice, first byusing only transcripts that had rice annotation and then byexcluding redundant rice accession numbers. Therefore, thelist of DETs was reduced by about 45% to 1,388 genes inthe R genotype (837 upregulated and 551 downregulated)and by 40% to 1,007 genes (514 upregulated and 493downregulated) in the S genotype. The enrichment analysesof GO terms based on biological processes identified 30enriched GOs among the upregulated DETs (Table 1); 19 ofthem were enriched in both genotypes, such as ‘embryonicdevelopment’ which includes seed maturation and lateembryogenesis abundant (LEA) proteins that are involvedin response to drought stress and drought resistance (Choiet al. 1999; Suprunova et al. 2004). Notably, these genes

Fig. 1 Venn diagrams summarizing regulation patterns of DETs andfurther filtering of DETRs and DETSs in wild emmer wheat. aDifferentially and commonly expressed (up- and downregulated)transcripts in the resistance (R) and sensitive (S) genotypes; b DETsfiltering procedure and the resulted DETSs and DETRs, each showingsignificant G, E and G × E effects (P<0.05) and FC>1.4 in thecomparisons between: (a) R and S genotypes under water-stress (in thedashed red circles) and (b) water-stress vs. well-watered control ineach of the genotypes

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were not only highly represented, but most of them werecharacterized by high fold change in response to drought.For example, 26 transcripts annotated as LEA genes and 17transcripts annotated as dehydrin genes were upregulated,most of them in both genotypes. The expression pattern ofsuch LEA gene (Ta.14145.S1._at) was confirmed in the twogenotypes by qPCR (Fig. S1). Interestingly, most of theGOs of group C (Table 1) that are associated withmultilevel regulation were enriched only in the R genotype.The enrichment analyses based on molecular functionrevealed that ‘nucleotide binding’ and ‘RNA binding’

were exclusively enriched in the R genotype. Additionally,‘translation’ GO group that was enriched in both genotypesincluded a 56% higher number of genes in the R genotype.

The annotation analyses were further focused on thefiltered DETRs (Fig. 2), of which only 84 could beannotated and classified into functional groups; these werecompared with the 30 annotated DETSs (Tables S2 and S3,respectively). The DETRs were classified into three mainfunctional categories (Fig. 2): (a) multilevel regulation(39%), included transcriptional and post-transcriptionalregulation, translation activity, signalling and plant hor-

Table 1 Biological process enrichment analyses of the upregulated DETs in the resistance (R) and sensitive (S) genotypes

Biological process GO term Number of transcripts and cluster frequencies Genome frequency of 56,985used rice genes [n (%)]

R genotype - out of 837 DETs -[n (%)]

S genotype - out of 514 DETs[n (%)]

A Response to stress 104 (12.4) 61 (11.9) 2,516 (4.4)

Response to endogenous stimulus 76 (9.1) n.s. 3,963 (7.0)

Response to abiotic stimulus 70 (8.4) 48 (9.3) 2,308 (4.1)

Response to biotic stimulus 47 (5.6) 33 (6.4) 2,019 (3.5)

Response to external stimulus 27 (3.2) 14 (2.7) 797 (1.4)

Response to stimulus 180 (21.5) 121 (23.5) 6,182 (10.8)

B Transport 64 (7.6) 44 (8.6) 1,451 (2.5)

Biosynthetic process 92 (11.0) 51 (9.9) 4,164 (7.3)

Primary metabolic process 205 (24.5) 110 (21.4) 8,150 (14.3)

Catabolic process 27 (3.2) n.s. 865 (1.5)

Metabolic process 240 (28.7) 137 (26.7) 9,177 (16.1)

Protein metabolic process 104 (12.4) 55 (10.7) 3,869 (6.8)

Cellular metabolic process 130 (15.5) n.s. 6,347 (11.1)

Cellular biopolymer metabolic process 68 (8.1) n.s. 2,931 (5.1)

Cellular protein metabolic process 56 (6.7) n.s. 2,701 (4.7)

Lipid metabolic process 29 (3.5) 18 (3.5) 1,005 (1.8)

Carbohydrate metabolic process 23 (2.7) 18 (3.5) 666 (1.2)

Cellular amino acid and derivativemetabolic process

27 (3.2) n.s. 1,153 (2.0)

Macromolecule metabolic process 131 (15.7) n.s. 5,877 (10.3)

C Translation 30 (3.6) 12 (2.3) 566 (1.0)

Nucleobase, nucleoside, nucleotideand nucleic acid metabolic process

67 (8.0) n.s. 2,906 (5.1)

Gene expression 53 (6.3) n.s. 2,394 (4.2)

DNA metabolic process 15 (1.8) n.s. 557 (1.0)

D Embryonic development 13 (1.6) 11 (2.1) 51 (0.1)

Developmental process 51 (6.1) 38 (7.4) 1,915 (3.4)

Multicellular organismal development 42 (5.0) 31 (6.0) 1,469 (2.6)

Cellular component organization 28 (3.3) n.s. 988 (1.7)

Cellular process 265 (31.7) 147 (28.6) 9,610 (16.9)

Reproduction 30 (3.6) 21 (4.1) 1,012 (1.8)

GO enrichment analyses identified clusters of genes which were significantly enriched (p≤ 0.05 FDR as the cutoff) as compared with 56,985frequently used rice genes. The analysis was performed using Plant MetGenMAP (http://bioinfo.bti.cornell.edu/cgi-bin/MetGenMAP/home.cgi;described by Joung et al. 2009)

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mone metabolism and signalling. The relatively high propor-tion of multilevel regulation and signalling genes, in particulargenes involved in plant hormones, is much higher than thoseidentified previously in leaves (26%) of the same genotypeunder terminal drought (Krugman et al. 2010); (b) metabolism(29%), included protein, lipid and carbohydrate metabolism,metabolic and catalytic activities and (c) binding, transportand energy proteins (26%). The remaining genes (6%) wereclassified as stress and membrane proteins. Of the 30annotated DETSs, 25% were classified as associated withmultilevel regulation genes (25%) while 20% were classifiedas stress-induced proteins (20%). We further review thegenes known to be involved in multilevel regulation, inparticular in plant hormone homeostasis.

