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Genetics. Dec 2006; 174(4): 1811–1824.
PMCID: PMC1698639

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

Abstract

The identification and analysis of genes exhibiting large expression responses to several different types of stress may provide insights into the functional basis of multiple stress tolerance in plant species. This study considered whole-genome transcriptional profiles from Arabidopsis thaliana root and shoot organs under 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 different stress treatments peaked following 1 hr of stress exposure, while in shoots, correlations tended to increase following 6 hr of stress exposure. The generality of stress responses at the transcriptional level was therefore time and organ dependent. A total of 67 genes were identified as exhibiting a statistically significant pattern of gene expression characterized by large transcriptional responses to all nine stress treatments. 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 functional classes were overrepresented, providing novel insight into the functional basis of multiple stress tolerance in Arabidopsis.

THE genetic effects of environmental stress have been 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 increasingly, biotechnology (Holmberg and Bülow 1998; Kasuga et al. 1999; Wang et al. 2003; Pellegrineschi et al. 2004; Denby and Gehring 2005; Vinocur and Altman 2005). In plant species, environmental stress can be a large source of mortality because plants are unable to avoid environmental extremes through migration. Stress is thus a powerful force influencing the evolution of plant populations in the wild (Hoffmann and Parsons 1997), as well as a key factor limiting economic yield in commercially valuable species (Boyer 1982; Blum 1988). The susceptibility of plants to environmental extremes has driven the evolution of a wide range of stress-resistance and tolerance mechanisms (Singh et al. 2002; Mahalingam et al. 2003; Chen and Zhu 2004; Bohnert et al. 2006). In the model system Arabidopsis thaliana, the physiological basis of these resistance mechanisms has been pursued with the ultimate goal of elucidating the biochemical pathways involved in stress perception, signal transduction, and adaptive 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 some stress responses are general and potentially confer tolerance to multiple types of stress (Chinnusamy et al. 2004; Kim et al. 2004). The genes associated with these general stress responses may yield insight into biochemical networks underlying stress resistance and may provide targets for stress-resistance engineering in plant species.

The functional basis of multiple-stress tolerance has been explained from both mechanistic and energetic perspectives. The mechanistic viewpoint has largely emerged from studies focusing on plant model systems, in which similarities between cellular responses to different types of stress have been explained in terms of the shared effects of different stress treatments on cellular water potential (Verslues et al. 2006). This common effect has frequently been cited to explain associations found among cold, drought, and salinity stress responses in Arabidopsis and other plant species (e.g., Munns 2002; Denby and Gehring 2005; Verslues et al. 2006). The energetic viewpoint has attempted to account for cross-tolerance mechanisms more broadly in terms of the common effect that different stress conditions have on energy allocation (Hoffmann and Parsons 1991, Chap. 6). In stressful environments, organisms must free energetic resources that enable mechanisms promoting tolerance and survival. Many mechanisms are likely to be stress specific, but metabolic shifts that reallocate energy to these stress-specific mechanisms represent a general response that may occur under many types of adverse conditions (Hoffmann and Parsons 1989a,b, 1991, 1997; Bubliy and Loeschcke 2005). The mechanistic and energetic perspectives suggest the existence of stress-resistance mechanisms that confer tolerance to a wide range of adverse conditions. Evidence in support of such mechanisms has been obtained at both the quantitative genetic and the molecular levels. Quantitative genetic studies have identified genetic correlations among stress-resistance traits, such that selection for resistance to one type of stress has been associated with resistance to another type of stress as a correlated selection response (e.g., Hoffmann and Parsons 1989a,b; Hoffmann and Harshman 1999; Agrawal et al. 2004; Bubliy and Loeschcke 2005). At the molecular level, certain heat-shock proteins are commonly elicited in response to various stress conditions (Vierling 1991; Feder 1999; Sørensen et al. 2003). Likewise, in the Arabidopsis model system, shared elements among drought, salinity, and temperature extreme response pathways have been identified, such as the DREB transcription factors and the phytohormone abscisic acid (ABA) (Liu et al. 1998; Kim et al. 2004; Mauch-Mani and Mauch 2005).

