Open Collections

UBC Faculty Research and Publications

RNA-Seq analysis identifies genes associated with differential reproductive success under drought-stress… Hübner, Sariel; Korol, Abraham B; Schmid, Karl J Jun 9, 2015

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-12870_2015_Article_528.pdf [ 3.01MB ]
JSON: 52383-1.0132632.json
JSON-LD: 52383-1.0132632-ld.json
RDF/XML (Pretty): 52383-1.0132632-rdf.xml
RDF/JSON: 52383-1.0132632-rdf.json
Turtle: 52383-1.0132632-turtle.txt
N-Triples: 52383-1.0132632-rdf-ntriples.txt
Original Record: 52383-1.0132632-source.json
Full Text

Full Text

RESEARCH ARTICLERNA-Seq analysis identifiearpWild barley (Hordeum spontanuem) is the direct progenitorof cultivated barley (Hordeum vulgare) and the two subspe-stantial differences between years, especially in the Levant,where Mediterranean and desert climates meet [7].important fit-]. Measuringtural popula-sed indirectlye differentialble and con-umber of off-in subsequentHübner et al. BMC Plant Biology  (2015) 15:134 DOI 10.1186/s12870-015-0528-zis affected by overall plant growth and development, butof Hohenheim, D-70593 Stuttgart, GermanyFull list of author information is available at the end of the articlegenerations, it still constitutes a major component of fitnessand is of interest for breeding purposes [12]. In plants, RS* Correspondence: karl.schmid@uni-hohenheim.de2Institute of Plant Breeding, Seed Science and Population Genetics, Universitybarley was long recognized as a source of useful geneticvariation for introgression into modern cultivars to breedmore robust varieties that are better adapted to environ-mental stresses [2–5]. Wild barley occurs in different habi-tats along the Fertile Crescent including extreme desertenvironments where it is frequently found in large standsof stable populations [6]. The core region of wild barley isunfavorable environmental conditions is anness component of individual plants [8Darwinian fitness of single genotypes in nations is challenging [9], but can be assesby measuring a fitness-associated trait likreproductive success (RS) under comparatrolled conditions [10, 11]. Although the nspring does not necessarily reflect successcies do not show a reproductive barrier [1]. Therefore, wild The ability to survive and reproduce under variable andBackground: The evolutionary basis of reproductive success in different environments is of major interest in thestudy of plant adaptation. Since the reproductive stage is particularly sensitive to drought, genes affecting reproductivesuccess during this stage are key players in the evolution of adaptive mechanisms. We used an ecological genomicsapproach to investigate the reproductive response of drought-tolerant and sensitive wild barley accessions originatingfrom different habitats in the Levant.Results: We sequenced mRNA extracted from spikelets at the flowering stage in drought-treated and control plants. Thebarley genome was used for a reference-guided assembly and differential expression analysis. Our approach enabled todetect biological processes affecting grain production under drought stress. We detected novel candidate genesand differentially expressed alleles associated with drought tolerance. Drought associated genes were shown tobe more conserved than non-associated genes, and drought-tolerance genes were found to evolve more rapidlythan other drought associated genes.Conclusions: We show that reproductive success under drought stress is not a habitat-specific trait but a sharedphysiological adaptation that appeared to evolve recently in the evolutionary history of wild barley. Exploring thegenomic basis of reproductive success under stress in crop wild progenitors is expected to have considerable ecologicaland economical applications.Keywords: Drought tolerance, Hordeum spontaneum (wild barley), Reproductive success, Adaptation, RNA-SeqBackground characterized by a wide range of environments with sub-associated with differentisuccess under drought-stof wild barley Hordeum sSariel Hübner1,3, Abraham B. Korol1 and Karl J. Schmid2*Abstract© 2015 Hübner et al. This is an Open Access a(,provided the original work is properly Accesss genesl reproductiveess in accessionsontaneumrticle distributed under the terms of the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium,. The Creative Commons Public Domain Dedication waiver (http://) applies to the data made available in this article, unless otherwise stated.Hübner et al. BMC Plant Biology  (2015) 15:134 Page 2 of 14the most sensitive stages to both elevated temperatures anddrought are meiosis and early grain maturation [13]. There-fore, the ability to tolerate unfavorable environmental con-ditions such as drought during reproductive developmentis a key component of plant RS [14].The sessile nature of most plant species entails two dif-ferent strategies to overcome unfavorable environmentalconditions: avoidance and tolerance [15]. The avoidancestrategy consists of a high growth rate and early floweringtime to complete the sensitive reproductive stage beforeunfavorable environmental conditions decrease reproduct-ive efficiency. This strategy is a major adaptive trait in wildbarley [16]. However, early maturation may lead to fewerand smaller grains under cool climatic conditions becausesensitive reproductive tissues could be damaged andfertilization may be suppressed [17]. An avoidance strategyis also disadvantageous in years with early drought duringthe flowering stage, which forces plants to reproduceunder unfavorable conditions [18]. Under a large-scale cli-mate change, a pure escape strategy may not be sufficientif environmental change becomes more extreme and vari-able between years. A second strategy is to tolerate stressby adaptive mechanisms and to continue with reproduct-ive development in spite of unfavorable conditions. Thisstrategy enables the completion of the growing stage andmay allow reproduction under a wider range of environ-mental conditions. It involves different mechanisms likedownregulation of metabolism, partitioning of amphiphiliccompounds and immobilization of cytoplasm, which mayvary according to the level of dehydration and maintainsustainable populations at periods of adverse conditions[19]. In wild barley, both fecundity and maternal invest-ment are sensitive to environmental changes and subjectto natural selection (e.g., [20]). Therefore, plants that re-produce in adjacent years with similar or different envir-onmental conditions are under constant selection, whichenhances adaptation to fluctuating environments [8]. Boththe escape and tolerance strategies are adaptive responsesto environmental selective pressures and may coexist in apopulation, but the tolerance strategy helps to maintainstable populations over time and is of interest for breedingvarieties with a higher yield stability in changing environ-ments [21].The identification of reproductive drought-tolerancegenes is essential for understanding the molecular mech-anisms of drought tolerance and plant adaptation. Oneapproach to identify such genes is to compare transcriptlevels at the reproductive stages among drought-tolerantand sensitive accessions that were exposed to droughttreatment [22]. A differential expression analysis to de-tect drought-tolerance genes in the wild ancestor of amajor crop may contribute to a better understanding ofRS mechanisms and the utilization of beneficial allelesfor breeding of more robust varieties.Expression profiling by massively parallel cDNA sequen-cing (RNA-Seq; [23]) is a cost-effective way to survey tran-scriptomes of different tissues and developmental stages.RNA-Seq accurately identifies gene expression profiles[24] with an appropriate experimental design, and maynot require a validation step with another method such asquantitative PCR [25, 26]. Thus, RNA-Seq is becomingthe technology of choice for studying expression profilesof non-model organisms [27, 28]. Since RNA-Seq enablesto combine gene discovery with the identification of allelicvariation, sequence variants associated with