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Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach Shaar-Moshe, Lidor; Hübner, Sariel; Peleg, Zvi May 3, 2015

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RESEARCH ARTICLE Open AccessIdentification of conserved drought-adaptivegenes using a cross-species meta-analysisapproachMicroarray, Osmotic adjustmentShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 DOI 10.1186/s12870-015-0493-6The Hebrew University of Jerusalem, Rehovot 7610001, IsraelFull list of author information is available at the end of the article* Correspondence: zvi.peleg@mail.huji.ac.il†Equal contributors1The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture,Lidor Shaar-Moshe1†, Sariel Hübner1,2† and Zvi Peleg1*AbstractBackground: Drought is the major environmental stress threatening crop-plant productivity worldwide. Identificationof new genes and metabolic pathways involved in plant adaptation to progressive drought stress at the reproductivestage is of great interest for agricultural research.Results: We developed a novel Cross-Species meta-Analysis of progressive Drought stress at the reproductive stage(CSA:Drought) to identify key drought adaptive genes and mechanisms and to test their evolutionary conservation.Empirically defined filtering criteria were used to facilitate a robust integration of 17 deposited microarray experiments(148 arrays) of Arabidopsis, rice, wheat and barley. By prioritizing consistency over intensity, our approach was able toidentify 225 differentially expressed genes shared across studies and taxa. Gene ontology enrichment and pathwayanalyses classified the shared genes into functional categories involved predominantly in metabolic processes (e.g.amino acid and carbohydrate metabolism), regulatory function (e.g. protein degradation and transcription) andresponse to stimulus. We further investigated drought related cis-acting elements in the shared gene promoters,and the evolutionary conservation of shared genes. The universal nature of the identified drought-adaptive geneswas further validated in a fifth species, Brachypodium distachyon that was not included in the meta-analysis. qPCRanalysis of 27, randomly selected, shared orthologs showed similar expression pattern as was found by the CSA:Drought.In accordance, morpho-physiological characterization of progressive drought stress, in B. distachyon,highlighted the key role of osmotic adjustment as evolutionary conserved drought-adaptive mechanism.Conclusions: Our CSA:Drought strategy highlights major drought-adaptive genes and metabolic pathways thatwere only partially, if at all, reported in the original studies included in the meta-analysis. These genes include agroup of unclassified genes that could be involved in novel drought adaptation mechanisms. The identifiedshared genes can provide a useful resource for subsequent research to better understand the mechanisms involved indrought adaptation across-species and can serve as a potential set of molecular biomarkers for progressive droughtexperiments.Keywords: Brachypodium distachyon, Cross-species meta-analysis, Drought stress, Evolutionary conservation,© 2015 Shaar-Moshe et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public DomainDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,unless otherwise stated.Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 2 of 18BackgroundDrought stress adversely affects plant growth and product-ivity worldwide. It is estimated that about 40% of all crop-lands are affected by moderate to extreme water stress(http://www.wri.org/applications/maps/agriculturemap).Moreover, agro-ecological conditions expected to de-teriorate, due to foreseen global climatic changes, to-wards reduced availability and increased variability ofwater resources. The ever-increasing human populationthat is expected to exceed 9 billion people by 2050(http://www.fao.org/wsfs/world-summit/en) together withthe loss of agricultural land, poses serious challenges toagricultural plant research. Thus, developing drought-resistance crop-plants with enhanced productivity and im-proved water-use efficiency is the most promising solutionfor alleviating future threats to food security.Plants have evolved various adaptive mechanisms tocope with drought stress at multiple levels such as mo-lecular, cellular, tissue, anatomical, morphological andwhole-plant physiological level [1-3]. Transcriptional pro-filing analyses, in various species, have been widely usedto identify drought-related genes (e.g. [4-7]). These experi-ments resulted in condition- and/or genotype-specificgenes with little overlaps across studies (reviewed by [8]).Meta-analysis is a powerful strategy to exploit the po-tential of transcriptome studies [9]. The combination ofmultiple studies, addressing similar experimental setups,enhances the reliability of the results by increasing thestatistical power to reveal a more valid and precise set ofdifferentially expressed genes (DEGs) [10]. Moreover,combining gene expression information across speciescan improve the ability to identify core gene sets withhigh evolutionary conservation. These genes are conservedin both sequence and expression across multiple speciesand are thus key components of the biological responsesbeing studied [11]. In animals, microarray meta-analyseshave been extensively used for gene discovery (reviewed by[12,13]). However, only few microarray meta-analyses werereported in plants, with the majority conducted in Arabi-dopsis (Arabidopsis thaliana) [14-22]. Even fewer studiesinvolved more than one plant species (e.g. [23-25]). Todate, an extensive amount of transcriptome data, fromvarious plant species, developmental stages, tissues andexperimental conditions, are publicly available. Thus,re-analyzing published data using a meta-analysis and across-species approach could promote detection of con-served key genes and pathways that were overlooked usingother analytical approaches and facilitate prediction offunctional drought responses in non-model species.In the current study, we developed a novel Cross-Spe-cies meta-Analysis of progressive Drought stress at thereproductive stage (CSA:Drought), using Arabidopsis,rice, wheat and barley microarray studies. Based on thisdataset we identified shared key genes and metabolicpathways involved in whole plant adaptation to progres-sive drought stress across-species. We further evaluatedthe level of sequence conservation between shared andspecies-specific DEGs and detected common regulatorycis-acting elements in their promoters. Finally, based ontranscriptional and morpho-physiological analyses, we val-idated the universal nature and functional conservation ofselected shared DEGs in a fifth species, Brachypodiumdistachyon.ResultsMeta-analysis of microarray progressive drought stressstudiesA schematic workflow, summarizing each step of theCSA:Drought strategy is described in Figure 1. A widesurvey of deposited drought related microarray studies,in various plant and crop species, was conducted. Focuswas given to studies involving progressive drought stressat the reproductive stage. Most of the microarray studiesfound in databases (~4,000) were conducted in Arabidop-sis (~3000), with only 15 studies involving drought stressat the reproductive stage. Among other plant species, onlyrice (10 studies), wheat (5 studies) and barley (2 studies)included more than one drought stress experiment at thereproductive stage. Altogether, 32 studies, conducted atthe reproductive stage, from four different plant species,were found in our survey. To further homogenize theexperimental setup, only Affymetrix GeneChip plat-form and aboveground tissues of soil grown wild type(WT) plants were included. It is worth noted that allselected Arabidopsis experiments used Col-0 ecotype,while, for other plants, different genotypes were included,due to low number of studies from the same genetic back-ground (Additional file 1: Table S1). Following a hierarch-ical clustering analysis to assess the quality of the studies,additional eight arrays were removed due to inconsistentexpression profile across biological replicates within thesame experiment (Additional file 2: Figure S1). In total,148 arrays corresponding to 17 progressive drought stressstudies, from four different plant species, were included inthe CSA:Drought pipeline (Table 1).Microarray data from each species was integrated into acomparable meta-analysis platform using the rank productapproach. The number of significant DEGs detected forArabidopsis (3.5 k), rice (7.3 k), wheat (2.4 k) and barley(2.7 k) (Figure 2A and Additional file 3: Table S2) was notaffected by the array size (r = −0.05, P = 0.9). However, thenumber of studies integrated in the meta-analysis affectedthe number of significant DEGs detected in each species(r = −0.88, P = 0.004). This effect is inherent to meta-analysis and was previously reported (e.g. [20]). Despitethe negative effect of less overlapping DEGs when increas-ing number of studies, the improved statistical power andaugmented stringency further supported the inclusion ofSurvey of publically available microarray drought experimentsand identification of relevant dataRank product analysisIntra species analysisInter species analysisronanrese rgrefrogee uchalyinShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 3 of 18Functional analysisGO enrichmentPaFunctional analysiseIdentification of key pathways and gendrought stress at thFigure 1 A schematic overview of the Cross-Species meta-Analysis of proFollowing selection of relevant microarray drought stress studies, raw data,analysis. This statistical method generated lists of up- and down-regulatedwithin each species. Significantly differentially expressed genes (DEGs), werterms and to classify genes into functional pathways. Next, DEGs within eamethod was used to combine P-value distributions across species meta-antheir universal nature was validated in a fifth species that was not includedmore studies over the cost of false negative calls. The per-centage