Genomewide quantification of transcript levels by DNA microarrays provides a promising approach toward the identification and functional analysis of genes underlying multiple-stress tolerance (Clarke and Zhu 2006). This technology has been well developed in the Arabidopsis model system, in which a number of studies have now obtained gene expression measurements under a 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). These studies identified gene sets that are strongly up- or downregulated under different stress conditions and have examined the overlap among these gene sets (e.g., through Venn-diagram analysis). It has generally been found that regions of overlap do occur, although these overlapping regions may represent only a small proportion of all stress-responsive genes. The study of Seki et al. (2002), for example, identified 351 genes among 7000 Arabidopsis cDNAs in which expression changed more than fivefold under cold, drought, or salinity stress and found that only 22 (6.3%) of these genes showed fivefold induction under all three stress conditions. These associations may be influenced by how long plants are placed under stress. The study of Kreps et al. (2002), for instance, selected 2409 of 8100 Arabidopsis cDNAs showing 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 after 3 hr of stress, but that only 12 genes (0.5%) were induced twofold under all three stress types after 27 hr. This suggested that mechanisms underlying stress resistance may shift from stress general to stress specific during continued stress exposure. In previous studies, analyses have been limited to the among-stress associations with respect to the most stress-responsive genes only. The genomewide associations among expression responses to different types of stress have not been considered, even though the genetic basis of stress-resistance traits may depend on a large number of genes with minor effects (Barton and Turelli 1990; Hoffmann and Parsons 1991). Furthermore, when analyses have focused on the most stress-responsive genes only, no statistical framework has been used to assign a significance level to genes identified as commonly induced by more than one stress condition.

In this study, I performed a statistical analysis of microarray data (transcriptional profiling) to study the association and overlap among gene expression responses to nine different types of abiotic stress in the plant A. thaliana. Previous studies of Arabidopsis gene expression under stress have been limited to cDNA subsets representing relatively small portions of the entire transcriptome. This study considers a large microarray data set based upon expression measurements from 22,810 genes, which represents >80% of all known genes in Arabidopsis (Redman et al. 2004; Schmid et al. 2005). The entire data set has been made publicly available for data mining by the AtGenExpress consortium. It includes gene expression measurements from Arabidopsis roots and shoots under nine distinct environmental stress conditions (cold, osmotic stress, salt, drought, genotoxic stress, ultraviolet light, oxidative stress, wounding, and high temperature). For each stress condition, gene expression measurements were obtained at six different time points (0.5, 1, 3, 6, 12, and 24 hr). The goals of this study were to quantify the overall association among gene expression responses to the nine stress conditions, to uncover temporal trends in these associations, and to functionally characterize genes with stress-general expression patterns. Two main approaches were employed. First, the associations among gene expression responses to stress on a genomewide scale were considered. This approach was based on genomewide correlations among expression responses to the nine stress conditions and the temporal dynamics of these correlations. Second, to obtain gene-specific functional information, genes exhibiting large expression responses to all nine environmental stress conditions were identified. This was done using a Monte Carlo resampling procedure combined with differential expression analysis, which allowed the statistical significance of highlighted genes to be specified.

MATERIALS AND METHODS

The data analyzed in this study consisted of gene expression measurements performed on A. thaliana (col-0) roots and shoot organs under a benign control condition and nine environmental 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 (0.5, 1, 3, 6, 12, and 24 hr). The entire data set was generated and made publicly available by the AtGenExpress consortium and can be downloaded at http://www.weigelworld.org/resources/microarray/AtGenExpress/. All gene expression measurements were obtained using the ATH1 Affymetrix microarray platform (Hennig et al. 2003; Redman et al. 2004) with duplicate biological replications, and expression estimates were obtained using gcRMA normalization (Wu et al. 2004). The 64 Affymetrix control probes included on the ATH1 array were excluded from the present analysis, such that results are based on a total of 22,746 Arabidopsis genes. Overviews of the nine environmental stress treatments are available at http://www.uni-tuebingen.de/plantphys/AFGN/atgenextable2.htm, while complete descriptions can be obtained 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 treatments included cold (4°), osmotic stress (300 mm mannitol), salt (150 mm NaCl), drought (15 min dry air stream leading to 10% loss of fresh weight), genotoxic stress (1.5 μg/ml bleomycin, 22 μg/ml mitomycin), oxidative stress (10 μm methyl viologen), UV-B light stress (15 min exposure, 1.18 W/m2 Phillips TL40W/12), wounding (pin puncture), and high temperature (38°).

Genomewide analysis:

Genomewide relationships among expression responses to the nine stress treatments were assessed by the Spearman rank correlation. At a given organ–time combination, let equation M1 denote the mean log2 intensity corresponding to the ith gene under the jth environmental treatment (i = 1,  , N, j = 0,  , 9). The mean log2 intensity of the control treatment is denoted as j = 0, while intensities corresponding to stress treatments are denoted as j = 1,  , 9. The effect of stress j on the expression of gene i was quantified by the log2 fold change, which is the difference between equation M2 and equation M3:

equation M4
(1)

