Copyright � 2006 by the Genetics Society of AmericaDOI: 10.1534/genetics.106.061374

The Association Among Gene Expression Responses to Nine Abiotic StressTreatments in Arabidopsis thaliana

William R. Swindell1

Department of Probability and Statistics, Michigan State University, East Lansing, Michigan 48824

Manuscript received May 26, 2006Accepted for publication September 11, 2006

ABSTRACT

The identification and analysis of genes exhibiting large expression responses to several different typesof stress may provide insights into the functional basis of multiple stress tolerance in plant species. Thisstudy considered whole-genome transcriptional profiles from Arabidopsis thaliana root and shoot organsunder nine abiotic stress conditions (cold, osmotic stress, salt, drought, genotoxic stress, ultraviolet light,oxidative stress, wounding, and high temperature) and at six different time points of stress exposure (0.5,1, 3, 6, 12, and 24 hr). In roots, genomewide correlations between transcriptional responses to differentstress treatments peaked following 1 hr of stress exposure, while in shoots, correlations tended to increasefollowing 6 hr of stress exposure. The generality of stress responses at the transcriptional level wastherefore time and organ dependent. A total of 67 genes were identified as exhibiting a statisticallysignificant pattern of gene expression characterized by large transcriptional responses to all nine stresstreatments. Most genes were identified from early to middle (1–6 hr) time points of stress exposure.Analysis of this gene set indicated that cell rescue/defense/virulence, energy, and metabolism functionalclasses were overrepresented, providing novel insight into the functional basis of multiple stress tolerancein Arabidopsis.

THE genetic effects of environmental stress havebeen extensively studied from the standpoint of

cellular physiology (Singh et al. 2002; Mahalingam

et al. 2003; Chen and Zhu 2004), evolutionary biology(Hoffmann and Parsons 1991, 1997), and increas-ingly, biotechnology (Holmberg and Bulow 1998;Kasuga et al. 1999; Wang et al. 2003; Pellegrineschi

et al. 2004; Denby and Gehring 2005; Vinocur andAltman 2005). In plant species, environmental stresscan be a large source of mortality because plants areunable to avoid environmental extremes through mi-gration. Stress is thus a powerful force influencing theevolution of plant populations in the wild (Hoffmann

and Parsons 1997), as well as a key factor limiting eco-nomic yield in commercially valuable species (Boyer

1982; Blum 1988). The susceptibility of plants to en-vironmental extremes has driven the evolution of awide range of stress-resistance and tolerance mecha-nisms (Singh et al. 2002; Mahalingam et al. 2003; Chen

and Zhu 2004; Bohnert et al. 2006). In the modelsystem Arabidopsis thaliana, the physiological basis ofthese resistance mechanisms has been pursued with theultimate goal of elucidating the biochemical pathwaysinvolved in stress perception, signal transduction, andadaptive response (e.g., Seki et al. 2001, 2002; Kreps

et al. 2002; Hazen et al. 2003; Takahashi et al. 2004; Liu

et al. 2005). While many stress responses appear to be

specific to different forms of stress, it is clear that somestress responses are general and potentially confer tol-erance to multiple types of stress (Chinnusamy et al.2004; Kim et al. 2004). The genes associated with thesegeneral stress responses may yield insight into bio-chemical networks underlying stress resistance and mayprovide targets for stress-resistance engineering in plantspecies.

The functional basis of multiple-stress tolerance hasbeen explained from both mechanistic and energeticperspectives. The mechanistic viewpoint has largelyemerged from studies focusing on plant model systems,in which similarities between cellular responses to dif-ferent types of stress have been explained in terms of theshared effects of different stress treatments on cellularwater potential (Verslues et al. 2006). This commoneffect has frequently been cited to explain associationsfound among cold, drought, and salinity stress re-sponses in Arabidopsis and other plant species (e.g.,Munns 2002; Denby and Gehring 2005; Verslues et al.2006). The energetic viewpoint has attempted to ac-count for cross-tolerance mechanisms more broadlyin terms of the common effect that different stressconditions have on energy allocation (Hoffmann andParsons 1991, Chap. 6). In stressful environments,organisms must free energetic resources that enablemechanisms promoting tolerance and survival. Manymechanisms are likely to be stress specific, but metabolicshifts that reallocate energy to these stress-specific mech-anisms represent a general response that may occur1Author e-mail: swindel5@msu.edu

Genetics 174: 1811–1824 (December 2006)

under many types of adverse conditions (Hoffmann andParsons 1989a,b, 1991, 1997; Bubliy and Loeschcke

2005). The mechanistic and energetic perspectives sug-gest the existence of stress-resistance mechanisms thatconfer tolerance to a wide range of adverse conditions.Evidence in support of such mechanisms has been ob-tained at both the quantitative genetic and the molec-ular levels. Quantitative genetic studies have identifiedgenetic correlations among stress-resistance traits, suchthat selection for resistance to one type of stress hasbeen associated with resistance to another type of stressas a correlated selection response (e.g., Hoffmann andParsons 1989a,b; Hoffmann and Harshman 1999;Agrawal et al. 2004; Bubliy and Loeschcke 2005).At the molecular level, certain heat-shock proteins arecommonly elicited in response to various stress con-ditions (Vierling 1991; Feder 1999; Sørensen et al.2003). Likewise, in the Arabidopsis model system, sharedelements among drought, salinity, and temperature ex-treme response pathways have been identified, such asthe DREB transcription factors and the phytohormoneabscisic acid (ABA) (Liu et al. 1998; Kim et al. 2004;Mauch-Mani and Mauch 2005).

Genomewide quantification of transcript levels byDNA microarrays provides a promising approach to-ward the identification and functional analysis of genesunderlying multiple-stress tolerance (Clarke and Zhu

2006). This technology has been well developed in theArabidopsis model system, in which a number of studieshave now obtained gene expression measurements undera range of stressful conditions (e.g., Seki et al. 2001,2002, 2004; Cheong et al. 2002; Kreps et al. 2002; Hazen

et al. 2003; Takahashi et al. 2004; Liu et al. 2005). Thesestudies identified gene sets that are strongly up- ordownregulated under different stress conditions andhave examined the overlap among these gene sets (e.g.,through Venn-diagram analysis). It has generally beenfound that regions of overlap do occur, although theseoverlapping regions may represent only a small pro-portion of all stress-responsive genes. The study of Seki

et al. (2002), for example, identified 351 genes among7000 Arabidopsis cDNAs in which expression changedmore than fivefold under cold, drought, or salinity stressand found that only 22 (6.3%) of these genes showedfivefold induction under all three stress conditions.These associations may be influenced by how long plantsare placed under stress. The study of Kreps et al. (2002),for instance, selected 2409 of 8100 Arabidopsis cDNAsshowing greater than twofold induction under salt,osmotic, or cold stress and found that 118 genes (4.9%)were induced twofold under all three stress types after3 hr of stress, but that only 12 genes (0.5%) were in-duced twofold under all three stress types after 27 hr.This suggested that mechanisms underlying stress re-sistance may shift from stress general to stress specificduring continued stress exposure. In previous studies,analyses have been limited to the among-stress associa-

tions with respect to the most stress-responsive genesonly. The genomewide associations among expressionresponses to different types of stress have not beenconsidered, even though the genetic basis of stress-resistance traits may depend on a large number ofgenes with minor effects (Barton and Turelli 1990;Hoffmann and Parsons 1991). Furthermore, whenanalyses have focused on the most stress-responsivegenes only, no statistical framework has been used toassign a significance level to genes identified as com-monly induced by more than one stress condition.