differentiallyexpressed genes in response to a treatment can be identi-fied. Such trait-associated variants are of prime interest forapplying marker-assisted selection in advanced breedingprograms [29].In this study, we addressed three objectives: (i) to pheno-typically discriminate between drought-tolerant and sensi-tive accessions with respect to RS under terminal droughtstress, (ii) to detect differentially expressed genes associatedwith drought tolerance, and (iii) to investigate ecologicaland evolutionary aspects of drought responsive genes inwild barley originating from different eco-geographical re-gions in Israel. We applied RNA-Seq to drought-tolerantand sensitive wild barley accessions grown in a common-garden experiment to study the genomic basis of RS underdrought and identified numerous candidate genes involvedin the response to drought during reproductive develop-ment. This study provides ecological and evolutionary in-sights into plant adaptation and an applied perspective forcrop breeding.ResultsSelection of drought-tolerant and sensitive accessionsThe Barley1K collection consists of wild barley ecotypesrepresenting the wide eco-geographical diversity in theSouthwestern part of the Fertile Crescent [30]. The 35selected accessions reflect the different eco-geographicalregions and phenological ecotypes present in this collec-tion [31]. We previously verified that all accessions used inthis study are free of traces of recent introgressions fromcultivated barley [32]. A common-garden experiment withthese accessions was conducted in a greenhouse duringthe winter of 2010 to evaluate their reproductive successunder terminal drought treatment (Fig. 1, Additionalfile 1: Figure S1 A,B). We define reproductive successas relative grain loss between treated and untreated plantsdue to water deficit during flowering and early maturation.The standard deviation of relative grain loss correlated withmean grain number (r2 = 0.24, p = 0.004). To reduce thisscaling effect we transformed the calculated difference be-tween drought and control treatments within blocks to alogarithmic scale (r2 = 0.19, p = 0.01). Since the extent ofgrain loss due to drought indicates the ability of a plant toreproduce despite unfavorable drought conditions, weB1K45021.01.5ins/Spike5ngHübner et al. BMC Plant Biology  (2015) 15:134 Page 3 of 14B1K3519B1K0903B1K2405B1K0412B1K1707B1K3615B1K4615B1K2711B1K2108B1K1703B1K3620B1K0205B1K0813B1K13100.00.5LogDiffNumGraFig. 1 Number of grains lost by the drought treatment for each of the 3in 2010. The dark horizontal line indicates the median, boxes represent the raconsidered smaller differences between treatments ashigher drought tolerance. We first tested whether RS inthe first year experiment was correlated with eco-geographic gradients in the native distribution range[30]. Mean grain loss in response to drought did notcorrelate with level of precipitation (Pearson’s r = −0.19,p = 0.26), geographic distance (r = −0.06, p = 0.12) orgenetic distance calculated from 42 microsatellitemarkers [30],[32] (r = 0.04, p = 0.3). The latter result in-dicates that differences in drought tolerance do not re-sult from genetic drift and population structure.Based on the common-garden experiment, we selectedtwo tolerant and two sensitive accessions for further ana-lysis (Table 1, Fig. 2) and repeated the experiment with thesame design in the following year under controlledTolerant accessions are marked with a green and sensitive accessions with a redTable 1 The four wild barley accessions selected from the Barley1Kprofiling after the phenotypic greenhouse trials in Atlit and HohenhAccession ID Response Sampling site CoordinB1K0412 Tolerant Ein Prat 353056EB1K3615 Tolerant Amiad 355318EB1K3516 Sensitive Beit Govrin 348992EB1K4620 Sensitive Amud stream 355028EAll accessionsDays to heading and difference (Δlog) in number of seeds/spike are indicated for thB1K0918B1K2611B1K3711B1K1013B1K0910B1K3404B1K3414B1K0210B1K4607B1K0307B1K4319B1K1507B1K3719B1K3516B1K1001B1K0711B1K2120B1K1105B1K3420B1K4620accessions in the drought experiment conducted at the Aaronson farme between first and third quartiles and whiskers extend to the extremes.conditions in a greenhouse at the University of Hohenheim,Germany (Additional file 1: Figure S1 C,D). A two-wayANOVA was performed with the four accessions to test theeffect of drought treatment applied to the selected acces-sions in the two environments (Atlit and Hohenheim) onthe number of grains per spike. The treatment (drought vs.control) caused strong differences in grain number(MS = 802.4, F = 44.09, p < 10−7), while the environment(MS = 4.7, F = 0.26, p = 0.61) and treatment × environ-ment interactions (MS = 0.7, F = 0.04, p = 0.84) had noeffect, which may reflect the controlled conditions ofthe experiment. A further two-way ANOVA quantifiedthe effects of the environment and a classification intodrought-tolerant or sensitive accessions on the extentof grain loss in response to the drought treatment. Theboxcollection for drought response screening and expressioneimates Mean days to heading(control/drought)ΔLog Grains/Spike2010 (sd), 318346 N 92/95.8 0.88 (0.50), 329259 N 101/99 1.01 (0.15), 315952 N 100/105 1.31 (0.16), 328723 N 102/99 1.58 (0.09)101.3/101.4 1.17 (0.38)e 2010 experimentHübner et al. BMC Plant Biology  (2015) 15:134 Page 4 of 14two tolerant accessions differed significantly from thesensitive accessions in the number of grain loss (MS = 0.87,F = 25.73, p = 5.28 × 10−5) regardless of the environmentaleffect or the classification × environment interaction(MS = 0, F = 0, p = 0.99 and MS = 0.04, F = 1.24, p = 0.28,respectively), which confirmed the results of the first yearexperiment.RNA-Seq and reference transcript assemblyIn the second-year experiment (U. Hohenheim), we sam-pled spikelets at the fertilization stage from each accessionunder treatment and control conditions for further analysis.Sampled mRNA was sequenced (RNA-Seq) to identify can-didate genes that are associated with differential reproduct-ive success under drought stress. We pooled the twospikelets to create a single sample for each of two tillerstaken from each of eight plants (4 genotypes x 2 treat-ments) during flowering stage (Fig. 2). We extracted totalFig. 2 General workflow of the study. a Analysis of differentially expressedcandidate genes. b Sampling strategy to produce the 16 sequenced cDNApooled spikelets were sampled at two replicates for each drought (dark) anRNA from each of the 16 samples and obtained 80 Gb ofpoly(A)-selected RNA sequences with an average of 50 mil-lion single-end reads with lengths of 96 bp per library(Additional file 2: Table S1). The preprocessing step re-moved 16-19 % of reads per library due to low quality. Readmapping to the Morex reference assembly with TopHatand Cufflinks produced assemblies of more than 40 millionreads per library with an average of 70x per-base coverage.These assemblies correspond to a total of 189,919 isoformsincluding 95,387 multi-exon transcripts and 20,905 multi-transcript loci with approximately 2.1 transcripts per locusover all accessions. Integrating the reference annotation filein the transcript assembly pipeline identified 49,340 codingsequences including novel transcripts in each accession.Among all libraries, 6.8-9.8 % of assembled loci, 5.2-6.5 %of exons and 6.3-7.4 % of introns were novel with respectto the reference genome. We further analyzed the assem-blies with SAMTools to call SNPs and short indels in thegenes associated with drought tolerance from greenhouse trials tolibraries. For each tolerant (green) and sensitive (red) accessions twod control (light) treatments for RNA extraction and sequencingfour wild barley accessions. Altogether, 298,778 SNPs andshort indels with a phred-quality >20 were identified.Interestingly, more polymorphisms segregated in thetwo Northern (mean = 229,828) than in the Southernaccessions (mean = 226,224) regardless of the numberof isoforms in each accession (r = 0.1; p = 0.9). This mayreflect a higher level of genetic variation in Northernwith their