The value of M was calculated for all genes and stress treatments at each of the 12 organ–time combinations (2 organ types × 6 time points). Spearman rank correlations were then calculated to evaluate the genomewide association between M-values from any two stress conditions at each organ–time combination. The Spearman rank correlation measures the degree of association between two vectors independently of the algebraic form of the relationship (Sokal and Rohlf 1995). It is therefore an appropriate metric for quantifying associations in which there is no a priori expectation regarding how vectors are related. For each organ–time combination, there were 9(9 − 1)/2 = 36 possible pairwise correlations among the nine stress conditions. These 36 correlation coefficients were calculated separately for each of the 12 organ–time combinations, yielding 12 × 36 = 432 correlation coefficients in total. Temporal trends were evaluated by examining the consistency of changes in corresponding correlations across 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 of gene expression is defined as an improbably large degree of up- and/or downregulation across all nine environmental stress treatments at a given organ–time combination. Two approaches were used to identify stress-general genes. The first approach was based on mean expression values alone and identified genes for which the log2 fold change (M) was large among all stress treatments. The second approach accounted for variability in duplicate gene expression measures and identified genes for which the effects of stress treatments were large relative to variability associated with duplicate expression measurements (differential expression analysis). These two approaches correspond to criteria that have been used in previous studies to identify genes exhibiting expression responses 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. Let equation M5 represent the genomewide rank of the M-value corresponding to gene i under environmental stress treatment j. Values of equation M6 thus ranged between 1 and N for all genes under all stress conditions, where N is the number of genes in the genome. Genes strongly downregulated under a certain stress condition (large negative M) were associated with small values of equation M7, while genes strongly upregulated under a certain stress condition (large positive M) were associated with large values of W. A strongly downregulated gene was indicated by a low value of W, whereas a strongly upregulated gene was indicated by a low value of N + 1 − W. The genes exhibiting extreme stress-general expression patterns were therefore those for which min(W, N + 1 − W) was small on average across the nine stress conditions. For any gene i, therefore, stress-general expression patterns were indicated by low values of the ψ-statistic defined by Equation 2:

equation M8
(2)

The statistical significance of observed ψ-statistics was evaluated by comparing observed values to those found to occur in randomized data sets generated by a Monte Carlo resampling procedure. For the ith gene, a stress-general expression pattern (defined above) represents a specified type of nonrandom association among equation M9, equation M10,;…, equation M11, the mean expression values in the control treatment and among the nine environmental stress treatments. The test performed is therefore premised on the principle that stress-general genes must be associated with an observed ψ-statistic that is sufficiently small, such that the observed ψ-statistic is unlikely to have arisen if equation M12 is randomly associated with equation M13 for every jk. Null distributions were therefore designed to reflect the null hypothesis that, for all genes, mean expression values corresponding to the control and nine stress treatments were randomly associated with one another, such that the following statement is true:

equation M14
(3)

Under this null hypothesis, there are N10 possible equation M15, equation M16} sets that are equally likely to occur at each organ–time combination. To generate a randomized data set consisting of N of the N10 possible sets, the permutation procedure implemented by Munneke et al. (2005) was used, in which mean gene expression values (equation M17) were permuted across genes within each of the nine stress treatments. This permutation procedure yields a randomized data set containing N of the N10 possible {equation M18, equation M19,  , equation M20} sets that may occur under the null hypothesis.

A total of 10,000 randomized data sets were generated for each organ–time combination. Let equation M21 represent the minimum ψ-value found to occur within each data set, and let equation M22 represent the maximum ψ-value. From among all 10,000 data sets generated for each organ–time combination, the null distributions of equation M23 and equation M24 were estimated. The observed test statistic (equation M25) for a given gene is significant if, among all 10,000 randomized data sets, the probability that equation M26 is within the span of equation M27,  , equation M28 is less than α:

equation M29
(4)

Since genes of interest were those for which equation M30 was very small, equation M31, and the above definition can be stated in terms of equation M32 alone:

equation M33
(5)

The significance of observed ψ-statistics was therefore evaluated by comparison against the distribution of equation M34 under the null hypothesis. Accordingly, P-values were calculated from null distributions of equation M35 by determining the probability that equation M36 for a given gene. It should be noted that null distributions account for spuriously low values of ψ that may arise due to the fact that, at each organ–time combination, expression responses to the nine stress conditions were calculated using a common control treatment as a reference.