In this study, I performed a statistical analysis ofmicroarray data (transcriptional profiling) to study theassociation and overlap among gene expression re-sponses to nine different types of abiotic stress in theplant A. thaliana. Previous studies of Arabidopsis geneexpression under stress have been limited to cDNA sub-sets representing relatively small portions of the entiretranscriptome. This study considers a large microarraydata set based upon expression measurements from22,810 genes, which represents .80% of all knowngenes in Arabidopsis (Redman et al. 2004; Schmid et al.2005). The entire data set has been made publiclyavailable for data mining by the AtGenExpress consor-tium. It includes gene expression measurements fromArabidopsis roots and shoots under nine distinct en-vironmental stress conditions (cold, osmotic stress, salt,drought, genotoxic stress, ultraviolet light, oxidativestress, wounding, and high temperature). For each stresscondition, gene expression measurements were ob-tained at six different time points (0.5, 1, 3, 6, 12, and24 hr). The goals of this study were to quantify theoverall association among gene expression responses tothe nine stress conditions, to uncover temporal trendsin these associations, and to functionally characterizegenes with stress-general expression patterns. Two mainapproaches were employed. First, the associations amonggene expression responses to stress on a genomewidescale were considered. This approach was based ongenomewide correlations among expression responsesto the nine stress conditions and the temporal dynamicsof these correlations. Second, to obtain gene-specificfunctional information, genes exhibiting large expres-sion responses to all nine environmental stress con-ditions were identified. This was done using a MonteCarlo resampling procedure combined with differentialexpression analysis, which allowed the statistical signif-icance of highlighted genes to be specified.

MATERIALS AND METHODS

The data analyzed in this study consisted of gene expressionmeasurements performed on A. thaliana (col-0) roots andshoot organs under a benign control condition and nineenvironmental stress conditions. For each stress condition,gene expression measurements were obtained from 16- to 18-day-old plants at six different time points of stress exposure

1812 W. R. Swindell

(0.5, 1, 3, 6, 12, and 24 hr). The entire data set was generatedand made publicly available by the AtGenExpress consor-tium and can be downloaded at http://www.weigelworld.org/resources/microarray/AtGenExpress/. All gene expressionmeasurements were obtained using the ATH1 Affymetrixmicroarray platform (Hennig et al. 2003; Redman et al.2004) with duplicate biological replications, and expressionestimates were obtained using gcRMA normalization (Wu

et al. 2004). The 64 Affymetrix control probes included onthe ATH1 array were excluded from the present analysis, suchthat results are based on a total of 22,746 Arabidopsis genes.Overviews of the nine environmental stress treatments areavailable at http://www.uni-tuebingen.de/plantphys/AFGN/atgenextable2.htm, while complete descriptions can be ob-tained from The Arabidopsis Information Resource (TAIR)(http://www.arabidopsis.org/) (submission nos.: ME00325,ME00326, ME00327, ME00328, ME00329, ME00330,ME00338, ME00339, and ME00340). In brief, stress treat-ments included cold (4�), osmotic stress (300 mm mannitol),salt (150 mm NaCl), drought (15 min dry air stream leading to10% loss of fresh weight), genotoxic stress (1.5 mg/ml bleo-mycin, 22 mg/ml mitomycin), oxidative stress (10 mm methylviologen), UV-B light stress (15 min exposure, 1.18 W/m2

Phillips TL40W/12), wounding (pin puncture), and hightemperature (38�).

Genomewide analysis: Genomewide relationships amongexpression responses to the nine stress treatments were as-sessed by the Spearman rank correlation. At a given organ–time combination, let �uij denote the mean log2 intensitycorresponding to the ith gene under the jth environmentaltreatment (i ¼ 1, . . . , N, j ¼ 0, . . . , 9). The mean log2 intensityof the control treatment is denoted as j ¼ 0, while intensitiescorresponding to stress treatments are denoted as j¼ 1, . . . , 9.The effect of stress j on the expression of gene i was quantifiedby the log2 fold change, which is the difference between �uij

and �ui0:

Mij ¼ �uij � �ui0: ð1Þ

The value of M was calculated for all genes and stresstreatments at each of the 12 organ–time combinations (2organ types 3 6 time points). Spearman rank correlationswere then calculated to evaluate the genomewide associationbetween M-values from any two stress conditions at each organ–time combination. The Spearman rank correlation measuresthe degree of association between two vectors independentlyof the algebraic form of the relationship (Sokal and Rohlf

1995). It is therefore an appropriate metric for quantifyingassociations in which there is no a priori expectation regardinghow vectors are related. For each organ–time combination,there were 9(9 � 1)/2 ¼ 36 possible pairwise correlationsamong the nine stress conditions. These 36 correlation co-efficients were calculated separately for each of the 12 organ–time combinations, yielding 12 3 36 ¼ 432 correlation coef-ficients in total. Temporal trends were evaluated by examiningthe consistency of changes in corresponding correlationsacross each of the five time intervals (i.e., 0.5–1, 1–3, 3–6,6–12, and 12–24 hr).

Analysis of individual genes: A stress-general pattern ofgene expression is defined as an improbably large degree ofup- and/or downregulation across all nine environmentalstress treatments at a given organ–time combination. Two ap-proaches were used to identify stress-general genes. The firstapproach was based on mean expression values alone andidentified genes for which the log2 fold change (M) was largeamong all stress treatments. The second approach accountedfor variability in duplicate gene expression measures and iden-tified genes for which the effects of stress treatments were large

relative to variability associated with duplicate expressionmeasurements (differential expression analysis). These twoapproaches correspond to criteria that have been used inprevious studies to identify genes exhibiting expression re-sponses to multiple stress treatments (e.g., Seki et al. 2001,2002, 2004; Kreps et al. 2002; Hazen et al. 2003; Rensink et al.2005; Wong et al. 2006).