of DEGs (with respect to the transcriptome size)highlighted Arabidopsis as the most drought-responsivespecies (16% DEGs), followed by rice and barley (12%DEGs). Wheat had the lowest percentage (4%) of DEGs,which may be to the outcome of partial representation oftranscripts on the Affymetrix array. Completion of thewheat genome sequence will facilitate the discovery ofadditional and novel drought-adaptive DEGs. Notably, thepercentages of the identified DEGs were not associatedwith the different number of studies (r = −0.18, P = 0.82),and therefore reflect true differences between species.Gene ontology characterization in each speciesThe significant DEGs, in each species, were subjected togene ontology (GO) enrichment analysis for functionalcharacterization of their biological processes (AdditionalTable 1 Overall summary of within species microarray meta-aPlant species Clade Studiesa AArabidopsis Eudicot 8 4Rice Monocot 3 3Wheat Monocot 4 3Barley Monocot 2 3a. Details of the individual microarray studies that were included in the CSA:Droughb. Affymetrix Genechip® Microarray of Arabidopsis, rice, wheat and barley.c. Differentially expressed genes, false-positive prediction (PFP) ≤ 0.05.d. Enriched gene ontology biological processes (FDR ≤ 0.05).OrthologgenesQuantitative  validationConservationanalysis moter lysis GO ichment involved in adaptation to progressive eproductive stagessive Drought stress at the reproductive stage (CSA:Drought) approach.m each species, was integrated into separate datasets using rank productnes based on their expression (i.e. rank) across the individual experimentssed for intra-species analysis to retrieve enriched gene ontology (GO)species were transformed to rice orthologs and the penalized Fishersis. Finally, the shared drought-adaptive DEGs were characterized andthe meta-analysis.file 4: Figure S2). The highest number of significantlyenriched biological-processes was found in Arabidopsis(663), followed by rice (180), wheat (86) and barley (27)(Figure 2B and C and Table 1). Strikingly, 81% of thebiological-processes detected in Arabidopsis were species-specific while rice, wheat and barley had only 48%, 34%and 7% of species-specific enriched biological-processes,respectively (Figure 2B and C). The substantial differencesin the number and uniqueness of the GO biological-processes in each species may reflect the considerable lagin research and gene annotations that characterizes crop-plants.To test the ability of the meta-analysis to identify newbiological processes, we compared Arabidopsis GO list,obtained by the meta-analysis, with a subset of three ori-ginal GO lists, obtained from WT Arabidopsis studiesincluded in the meta-analysis. Interestingly, only 34%nalysisrrays Probe-setsb DEGsc GOsd0 22 k 3.5 k 6634 57 k 7.3 k 1808 61 k 2.4 k 866 22 k 2.7 k 27t is given in Additional file 1: Table S1.Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 4 of 18similarity was observed (Additional file 5: Figure S3),and all common biological-processes, found among thethree individual lists, were also detected by the meta-analysis approach. The ability of the meta-analysis ap-proach to detect additional 66% biological-processesdemonstrates its analytic power to reveal new pathwaysthat have been overlooked by individual studies.Identification of drought-adaptive genes using cross-speciesmeta-analysisA comparative platform across-species was developed bycombining the fold-change scores obtained for each genein the meta-analysis. To accomplish this, an injective(one-to-one) orthology relationship was defined, usingthe Model Genome Interrogator (MGI) and predictedorthologs among the four species were identified. Therice database was used as a reference for all species dueto the high number of orthologs detected compared withArabidopsis (9,104 vs. 4,939 for rice and Arabidopsisorthologs, respectively; Additional file 6: Table S3). Thetransformation to rice orthologs reduced dramaticallythe number of detected genes. From a total of 15,953 de-tected genes across the four species in the meta-analysisFigure 2 Within species microarray meta-analysis. (A) Expression profiles othe rank product analysis. Length of heatmap is proportional to number ofgene ontology biological processes (FDR≤ 0.05) based on significantly diffe(D) up- and (E) down-regulated orthologs (FDR≤ 0.05).(Table 1 and Additional file 3: Table S2), 8,471 orthologsremained (53%; Additional file 6: Table S3), of which5,520 orthologs belong to rice. A prominent reductionin gene number was observed for Arabidopsis and wheat(73% and 74% loss, respectively) followed by barley(49%) and rice (25%). The reduced number of wheatorthologs could result from an incomplete database,which may explain the substantial difference betweenthe number of orthologs common to rice and barley(264 genes) compared with the number of orthologscommon to rice and wheat (83 genes). It may also accountfor the low number of orthologs (28 genes) present in allthree monocots (Figure 2D and E and Additional file 7:Table S4). In Arabidopsis, the reduced number of ortho-logs could also be explained by the high evolutionarydistance from rice (i.e. eudicot vs. monocots).Another analytical challenge in combining datasets ofvarious species is to overcome species-specific residualvariation in fold-change and substantial differences indatabase size. Penalized Fisher method was used to com-bine P-value distributions from each species meta-analysis.Significant cross-species DEGs were detected using ad-justed P-value cutoff of 0.05 without setting a cross-speciesf significantly differentially expressed genes in each species based onprobe-sets. Unique and common (B) up- and (C) down-regulatedrentially expressed genes within each species. Unique and commonShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 5 of 18fold-change threshold. The advantage of this analytical setupis its improved ability to detect genes with consistent ex-pression differences across taxa, which may have been over-looked due to their mild expression change. This approachresulted in identification of 225 DEGs across-species, com-prised of 162 up-regulated (Average FC= 1.42, SDFC = 0.20)and 63 down-regulated (Average FC = 1.38, SDFC = 0.17)shared orthologs (Table 2 and Additional file 8: Table S5).To compare the CSA:Drought results to the originalexperiments included in the meta-analysis we examinedtwo case studies using Arabidopsis and wheat experi-ments (Additional file 9: Figure S4). Among the 225shared DEGs, only five genes (two genes involved in pro-teolysis, two genes encoding transporters and one geneassociated with purine catabolism) were also reportedamong all three Arabidopsis studies [5,26,27]. The ma-jority (62%) of the shared drought-adaptive DEGs werenot reported in any of these experiments (Additional file9: Figure S4A and Additional file 10: Table S6). This pat-tern was even more prominent among wheat studies[28-30], where none of the shared DEGs was detected byall three individual studies. Moreover, 82% of the sharedDEGs were not reported in any of the three wheat studies(Additional file 9: Figure S4B and Additional file 10: TableS6). Remarkably, a higher number of overlapping geneswas detected among the three individual Arabidopsis ex-periments (e.g. 46 genes present in all three studies). Thesecommon DEGs may imply Arabidopsis specific adaptationsto drought stress rather than general plant droughtadaptations.Metabolic pathway analysis of shared drought-adaptive DEGsThe 225-shared drought-adaptive DEGs were further an-alyzed for their associated GO biological-process termsand functional categories. GOs describe gene productsin a species-independent manner [31], making it a usefulfunctional classification for cross-species comparisons.REVIGO clustering highlighted response to abiotic stimu-lus and carbohydrate metabolism among up-regulatedbiological processes, whereas, metabolism of amines andaromatic compounds, and transport were includedamong down-regulated biological processes (FDR ≤ 0.05)(Additional file 11: Table S7). To complement this ap-proach, the 225-shared drought-adaptive DEGs wereanalyzed for their corresponding functional categoriesbased on the species-specific MapMan annotations.Additional effort to minimize the number of DEGs withunknown function or classification was undertakenusing the BLAST2GO program (Figure 3 and Table 2).The largest functional group (41%) of DEGs was associ-ated with metabolic processes (e.g. metabolism of lipids,nucleotides, secondary metabolites and cell wall), suggest-ing a considerable rearrangement in plant metabolism aspart of progressive drought adaptation. Thirty-five of thesegenes were involved in carbohydrate and amino acidmetabolism (e.g. up-regulation in synthesis of stress-related sugars such as raffinose, galactinol and trehal-ose and synthesis of proline and GABA). Several ofthese genes were shown to be involved in synthesis ofosmoprotectants, which ameliorate the detrimental ef-fects of drought (reviewed by [32]). Up to 29% of theshared DEGs were involved in putative regulatory func-tions (e.g. transcription regulation, signaling, proteindegradation, post-translational modifications and hor-mones). The expression of genes involved in abscisicacid transduction and synthesis was found to be up-regulated, whereas genes associated with gibberellinbiosynthesis and regulation exhibited down-regulation.Additional functional group of genes associated withresponse to stimulus (9%) was largely up-regulated (e.g.heat stress and xenobiotics degradation). Up-regulation ofheat stress responsive genes was in accordance with up-regulation of heat-shock transcription factors. It is note-worthy, that 8% of the shared DEGs remained unclassified.These unassigned genes are intriguing since they hold thepotential to contribute to drought adaptation and henceare novel drought-adaptive genes (Table 2).Promoter analysis of shared DEGsTo test whether putative regulatory regions, spanningDEG promoters, are enriched with cis-acting elements,across-species, DEG promoter