The genes associated with significant ψ-statistics exhibit large expression responses across all nine stress treatments, which are unlikely under the null hypothesis stated by Equation 3 above. A significant ψ-statistic does not imply, however, that there exists evidence of differential expression with respect to every stress treatment. In addition to the above analysis, therefore, genes were identified for which ψ was nonsignificant, but there existed evidence of significant differential expression with respect to each of the nine environmental stress treatments (Allison et al. 2006). This was done using the Limma linear modeling package available in the R Bioconductor software suite (Smyth 2004). P-values were adjusted for multiple comparisons using the Benjamini and Hochberg method (Benjamini and Hochberg 1995). The Benjamini and Hochberg method is robust to certain types of dependency among contrasts, but the range of dependency structures for which the procedure is valid has not been established (Reiner et al. 2003). This method of P-value adjustment should therefore be regarded as nonconservative. In the results, gene identifications remaining significant following the conservative Holm step-down Bonferroni method of P-value adjustment are also indicated (Holm 1979).

Functional classifications of significant stress-general genes were obtained using the web-based Functional Classification SuperViewer (Provart and Zhu 2003). This classification tool is based upon functional information available from the Munich Information Center for Protein Sequences (MIPS) database (Schoof et al. 2004). Class scores were obtained to determine whether certain functional classes were overrepresented among significantly stress-general genes (Provart and Zhu 2003). Class score means and standard errors were computed on the basis of 100 bootstrap samples of the input gene list as described by Provart and Zhu (2003). The web-based GOstat tool was used to find statistically overrepresented gene ontology categories among identified stress-general genes (Beissbarth and Speed 2004). The GOstat tool was implemented using the TAIR gene-association database and false discovery rate correction for multiple testing.

RESULTS

Marker genes:

The nine abiotic stress conditions induced significant expression responses of selected marker genes that had been found responsive to similar treatments in previous experiments. In both cold and drought treatments, for example, the DREB1A transcription factor was associated with significant differential expression at multiple points in the time course (P < 0.05), with greater than sevenfold induction under the cold stress treatment. Similarly, in the heat stress treatment, large and significant induction of a selected heat-shock protein (HSP18.2) and a heat-shock factor (HSF4) occurred over most time points of measurement. For each individual stress treatment, expression responses of marker genes are described in section 1 of the supplemental data file (available at http://www.genetics.org/supplemental/).

Genomewide analysis:

The 36 pairwise correlations between expression responses among the nine stress treatments tended to be positive at each organ–time combination. In roots, only 6 of 216 correlation coefficients were negative, while in shoots, only 4 of 216 correlation coefficients were negative. In Table 1, the mean correlations obtained by averaging across each of the six time points are displayed for root and shoot organ types. On average, the correlation between salt and osmotic stress was the largest in both organs (r = 0.642 in roots; r = 0.565 in shoots). Similarly, in both organ types, the second largest correlation was between genotoxic and oxidative stress (r = 0.509 in roots; r = 0.486 in shoots). The smallest correlation in roots was between cold and heat stress (r = 0.101), while the smallest correlation in shoots was between cold and oxidative stress (r = 0.128).

TABLE 1
Genomewide Spearman rank correlations between transcriptional responses to nine different abiotic stress treatments

The strongest temporal trends related to genomewide correlations were found to occur over the 0.5- to 1- and 1- to 3-hr time intervals in root cells and the 3- to 6-hr time interval in shoot cells (Table 2). In root cells, 32 of 36 pairwise correlations increased between the 0.5- and 1-hr time points of stress exposure, and subsequently 30 of 36 correlation coefficients decreased between the 1- and 3-hr time points. In shoot cells, 30 of 36 correlation coefficients increased between 3 and 6 hr of stress exposure. The temporal profiles of correlation coefficients are shown in Figures 1 and and22 for root and shoot cells, respectively (see also supplemental data file, section 2, at http://www.genetics.org/supplemental/). To quantify the overall temporal trend, each of the 36 pairwise correlations was regressed onto the logarithm of time. In root cells, 24 of the 36 least-squares slopes were negative with a median value of −0.054. In contrast, for shoot cells, correlations among expression responses to different stressors tended to increase over time. Regressions of the 36 correlation coefficients on the logarithm of time showed that 27 of 36 least-squares slopes were positive and the median slope was positive (0.065).

Figure 1.
Spearman rank correlations between gene expression responses to different types of stress in root cells. Time points along the horizontal axis correspond to 0.5, 1, 3, 6, 12, and 24 hr of stress exposure. Genomewide correlations between response to one ...
Figure 2.
Spearman rank correlations between gene expression responses to different types of stress in shoot cells. Time points along the horizontal axis correspond to 0.5, 1, 3, 6, 12, and 24 hr of stress exposure. Genomewide correlations between response to one ...
TABLE 2
Temporal trends influencing genomewide correlations among expression responses to nine abiotic stress treatments

Associations between transcriptional responses to stress treatments were also quantified by analyzing the overlap among gene sets differentially expressed under each stress treatment. This analysis is provided in section 3 of the supplemental data file (at http://www.genetics.org/supplemental/) along with comparable results from previous Arabidopsis gene expression studies examining multiple-stress treatments (e.g., Seki et al. 2001, 2002; Kreps et al. 2002). The temporal trends evident from Table 2 and Figures 1 and and22 were also discernable in this analysis.