A rank-based statistic was developed to identify stress-general genes on the basis of mean expression values. LetWij represent the genomewide rank of the M-value corre-sponding to gene i under environmental stress treatment j.Values of Wij thus ranged between 1 and N for all genes underall stress conditions, where N is the number of genes in thegenome. Genes strongly downregulated under a certain stresscondition (large negative M) were associated with small valuesof W , while genes strongly upregulated under a certain stresscondition (large positive M) were associated with large valuesof W. A strongly downregulated gene was indicated by a lowvalue of W, whereas a strongly upregulated gene was indicatedby a low value of N 1 1 � W. The genes exhibiting extremestress-general expression patterns were therefore those forwhich min(W, N 1 1�W) was small on average across the ninestress conditions. For any gene i, therefore, stress-general ex-pression patterns were indicated by low values of the c-statisticdefined by Equation 2:

ci ¼1

9

� �X9

j¼1

minðWij ;N 1 1�WijÞ: ð2Þ

The statistical significance of observed c-statistics was eval-uated by comparing observed values to those found to occur inrandomized data sets generated by a Monte Carlo resamplingprocedure. For the ith gene, a stress-general expression pat-tern (defined above) represents a specified type of nonran-dom association among �ui0, �ui1, . . . , �ui9, the mean expressionvalues in the control treatment and among the nine environ-mental stress treatments. The test performed is thereforepremised on the principle that stress-general genes must beassociated with an observed c-statistic that is sufficiently small,such that the observed c-statistic is unlikely to have arisen if �uij

is randomly associated with �uik for every j 6¼ k. Null distribu-tions were therefore designed to reflect the null hypothesisthat, for all genes, mean expression values corresponding tothe control and nine stress treatments were randomly associ-ated with one another, such that the following statement istrue:

H0: Pð�uij ; �uikÞ ¼ Pð�uij ; �umkÞ"i; j ; i 6¼ m; k 6¼ j : ð3Þ

Under this null hypothesis, there are N 10 possible {�ui0,�ui1, . . . , �ui9} sets that are equally likely to occur at each organ–time combination. To generate a randomized data set consist-ing of N of the N10 possible sets, the permutation procedureimplemented by Munneke et al. (2005) was used, in whichmean gene expression values (�u) were permuted across geneswithin each of the nine stress treatments. This permutationprocedure yields a randomized data set containing N of the N10

possible {�ui0, �ui1, . . . , �ui9} sets that may occur under the nullhypothesis.

A total of 10,000 randomized data sets were generated foreach organ–time combination. Let cð1Þ represent the mini-mum c-value found to occur within each data set, and let cðN Þrepresent the maximum c-value. From among all 10,000 datasets generated for each organ–time combination, the null dis-tributions of cð1Þ and cðN Þ were estimated. The observed teststatistic (ci) for a given gene is significant if, among all 10,000

Gene Expression Responses to Nine Stress Treatments 1813

randomized data sets, the probability that ci is within the spanof cð1Þ, . . . , cðN Þ is less than a:

P ½c . cð1Þ \ c , cðN Þ�, a: ð4Þ

Since genes of interest were those for which ci was verysmall, P[ci , cðN Þ]� 1, and the above definition can be statedin terms of cð1Þ alone:

P ½ci . cð1Þ�, a: ð5Þ

The significance of observed c-statistics was therefore eval-uated by comparison against the distribution of cð1Þ under thenull hypothesis. Accordingly, P-values were calculated fromnull distributions of cð1Þ by determining the probability thatci . cð1Þ for a given gene. It should be noted that null dis-tributions account for spuriously low values of c that may arisedue to the fact that, at each organ–time combination, expres-sion responses to the nine stress conditions were calculatedusing a common control treatment as a reference.

The genes associated with significant c-statistics exhibitlarge expression responses across all nine stress treatments,which are unlikely under the null hypothesis stated by Equa-tion 3 above. A significant c-statistic does not imply, however,that there exists evidence of differential expression withrespect to every stress treatment. In addition to the above anal-ysis, therefore, genes were identified for which c was non-significant, but there existed evidence of significant differentialexpression with respect to each of the nine environmentalstress treatments (Allison et al. 2006). This was done using theLimma linear modeling package available in the R Biocon-ductor software suite (Smyth 2004). P-values were adjusted formultiple comparisons using the Benjamini and Hochbergmethod (Benjamini and Hochberg 1995). The Benjaminiand Hochberg method is robust to certain types of depen-dency among contrasts, but the range of dependency struc-tures for which the procedure is valid has not been established(Reiner et al. 2003). This method of P-value adjustmentshould therefore be regarded as nonconservative. In the re-sults, gene identifications remaining significant following theconservative Holm step-down Bonferroni method of P-valueadjustment are also indicated (Holm 1979).

Functional classifications of significant stress-general geneswere obtained using the web-based Functional ClassificationSuperViewer (Provart and Zhu 2003). This classification toolis based upon functional information available from theMunich Information Center for Protein Sequences (MIPS)database (Schoof et al. 2004). Class scores were obtained todetermine whether certain functional classes were overrepre-sented among significantly stress-general genes (Provart andZhu 2003). Class score means and standard errors werecomputed on the basis of 100 bootstrap samples of the inputgene list as described by Provart and Zhu (2003). The web-based GOstat tool was used to find statistically overrepresentedgene ontology categories among identified stress-generalgenes (Beissbarth and Speed 2004). The GOstat tool wasimplemented using the TAIR gene-association database andfalse discovery rate correction for multiple testing.

RESULTS

Marker genes: The nine abiotic stress conditionsinduced significant expression responses of selectedmarker genes that had been found responsive to similartreatments in previous experiments. In both cold anddrought treatments, for example, the DREB1A tran-

scription factor was associated with significant differ-ential expression at multiple points in the time course(P , 0.05), with greater than sevenfold induction underthe cold stress treatment. Similarly, in the heat stresstreatment, large and significant induction of a selectedheat-shock protein (HSP18.2) and a heat-shock factor(HSF4) occurred over most time points of measure-ment. For each individual stress treatment, expressionresponses of marker genes are described in section 1 ofthe supplemental data file (available at http://www.genetics.org/supplemental/).

Genomewide analysis: The 36 pairwise correlationsbetween expression responses among the nine stresstreatments tended to be positive at each organ–timecombination. In roots, only 6 of 216 correlation coef-ficients were negative, while in shoots, only 4 of 216correlation coefficients were negative. In Table 1, themean correlations obtained by averaging across eachof the six time points are displayed for root and shootorgan types. On average, the correlation between saltand osmotic stress was the largest in both organs (r ¼0.642 in roots; r ¼ 0.565 in shoots). Similarly, in bothorgan types, the second largest correlation was betweengenotoxic and oxidative stress (r ¼ 0.509 in roots; r ¼0.486 in shoots). The smallest correlation in roots wasbetween cold and heat stress (r ¼ 0.101), while thesmallest correlation in shoots was between cold andoxidative stress (r ¼ 0.128).

