Biochimica et Biophysica Acta 1819 (2012) 166–175

Contents lists available at SciVerse ScienceDirect

Biochimica et Biophysica Acta

j ourna l homepage: www.e lsev ie r .com/ locate /bbagrm

Prerequisites, performance and profits of transcriptional profiling the abioticstress response☆

Joachim Kilian, Florian Peschke, Kenneth W. Berendzen, Klaus Harter, Dierk Wanke ⁎Center of Plant Molecular Biology (ZMBP)—Plant Physiology, University of Tuebingen, Auf der Morgenstelle 1, D-72076 Tübingen, Germany

☆ This article is part of a Special Issue entitled: Plantabiotic stress.⁎ Corresponding author. Tel.: +49 7071 29 73087; fa

E-mail addresses: joachimkilian@zmbp.uni-tuebingeflorian.peschke@uni-bielefeld.de (F. Peschke),kenneth.berendzen@zmbp.uni-tuebingen.de (K.W. Bereklaus.harter@zmbp.uni-tuebingen.de (K. Harter),dierk.wanke@zmbp.uni-tuebingen.de (D. Wanke).

1874-9399/$ – see front matter © 2011 Elsevier B.V. Aldoi:10.1016/j.bbagrm.2011.09.005

a b s t r a c t

a r t i c l e i n f o

Article history:Received 6 May 2011Received in revised form 27 September 2011Accepted 28 September 2011Available online 6 October 2011

Keywords:AtGenExpressAbiotic stressMicroarrayCore environmental stress responsePlant-CESRExperimental comparability

During the last decade, microarrays became a routine tool for the analysis of transcripts in the model plantArabidopsis thaliana and the crop plant species rice, poplar or barley. The overwhelming amount of datagenerated by gene expression studies is a valuable resource for every scientist. Here, we summarize themost important findings about the abiotic stress responses in plants. Interestingly, conserved patterns ofgene expression responses have been found that are common between different abiotic stresses or thatare conserved between different plant species. However, the individual histories of each plant affect theinter-comparability between experiments already before the onset of the actual stress treatment. This re-view outlines multiple aspects of microarray technology and highlights some of the benefits, limitationsand also pitfalls of the technique. This article is part of a Special Issue entitled: Plant gene regulation in re-sponse to abiotic stress.

gene regulation in response to

x: +49 7071 29 3287.n.de (J. Kilian),

ndzen),

l rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Microarrays have been one of the breakthrough technologies in thelife sciences. Since their first use for global expression analysis about15 years ago [1], they have become a routine tool for the analysis of tran-script abundance in model organisms. In Arabidopsis thaliana and thecrop species Oryza sativa, Populus spp., and Hordeum vulgare, studieswith microarrays are providing a powerful and widely used method toanalyze the effects of various external inputs [2–18]. For the analysis ofdefined stimuli such as hormone or abiotic stress treatments, transcrip-tional profiling has particularly been informative. As plants are unable toescape from abiotic factors that are outside their physiological optimum,they needed to evolve a wide range of ecological plasticity that allowsthem to tolerate or even adapt to altered environmental condi-tions [9,13,14,19–26].

During the last decade the number of publications applyingmicroarray techniques increased dramatically. Likewise, more andmore different commercial or community microarray platforms becameavailable that vary in architectures (e.g. exon arrays or tiling arrays),methods for labeling, hybridization and read-outs, as well as the number

and lengths of the complementary probes [8,10,18]. All of these havebeen applied to monitor transcriptional changes before, during andafter stress treatments [13,14,22,27–29]. In the beginning, microarrayswere used only for transcriptional profiling of specific responses or esti-mating mRNA abundance in tissues or special cells types [7,22–24,30–33]. More recently, these versatile platforms are also used for severalother applications in plant science on a whole genome scale, which in-clude the discovery of novel sense/antisense transcripts, alternativesplicing, genetic mapping of mutations or trait loci, the study of DNA-methylation, investigation of histone modifications and the in vivoidentification of transcription factor binding sites [10,25,34–41]. Abioticstress research also is including some of these new discoveries, as re-sponses to stress do not only affect the immediate organism state, butalso can have persistent epigenetic effects.

Therefore, transcriptional profiling provides much more informationon plants' biology than just the changes in transcript abundance. Fig. 1summarizes some of the data that can be extracted from well designed,executed and documented transcriptional profiling experiments. Whendealing with stress, it is imperative that each individual history isaccounted for, as not only the developmental state plays a role in howthe plant will react. As such, the individual response upon stress differsbetween the various organs (or cell types) [42] and their previousgrowth conditions (light, temp, pathogen, etc.)—all of which, desiredor not—influence the transcriptional “stress” readout. Therefore, thedocumentation that accompanies every microarray should be as de-tailed as possible, especially for environmental and stress parameters.By careful interpretation of the data, one can try to identify principalfactors of stress response, but also to predict putative protein–protein

Previous stress

Individualhistory

Outputinterpretation

Tissue / organ specificity

Diurnal changes and circadian rhythm

Organism responses

• basic research about stress

• stress adaptation & tolerance

• cross-protection / priming

Genetic engineering

• application tocrop-science

• phytoremediation

Species level responses

• evolutionary conservedresponse pattern

• epigenetic stress memory

‘Omics analysis

• expression correlation

• metabolic correlation

• protein-protein interactions

• biosynthetic pathways

• conditional splicing & splice variants

Network analysis

• gene regulatory networks

• protein phosphorylation / activation / inactivation

• sRNA networks

Do

cum

entatio

n [M

IAM

E]

microarray

probe arrays

tiling array

laboratory / environmentalsetting

Stress

Developmental stage

Fig. 1. Schematic summary of different types of data that can be extracted fromwell designed, executed and documented transcriptional profiling experiments. Physiological conditions ofthe organism prior to the experiment, i.e. the individual history, are frequently overlooked causes for background noise in microarray experiments. Subsequent to the experiment, differentpossibilities exist for the interpretation of the experimental outcomes.

167J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

interaction networks, conclude on metabolic pathways, capture the in-fluence of previous historical states and optimize gene regulatory net-works (Fig. 1). Although one only looks at mRNA abundance,microarray analyses allow us a valuable insight into plant cellular biologyand stress responses in general [2,9,43,44].

1.1. A generalized view on the abiotic stress response in plants

Stress is defined as a sudden change in the environment that exceedsthe organism's optimum and causes homeostatic imbalance, whichmust be compensated for. Little is known about the receptors that per-ceive stress information, while many downstream signaling pathwayshave been thoroughly studied, some of thosewithmicroarray based tech-niques [10,13,14,22,27–29]. Interestingly, besides treatment-specificgene expression responses, it has been found that many of the differentstresses enroll the same set of genes and overlapping signaling cascades[9,13,14,20,22,45]. Concerted expression changes common to severalstresses might in part be mediated by the same or similar upstreamsignaling components and involve some of the same plant hormones,e.g. abscisic acid (ABA), salicylic acid (SA), methyl-jasmonate (MJ) orethylene [7,43,46–49]. Comparative analyses of the same or similarabiotic stress treatments but with two different species haverevealed that several orthologous genes have been found equally re-sponsive, which suggest generally conserved stress response path-ways in plants [9,50–52].

As environmental stress signals are somehow perceived and trans-duced into the cell to orchestrate specific gene expression responses,this entire signal transduction pathway possibly consists of a largerset of proteins that are already preformed and await external stimula-tion [10,13,14,22,26,27,29,53]. Thus, the very initial points of stress re-sponses can probably not be analyzed by microarrays as they do notutilize changes in gene transcription. Conversely, the subsequentphases of immediate-early and indirect-late transcriptional responses

have been extensively investigated with microarray technology formany abiotic stresses and in several plant species.

Irrespective of the applied stress treatment, the plant species or themicroarray platform used for investigation, one can extract a schematicmodel that is applicable to many, if not all, stress responses (Fig. 2).Immediate-early expression changes are characterized by eitherstress-specific responsive genes or genes that are unspecific to aparticular stress, but show very rapid expression changes (≤1 h)[9,13,14,21,22,32,45, 54]. It was shown for UV-B light irradiationand drought stress that immediate-early responses occur withinless than 15 min after the onset of the stress treatment and were in-conspicuous already after 30 min [13,22]. Commonly expressedgenes might constitute a plant core environmental stress response(PCESR) [13,22,55], which are described in more detail in a latersection. Interestingly, a fraction of these PCESR genes is responsive inboth, the treated and the untreated plant organs, which is indicativeof a basal systemic stress response and possibly needed for priming.Immediate-early response genes might be triggered in part via oxida-tive signals, such as reactive oxygen species (ROS) or nitric oxide(NO) that are also common to most stresses [13,56–59]. Another com-mon observation is the rapid regulation of genes that bind cytosolic cal-cium or those that are enrolled in calcium-dependent signalingcascades [21,60–62]. Some studies have shown that oxidative signalscan predate calcium spikes in the cytosol [21,58,60–63], neverthelessbothmolecules appear to precede and accompany the immediate-early re-sponses (Fig. 2).

The most important group of primary stress responsive genes,however, is transcription factors, with WRKYs, AP2/ERFs, bZIPs,NACs and MYBs among the most prominent ones [45,47,48,64–69].Transcription factor proteins probably initiate the indirect-late phaseof responses by binding to specific DNA-motifs in the upstream regula-tory sequences of their target genes, while their expression marks theearly phase of the stress already (Fig. 2). The indirect-late response ismainly characterized by stress specific gene expression patterns that

Preformed signaling cascades

Stress memory

Hormones(ABA, MJ, Ethylene)

Transcription

PCESR genescis TranscriptionTranscription

mRNA

Stress

Ac

Me

Me

AcAcAc

Ac

Me

Me Ac Ac

Me

Me

AcAcAc

Ac

Me

Me

Chromatin Remodeling

Systemic alarm stress

response

Imm

edia

te e

arly

res

pons

esI n

dire

ct la

te r

espo

nses

Tim

e after onset of stre ss

Ca2+ Oxidative signal

?

Primary stress specific response genescis Transcriptioncis TranscriptionTranscription

PCESR

Target genecis

Transcription

TFs

Stress tolerance & adaptationGrowth & development

Transcription

cisTarget gene

sRNA

WRKY, AP2/ERF,NAC, MYB, …

Protein turnover(synthesis, degradation, stability)

Dehydration response Stress specific response Mechanical defense(cell wall modification)

Fig. 2. Schematicmodel of immediate-early and indirect-lategene expression responsesuponabiotic stresses. Preformed signaling cascades receive the stress signal and subsequently orchestrateeither common or specific sets of differentially expressed genes. Several stress specific genes are typically encoding transcription factors that control the indirect-late responsive genes. Genesthat are involved in several stressesmight control a plant core environmental stress response (PCESR) that is also found in systemic tissues. Indirect-late stress responsive genes could be sortedinto distinct categories that involve functions such as protein turnover, cell-wall modifications or dehydration response. In addition, groups of stress, tissue or species specific responsive genesare found.