Analysis of individual genes:

A total of 67 genes were identified as exhibiting a stress-general expression pattern. Annotations associated with these genes are provided in section 4 of the supplemental data file at http://www.genetics.org/supplemental/, along with gene ontology biological process, cellular component, and molecular function terms. The identified genes were specific to both organs and time points, such that no single gene was identified in both roots and shoots or at more than one time point. A total of 26 genes were identified from roots and 41 genes were identified from shoots. The number of genes identified at each time point corresponded well with observations made in the genomewide correlation analysis (see Table 2). In roots, 21 of 26 genes were identified at the 1-hr time point, 2 genes were identified at the 3-hr time point, 1 gene was identified 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 time point, while 0, 2, 4, 3, and 1 genes were identified from the 0.5-, 1-, 3-, 12-, and 24-hr time points, respectively. These temporal patterns suggest that stress-general gene expression in roots peaks at the 1-hr point of stress exposure, while in shoots stress-general gene expression peaks at the 6-hr stage of stress exposure.

Table 3 lists the 26 stress-general genes identified in root cells. Within this gene set, 13 genes were identified on the basis of a significant ψ-statistic, 12 genes were identified on the basis of differential expression across all nine stress treatments, and 1 gene was identified on the basis of both criteria (germin-like protein At5g38910 at the 1-hr time point). Figure 3 shows the log2 fold changes observed across all stress treatments for the 26 genes identified in roots (see also supplemental data file, section 6, at http://www.genetics.org/supplemental/). Most genes are consistently downregulated across all stress treatments (15 of 26 genes), while slightly fewer are consistently upregulated across all stress treatments (10 of 26 genes). Only 1 gene alternated between up- and downregulation across stress treatments (iron-responsive transporter At4g19690 at the 6-hr time point).

Figure 3.
Log2 fold changes (M) associated with each stress treatment for the 26 stress-general genes identified in root cells. Fold changes of genes identified at (A) 0.5, (B) 1, (C) 3, and (D) 6–24 hr are shown. The dashed line denotes a log2 fold change ...
TABLE 3
Identification of 26 genes exhibiting stress-general expression responses in root cells

Table 4 lists the 41 stress-general genes identified in shoot cells. The majority of genes (37 of 41) were identified on the basis of differential expression across all nine environmental stress treatments, while only 3 of 41 genes were identified on the basis of a significant ψ-statistic. One gene was identified on the basis of both criteria (zinc finger protein At1g27730 at the 12-hr time point). Figure 4 shows the log2 fold changes observed across all stress treatments for the 41 genes identified in shoots (see also supplemental data file, section 6, at http://www.genetics.org/supplemental/). Most genes (33 of 41) were consistently downregulated across all nine environmental stress treatments. Only a small number (5 of 41) of identifications involved genes consistently upregulated across all stress treatments, while 3 identifications involved genes that alternated between up- and downregulation across stress treatments. A total of 23 genes identified in shoots were weakly expressed in the control treatment and the majority of stress conditions (indicated in Table 4). Genes with low expression levels should be interpreted with some caution, since weakly expressed genes have an elevated false-positive rate in differential expression analysis (McClintick and Edenberg 2006). For all identified genes, raw signal intensities reflecting absolute gene expression levels are provided in the supplemental data file (section 7 at http://www.genetics.org/supplemental/).

Figure 4.
Log2 fold changes (M) associated with each stress treatment for the 41 significantly stress-general genes identified in shoot cells. Fold changes of genes identified at (A) 0.5–1, (B) 3, (C) 6, and (D) 12–24 hr are shown. The dashed line ...
TABLE 4
Identification of 41 genes exhibiting stress-general expression responses in shoot cells

The entire set of 67 stress-general genes was analyzed to determine if certain functional categories were overrepresented. Class scores (±SE) associated with cell rescue/defense/virulence (1.58 ± 0.43), energy (1.54 ± 0.50), and metabolism (1.42 ± 0.28) were more than one standard deviation greater than one, which provided strong evidence of overrepresentation with respect to these broad functional categories. The complete set of GOstat results listing significantly overrepresented gene ontologies is available in section 5 of the supplemental data file at http://www.genetics.org/supplemental/. A total of 16 gene ontologies were significantly overrepresented among the 67 stress-general genes (P < 0.05). The 5 overrepresented biological process ontologies were related to cellular metabolism and photosynthesis (GO:0006118, GO:0006091, GO:0015979, GO:0019684, and GO:0009767), while the two overrepresented cell component ontologies were associated with the thylakoid membrane and photosystem II protein complex (GO:0009579 and GO:0009523). Among the 9 overrepresented molecular function ontologies, 5 were related to catalytic oxioreductase activity (GO:0016491, GO:0045157, GO:0004497, GO:0004152, and GO:0004158), while the remaining were associated with binding of tetrapyrrole (GO:0046906 and GO:0020037), oxygen (GO:0019825), or iron ions (GO:0005506).