The strongest temporal trends related to genome-wide correlations were found to occur over the 0.5- to1- and 1- to 3-hr time intervals in root cells and the 3- to6-hr time interval in shoot cells (Table 2). In root cells,32 of 36 pairwise correlations increased between the0.5- and 1-hr time points of stress exposure, and sub-sequently 30 of 36 correlation coefficients decreasedbetween the 1- and 3-hr time points. In shoot cells, 30 of36 correlation coefficients increased between 3 and 6 hrof stress exposure. The temporal profiles of correlationcoefficients are shown in Figures 1 and 2 for root andshoot cells, respectively (see also supplemental data file,section 2, at http://www.genetics.org/supplemental/).To quantify the overall temporal trend, each of the 36pairwise correlations was regressed onto the logarithmof time. In root cells, 24 of the 36 least-squares slopeswere negative with a median value of �0.054. In con-trast, for shoot cells, correlations among expressionresponses to different stressors tended to increase overtime. Regressions of the 36 correlation coefficients onthe logarithm of time showed that 27 of 36 least-squaresslopes were positive and the median slope was positive(0.065).

Associations between transcriptional responses tostress treatments were also quantified by analyzing theoverlap among gene sets differentially expressed undereach stress treatment. This analysis is provided in sec-tion 3 of the supplemental data file (at http://www.genetics.org/supplemental/) along with comparable

1814 W. R. Swindell

results from previous Arabidopsis gene expression stud-ies examining multiple-stress treatments (e.g., Seki et al.2001, 2002; Kreps et al. 2002). The temporal trendsevident from Table 2 and Figures 1 and 2 were also dis-cernable in this analysis.

Analysis of individual genes: A total of 67 geneswere identified as exhibiting a stress-general expressionpattern. Annotations associated with these genes areprovided in section 4 of the supplemental data fileat http://www.genetics.org/supplemental/, along withgene ontology biological process, cellular component,and molecular function terms. The identified geneswere specific to both organs and time points, such thatno single gene was identified in both roots and shoots orat more than one time point. A total of 26 genes were

identified from roots and 41 genes were identified fromshoots. The number of genes identified at each timepoint corresponded well with observations made in thegenomewide correlation analysis (see Table 2). In roots,21 of 26 genes were identified at the 1-hr time point, 2genes were identified at the 3-hr time point, 1 gene wasidentified at each of the 0.5-, 6-, and 12-hr time points,and no genes were identified from the 24-hr time point.In shoots, 31 of 41 genes were identified at the 6-hr timepoint, while 0, 2, 4, 3, and 1 genes were identified fromthe 0.5-, 1-, 3-, 12-, and 24-hr time points, respectively.These temporal patterns suggest that stress-general geneexpression in roots peaks at the 1-hr point of stressexposure, while in shoots stress-general gene expressionpeaks at the 6-hr stage of stress exposure.

Table 3 lists the 26 stress-general genes identified inroot cells. Within this gene set, 13 genes were identifiedon the basis of a significant c-statistic, 12 genes wereidentified on the basis of differential expression acrossall nine stress treatments, and 1 gene was identifiedon the basis of both criteria (germin-like proteinAt5g38910 at the 1-hr time point). Figure 3 shows thelog2 fold changes observed across all stress treatmentsfor the 26 genes identified in roots (see also supple-mental data file, section 6, at http://www.genetics.org/supplemental/). Most genes are consistently downregu-lated across all stress treatments (15 of 26 genes), whileslightly fewer are consistently upregulated across allstress treatments (10 of 26 genes). Only 1 gene alter-nated between up- and downregulation across stresstreatments (iron-responsive transporter At4g19690 atthe 6-hr time point).

Table 4 lists the 41 stress-general genes identified inshoot cells. The majority of genes (37 of 41) wereidentified on the basis of differential expression acrossall nine environmental stress treatments, while only 3 of41 genes were identified on the basis of a significant c-statistic. One gene was identified on the basis of bothcriteria (zinc finger protein At1g27730 at the 12-hr time

TABLE 1

Genomewide Spearman rank correlations between transcriptional responses to nine different abiotic stress treatments

cs os ss ds gs oxs uv ws hs

cs — 0.236 0.275 0.196 0.140 0.128 0.260 0.204 0.243os 0.295 — 0.565 0.425 0.334 0.232 0.391 0.308 0.131ss 0.323 0.642 — 0.395 0.374 0.249 0.249 0.205 0.209ds 0.149 0.353 0.293 — 0.455 0.425 0.326 0.382 0.216gs 0.219 0.364 0.319 0.293 — 0.486 0.250 0.193 0.192oxs 0.228 0.398 0.301 0.348 0.509 — 0.226 0.206 0.201uv 0.181 0.296 0.209 0.292 0.463 0.447 — 0.406 0.237ws 0.222 0.313 0.241 0.327 0.489 0.469 0.432 — 0.199hs 0.101 0.284 0.160 0.196 0.291 0.273 0.251 0.242 —

For each pairwise correlation, the listed values were obtained by calculating the average of correlations computed at each of thesix time points individually (0.5, 1, 3, 6, 12, and 24 hr). Correlations below the diagonal correspond to roots, while correlationsabove the diagonal correspond to shoots. Environmental treatments include cold stress (cs), osmotic stress (os), salt (ss), drought(ds), genotoxic stress (gs), oxidative stress (oxs), ultraviolet light (uv), wounding (ws), and heat (hs).

TABLE 2

Temporal trends influencing genomewide correlations amongexpression responses to nine abiotic stress treatments

Interval (hr)Increased

correlationsDecreased

correlations

Roots0.5–1 32 41–3 6 303–6 22 146–12 14 2212–24 15 21

Shoots0.5–1 22 141–3 15 213–6 30 66–12 12 2412–24 20 16

The 9(9� 1)/2¼ 36 possible pairwise correlations betweenexpression responses among the nine stress treatments wereevaluated at each organ–time combination, and the numbersof correlations that increased or decreased across time inter-vals are listed.

Gene Expression Responses to Nine Stress Treatments 1815

point). Figure 4 shows the log2 fold changes observedacross all stress treatments for the 41 genes identified inshoots (see also supplemental data file, section 6, athttp://www.genetics.org/supplemental/). Most genes(33 of 41) were consistently downregulated across allnine environmental stress treatments. Only a small num-ber (5 of 41) of identifications involved genes consis-tently upregulated across all stress treatments, while 3identifications involved genes that alternated betweenup- and downregulation across stress treatments. A totalof 23 genes identified in shoots were weakly expressedin the control treatment and the majority of stressconditions (indicated in Table 4). Genes with low ex-pression levels should be interpreted with some cau-tion, since weakly expressed genes have an elevatedfalse-positive rate in differential expression analysis(McClintick and Edenberg 2006). For all identifiedgenes, raw signal intensities reflecting absolute gene ex-pression levels are provided in the supplemental data file(section 7 at http://www.genetics.org/supplemental/).