eco-geographic origin (i.e., Mediterranean vs.desert climate). In addition, we found no correlation be-tween geographic and genetic distances calculated from723 SNPs detected in drought-tolerance genes (r = −0.06,p = 0.91), nor between genetic distance and differential ex-pression profiles of these genes (r = −0.36, p = 0.47). Takentogether, the results indicate that the drought-responsechgeHübner et al. BMC Plant Biology  (2015) 15:134 Page 5 of 14ecotypes. Additional alignment information for eachaccession is given in Table 2.Differential RNA expression in drought tolerant andsensitive accessionsTo identify genes associated with response to droughtstress in tolerant and sensitive accessions, we quantifiedgene expression in spikelets sampled at the early floweringstage (see ‘Materials and Methods’). The experimental de-sign and replicated sampling allowed us to control for re-sidual variation within each accession (Fig. 2). To testwhether genetic drift (isolation-by-distance effects) andphysiological adaptation contribute to expression differ-ences, we compared expression, genetic, and geographicdistances between accessions. The two Northern acces-sions had approximately six times more differentiallyexpressed genes in common than the Southern accessionsafter correcting for the total number of genes (Fig. 3a).Among the top 50 differentially expressed genes in eachaccession (Fig. 3b), 8 genes were shared among sensitiveaccessions, 15 among tolerant accessions, none amongsouthern accessions, 12 among northern accessions andnone among all accessions. Hierarchical clustering ofSNPs in genes that are constitutively expressed across ac-cessions grouped the four individuals in accordance withtheir geographic origin (north/south) as expected by theeffect of neutral genetic drift (Fig. 3c). However, the sameclustering analysis based on SNPs in genes that were differ-entially expressed in drought-tolerant accessions but not indrought-sensitive accessions (drought-tolerance genes) re-sulted in a weaker geographic clustering as indicated bythe corresponding bootstrap values (Fig. 3c). Moreover,clustering the accessions based on their expression profilesgrouped the accessions in accordance with their pheno-typic drought-response classification (tolerant/sensitive)rather than their geographic origin and showed that theexpression of drought response genes is not consistentTable 2 Summary of transcript assemblies and annotations for eaAccession ID Average number of alignedreads (sd)Average coveraB1K0412 47,259,206 (5,881,069) 69.96 (11.16)B1K3516 51,879,703 (14,491,956) 66.83 (10.55)B1K3615 53,149,174 (13,822,620) 78.18 (16.11)B1K4620 45,748,092 (11,603,178) 70.01 (6.08)For each accession the average number of aligned reads and coverage from thedeviations are indicatedphenotype and the associated transcriptome patterns arenot associated with a putative local adaptation to majorhabitats (Mediterranean vs. desert) but represent a poly-morphic physiological response mechanism.To test the hypothesis that the genetic basis of droughttolerance represents a recent adaptation, we comparedgenetic diversity and the long-term evolutionary con-servation of genes representing the three major types ofexpression patterns: constitutively expressed in all acces-sions, drought-responsive (differentially expressed in all ac-cessions), and drought-tolerant (differentially expressed indrought-tolerant and constitutively expressed in drought-sensitive accessions). We selected a random set of 50 genesfrom each group to achieve balance between representationand randomness with respect to the total number of genesin each of the three groups (see Fig. 3a). The number ofSNPs was used as a measurement for genetic diversity aftercorrecting for transcript length. Constitutively expressedgenes showed a lower diversity (4.51 SNPs/Kb) than bothdrought-tolerance (6.63 SNPs/Kb, twelch = 2.26, p = 0.02)and drought-responsive genes (6.18 SNPs/Kb, twelch = 2.67,p = 0.008). The average diversity of drought-tolerance geneswas not significantly higher than of drought-responsivegenes (twelch = 0.23, p = 0.82). To characterize the evolution-ary conservation of drought-responsive compared withnon-responsive genes we randomly sampled 100 genesshowing differential or non-differential expression in re-sponse to the drought treatment separately for each acces-sion (Fig. 4). We determined the level of conservation bysequence comparison to homologs in Brachypodium dis-tachyon, Oryza sativa, and Sorghum bicolor using the se-quences from the barley reference assembly (Morex) asquery sequence to reduce any mismatch effect resultingfrom sequence diversity in the wild barley accessions. Thegroup of drought-responsive genes (differentially expressedin all accessions) showed more hits against the three speciesthan non-responsive genes (tWelch,= 8.13, p = 0.004)of the four accessions analyzed(sd) Isoforms SNPs Introns Exons152,061 227,441 149,604 323,806156,474 225,007 151,973 330,710156,951 230,152 151,794 330,838155,644 229,505 151,416 329,419corresponding four libraries (two drought and two control) and standardHübner et al. BMC Plant Biology  (2015) 15:134 Page 6 of 14indicating that drought-responsive genes tend to be moreconserved. The drought-responsive genes were also moreconserved than drought-tolerance genes, which are differ-entially expressed only in drought tolerant accessions(t = 9.77, p = 0.01; Fig, 4d).Fourteen genes were differentially expressed in responseto drought treatment across all accessions, of which 12genes are associated with drought stress (e.g., Pairedamphipathic helix protein LEA [33], expansin [34] andVQ-motif transcription factor [35]; Additional file 3:Table S2). Overall, more differentially expressed genes werefound in the sensitive (B1K4620 = 1,345, B1K3516 = 821)Fig. 3 Analysis of differential expression in ‘drought’ versus ‘control’ treatments.corresponding number of significantly enriched (FDR< 0.05) gene ontology in rexpressed genes between drought and control treatments for each accession. cdistances based on differentially (DEGs) and non-differentially expressed genes (differentially expressed genes (DEGs). Drought tolerant accessions are printed in(north/south) is indicated below. Bootstrap probability values (bp) are printed inthan in the tolerant accessions (B1K3615 = 348,B1K0412 = 254). Out of 99 differentially expressedgenes in drought tolerant accessions, 85 were detectedonly in the two tolerant accessions and not in the sensitiveaccessions of which 6 are drought-associated transcriptionfactors (e.g., WRKY, BZIP, MADS-Box), 5 unclassifiedretrotransposon proteins and transposase, 5 fertility-associated genes (e.g., Chalcone synthase, Squalene syn-thase, and Prostaglandin E synthase), and 13 genes ofunknown function. We consider these as candidate genesthat contribute to reproductive success under droughtstress in wild barley (Additional file 4: Table S3). Ina Venn diagram of overall differentially expressed genes and theesponse to drought treatment. b The top 50 significant differentiallyDendrograms of geographic distances between accessions, geneticNon-DEGs), and expression distance calculated from the log-fold change ingreen and sensitive accessions in red and their region of originpurple and approximate unbiased probability (au) values are printed in blueBrHübner et al. BMC Plant Biology  (2015) 15:134 Page 7 of 14Fig. 4 Evolutionary conservation based on the proportion of BLAST hits toaddition, 396 genes were differentially expressed in thedrought-sensitive but not in the tolerant accessions. Amongthese genes, several drought-induced genes were detected(e.g., AP2, U-box, Serine proteases, and Peroxidase), and 50genes of unknown function, which are candidate genes forfurther studies (Additional file 5: Table S4).Functional annotation of differentially expressed allelsTo infer the biological processes and functions ofgenes associated with drought stress response, we con-ducted a gene ontology (GO) analysis separately foreach accession (Additional file 6: Table S5). Althoughmore