DISCUSSION

The relationships among gene expression responses to different types of stress represent a fundamental basis for understanding the genetic and functional foundation of multiple-stress tolerance. Genomic responses of A. thaliana to different types of environmental stress have often been studied independently of one another. There has been an increasing recognition, however, of the genetic and physiological elements that are shared 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 present study has examined associations among gene expression responses to nine types of abiotic stress, which represents the most inclusive analysis of stress-induced transcriptional changes currently available in the Arabidopsis model system. The results show that associations among expression responses to different types of stress are dependent on the type of organ being considered and the timescale of stress exposure. This finding was supported by observations made both at a genomewide scale and with respect to individual genes showing the greatest transcriptional induction under stress. A total of 67 genes were identified as exhibiting statistically significant patterns of gene expression characterized by large expression responses to all nine stress treatments. In comparison with the rest of the Arabidopsis genome, these genes were disproportionately associated with cell rescue/defense/virulence, energy, and metabolism functional classifications. These findings have implications related to the structure of gene networks coordinating stress response in Arabidopsis, the functional basis of multiple stress tolerance, and stress-resistance engineering in plant species.

The associations among gene expression responses to different types of stress are strongly dependent upon the duration of stress exposure. In general, expression responses to different stress conditions are more strongly associated at early to middle time points of stress exposure. In root cells, nearly all genomewide correlations among stress responses peaked following 1 hr of stress exposure. In accordance with this observation, 23 of 28 stress-general genes identified in root cells were specific to the 1-hr time point, while only 2 genes were identified beyond the 3-hr time point of stress exposure. In shoot cells, a similar pattern appeared to be shifted to later time points, since most genomewide correlations among expression responses increased between 3 and 6 hr of stress 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 the results of some previous studies (Kreps et al. 2002; Denby and Gehring 2005) and lend support to the hypothesis that transcriptional changes under stress are more generalized during certain early stages of stress exposure. This observation may be explainable on the basis of overlapping stress-perception and signal transduction mechanisms among the nine abiotic stress treatments examined (Kreps et al. 2002; Denby and Gehring 2005). In root cells, for example, such overlapping stress perception and signal transduction pathways may be most critical at the 1-hr stage of stress exposure. For shoot cells, in contrast, shared components among different stress regulatory networks may play the greatest role following 6 hr of stress exposure.

Transcriptional responses to abiotic stress treatments have previously been viewed as progressing from general to specific over early to late stages of stress exposure (e.g., Kreps et al. 2002). The present analysis offers some support for this viewpoint, particularly with regard to expression patterns in root organs. At the same time, however, any such general-to-specific trend had a number of exceptions, and it is clear that a more complex characterization is appropriate in some cases. For instance, in root cells, the salt–osmotic stress, oxidative–osmotic, ultraviolet light–salt, ultraviolet light–osmotic genomewide correlations among expression responses increased over the latest time points. In shoot cells, moreover, most of the 36 pairwise correlations among expression responses were associated with positive slopes when regressed on different levels of time. On average, therefore, genomewide transcriptional changes under stress in shoot cells are less stress specific at later stages of stress exposure. It would be interesting to determine whether this pattern holds over durations of stress exposure >24 hr in shoot cells. In comparison to leaf tissues, roots possess a simpler anatomical structure, such that shoots perceive and respond to stress more slowly than root cells (Weigel and Glazebrook 2002). Expression responses in shoots may therefore be delayed, and over a greater timescale (>24 hr) it is possible that temporal patterns found in shoots would more closely resemble those observed in roots (i.e., more general-to-specific).