The entire set of 67 stress-general genes was analyzedto determine if certain functional categories were over-represented. Class scores (6SE) associated with cell res-cue/defense/virulence (1.58 6 0.43), energy (1.54 6

0.50), and metabolism (1.42 6 0.28) were more than onestandard deviation greater than one, which providedstrong evidence of overrepresentation with respect tothese broad functional categories. The complete set ofGOstat results listing significantly overrepresented gene

ontologies is available in section 5 of the supplementaldata file at http://www.genetics.org/supplemental/. Atotal of 16 gene ontologies were significantly over-represented among the 67 stress-general genes (P ,

0.05). The 5 overrepresented biological process on-tologies were related to cellular metabolism and photo-synthesis (GO:0006118, GO:0006091, GO:0015979, GO:0019684, and GO:0009767), while the two overrepre-sented cell component ontologies were associated withthe thylakoid membrane and photosystem II proteincomplex (GO:0009579 and GO:0009523). Among the9 overrepresented molecular function ontologies, 5 wererelated to catalytic oxioreductase activity (GO:0016491,GO:0045157, GO:0004497, GO:0004152, and GO:0004158), while the remaining were associated withbinding of tetrapyrrole (GO:0046906 and GO:0020037),oxygen (GO:0019825), or iron ions (GO:0005506).

DISCUSSION

The relationships among gene expression responsesto different types of stress represent a fundamental ba-sis for understanding the genetic and functional foun-dation of multiple-stress tolerance. Genomic responsesof A. thaliana to different types of environmental stresshave often been studied independently of one another.There has been an increasing recognition, however, ofthe genetic and physiological elements that are shared

Figure 1.—Spearmanrank correlations betweengene expression responsesto different types of stressin root cells. Time pointsalong the horizontal axiscorrespond to 0.5, 1, 3, 6,12, and 24 hr of stress expo-sure. Genomewide correla-tions between response toone stress and responsesto the eight other stressconditions are shown. Cor-relations involving cold(A), osmotic stress (B), salt(C), drought (D), geno-toxic stress (E), oxidativestress (F), ultraviolet light(G), wounding (H), andheat (I) are shown. Thedashed line denotes a cor-relation coefficient of zero(no association betweenstress responses). Temporalprofiles associated with in-dividual pairwise combina-tions of stress treatmentscan be viewed in section 2of the supplemental data fileat http://www.genetics.org/supplemental/.

1816 W. R. Swindell

among otherwise distinct stress-response pathways(Cheong et al. 2002; Kreps et al. 2002; Chinnusamy

et al. 2004; Kim et al. 2004; Rensink et al. 2005; Ma et al.2006; Mittler 2006; Rossel et al. 2006). The presentstudy has examined associations among gene expres-sion responses to nine types of abiotic stress, whichrepresents the most inclusive analysis of stress-inducedtranscriptional changes currently available in the Arabi-dopsis model system. The results show that associationsamong expression responses to different types of stressare dependent on the type of organ being consideredand the timescale of stress exposure. This finding wassupported by observations made both at a genomewidescale and with respect to individual genes showing thegreatest transcriptional induction under stress. A totalof 67 genes were identified as exhibiting statisticallysignificant patterns of gene expression characterized bylarge expression responses to all nine stress treatments.In comparison with the rest of the Arabidopsis genome,these genes were disproportionately associated withcell rescue/defense/virulence, energy, and metabolismfunctional classifications. These findings have implica-tions related to the structure of gene networks coordi-nating stress response in Arabidopsis, the functionalbasis of multiple stress tolerance, and stress-resistanceengineering in plant species.

The associations among gene expression responses todifferent types of stress are strongly dependent uponthe duration of stress exposure. In general, expressionresponses to different stress conditions are more strongly

associated at early to middle time points of stress ex-posure. In root cells, nearly all genomewide correlationsamong stress responses peaked following 1 hr of stressexposure. In accordance with this observation, 23 of 28stress-general genes identified in root cells were specificto the 1-hr time point, while only 2 genes were identifiedbeyond the 3-hr time point of stress exposure. In shootcells, a similar pattern appeared to be shifted to latertime points, since most genomewide correlations amongexpression responses increased between 3 and 6 hr ofstress exposure, and the majority (31 of 41) of stress-general genes were identified at the 6-hr time point.Taken together, these findings are consistent with theresults of some previous studies (Kreps et al. 2002; Denby

and Gehring 2005) and lend support to the hypothe-sis that transcriptional changes under stress are moregeneralized during certain early stages of stress expo-sure. This observation may be explainable on the basisof overlapping stress-perception and signal transduc-tion mechanisms among the nine abiotic stress treat-ments examined (Kreps et al. 2002; Denby and Gehring

2005). In root cells, for example, such overlapping stressperception and signal transduction pathways may bemost critical at the 1-hr stage of stress exposure. Forshoot cells, in contrast, shared components among dif-ferent stress regulatory networks may play the greatestrole following 6 hr of stress exposure.

Transcriptional responses to abiotic stress treatmentshave previously been viewed as progressing from gen-eral to specific over early to late stages of stress exposure

Figure 2.—Spearmanrank correlations betweengene expression responsesto different types of stressin shoot cells. Time pointsalong the horizontal axiscorrespond to 0.5, 1, 3, 6,12, and 24 hr of stress expo-sure. Genomewide correla-tions between response toone stress and responsesto the eight other stressconditions are shown. Cor-relations involving cold(A), osmotic stress (B), salt(C), drought (D), geno-toxic stress (E), oxidativestress (F), ultraviolet light(G), wounding (H), andheat (I) are shown. Thedashed line denotes a cor-relation coefficient of zero(no association betweenstress responses). Temporalprofiles associated with in-dividual pairwise combina-tions of stress treatmentscan be viewed in section 2of the supplemental datafile at http://www.genetics.org/supplemental/.

Gene Expression Responses to Nine Stress Treatments 1817

(e.g., Kreps et al. 2002). The present analysis offers somesupport for this viewpoint, particularly with regard toexpression patterns in root organs. At the same time,however, any such general-to-specific trend had a num-ber of exceptions, and it is clear that a more complexcharacterization is appropriate in some cases. For in-stance, in root cells, the salt–osmotic stress, oxidative–osmotic, ultraviolet light–salt, ultraviolet light–osmoticgenomewide correlations among expression responsesincreased over the latest time points. In shoot cells, more-over, most of the 36 pairwise correlations among expres-sion responses were associated with positive slopes whenregressed on different levels of time. On average, there-fore, genomewide transcriptional changes under stressin shoot cells are less stress specific at later stages of stressexposure. It would be interesting to determine whetherthis pattern holds over durations of stress exposure .24hr in shoot cells. In comparison to leaf tissues, rootspossess a simpler anatomical structure, such that shootsperceive and respond to stress more slowly than rootcells (Weigel and Glazebrook 2002). Expressionresponses in shoots may therefore be delayed, and overa greater timescale (.24 hr) it is possible that temporalpatterns found in shoots would more closely resemblethose observed in roots (i.e., more general-to-specific).