genes were differentially expressed in sensitive(410) than tolerant accessions (99), more GO categor-ies were enriched in tolerant compared with sensitiveaccessions. Altogether, 90 categories were enriched(FDR < 0.05) in all samples, and DNA repair was theonly category enriched across all accessions. Two cat-egories (hydrolase activity and DNA repair) wereenriched in the sensitive accessions and 12 categories(e.g., DNA helicase activity, thiol oxidase activity, andglycine biosynthetic process) in the tolerant accessions(Figs. 3a and 5). Of the 12 categories enriched in thetolerant accessions, at least four categories areprotein databases. The dark horizontal line indicates the median, boxes represthe extremes. For each comparison A-D, t scores and p-values are indicated inversus non-responsive genes (Non-DEGs), b drought-tolerance genes (DEGs Tin all accessions versus drought-tolerance genes, and d drought-responsi(red) conservation along time scale since divergence from barleyachypodium distachyon, Oryza sativa, and Sorghum bicolor non-redundantassociated with carbon metabolism, which has an im-portant role in enhanced stress tolerance in plants.We further investigated sequence variation in drought-tolerance genes. The 99 genes differentially expressedin the tolerant accessions harbor 1,056 high quality(phred score > 20) SNPs and short indels, of which 42polymorphisms differentiate between the two drought-tolerant and the sensitive accessions. To examinewhether alleles that are specific to drought-tolerantaccessions and different from the Morex reference area potential source of useful genetic variation, we se-lected four candidate drought-tolerance genes andcharacterized potential functional effects of allelicvariation with the SnpEff program (Table 3, Additionalfile 7: Figure S2). Two genes were previously associatedwith drought response (AK362742, AK368692), one withpollen viability (MLOC_67950.1), and one is of un-known function (AK370720). In AK368692, one vari-ant was located upstream to the coding region and inAK362742 two variants were synonymous substitu-tions. In MLOC_67950.1, a non-synonymous substitu-tion (GtG/GcG: valine/alanine) was found in thecoding region and one allele in AK370720 was de-tected (aAG/tAG: Lysine/stop) as leading to prema-ture stop codon.ent the range between first and third quartiles and whiskers extend totop-right box. a drought-responsive genes in all accessions (DEGs All)olerance) versus non-responsive genes, c The drought-responsive genesve genes in all accessions (green) versus drought-tolerance genesFig. 5 Functional annotation analysis of overall gene expression. Distribution of significantly enriched GOs in all accessions. Shared category among allaccessions is colored with red, shared categories among tolerant accessions is colored in green, and shared categories among sensitive accessions iscolored in orangeTable 3 Differentially expressed alleles between tolerant and sensitive accessionsContig Gene ID Gene Description POS Chr REF ALT QUAL Effect B1K0412 B1K3516 B1K3615 B1K4620morex_contig_53956 MLOC_67950.1 GPI mannosyltransferase 7720 5HL A G 243 Moderate 1/1 0/0 1/1 0/0morex_contig_65282 AK362742 Dehydrogenase/reductase3352 6HS C T 28.6 Low 1/1 0/0 1/1 0/0morex_contig_1575714 AK368692 Vacuolar protein 4665 6HL C T 80.4 Modifier 1/1 0/0 1/1 0/0morex_contig_1734244 AK370720 unknown protein 82 7HL A T 48.8 High 1/1 0/0 1/1 0/0Candidate variants after filtering for heterozygosity and including only non-reference alleles associated with drought tolerance. The contig name in the Morexassembly is indicated, the annotated gene (Gene ID), variant position within contig (POS), chromosome arm (Chr), the reference (REF) and alternative (ALT)variants, the phred-quality (QUAL), the variant impact (Effect), and the genotype of each accessionHübner et al. BMC Plant Biology  (2015) 15:134 Page 8 of 14Evolution of drought-tolerance genesHübner et al. BMC Plant Biology  (2015) 15:134 Page 9 of 14DiscussionIn this study, we combined phenotypic analysis withRNA-Seq to investigate the phenotypic variation andtranscriptomic basis of reproductive success underdrought stress in the wild ancestor of cultivated barley.We observed a substantial level of phenotypic variationamong accessions and found that gene expression patternsare similar between drought tolerant accessions with dif-ferent genetic background and geographic origin.Detection of drought-tolerant accessions from naturalpopulationsThe 35 accessions selected from the Barley1K collec-tion for this study represent the three major ecotypesin the Levant [30–32]. These accessions were screenedto test whether they differ in reproductive successunder terminal drought stress in controlled conditions.We further verified that the four selected accessions forthe expression analysis truly represent differences in re-sponse to drought and that our drought-treatments wasthe major contributor to reduction in the number ofseeds for each accession. Both tests confirmed our ex-perimental setup and indicated a marginal contributionof the environment and environment × genotype inter-actions. Although isolating the factors of interest is themajor benefit of a common-garden experiment, the re-sponse to drought under natural conditions is an en-semble of interactions with many abiotic and bioticfactors involved. The comparison of the relative num-ber of grains produced under drought with geographicdistance, genetic distance, and precipitation gradientrevealed different levels of reproductive success withinecotypes regardless of their eco-geographic location.This result contrasts previous studies [36] and suggeststhat reproductive drought tolerance is not solely re-stricted to areas of low precipitation, which is consist-ent with the hypothesis that alternating selection (e.g.,through changing the physiological optimum) may actto maintain a population under changing environmen-tal conditions [37]. Therefore, accessions with high re-productive success under unfavorable environmentalconditions are expected to occur also in regions withchanging precipitation in adjacent years. Clustering ofexpression data further supported our observation thatsimilar physiological responses are found in differentecotypes (desert vs. Mediterranean). In contrast, theSNPs identified in the transcriptome sequences clearlygrouped the four accessions in accordance with their eco-geographic origin, thereby supporting the previous popu-lation genetic and phenotypic analyses of the Barley1Kcollection [30–32]. The clustering analysis with SNPs fromdrought-tolerance associated genes differentiated the eco-types less, which suggests that these genes evolved differ-ently than other parts of the transcriptome. AlthoughThe differential gene expression in plants under droughtand control treatments for both tolerant and sensitiveaccessions enabled us to identify sets of genes associ-ated with reproductive success under terminal droughtin accessions from different eco-geographical regions.Drought-responsive genes common to all accessionsare more evolutionarily conserved than non-differentiallyexpressed genes. High evolutionary conservation is ex-pected for functionally important genes due to purify-ing selection that reduces the rate of evolution relativeto neutrality [45, 46]. In genes associated with anavoidance-strategy like flowering time variation, differ-ent alleles may be fixed along eco-geographic gradients[16], whereas drought-tolerance genes are expected toevolve under balancing selection in different geographicregions [47]. Our results support this observation be-cause of a higher genetic diversity in drought-responsivethan non-responsive genes. In addition, genes associatedwith drought-tolerance, which are differentially expressedonly in tolerant accessions, tend to evolve faster than otherdrought-responsive genes (differentially expressed in allaccessions). Relative position in the signaling pathwayassociated with the response to drought may be aplausible explanation in linear biochemical networks[48]. However, in