168 J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

partially overlap with known hormone responses [7,9,13,14,47,70–72].Therefore, one can conclude that the fine-tuned, specific activation ofhormone synthesis, e.g. ABA [47], MJ or ethylene [7,46,48,49,58,73], isalso one of the crucial steps during the indirect-late phase of the stressresponse. Indirect-late responsive genes are often enriched in distinctfunctional categories that suggest functions in protein turnover (syn-thesis, degradation and protein stability), in the dehydration responseand in cell wall modifications as a means of mechanical defense(Fig. 2) [9,11–13,22,47,71,74]. In photosynthetically active tissues,genes involved in photosynthesis or associated with plastid develop-ment have been found to be infrequently altered in expression—asmost of these are not immediately stress responsive [2,12,22,54]. Be-sides all these known processes, there are plenty of responsive geneswith stress related or yet unknown functions.

In addition to the previously described gene expression changes, it hasbeen experimentally verified that environmental stimuli affect chromatinremodeling and that under a certain stress condition, epigenticmemory ismanifested [75–77]. In addition, ABA has recently been linked with chro-matin remodeling and factors that modify histones have been associatedwith ABA-responsive gene expression [47,78,79]. Finally, all changes ingene expression and chromatin states will finally lead to stress toleranceand adaptation (Fig. 2), probably at the cost of growth, development andpossibly reproductive success [9,63,80–83].

1.2. Systemic stress responses

Plant cells and organs are known to be autonomous and are thuscapable of responding to environmental stimuli independently ofneighboring cells or organs [42]. Nevertheless, plants still coordinate

their cells and organs so that the organism survives until reproduc-tion, despite their lack of a circulatory system analogous to animals.This coordination is achieved through systemic signaling from onepart of the plant to another, albeit cell-to-cell or organ-to-organ(Fig. 2). Plant hormones are systemic signals, which are required tocoordinate the various cells of a multi-cellular organism for growthand development [7,22,84–92]. A hormone is produced in one organand transported to another, where it is recognized by a receptor andsubsequently leads to cell-specific responses. Expression profilingrevealed that some hormones are more stress-related and stress-triggered than others [7,46,47,49,58,71,93,94]. Of the classicalplant hormones, ABA, ethylene, salycic acid (SA) and jasmonic acid(JA) were originally described as stress-specific [87], howevermany stress-related and developmental functions are known today[7,47–49,53,92,95,96]. Besides the possibility that hormones consti-tute systemic mobile signals during stress responses, there are otherpossibilities, including small RNAs that transport distinct informa-tion through the phloem [97].

1.3. Abscisic acid

Although initially related to drought, ABA is possibly an importanthormone for many stress responses [47,53,66,95,98]. About 10% of allprotein coding genes were found differentially expressed after ABA-treatment in A. thaliana [7,47,94,95] and most of these genes werealso regulated during various biotic and abiotic stresses [14,22,61,92].It has been found that the perception, signal transduction and the cross-talk of ABA during developmental and cellular processes appears to beconserved in all plants from bryophytes through ferns to gymnosperms

169J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

to angiosperms [47,84]. ABA has a long standing history as a signalingmolecule, as it is not restricted to plants and can be found in all or-ganism kingdoms [47,95,99]. Two subgroups of bZIP transcriptionfactors, abscisic acid response-element binding factors (AREB/ABFs)and G-box binding factors (GBFs), are major integrators of the ABAsignal and triggers of ABA-dependent gene expression responses[47,95].

1.4. Salicylic acid and jasmonic acid

Salicylic acid (SA) is the most commonly recognized hormonethat is nearly exclusive to stress. SA is best known for its involvementin systemic acquired resistance (SAR) [92,96,100]. In addition to SA,azelaic acid and a secreted protein, AZI1, are also involved in system-ic signaling for SAR [95,96]. Beyond stress, SA is also involved in de-velopmental processes like senescence [100]. JA on the other hand isbest known for its role for signaling herbivore attack or wounding,not just from one organ to the next, but also between organisms[49]. From transcriptional profiling we learned that JA also mitigates re-sponses to ozone, UV radiation and other abiotic stresses [7,46,58]. Themost active formof JA is the amino-acid conjugate, Jasmonate–Isoleucine(JA–Ile). Binding of JA–Ile by the E3-ligase COI1 facilitates the high affinitybinding and degradation of JAZ repressor proteins leading to the activa-tion of JA-dependent transcriptional responses [101,102]. Developmentalroles for JA include flower development, pollen development, senescenceand carbonpartitioning [46,49]. SomeWRKY transcription factors, knownfor their role in abiotic and biotic stress responses, have been placed bothdownstream and upstream of SA- and JA-dependent signaling [68–70,103].

1.5. Hydrogen peroxide and nitric oxide

Two types of free radial forming molecules, nitric oxide (NO) andhydrogen peroxide (H2O2) are well documented as having both localand systemic effects. NO is involved in innate immunity of both plantsand animals and is a key factor in abiotic stress signaling as well[56,58,59,104]. In plants, NO is needed for the hypersensitive responseand SAR [59,89,105] and it is in thisway that NO has a systemic functionover short distances, although it is not a systemic signal itself [59]. ROSmolecules, like H2O2, are produced by diverse stresses and are knownfor the initiation of immediate-early signaling cascades that often enrollcytosolic calcium [23]. ROS signals are possibly generated by plasmalemma localized NADPH oxidases and have also been shown to propa-gate systemically [60,88]. Besides its function as ethylene receptor, theER-localized ETR1 has been proposed to also be a H2O2 receptor inguard cells [106,107]. Some developmental processes, like root-hairgrowth, require both ROS and NO [60,108,109]. Interestingly, NO treat-ment activates genes that reduce ROS and other strong radicals [104].

1.6. Priming

Priming is a physiological state where the organism is able tomounta responsemore quickly or better to a secondary stress event after a pri-mary stress event [75,96,110]. Priming is also known as induced resis-tance (IR), enhanced stress response, systemic acquired acclimationand cross-protection. Priming has been observed by either applying aprimary stress event or mimicked by application of JA [111], SA [92],BTH [112], NO [91]. In this way the “pre-treatment” prompts the plantto prepare for another attack of similar or different origin. It wasshown that there is an energy cost, which appears to be strongest fordefense against biotic stressors: cpr1plants have growth/yield penalties[83],NPR1 overexpressors can compromise normal responses [113], butedr1 mutants are more resistant without expression of defense genes[114]. However, it is not known whether NPR1 or EDR1 are necessaryfor the establishment of a primed state also after abiotic stresses. Toachieve a primed state one would expect that some type of defense

mechanism, either as mRNA or as protein, is preformed to constitutivelyelevated levels compared with the non-primed state. This is the case forthe mitogen-activated protein kinases 3 (MPK3) and MPK6, which havebeen shown to be required for full priming inArabidopsis [115]. However,it is yet unclear whether only MPK3 and MPK6 are the sole requisites forpriming of abiotic stress or constitute just a part of priming it. Priming isparticularly interesting when one considers that one abiotic stress canprime for another [75,96,110]. A possible explanation for such a cross-re-action is that there are common components, most likely gene products,which are needed in nearly any stress event.

1.7. Alarm responses to stress

Many stresses activate a similar battery of genes locally and sys-temically as a rather unspecific alarm or general stress response[22,116], which prepares priming, but is not necessarily a part of it.It is in this sense that core environmental stress response (CESR)genes have been proposed. CESR genes exhibit stereotypical genestate changes that occur due to nearly all stresses. Theywere originallydescribed as a set of genes, whose regulation by stresses is conservedbetween Saccharomyces cerevisiae (CER, core environmental response)[117] and Schizosaccharomyces pombe (CESR) [118] and recently pro-posed to also exist as a plant core environmental stress response(PCESR) genes (Fig. 2) [22]. ZAT12, fromArabidopsis, is a good candidatefor a PCESR gene. It is one of the best characterized genes activated bymany different stresses [119,120] and needed for systemic H2O2 signal-ing [88]. ZAT10, another PCESR candidate, on the converse is not onlysystemically regulated but also involved in systemic acquired acclima-tion (SAA) [90] and is induced by many stresses [22, 116, 120], makingit also a candidate for priming. Overexpression of either ZAT10 or ZAT12leads to enhanced resistance to light stress and quicker responses toH2O2 [90,119]. Conservation of induction and function is one of themajor arguments that core environmental stress responses exist. Pro-vokingly, AtCOR413-tm1 and -pm1 genes are activated by water-stress,ABA, light, freezing tolerance and these responses are conserved be-tweenwheat and Arabidopsis [121]. As CESR changes are transcriptionalchanges, microarray data has beenwell suited for detecting common setsof genes that are regulated by many stresses. Such experiments havealready been done [22,66], but the full implications have yet to beexplored.

1.8. Designing microarray experiments

Although one might think that it is not worth mentioning, but theconceptual design of a microarray experiment is not just choosing thebest control plants or conditions. One also has to critically review theapplication of a stimulus and what side-effects that might influencean experiment. As with any other experimental setup, the aim is toeliminate disturbing influences or at least to minimize them so thatthe one (stress) treatment shapes the principal component. Thus, itis useful to keep the following strategy in mind: (1) choose plants inthe same developmental state, (2) exclude all external influences, (3)pick the appropriate RNA isolation protocol, (4) perform all necessarycontrol experiments, (5) pick and standardize the hybridization proto-col, (6) choose the appropriate normalization strategy and (7) apply thenecessary statistical tests and visualization strategies. Accordingly, sys-tematic errors that can bemade in the application of microarray analysesmostly fall into fourmajor classes: (1) unequal growth conditions, (2) in-accurate sampling, (3) lack of knowledge in data processing or (4) datainterpretation.

Growth conditions, or the plants' individual histories, have biginfluences on gene expression. Potting can have a tremendous effecton the induction and repression of genes; for example close proximitycan result in shade avoidance responses, which are regulated by lightperception and are characterized by elongated stems and petioles[122,123]. Furthermore, light intensity and quality influence gene

170 J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

expression strongly and can be an unsuspected source of variation. Es-pecially, diurnal changes and circadian rhythms affect gene expressionand are an inherent background signature of several expression experi-ments [2,9,22,124,125].