Changes in the expression of genes encoding hormone-associated pathways

A comparison between the DETRs and DETSs revealeddifferential expression of genes encoding enzymes mediat-ing hormone biosynthesis and homeostasis in addition todownstream hormone-mediated processes. In general, ABAand GA were differentially expressed between the R and Sgenotype, while genes involved in IAA were differentiallyexpressed in response to drought but not between geno-types. Based on these findings, we further searched our fulldata of the non-filtered DETs for additional genes that maybe associated with these three plant hormones. Theidentified hormone-related transcripts are described inTable 2, and their pattern of expression in the twogenotypes is illustrated by the heat map Fig. 3.

ABA Genes associated with ABA biosynthesis, signallingor response were up- or downregulated in the R genotypeunder drought stress (Fig. 3a; Table 2). The biosynthesis ofABA is associated with increased rates of carotenoid andepoxycarotenoid synthesis, in addition to increased epoxy

carotenoid cleavage by 9-cis-epoxycarotenoid dioxygenase(NCED) that are needed to elevate ABA biosynthesis. Inthe current study, while NCED was not identified asdifferentially expressed, other essential genes in the ABAbiosynthesis pathway were highly upregulated in the Rgenotype, including zeaxanthin epoxidase (ZEP) which isknown to be activated upstream of NECD and aldehydeoxidase AAO1 and AAO3, which catalyse the final step inthe biosynthesis of ABA through abscisic aldehyde intoABA. Interestingly, NECD was not differentially expressedbetween these two genotypes also in the leaves (Krugmanet al. 2010). The expression patterns of AAO1 as well as theABA-responsive gene PM19 were validated by qPCR(Fig. S1). In addition to ABA metabolism, importantregulators were identified in both genotypes, includingPP2C genes (Ta.12348.1.A1_at, Ta.12348.1.S1_at andTa.26201.1.S1_at) that are structurally related to ABI1 andHAB2 known as negative regulators of the ABA response,OST1/SNRK2E (Ta.6040.1.S1_a_at; open stomata 1 SNF1-related protein kinase 2.6) and the nuclear cap-bindingprotein CBP80/ABH1 known as important regulation ofABA modulation via post-transcriptional mRNA process-ing (Table 2). Furthermore, several ABA-responsive geneswere highly upregulated in both genotypes, such asdehydration-responsive genes and several members of thedehydrin gene family (e.g. RD20, RAB18, WZY, dhn1, dhn2and dhn2). Further metabolic analysis confirmed that ABAcontent in the R genotype was significantly higher in thewell-watered treatment and further increased in response todrought, while in the S genotype ABA was invariant incomparison with the control treatment (Fig. 4). Interest-ingly, the dehydration-responsive gene RD22 (Ta.26910.1.S1_at), which was commonly used in many studies as anindicator for ABA response (Yamaguchi-Shinozaki andShinozaki 1993; Abe et al. 2003), was highly upregulatedin the roots of the R genotype under the well-wateredconditions and downregulated under the drought treatment.RD22 was previously highly expressed under drought in the

Fig. 2 Gene products of 84DETRs classified into function-al categories. The main catego-ries are marked in colours: (1)multilevel regulation in red, in-cluding transcription and post-transcriptional regulation, sig-nalling, kinase activity and hor-mone signalling; (2) metabolicand catalytic activity in blue,including proteins, carbohy-drates and lipids; (3) transportand energy transfer in green; (4)stress-induced proteins in black;(5) other in brown

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leaves of the R genotype; therefore, we confirmed its patternof regulation by qPCR, using the same PCR primers thatwere used in the leaves. The reduced expression of RD22that was indentified in the roots of the R genotype underdrought may be related to IAA and ABA cross talk that ismodulated by the transcription factor MYB96 in the roots(Seo et al. 2009). A second gene that was expected to be

upregulated under drought is PM19 (Ta.24312.1.S1_at),which is also well-known as an ABA-responsive gene(Ranford et al. 2002). Surprisingly, PM19 was downregu-lated in the current study only in roots of the R genotype butwas highly upregulated in the S genotype under drought(Table 2). It has been shown by Mashiguchi et al. (2008) thatGA can downregulate several genes related to ABA function

Table 2 Genes involved in regulation, biosynthesis, transport and response of the plant hormones abscisic acid (ABA), gibbereline (GA) andauxin (IAA), altered in roots of the resistance (R) and the sensitive (S) genotypes in response to water-stress

Role Affymetrix probe-set ID aFC/S aFC/R bRice/Arabidopsis description cReference

ABA mRNA processing Ta.5831.2.S1_at 0.10 0.44* MIF4G/ABH1 (CBP80) Hugouvieux et al. 2001, 2002;Kuhn et al. 2008

Signalling Ta.1318.2.S1_x_at 0.02 0.40* 90 kDa heat shock ATPase Merlot et al. 2007

Ta.6040.1.S1._a_at 1.33* 1.65* CAMK calcium-dependentkinase/OST1-SNRK2

Yoshida et al. 2010

Regulation Ta.12348.1.A1_at 2.69* 2.67* PP2C/ABI1; HAB1 Miyazono et al. 2009;Park et al. 2009Ta.23176.1.S1_at 0.99* 1.17* PP2C/ABI2; HAB2

Biosynthesis Ta.404.1.S1_x_at 0.17 0.59* Zeaxanthin epoxidase/ZEP; ABA1(ABA deficient1)