The functional properties of stress-general genes provide insight into the mechanisms that may be shared among cellular responses to the stress treatments examined. The striking increase in stress-general expression responses at early to middle time points in roots and shoots, for example, may partly be due to the common role of calcium as a second messenger under many 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 EF hand family protein (At5g39670) was upregulated under all stress treatments. At the 6-hr time point in shoot cells, a calmodulin family-binding protein (At3g52290) and annexin-like protein nearly identical to calcium-binding protein annexin 6 (At5g10220) were downregulated under all stress treatments. Cytosolic calcium oscillations have been found to occur in response to several abiotic stress stimuli and are thought to be essential for eliciting stomatal closure (Allen et al. 2000; Posas et al. 2000). The up- or downregulation of the identified genes may play a role in the control of cytosolic calcium concentration oscillations, possibly by aiding or inhibiting the sequestration of calcium ions into intracellular stores or across the plasma membrane (Allen et al. 2000). Stress-general expression responses may also arise from the activities of various transcription factors, which have been viewed as a source of mechanistic interaction among otherwise distinct abiotic stress response pathways (Chen et al. 2002; Chen and Zhu 2004). At the 1-hr time point in root cells, a NAM family protein (At2g43000) was identified, which has been associated with transcription factor activity (Riechmann et al. 2000). At the 12-hr time point in shoot cells, a zinc finger protein (Zat10, At1g27730) with transcription factor 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 that has been associated with a wide range of stressful stimuli (Pastori and Foyer 2002). Several ROS-generating p450 hemoproteins were included among stress-general genes (At2g30770, At1g69500, At3g14620, and At2g30750), along with genes functioning in the transfer of glutathione (At1g02930 and At1g74590), which is a thiol compound with a well-established role in ROS detoxification (Penninckx 2000; Maughan and Foyer 2006). Some identified stress-general genes have previously been associated with responsiveness to a single type of stress condition, but may have a broader role in stress response. For instance, auxin-responsive (At3g20220) and auxin-regulated (At2g21210) chloroplast proteins were identified, both of which were downregulated across all stress treatments. The downregulation of auxin-responsive genes has been documented as a response to wounding stress, but such downregulation has not previously been noted under other abiotic stress treatments (Cheong et al. 2002). A number of additional identified genes have previously been associated with defense against parasite and pathogen attack [At1g72900 (disease resistance protein), At3g57260 (beta 1, 3-glucanase), At4g36010 (thaumatin-like pathogenesis-related protein), and At2g43600 (putative endochitinase)], suggesting possible overlap between the mechanisms underlying abiotic and biotic stress response pathways. Two defense-related genes were exclusively downregulated among all nine abiotic stress treatments (At3g57260 and At2g43600). Such downregulation could arise from stress-induced elevations of ABA phytohormone, which has been found to promote the downregulation of defense-related genes (Anderson et al. 2004) and may therefore account for downregulation of defense-related genes under abiotic stress treatments (e.g., see Wong et al. 2006). Given that the stress-general genes highlighted above have been identified on the basis of microarray data alone, it should be emphasized that the present analysis represents only a first step toward confirming and understanding their role in stress regulatory pathways. Future experimental investigations of stress-general genes, including investigations with real-time PCR and analysis of post-transcriptional processes, will be necessary to elucidate how these genes may contribute to abiotic stress tolerance.

The functional basis of multiple-stress tolerance has previously been viewed from both mechanistic and energetic perspectives. Multiple-stress tolerance may arise from similarities in the specific types of biological damage inflicted by different stress treatments (e.g., decreased cellular water potential). A broader explanation of multiple-stress tolerance, however, is that different types of stress impose a similar demand on metabolic processes and patterns of energetic resource allocation (Hoffmann and Parsons 1991). These viewpoints are not mutually exclusive explanations of multiple-stress tolerance, but the extent to which each explanation properly accounts for the functional basis of multiple stress tolerance is not clear. This study found that genes associated with energy functional classes were 54% more frequent among significant stress-general genes in comparison to the rest of the Arabidopsis genome, while genes associated with metabolic functional classes were 42% more frequent. The gene ontology corresponding to generation of precursor metabolites and energy (GO:0006091) was overrepresented among stress-general genes, and, in general, significantly overrepresented biological process ontologies were associated with cellular metabolism and photosynthesis. These findings are consistent with the view that genes with a role in multiple-stress tolerance are involved in metabolic processes and allocation of energetic resources (Hoffmann and Parsons 1989a,b, 1991, 1997; Bubliy and Loeschcke 2005). The apparent role of energy and metabolism with respect to many stress treatments provides insight into connections that have been made between stress tolerance and seemingly different processes, such as senescence and inbreeding depression. A number of recent microarray studies have found that metabolic genes are disproportionately influenced by environmental stress, senescence, and inbreeding treatments (Girardot et al. 2004; Landis et al. 2004; Wang et al. 2004; Englander 2005; Kristensen et al. 2005). At a broad level, the role of metabolism in these processes may account for interactions between environmental stress and the magnitude of inbreeding depression (Crnokrak and Roff 1999; Keller and Waller 2002; Armbruster and Reed 2005), quantitative genetic correlations between stress resistance and extended life span (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 the molecular effects of stress, inbreeding, and aging treatments 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 genes with stress-general expression patterns on the basis of large 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 across multiple-stress treatments (Rensink et al. 2005; Wong et al. 2006). Both of these approaches were utilized to identify stress-general genes in the present study. The greatest confidence should be assigned to genes that scored highly on the basis of both approaches [e.g., At5g38910 (putative respiration germin-like protein), At5g39670 (calcium-binding protein), and At1g27730 (Zat10 salt tolerance finger protein)], since such genes exhibit stress responses that are large relative to all other genes in the genome and large relative to variation between duplicate expression measurements. When identifying stress-general genes on the basis of log2 fold change, previous studies have isolated sets of genes exceeding a particular level of fold change under each stress treatment individually (e.g., more than twofold) and then identified stress-general genes by examining the intersection of these gene sets (e.g., Kreps et al. 2002; Seki et al. 2002). The present study improved upon this strategy in two ways. First, rather than choose a “cutoff” level of fold change, a null hypothesis was formulated and resampling methods were used to assign statistical significance to highlighted genes. Second, log2 fold changes (M) associated with genes were replaced by their corresponding genomewide ranks. This normalized vectors of expression responses, which allowed all stress conditions to be weighted equally in identifying stress-general genes. Furthermore, averaging ranks across stress treatments allowed a meaningful statistic to be developed that reflects the continuity of available evidence.