more complex networks (as in ourgenetic drift leads to genomic divergence in accordancewith isolation by distance, physiological adaptation tosimilar types of stress in different regions may occurthrough a small number of genetic changes, which influ-ences the clustering mode. Additional causes for the dif-ferential response to drought and the underlying geneexpression may involve changes in gene regulation bystructural variation [38], epigenetic modifications of chro-matin state [39], transposable elements activity [40], or acombination of more than one mechanism.Drought is a major selective constraint in the evolu-tion of plants. However, the relative contribution of se-lection acting on new and standing genetic variation, orphenotypic plasticity is still unknown. Although drought isseen as a diversifying factor in population dynamics[30] we show that in contrast to previous studies [41]and in accordance to others [42, 43], a substantial vari-ation exist within diverged populations in drought re-sponse, supported by both phenotypic and transcriptomeanalysis. Further analysis is required to quantify therelative contribution of adaptive phenotypic plasticityand pleiotropic gene action to drought tolerance inplants [44] as well as the role of genetic and the correlation between function and rate of evo-lution is less obvious.Hübner et al. BMC Plant Biology  (2015) 15:134 Page 10 of 14The genetic basis of reproductive success under droughtin wild barleyDrought stress during reproductive stages may reduce yieldby up to 60 %, mostly due to reduction in grain number[49]. The traits most sensitive to reproduction-associateddrought stress are pollen viability, stigma receptivity,panicle exertion, anther dehiscence, and early grain devel-opment [13]. We found differentially expressed genes asso-ciated with these traits in this study ( Additional file 3:Table S2). The most prominent biological process enrichedin all accessions in response to drought was DNA repairwhich plays a critical role during meiosis [50] and seed de-velopment [51]. Several genes associated with reproductivesuccess under stress were detected exclusively among thedrought-tolerant accessions, and could potentially be usedfor breeding of more drought tolerant varieties ( Additionalfile 4: Table S3). For example, the flavonoid synthesis path-way gene Chalcone synthase was identified among the can-didates (Log-fold change = 2.87; Additional file 4: Table S3).Although its mechanistic role in drought stress is still un-known, Chalcone synthase was previously reported as acontributor to reproductive success under heat stress [52].Another group of genes associated with drought tolerancewere bZIP transcription factors that are involved in both re-sponse to stress and reproductive development success[53]. Several genes associated with response to droughtstress were detected among the drought-sensitive acces-sions. Interestingly, AP2 of the super-family of DREB geneswas found among the over-expressed genes in response todrought. The DREB protein family comprises importantplant transcription factors that regulate the expression ofnumerous stress-responsive genes, and DREB proteins as-sociated with enhanced stress tolerance [54]. A possible ex-planation for the higher expression of DREB proteinsamong sensitive than tolerant accessions is that thedrought-tolerance mechanisms during the vegetative state(in which AP2 is expressed) is different from the mechan-ism acting during fertilization and reproduction [12]. Theadaptive value of genes expressed in sensitive accessions isunknown and requires further study.Among the tolerant accessions, several categories associ-ated with carbon metabolism were enriched. Droughtstress can affect plant viability through carbon starvation,which is tightly interdependent on both the avoidance andoccurrence of hydraulic failure through impacts onmaintenance metabolism [55]. An increased carbohy-drate content threshold in tolerant accessions is a pos-sible mechanism by which increased fitness underdrought stress is achieved. Another enriched processassociated with drought tolerance involves thiol metab-olism (three enriched categories), which is a centralmechanism of protecting plants from oxidative damagecaused by environmental stresses such as drought [56]. Tobetter understand the contribution of these biologicalfunctions to drought tolerance further support is neededfrom metabolic pathways analysis and eQTL mapping in asegregating population [57].One advantage of RNA-Seq is the combination of differ-ential expression analysis with sequence polymorphism de-tection, which allows to associate differentially expressedalleles with a trait of interest and to identify potential effectson protein function [58, 59]. In this study, we predicted theexpected effect of differentially expressed alleles in proteinfunction in four candidate genes after filtering for low qual-ity SNPs [60]. Three of these candidate genes were identi-fied as drought responsive genes and one is of unknownfunction. These genes are known to be associated withpollen viability, ABA biosynthesis [61], and vacuolar pro-cesses, which contribute to an increased flexibility to copewith environmental changes [62]. These genes are majorcandidates for increasing RS under drought stress and canserve as a lead for further functional and physiological stud-ies to unravel the complex mechanisms associated withdrought tolerance in plants. It should be noted that SNPannotations and effect predictions need to be addressedwith caution because they rely on robust sequence annota-tion which is still under development for barley.Here we showed that the differential response to droughtstress of tolerant and sensitive plants during reproductivedevelopment is the outcome of adaptation to commonenvironmental stress regardless of eco-geographic andgenetic distances. Reproductive success in wild barleyunder drought stress is not an ecotype-specific traitthat evolved as a local adaptation, but appears to be aphysiological adaptation which evolved similarly in dif-ferent regions and which is characterized by an in-creased evolutionary flexibility.ConclusionsReproductive success under drought stress is an import-ant trait in the study of fitness and adaptation in naturalpopulations and for breeding high yielding varieties thatcan sustain harsh environments. Using transcriptomeanalysis of a common-garden experiment we show thatreproductive success under drought stress has evolvedsimilarly in different habitats indicating a shared physio-logical adaptation. Moreover, drought responsive geneswere found to be more conserved in evolution thannon-responsive genes and drought-tolerant genes werefound to evolve recently in the evolutionary history ofwild barley.MethodsPlant material and field trialsWe selected representative wild barley accessions from dif-ferent eco-geographical regions in the southwestern part ofthe Fertile Crescent from the Barley1K collection [30].These accessions were grown in a greenhouse underHübner et al. BMC Plant Biology  (2015) 15:134 Page 11 of 14common-garden conditions in the winter of 2010 at theAaronson farm in Atlit, Israel (32°42’35”N, 45°33’34”E).Each of the selected 35 accessions was sterilized with 4 %NaOH, wrapped in germination paper, and placed in a colddark room (4 °C) for one week to break dormancy and im-prove germination. Germinating seeds were transplantedinto 5-l pots containing a sand and pit mixture (1:1) andplaced in a greenhouse under full irrigation regime with adaily amount of 200 cm3 water per plant. The experimentwas conducted in a randomized block design (RBD) whereboth ‘drought’ and ‘control’ treatments for each accessionwere replicated once within each of six blocks. This experi-mental design enabled to evaluate the differences betweentreatments with the lowest experimental variation.All plants were irrigated daily using a drip system untilthe flag leaf emerged in 90 % of the plants. The droughttreatment was implemented for half of the plants by pre-venting watering for 12 days, until