motif enrichment analysiswas conducted. Motif enrichment was limited to Arabi-dopsis and rice due to insufficient database support forwheat and barley. Significant motif enrichment was foundonly for the putative promoters of up-regulated DEGs. InArabidopsis, three putative enriched motifs (GaCACGtg,GACACGTgTC and GacACGTGTC), found in 22 outof the 100 DEG promoters, are highly similar to theCACGTG core G-box motif (Additional file 12: FigureS5A). G-box was suggested to regulate gene expressionin response to phytohormones and abiotic stimuli [33].G-box motif can also be part of the ABA-ResponsiveElement (ABRE; ACGTGT), to which the two latterputative motifs are highly similar. In rice, three putativeenriched motifs were identified (CGCACGc, TGCGTGand gCGTGCG; Additional file 12: Figure S5B) in 50 outof the 150 DEG promoters. The first motif (CGCACGc)is highly similar to a rice motif (GCACGC) that wasenriched among dehydration inducible promoters [34]. Theother two motifs contain the core sequence of XenobioticResponse Element (XRE; GCGTG), which was found inpromoters of animal genes, encoding xenobiotic metabolicenzymes [35], as well as in promoters of plant genes [36].Conservation analysis of drought-adaptive DEGsFunctional and sequence conservation of the drought-adaptive DEGs across-species were further investigatedTable 2 Functional classification of the shared drought-adaptive DEGs across-speciesGeneralcategoryMain functionalcategoryRice genes and their Arabidopsis orthologs as predicted by MapMan and BLAST2GOUp-regulated Down-regulatedRegulatory functionsRNA regulation Transcriptionregulationloc_os02g02390 (AT1G12800, S1 RNA-binding domain-containing protein), loc_os06g35960 (AT3G24520, HSFC1),loc_os05g38820 (AT2G37060, nuclear factor yb2)loc_os12g42610 (AT2G26580, YAB5),loc_os03g08790 (AT1G09750, chloroplastnucleoid DNA-binding protein-related)RNA binding,transcriptionloc_os03g17060 (AT2G37510, RNA-binding), loc_os03g44484(AT4G21710, NRPB2), loc_os08g30820 (AT4G29820, CFIM-25)Signaling Calcium loc_os02g03020, loc_os06g46950(AT2G46600, calcium-binding protein),loc_os03g20370 (AT2G27030, CAM5)Light loc_os03g10800 (AT2G14820, NPY2), loc_os07g08160(AT3G22840, ELIP1)G-proteins andmiscellaneousloc_os03g05280 (AT5G03530, RAB ALPHA), loc_os07g33850(AT5G54840, SGP1),loc_os07g44410 (AT4G01870, tolBprotein-related)Protein Degradation loc_os01g12660 (AT1G64110, DAA1), loc_os01g52110(AT5G25560, zinc finger family protein), loc_os04g45470,loc_os02g43010 (AT1G62710, β-VPE), loc_os08g38700(AT1G55760, BTB/POZ domain-containing protein),loc_os02g02320 (AT3G10410, scpl49), loc_os02g27030(AT4G39090, RD19), loc_os05g44130 (AT1G78680, GGH2),loc_os06g21380 (AT3G57680, peptidase S41 family protein),loc_os11g26910 (AT5G42190, ASK2), loc_os02g13140(AT4G29490, aminopeptidase), loc_os03g54130 (AT5G45890,SAG12), loc_os05g35110 (AT1G21410, SKP2A)loc_os12g24390 (AT3G54780, zinc fingerfamily protein), loc_os06g03580 (AT3G63530,BB), loc_os02g48870 (AT5G10770, chloroplastnucleoid DNA-binding protein)Postranslationalmodificationloc_os03g27280 (AT1G78290, SNRK2.8), loc_os01g40094(AT1G17550, HAB2), loc_os01g64970 (AT1G10940 ,SNRK2.4),loc_os01g10890 (AT5G45820, CIPK20), loc_os01g35184(AT4G24400, CIPK8), loc_os09g25090 (AT5G25110, CIPK25),loc_os12g02200 (AT5G07070, CIPK2), loc_os06g08280(AT3G46920 ,protein kinase family protein)loc_os01g70130 (AT5G50860, proteinkinase family protein), loc_os05g51420(AT5G62740, HIR1)Folding andtargetingloc_os06g02380 (Chaperonin-60BETA2), loc_os12g02390(AT3G52850, VSR1)Synthesis loc_os05g31020 (AT1G12920, ERF1-2), loc_os05g51500(AT1G76810, elF-2 family protein)ChromatinstructureHistone loc_os01g05630 (AT5G22880, H2B)Development LEA protein,unspecifiedloc_os06g23350 (AT3G22490, LEA protein), loc_os05g46480(LEA3), loc_os03g21060 (AT1G69490, NAP), loc_os12g41680(AT1G56010, NAC1), loc_os02g53320 (AT3G03270, USP familyprotein), loc_os04g43200 (AT2G33380, RD20),loc_os01g66120 (AT1G01720, ATAF1), loc_os03g26870(AT1G78070, Transducin/WD40 repeat-like superfamilyprotein)loc_os12g32620 (AT1G10200, WLIM1),loc_os09g36600 (AT4G34950, nodulinfamily protein)HormonemetabolismAbscisic acid loc_os02g52780 (AT3G19290, ABF4), loc_os03g57680(AT5G20960, AAO1), loc_os05g49440 (AT1G05510)Gibberelic acid loc_os06g15620 (AT1G74670, GASA6),loc_os03g42130 (AT3G19000, oxidoreductase)Ethylene loc_os01g32780 (AT1G68300, USP family protein),loc_os12g36640 (AT2G47710, UPS family protein),loc_os01g51430 (AT2G26070, RTE1)Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 6 of 18Table 2 Functional classification of the shared drought-adaptive DEGs across-species (Continued)GeneralcategoryMain functionalcategoryRice genes and their Arabidopsis orthologs as predicted by MapMan and BLAST2GOUp-regulated Down-regulatedResponse to stimulusAbiotic andbiotic stressHeat, drought loc_os02g32520 (AT5G51070, ERD1), loc_os05g44340(AT1G74310, HSP101), loc_os03g16030, loc_os01g04380(AT5G59720, HSP18.2), loc_os03g11910 (AT2G32120, HSP70T-2),loc_os03g31300 (AT5G15450, APG6), loc_os05g38530(AT3G12580, HSP70), loc_os11g47760 (AT5G02500,HSP70.1),loc_os11g26760 (dehydrin Rab16C)loc_os02g04120 (AT2G18250, COAD),loc_os04g33060 (AT1G32220,dehydratasefamily protein)Signaling loc_os02g10350 (AT4G02600, MLO1)Unspecified andbiotic stressloc_os06g40120 (AT5G20150, SPX1), loc_os03g18850 (PR1),loc_os11g10480 (AT1G77120, ADH1)loc_os08g35760 (AT5G20630, GLP3),loc_os04g38450 (AT4G39640 ,GGT1),loc_os01g28500 (AT2G14610, PR1)Biodegradationof Xenobioticsloc_os01g47690 (AT1G53580, GLX2-3), loc_os06g20200(AT5G23530, CXE18)Localization & organizationTransport TIP/NIP loc_os03g05290 (AT2G36830, TIP1;1),loc_os06g22960 (AT3G16240, TIP2;1),loc_os10g36924 (AT4G10380, NIP5;1),loc_os06g12310 (AT5G37820, NIP4;2)Sugars loc_os02g17500 (AT1G67300, hexose transporter) loc_os07g39350, loc_os03g10090 (AT3G18830,ATPLT5), loc_os07g01560 (AT1G11260, STP1)Amino acids loc_os02g54730 (AT2G41190, amino acid transporter familyprotein)loc_os07g04180 (AT5G49630, AAP6)Nitrate loc_os04g40410 (AT5G50200, NRT3.1)Peptides and misc. loc_os05g32630 (AT3G05290, PNC1), loc_os08g06010(AT3G47420, G3PP1), loc_os03g43720 (AT3G13050, NIAP),loc_os02g39930 (AT5G58070, ATTIL), loc_os04g36560(chloride channel)loc_os10g22560 (AT2G02040, PTR2-B),loc_os04g57200 (metal ion transport)Metal handling Metal binding loc_os04g17100 (AT5G66110, metal ion binding),loc_os04g32030 (AT5G50740, metal ion binding)Cell Organization loc_os07g37560 (AT1G50360, VIIIA) loc_os07g38730 (AT5G19780, TUA5)Death loc_os03g05310 (AT3G44880, ACD1)EnergyMitochondrialelectrontransportElectron transferflavoproteinloc_os04g10400 (AT5G43430, ETFBETA), loc_os03g61920(AT1G50940 ETFALPHA)Cytochrome creductaseloc_os02g33730 (AT1G15120, ubiquinol-cytochrome Creductase complex 7.8 kDa protein)Photosynthesis Light reaction andCalvin cycleloc_os01g12710 (AT4G13250, SDR family protein) loc_os07g05360 (AT1G79040, PSBR),loc_os11g47970 (AT2G39730, RCA),loc_os05g22614 (AT3G46780, PTAC16)Metabolic processesCarbohydratemetabolismStarch synthesis anddegradationloc_os05g50380 (AT1G27680, APL2), loc_os07g22930(AT1G32900 , Starch synthase), loc_os03g04770 (AT3G23920,BAM1), loc_os09g29404 (AT4G09020, ISA3)loc_os10g40640 (AT4G16600, transferase)Sucrose synthesisand degradationloc_os08g20660 (AT5G20280, SPS1F), loc_os04g33490(AT5G22510, INV-E), loc_os02g01590 (AT1G12240 , VAC-INV),loc_os05g45590 (AT4G29130, ATHXK1), Loc_os09g33680(AT1G02850, BGLU11)Raffinose andgalactinol synthesisloc_os07g48830 (AT1G56600, AtGolS2), loc_os03g20120(AT2G47180, AtGolS1) loc_os08g38710 (AT1G55740, AtSIP1)Galactosemetabolismloc_os10g35070 (AT5G08380, AGAL1), loc_os07g48160(AT3G56310, AGAL putative), loc_os01g33420 (AT3G26380,AGAL putative), loc_os05g51670 (AT4G10960, UGE5)loc_os04g38530 (AT5G15140, Galactosemutarotase-like superfamily protein)Trehalose synthesis loc_os10g40550 (AT4G22590, TPPG), loc_os02g44230(AT5G51460, TPPA)Miscellaneous loc_os03g45390 (AT1G64760, glycosyl hydrolase family 17protein), loc_os03g15020 (AT2G28470, BGAL8),loc_os07g23880 (AT3G23640, HGL1)Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 7 of 18Table 2 Functional classification of the shared drought-adaptive DEGs across-species (Continued)GeneralcategoryMain functionalcategoryRice genes and their Arabidopsis orthologs as predicted by MapMan and BLAST2GOUp-regulated Down-regulatedAmino acidmetabolismSynthesis loc_os05g38150 (AT2G39800, P5CS1), loc_os04g52450(AT3G22200, GABA-T)loc_os04g33390 (AT1G08250, ADT6),loc_os07g42960 (AT1G22410, DAHP synthase),loc_os03g63330 (AT5G13280, AK1)Degradation loc_os05g03480 (AT3G45300, IVD), loc_os03g44150(AT5G46180, Δ-OAT), loc_os06g01360 (AT5G54080, HGO),loc_os05g39770 (AT3G08860, PYD4)loc_os04g53230 (AT1G11860,aminomethyltransferase), loc_os04g43650(AT1G08630, THA1)Miscellaneous loc_os04g20164 (AT4G12290, amine oxidase)PolyaminemetabolismSpermidinesynthaseloc_os06g33710 (AT5G53120, SPDS3)TCA\organictransformationOrganic acidtransformaitons,carbonic anhydrasesloc_os02g07760 (AT1G79440, ALDH5F1), loc_os09g28910(AT4G33580, BCA5), loc_os01g11054 (AT3G14940, ATPPC3)loc_os04g33660 (AT3G52720, ACA1)Fermentation Aldehydedehydrogenaseloc_os09g26880 (AT1G54100 ,ALDH7B4), loc_os08g32870(AT1G74920, ALDH10A8)loc_os02g43194 (AT4G36250, ALDH3F1)Pyruvatedecarboxylaseloc_os01g06660 (AT4G33070, PDC1)LipidmetabolismSynthesis loc_os11g05990 (AT3G11670, DGD1), loc_os09g21230(AT5G23050, AAE17), loc_os12g04990 (AT5G27600, LACS7),loc_os01g57420 (AT2G20900, DGK5), loc_os10g39810(AT4G12110, SMO1-1)Degradation loc_os09g37100 (AT4G35790, PLDDELTA), loc_os07g47250(AT5G18640, lipase class 3 family protein), loc_os07g47820(T3G06810, IBR3), loc_os11g39220 (AT5G65110, ACX2),loc_os10g04620 (AT5G16120, hydrolase), loc_os03g07180(embryonic protein DC-8)loc_os03g40670 (AT5G08030, GDPD6)Desaturation,transferloc_os11g24070 loc_os03g18070 (AT3G11170, FAD7)SecondarymetabolismIsoprenoids loc_os02g07160 (AT1G06570, PDS1), loc_os01g02020(AT5G47720, AACT1)loc_os02g04710 (AT2G07050, CAS1)Phenylpropanoidsand misc.loc_os07g42250 (AT3G51420, SSL4) loc_os04g15920 (AT4G39330, CAD9),loc_os11g32650 (AT5G13930, CHS)TetrapyrrolesynthesisGlutamyl-tRNAreductaseloc_os10g35840 (AT1G58290, HEMA1)NucleotidemetabolismSynthesis, adeninesalvageloc_os05g49770 (AT3G12670, emb2742) loc_os02g40010 (AT1G80050, APT2)Degradation loc_os02g50350 (AT3G17810, PYD1), loc_os08g13890(AT1G67660, exonuclease)loc_os04g58390 (AT4G04955, ALN)Cell wall Modification loc_os06g48200 (AT5G57550, XTR3),loc_os01g60770 (AT1G69530, EXPA1),loc_os10g40720 (AT1G65680, ATEXPB2),loc_os05g39990 (AT2G40610, ATEXPA8)Degradation loc_os09g31270 (AT3G57790, Pectin lyase-like superfamilyprotein), loc_os03g53860 (AT5G20950, glycosyl hydrolasefamily protein),Redox Ascorbate,glutathioneloc_os12g29760 (AT4G33670, L-GalDH) loc_os02g44500 (AT4G11600, GPX6)Heme loc_os02g33020 (AT3G10130, SOUL heme-binding familyproteinMiscellaneous loc_os03g16210 (AT5G06060, tropinone reductase), loc_os03g04660 (AT4G39490, CYP96A10),loc_os07g48020 (AT5G05340, peroxidase),loc_os07g48050 (AT5G05340, peroxidase)Unspecifiedprocessesloc_os03g17470 (AT3G55040, GSTL2), loc_os01g08440(AT4G15550, IAGLU), loc_os01g05840 (AT2G37540, SDRfamily protein), loc_os02g51930 (AT1G22400, UGT85A1),loc_os10g40570 (AT1G63370, FMO family protein),loc_os12g21789 (AT3G49880, glycosyl hydrolase familyprotein 43), loc_os11g03730 (AT3G10740, ASD1),loc_os06g22080 (AT3G51520, diacylglycerol acyltransferasefamily), loc_os06g49990 (AT3G51130)Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 8 of 18by comparing the expression profiles and sequences of theidentified DEGs. Due to substantial differences among spe-cies, only genes for which orthology could be determinedin all four species were included in the analysis. A hierar-chal clustering of pair-wise distance matrix, based on theexpression fold-change in ortholog genes across species, re-capitulated the known plant phylogeny (Figure 4A). Se-quence conservation in shared versus species-specific DEGswas evaluated by comparing the corresponding sequences(tWelch = 5.91, P ≤ 0.0001) and wheat (tWelch = 14.13, P ≤0.0001) (Figure 4B). The non-significant difference found inArabidopsis, is presumably the consequence of the amplegenetic distance between monocots and eudicots, indicatedby a general lower sequence similarity and resolution.A case study of drought-adaptive genes in BrachypodiumdistachyonTo validate the identified shared DEGs and evaluate theirTable 2 Functional classification of the shared drought-adaptive DEGs across-species (Continued)GeneralcategoryMain functionalcategoryRice genes and their Arabidopsis orthologs as predicted by MapMan and BLAST2GOUp-regulated Down-regulatedUnclassifiedloc_os10g32680 (AT1G07040), loc_os11g37560 (AT3G55760),loc_os01g46600 (PM41), loc_os03g51350, loc_os01g40280(AT5G35460), loc_os09g20930, loc_os03g45280 (dehydrin),loc_os04g34610 (AT1G43245), loc_os03g48380 (AT1G27150),loc_os08g33640 (AT1G23110), loc_os01g58114 (AT4G27020),loc_os05g33820 (AT1G10740), loc_os02g48630 (AT5G48020),loc_os05g48230 (AT4G13400), loc_os09g04100 (AT4G31830),loc_os01g26920 (AT2G39080)loc_os02g38240 (AT4G24750), loc_os07g12730(AT5G01750) strentlatioatioShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 9 of 18between the rice ortholog and each species (excluding aself-comparison for rice). For both shared and species-specific DEGs, higher sequence conservation was foundamong rice-barley and rice-wheat than for rice-Arabidopsiscomparison (Figure 4B). Both functional and sequence con-servation patterns found among species further support theCSA:Drought detection of cross-species DEGs. Significantlyhigher sequence conservation level of shared DEGs com-pared with species-specific DEGs, was found for barleySignalingProteinsChromatinDevelopmHormonesRNA reguStressBiodegradTransportRegulatory functions (29%)Response to stimulus (9%)Metal bindingCellMitochondriaPhotosynthesCarbohydrateAmine metabTCA and fermLipid metaboSecondary mTetrapyrrole sNucleotide mCell wallRedoxUnspecified pMetabolic processes (41%)Energy  (3%)Localization & organization (10%)Unclassified (8%)Figure 3 Functional classification of shared drought-adaptive DEGs baseduniversality, we used the model grass B. distachyon [37] asa case study. Morpho-physiological characterization ofplant adaptation to drought stress resulted in dramaticeffects on plant growth (Figure 5A), spike morphology(Figure 5B) and root development (Figure 5C). More-over, a significant reduction in culm length (P = 0.0001;Figure 5D), total biomass (P = 0.0001; Figure 5E) andyield production (P = 0.002; Figure 5F) was observed.Under drought stress, plants exhibited significant lower65251862352213257 11ucturenn of xenobioticsDown-regulatedUp-regulated22 131 3217 626 212 23 313 22 43 49216l electron transportis metabolismolismentationlismetabolismynthesisetabolismrocesseson MapMan and BLAST2GO annotations.Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 10 of 18AWheatBarley8291chlorophyll content (P = 0.02) based on transformedchlorophyll absorbance in reflectance index (TCARI;Figure 5G), higher osmotic potential (net solute accumula-tion in the cell: −1.19 ± 0.05 compared with −1.74 ± 0.04for the control and drought treatment, respectively;Figure 5H) and a minor reduction in RWC (Figure 5I).A subset of 27 drought-adaptive DEGs, identified inthe CSA:Drought, with various expression patterns, wasselected for qPCR validation in B. distachyon. In general,this assay showed similar expression pattern as the CSA:Drought (except for BdGOLS1), with 20 significant genes(Figure 6, Additional file 13: Figure S6 and AdditionalBBit scoreArabidopsis Wheat Barley100200300400Species-specific DEGsShared DEGs ******500ArabidopsisRice1000.5Log FCFigure 4 Conservation analysis. (A) Hierarchal clustering of pair-wisedistance matrix based on expression profile of orthologs in eachspecies. Bootstrap scores supporting the consensus tree (percentage)are indicated at each node. (B) Sequence conservation of sharedDEGs versus species-specific DEGs. For each species, the bit score,obtained from the permutated blastn analysis, was compared betweenshared DEGs and species-specific DEGs. Bold horizontal bars indicatethe average, boxes indicate the upper and lower 0.25 quartile, dashedbars indicate the max/min scores (excluding extremes), circles indicatethe extremes, and notch overlaps indicate non-significant differences(P≤ 0.05).file 14: Table S8). These genes included carbohydratemetabolic enzymes as Granule-bound starch synthase 1(GBSS1, regulator of amylose synthesis), β-Amylase 1(BAM1, involves in starch degradation), Trehalose-6-phosphate phosphatase G (TPPG, involves in trehalosesynthesis), Alkaline/neutral invertase E (INV-E, hydroly-ses sucrose into hexoses) and Hexokinase 1 (HXK1, in-volves in hexoses catabolism and sugar signaling). Genesthat encoded amino acid metabolic enzymes as Homo-gentisate 1,2-dioxygenase (HGO, involves in tyrosine deg-radation), 3-Deoxy-D-arabino-heptulosonate 7-phosphatesynthase (DAHPS, the first committed enzyme of theshikimate pathway), Delta1-pyrroline-5-carboxylate syn-thetase (P5CS1, the rate-limiting enzyme in proline bio-synthesis) and Aspartate kinase 1 (AK1, catalyzes thefirst reaction of lysine synthesis). Genes related to pro-tein degradation as Early responsive to dehydration 1(ERD1, encodes a Clp protease regulatory subunit) andSerine carboxypeptidase-like 49 (SCPL49, involves inproteolysis). Hormone metabolic enzymes and tran-scription factors, including ABRE binding factor 4(ABF4, a bZIP transcription factor that mediates ABA-dependent stress responses), SNF1-related kinase 2.4(SnRK2.4, involves in osmotic stress responses and ABAsignaling), Gibberellin 20 oxidase 2 (GA20ox2, a keyenzyme in gibberellin synthesis) and NAC domain con-taining protein 1 (NAC1, involves in transcriptional regula-tion). Additionally, a random set of unknown function(putative late embryogenesis abundant protein, group 3,LEA3) and unclassified (BRADI2G17170, BRADI3G28120and BRADI2G42030) genes were also analyzed.The similar expression pattern, obtained in a fifth spe-cies that was not included in the CSA:Drought, reinforcesthe consistency of the shared DEGs as key genes involvedin adaptation to progressive drought stress across-species(Figure 6).DiscussionTraditionally, comparisons between two contrasting waterregimes were used to identify drought-related DEGs. Thisstrategy yielded hundreds to thousands of DEGs, depend-ing on the selected significance threshold, however, focuswas predominantly given to genes with high fold-change(usually ≥ 2), overlooking functionally and biologically im-portant genes with relative mild expression differences.Moreover, in most cases very limited overlaps were foundamong different studies. Our working hypothesis is thatplant adaptation to drought stress involves combination ofevolutionary conserved pathways, as well as, species-specific genes. Here we developed a novel cross-speciesmeta-analysis platform to reveal a core set of shared genesand pathways by integrating transcriptional data fromArabidopsis, rice, wheat and barley into one meaningfulanalytical framework.Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 11 of 18Culm length, cmDFeight, g plant-1Control Drought A B cmMost (75%) drought transcriptome studies have beenconducted on Arabidopsis under artificial and extremeconditions (e.g. detached leaves and shocks) for short pe-riods (e.g. minutes to hours) at the vegetative phase (e.g.young seedlings), with survival or recovery as selectivetraits. In addition, while functional analysis of candidategenes significantly improved drought resistance in trans-genic lines under laboratory conditions, limited successwas reported for transgenic crop-plants under field condi-tions [38], where crop-plants are often exposed to longerepisodes of slowly developing drought stress [39]. There-fore, we focused our CSA:Drought strategy on progressivedrought stress studies at the reproductive stage. This ap-proach enabled detection of 225-shared drought-adaptiveDEGs with enhanced functional and evolutionary conser-vation across-species (Figures 3, 4 and Table 2). Moreover,we were able to detect with the CSA:Drought approach128 and 178 shared ortholog DEGs in Arabidopsis andHConSpike wOsmotic potential, MPaControl DroughtCFigure 5 Brachypodium distachyon as a case study to validate the shared Ddrought conditions. (B) Spike morphology, (C) Roots biomass, (D) Culm lenabsorbance in reflectance (TCAR) index, (H) Osmotic potential, and (I) Relatare mean ± SD (n = 5). *, ** and *** indicate significant differences betweenAR index****GTotal biomass, g plant- 1*****Ewheat, respectively, that were missed by the original stud-ies (Additional file 9: Figure S4). It is worth noted thatwhile in Arabidopsis only treatment differed betweenstudies (i.e. all studies conducted using Col-0 ecotype), inwheat both genotypes (e.g. genotypes Creso, ChineseSpring, Y12-3 and A24-39) and treatments differed, whichmay account for the limited overlaps compared with theshared DEGs. Additionally, in most cases, transcriptomeanalyses use arbitrary fold-change thresholds combinedwith significance levels to reduce the number of detectedDEGs from few hundreds/thousands to a tractable sub-set. Such an approach highlights mostly species- and/ortreatment-specific DEGs. In contrast, meta-analysis strat-egy facilitates detection of consistent and biologically im-portant DEGs, which were overlooked in the originalstudies due to relatively low fold-change.Relatively high level of sequence conservation was foundamong the shared DEGs compared with the species-TCtrol DroughtRelative water content, %***IEGs detected by CSA:Drought. (A) Plants grown under control andgth, (E) Total biomass, (F) Spike weight, (G) Transformed chlorophyllive water content (RWC) under control and drought conditions. Valuestreatments at P ≤ 0.05, 0.01 and 0.001, respectively.CA:Dd(n =ls-6-isa1.actnhaShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 12 of 18CSA qPCR Gene nameGBSS1BAM1TPPINV-EGOLS1AHGODAHPSBCEDBRADI2G41590BRADI1G75610BRADI3G32970BRADI5G09200BRADI2G18877BRADI1G64120Gene IDBRADI1G52290BRADI2G23507BRADI1G21330BRADI1G01800HXK1 §AK1 §P5CS1 §Figure 6 Heat-map of selected drought-adaptive genes detected by CSRed and blue represent high and low relative expression when compareis log2 of mean expression value. qPCR values, representing mean ± SDdehydrogenase and S-adenosylmethionine decarboxylase as internal controGBSS1, Granule-bound starch synthase 1; BAM1, β-Amylase 1; TPP, Trehalose1; GOLS1, Galactinol synthase 1. (B) Amino acid metabolism: HGO, Homogent3-Deoxy-D-arabino-heptulosonate 7-phosphate synthase; AK1, Aspartate kinaseSerine carboxypeptidase-like 49. (D) Hormone metabolism and transcription fGibberellin 20 oxidase 2; NAC1, NAC domain containing protein 1. (E) Unknow3. § indicates significant differences of qPCR analysis at P ≤ 0.1. Fold cAdditional file 13: Figure S6.specific DEGs (Figure 4B). This result should be consid-ered in the light of the evolutionary distance between thefour species and recent genetic bottlenecks involved indomestication and consciously evolution under domes-tication of rice, wheat and barley. It is worth notice thatwe cannot determine by our analysis if these geneswere converged among species sometime during theirseparated evolutionary history. Although this seems un-likely, the sample size used in this study and the experi-mental design used in the original studies prevent usfrom completely rule out this option. Whether the se-quence and functional similarity found among thesegenes is a consequence of conservation or convergence(or both), this shows that the shared DEGs play funda-mental roles in drought adaptation.Classification of the shared DEGs into functional categor-ies suggests the involvement of various mildly expressedregulatory and metabolic pathways that jointly elicit an or-chestrated drought adaptation (Figure 3 and Table 2).Among the metabolic processes carbohydrate and aminemetabolisms are assigned as the largest sub-category (39%),which is involved in biosynthesis and accumulation ofcompatible solutes (Additional file 15: Figure S7). Thefunctional conservation of these genes was demonstratedin an additional species. A randomly selected subset of 11carbohydrate and amine metabolic B. distachyon orthologsshowed similar expression pattern as CSA:Drought. Inaccordance, a higher osmotic potential was measuredERD1SCPL49LEA3BRADI2G42030BRADI2G17170BRADI3G28120ABF4GA20ox2NAC1SnRK2.4BRADI3G44640BRADI3G01320BRADI3G57960BRADI2G56267BRADI1G14580BRADI4G02060BRADI2G18090SA qPCR Gene nameGene ID-2-112UnknownUnknownUnknownrought and validated by qPCR analysis in Brachypodium distachyon.to the mean value of expression across all samples, respectively. Scale6), were calculated and normalized using Glyceraldehyde 3-phosphateand presented as fold-change (P≤ 0.05). (A) Carbohydrate metabolism:phosphate phosphatase; INV-E, Alkaline/neutral invertase E; HXK1, Hexokinasete 1,2-dioxygenase; P5CS1, Delta1-pyrroline-5-carboxylate synthetase; DAHPS,(C) Protein degradation: ERD1, Early responsive to dehydration 1; SCPL49,ors: ABF4, ABRE binding factor 4; SnRK2.4, SNF1-related kinase 2.4; GA20ox2,and unclassified: LEA3, Late embryogenesis abundant protein, groupnge values and statistical analysis for each gene can be found inin drought stressed compared to control B. distachyonplants. Compatible solutes are small, nontoxic mole-cules that include sugars (maltose and trehalose), sugaralcohols (galactinol and mannitol), amino acids (pro-line) and amines (spermidine and glycine betaine)(reviewed by [40]). Compatible solutes are an importantadaptive mechanism under drought stress as well asunder additional abiotic stresses as salinity and extremetemperatures. Osmoprotectants facilitate maintenanceof cell turgor and cellular water potential under stress,as well as acting in membrane and macromoleculesstabilization and ROS scavenging (reviewed by [41]).Some of these osmoregulation-related shared geneshave already been shown to improve drought tolerance.TPPA and TPPG, genes involved in trehalose synthesis,were included among up-regulated shared DEGs. Over-expression of yeast TPS-TPP in tobacco, Arabidopsis,rice and alfalfa significantly improved the transgenicplant drought tolerance [42-45]. Invertases mediate su-crose hydrolysis to glucose and fructose, which con-tributed to better osmoregulation [46]. Accordingly,INV-E was up-regulated under drought (Figure 6 andAdditional file 13: Figure S6). Complex mechanismsoperate in plants to coordinate the interactions be-tween carbon assimilation and nitrogen metabolism[47]. Carbon and nitrogen balance is a key componentin plant adaptation to drought stress [48]. Proline, syn-thesized via the glutamate pathway (P5CS), or fromShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 13 of 18ornithine (Δ-OAT) [49], is believed to act as a store ofcarbon and nitrogen, as well as in ROS scavenging[50]. Both P5CS1 and Δ-OAT expression levels wereup-regulated under drought (Additional file 15: FigureS7). Accordingly, several studies have shown that over-expression of either P5CS, or Δ-OAT, in different plantspecies resulted in increased proline levels, whichcould contribute to enhanced stress tolerance [51-53].Remarkably, among DEGs reported in studies includedin the meta-analysis, only 16 osmoregulation-relatedshared genes were detected, with majority of these genes(10) present only in one study (Additional file 10: TableS6). It is worth noted that all Arabidopsis microarrayexperiments included in the meta-analysis overlooked theosmoregulation-related genes [5,26,27], and for other spe-cies only partial results were discussed [4,7,28-30,54].Carbohydrate metabolism and lipid degradation may alsobe involved in supplying energy that is required for main-tenance of drought adaptation and osmoprotectant syn-thesis through breakdown of energy reserves. Additionallarge group of genes were assigned to protein regulationand metabolism. Apart from its regulatory function, pro-tein degradation during drought-induced leaf senescenceresults in increment of the free amino acid pool availablefor osmotic adjustment [48,55].Phytohormone homeostasis is a key factor in plantdrought adaptation that mediates a wide range of adaptiveresponses (reviewed by [1]). One of the fastest responsesof plants to drought stress is synthesis of ABA, which in-duces gene expression, triggers stomata closure and even-tually restricts cellular growth, leading to whole plantgrowth retardation. In accordance with ABA effects onreproductive tissue development, through transcrip-tional reprogramming [56] and ABA gene expressionregulation during drought, which is mediated by tran-scription factors such as ABF4 (Figure 6), promoters ofshared Arabidopsis orthologs were enriched with thecis-acting element ABRE (Additional file 12: Figure S5).ABRE involvement in ABA-regulated gene expressionoccurs after the accumulation of ABA and thereforemany ABA-inducible transcription factors are involvedmainly in late and adaptive drought processes [57].Among the enriched ABRE genes included those involvedin starch degradation and accumulation of compatiblesolutes [56], as detected by CSA:Drought and validatedin B. distachyon, both transcriptionally and physiologically(Figures 5 and 6).Interestingly, several genes that are known to regulaterapid drought-induced gene expression, were also detectedby the CSA:Drought analysis. These genes included tran-scription factors as SnRK2.4 and SnRK2.8, and a proteaseregulatory subunit as ERD1. Most drought-induced geneswere detected under extreme drought conditions andshort period assays, which might explain their annotationsas early drought-responsive genes. However, the inductionof these genes also during long, mild drought stress mightimply on their involvement in maintenance of study-stategene expression level as part of drought adaptation. Thesediscrepancies emphasize the importance of using physiolo-gically oriented approach when designing stress assays.ConclusionsOur CSA:Drought strategy identified a set of 225 keydrought-adaptive genes that were only partially, if at all,reported in the studies included in the meta-analysis.Functional categorization