Additional sources of experimental noise due to inequalities in plantgrowth and development can be alternating water supply, nutrients(e.g. dirt, hydroponics, sugar in the media, etc.), pathogens in growthchamber, temperature effects (e.g. cold or heat) [126]. Population vari-ance can also cause noise in the system: a plant line derived from single-seed decentmay respondmore uniformly to the treatment compared toa mixed-population wild-type. This means that either a wild-type con-trol is needed, which is also from single-seed decent, or the genetic vari-ability is compensated, e.g. by pooling several individuals in one biologicalsample.

1.9. Sampling the stress, not the scientist

Even under extra careful practice, the researcher is a prominentsource for expression variation: Moving or wobbling of pots has awell known influence on gene expression of ‘very’ touch sensitivegenes, e.g. the thigmomorphogenetic calmodulin TOUCH1, MPK3 orthe xyloglucan endotransglucosylase TOUCH4 [127]. Changing tem-peratures and light during transport can affect expression of distinctsets of genes, which will create certain background noise. In addition,even though many laboratories are climatized, the season still canhave an impact on expression changes as well. As mentioned before,all plants samples have to be in the same developmental stage toavoid disturbing effects on gene expression. Even a difference in ageof several days or the mixed sampling of root and shoot organs willresult in the identification of a totally different set of regulatedgenes [126–128]. Obviously, sampling needs to be well pre-plannedand tested for sampling options, RNA isolation, cDNA synthesis and,if possible, even chip hybridization; pre-experiments from qPCR orat least RT-PCR are beneficial in this regard [126,128]. In addition,we found it best to execute the entire experiment(s) within theshortest time frame possible and with the highest precision possible.For validation, the usage of the same RNA used for the microarray isrecommended since this will minimize biological variation [129].Nevertheless, independent experiments need to be conducted to val-idate the observations and support conclusions.

1.10. Documentation of microarray experiments for the community

The ever growing number of microarray variants and their expres-sion data is often archived in public databases such as GEO [129] orArrayExpress [129,130], where some curation takes place. TheMIAME (Minimum Information About a Microarray Experiment) stan-dard suggests the minimal information one has to deliver about micro-array experiments to ensure the versatility of the data [130,131]. AMIAME counterpart is the Microarray Gene Expression Data (MGED)Ontology (MO) that proposes specific terms for the annotation of allpossible aspects of amicroarray experiment—from the design of the ex-periment, through array layout and preparation of the biological probesto the hybridization and data analysis [127]. The idea of the bothMIAMEand MGED initiatives was to establish standards for recording andreporting of microarray-based gene expression data [7,13,22,33]. Weencourage that both are followed in as much detail as possible since itis necessary to have a detailed knowledge of the environmental and de-velopmental history of samples [13,22,132].

1.11. Data processing, visualization, interpretation and integration

The subsequent processing of raw data depends on what type ofmicroarray platform was used [133]. Remarkably, precision will stillnot guarantee that the probes are gene specific, as this strictly dependson the knowledge about a genome, the quality of its annotation, and

how the probes were designed.With photolithographic manufacturing,it is possible to immobilize several million probes onto one single array[40,130]. Such high density arrays are usually used as tilling arrays,which allowmany other applications other than only expression profil-ing [10,40]. However, this big varietymakes comparisons or integrationfrom different platforms extremely difficult. Nevertheless, comparableresults for medium and highly expressed genes could successfully beobtained from some experiments [130].

Data analysis and visualization of microarray data can be performedwith commercial programs like GeneSpring or free software, e.g. thebiology tailored BioConductor (www.bioconductor.org) package inR (www.r-project.org) [133]. In addition, there are a lot of web-basedapplications that can process or display raw and/or processed data,e.g. GEPAS 4.0 [134] or MIDAW [135]. Visualization tools like AtGenEx-press Visualization Tool (AVT) (http://jsp.weigelworld.org/expviz/expviz.jsp) or the eFP-browser show expression for selected genes inArabidopsis [136]; both ofwhichmakeuse of the high-quality AtGenEx-press datasets. The eFP-Browser is part of the Bio-Array Resource [93]that also includes more tools for microarray related analysis includingGeneMANIA [137], Sample Angler (BAR.utoronto.ca) and the Arabidop-sis Interactions Viewer, just to name a few [93,137,138]. Co-expressionnetworks are becoming all the rage, as it has been shown that biologicallyrelevant information can be derived from them [28,139]. ATTED-II inte-grates not only co-expression networks based on many microarray ex-periments, but also integrates KEGG pathways and protein–proteinnetwork information [72]. In fact, network analysis is now a directivegoal of modern bioinformatic work and several different analysis suitesare available, such as FunNet [140], EGAN [141], Genvestigator [142];MeV part of TM4 Analysis Suite [143]. Networks can be viewed, exploredand analyzedusing freely available software like Cytoscape [144] or pack-ages in BioConductor.

1.12. Comparability of expression data

A largely ignored problem is the incomparability between indepen-dent microarray experiments, which is of multifaceted nature, but canalso result from insufficient documentation. Howmuch environmentaland developmental histories can affect experimental outcomes canbe seen by comparing the same or similar experiments from differ-ent laboratories. As has been demonstrated for inter-laboratorystudies, only strictly controlled species accessions and stringentlyfollowed experimental conditions allow some reproducibility oftranscriptome data [129,130]. However, this was only consistent forhighly expressed or strongly responding genes as inter-laboratory vari-ability was higher than intra-laboratory variability [130,131,145], makingsubsets of genes nearly impossible to replicate. Massonnet et al. [129]observed that even with the same plant line grown under the sameconditions, only 4 out of 10 labs had the same leaf phenotype, whilenone of the metabolic data was consistent. Surprisingly though, themicroarray expression data was consistent after applying stringentfiltering [129], consistent with what was shown in the other studies[130,131,145]. These experiments point to a major limitation instudying stress responses: How can one get an accurate picture ofstress responses, when it is nearly impossible to properly reproducedata from multiple laboratories or multiple experiments? Should webe content with capturing only principal expression changes or whatcan be done to improve this? We ask these questions since re-searchers need to be critical about cross validation via comparingpreviously published microarray experiments. We try to illustratethe importance of documentation for comparison and reproductionof seven published microarray data for the cold stress response inArabidopsis in Fig. 3; the total variances in gene expression changesbetween the samples from four independent research groups dis-played only little overlap (Fig. 3B,C). All of the experiments fulfill theMIAME standards, but the amount of data provided to the community dif-fers, can even be incomplete or partially missing (Fig. 3A). Nevertheless,

171J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

every accurate microarray experiment will provide a certain snapshot ofgene expression changes thatwill likely be informative for a specific ques-tion, although it might be very difficult for others to reproduce and com-pare entire experimental outcomes due to the individual histories andlaboratory conditions.

1.13. The AtGenExpress abiotic stress experiment

To conduct a comparable and comprehensive expression atlas for themodel plant A. thaliana (AtGenExpress), the DFG funded the ArabidopsisFunctional Genomics Network (AFGN) project as a multi-national effortto uncover the transcriptome with the help of a unified microarray plat-form (http://www.uni-tuebingen.de/plantphys/AFGN/atgenex.htm). Bythe end of 2005, a total of 41 different experimental core conditionshad been completed andmade publicly available, consisting of 1295 inde-pendent, high quality microarray hybridizations [7,13,22,33]. The abioticstress experiment covered nine different stress conditions (heat, cold,salt, high osmolarity, drought, UV-B light, wounding, genotoxic andoxidative stress) [13,22,132]. To ensure intercomparable expressiondata with the least background noise all the experimental stresstreatments were performed simultaneously in the same laboratoryafter scrutinous planning. Besides the different treatments, roots

PC 1 (15.7%)

PC

2 (

13.7

%)

-100 -50 0 50 100

-100

-50

050

100

A

B

GSE3326 GSE5535 GSE5534

Stratification n.g. 4 days 4 days

Growth media MS agar 3% sucrose soil MS agar 0.8%

22°C 22°C 24°C

16/8 light/dark continous light continous light

Dev. stage / age 14 days 18 days 10 days

cold (0°C) cold (4°C) cold (4°C)

Treatment time 24 hrs 7 days 7 daysTime series yes yes yes

Platform ATH1-121501 ATH1-121501 ATH1-121501

Growth conditions

Stress condition

Fluence rate (Fr)

@ Fluence rate (Fr)

100 100

25 25

Light regime

Temperature

n.g.

n.g.

Fig. 3. Inter-comparability of cold stress response experiments. (A) Examples of seven differeninformation was taken from the respective microarray repositories, GEO (www.ncbi.nlm.nih.glaboratory stress conditions and differences in documentation. The first four experiments in (as they were all performed on the same array platform and grown under similar conditions andcroarray afterMAS5 normalization. One observes that the populations of data are non-overlapptreatment. Despite the seemingly similar stress treatments, the principal differences in these expthe two respective databases; (Fr; Fluence rate) μmol m−2 s−1.

and shoots were sampled independently at 0 min, 30 min, 1 h, 3 h,6 h, 12 h and 24 h after stress onset [13,22,116,132]. Unintentionally,the AtGenExpress abiotic stress experiment constitutes the first four-di-mensional expression profiling dataset in plant science (gene×condi-tion×time×tissue) requiring and inspiring new methods for its analysis[13,125,132,146]. The AtGenExpress stress data set also comprises oneof the largest systemic datasets between shoots and roots for those stres-ses, which were only applied to one major organ. One major impressionfrom these intercomparable abiotic stress experimentswas that a generalstress response pattern exists, which is common to all of the nine treat-ments [13,22]. On the basis of these experiments, however, we hadalso been able to identify genes that were exclusively responsiveunder only one of the stress conditions [13,22,125] and propose themas possible marker genes for various stress studies (Table 1).

1.14. Evolutionary implications

Meta-analysis is the use of different independent sources of infor-mation from which meaningful data can be derived. One example isthe interspecies comparison between more distantly related species,which can reveal conserved transcriptional signatures. At present,cross-species analyses are made between well established model

-100

-100 -50 0 50 100

-50

050

100

PC 1 (15.7%)

PC

3 (

10.9

%)

C

GSE5621 GSE13295 GSE19254 E-MEXP-2173

2 days n.g. 2 days n.g.

raft on 0.5 MSliquid 0.5 MS agar soil peat moss

24°C 22°C

16/8 light/dark n.g.