tensiometers placedinto pots indicated that one-third of the field capacityand the wilting point (yellow leaves) had been reached.Plants under the drought treatment were then irrigatedwith 200 cm3 water per plant every fourth day to main-tain the stress until all plants were harvested. Duringthis time, a full irrigation regime was implemented tocontrol plants. In each plant, five spikes were covered toavoid grain loss from seed shattering. The total numberof filled seeds was counted for each plant, and the differ-ence between control and drought treatments withineach block was calculated as a measurement for the re-productive response to drought. Reproductive responsemeasurements were then transformed to a logarithmicscale due to standard deviation-mean dependence forthe number of grain loss i.e., standard deviation increasewith average number of seeds [63]. In the following year,the experiment was repeated with selected accessions ina greenhouse at the University of Hohenheim, Germany(48°71'38''N, 9°20'88''E). The same experimental proced-ure was conducted in three replicates and compared tothe results from previous year. Two tolerant and twosensitive accessions were selected for mRNA extractionin the second year conducted in Hohenheim based ontheir performance in the first year experiment i.e., theaverage reduction in seed set in response to droughtwith minor spread of data (see Figs. 1 and 2). From eachplant, two spikelets were sampled from the middle oftwo different spikes at the early flowering stage (booting,Zadok scale: Z49; [64]) when fertilization occurs andstress has the strongest effect on yield reduction [65].RNA extraction and sequencingSamples were immediately frozen in liquid nitrogen andstored in −80 °C until RNA extraction. Total RNA wasextracted from 16 samples (Fig. 2) using the Bioline MiniKit protocol for plants (Bioline GmbH, Germany). Anamount of 1.5-10 μg was determined using an electro-photometer with an OD 260/280 ratio of at least 1.8 andgel electrophoresis. Quality was determined by using aRNA integrity number (RIN) of at least 8. ExtractedRNA samples were further processed by SciLifeLab,Stockholm (School of Biotechnology, KTH) and includedthe purification of polyadenylated RNA, RNA fragmen-tation, cDNA synthesis, polymerase chain reaction(PCR) amplification, and RNA sequencing according tothe Illumina RNA-Seq protocol (Illumina, Inc., SanDiego, CA). Single read non-strand-specific RNA se-quences (RNA-Seq) were generated using a HiSeq2000in one flow cell with eight lanes producing 50 millionreads of 100 bp per sample. Each of the 16 libraries wastagged and split between two lanes. Within each lane,random combinations of four different half libraries wereinjected to reach a balanced experimental design [66]and to reduce sequencing bias resulting from differencesin sample amplification efficiency of different libraries.Identification of differentially expressed transcriptsSequenced Illumina reads were mapped to the annotatedbarley genome assembly of the Morex variety [67] usingBowtie2 v2.0.5 [68] and TopHat v2.0.6 [69]. Each of the16 transcriptome libraries was separately mapped to thereference genome after trimming tags and filtering low-quality reads based on the distribution of phred-likescores at each sequencing cycle with the FASTX toolkit( using the command:fastq_quality_filter -v -q 20 -p 80. TopHat was run withdefault parameters and a gene model annotation file inthe GTF format was used to enable Bowtie2 to first aligntranscript sequences to the transcriptome and then tomap only unmapped reads to the genome.To measure the level of expression, the quantificationof transcript abundance in the samples was conductedwith Cufflinks v2.0.2 [70]. Assemblies were producedseparately for each of the 16 libraries and then parsimo-niously merged with the reference genome annotationfor each accession (four libraries each) using Cuffmerge.This step performs an assembly based on the annotationto the reference [70] and produces an annotation file fordownstream analysis. Since annotation files were gener-ated separately for each accession, an additional stepmatched transcripts from different samples and pro-duced an annotation file that combined the transcribedfragments (transfrags) from all accessions and the refer-ence annotation file. This step was carried out with Cuff-compare and utilized the reference genome annotationto produce a single annotation file for downstream ana-lysis of differential expression. Changes in the relativeabundance of transcripts between drought treatmentand controls were estimated for each accession using theCuffdiff program, which calculates the number of readsHübner et al. BMC Plant Biology  (2015) 15:134 Page 12 of 14per kilobase of exon per million reads mapped (RPKM)for each transcript and summarizes them for each groupof transcripts [23]. Cuffdiff output files were furtherprocessed with the R package CummeRbund [71] to in-tegrate the output from different accessions into a singledata set for differential expression analysis andvisualization. Differentially expressed genes were consid-ered significant at loge of fold change of 1.5 and FDRlevel of 0.05 using the Benjamini-Hochberg method [72].Clustering of genes classified as differentially expressedwas conducted with the pvclust package in R [73]. Ex-pression distances among accessions were calculatedfrom the loge of fold change between treatments. Gen-etic distances were calculated from single nucleotidepolymorphisms (SNPs) using the pair-wise Hammingdistance between accessions. Geographic distances werecalculated using the pair-wise Euclidean distance basedon the Barley1K sampling site coordinates. Clusteringtrees were bootstrapped 1,000 times to evaluate the ro-bustness of clustered nodes.Evolutionary sequence conservationTo assess the evolutionary conservation of genes associ-ated with the response to drought, a sample of 100 genesthat were differentially expressed between drought and con-trol, and 100 non-differentially expressed genes were ran-domly sampled from each accession to balance betweengood representation of the data sets and randomness.This sub-sampling step was conducted to avoid a biasintroduced by differences in sample size between thetwo data sets. For each gene, the corresponding se-quences were retrieved from the reference genome toreduce the effect of inter-accession polymorphism on thecross-species mapping success. Sequences were mappedto protein databases of Brachypodium distachyon, Oryzasativa, and Sorghum bicolor ( BLASTX [74] with an E-value cutoff of 10−3, andthe number of hits and best hit E-values were recorded.Gene ontology analysisTo study the biological significance of differentiallyexpressed genes, an enrichment analysis of gene ontology(GO) terms was conducted with the Bioconductor pack-age goseq [75], which accounts for biases inherent inRNA-Seq data. Statistically significant over-representationof GO categories in response to the drought treatmentwas determined separately for each accession. A list ofdifferentially expressed genes, after correcting for mul-tiple comparisons (adjusted p-value < 0.05), and of non-differentially expressed genes was used to generate theprobability weighting function which enables to correctfor the gene coding length bias. To save computingpower, the Wallenius distribution method [75] wasused as an approximation for the null distribution. Thelength of each transcribed gene was calculated from itscoordinates in the reference genome and a correspondingcategory mapping file was generated using available annota-tion database ( Significantly over-representedGO terms were finally corrected for multiple compari-sons at FDR level of 0.05 using the Benjamini-Hochberg method [72].Identification of SNPsTo study differentially expressed alleles that are associ-ated with drought tolerance, single nucleotide polymor-phisms (SNPs) were detected in each accession bycomparing the transcript sequences to the Morex refer-ence assembly. For each accession, all four libraries wereconcatenated after