of the shared DEGs underlinedvarious regulatory and metabolic pathways as conserveddrought-adaptive mechanisms across species. Physio-logical and transcriptional characterization of droughtstressed B. distachyon, further supported these results.Additionally, we have identified and validated a group ofunclassified genes (8%) that could be further investigatedof their functional prospective roles in drought adaptationmechanisms. The shared DEGs provide useful resourcefor subsequent research and can serve as a potential setof molecular biomarkers for drought experiments andas candidate genes for engineering drought-tolerantcrop-plants.MethodsMicroarray meta-analysisRaw microarray data files (.CEL) of progressive droughtstress studies at the reproductive stage were obtained fromGene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and ArrayExpress (http://www.ebi.ac.uk/arrayexpress).Description of the obtained studies depicted in Additionalfile 1: Table S1. Both species-specific probe-set annotationfile and the corresponding probe-gene maps were down-loaded from the Affymetrix site (http://www.affymetrix.com). For each species, Affymetrix raw data files were con-verted and normalized in R (http://www.r-project.org)using the bioconductor ‘affy’ package [58]. Quality controlanalyses of the obtained microarrays included quantilenormalization for each array, followed by across arrayrobust multichip average (RMA) normalization and trans-formation to log2 scale.Meta-analysis was conducted using the rank productstatistics [59], which enabled to combine data of differentorigins and identify DEGs between treatment and controlconditions. This non-parametric test was conducted overall replicates within species to decrease the residual effectof each study and increase statistical power to identifyDEGs across experiments using the Bioconductor ‘Rank-Prod’ package [60]. Briefly, genes are ranked based ontheir expression (up- or down-regulation) in response todrought in each experiment individually. The null hypoth-esis is that the order of genes in an experiment is random,hence the probability to detect a gene ranked among theShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 14 of 18top genes equals to its rank among the total number ofgenes in each experiment. For each gene a combinedprobability was calculated as the product of ranks acrossexperiments and its significance was determined using100 permutations to accurately estimate P-values [61].DEGs were selected after correcting for multiple testingusing the percentage of false-positive prediction, whichalso controls for the accumulated false positives with acutoff of 0.05. For each species, DEGs heat-map was con-structed using ggplot2 package [62]. To be able to com-pare between species, the number of detected DEGs wasdivided by the corresponding species array size.Gene ontology analysisThe DEGs were subjected to enrichment of gene ontol-ogies (GOs) using the AgriGO toolkit (http://bioinfo.cau.edu.cn/agriGO). GO enrichment was based on the hyper-geometric statistics followed by a 0.05 FDR correction formultiple comparisons with a minimum of five entitiesmapped to each category. The enriched GO biologicalprocesses were clustered and visualized using the web-server REVIGO (http://revigo.irb.hr). REVIGO clusteringalgorithm finds a single representative GO term, for clus-ters of semantically similar GO terms, thus resulting in re-duced, non-redundant GO term sets (i.e. superclusters).The size of each supercluster reflects its P-value.Cross-species meta-analysisWe used the Model Genome Interrogator (MGI) tool inPLEXdb (http://www.plexdb.org) to retrieve predictedorthologs between each species and homologous loci inthe rice model genome. The MGI matches one or morepredicted orthologs to a selected microarray probe-setusing GeneSeqer (parameters: −x 12 -y 16 -z 24 -w 0.2)followed by blastx to protein database and blastn to FL-cDNA sequence database (both with E-value < 1e-20),and back, producing a quality score for each pair. To de-fine an injective (one-to-one) orthology between genes,only best alignment score for each probe-set-orthologhit was considered. Shared DEGs were identified usingthe penalized Fisher method that combines the P-valuedistributions from all four species:X2g ¼Xki¼1−2loge Pgi where Pgi is the probability that gene g was not differen-tially expressed between treatments (based on false-positive prediction). This method could be affected by dif-ferences in dataset size between species, i.e. small P-valuesin one species may lead to subsequent small P-values inthe cross-species combined distribution, as was detectedfor the non-normalized data (data not shown). Therefore,P-values were quantile normalized within each speciesprior to the penalized Fisher method. The combinedP-values were further corrected using the FDR adjustment[63]. To enable the detection of significant items evenwhen not present in all datasets, missing items from atmost one dataset were included, dragging a P-value pen-alty equals to one instead of a missing value. Z-transformnormalization was also examined, but was found to besensitive to the use of penalty (not shown), due to summa-tion compared with multiplication in the penalized Fishermethod. For each DEG the average fold-change across-species was calculated using the geometric mean:Dg ¼ exp 1kXki¼1loge Dgi  !where D is the expression fold-change of gene g in speciesi, and k is the number of species from which the averagefold-change was calculated.Metabolic-pathway analysisDEGs were assigned to processes and pathways usingMapMan software, which organizes genes in blocks, ratherthan as pathways. This designation allows genes to be ten-tatively assigned, even when their function is only roughlyknown [64]. Unassigned genes were further annotatedwith the program BLAS2GO (http://www.blast2go.com)using default parameters.Promoter analysisSequences of shared DEGs were extracted from GrameneBioMart (http://www.gramene.org) with 1 kb upstream tothe transcription start site. Promoter analysis was con-ducted on the two model species Arabidopsis and rice,since wheat genome is not supported by BioMart, andapproximately third of the barley gene sequences arenot at adequate quality (i.e. < 800 bp or >200 N). Ana-lysis of significantly overrepresented motifs within pro-moter sequences was conducted in BioProspector program[65] integrated in the Tmod software [66]. To modelthe base dependencies of each species, the second-order Markov background models were constructedbased on a random sample of 100 and 150 promoters,which are equivalent to the size of up-regulated across-species genes in Arabidopsis and rice, respectively. Sinceseveral cis-acting elements, involved in plant responsesto drought, e.g. ABA-responsive element (ABRE) anddehydration-responsive element (DRE), contain corehexamer sequences [67,68], a fixed motif width was setto 6 bp. For all other parameters the default settingswere used and a null score was obtained based on thedistribution of 100 Monte-Carlo simulations. The de-tected motifs, were further optimized and validatedusing the BioOptimizer program [69]. Logos wereShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 15 of 18generated using WebLogo program (http://weblogo.berkeley.edu).Evolutionary analysisTo study the functional clustering of the four species, apair-wise distance matrix was calculated using the ex-pression profile of each species. The Euclidean distancebetween orthologs, as were determined by the ModelGenome Interrogator and the following filtering proced-ure, was calculated using the expression fold-change inresponse to drought of all genes expressed across-species. A hierarchical clustering was conducted in Rusing a complete agglomeration of the pair-wise distancematrix and a phylogenetic tree was constructed after 100bootstraps.Shared DEGs were further analyzed for their DNA se-quence conservation among the four species. For eachshared DEG, the ortholog in rice was determined usingthe MGI tool and was used as a transitive anchoracross species. The corresponding sequence in rice wasobtained from the Rice Genome Annotation Project(http://rice.plantbiology.msu.edu) and mapped to thebarley genome (Morex assembly [70]), wheat draft gen-ome (LCG assembly [71]), and Arabidopsis genome(TAIR10; http://www.arabidopsis.org). The blastn pro-gram was used to compare all rice ortholog sequencesto the other three species genomes with an E-value cut-offof e−10 and the bit-score was considered as a measurementfor similarity between sequences. The use of bit-score en-abled to reduce the bias introduced by the size of thesearched database [72], which varies extensively betweenspecies. To avoid the residual variation introduced by geneduplication after speciation (paralogy), whole genomeduplication (ohnology) or polyploidization (homeology)(in wheat), only the best hit (i.e. lowest E-value) wasconsidered. The conservation of shared DEGs was fur-ther compared with DEGs uniquely detected in eachspecies (i.e. species-specific DEGs). The ortholog se-quences of unique DEGs in each species were obtainedfrom the rice genome. A random sample of 50 geneswas selected from each of the two DEGs lists of eachspecies. The rice ortholog sequences were then com-pared to the corresponding species genome usingblastn with same settings as previously described andthe average bit score was recorded. This procedure waspermutated 100 times with replacement and the averagebit score over all samples was compared between the twoDEG lists for each species using the Welch t-test.Physiological characterization of drought adaptation inBrachypodium distachyonSeeds of B. distachyon accession 21–3 were obtained fromthe National Small Grains Collection (NSGC). Seeds weresown in trays containing soil mixture (Tuff Merom Golan,Israel) and stored in 4°C for 48 h followed by 5d in darkroom (15°C). Seedling were transferred to greenhouse(22°C/16°C day/night, 10 h light/14 h dark) and plantedin pre-weighted 1 L pots. Plants were fully irrigatedthree times a week