20°C

16/8 light/dark

18 days n.g.1.14 1.07-1.08

cold (4°C) 4°C vs. 22°C cold (4°C) cold (4°C)

24 hrs 4 hrs 10 days 4 daysyes no no yes

ATH1-121501 CATMA_v2.3 ATH1-121501 A-AGIL-4

(21 days)

150 40±10

25 n.g. 100 40±10

8/16 light/dark

100

(38 days)

t experiments that includedwild-type Col-0 controls for comparison. For each experimentov/geo/) or ArrayExpress (http://www.ebi.ac.uk/arrayexpress/); Note the wide variety ofA; blue, red, orange, green) were taken for principal components analysis (PCA) (B+C),sampling times after treatment. Each character represents a single hybridization of a mi-

ing and, thus, they sharemore similarities within each laboratory thanwith the cold stresseriments are the laboratory and environmental settings. (n.g.) documentationnot given in

Table 1Possible stress specific marker genes in Arabidopsis thaliana.

Stress Affy ID AGI Gene name

Cold 254066_AT At4g25480 CBF3, dehydration response element B1A (DREB1a)254075_AT At4g25470 DRE/CRT-binding protein (DREB1C)

Osmotic 248428_AT At5g51760 Putative protein phosphatase 2C (PP2C)248750_AT At5g47530 Auxin-responsive dopamine beta-monooxygenase265084_AT At1g03790 Zinc finger (CCCH-type) family protein

Salt 253060_AT At4g37710 VQ motif-containing protein245033_AT At2g26380 Disease resistance protein-related / LRR protein-related245531_AT At4g15100 Serine carboxypeptidase S10 family protein

Drought 265984_AT At2g24210 Myrcene/ocimene synthase (TPS10)249732_AT At5g24420 Glucosamine/galactosamine-6-phosphate isomeraserelated245275_AT At4g15210 Beta-amylase (ATBETA-AMY)

Genotoxic 254443_AT At4g21070 C3HC4-type Ring finger family protein/BRCT domaincontaining protein246132_AT At5g20850 DNA repair protein RAD51

Oxidative 257670_AT At3g20340 Expressed proteinHeat 266841_AT At2g26150 Heat Shock Factor A2 (HSFA2)

248657_AT At5g48570 Peptidyl-prolyl cis-trans isomerase ROF1248332_AT At5g52640 Heat shock protein 81-1 (HSP81-1)

UV-B 261242_AT At1g32960 subtilase family protein259559_AT At1g21240 Wall associated kinase 3 (WAK3)255406_AT At4g03450 Ankyrin repeat family protein

Wounding 263754_AT At2g21510 DNAJ heat shock N-terminal domain-containing protein261101_AT At1g63030 Dwarf and delayed flowering 2 (DDF2)

172 J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

species such as Arabidopsis and rice or barley, poplar or even banana[9,15,20,51,52,121,147]. One important notion is that many ortholo-gous genes retained similar expression patterns and by conjecturefulfill similar functions [3,9,27,51,52,99,148,149]. Interestingly, com-parative analyses discovered that orthologs of specific genes in riceor barley are also responsive during stress responses in A. thaliana[9,20,52]. It is not a farfetched assumption that conserved expressionpatterns of orthologs imply an ancient origin of those response path-ways and possibly predates the separation of the investigated spe-cies' lineages. Such observations are very relevant for the transferof stress research information from one species to another.

Nonetheless, the current oligonucleotide microarray analysesharbor one major drawback: all of the cross-species information isderived from referenced sequence similarity searches to the A.thaliana genome. Likewise, there are tools now available now for thefunctional categorization of e.g. rice or barley genes by using the wellknown Gene Ontologies (GO), but these GO terms are typically takenfrom ‘best sequence hits’ in the A. thaliana genome. Hence, real func-tional data for many of these genes in other important model or cropspecies is still lacking.

Nevertheless, comparison of the barley and Arabidopsis heat stressresponse revealed conserved response patterns besides the regulationof the well known heat shock proteins [9]. The comparative analysisof Arabidopsis and barley seeds uncovered sets of genes thatwere com-monly regulated throughout development. This is somehow surprisingas Arabidopsis stores oil, while grasses have a starchy endosperm [2,9].While rice and barley are bothmembers of the Poaceae family and, thus,share basic developmental programs, they differ tremendously in theirecological plasticity [9,15,20,52,150–152]. Barley tolerates a remark-able set of environmental factors, like cold, drought, and high salinitybetter than rice [15,20,81,150,153] but the molecular mechanisms ofthis tolerance are widely unknown. However, comparative analysesidentified analogous and contrasting gene expression patterns be-tween rice and barley that might likely be responsible for the differ-ences observed between the two species [20].

1.15. Outlook on genetic engineering

The data obtained from analyses of stress transcriptome data maycontribute to the generation of plants that are more tolerant to certainabiotic stresses and in general, this goal can be achieved either by con-ventional breeding or by genetic engineering. Basic improvements havebeen made for soybean and sugar beet [19,154]. Marker-assisted

selection (MAS) of specific traits related to stress tolerance, as well asquantitative trait loci (QTLs), is being used alongside conventionalbreeding strategies [15,55]. Genetic engineering of plants includes theintrogression of other plant genes that are known to be involved instress response and tolerance. Initial approaches were based on the in-sertion of “single action” genes. Examples of these are osmoprotectants(amino acids, amines, sugars and sugar alcohols), detoxifying genes(glutathione peroxidase, glutathione reductase), late embryogenesisabundant (LEA) genes (HVA1), transporters (HLA1, AtCLCd), and lipidbiosynthesis genes (glycerol-3-phosphate acyltransferase (GPAT)), aswell as heat shock protein genes. New strategies make use of engineer-ing regulatory mechanisms. For this purpose, transcription factors orother signaling components are inserted into plant genomes to controlthe expression of many stress-responsive genes synchronously andspecifically [15,155]. Progenitors of crop plants may also function asan alternative source for possible ‘transgenes’ for stress tolerance. Thisapproach was successfully used for the improvement of Triticeae, in-cluding wheat and barley [15]. Furthermore, the introduction of genesfrom stress-adapted species such as desert and halo-tolerant plantsand organisms, such as freezing-tolerant fish, might also increasestress-tolerance [55,156–158]. Understanding the physiological and bio-chemical responseswithin their gene-regulatory networks is essential forthe successful generation of stress-tolerant plants. Recent advances inbreeding programs, as well as genome sequencing and genomic tech-nologies, provide great opportunities for understanding global patternsof gene expression in concert with biochemical data [9,55,157,159].

1.16. Concluding remarks

Probing the plant's transcriptional response to abiotic stresseswith microarray technology has significantly contributed to a betterunderstanding of the physiological processes during the initialalarm state and the mechanisms that lead to stress acclimation andtolerance. We see this already as microarrays are being used tostudy both model and crop plant species to search for evolutionaryconserved molecular mechanisms. Although next generation sequenc-ing technologies are starting to becomemore andmore affordable and in-corporated into standard evaluation procedures, current trends intranscriptome research will still be in favor of microarray technology fortranscriptional profiling in the future. In addition, diverse methodologieshave occurred over the last decade that capture quantitative changes ofthe plant's proteome, interactome and metabolome. Newer technologiesthat combine the separation of (macro-)molecules with subsequent

173J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

mass-spectrometry will allow the identification of the preformed com-plexes that are involved in the perception of the external stimuli. There-fore, what we see is that powerful algorithms need to be developed thatare capable of analyzing the different types and formats of transcriptome,proteome and metabolome data, if we are to delve even deeper into thecellular responses to abiotic stress.

Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft(DFG) (grant no. HA2146/11-1).

References

[1] J. DeRisi, L. Penland, P.O. Brown, M.L. Bittner, P.S. Meltzer, M. Ray, Y. Chen, Y.A.Su, J.M. Trent, Use of a cDNA microarray to analyse gene expression patternsin human cancer, Nat. Genet. 14 (1996) 457–460.

[2] E. Mangelsen, D. Wanke, J. Kilian, E. Sundberg, K. Harter, C. Jansson, Significanceof light, sugar, and amino acid supply for diurnal gene regulation in developingbarley caryopses, Plant Physiol. 153 (2010) 14–33.

[3] J. Schrader, R. Moyle, R. Bhalerao, M. Hertzberg, J. Lundeberg, P. Nilsson, R.P.Bhalerao, Cambial meristem dormancy in trees involves extensive remodellingof the transcriptome, Plant J. 40 (2004) 173–187.

[4] A. Andersson, J. Keskitalo, A. Sjodin, R. Bhalerao, F. Sterky, K. Wissel, K. Tandre, H.Aspeborg, R. Moyle, Y. Ohmiya, A. Brunner, P. Gustafsson, J. Karlsson, J. Lundeberg,O. Nilsson, G. Sandberg, S. Strauss, B. Sundberg, M. Uhlen, S. Jansson, P. Nilsson, Atranscriptional timetable of autumn senescence, Genome Biol. 5 (2004) R24.

[5] R. Gu, S. Fonseca, L.G. Puskas, L. Hackler Jr., A. Zvara, D. Dudits, M.S. Pais, Transcriptidentification and profiling during salt stress and recovery of Populus euphratica,Tree Physiol. 24 (2004) 265–276.

[6] W. Chen, N.J. Provart, J. Glazebrook, F. Katagiri, H.S. Chang, T. Eulgem, F. Mauch, S.Luan, G. Zou, S.A. Whitham, P.R. Budworth, Y. Tao, Z. Xie, X. Chen, S. Lam, J.A.Kreps, J.F. Harper, A. Si-Ammour, B. Mauch-Mani, M. Heinlein, K. Kobayashi, T.Hohn, J.L. Dangl, X.Wang, T. Zhu, Expression profilematrix of Arabidopsis transcrip-tion factor genes suggests their putative functions in response to environmentalstresses, Plant Cell 14 (2002) 559–574.

[7] H. Goda, E. Sasaki, K. Akiyama, A. Maruyama-Nakashita, K. Nakabayashi, W. Li,M. Ogawa, Y. Yamauchi, J. Preston, K. Aoki, T. Kiba, S. Takatsuto, S. Fujioka, T.Asami, T. Nakano, H. Kato, T. Mizuno, H. Sakakibara, S. Yamaguchi, E. Nambara,Y. Kamiya, H. Takahashi, M.Y. Hirai, T. Sakurai, K. Shinozaki, K. Saito, S. Yoshida,Y. Shimada, The AtGenExpress hormone and chemical treatment data set: experi-mental design, data evaluation, model data analysis and data access, Plant J. 55(2008) 526–542.