Thompson et al. 2007

Ta.16894.1.S1_at 0.12 0.39* Aldehyde oxidase/AAO1 Koiwai et al. 2004Ta.6172.3.A1_a_at 0.43 0.85* AAO3

TaAffx.97075.1.S1_at 0.87* 0.94* AAO3

Ta.8837.1.S1_at 0.46 1.63* AAO1

Response Ta.2638.1.S1_at 4.85* 5.19* RAB18 (DHN1, DHN2,DHN3 and others)

TIGR

Ta.30801.1.S1_s_at 0.46* 0.36 bZIP/AREB3 (ABA-responsiveelement binding protein 3)

TIGR

TaAffx.132154.1.S1_at −0.6* −0.72* bZIP/AREB3 (ABA-responsiveelement binding protein 3)

TIGR

Ta.24312.1.S1_at 1.32* 0.16 PM19 Ranford et al. 2002

Ta.26910.1.S1_at −0.5 −2.5* RD22 Abe et al. 2003; Xu et al. 2010

GA Biosynthesis TaAffx.12175.1.S1_at −1.41* −1.34* GA 2-beta-dioxygenase/ATGA2OX1 Yamaguchi et al. 2007

Receptor TaAffx.78360.1.S1_at 0.13 0.46* GA receptor GID1L2 Murase et al. 2008; Achardet al. 2009; Ubeda-Tomáset al. 2008

Regulation Ta.14990.1.S1_at 0.14 0.58* SEC (secret agent) Thornton et al. 1999

Response TaAffx.88791.1.S1_at 0.09 0.53* Chitin-inducible GA responsive/CIGR1 Day et al. 2004

Ta.4968.1.S1_at −0.51* −0.69* CIGR/SCARECROW like (SCL1) Pysh et al. 2002; Kamiya et al.2003; Cui and Benfey 2009

Ta.11425.1.S1_s_at 1.13* 1.09* Zinc finger, C3HC4 type/XERICO TIGR

Ta.19664.1.A1_at −0.51* −0.79* WD-40 repeat family/GAMYB Gubler et al. 1999

IAA Transport Ta.12796.2.S1_s_at 0.63* 0.95* Auxin efflux carrier component TIGR

Ta.5171.1.S1_at −2.26* −2.16* Auxin efflux carrier component/PIN3 Harrison and Masson 2008

TaAffx.95321.1.S1_at 0.56* 1.29* Auxin efflux carrier component TIGR

Binding Ta.14175.1.S1_at 0.69* 0.56* Auxin-binding protein 4 precursor/ABP1 TIGR

Response Ta.28852.1.S1_at 0.53* 1.14* Auxin response factor 15/ETT (ETTIN) TIGRTaAffx.44251.1.A1_at −0.16 −0.39* OsSAUR37—auxin responsive

Ta.18811.1.S1_at 0.33* 0.13 OsSAUR38—auxin responsive TIGRTa.1454.2.S1_at 1.19* 1.79* Auxin-repressed protein

*P<0.05 (FC values that are significantly regulated)a Fold change between well-watered C and drought treatments of the S (FC S) or R (FC R) genotypes is described as log2b Annotation is based on best BLASTX rice description (TIGR database) and/or best BLASTX Arabidopsisc The role of the gene in hormone biosynthesis or signalling pathway is described by the authors or in TIGR database

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including PM19; therefore, the reduction of the ABA-responsive gene PM19 in the roots of the R genotype maybe explained by ABA and GA cross talk.

GA Genes involved in GA biosynthesis, signalling andresponse were differentially expressed in the R and Sgenotyped under stress. GA 2-beta-dioxygenase (GA2OX1)was downregulated in both genotypes (Table 2); as wasshown by Yamaguchi et al. (2007), downregulation ofGA2OX1 may cause an increase in the active GA. OtherGA-responsive and regulation genes listed in Table 2showed higher upregulation in the R genotype. These

include: GID1L2 which encodes the receptor that bindsGA, and O-linked N-acetyl glucosamine (O-GlcNAc)transferase that may be involved in the activity or stabilityof DELLA proteins by modification or phosphorylation viathe action of SPINDLY (SPY) as a negative regulator of GAsignalling. The dissection of GA biosynthesis, signallingand further downstream transcriptional events is highlycomplex also due to gene-specific expression, cell typesand developmental stages within the roots, as was describedin Arabidopsis (Birnbaum et al. 2003). This complexity isindicated in the current study by the alteration of severalgenes, including the chitin-inducible GA-responsive genes

Fig. 3 Expression pattern ofhormone-associated genes inthe resistance (R) and sensitive(S) genotypes in response towater-stress. a abscisic acid(ABA), b gibbereline (GA) andc auxin (IAA). Well-wateredcontrol (C), water-stress (D).Red and green represent highand low relative expressionwhen compared to the meanvalue of expression across allsamples, respectively. Scale islog2 of mean expression value.Annotation of transcripts andfull names of the putative genesare detailed in Table 2

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(CIGR1), which is a member of the GRAS gene family inrice that was upregulated while another CIGR gene showingsimilarity to SCARECROW (SCR; Ta.4968.1.S1_at) wasdownregulated. The SCR gene is a key regulator of theasymmetric cell division, which is one of the most importantmechanisms in the diversification of cell function and fate.Additionally, the transcription factor GAMYB (Ta.19664.1.A1_at) was downregulated in both genotypes. GAMYB isresponsible for the tissue specific expression of GA-regulated genes in cereal aleurone cells (Gubler et al. 1999).