removing low-quality sequences togenerate the deepest and widest possible transcriptomerepresentation. Each of the four concatenated files wasmapped to the Morex assembly using Bowtie2. Variantcalling was conducted using the mpileup function inSAMTools v1.18 [76] and the BCFTools package wasused to filter SNPs with a minimum phred quality of 20and a maximum read depth of 100. Heterozygote SNPs,which could be the outcome of sequencing errors, col-lapsed paralogous genes or remnants of a past intro-gression were filtered out as well. Candidate variantsfrom differentially expressed genes were further filteredand their functional annotation was predicted withSnpEff [60].Availability of supporting dataSequence reads were submitted to the European NucleotideArchive under accession number PRJEB8700. All aggre-gated data and analysis scripts are available from the Dryadpublic archive ( under accession num-ber doi:10.5061/dryad.kt69d.Additional filesAdditional file 1: Figure S1. Photographs of the experimentsconducted in Atlit and Hohenheim. A) Atlit control, B) Atlit drought, C)Hohenheim control, and D) Hohenheim drought.Additional file 2: Table S1. List of sequence data used in this study.Additional file 3: Table S2. List of differentially expressed genes inresponse to drought stress common to all accessions.Additional file 4: Table S3. List of candidate genes associated withtolerant response to drought stress.Additional file 5: Table S4. List of candidate genes associated withsensitive response to drought stress.Additional file 6: Table S5. List of gene ontology terms anddescription found for each of the four accessions.Additional file 7: Figure S2. Zoom-in on differentially expressed allelesassociated with drought tolerance. The figure was generated in integrativegenomic viewer browser [77] for differentially expressed allele inMLOC_67950.1 (morex_contig_53956).Hübner et al. BMC Plant Biology  (2015) 15:134 Page 13 of 14AbbreviationsRS: Reproductive success; SD: Standard deviation; RBD: Randomized blockdesign; RPKM: Reads per kilobase of exon per million reads mapped;GO: Gene ontology; FDR: False discovery rate.Competing interestsThe authors declare no competing interests.Author’s contributionsSH, ABK and KJS designed the study. SH did the experimental work andanalyzed data. SH, ABK and KJS wrote the paper.AcknowledgementsWe thank Anna Westerbergh and Girma Bedada (SLU Uppsala) for theirassistance with the sequencing procedure. To Elisabeth Kokai-Kota and InkaGawenda for their technical assistance with preparing samples for sequencing.We also thank Tamar Krugman and Souad Khalifa (Institute of Evolution, Universityof Haifa) for their assistance with greenhouse experiment conducted in Israel andto Shany Satt for her considerable help with plant management andphenotyping. Finally, we thank Assaf Malik (University of Haifa) andThomas Müller (Hohenheim University) for their useful bioinformatics advice. Thiswork was supported by the German Academic Exchange Service grant (DAAD;S.H.), and FORMAS grant 2008–1220 (K.S.).Author details1Department of Evolutionary and Environmental Biology, University of Haifa,Mt. Carmel 31905, Haifa, Israel. 2Institute of Plant Breeding, Seed Science andPopulation Genetics, University of Hohenheim, D-70593 Stuttgart, Germany.3Current address: Department of Botany, University of British Columbia,Vancouver, Canada.Received: 28 December 2014 Accepted: 20 May 2015References1. Harlan JR, Zohary D. Distribution of wild wheats and barley. Science.1966;153:1074–80.2. Aaronson A: Agricultural and botanical exploration in Palestine. Bulletin PlantIndustry. USDA, Washington DC, USA 1910, 180:1–63.3. Vavilov NI. The origin, variation, immunity and breeding of cultivated plants.Chronology Botany. 1951;13:1–366.4. Ellis RP, Forster BP, Robinson D, Handley LL, Gordon DC, Russell JR, et al.Wild barley: a source of genes for crop improvement in the 21st century?J Exp Bot. 2000;51:9–17.5. Nevo E, Korol AB, Beiles A, Fahima T. Evolution of wild emmer and wheatimprovement: population genetics, genetic resources and genomeorganization of wheat’s progenitor, Triticum dicoccoides. Berlin, Germany:Springer; 2002.6. Nevo, E: Origin, evolution, population genetics and resources for breedingof wild barley, Hordeum spontaneum, in the Fertile Crescent. In: Shewry PRed. Barley: Genetics, Molecular Biology and Biotechnology. Wallingford, UK:C.A.B. International 1992, 19–43.7. Roberts N, Eastwood WJ, Kuzucuoglu C, Fiorentino G, Caracuta V. Climatic,vegetation and cultural change in the eastern Mediterranean during themid-Holocene environmental transition. The Holocene. 2011;21:147–62.8. Levitt J. Responses of plants to environmental stresses. NY, USA: Academic; 1980.9. Ariew A, Lewontin R. The confusions of fitness. Br J Philos Sci. 2004;55:347–63.10. Primack RB, Antonovics J. Experimental ecological genetics in Plantago V.Components of seed yield in the ribwort plantain Plantago lanceolata L.Evolution. 1981;35:1069–79.11. Ackerly DD, Dudley SA, Sultan SE, Schmitt J, Coleman JS, Linder CR, et al. Theevolution of plant ecophysiological traits: recent advances and future directionsnew research addresses natural selection, genetic constraints, and the adaptiveevolution of plant ecophysiological traits. Bioscience. 2000;5:979–95.12. Dolferus R, Xuemei J, Richard AR. Abiotic stress and control of grain numberin cereals. Plant Sci. 2011;181:331–41.13. Barnabás B, Jäger K, Fehér A. The effect of drought and heat stress onreproductive processes in cereals. Plant, Cell and Environment.2008;31:11–38.14. Saini HS, Westgate ME. Reproductive development in grain crop duringdrought. In: Spartes DL, editor. Advances in Agronomy, CA, USA: AcademicPress San Diego, vol. 68. 2000. p. 59–96.15. Blum A. Plant breeding for stress environments. Boca Raton, FL, USA: CRCpress; 1988.16. Verhoeven KJF, Poorter H, Nevo E, Biere A. Habitat-specific natural selectionat a flowering-time QTL is a main driver of local adaptation in two wildbarley populations. Mol Ecol. 2008;17:3416–24.17. Mahajan S, Tuteja N. Cold, salinity and drought stresses: an overview. ArchBiochem Biophys. 2005;444:139–58.18. Cattivelli L, Rizza F, Badeck FW, Mazzucotelli E, Mastrangelo AM, Francia E,et al. Drought tolerance improvement in crop plants: An integrated viewfrom breeding to genomics. Field Crop Res. 2008;105:1–14.19. Hoekstra FA, Golovina EA, Buitink J. Mechanisms of plant desiccationtolerance. Trends Plant Sci. 2001;6:431–8.20. Volis S, Verhoeven KJF, Mendlinger S, Ward D. Phenotypic selection andregulation of reproduction in different environments in wild barley. J EvolBiol. 2004;17:1121–31.21. Boyer JS. Plant productivity and environment. Science. 1982;218:443–8.22. 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;60:3531–44.23. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and quantifyingmammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:1–8.24. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment oftechnical reproducibility and comparison with gene expression arrays. GenomeRes. 2008;18:1509–17.25. Kugler KG, Siegwart G, Nussbaumer T, Ametz C, Spannagl M, Steiner B, et al.Quantitative trait loci-dependent analysis of a gene co-expression networkassociated with Fusarium head blight resistance in bread wheat (Triticumaestivum L). BMC genomics. 2013;14:728.26. Paz Celorio‐Mancera M, Wheat CW, Vogel H, Söderlind L, Janz N, Nylin S.Mechanisms of macroevolution: polyphagous plasticity in butterfly larvaerevealed by RNA‐Seq. Molecular ecology. 2013;22:4884–95.27. Mizrachi E, Hefer CA, Ranik M, Joubert F, Myburg AA. De novo assembledexpressed gene catalog of a fast-growing Eucalyptus tree produced byIllumina mRNA-Seq. BMC Genomics. 