[8] S. Laubinger, G. Zeller, S.R. Henz, T. Sachsenberg, C.K. Widmer, N. Naouar, M.Vuylsteke, B. Scholkopf, G. Ratsch, D. Weigel, At-TAX: a whole genome tilingarray resource for developmental expression analysis and transcript identifica-tion in Arabidopsis thaliana, Genome Biol. 9 (2008) R112.

[9] E. Mangelsen, J. Kilian, K. Harter, C. Jansson, D. Wanke, E. Sundberg, Transcriptomeanalysis of high-temperature stress in developing barley caryopses: early stress re-sponses and effects on storage compound biosynthesis, Mol. Plant 4 (2011) 97–115.

[10] H. Rehrauer, C. Aquino,W.Gruissem, S.R. Henz, P. Hilson, S. Laubinger, N. Naouar, A.Patrignani, S. Rombauts, H. Shu, Y. Van de Peer, M. Vuylsteke, D.Weigel, G. Zeller, L.Hennig, AGRONOMICS1: a new resource for Arabidopsis transcriptome profiling,Plant Physiol. 152 (2010) 487–499.

[11] H. Walia, C. Wilson, P. Condamine, X. Liu, A.M. Ismail, L. Zeng, S.I. Wanamaker, J.Mandal, J. Xu, X. Cui, T.J. Close, Comparative transcriptional profiling of two con-trasting rice genotypes under salinity stress during the vegetative growth stage,Plant Physiol. 139 (2005) 822–835.

[12] D.Wang, Y. Pan, X. Zhao, L. Zhu, B. Fu, Z. Li, Genome-wide temporal–spatial gene ex-pression profiling of drought responsiveness in rice, BMC Genomics 12 (2011) 149.

[13] D. Wanke, K.W. Berendzen, J. Kilian, K. Harter, Insights into Arabidopsis abiotic stressresponse from the AtGenExpress expression profile dataset, in: H. Hirt (Ed.), PlantStress Biology: Genomics Goes Systems Biology, WILEY-VCH, Weinheim, 2009,pp. 199–225.

[14] G. Zeller, S.R. Henz, C.K.Widmer, T. Sachsenberg, G. Ratsch, D.Weigel, S. Laubinger,Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed usingwhole-genome tiling arrays, Plant J. 58 (2009) 1068–1082.

[15] E. Nevo, G. Chen, Drought and salt tolerances inwild relatives for wheat and barleyimprovement, Plant Cell Environ. 33 (2010) 670–685.

[16] W.A. Rensink, C.R. Buell,Microarray expression profiling resources for plant genomics,Trends Plant Sci. 10 (2005) 603–609.

[17] D.W. Galbraith, DNA microarray analyses in higher plants, OMICS 10 (2006)455–473.

[18] J.C. Redman, B.J. Haas, G. Tanimoto, C.D. Town, Development and evaluation ofan Arabidopsis whole genome Affymetrix probe array, Plant J. 38 (2004)545–561.

[19] M. Hajheidari, M. Abdollahian-Noghabi, H. Askari, M. Heidari, S.Y. Sadeghian,E.S. Ober, G.H. Salekdeh, Proteome analysis of sugar beet leaves under droughtstress, Proteomics 5 (2005) 950–960.

[20] H. Walia, C. Wilson, A.M. Ismail, T.J. Close, X. Cui, Comparing genomic expressionpatterns across plant species reveals highly diverged transcriptional dynamicsin response to salt stress, BMC Genomics 10 (2009) 398.

[21] V. Chinnusamy, K. Schumaker, J.K. Zhu, Molecular genetic perspectives on cross-talk and specificity in abiotic stress signalling in plants, J. Exp. Bot. 55 (2004)225–236.

[22] J. Kilian, D. Whitehead, J. Horak, D. Wanke, S. Weinl, O. Batistic, C. D'Angelo, E.Bornberg-Bauer, J. Kudla, K. Harter, The AtGenExpress global stress expressiondata set: protocols, evaluation and model data analysis of UV-B light, droughtand cold stress responses, Plant J. 50 (2007) 347–363.

[23] R. Mittler, Oxidative stress, antioxidants and stress tolerance, Trends Plant Sci. 7(2002) 405–410.

[24] N.J. Provart, P. Gil, W. Chen, B. Han, H.S. Chang, X. Wang, T. Zhu, Gene expressionphenotypes of Arabidopsis associated with sensitivity to low temperatures,Plant Physiol. 132 (2003) 893–906.

[25] S.H. Shiu, J.O. Borevitz, The next generation of microarray research: applicationsin evolutionary and ecological genomics, Heredity 100 (2008) 141–149.

[26] W.R. Swindell, M. Huebner, A.P. Weber, Plastic and adaptive gene expressionpatterns associated with temperature stress in Arabidopsis thaliana, Heredity99 (2007) 143–150.

[27] T. Hirayama, K. Shinozaki, Research on plant abiotic stress responses in thepost-genome era: past, present and future, Plant J. 61 (2010) 1041–1052.

[28] B. Usadel, T. Obayashi, M. Mutwil, F.M. Giorgi, G.W. Bassel, M. Tanimoto, A. Chow,D. Steinhauser, S. Persson, N.J. Provart, Co-expression tools for plant biology:opportunities for hypothesis generation and caveats, Plant Cell Environ. 32(2009) 1633–1651.

[29] D. Wanke, A. Hahn, J. Kilian, K. Harter, K.W. Berendzen, Regulatory networks: infer-ring functional relationships through Co-expression, in: L. Arccardi,W. Freudenberg,M. Ohya (Eds.), Quantum Bio-Informatics III: From Quantum Information to Bio-Informatics, vol. 26, World Scientific, Singapore, 2010, pp. 405–424.

[30] M. Seki, M. Narusaka, H. Abe, M. Kasuga, K. Yamaguchi-Shinozaki, P. Carninci, Y.Hayashizaki, K. Shinozaki, Monitoring the expression pattern of 1300 Arabidopsisgenes under drought and cold stresses by using a full-length cDNA microarray,Plant Cell 13 (2001) 61–72.

[31] S. Fowler, M.F. Thomashow, Arabidopsis transcriptome profiling indicates thatmultiple regulatory pathways are activated during cold acclimation in additionto the CBF cold response pathway, Plant Cell 14 (2002) 1675–1690.

[32] J.A. Kreps, Y. Wu, H.S. Chang, T. Zhu, X. Wang, J.F. Harper, Transcriptome changesfor Arabidopsis in response to salt, osmotic, and cold stress, Plant Physiol. 130(2002) 2129–2141.

[33] M. Schmid, T.S. Davison, S.R. Henz, U.J. Pape, M. Demar,M. Vingron, B. Scholkopf, D.Weigel, J.U. Lohmann, A gene expressionmap of Arabidopsis thaliana development,Nat. Genet. 37 (2005) 501–506.

[34] M. Lafos, P. Kroll, M.L. Hohenstatt, F.L. Thorpe, O. Clarenz, D. Schubert, Dynamicregulation of H3K27 trimethylation during Arabidopsis differentiation, PLoSGenet. 7 (2011) e1002040.

[35] J.D. Werner, J.O. Borevitz, N. Warthmann, G.T. Trainer, J.R. Ecker, J. Chory, D. Weigel,Quantitative trait locus mapping and DNA array hybridization identify an FLM dele-tion as a cause for natural flowering-time variation, Proc. Natl. Acad. Sci. U.S.A. 102(2005) 2460–2465.

[36] X. Zhang, J. Yazaki, A. Sundaresan, S. Cokus, S.W. Chan, H. Chen, I.R. Henderson,P. Shinn, M. Pellegrini, S.E. Jacobsen, J.R. Ecker, Genome-wide high-resolutionmapping and functional analysis of DNA methylation in arabidopsis, Cell 126(2006) 1189–1201.

[37] C. Costas, M. de la Paz Sanchez, H. Stroud, Y. Yu, J.C. Oliveros, S. Feng, A. Benguria,I. Lopez-Vidriero, X. Zhang, R. Solano, S.E. Jacobsen, C. Gutierrez, Genome-widemapping of Arabidopsis thaliana origins of DNA replication and their associatedepigenetic marks, Nat. Struct. Mol. Biol. 18 (2011) 395–400.

[38] P.K. Gupta, S. Rustgi, R.R. Mir, Array-based high-throughput DNA markers forcrop improvement, Heredity 101 (2008) 5–18.

[39] R.K. Chodavarapu, S. Feng, Y.V. Bernatavichute, P.Y. Chen, H. Stroud, Y. Yu, J.A. Hetzel,F. Kuo, J. Kim, S.J. Cokus, D. Casero, M. Bernal, P. Huijser, A.T. Clark, U. Kramer, S.S.Merchant, X. Zhang, S.E. Jacobsen, M. Pellegrini, Relationship between nucleosomepositioning and DNA methylation, Nature 466 (2010) 388–392.

[40] T.C. Mockler, S. Chan, A. Sundaresan, H. Chen, S.E. Jacobsen, J.R. Ecker, Applicationsof DNA tiling arrays for whole-genome analysis, Genomics 85 (2005) 1–15.

[41] F. Thibaud-Nissen, H.Wu,T. Richmond, J.C. Redman, C. Johnson, R. Green, J. Arias, C.D.Town, Development of Arabidopsis whole-genome microarrays and their applica-tion to the discovery of binding sites for the TGA2 transcription factor in salicylicacid-treated plants, Plant J. 47 (2006) 152–162.

[42] J.R. Dinneny, T.A. Long, J.Y.Wang, J.W. Jung, D. Mace, S. Pointer, C. Barron, S.M. Brady,J. Schiefelbein, P.N. Benfey, Cell identity mediates the response of Arabidopsis rootsto abiotic stress, Science 320 (2008) 942–945.

[43] J. Dugardeyn, F. Vandenbussche, D. Van Der Straeten, To grow or not to grow: whatcanwe learn on ethylene-gibberellin cross-talk by in silico gene expression analysis?J. Exp. Bot. 59 (2008) 1–16.

[44] C. Pinheiro, M.M. Chaves, Photosynthesis and drought: can we make metabolicconnections from available data? J. Exp. Bot. 62 (2010) 869–882.

[45] W.R. Swindell, M. Huebner, A.P. Weber, Transcriptional profiling of Arabidopsisheat shock proteins and transcription factors reveals extensive overlap betweenheat and non-heat stress response pathways, BMC Genomics 8 (2007) 125.