IAA Genes involved in IAA transport and binding hadhigher or lower expression in response to drought in bothgenotypes (Table 3; Fig. 3c). Nonetheless, several IAAefflux carriers had a slightly higher expression in the Rgenotype. The IAA efflux carrier (PIN3; Ta.5171.1.S1_at)was highly downregulated in both genotypes (Fig. 3c;Table 2). PIN3 has been hypothesized to function in theredirection of auxin toward the new lower flank of gravi-stimulated roots. In gravi-stimulated roots, PIN3 re-localizesto the lower side of statocytes, a process that is thought toplay a role in the asymmetrical redistribution of auxin(Harrison and Masson 2008).

Transcriptional and post-transcriptional regulation

Transcriptional regulation Large number of DETs annotatedas transcription factors (TFs) belonging to different gene

families were identified in the roots in response to drought.The most abundant TFs were members of MYB and bZIPgene families. Of the MYB gene family, six members weredown- and four were upregulated in response to drought; theMyb1 (Ta.26049.1.S1_a_at), a transcription activator in-volved in the regulation of flavonoid biosynthesis, was oneof the most highly upregulated TF (FC log2 4.1 and 3.87 in Sand R genotypes, respectively) in both R and S genotypes.Myb1 was previously reported to be induced in roots ofwheat in response to hypoxia and other abiotic stresses (Leeet al. 2007). Of this gene family MCB2; (DETRTaAffx.5070.1.S1_at) was the only differentially expressedgene in the R genotype. Members of the bZIP family havean important role in ABA response and regulation ofoxidative and pathogen defence responses (Lu et al.2009). Two of the bZIP transcripts (Ta.30801.1.S1_s_atand TaAffx.132154.1.S1_at) were up- or downregulatedin both R and S genotypes, respectively. These transcriptswere annotated as AREB3 (ABA-responsive elementbinding protein 3) in Arabidopsis. MADS-box genes arekey components of the networks that control the transi-tion to flowering and flower development, but their rolein vegetative development is poorly understood. Onemember of this gene family, MADS-box26 (DETRTa.26215.2.S1_s_at), was upregulated in the R genotype(FC>2). It shows similarity to MIKCc type-box26 in riceand to AGL12 (AGAMOUS-LIKE 12/XAL1), which isrequired for normal root development and proper transi-tion to flowering in Arabidopsis, and was suggested tomediate auxin participation in root meristematic cells andin root cell elongation (Tapia-Lopez et al. 2008).

Post-transcriptional regulation Genes encoding helicasesand RNA binding proteins were significantly enriched(Table 1) and showed a trend of higher expression in theR genotype in response to drought (Fig. 5a; supplementaryTable S4). Notably, 15 transcripts annotated as DEAD-boxATP-dependent RNA helicase were upregulated in theroots, while none were downregulated. Ta.7609.1.S1_atthat was upregulated in the R genotype was annotated asDEAD-box RNA helicase STRS1 (stress response suppres-sor 1). It was shown that mutations in this gene mayattenuate the expression of stress-responsive transcriptionalactivators that function in ABA-dependent and ABA-independent abiotic stress-signalling networks (Kant et al.2007). An additional type of post-transcriptional regulationin the R genotype is related to the nuclear cap-binding proteinCBP80/ABH1 (ABA hypersensitive 1; DETR Ta.5831.2.S1_at) which encodes the large subunit (CBP80) of thenuclear cap-binding complex. RNA processing by ABH1/CBP80 contributes to ABA signal transduction as a negativeregulator (Hugouvieux et al. 2001, 2002; Kuhn et al. 2008;Table 2).

Fig. 4 Abscisic acid level in the resistance (R) and the sensitive (S)genotypes in the well-watered control and water-stress treatments. Valuesare means±SE of five biological replicates. *P<0.05 indicates valuessignificantly different between treatments of each genotype (Student t test)

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Translation efficiency ‘RNA charging’ was one of the mostsignificantly changed pathways in the R genotype (analysisconducted by the Plant MetGenMAP package). Therefore,

the non-filtered DETs data were searched for the genesinvolved. Higher abundance of tRNA ligases were identi-fied (Table S4); their pattern of regulation is presented by

Table 3 Relative content of metabolites significantly different (P value≤0.05) between R and S genotypes under well-watered conditions RC andSC and water-stress conditions (RD and SD)

Metabolites level under well-watered condition Metabolites level under water-stress condition

Metabolites RC ± SE SC ± SE Metabolites RD ± SE SD ± SE

Amino acids Arginine 5.588±0.195 9.045±0.77 Arginine 5.941±0.441 14.34±1.118

Asparagine 2.366±0.136 5.619±0.48 Asparagine 3.408±0.233 5.648±0.723

Cystathionine 2.639±0.145 5.406±0.42 Cystathionine 2.438±0.167 4.957±0.744

Glycine 0.234±0.007 0.494±0.05 Glycine 0.531±0.043 0.289±0.061

Lysine 0.056±0.002 0.126±0.01 Lysine 0.088±0.009 0.176±0.024

Threonine 1.389±0.133 2.121±0.21 Histidine 0.365±0.022 0.828±0.121

Glutamine 1.128±0.062 1.468±0.17 Serine 0.403±0.026 0.638±0.057

Alanine 0.467±0.056 0.208±0.02

Sugars Galactopyranoside-1-Omethyl β

11.71±0.657 19.16±2.95 Galactopyranoside-1-O-methyl β

13.88±0.648 20.62±1.409

Glucoheptose 1.164±0.059 2.285±0.17 Glucoheptose 0.715±0.069 2.869±0.247

Gentiobiose 0.316±0.025 0.783±0.06 Galactose 3.533±0.285 5.648±0.723

Glucose 1.346±0.117 3.381±0.24 Glucose 7.585±0.455 5.68±0.52

Glucopyranose 1.646±0.112 3.574±0.18 Glucopyranose D 0.775±0.065 3.031±0.469

Glyceraldehyde 17.05±0.588 8.643±0.38 Glyceraldehyde 2.997±0.307 5.209±0.409

Melibiose 1.444±0 10.02±1.16 Melibiose 2.183±0.172 12.65±1.582

Raffinose 48.76±2.752 88.31±10.4 Raffinose 57.11±1.82 121.4±6.774

Sucrose 2.264±0.066 3.447±0.27 Sucrose 1.912±0.19 3.766±0.686

Erythritol 17.81±1.138 1.957±0.24 Erythritol 8.05±0.483 1.372±0.129

Galactinol 1.119±0.089 1.792±0.18 Galactinol 3.318±0.141 7.608±1.119

Maltitol 0.302±0.016 0.783±0.06 Tagatose 3.925±0.301 1.597±0.135

Glycerol-2-phosphate 0.132±0.004 0.268±0.03 Glycerol-2-phosphate 0.239±0.023 15.34±2.803