The functional properties of stress-general genes pro-vide insight into the mechanisms that may be sharedamong cellular responses to the stress treatments ex-amined. The striking increase in stress-general expres-sion responses at early to middle time points in rootsand shoots, for example, may partly be due to thecommon role of calcium as a second messenger undermany types of stress (Sanders et al. 1999; Allen et al.2000; Knight 2000; Posas et al. 2000; Kreps et al. 2002).At the 1-hr time point in root cells, a calcium-binding EFhand family protein (At5g39670) was upregulated un-der all stress treatments. At the 6-hr time point in shootcells, a calmodulin family-binding protein (At3g52290)and annexin-like protein nearly identical to calcium-binding protein annexin 6 (At5g10220) were down-regulated under all stress treatments. Cytosolic calciumoscillations have been found to occur in response toseveral abiotic stress stimuli and are thought to be es-sential for eliciting stomatal closure (Allen et al. 2000;Posas et al. 2000). The up- or downregulation of theidentified genes may play a role in the control of cy-tosolic calcium concentration oscillations, possibly byaiding or inhibiting the sequestration of calcium ionsinto intracellular stores or across the plasma membrane(Allen et al. 2000). Stress-general expression responsesmay also arise from the activities of various transcriptionfactors, which have been viewed as a source of mecha-nistic interaction among otherwise distinct abiotic stressresponse pathways (Chen et al. 2002; Chen and Zhu

2004). At the 1-hr time point in root cells, a NAM fam-ily protein (At2g43000) was identified, which has beenassociated with transcription factor activity (Riechmann

TABLE 3

Identification of 26 genes exhibiting stress-general expressionresponses in root cells

AGI code (U/D) c (P-value) d (d*)

Roots, 0.5 hrAt3g29970 (D) 27.22 (0.002) 1 (0)

Roots, 1 hrAt5g01740 (D)a 18.11 (0.001) 8 (1)At2g21210 (D)b 23.56 (0.002) 8 (0)At5g38910 (U) 37.00 (0.005) 9 (0)At2g33790 (D) 56.78 (0.016) 1 (0)At3g46880 (D) 65.67 (0.024) 8 (1)At4g08300 (D) 67.67 (0.026) 6 (0)At5g44130 (D) 74.22 (0.033) 7 (0)At3g59340 (D) 78.89 (0.040) 8 (0)At4g18510 (D) 79.33 (0.041) 6 (0)At5g39670 (U) 102.89 (0.085) 9 (8)At3g14620 (U) 119.11 (0.123) 9 (8)At2g30750 (U) 142.33 (0.193) 9 (0)At5g46040 (D) 253.56 (0.683) 9 (5)At1g06830 (D) 256.22 (0.694) 9 (0)At1g72900 (U)c 322.44 (0.902) 9 (0)At1g02930 (U) 352.22 (0.951) 9 (4)At1g74590 (U) 352.56 (0.951) 9 (7)At2g43000 (U) 493.22 (1.00) 9 (5)At2g26690 (U)d 584.56 (1.00) 9 (0)At4g28150 (U) 966.78 (1.00) 9 (4)At5g01220 (D) 1252.78 (1.00) 9 (2)

Roots, 3 hrAt1g08090 (D) 8.00 (, 0.001) 5 (0)At2g30770 (D) 51.67 (0.014) 6 (0)

Roots, 6 hrAt4g19690 (U/D) 46.78 (0.011) 7 (2)

Roots, 12 hrAt3g12900 (D)e 52.333 (0.017) 1 (1)

The listed time points correspond to 0.5, 1, 3, 6, or 12 hr ofstress exposure. The letters in parentheses next to each Ara-bidopsis Genome Initiative (AGI) code indicate that a gene isexclusively upregulated (U), exclusively downregulated (D),or both up- and downregulated (U/D) across the nine abioticstress treatments. The c-statistic is defined by Equation 2 (seematerials and methods) and is inversely related to the av-erage log2 fold change across stress treatments. The third col-umn lists the number of stress treatments in which the geneexhibited significant differential expression following eitherBenjamini–Hochberg (d) or Holm P-value (d*) adjustments.See section 4 of the supplemental data file at http://www.genetics.org/supplemental/ for annotations and gene ontol-ogy terms associated with listed genes.

a Wound-inducible protein.b Auxin-responsive protein.c Disease resistance, responsive to pathogen.d Response to jasmonic acid stimulus, wounding.e Weak expression under control treatment and majority of

stress treatments.

1818 W. R. Swindell

et al. 2000). At the 12-hr time point in shoot cells, a zincfinger protein (Zat10, At1g27730) with transcriptionfactor activity was identified (Riechmann et al. 2000),which has previously been found responsive to salt,wounding, and chitin stimuli (Lippuner et al. 1996; Taki

et al. 2005).The generation of reactive oxygen species (ROS),

such as hydrogen peroxide, is a cellular response thathas been associated with a wide range of stressful stimuli(Pastori and Foyer 2002). Several ROS-generatingp450 hemoproteins were included among stress-general genes (At2g30770, At1g69500, At3g14620, andAt2g30750), along with genes functioning in the trans-fer of glutathione (At1g02930 and At1g74590), whichis a thiol compound with a well-established role inROS detoxification (Penninckx 2000; Maughan andFoyer 2006). Some identified stress-general genes havepreviously been associated with responsiveness to asingle type of stress condition, but may have a broaderrole in stress response. For instance, auxin-responsive(At3g20220) and auxin-regulated (At2g21210) chloro-plast proteins were identified, both of which weredownregulated across all stress treatments. The down-regulation of auxin-responsive genes has been docu-mented as a response to wounding stress, but suchdownregulation has not previously been noted underother abiotic stress treatments (Cheong et al. 2002). Anumber of additional identified genes have previouslybeen associated with defense against parasite andpathogen attack [At1g72900 (disease resistance protein),At3g57260 (beta 1, 3-glucanase), At4g36010 (thaumatin-like pathogenesis-related protein), and At2g43600 (pu-tative endochitinase)], suggesting possible overlap be-tween the mechanisms underlying abiotic and bioticstress response pathways. Two defense-related geneswere exclusively downregulated among all nine abio-tic stress treatments (At3g57260 and At2g43600). Suchdownregulation could arise from stress-induced eleva-

tions of ABA phytohormone, which has been found topromote the downregulation of defense-related genes(Anderson et al. 2004) and may therefore account fordownregulation of defense-related genes under abioticstress treatments (e.g., see Wong et al. 2006). Given thatthe stress-general genes highlighted above have beenidentified on the basis of microarray data alone, itshould be emphasized that the present analysis repre-sents only a first step toward confirming and under-standing their role in stress regulatory pathways. Futureexperimental investigations of stress-general genes, in-cluding investigations with real-time PCR and analysisof post-transcriptional processes, will be necessary toelucidate how these genes may contribute to abioticstress tolerance.