Associations among expression responses to different stress treatments have often been examined with respect to the most stress-responsive genes only (e.g., Kreps et al. 2002; Seki et al. 2002). It is not necessarily true, however, that genes exhibiting large fold changes under stress occupy the most prominent role in coordinating physiological responses to stress (Feder and Walser 2005). In addition, it is plausible that a large number of genes with minor effects underlie both specific and general responses to abiotic stress treatments. For instance, in the present study, temporal patterns influencing stress generality among the most stress-responsive genes were also evident at the genomewide level, suggesting the possible involvement of a large number of genes. These considerations underscore the importance of investigating associations among expression responses to different types of stress on a genomewide scale in addition to within selected subsets of genes. The degree to which stress-resistance traits depend on a large vs. a small number of loci is unclear and has been the subject of long-standing debate (Hoffmann and Parsons 1991). A number of quantitative genetic studies have attributed stress-resistant phenotypes to one or a 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 findings have often been difficult to interpret, since it is generally difficult to distinguish between the effects of many loci of 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 of several kinds of stress in combination (Mittler 2006). Understanding the genetic and functional basis of multiple-stress tolerance will therefore be an important step toward increasing plant productivity through bioengineering approaches. The set of 67 stress-general genes identified in this study represents targets for direct bioengineering approaches or, alternatively, genetic markers that may facilitate the creation of stress-resistant genotypes through artificial selection. Such markers may play a pivotal role in systemic strategies for achieving plant-breeding goals through unification of molecular and quantitative genetic approaches (Arnholdt-Schmitt 2005). A primary challenge, however, may reside in manipulating the expression of genes underlying multiple-stress tolerance without associated costs affecting plant fitness components, particularly if such genes are involved in central metabolic processes (Vinocur and Altman 2005). This challenge has been foreshadowed by decades of artificial selection experiments, in which diminished growth and productivity have often been found to result as a correlated response to selection for increased stress tolerance (Service and Rose 1985; Hoffmann and Parsons 1989a; Verhoeven et al. 2004; Sinebo 2005; but see Araus et al. 2003). Similar growth and productivity costs have more recently been found in strains bioengineered for increased tolerance to adverse conditions (e.g., Kasuga et al. 1999; Ito et al. 2006) and have been an obstacle toward successful bioengineering of stress tolerance in plants (Vinocur and Altman 2005). Genes exhibiting strong expression responses to many types of environmental stress represent a possible intersection point among otherwise independent stress-response pathways. In addition to their potential role as gene targets, therefore, analysis of these genes may prove useful for developing a more comprehensive systems-oriented knowledge of stress physiology, which may be necessary for successful bioengineering approaches to increasing stress resistance in plant species.

Acknowledgments

The author thanks Andreas Weber, Marianne Huebner, Dong-Yun Kim, and two anonymous reviewers for helpful discussions and comments on this manuscript. In addition, members of the AtGenExpress consortium (Thomas Altmann, Pascal von Koskull-Döring, Jörg Kudla, Lutz Nover, and Detlef Weigel) and the Arabidopsis Functional Genomics Network are gratefully acknowledged for providing the gene expression data analyzed in this study. This work was supported by a research grant from the Michigan State University quantitative biology and modeling initiative.

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