Threitol 18.81±1.757 2.1±0.35 Threitol 3.3±0.242 1.372±0.129

Aconitate 1.208±0.151 5.284±0.58 Glycerol 1.164±0.084 1.676±0.165

Butanoate-4-hydroxy 1.08±0.054 1.459±0.13 Butanoate-4-hydroxyl 0.686±0.047 1.131±0.062

Malonate 8.673±0.083 12.2±1.11 Malonate 7.259±0.895 12.44±0.498

Gluconate 0.139±0.013 0.258±0.03 Glycerate 0.482±0.042 1.245±0.073

Lactate 8.16±0.497 12.62±1.08 Mannose 3.408±0.233 5.648±0.723

Sorbose 0.658±0.054 1.505±0.11

Carboxylic/phosphate Glycerate-3-phosphate 6.38±0.361 13.64±0.66 Glycerate-3-phosphate 9.424±0.427 15.42±0.612

Sugar phosphates Glucose-6-phosphate 24.4±0.958 50.39±3.34 Glucose-6-phosphate 29.08±0.97 52.17±2.766

Fructose-1-6 diphosphate 0.256±0.018 0.414±0.03

Organic compounds Oxalate 2.162±0.143 1.428±0.11 Oxalate 2.942±0.3 1.876±0.122

Fumarate 0.324±0.006 0.451±0.04 Fumarate 0.24±0.031 0.119±0.011

Isocitrate 0.864±0.112 1.747±0.25 Isocitrate 2.968±0.191 7.398±0.423

Malate 19.81±1.179 10.24±0.61 Malate 3.661±0.175 1.372±0.129

Nicotianamine 5.538±0.447 7.055±0.5

Succinate 2.345±0.075 1.242±0.11

Pyruvate 0.481±0.032 0.968±0.12

Citrate 0.096±0.003 0.172±0.01

Fatty acids C18:2 0.099±0.006 0.166±0.02 C12:0 92.38±12.45 155.4±14.17

Peak intensities are normalised on internal standard ribitol and gr FW

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heat map (Fig. 5b). As described by Kwapisz et al. (2002),a higher level of tRNA is indicated to increase translationalfidelity, but not the rate of translation, and independently

may affect other cellular processes that contribute to therespiratory growth. An additional indication that may alsocontribute to translation fidelity is the downregulation of

Fig. 5 Expression patterns ofDETs associated with post-transcriptional regulation. aRNA binding proteins; btRNAs. Well-watered control(C), water-stress (D). Red andgreen represent high and lowrelative expression when com-pared to the mean value ofexpression across all samples,respectively. Scale is log2 ofmean expression value. Annota-tion of transcripts and full namesof the putative genes are detailedin Table S4

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DETR Ta.2959.1.A1_at, annotated as Maf1, which is anessential and specific mediator of transcriptional repressionin the RNA polymerase III system. Maf1-dependentrepression occurs in response to a wide range of conditions,suggesting that the protein itself is targeted by the majornutritional and stress-signalling pathways (Ciesla et al.2007; Hopper and Shaheen 2008).

Signalling and second messengers

Calcium signalling Genes encoding proteins known assecond messengers or as involved in signal transductionshowed higher abundance in the R genotype in response todrought, including 14 transcripts annotated as calcium-dependent protein kinases and several types of calciumsensors, such as a calmodulin-related calcium sensor(DETR TaAffx.107453.1.S1_at) that was further validatedby qPCR (Table S1 and Fig. S1), calcium binding protein(Ta.7626.3.S1_at), caleosine (Ta.9830.1.A1_at andTa.7279.1.S1_at) and annexin (Ta.1577.1.S1_at andTa.14590.3.S1_at). Caleosine is known as a calcium andphosphate binding protein, responsive to a range ofenvironmental stresses and ABA in leaves and roots(Partridge and Murphy 2009). Annexin acts as a target ofcalcium signal in eukaryotic cells, binding phospholipidmembranes in a calcium-dependent manner under droughtstress in Arabidopsis (Konopka-Postupolska et al. 2009)and cold stress in wheat (Breton et al. 2000).

Phospholipid signalling The phosphatidylinositol-4-phosphate 5-Kinase (PIP5K; TaAffx.66666.1.A1_at) showedhigher upregulation in the R genotype. PIP5K is involved insignal transduction pathways and is also related to droughtstress stimuli in leaves and roots (Lee et al. 2008b; Chen andXiong 2010; Munnik and Vermeer 2010). Phosphatidylino-sitol (PtdIns) is a major phospholipid molecule in eukaryoticcells, and its phosphorylated derivatives are involved inmany cellular processes in animal and plant cells, mainly inlipid signalling. It catalyses the phosphorylation ofphosphatidylinositol-4-phosphate (PIP) to producephosphatidylinositol-4,5-bisphosphate (PIP2). PIP2 is hydro-lyzed by phosphatidylinositol-specific phospholipase C toproduce two second messengers, inositol-1,4,5-triphosphate,which is involved in the release of Ca2+ from intracellularstores, and diacylglycerol (Mikami et al. 1998). Additionally,three transcripts (Affx.83351.1_at, Ta.24282.1.S1_at andTa.465.1.S1_at) annotated as SEC14 proteins/phosphatidyli-nositol/phosphatidylcholine transfer proteins were upregu-lated in response to drought in the current study. QuantitativePCR (Fig. S1) showed that although the expression of DETRTaAffx.66666.1.A1_at was higher in the stressed R geno-type, the difference was not significant. Nevertheless,

metabolic analysis indicated a 1.5-fold increase in myo-inositol 1 phosphate in the R genotype (SupplementaryTable S6), while there was no change in the S genotype,supporting the expression results and suggesting that myo-inositol metabolism was increased in the R genotype.