[46] L. Katsir, H.S. Chung, A.J. Koo, G.A. Howe, Jasmonate signaling: a conservedmechanism of hormone sensing, Curr. Opin. Plant Biol. 11 (2008) 428–435.

[47] Y. Fujita, M. Fujita, K. Shinozaki, K. Yamaguchi-Shinozaki, ABA-mediatedtranscriptional regulation in response to osmotic stress in plants, J. Plant Res.124 (2011) 509–525.

[48] K.J. Dietz, M.O. Vogel, A. Viehhauser, AP2/EREBP transcription factors are part of generegulatory networks and integratemetabolic, hormonal and environmental signals instress acclimation and retrograde signalling, Protoplasma 245 (2010) 3–14.

174 J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

[49] C. Wasternack, Jasmonates: an update on biosynthesis, signal transductionand action in plant stress response, growth and development, Ann. Bot.100 (2007) 681–697.

[50] D.H. Sanchez, F.L. Pieckenstain, J. Szymanski, A. Erban, M. Bromke, M.A. Hannah,U. Kraemer, J. Kopka, M.K. Udvardi, Comparative functional genomics of saltstress in related model and cultivated plants identifies and overcomes limita-tions to translational genomics, PLoS One 6 (2011) e17094.

[51] A. Mustroph, S.C. Lee, T. Oosumi, M.E. Zanetti, H. Yang, K. Ma, A. Yaghoubi-Masihi, T.Fukao, J. Bailey-Serres, Cross-kingdom comparison of transcriptomic adjustments tolow-oxygen stress highlights conserved and plant-specific responses, Plant Physiol.152 (2010) 1484–1500.

[52] O. Cotsaftis, D. Plett, A.A. Johnson, H. Walia, C. Wilson, A.M. Ismail, T.J. Close, M.Tester, U. Baumann, Root-specific transcript profiling of contrasting rice geno-types in response to salinity stress, Mol. Plant 4 (2011) 25–41.

[53] T. Umezawa, Systems biology approaches to abscisic acid signaling, J. Plant Res.124 (2011) 539–548.

[54] N.R. Street, S. Jansson, T.R. Hvidsten, A systems biology model of the regulatorynetwork in Populus leaves reveals interacting regulators and conserved regula-tion, BMC Plant Biol. 11 (2011) 13.

[55] R. Mittler, E. Blumwald, Genetic engineering for modern agriculture: challengesand perspectives, Annu. Rev. Plant Biol. 61 (2010) 443–462.

[56] S.J. Neill, R. Desikan, A. Clarke, R.D. Hurst, J.T. Hancock, Hydrogen peroxide andnitric oxide as signalling molecules in plants, J. Exp. Bot. 53 (2002) 1237–1247.

[57] W. Qiao, L.M. Fan, Nitric oxide signaling in plant responses to abiotic stresses, J.Integr. Plant Biol. 50 (2008) 1238–1246.

[58] X. Hu, W. Li, Q. Chen, Y. Yang, Early signal transduction linking the synthesis ofjasmonic acid in plant, Plant Signal. Behav. 4 (2009) 696–697.

[59] A.N. Misra, M. Misra, R. Singh, Nitric oxide: A ubiquitous signaling molecule withdiverse role in plants, Afr. J. Plant Sci. 5 (2011) 57–74.

[60] Y. Ogasawara, H. Kaya, G. Hiraoka, F. Yumoto, S. Kimura, Y. Kadota, H. Hishinuma, E.Senzaki, S. Yamagoe, K. Nagata, M. Nara, K. Suzuki, M. Tanokura, K. Kuchitsu, Syn-ergistic activation of the Arabidopsis NADPH oxidase AtrbohD by Ca2+ and phos-phorylation, J. Biol. Chem. 283 (2008) 8885–8892.

[61] R. Das, G.K. Pandey, Expressional analysis and role of calcium regulated kinasesin abiotic stress signaling, Curr. Genomics 11 (2010) 2–13.

[62] S. Franz, B. Ehlert, A. Liese, J. Kurth, A.C. Cazale, T. Romeis, Calcium-dependentprotein kinase CPK21 functions in abiotic stress response in Arabidopsis thaliana,Mol. Plant 4 (2011) 83–96.

[63] T. Sano, T. Higaki, K.Handa, Y. Kadota, K. Kuchitsu, S. Hasezawa, A. Hoffmann, J. Endter,U. Zimmermann, R. Hedrich, T. Roitsch, Calcium ions are involved in the delay of plantcell cycle progression by abiotic stresses, FEBS Lett. 580 (2006) 597–602.

[64] C. Dubos, R. Stracke, E. Grotewold, B. Weisshaar, C. Martin, L. Lepiniec, MYB tran-scription factors in Arabidopsis, Trends Plant Sci. 15 (2010) 573–581.

[65] D. Qiu, J. Xiao, W. Xie, H. Cheng, X. Li, S. Wang, Exploring transcriptional signallingmediated by OsWRKY13, a potential regulator of multiple physiological processesin rice, BMC Plant Biol. 9 (2009) 74.

[66] G.P. Srivastava, P. Li, J. Liu, D. Xu, Identification of transcription factor's targetsusing tissue-specific transcriptomic data in Arabidopsis thaliana, BMC Syst. Biol.4 (Suppl. 2) (2010) S2.

[67] M. Nuruzzaman, R. Manimekalai, A.M. Sharoni, K. Satoh, H. Kondoh, H. Ooka, S.Kikuchi, Genome-wide analysis of NAC transcription factor family in rice, Gene465 (2010) 30–44.

[68] H. Chen, Z. Lai, J. Shi, Y. Xiao, Z. Chen, X. Xu, Roles of arabidopsis WRKY18,WRKY40 and WRKY60 transcription factors in plant responses to abscisic acidand abiotic stress, BMC Plant Biol. 10 (2010) 281.

[69] P. Mao, M. Duan, C. Wei, Y. Li, WRKY62 transcription factor acts downstream ofcytosolic NPR1 and negatively regulates jasmonate-responsive gene expression,Plant Cell Physiol. 48 (2007) 833–842.

[70] X. Ren, Z. Chen, Y. Liu, H. Zhang, M. Zhang, Q. Liu, X. Hong, J.K. Zhu, Z. Gong,ABO3, a WRKY transcription factor, mediates plant responses to abscisic acidand drought tolerance in Arabidopsis, Plant J. 63 (2010) 417–429.

[71] C. Seiler, V.T. Harshavardhan, K. Rajesh, P.S. Reddy, M. Strickert, H. Rolletschek,U. Scholz, U. Wobus, N. Sreenivasulu, ABA biosynthesis and degradation contrib-uting to ABA homeostasis during barley seed development under control andterminal drought-stress conditions, J. Exp. Bot. 62 (2011) 2615–2632.

[72] T. Obayashi, K. Nishida, K. Kasahara, K. Kinoshita, ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications toa broad range of flowering plants, Plant Cell Physiol. 52 (2011) 213–219.

[73] S.J. Wi, S.J. Jang, K.Y. Park, Inhibition of biphasic ethylene production enhancestolerance to abiotic stress by reducing the accumulation of reactive oxygen speciesin Nicotiana tabacum, Mol. Cells 30 (2010) 37–49.

[74] S. Ray, P.K. Dansana, J. Giri, P. Deveshwar, R. Arora, P. Agarwal, J.P. Khurana,S. Kapoor, A.K. Tyagi, Modulation of transcription factor and metabolic path-way genes in response to water-deficit stress in rice, Funct. Integr. Genomics11 (2010) 157–178.

[75] M. Jaskiewicz, U. Conrath, C. Peterhansel, Chromatin modification acts as amemory for systemic acquired resistance in the plant stress response, EMBORep. 12 (2010) 50–55.

[76] J.M. Kim, T.K. To, T. Nishioka, M. Seki, Chromatin regulation functions in plantabiotic stress responses, Plant Cell Environ. 33 (2010) 604–611.

[77] S.V. Kumar, P.A. Wigge, H2A.Z-containing nucleosomes mediate the thermosensoryresponse in Arabidopsis, Cell 140 (2010) 136–147.

[78] V. Chinnusamy, Z. Gong, J.K. Zhu, Abscisic acid-mediated epigenetic processes inplant development and stress responses, J. Integr. Plant Biol. 50 (2008)1187–1195.

[79] A. Kapazoglou, A. Tondelli, D. Papaefthimiou, H. Ampatzidou, E. Francia, M.A.Stanca, K. Bladenopoulos, A.S. Tsaftaris, Epigenetic chromatin modifiers in barley:IV. The study of barley polycomb group (PcG) genes during seed developmentand in response to external ABA, BMC Plant Biol. 10 (2010) 73.

[80] M. Todesco, S. Balasubramanian, T.T. Hu,M.B. Traw,M.Horton, P. Epple, C. Kuhns, S.Sureshkumar, C. Schwartz, C. Lanz, R.A. Laitinen, Y. Huang, J. Chory, V. Lipka, J.O.Borevitz, J.L. Dangl, J. Bergelson, M. Nordborg, D. Weigel, Natural allelic variationunderlying a major fitness trade-off in Arabidopsis thaliana, Nature 465 (2010)632–636.

[81] T. Abebe, K. Melmaiee, V. Berg, R.P. Wise, Drought response in the spikes of barley:gene expression in the lemma, palea, awn, and seed, Funct. Integr. Genomics 10(2010) 191–205.

[82] K.E. Zinn, M. Tunc-Ozdemir, J.F. Harper, Temperature stress and plant sexual re-production: uncovering the weakest links, J. Exp. Bot. 61 (2010) 1959–1968.

[83] M. van Hulten, M. Pelser, L.C. van Loon, C.M. Pieterse, J. Ton, Costs and benefits ofpriming for defense in Arabidopsis, Proc. Natl. Acad. Sci. U.S.A. 103 (2006)5602–5607.

[84] D. Takezawa, K. Komatsu, Y. Sakata, ABA in bryophytes: how a universal growthregulator in life became a plant hormone? J. Plant Res. 124 (2011) 437–453.

[85] A.J. Koo, G.A. Howe, The wound hormone jasmonate, Phytochemistry 70 (2009)1571–1580.

[86] A.J. Koo, X. Gao, A.D. Jones, G.A. Howe, A rapid wound signal activates the systemicsynthesis of bioactive jasmonates in Arabidopsis, Plant J. 59 (2009) 974–986.

[87] W. Busch, P.N. Benfey, Information processing without brains—the power ofintercellular regulators in plants, Development 137 (2010) 1215–1226.