The functional basis of multiple-stress tolerance haspreviously been viewed from both mechanistic and en-ergetic perspectives. Multiple-stress tolerance may arisefrom similarities in the specific types of biologicaldamage inflicted by different stress treatments (e.g.,decreased cellular water potential). A broader expla-nation of multiple-stress tolerance, however, is that dif-ferent types of stress impose a similar demand onmetabolic processes and patterns of energetic resourceallocation (Hoffmann and Parsons 1991). Theseviewpoints are not mutually exclusive explanations ofmultiple-stress tolerance, but the extent to which eachexplanation properly accounts for the functional basisof multiple stress tolerance is not clear. This study foundthat genes associated with energy functional classeswere 54% more frequent among significant stress-generalgenes in comparison to the rest of the Arabidopsis ge-nome, while genes associated with metabolic functionalclasses were 42% more frequent. The gene ontology cor-responding to generation of precursor metabolites andenergy (GO:0006091) was overrepresented among stress-general genes, and, in general, significantly overrep-resented biological process ontologies were associated

Figure 3.—Log2 fold changes (M) associatedwith each stress treatment for the 26 stress-general genes identified in root cells. Foldchanges of genes identified at (A) 0.5, (B) 1,(C) 3, and (D) 6–24 hr are shown. The dashedline denotes a log2 fold change of zero (no ex-pression response to stress). The abbreviationsgiven along the horizontal axis represent eachof the nine environmental stress treatmentsand correspond to those given in the Table 1 leg-end. Log2 fold-change (M) plots associated witheach individual stress-general gene can be viewedin section 6 of the supplemental data file athttp://www.genetics.org/supplemental/.

Gene Expression Responses to Nine Stress Treatments 1819

with cellular metabolism and photosynthesis. Thesefindings are consistent with the view that genes with arole in multiple-stress tolerance are involved in meta-bolic processes and allocation of energetic resources(Hoffmann and Parsons 1989a,b, 1991, 1997; Bubliy

and Loeschcke 2005). The apparent role of energyand metabolism with respect to many stress treatmentsprovides insight into connections that have been madebetween stress tolerance and seemingly different pro-cesses, such as senescence and inbreeding depression. Anumber of recent microarray studies have found thatmetabolic genes are disproportionately influenced byenvironmental stress, senescence, and inbreeding treat-

ments (Girardot et al. 2004; Landis et al. 2004; Wang

et al. 2004; Englander 2005; Kristensen et al. 2005). At abroad level, the role of metabolism in these processesmay account for interactions between environmentalstress and the magnitude of inbreeding depression(Crnokrak and Roff 1999; Keller and Waller 2002;Armbruster and Reed 2005), quantitative geneticcorrelations between stress resistance and extended lifespan (Service et al. 1985; Rose et al. 1992; Force et al.1995; Zwaan et al. 1995; Chippindale et al. 1998; Norry

and Loeschcke 2003), and similarities among themolecular effects of stress, inbreeding, and aging treat-ments observed in several species (Chen et al. 2002;Brunet et al. 2004; Lamming et al. 2004; Colotti et al.2005; Kristensen et al. 2005).

Previous microarray analyses have identified geneswith stress-general expression patterns on the basis oflarge fold change under several stress conditions (Seki

et al. 2001, 2002; Cheong et al. 2002; Kreps et al. 2002;Takahashi et al. 2004; Ma et al. 2006) or, less commonly,on the basis of significant differential expression acrossmultiple-stress treatments (Rensink et al. 2005; Wong

et al. 2006). Both of these approaches were utilized toidentify stress-general genes in the present study. Thegreatest confidence should be assigned to genes thatscored highly on the basis of both approaches [e.g.,At5g38910 (putative respiration germin-like protein),

TABLE 4

Identification of 41 genes exhibiting stress-general expressionresponses in shoot cells

AGI code (U/D) c (P-value) d (d*)

Shoots, 1 hrAt3g57260 (D)a 12.44 (0.001) 0 (0)At1g05680 (D)a 33.44 (0.011) 0 (0)

Shoots, 3 hrAt1g15040 (D)a 54.00 (0.041) 0 (0)At4g36010 (U)b 172.22 (0.583) 9 (9)At1g69500 (D)a 251.67 (0.917) 9 (0)At3g17810 (U/D) 1440.11 (1.00) 9 (4)

Shoots, 6 hrAt3g63010 (U) 303.00 (0.981) 9 (4)ndhG (D) 331.56 (0.994) 9 (1)petG (D) 386.33 (0.999) 9 (2)At3g17990 (U/D) 491.00 (1.00) 9 (1)rps15 (D) 546.78 (1.00) 9 (4)psbB (D) 615.44 (1.00) 9 (1)psbD (D) 642.89 (1.00) 9 (1)At3g43680 (D)a 742.56 (1.00) 9 (0)At1g43320 (D)a 757.44 (1.00) 9 (1)At2g23800 (D)a 958.44 (1.00) 9 (0)At1g16910 (D)a 963.22 (1.00) 9 (0)At2g07739 (D) 977.33 (1.00) 9 (2)At4g21560 (U) 1182.67 (1.00) 9 (2)At1g09230 (U/D) 1216.44 (1.00) 9 (0)At2g03740 (D)a,c 1254.78 (1.00) 9 (0)At5g38820 (D) 1707.78 (1.00) 9 (1)At1g31100 (D)a 1846.00 (1.00) 9 (4)At1g63940 (D) 1852.67 (1.00) 9 (4)At3g01620 (D)a 1909.22 (1.00) 9 (0)At1g05450 (D)a 2036.22 (1.00) 9 (0)At5g19720 (D)a 2104.56 (1.00) 9 (0)At5g15560 (D)a 2201.33 (1.00) 9 (0)At4g00190 (D)a 2248.67 (1.00) 9 (0)At5g10220 (D)a 2408.56 (1.00) 9 (1)At3g52290 (D) 2474.67 (1.00) 9 (2)At2g10650 (D) 2656.78 (1.00) 9 (0)At5g23970 (D)a 2692.78 (1.00) 9 (0)At2g15510 (D)a 2693.89 (1.00) 9 (0)At5g48210 (D)a 3033.44 (1.00) 9 (0)At1g05400 (D)a 3063.00 (1.00) 9 (0)At5g58820 (D)a 3885.44 (1.00) 9 (0)

TABLE 4

(Continued)

AGI code (U/D) c (P-value) d (d*)