2010;11:681.28. Lai Z, Kane NC, Kozik A, Hodgins KA, Dlugosch KM, Barker MS, et al.Genomics of Compositae weeds: EST libraries, microarrays, and evidence ofintrogression. Am J Bot. 2012;99:209–18.29. Perez-de-Castro AM, Vilanova S, Canizares J, Pascual L, Blanca JM, Diez MJ,et al. Application of genomic tools in plant breeding. Current Genomics.2012;13:179–95.30. Hübner S, Hüffken M, Oren E, Haseneyer G, Stein N, Graner A, et al. Strongcorrelation of wild barley (Hordeum spontaneum) population structure withtemperature and precipitation variation. Mol Ecol. 2009;18:1523–36.31. Hübner S, Bdolach E, Ein Gedy S, Schmid KJ, Korol A, Fridman E. Phenotypiclandscapes: phenological patterns in wild and cultivated barley. Journal ofEvolutionary Biology. 2013;26:163–74.32. Hübner S, Günther T, Flavell A, Fridman E, Graner A, Korol A, et al. Islandsand streams: clusters and gene flow in wild barley populations from theLevant. Mol Ecol. 2012;21:1115–29.33. Song CP, Agarwal M, Ohta M, Guo Y, Halfter U, Wang P, et al. Role of anArabidopsis AP2/EREBP-type transcriptional repressor in abscisic acid anddrought stress responses. The Plant Cell. 2005;17:2384–96.34. Harb A, Krishnan A, Ambavaram MM, Pereira A. Molecular and physiologicalanalysis of drought stress in Arabidopsis reveals early responses leading toacclimation in plant growth. Plant Physiol. 2010;154:1254–71.35. Kim DY, Kwon SI, Choi C, Lee H, Ahn I, Park SR, et al. Expression analysisof rice VQ genes in response to biotic and abiotic stresses. Gene.2013;529:208–14.36. Volis S, Mendlinger S, Ward D. Adaptive traits of wild barley plants ofMediterranean and desert origin. Oecologia. 2002;133:131–8.37. Holt RD. The microevolutionary consequences of climate change. Trends inEcology and Evolution. 1990;5:311–5.38. Gordon SP, Priest H, Des Marais DL, Schackwitz W, Figueroa M, Martin J,et al. Genome diversity in Brachypodium distachyon: deep sequencing ofhighly diverse inbred lines. The Plant Journal. 2014;79:361–74.39. Widiez T, Symeonidi A, Luo C, Lam E, Lawton M, Rensing SA. Thechromatin landscape of the moss Physcomitrella patens and its2009;10:R25.69. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions withRNA-Seq. Bioinformatics. 2009;25:1105–11.70. Roberts A, Pimentel H, Trapnell C, Pachter L. Identification of noveltranscripts in annotated genomes using RNA-Seq. Bioinformatics.2011;27:2325–9.71. Goff L, Trapnell C: cummeRbund: Analysis, exploration, manipulation, andvisualization of Cufflinks high-throughput sequencing data. 2011 []72. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practicaland powerful approach to multiple testing. J R Stat Soc. 1995;289–300.73. Suzuki R, Shimodaira H. Pvclust: an R package for assessing the uncertaintyin hierarchical clustering. Bioinformatics. 2006;22:1540–2.74. Altschul SF, Gish W, Miller W, Mayers EW, Lipman DJ. Basic local alignmenttool. J Mol Biol. 1990;215:403–10.75. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Method gene ontologyanalysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14.76. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequencealignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.77. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G,et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–6.Hübner et al. BMC Plant Biology  (2015) 15:134 Page 14 of 14dynamics during development and drought stress. The Plant Journal.2014;79:67–81.40. Lopes FR, Jjingo D, da Silva CRM, Andrade AC, Marraccini P, Teixeira JB, et al.Transcriptional activity, chromosomal distribution and expression effects oftransposable elements in Coffea genomes. PLoS One. 2013;8, e78931.41. Ramirez‐Valiente JA, Lorenzo Z, Soto A, Valladares F, Gil L, Aranda I.Elucidating the role of genetic drift and natural selection in cork oakdifferentiation regarding drought tolerance. Molecular Ecology.2009;18:3803–15.42. Kooyers NJ, Greenlee AB, Colicchio JM, Oh M, Blackman BK. Replicatealtitudinal clines reveal that evolutionary flexibility underlies adaptation todrought stress in annual Mimulus guttatus. New Phytol. 2014. doi:10.1111/nph.13153.43. Sletvold N, Ågren J. Variation in tolerance to drought among Scandinavianpopulations of Arabidopsis lyrata. Evol Ecol. 2012;26:559–77.44. Juenger TE. Natural variation and genetic constraints on drought tolerance.Curr Opin Plant Biol. 2013;16:274–81.45. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al.Evolutionarily conserved elements in vertebrate, insect, worm, and yeastgenomes. Genome Res. 2005;15:1034–50.46. Lawrie DS, Petrov DA, Messer PW. Faster than neutral evolution ofconstrained sequences: the complexity interplay of mutational biases andweak selection. Genome Biology and Evolution. 2011;3:383–95.47. Peleg Z, Fahima T, Abbo S, Krugman T, Nevo E, Yakir D, et al. Geneticdiversity for drought resistance in wild emmer wheat and itsecogeographical associations. Plant, Cell and Environment. 2005;28:176–91.48. Xia H, Camus-Kulandaivelu L, Stephan W, Tellier A, Zhang Z. Nucleotidediversity patterns of local adaptation at drought related candidate genes inwild tomatoes. Mol Ecol. 2010;19:4144–54.49. Garrity DP, O'Toole JC. Screening rice for drought resistance at thereproductive phase. Field Crop Res. 1994;39:99–110.50. Rosa M, Von Harder M, Cigliano RA, Schlögelhofer P, Scheid OM. TheArabidopsis SWR1 chromatin-remodeling complex is important for DNArepair, somatic recombination, and meiosis. The Plant Cell Online.2013;25:1990–2001.51. Balestrazzi A, Confalonieri M, Macovei A, Donà M, Carbonera D. Genotoxicstress and DNA repair in plants: emerging functions and tools for improvingcrop productivity. Plant Cell Rep. 2011;30:287–95.52. Coberly LC, Rausher MD. Analysis of a chalcone synthase mutant inIpomoea purpurea reveals a novel function for flavonoids: amelioration ofheat stress. Mol Ecol. 2003;12:1113–24.53. Jakoby M, Weisshaar B, Dröge-Laser W, Vicente-Carbajosa J, Tiedemann J,Kroj T, et al. bZIP transcription factors in Arabidopsis. Trends Plant Sci.2002;7:106–11.54. Lata C, Prasad M. Role of DREBs in regulation of abiotic stress responses inplants. J Exp Bot. 2011;62:4731–48.55. McDowell NG. Mechanisms linking drought, hydraulics, carbon metabolism,and vegetation mortality. Plant Physiol. 2011;155:1051–9.56. Yi H, Galant A, Ravilious GE, Preuss ML, Jez JM. Sensing sulfur conditions:simple to complex protein regulatory mechanisms in plant thiolmetabolism. Mol Plant. 2010;3:269–79.57. Hammond JP, Mayes S, Bowen HC, Graham NS, Hayden RM, Love CG, et al.Regulatory hotspots are associated with plant gene expression undervarying soil phosphorus supply in Brassica rapa. Plant Physiol.2011;156:1230–41.58. Fraser HB. Genome wide approaches to the study of adaptive geneexpression evolution. Bioessays. 2011;33:469–77.59. Gillmor CS, Lukowitz W, Brininstool G, Sedbrook JC, Hamann T, Poindexter P,et al. Glycosylphosphatidylinositol-anchored proteins are required for cellwall synthesis and morphogenesis in Arabidopsis. The Plant Cell.2005;17:1128–40.60. Cingolani P, Platts A, Coon M, Nguyen T, Wang L, Land SJ, et al. A programfor annotating and predicting the effects of single nucleotidepolymorphisms, SnpEff: SNPs in the genome of Drosophila melanogasterstrain w1118; iso-2; iso-3. Fly. 2012;6:80–92.61. Cheng WH, Endo A, Zhou L, Penney J, Chen HC, Arroyo A, et al. A uniqueshort-chain dehydrogenase/reductase in Arabidopsis glucose signaling andabscisic acid biosynthesis and functions. Plant Cell. 2002;14:2723.62. Xiang L, Etxeberria E, den Ende W. Vacuolar protein sorting mechanisms inplants. FEBS J. 2013;280:979–93.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 redistribution63. Little TM, Hills FJ. Agricultural experimentations: Design and analysis. NewYork: Wiley; 1978. 350.64. Zadoks JC, Chang TT, Konzak CF. A decimal code for the growth stages ofcereals. Weed Res. 1974;14:415–21.65. Turner NC. Agronomic options for improving rainfall-use efficiency of cropsin dryland farming systems. J Exp Bot. 2004;55:2413–25.66. Auer PL, Doerge RW. Statistical design and analysis of RNA-Seq data.Genetics. 2010;185:405–16.67. International Barley Genome Sequencing Consortium. A physical, geneticand functional sequence assembly of the barley genome. Nature.2012;491:711–6.68. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficientalignment of short DNA sequences to the human genome. Genome Biol.Submit your manuscript at


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items