WD-40 proteins Genes encoding proteins containing WD-40 repeats showed higher abundance in the R genotypeunder drought conditions; 23 WD-40 proteins were upre-gulated in the R genotype, as compared to only two in the Sgenotype. WD-40 repeat proteins play a key role in signaltransduction, cytoskeletal dynamics, protein trafficking,nuclear export, ribosomal RNA biogenesis and especiallyin chromatin modification and transcription (van Nockerand Ludwig 2003), also recognized as involved in stress(Huang et al. 2008). In all proteins with WD-40 domains,the domains act as sites for interactions with other proteins,but the range of proteins with which they interact is large.Genetic analyses and yeast two-hybrid experiments haveidentified these partners as belonging to the bHLH andMYB classes of proteins (Ramsay and Glover 2005) thatwere also found to be up- or downregulated in response todrought in the current study.

Metabolomic profiling in wild emmer roots under well-watered and water-stress conditions

The GC-MS-based metabolic analysis data (presented assupplementary data Table S5) were first analysed by PCA(Saeed et al. 2004) which revealed a clear separation of thetwo genotypes based mainly on water regimes. The firstcomponent (PC1) represented the effect of drought,accounting for about 62% of the variance within the dataset(Fig. S2). Differences between genotypes were identifiedalso under the well-watered conditions.

Metabolic differences between genotypes under well-watered conditions One of the most significant differencesbetween the genotypes included a 2-fold higher level ofmalate and related succinate in the R genotype as comparedto the S genotype (Table 3). The R genotype was alsocharacterized by very high levels (10-fold higher than the Sgenotype) of polyol that is recognized by the MS library asthreitol or erythritol (Schauer et al. 2005). Polyols whichare considered to be primary photosynthesis products areknown to be produced under stress (Farooq et al. 2009).Therefore, their increased levels in the well-watered Rgenotype were unexpected. Plants have been found toaccumulate polyols to compensate for reduced cell waterpotential under drought conditions (Noiraud et al. 2001).On the other hand, there are examples in the literature ofresistant plants that have constitutively higher levels of

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stress-related metabolites, a feature possibly geneticallyevolved in the regulation of stress-related metabolic path-ways (Kant et al. 2006). Interestingly, under the well-watered condition the amount of trehalose together withsugars such as maltose, raffinose and sucrose wererelatively high in the S genotype as compared with the Rgenotype (Table S5). Many other metabolites had highercontent in the S genotype under well-watered condition,including arginine, asparagine, cystathionine, galactinol,glucoheptose, glucuronic acid, glucopyranose and glycine(Table 3).

Metabolic differences between genotypes in response todrought Drought caused significant (P<0.05) alteration inthe level of metabolites in both genotypes (Table 3): Whilethe content of 23 metabolites increased significantly in theR genotype, the level of only 17 metabolites increased inthe S genotype. Some of the metabolites displayed anopposite pattern of change, accumulating in the R genotypewhile decreasing in the S genotype, including citrate,glutarate, pyruvate, trehalose, glycine, maltitol and proline(Fig. 6). Whilst metabolite to transcript relations have beenshown as non-linear in previous studies combining omicstechnologies, the accumulation of the above-mentionedmetabolites could be partly supported by induction ofmetabolic genes. For example, accumulation of glucose

was supported by the induction of sucrose synthase (SUS3and SUS4; TaAffx.51190.1.S1_at) and sucrose-phosphatesynthase (Ta.3972.1.S1_at). It was shown that the accumu-lation of glucose and increased SUS enzymes consume lessenergy, a feature that might be relevant under stressconditions (Baena-González 2010). The stress-related di-saccharide trehalose increased 3-fold in the drought treatedR genotype, twice the change measured in the S genotype(Fig. 6). Nonetheless, several transcripts (e.g. Ta.5372.1.S1_at, Ta.30416.1.S1_at) encoding for trehalose-6-phosphate phosphatase (TPP), which is the second enzymein the trehalose biosynthesis, were downregulated in bothgenotypes growing under stress. The non-correlated tran-scriptomic and metabolomic results (e.g. accumulation oftrehalose under drought and reduction of TPP) could aim atmaintaining trehalose-6-phosphate (T6P) levels, whichwere shown to regulate sugar phosphates and sugarmetabolism in general. Indeed, Schluepmann et al. (2003)showed that upon depletion of T6P, sugar phosphatesaccumulate. An alternative explanation to the oppositechange of metabolite and transcript levels could involvepost-translational factors in the regulation of metabolism(Fernie et al. 2004).

Proline was accumulated (1.5-fold) in response todrought in the roots of the R genotype while a slightreduction was observed in the S genotype (Table S6). Theresults of transcriptome analysis indicated that transcriptTa.7091.1.S1_at annotated as delta 1 pyrroline-5-carboxylatesynthase 2 (P5CS) was highly upregulated in both genotypes.P5CS is the first and the rate-limiting enzyme in prolinebiosynthesis in plants. This transcript was also highlyupregulated in both genotypes in the leaves under drought.The non-correlated trend between the upregulation ofP5CS inthe S genotype and the lower accumulation of proline in thisgenotype may be also related to complex control mecha-nisms, at the transcriptional, post-transcriptional and post-translational levels of proline biosynthesis, not only by theactivation of P5CS but also by feedback regulation ofproline, the end product of the pathway that caninfluence the amount of proline in the cell (Hong et al.2000). The accumulation of glycine (Table S5), which isthe precursor of glycine betaine, was correlated with theactivation of several transcripts annotated as betaine-aldehyde dehydrogenases (e.g. Ta.2585.1.S1_at) in the Rgenotype.