and fertilized with 1 g L−1 N:P:K(20% nitrogen, 20% phosphorus, 20% potassium) +micronutrients, two months after germination. Plantswere transferred to a long day regime (15 h light/9 hdark) 10 weeks after germination (six replicates in eachtreatment). At booting stage (BBCH scale 4.5 [73])drought was applied gradually and maintained at 40%relative soil water content for 17d.Measurements of osmotic potential and relative watercontent (RWC) were conducted on third leaf at mid-day.For osmotic potential analysis, leaves were placed in vialscontaining double-distilled water and kept in dark coldroom for 4 h. Leaves were then dried and frozen in li-quid nitrogen. Osmotic potential of the leaf sap wasassessed using a vapor pressure osmometer (Vapro5600,Wescor Inc., USA). For RWC analysis, leaves wereplaced in pre-weighted vials. Vials were immediatelyweighted to obtain fresh weight (FW) followed by hydra-tion for 6 h to full turgid. Samples were weighted to obtainleaf turgid weight (TW) and then oven dried at 75°C for72 h to obtain dry weight (DW). RWC was calculated as:RWC¼ FW ‐DW=TW ‐DWð Þ  100Leaf spectral reflectance, at wavelengths from 400 to1000 nm with an interval of ~0.2 nm, was measured atmid-day using a portable narrow-band width spectrometer(CI-700, CID Bio-Science Inc., USA). Leaf chlorophyllconcentration was estimated using transformed chloro-phyll absorption in reflectance index (TCARI) [74]:TCARI ¼ 3½ W700−W670ð Þ−0:2W700−W550ð Þ W700=W670ð ÞCulm length was measured from soil to spike base.Spikes and vegetative dry matter were harvested separatelyat the end of the experiment and oven dried (75°C for72 h). Samples were weighed and total biomass wascalculated.RNA extraction and qPCR assayFlag and second leaf samples from six independentplants were collected in the morning of the 17th day ofdrought stress and immediately frozen in liquid nitro-gen. Total RNA was extracted using Plant/Fungi TotalRNA Purification Kit (Norgen Biotek Corp., Canada)with on-column DNase treatment (Qiagen, Germany).RNA integrity was assessed with 2100 Bioanalyzer (Agi-lent Technologies Inc., Germany) and first strand cDNAwas synthesized using qScript™ cDNA Synthesis Kit(Quanta Biosciences Inc., USA) following manufacturer’sInc., USA). Gene-specific primers were designed usingincluded in the meta-analysis.Additional file 12: Figure S5. Enriched motifs in promoters ofSchmelz EA, et al. ABA is an essential signal for plant resistance toShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 16 of 18up-regulated shared drought-adaptive DEGs in (A) Arabidopsis and (B) rice.Additional file 13: Figure S6. Relative expression of shareddrought-adaptive orthologs under controlled and drought stressedBrachypodium distachyon plants.Additional file 14: Table S8. List of primers used for the qPCR assay.Additional file 15: Figure S7. Alteration in expression of carbohydrateand amino acid metabolic genes, involved in osmoregulation underdrought, that were detected by CSA:Drought.AbbreviationsABA: Abscisic acid; ABRE: ABA-Responsive Element; BP: Biological process;DEG: Differentially expressed gene; FC: Fold-change; GO: Gene ontology.Additional file 10: Table S6. Common genes between shared DEGsand independent lists obtained from Arabidopsis or wheat studies thatwere included in the meta-analysis.Additional file 11: Table S7. Significant up- and down-regulatedshared biological processes across-species.Primer-BLAST software [75] (Additional file 14: Table S8).The 2-ΔΔCT method [76] was used to normalize and cali-brate transcript values relative to two housekeeping genesGlyceraldehyde 3-phosphate dehydrogenase (GAPDH,BRADI3G14120) and S-adenosylmethionine decarboxylase(SamDC, BRADI2G02580) [77], whose their expressiondid not change in response to drought.Availability of supporting dataThe datasets supporting the results of this article are in-cluded within the article and its Additional files.Additional filesAdditional file 1: Table S1. Summary of studies and arrays that wereincluded in the CSA:Drought.Additional file 2: Figure S1. Hierarchal clustering of expression profilesin each species.Additional file 3: Table S2. Significant DEGs in each species, ascalculated by rank product statistics.Additional file 4: Figure S2. Significant up- and down-regulated GOsin each species.Additional file 5: Figure S3. A comparison between the shared GOsdetected by CSA:Drought and three independent Arabidopsis studies.Additional file 6: Table S3. Orthologs in each species, based on MGItool.Additional file 7: Table S4. Common and unique orthologs foundamong the four species.Additional file 8: Table S5. Shared drought-adaptive DEGs across-species,based on penalized Fisher method.Additional file 9: Figure S4. A comparison between the shared DEGsand independent lists obtained from (A) Arabidopsis or (B) wheat studiesinstructions. qPCR was carried out using PerfeCTa®SYBR® Green FastMix® (Quanta Biosciences Inc., USA)on the PikoReal RT-PCR system (Thermo Fisher scientificCompeting interestsThe authors declare that they have no competing interests.pathogens affecting JA biosynthesis and the activation of defenses inArabidopsis. Plant Cell. 2007;19(5):1665–81.15. Covington MF, Maloof JN, Straume M, Kay SA, Harmer SL. Globaltranscriptome analysis reveals circadian regulation of key pathways in plantgrowth and development. Genome Biol. 2008;9(8):R130.16. Ehlting J, Chowrira SG, Mattheus N, Aeschliman DS, Arimura G, Bohlmann J.Authors’ contributionLSM, SH and ZP designed the research and interpreted the results. LSM andSH analyzed the microarray data. LSM conducted the physiological andtranscriptional assays in B. distachyon. LSM, SH and ZP wrote the paper. Allauthors have read and approved the final manuscript.AcknowledgmentsThis research was supported by the United States-Israel Binational ScienceFoundation (BSF) (grant #2011310) and The Hebrew University of JerusalemIntramural Research Found Career Development. LSM was supported by TheIsraeli President’s Scholarship for Scientific Excellence and Innovation. Wethank Prof. A. Korol (University of Haifa) for the computational resourcesmade available for this study.Author details1The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture,The Hebrew University of Jerusalem, Rehovot 7610001, Israel. 2Presentaddress: Department of Botany, University of British Columbia, Vancouver, BC,Canada.Received: 16 February 2015 Accepted: 16 April 2015References1. Peleg Z, Blumwald E. Hormone balance and abiotic stress tolerance in cropplants. Curr Opin Plant Biol. 2011;14(3):290–5.2. Claeys H, Inze D. The agony of choice: how plants balance growth andsurvival under water-limiting conditions. Plant Physiol. 2013;162(4):1768–79.3. Nakashima K, Yamaguchi-Shinozaki K, Shinozaki K. The transcriptionalregulatory network in the drought response and its crosstalk in abioticstress responses including drought, cold, and heat. Front Plant Sci.2014;5:170.4. Abebe T, Melmaiee K, Berg V, Wise RP. Drought response in the spikes ofbarley: gene expression in the lemma, palea, awn, and seed. Funct IntegrGenomics. 2010;10(2):191–205.5. van Dijk K, Ding Y, Malkaram S, Riethoven JJM, Liu R, Yang JY, et al.Dynamic changes in genome-wide histone H3 lysine 4 methylation patternsin response to dehydration stress in Arabidopsis thaliana. BMC Plant Biol.2010;10:238.6. Peleg Z, Reguera M, Tumimbang E, Walia H, Blumwald E. Cytokinin-mediatedsource/sink modifications improve drought tolerance and increase grain yieldin rice under water-stress. Plant Biotech J. 2011;9(7):747–58.7. Ding XP, Li XK, Xiong LZ. Insight into differential responses of upland andpaddy rice to drought stress by comparative expression profiling analysis.Int J Mol Sci. 2013;14(3):5214–38.8. Deyholos MK. Making the most of drought and salinity transcriptomics.Plant Cell Environ. 2010;33(4):648–54.9. Feichtinger J, Thallinger G, McFarlane R, Larcombe L. Microarray meta-analysis: from data to expression to biological relationships. In: Trajanoski Z,editor. Computational Medicine. Vienna: Springer; 2012. p. 59–77.10. Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting ameta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9):e184.11. Lu Y, Huggins P, Bar-Joseph Z. Cross species analysis of microarrayexpression data. Bioinformatics. 2009;25(12):1476–83.12. Tseng GC, Ghosh D, Feingold E. Comprehensive literature review andstatistical considerations for microarray meta-analysis. Nucleic Acids Res.2012;40(9):3785–99.13. Rung J, Brazma A. Reuse of public genome-wide gene expression data.Nat Rev Genet. 2013;14(2):89–99.14. Adie BA, Perez-Perez J, Perez-Perez MM, Godoy M, Sanchez-Serrano JJ,Comparative transcriptome analysis of Arabidopsis thaliana infested bydiamond back moth (Plutella xylostella) larvae reveals signatures of stressresponse, secondary metabolism, and signalling. BMC Genomics. 2008;9:154.Shaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 17 of 1817. Lee I, Ambaru B, Thakkar P, Marcotte E, Rhee SY. Rational association ofgenes with traits using a genome scale gene network for Arabidopsisthaliana. Nat Biotechnol. 2010;28(2):149–56.18. Cohen D, Bogeat-Triboulot MB, Tisserant E, Balzergue S, Martin-MagnietteML, Lelandais G, et al. Comparative transcriptomics of drought responses inPopulus: a meta-analysis of genome-wide expression profiling in matureleaves and root apices across two genotypes. BMC Genomics. 2010;11:630.19. Bassel GW, Lanc H, Glaab E, Gibbs DJ, Gerjets T, Krasnogor N, et al. Genomewide network model capturing seed germination reveals coordinatedregulation of plant cellular phase transitions. Proc Natl Acad Sci U S A.2011;108(23):9709–14.20. Bhargava A, Clabaugh I, To JP, Maxwell BB, Chiang YH, Schaller GE, et al.Identification of cytokinin-responsive genes using microarray meta-analysisand RNA-Seq in Arabidopsis. Plant Physiol. 