[88] G.Miller, K. Schlauch, R. Tam, D. Cortes,M.A. Torres, V. Shulaev, J.L. Dangl, R.Mittler,The plant NADPH oxidase RBOHDmediates rapid systemic signaling in response todiverse stimuli, Sci. Signal. 2 (2009) ra45.

[89] M.C. Romero-Puertas, M. Perazzolli, E.D. Zago, M. Delledonne, Nitric oxide signallingfunctions in plant–pathogen interactions, Cell. Microbiol. 6 (2004) 795–803.

[90] J.B. Rossel, P.B. Wilson, D. Hussain, N.S. Woo, M.J. Gordon, O.P. Mewett, K.A.Howell, J. Whelan, K. Kazan, B.J. Pogson, Systemic and intracellular responsesto photooxidative stress in Arabidopsis, Plant Cell 19 (2007) 4091–4110.

[91] G. Tanou, C. Job,M. Belghazi, A.Molassiotis, G. Diamantidis, D. Job, Proteomic signa-tures uncover hydrogen peroxide and nitric oxide cross-talk signaling network incitrus plants, J. Proteome Res. 9 (2010) 5994–6006.

[92] A.C. Vlot, D.A. Dempsey, D.F. Klessig, Salicylic acid, a multifaceted hormone tocombat disease, Annu. Rev. Phytopathol. 47 (2009) 177–206.

[93] G.W. Bassel, N.J. Provart, Gene expression analyses for elucidating mechanismsof hormonal action in plants, Methods Mol. Biol. 495 (2009) 21–37.

[94] J.L. Nemhauser, F. Hong, J. Chory,Different plant hormones regulate similar processesthrough largely nonoverlapping transcriptional responses, Cell 126 (2006) 467–475.

[95] S.R. Cutler, P.L. Rodriguez, R.R. Finkelstein, S.R. Abrams, Abscisic acid: emergenceof a core signaling network, Annu. Rev. Plant Biol. 61 (2010) 651–679.

[96] H.W. Jung, T.J. Tschaplinski, L. Wang, J. Glazebrook, J.T. Greenberg, Priming insystemic plant immunity, Science 324 (2009) 89–91.

[97] A. Buhtz, J. Pieritz, F. Springer, J. Kehr, Phloem small RNAs, nutrient stress responses,and systemic mobility, BMC Plant Biol. 10 (2010) 64.

[98] D. Wanke, The ABA-mediated switch between submersed and emersed life-stylesin aquatic macrophytes, J. Plant Res. 124 (2011) 467–475.

[99] J. Bassaganya-Riera, J. Skoneczka, D.G. Kingston, A. Krishnan, S.A.Misyak, A.J. Guri, A.Pereira, A.B. Carter, P. Minorsky, R. Tumarkin, R. Hontecillas, Mechanisms of actionand medicinal applications of abscisic acid, Curr. Med. Chem. 17 (2010) 467–478.

[100] K. Morris, S.A. MacKerness, T. Page, C.F. John, A.M. Murphy, J.P. Carr, V. Buchanan-Wollaston, Salicylic acid has a role in regulating gene expression during leaf senes-cence, Plant J. 23 (2000) 677–685.

[101] A. Chini, S. Fonseca, G. Fernandez, B. Adie, J.M. Chico, O. Lorenzo, G. Garcia-Casado, I.Lopez-Vidriero, F.M. Lozano, M.R. Ponce, J.L. Micol, R. Solano, The JAZ family of re-pressors is the missing link in jasmonate signalling, Nature 448 (2007) 666–671.

[102] B. Thines, L. Katsir, M. Melotto, Y. Niu, A. Mandaokar, G. Liu, K. Nomura, S.Y. He, G.A.Howe, J. Browse, JAZ repressor proteins are targets of the SCF(COI1) complex duringjasmonate signalling, Nature 448 (2007) 661–665.

[103] S.P. Pandey, M. Roccaro, M. Schon, E. Logemann, I.E. Somssich, Transcriptionalreprogramming regulated by WRKY18 andWRKY40 facilitates powdery mildewinfection of Arabidopsis, Plant J. 64 (2010) 912–923.

[104] M.H. Siddiqui, M.H. Al-Whaibi, M.O. Basalah, Role of nitric oxide in tolerance ofplants to abiotic stress, Protoplasma 248 (2011) 447–455.

[105] M.C. Romero-Puertas, M. Delledonne, Nitric oxide signaling in plant–pathogeninteractions, IUBMB Life 55 (2003) 579–583.

[106] R. Desikan, J.T. Hancock, J. Bright, J. Harrison, I. Weir, R. Hooley, S.J. Neill, A rolefor ETR1 in hydrogen peroxide signaling in stomatal guard cells, Plant Physiol.137 (2005) 831–834.

[107] C. Grefen, K. Stadele, K. Ruzicka, P. Obrdlik, K. Harter, J. Horak, Subcellular localizationand in vivo interactions of the Arabidopsis thaliana ethylene receptor family mem-bers, Mol. Plant 1 (2008) 308–320.

[108] S. Takeda, C. Gapper, H. Kaya, E. Bell, K. Kuchitsu, L. Dolan, Local positive feedbackregulation determines cell shape in root hair cells, Science 319 (2008) 1241–1244.

[109] J. Foreman, V. Demidchik, J.H. Bothwell, P. Mylona, H. Miedema, M.A. Torres, P.Linstead, S. Costa, C. Brownlee, J.D. Jones, J.M. Davies, L. Dolan, Reactive oxygenspecies produced by NADPH oxidase regulate plant cell growth, Nature 422(2003) 442–446.

[110] U. Conrath, C.M. Pieterse, B. Mauch-Mani, Priming in plant–pathogen interactions,Trends Plant Sci. 7 (2002) 210–216.

[111] I.T. Baldwin, Jasmonate-induced responses are costly but benefit plants underattack in native populations, Proc. Natl. Acad. Sci. U.S.A. 95 (1998) 8113–8118.

175J. Kilian et al. / Biochimica et Biophysica Acta 1819 (2012) 166–175

[112] A. Kohler, S. Schwindling, U. Conrath, Benzothiadiazole-induced priming for potenti-ated responses to pathogen infection, wounding, and infiltration of water into leavesrequires the NPR1/NIM1 gene in Arabidopsis, Plant Physiol. 128 (2002) 1046–1056.

[113] J. Quilis, G. Penas, J. Messeguer, C. Brugidou, B. San Segundo, The ArabidopsisAtNPR1 inversely modulates defense responses against fungal, bacterial, or viralpathogens while conferring hypersensitivity to abiotic stresses in transgenic rice,Mol. Plant Microbe Interact. 21 (2008) 1215–1231.

[114] M. Bartsch, E. Gobbato, P. Bednarek, S. Debey, J.L. Schultze, J. Bautor, J.E. Parker,Salicylic acid-independent ENHANCED DISEASE SUSCEPTIBILITY1 signaling in Arabi-dopsis immunity and cell death is regulated by the monooxygenase FMO1 and theNudix hydrolase NUDT7, Plant Cell 18 (2006) 1038–1051.

[115] G.J. Beckers, M. Jaskiewicz, Y. Liu, W.R. Underwood, S.Y. He, S. Zhang, U. Conrath,Mitogen-activated protein kinases 3 and 6 are required for full priming of stressresponses in Arabidopsis thaliana, Plant Cell 21 (2009) 944–953.

[116] W.R. Swindell, The association among gene expression responses to nine abioticstress treatments in Arabidopsis thaliana, Genetics 174 (2006) 1811–1824.

[117] H.C. Causton, B. Ren, S.S. Koh, C.T. Harbison, E. Kanin, E.G. Jennings, T.I. Lee, H.L.True, E.S. Lander, R.A. Young, Remodeling of yeast genome expression in responseto environmental changes, Mol. Biol. Cell 12 (2001) 323–337.

[118] D. Chen, W.M. Toone, J. Mata, R. Lyne, G. Burns, K. Kivinen, A. Brazma, N. Jones, J.Bahler, Global transcriptional responses of fission yeast to environmental stress,Mol. Biol. Cell 14 (2003) 214–229.

[119] S. Davletova, K. Schlauch, J. Coutu, R. Mittler, The zinc-finger protein Zat12 playsa central role in reactive oxygen and abiotic stress signaling in Arabidopsis, PlantPhysiol. 139 (2005) 847–856.

[120] H. Sakamoto, K. Maruyama, Y. Sakuma, T. Meshi, M. Iwabuchi, K. Shinozaki, K.Yamaguchi-Shinozaki, Arabidopsis Cys2/His2-type zinc-finger proteins functionas transcription repressors under drought, cold, and high-salinity stress conditions,Plant Physiol. 136 (2004) 2734–2746.

[121] G. Breton, J. Danyluk, J.B. Charron, F. Sarhan, Expression profiling and bioinformaticanalyses of a novel stress-regulated multispanning transmembrane protein familyfrom cereals and Arabidopsis, Plant Physiol. 132 (2003) 64–74.

[122] K.A. Franklin, Shade avoidance, New Phytol. 179 (2008) 930–944.[123] F. Peschke, T. Kretsch, Genome-wide analysis of light-dependent transcript accumu-

lation patterns duringearly stages of Arabidopsis seedlingdeetiolation, Plant Physiol.155 (2011) 1353–1366.

[124] H. Parkinson, U. Sarkans, N. Kolesnikov, N. Abeygunawardena, T. Burdett, M. Dylag,I. Emam, A. Farne, E. Hastings, E. Holloway, N. Kurbatova, M. Lukk, J. Malone, R.Mani, E. Pilicheva, G. Rustici, A. Sharma, E. Williams, T. Adamusiak, M. Brandizi, N.Sklyar, A. Brazma, ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments, Nucleic AcidsRes. 39 (2010) D1002–D1004.

[125] J. Supper, M. Strauch, D. Wanke, K. Harter, A. Zell, EDISA: extracting biclustersfrom multiple time-series of gene expression profiles, BMC Bioinformatics8 (2007) 334.

[126] A. Brazma, P. Hingamp, J. Quackenbush, G. Sherlock, P. Spellman, C. Stoeckert, J. Aach,W. Ansorge, C.A. Ball, H.C. Causton, T. Gaasterland, P. Glenisson, F.C.Holstege, I.F. Kim,V.Markowitz, J.C.Matese, H. Parkinson, A. Robinson, U. Sarkans, S. Schulze-Kremer, J.Stewart, R. Taylor, J. Vilo, M. Vingron, Minimum information about a microarrayexperiment (MIAME)-toward standards for microarray data, Nat. Genet. 29(2001) 365–371.