Alteration in the tricarboxylic acid cycle The metabolicanalysis indicated that the response to drought of the Rgenotype was characterized by altered levels of tricarbox-ylic acid (TCA) intermediates, including increased isoci-trate, citrate, an outstanding increase of aconitate (5-foldincrease) and a reduction in the amount of succinate andmalate (Fig. 7a). High accumulation of citrate and concom-

0

1

2

3

4

5

6

7

Gly Raffinose Pro Trehalose Glucose

Rel

ativ

e m

etab

olit

e co

nte

nt

(fo

ld c

han

ge

of

dro

ug

ht

to c

on

tro

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R

S

Fig. 6 Changes in the contents of drought-related metabolites in theroots of the resistance (R) and the sensitive (S) genotypes in responseto water-stress. Values represent the fold change to the mean responseof the well-watered control, where the control is given the value 0.Values are representative of at least five biological replicates of eachgenotype. Pro proline, Gly glycine

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itant decrease in malate content were also found inArabidopsis roots under oxidative stress (Lehmann et al.2009) and in drought-tolerant cotton under severe drought(Levi et al. 2011). As presented in Fig. 7a, b, the alterationin metabolites was correlated with the upregulation of theinvolved genes, including citrate synthase (Ta.970.1.S1_at), aconitate hydratase (TaAffx.6397.1.S1_s_at andTa.10193.1.S1_at), malate dehydrogenase (Ta.1039.1.S1_at) and succinate dehydrogenase (Ta.23753.1.A1_at).The latter is related to electron transport which might bealtered under stress conditions. Citrate, aconitate andisocitrate are upstream intermediates of 2-oxoglutaratewhich was also upregulated (Ta.28698.1.S1_at).

Discussion

Comparative transcriptome and metabolome profilingrevealed that drought stress induced major changes in theexpression of genes involved in multilevel regulation,

metabolism, energy and transport as well as in metabolitecontent, mainly in the roots of the R genotype. A similartrend of gene expression was previously found in the leavesof the same genotype (Krugman et al. 2010). However, amajor shift in expression of genes associated with planthormones, signalling and post-transcriptional regulation,were observed in the roots as compared with the leaves.

Cross talk between plant hormones results in synergeticor antagonist interactions play crucial roles in plantdevelopment and responses to abiotic stress (Wolters andJurgens 2009). The beneficial effect of modifying hormonehomeostasis for enhancement of abiotic stress tolerance incrop-plants was recently discussed by Peleg and Blumwald(2011). The biosynthesis of ABA in response to abioticstress is one of the most studied pathways in plants. ABA isalso involved in roots to shoots long-range signalling(Wilkinson and Davies 2010). In the current study, a higherabundance of genes related to ABA metabolism (ZEP,AAO1 and AAO3) and regulation (OST1, PP2C, HAB1 andHAB2) was found in the R genotype. Quantification ofABA in the roots further supported higher ABA levels in

Fig. 7 The TCA cycle—a me-tabolite accumulation and bgene expression patterns, identi-fied in the roots of R and Sgenotypes in response todrought. A simplified figure ofthe TCA cycle, based on KEGG(http://www.genome.jp/kegg-bin/show_pathway?ko00020+C00417). The metabolite foldchange level of the droughttreatment relative to the controlin the R and S genotypes areindicated by circle and square,respectively. The increase,decreased and no change foldlevels of metabolites and tran-scripts indicated with coloursgreen, red and black, respec-tively. The oval-shaped numbersindicate transcripts 1–6 that arepresented by the heat map (in b)as revealed by microarray anal-ysis correspond to the metabolicenzymes: 1 citrate hydro-lyase/aconitase, 2 2-oxoglutaratedehydrogenase, 3 citratehydro-lyase/aconitase, 4 citratesynthase/CSY3, 5 lactate/malatedehydrogenase, 6 succinatedehydrogenase

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the roots of R genotype under both irrigation regimes, withincreased levels under drought stress (Table 2; Figs. 3 and4). ABA was shown to stimulate changes in Ca2+ in anumber of plant tissues, including stomata guard cells andin roots. Furthermore, phosphoinositide signalling in plantsis known to be involved in the release of Ca2+ from cellularstores (Stenzel et al. 2008; Munnik and Vermeer 2010). Inagreement with that, several types of calcium bindingproteins (calmodulin, caleosine and annexin) and phospha-tidylinositol signalling (PIP5K and SEC14) were alsoupregulated in the R genotype.