2013;162(1):272–94.21. Ransbotyn V, Yeger-Lotem E, Basha O, Acuna T, Verduyn C, Gordon M,Chalifa-Caspi V, Hannah MA, Barak S. A combination of gene expressionranking and co-expression network analysis increases discovery rate inlarge-scale mutant screens for novel Arabidopsis thaliana abiotic stressgenes. Plant Biotechnol. J. 2015; In press (doi: 10.1111/pbi.12274).22. Shaik R, Ramakrishna W. Machine learning approaches distinguish multiplestress conditions using stress-responsive genes and identify candidategenes for broad resistance in rice. Plant Physiol. 2014;164(1):481–95.23. Mustroph A, Lee SC, Oosumi T, Zanetti ME, Yang HJ, Ma K, et al. Cross-kingdom comparison of transcriptomic adjustments to low-oxygenstress highlights conserved and plant-specific responses. Plant Physiol.2010;152(3):1484–500.24. Pinheiro C, Chaves MM. Photosynthesis and drought: can we makemetabolic connections from available data? J Exp Bot. 2011;62(3):869–82.25. Shaik R, Ramakrishna W. Genes and co-expression modules common todrought and bacterial stress responses in Arabidopsis and rice. PLoS One.2013;8(10):e77261.26. Harb A, Krishnan A, Ambavaram MMR, Pereira A. Molecular andphysiological analysis of drought stress in Arabidopsis reveals earlyresponses leading to acclimation in plant growth. Plant Physiol.2010;154(3):1254–71.27. Wilkins O, Brautigam K, Campbell MM. Time of day shapes Arabidopsisdrought transcriptomes. Plant J. 2010;63(5):715–27.28. Aprile A, Mastrangelo AM, De Leonardis AM, Galiba G, Roncaglia E, Ferrari F,et al. Transcriptional profiling in response to terminal drought stress revealsdifferential responses along the wheat genome. BMC genomics. 2009;10:279.29. Krugman T, Chague V, Peleg Z, Balzergue S, Just J, Korol AB, et al. Multilevelregulation and signalling processes associated with adaptation to terminaldrought in wild emmer wheat. Funct Integr Genomics. 2010;10(2):167–86.30. Kadam S, Singh K, Shukla S, Goel S, Vikram P, Pawar V, et al. Genomicassociations for drought tolerance on the short arm of wheat chromosome4B. Funct Integr Genomics. 2012;12(3):447–64.31. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Geneontology: tool for the unification of biology. The Gene OntologyConsortium. Nature Genet. 2000;25(1):25–9.32. Reguera M, Peleg Z, Blumwald E. Targeting metabolic pathways for geneticengineering abiotic stress-tolerance in crops. Biochim Biophys Acta.2012;1819(2):186–94.33. Menkens AE, Schindler U, Cashmore AR. The G-box: a ubiquitous regulatoryDNA element in plants bound by the GBF family of bZIP proteins. TrendsBiochem Sci. 1995;20(12):506–10.34. Maruyama K, Todaka D, Mizoi J, Yoshida T, Kidokoro S, Matsukura S, et al.Identification of cis-acting promoter elements in cold- and dehydration-induced transcriptional pathways in Arabidopsis, rice, and soybean. DNA Res.2012;19(1):37–49.35. Hankinson O. The aryl hydrocarbon receptor complex. Annu Rev Pharmacol.1995;35:307–40.36. Kernodle SP, Scandalios JG. Structural organization, regulation, andexpression of the chloroplastic superoxide dismutase Sod1 gene in maize.Arch Biochem Biophys. 2001;391(1):137–47.37. Mur L, Corke F, Doonan J. Brachypodium: A model temperate grass. In: eLS.John Wiley & Sons, Ltd, chichester; 2015.38. Hu H, Xiong L. Genetic engineering and breeding of drought-resistantcrops. Annu Rev Plant Biol. 2014;65:715–41.39. Blum A. Effective use of water (EUW) and not water-use efficiency (WUE) isthe target of crop yield improvement under drought stress. Field Crop Res.2009;112(2–3):119–23.40. Peleg Z, Apse MP, Blumwald E. Engineering salinity and water-stress tolerancein crop plants: getting closer to the field. Adv Bot Res. 2011;57:405–43.41. Rontein D, Basset G, Hanson AD. Metabolic engineering of osmoprotectantaccumulation in plants. Metab Eng. 2002;4(1):49–56.42. Garg AK, Kim JK, Owens TG, Ranwala AP, Do Choi Y, Kochian LV, et al.Trehalose accumulation in rice plants confers high tolerance levelsto different abiotic stresses. Proc Natl Acad Sci U S A.2002;99(25):15898–903.43. Karim S, Aronsson H, Ericson H, Pirhonen M, Leyman B, Welin B, et al.Improved drought tolerance without undesired side effects in transgenicplants producing trehalose. Plant Mol Biol. 2007;64(4):371–86.44. Miranda JA, Avonce N, Suarez R, Thevelein JM, Van Dijck P, Iturriaga G. Abifunctional TPS-TPP enzyme from yeast confers tolerance to multipleand extreme abiotic-stress conditions in transgenic Arabidopsis. Planta.2007;226(6):1411–21.45. Suarez R, Calderon C, Iturriaga G. Enhanced tolerance to multipleabiotic stresses in transgenic alfalfa accumulating trehalose. Crop Sci.2009;49(5):1791–9.46. Roitsch T, Gonzalez MC. Function and regulation of plant invertases: sweetsensations. Trends Plant Sci. 2004;9(12):606–13.47. Nunes-Nesi A, Fernie AR, Stitt M. Metabolic and signaling aspectsunderpinning the regulation of plant carbon nitrogen interactions. MolPlant. 2010;3(6):973–96.48. Reguera M, Peleg Z, Abdel-Tawab YM, Tumimbang EB, Delatorre CA,Blumwald E. Stress-Induced cytokinin synthesis increases droughttolerance through the coordinated regulation of carbon and nitrogenassimilation in rice. Plant Physiol. 2013;163(4):1609–22.49. Ahanger MA, Tyagi SR, Wani MR, Ahmad P. Drought tolerance: role oforganic osmolytes, growth regulators, and mineral nutrients. In:Physiological Mechanisms and Adaptation Strategies in Plants UnderChanging Environment. Springer, New York; 2014. p. 25–55.50. Verbruggen N, Hermans C. Proline accumulation in plants: A review. AminoAcids. 2008;35(4):753–9.51. Kishor P, Hong Z, Miao GH, Hu C, Verma D. Overexpression of Δ1-pyrroline-5-carboxylate synthetase increases proline production and confersosmotolerance in transgenic plants. Plant Physiol. 1995;108(4):1387–94.52. You J, Hu H, Xiong L. An ornithine d-aminotransferase gene OsOAT confersdrought and oxidative stress tolerance in rice. Plant Sci. 2012;197:59–69.53. Chen JB, Yang JW, Zhang ZY, Feng XF, Wang SM. Two P5CS genes fromcommon bean exhibiting different tolerance to salt stress in transgenicArabidopsis. J Genet. 2013;92(3):461–9.54. Guo P, Baum M, Grando S, Ceccarelli S, Bai G, Li R, et al. Differentiallyexpressed genes between drought-tolerant and drought-sensitive barleygenotypes in response to drought stress during the reproductive stage.J Exp Bot. 2009;60(12):3531–44.55. Munne-Bosch S, Alegre L. Die and let live: leaf senescence contributes toplant survival under drought stress. Funct Plant Biol. 2004;31(3):203–16.56. Sreenivasulu N, Harshavardhan VT, Govind G, Seiler C, Kohli A. Contrapuntalrole of ABA: does it mediate stress tolerance or plant growth retardationunder long-term drought stress? Gene. 2012;506(2):265–73.57. Yamaguchi-Shinozaki K, Shinozaki K. Transcriptional regulatory networks incellular responses and tolerance to dehydration and cold stresses. Annu RevPlant Biol. 2006;57:781–803.58. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy–analysis of AffymetrixGeneChip data at the probe level. Bioinformatics. 2004;20(3):307–15.59. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple,yet powerful, new method to detect differentially regulated genes inreplicated microarray experiments. FEBS Lett. 2004;573(1–3):83–92.60. Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J.RankProd: a bioconductor package for detecting differentially expressedgenes in meta-analysis. Bioinformatics. 2006;22(22):2825–7.61. Heskes T, Eisinga R, Breitling R. A fast algorithm for determining boundsand accurate approximate p-values of the rank product statistic forreplicate experiments. BMC Bioinform. 2014;15(1):367.62. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer;2009.63. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.64. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, et al. MAPMAN: auser-driven tool to display genomics data sets onto diagrams of metabolicpathways and other biological processes. Plant J. 2004;37(6):914–39.65. Liu X, Brutlag DL, Liu JS. BioProspector: discovering conserved DNA motifsin upstream regulatory regions of co-expressed genes. Pac Symp Biocomput.2001;6:127–38.66. Sun H, Yuan Y, Wu Y, Liu H, Liu JS, Xie H. Tmod: toolbox of motif discovery.Bioinformatics. 2010;26(3):405–7.67. Giraudat J, Parcy F, Bertauche N, Gosti F, Leung J, Morris PC, et al. Currentadvances in abscisic acid action and signalling. Plant Mol Biol.1994;26(5):1557–77.68. Yamaguchi-Shinozaki K, Shinozaki K. A novel cis-acting element in anArabidopsis gene is involved in responsiveness to drought, low-temperature,or high-salt stress. Plant Cell. 1994;6(2):251–64.69. Jensen ST, Liu JS. BioOptimizer: a Bayesian scoring function approach tomotif discovery. Bioinformatics. 2004;20(10):1557–64.70. Mayer KF, Waugh R, Brown JW, Schulman A, Langridge P, Platzer M, et al. Aphysical, genetic and functional sequence assembly of the barley genome.Nature. 2012;491(7426):711–6.71. Brenchley R, Spannagl M, Pfeifer M, Barker GL, D’Amore R, Allen AM, et al.Analysis of the bread wheat genome using whole-genome shotgun sequencing.Nature. 2012;491(7426):705–10.72. Kunin V, Ahren D, Goldovsky L, Janssen P, Ouzounis CA. Measuring genomeconservation across taxa: divided strains and united kingdoms. NucleicAcids Res. 2005;33(2):616–21.73. Hong SY, Park JH, Cho SH, Yang MS, Park CM. Phenological growth stagesof Brachypodium distachyon: codification and description. Weed Res.2011;51(6):612–20.74. Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L.Integrated narrow-band vegetation indices for prediction of cropchlorophyll content for application to precision agriculture. RemoteSens Environ. 2002;81(2–3):416–26.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionShaar-Moshe et al. BMC Plant Biology  (2015) 15:111 Page 18 of 1875. Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL.Primer-BLAST: a tool to design target-specific primers for polymerasechain reaction. BMC Bioinformatics. 2012;13:134.76. Livak KJ, Schmittgen TD. Analysis of relative gene expression datausing real-time quantitative PCR and the 2−ΔΔCT method. Methods.2001;25(4):402–8.77. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM. Exploring valid referencegenes for gene expression studies in Brachypodium distachyon by real-timePCR. BMC Plant Biol. 2008;8:112.Submit your manuscript at www.biomedcentral.com/submit

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