[127] P.L. Whetzel, H. Parkinson, H.C. Causton, L. Fan, J. Fostel, G. Fragoso, L. Game, M.Heiskanen, N. Morrison, P. Rocca-Serra, S.A. Sansone, C. Taylor, J. White, C.J.Stoeckert Jr., The MGED Ontology: a resource for semantics-based descriptionof microarray experiments, Bioinformatics 22 (2006) 866–873.

[128] A. Brazma, Minimum Information About a Microarray Experiment (MIAME)—successes, failures, challenges, ScientificWorldJournal 9 (2009) 420–423.

[129] C. Massonnet, D. Vile, J. Fabre, M.A. Hannah, C. Caldana, J. Lisec, G.T. Beemster,R.C. Meyer, G. Messerli, J.T. Gronlund, J. Perkovic, E. Wigmore, S. May, M.W.Bevan, C. Meyer, S. Rubio-Diaz, D. Weigel, J.L. Micol, V. Buchanan-Wollaston, F.Fiorani, S. Walsh, B. Rinn, W. Gruissem, P. Hilson, L. Hennig, L. Willmitzer, C.Granier, Probing the reproducibility of leaf growth and molecular phenotypes:a comparison of three Arabidopsis accessions cultivated in ten laboratories,Plant Physiol. 152 (2010) 2142–2157.

[130] W.P. Kuo, F. Liu, J. Trimarchi, C. Punzo, M. Lombardi, J. Sarang, M.E. Whipple, M.Maysuria, K. Serikawa, S.Y. Lee, D. McCrann, J. Kang, J.R. Shearstone, J. Burke, D.J.Park, X. Wang, T.L. Rector, P. Ricciardi-Castagnoli, S. Perrin, S. Choi, R. Bumgarner,J.H. Kim, G.F. Short 3rd, M.W. Freeman, B. Seed, R. Jensen, G.M. Church, E. Hovig,C.L. Cepko, P. Park, L. Ohno-Machado, T.K. Jenssen, A sequence-oriented comparisonof gene expressionmeasurements across differenthybridization-based technologies,Nat. Biotechnol. 24 (2006) 832–840.

[131] M.D. Piper, P. Daran-Lapujade, C. Bro, B. Regenberg, S. Knudsen, J. Nielsen, J.T.Pronk, Reproducibility of oligonucleotide microarray transcriptome analyses.An interlaboratory comparisonusing chemostat cultures of Saccharomyces cerevisiae,J. Biol. Chem. 277 (2002) 37001–37008.

[132] D.Wanke, J. Kilian, J. Supper, K.W. Berendzen, A. Zell, K. Harter, The analysis of geneexpression and cis-regulatory elements in large microarray expression data sets,in: L. Arccardi, W. Freudenberg, M. Ohya (Eds.), Quantum Bio-Informatics: FromQuantum Information to Bio-Informatics, vol. 21, World Scientific, Singapore,2008, pp. 294–314.

[133] A. Grewal, P. Lambert, J. Stockton, Analysis of expression data: an overview,Curr. Protoc. Bioinformatics (2007) 1 (Chapter 7, Unit 7).

[134] J. Tarraga, I. Medina, J. Carbonell, J. Huerta-Cepas, P. Minguez, E. Alloza, F. Al-Shahrour,S. Vegas-Azcarate, S. Goetz, P. Escobar, F. Garcia-Garcia, A. Conesa, D. Montaner, J.Dopazo, GEPAS, a web-based tool for microarray data analysis and interpretation,Nucleic Acids Res. 36 (2008) W308–W314.

[135] C. Romualdi, N. Vitulo,M. Del Favero, G. Lanfranchi,MIDAW: aweb tool for statisticalanalysis of microarray data, Nucleic Acids Res. 33 (2005) W644–W649.

[136] D. Winter, B. Vinegar, H. Nahal, R. Ammar, G.V. Wilson, N.J. Provart, An “ElectronicFluorescent Pictograph” browser for exploring and analyzing large-scale biologicaldata sets, PLoS One 2 (2007) e718.

[137] S. Mostafavi, D. Ray, D.Warde-Farley, C. Grouios, Q. Morris, GeneMANIA: a real-timemultiple association network integration algorithm for predicting gene function, Ge-nome Biol. 9 (Suppl. 1) (2008) S4.

[138] A. Sjodin, N.R. Street, G. Sandberg, P. Gustafsson, S. Jansson, The Populus GenomeIntegrative Explorer (PopGenIE): a new resource for exploring the Populus ge-nome, New Phytol. 182 (2009) 1013–1025.

[139] M. Mutwil, B. Usadel, M. Schutte, A. Loraine, O. Ebenhoh, S. Persson, Assembly ofan interactive correlation network for the Arabidopsis genome using a novelheuristic clustering algorithm, Plant Physiol. 152 (2010) 29–43.

[140] E. Prifti, J.D. Zucker, K. Clement, C. Henegar, FunNet: an integrative tool for exploringtranscriptional interactions, Bioinformatics 24 (2008) 2636–2638.

[141] J. Paquette, T. Tokuyasu, EGAN: exploratory gene associationnetworks, Bioinformatics26 (2009) 285–286.

[142] T. Hruz, O. Laule, G. Szabo, F. Wessendorp, S. Bleuler, L. Oertle, P. Widmayer, W.Gruissem, P. Zimmermann, Genevestigator v3: a reference expression databasefor the meta-analysis of transcriptomes, Adv. Bioinform. 2008 (2008) 420747.

[143] A.I. Saeed, N.K. Bhagabati, J.C. Braisted, W. Liang, V. Sharov, E.A. Howe, J. Li, M.Thiagarajan, J.A. White, J. Quackenbush, TM4 microarray software suite,Methods Enzymol. 411 (2006) 134–193.

[144] M.S. Cline, M. Smoot, E. Cerami, A. Kuchinsky, N. Landys, C.Workman, R. Christmas,I. Avila-Campilo, M. Creech, B. Gross, K. Hanspers, R. Isserlin, R. Kelley, S. Killcoyne,S. Lotia, S. Maere, J. Morris, K. Ono, V. Pavlovic, A.R. Pico, A. Vailaya, P.L. Wang, A.Adler, B.R. Conklin, L. Hood, M. Kuiper, C. Sander, I. Schmulevich, B. Schwikowski,G.J. Warner, T. Ideker, G.D. Bader, Integration of biological networks and geneexpression data using Cytoscape, Nat. Protoc. 2 (2007) 2366–2382.

[145] J.P. van Dijken, J. Bauer, L. Brambilla, P. Duboc, J.M. Francois, C. Gancedo, M.L.Giuseppin, J.J. Heijnen, M. Hoare, H.C. Lange, E.A. Madden, P. Niederberger, J.Nielsen, J.L. Parrou, T. Petit, D. Porro, M. Reuss, N. van Riel, M. Rizzi, H.Y.Steensma, C.T. Verrips, J. Vindelov, J.T. Pronk, An interlaboratory comparison ofphysiological andgeneticproperties of four Saccharomyces cerevisiae strains, EnzymeMicrob. Technol. 26 (2000) 706–714.

[146] C. Wrzodek, A. Schroder, A. Drager, D. Wanke, K.W. Berendzen, M. Kronfeld, K.Harter, A. Zell, ModuleMaster: a new tool to decipher transcriptional regulatorynetworks, Biosystems 99 (2010) 79–81.

[147] M.W. Davey, N.S. Graham, B. Vanholme, R. Swennen, S.T. May, J. Keulemans,Heterologousoligonucleotidemicroarrays for transcriptomics in a non-model species;a proof-of-concept study of drought stress inMusa, BMC Genomics 10 (2009) 436.

[148] E. Mangelsen, J. Kilian, K.W. Berendzen, U.H. Kolukisaoglu, K. Harter, C. Jansson,D. Wanke, Phylogenetic and comparative gene expression analysis of barley(Hordeum vulgare) WRKY transcription factor family reveals putatively retainedfunctions between monocots and dicots, BMC Genomics 9 (2008) 194.

[149] Y. Jiao, L. Ma, E. Strickland, X.W. Deng, Conservation and divergence of light-regulated genome expression patterns during seedling development in riceand Arabidopsis, Plant Cell 17 (2005) 3239–3256.

[150] P. Guo, M. Baum, S. Grando, S. Ceccarelli, G. Bai, R. Li, M. von Korff, R.K. Varshney,A. Graner, J. Valkoun, Differentially expressed genes between drought-tolerantand drought-sensitive barley genotypes in response to drought stress duringthe reproductive stage, J. Exp. Bot. 60 (2009) 3531–3544.

[151] H. Walia, C. Wilson, P. Condamine, A.M. Ismail, J. Xu, X. Cui, T.J. Close, Array-basedgenotyping and expression analysis of barley cv. Maythorpe and Golden Promise,BMC Genomics 8 (2007) 87.

[152] G.J. Norton, D.E. Lou-Hing, A.A. Meharg, A.H. Price, Rice–arsenate interactions in hy-droponics: whole genome transcriptional analysis, J. Exp. Bot. 59 (2008) 2267–2276.

[153] J.T. Svensson, C. Crosatti, C. Campoli, R. Bassi, A.M. Stanca, T.J. Close, L. Cattivelli,Transcriptome analysis of cold acclimation in barley albina and xantha mutants,Plant Physiol. 141 (2006) 257–270.

[154] E.A.H. Pilon-Smits, N. Terry, T. Sears, K. van Dun, Enhanced drought resistance infructan-producing sugar beet, Plant Physiol. Biochem. 37 (1999) 313–317.

[155] P. Bhatnagar-Mathur, V. Vadez, K.K. Sharma, Transgenic approaches for abiotic stresstolerance in plants: retrospect and prospects, Plant Cell Rep. 27 (2008) 411–424.

[156] B. Vinocur, A. Altman, Recent advances in engineering plant tolerance to abioticstress: achievements and limitations, Curr. Opin. Biotechnol. 16 (2005) 123–132.

[157] T.R. Sinclair, Challenges in breeding for yield increase for drought, Trends Plant Sci. 16(2011) 289–293.

[158] B. Zhu, A.S. Xiong, R.H. Peng, J. Xu, X.F. Jin, X.R. Meng, Q.H. Yao, Over-expressionof ThpI from Choristoneura fumiferana enhances tolerance to cold in Arabidopsis,Mol. Biol. Rep. 37 (2010) 961–966.

[159] S. Yang, B. Vanderbeld, J. Wan, Y. Huang, Narrowing down the targets: towards suc-cessful genetic engineering of drought-tolerant crops, Mol. Plant 3 (2010) 469–490.