The R genotype also exhibited an alteration in expres-sion of genes associated with GA biosynthesis (GA2OX1)and response (CIGR1; XERICO). Induction of the GAreceptor (GID1L2) in the R genotype suggests that GA mayplay a role in stress tolerance in the roots. Binding of GA toGID1 promotes interaction of GID1 with DELLA proteins,a subsequent poly-ubiquitination of DELLAs via the E3ubiquitin-ligase SCFSLY1 and the eventual destructions ofDELLAs. Furthermore, it is known that non-destructedDELLAs exert general plant inhibitory activity by bothreducing cell proliferation and expansion rates and thataccumulation of DELLA proteins repress root meristemsize (Murase et al. 2008; Achard et al. 2009; Ubeda-Tomáset al. 2008; Sun 2010). Fundamental research has revealedthat most of the dwarfing genes were involved in the GAbiosynthetic and signal transduction pathway in cereals (Jiaet al. 2009). The effects of GA on wheat root systems arenot clear (Wojciechowski et al. 2009 and reference therein),and further study will be needed to unravel its role. It isworth noting that genes annotated as auxin-related genes (e.g. OsSAUR37- and OsSAUR38-responsive genes; auxinefflux carrier), being key compounds in the fine tuning ofhormone signalling in the roots (Malamy 2005; Seo andPark 2009; Herder et al. 2010), showed in the current studydifferential expression in both genotypes in response todrought (Table 2). Recently, it was shown that the initiationand elongation of lateral roots is associated with a cross talkbetween auxin and ABA (Park et al. 2009) and between GAand IAA (Gou et al. 2010). The alteration in expression ofgenes related to biosynthesis and signalling of ABA, GAand IAA identified in the current study can supporthormonal shifts in the roots in response to drought.Furthermore, cross talk between the three plant hormonesis indicated by the expression patterns of RD22 and PM19.The transcription factor MADS-box26 was induced underdrought stress in the roots, as well as in the leaves(Krugman et al. 2010) of the R genotype. Similarly,MADS-box26 was upregulated in rice plants exposed todehydration stress (Arora et al. 2007; Diaz-Riquelme et al.2009). MAD-box26 is required for normal root developmentand was reported to be induced by auxin (Tapia-Lopez et al.2008). Transgenic rice plants overexpressing the OsMADS26

gene showed increased expression of JA and ethylenebiosynthesis (Lee et al. 2008a). The transgenics were similarto those previously reported for plants exposed to variousstresses including diminished leaf, shoot and root elongation,as well as enhanced formation of lateral roots. These authorsreported that, unexpectedly, a gene with high homology toGA β-hydroxylase, which converts GA20 to the active GA1,was also upregulated in the OsMADS26-overexpressingplants. However, they did not study the relationship betweenGA and OsMADS26 because GA is rarely indicated asinvolved in stress-related responses.

Genes involved in post-transcriptional regulation andtranslation efficiency had higher representation in the rootsof the same R genotype under drought stress as comparedwith the leaves (Krugman et al. 2010). These genesincluded RNA binding proteins and RNA helicases,which are the two main protein families determiningthe fate of pre-mRNAs and mRNAs from transcription totranslation (reviewed by Mazzucotelli et al. 2008).Interestingly, the cap-binding protein CBP80/ABH1 wasupregulated in the roots of the R genotype. This gene wasshown previously to be involved in ABA signal transduc-tion as a negative regulator (Hugouvieux et al. 2001,2002; Kuhn et al. 2008).

The R genotype was previously shown to have higherphotosynthetic capacity compared with the S genotype(Avneri 2009). The accumulation of malate and polyolsfound in the roots of R genotype may reflect this advantage.Malate has a central role in controlling the activity ofenzymes of primary metabolism through the TCA cycle(Charlton et al. 2008). The TCA cycle is a central elementof carbon metabolism in higher plants, which providesamong other things, electrons for oxidative phosphorylationin the inner mitochondrial membrane, intermediates foramino acid biosynthesis and oxaloacetate for gluconeogen-esis from succinate derived from fatty acids via theglyoxylate cycle in glyoxysomes (Fernie et al. 2004).Several studies have shown a correlation between TCAcycle activity and root growth (e.g. Carrari et al. 2003;Nunes-Nesi et al. 2005, 2007; Studart-Guimarães et al.2007; Sienkiewicz-Porzucek et al. 2008). Metabolomicanalysis of Arabidopsis plants treated with several indi-vidual phytohormones, such as ABA, GA, IAA andsalicylic acid, identified that ABA treatment caused amajor metabolic change including reduction in malate,fumarate and succinate and increased citrate and isocitrate(Okamoto et al. 2009), similar to the findings of thecurrent study.

Integrating transcriptome and metabolome profiles re-veal that ABA signalling lead to appropriate physiological,metabolic, transcriptional and translational modifications(Urano et al. 2009; Rao et al. 2010). In the current study,several metabolites, including glucose, trehalose and amino

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acids proline and glycine (a precursor of glycine betaine),that were shown to be associated with protecting cellularfunctions or with maintaining the structure of cellularcomponents under stress were increased in the R genotype(Elbein et al. 2003; Seki et al. 2007; Foito et al. 2009).Proline which is ABA-dependent (Urano et al. 2009) andglycine betaine are known as stress-related metabolites, andin general, the increased amino acid content represents aresponse of the plant to stress (Bartels and Sunkar 2005;Chen and Murata 2008). The metabolites accumulatingunder water-stress conditions can function also as osmo-lytes, signalling molecules, antioxidants, scavengers andmembrane-protective molecules that help plants to avoid orto tolerate dehydration stress (Bartels and Sunkar 2005).

In conclusion, integration of transcriptome and metab-olome profiling reveal genomic changes involved indrought-adaptive mechanisms in wild emmer wheat. Alter-ation in expression of genes involved in hormonal balancein the roots were associated with improved droughttolerance. Our results demonstrate the potential of the wildemmer genepool as a source for novel genes to improvedrought tolerance in modern wheat cultivars. Since thewheat genome remains mostly unknown, future advance-ment is wheat sequencing will enable the identification ofnovel genes for drought tolerance.

Acknowledgments This project was supported by the Program forSustainable Agriculture funded by the Israel Ministry of Science (# 01-21-00048), the French Ministry for Foreign Affairs and the FrenchMinistry for Education and Research. We also acknowledge the IsraelScience Foundation grant #1089/04 and equipment grants #048/99 and1478/04. Z. Peleg is indebted to the Israel Council for the HigherEducation Postdoctoral Fellowships Award. The authors thankA. Fahum,M. Goldshmit and S. Khalifa for their excellent technical assistance.

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