Shoots, 12 hrAt1g27730 (U)d 19.11 (0.002) 9 (4)At2g43600 (D)a,e 759.22 (1.00) 9 (0)At3g20220 (D)a,f 2942.33 (1.00) 9 (0)

Shoots, 24 hrAt2g39725 (U) 858.89 (1.00) 9 (3)

The listed time points correspond to 1, 3, 6, 12, or 24 hr ofstress exposure. The letters in parentheses next to each AGIcode indicate that a gene is exclusively upregulated (U), ex-clusively downregulated (D), or both up- and downregulated(U/D) across the nine abiotic stress treatments. The c-statisticis defined by Equation 2 (see materials and methods) andis inversely related to the average log2 fold change acrossstress treatments. The third column lists the number of stresstreatments in which the gene exhibited significant differen-tial expression following either Benjamini–Hochberg (d) orHolm P-value (d*) adjustments. See section 4 of the supple-mental data file at http://www.genetics.org/supplemental/for annotations and functional overviews of listed genes.

a Weak expression under control treatment and majority ofstress treatments.

b Pathogenesis-related, defense response.c LEA domain-containing protein, similar to cor15b.d Zat10 transcription factor, wounding and salt responsive.e Response to pest, pathogen, or parasite.f Response to auxin stimulus.

1820 W. R. Swindell

At5g39670 (calcium-binding protein), and At1g27730(Zat10 salt tolerance finger protein)], since such genesexhibit stress responses that are large relative to all othergenes in the genome and large relative to variation be-tween duplicate expression measurements. When iden-tifying stress-general genes on the basis of log2 foldchange, previous studies have isolated sets of genesexceeding a particular level of fold change under eachstress treatment individually (e.g., more than twofold)and then identified stress-general genes by examiningthe intersection of these gene sets (e.g., Kreps et al. 2002;Seki et al. 2002). The present study improved upon thisstrategy in two ways. First, rather than choose a ‘‘cutoff’’level of fold change, a null hypothesis was formulatedand resampling methods were used to assign statisticalsignificance to highlighted genes. Second, log2 foldchanges (M) associated with genes were replaced bytheir corresponding genomewide ranks. This normal-ized vectors of expression responses, which allowed allstress conditions to be weighted equally in identifyingstress-general genes. Furthermore, averaging ranks acrossstresstreatmentsallowedameaningfulstatistic tobedevel-oped that reflects the continuity of available evidence.

Associations among expression responses to differentstress treatments have often been examined with re-spect to the most stress-responsive genes only (e.g.,Kreps et al. 2002; Seki et al. 2002). It is not necessarilytrue, however, that genes exhibiting large fold changesunder stress occupy the most prominent role in co-ordinating physiological responses to stress (Feder andWalser 2005). In addition, it is plausible that a largenumber of genes with minor effects underlie both spe-cific and general responses to abiotic stress treatments.For instance, in the present study, temporal patternsinfluencing stress generality among the most stress-responsive genes were also evident at the genomewidelevel, suggesting the possible involvement of a largenumber of genes. These considerations underscore the

importance of investigating associations among expres-sion responses to different types of stress on a genome-wide scale in addition to within selected subsets of genes.The degree to which stress-resistance traits depend on alarge vs. a small number of loci is unclear and has beenthe subject of long-standing debate (Hoffmann andParsons 1991). A number of quantitative genetic stud-ies have attributed stress-resistant phenotypes to one ora few genes of major effect (e.g., Fatt and Dougherty

1963; Parsons et al. 1969; Blum 1988; Macnair 1991;Lenski and Bennett 1993). However, these findingshave often been difficult to interpret, since it is generallydifficult to distinguish between the effects of many lociof small effect and a few loci of major effect (Barton

and Turelli 1990).The productivity and yield of commercially valuable

plant species is often limited by the joint influence ofseveral kinds of stress in combination (Mittler 2006).Understanding the genetic and functional basis ofmultiple-stress tolerance will therefore be an importantstep toward increasing plant productivity through bio-engineering approaches. The set of 67 stress-generalgenes identified in this study represents targets fordirect bioengineering approaches or, alternatively, ge-netic markers that may facilitate the creation of stress-resistant genotypes through artificial selection. Suchmarkers may play a pivotal role in systemic strategiesfor achieving plant-breeding goals through unificationof molecular and quantitative genetic approaches(Arnholdt-Schmitt 2005). A primary challenge,however, may reside in manipulating the expression ofgenes underlying multiple-stress tolerance without as-sociated costs affecting plant fitness components, par-ticularly if such genes are involved in central metabolicprocesses (Vinocur and Altman 2005). This challengehas been foreshadowed by decades of artificial selec-tion experiments, in which diminished growth and pro-ductivity have often been found to result as a correlated

Figure 4.—Log2 fold changes (M) associatedwith each stress treatment for the 41 significantlystress-general genes identified in shoot cells. Foldchanges of genes identified at (A) 0.5–1, (B) 3,(C) 6, and (D) 12–24 hr are shown. The dashedline denotes a log2 fold change of zero (no ex-pression response to stress). The abbreviationsgiven along the horizontal axis represent eachof the nine environmental stress treatmentsand correspond to those given in the Table 1 leg-end. Log2 fold-change (M) plots associated witheach individual stress-general gene can be viewedin section 6 of the supplemental data file athttp://www.genetics.org/supplemental/.

Gene Expression Responses to Nine Stress Treatments 1821

response to selection for increased stress tolerance(Service and Rose 1985; Hoffmann and Parsons

1989a; Verhoeven et al. 2004; Sinebo 2005; but seeAraus et al. 2003). Similar growth and productivitycosts have more recently been found in strains bioen-gineered for increased tolerance to adverse conditions(e.g., Kasuga et al. 1999; Ito et al. 2006) and have beenan obstacle toward successful bioengineering of stresstolerance in plants (Vinocur and Altman 2005). Genesexhibiting strong expression responses to many types ofenvironmental stress represent a possible intersectionpoint among otherwise independent stress-responsepathways. In addition to their potential role as genetargets, therefore, analysis of these genes may proveuseful for developing a more comprehensive systems-oriented knowledge of stress physiology, which may benecessary for successful bioengineering approaches toincreasing stress resistance in plant species.

The author thanks Andreas Weber, Marianne Huebner, Dong-YunKim, and two anonymous reviewers for helpful discussions andcomments on this manuscript. In addition, members of the AtGen-Express consortium (Thomas Altmann, Pascal von Koskull-Doring,Jorg Kudla, Lutz Nover, and Detlef Weigel) and the ArabidopsisFunctional Genomics Network are gratefully acknowledged for pro-viding the gene expression data analyzed in this study. This work wassupported by a research grant from the Michigan State Universityquantitative biology and modeling initiative.

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Communicating editor: V. Sundaresan

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