JXB Advance Access originally published online on March 1, 2006
Journal of Experimental Botany 2006 57(5):1097-1107; doi:10.1093/jxb/erj098
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RESEARCH PAPER |
Dissecting salt stress pathways
1Department of Plant Biology, University of Illinois at Urbana-Champaign, 1201 W Gregory Drive, Urbana, IL 61801, USA
2Department of Crop Sciences, University of Illinois at Urbana-Champaign, 1201 W Gregory Drive, Urbana, IL 61801, USA
* To whom correspondence should be addressed. E-mail: bohnerth{at}life.uiuc.edu
Received 24 August 2005; Accepted 14 December 2005
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Abstract |
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Upon salt-stress treatment, Arabidopsis mobilizes a complex set of pathways that includes alterations in the regulation of gene expression and metabolic adjustments that attempt to establish a new energetic and developmental equilibrium. The responses share common elements with reactions to many other stresses, such as challenges by osmotic fluctuations, pathogens, mechanical interference, or cold stress. Also, hormones, such as ABA, ethylene, and jasmonic acid, play important roles in salt-stress signalling and adaptation. Publicly available and our own transcript profiling data are used here to dissect gene regulation under salt stress in A. thaliana Col-0. Applying the clustering method ‘fuzzy k-means clustering’ on 1500 strongly regulated genes, the salt-stress response could be categorized into distinct segments. Fewer than 25% of the regulated genes are salt stress-specific, while the majority also responded to other stresses and/or hormone treatments. Significantly, roots and shoots showed differences in hormone responsiveness, and early and late responses correlated with different signalling events. A network begins to emerge, revealing the basis of cross-talk between high salinity and other stresses.
Key words: Arabidopsis thaliana Col-0, cross-talk, fuzzy k-means clustering, salinity, transcript profiles
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Introduction |
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Decades of research into the effects of salinity on plant physiology and development have generated a wealth of information, among which the most advanced understanding is based on the detection and analysis of a signalling pathway (SOS) (Zhu, 2003
) and engineering of sodium storage by cells (Apse et al., 1999; Blumwald, 2003). Other results also pointed to the importance of the plant hormone ABA, the calcium sensor, calcineurin B-like 1 (CBL1), potassium homeostasis, and MAPK and CDPK genes in salt-stress responses that lead to protection (Hasegawa et al., 2000; Xiong et al., 2002; Albrecht et al., 2003; Cheong et al., 2003; Kim et al., 2004). However, current knowledge is still largely restricted to individual genes and pathways, and the unifying picture remains hidden.
Plants have evolved complex signalling pathways in response to various stimuli, such as salt, drought, cold, wounding, or pathogen invasion, and have acquired plasticity in metabolic functions and developmental switches to cope with changing environmental conditions (Genoud and Metraux, 1999). Cross-talk connecting different pathways appears to be a common feature in plants, as exemplified by biotic defences involving ethylene, salicylic acid, and jasmonic acid (Dong, 1998; Kunkel and Brooks, 2002), or by the DREB/CBF pathway on which signals from several abiotic stress conditions converge (Chinnusamy et al., 2004; Shinozaki and Yamaguchi-Shinozaki, 2000). The understanding of salinity stress will be greatly enhanced by identifying the convergent and divergent pathways between salinity and other abiotic stress responses and the nodes of signalling convergence. Indeed, several studies have addressed cross-talk between abiotic stresses and hormone signalling (Cheong et al., 2002; Kreps et al., 2002; Seki et al., 2002).
Recently, public efforts have been directed to Arabidopsis global transcript profiling that monitored the response of the plant under different treatments. Large sets of data have been made publicly available through several databases, such as TAIR, NASC, and Genevestigator (Garcia-Hernandez et al., 2002; Craigon et al., 2004; Zimmermann et al., 2004). Especially useful has been the AtGenExpress consortium project which had generated standard Affymetrix microarray data for Arabidopsis (http://www.arabidopsis.org/info/expression/ATGenExpress.jsp). Different methods, among them electronic northern and co-regulation analysis tools, have been created to integrate these data (Steinhauser et al., 2004; Zimmermann et al., 2004; Persson et al., 2005; Toufighi et al., 2005).
Salt-stress response pathways in Arabidopsis are dissected using the publicly available AtGenExpress data. In addition, microarray data generated by long oligo (70-mer) glass-array slides monitoring salt-stressed plants are compared with those deposited in AtGenExpress. This analysis revealed a well-defined salt-stress response in Arabidopsis that could be contrasted against reactions in response to other stresses. From the datasets, 1500 salt-regulated genes have been extracted and analysed by the fuzzy k-means clustering method (Gasch and Eisen, 2002). This analysis provided a distinction between genes that responded only to salinity from those that also responded to biotic, osmotic, low temperature stress, and hormone treatments. By assigning specificity and identifying nodes of cross-talk, general patterns of gene regulation in Arabidopsis upon salinity stress can be identified.
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Materials and methods |
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Affymetrix microarray data
The abiotic transcript profile data were downloaded from Weigel World (http://www.weigelworld.org/resources/microarray/AtGenExpress/), which has been processed via gcRMA (Wu et al., 2003
). For biotic and hormone treatments, the CEL files for Affymetrix microarray data were downloaded from TAIR and processed into expression estimates using gcRMA implemented in R with default settings. For each experiment, the log2 intensities for individual probe sets were averaged across two replicates for treatment and control, and their differences were used as log2 of fold changes. Among the 12 salt-stress experiments (roots or shoots, six time points), the maximum and minimum regulation values were used to extract, for this pilot analysis, the top 1000 up-regulated and top 500 down-regulated genes, which were analysed using the fuzzy k-means clustering method (Gasch and Eisen, 2002), using the parameter k=30. The process generated 22 centroids with each gene linked to every centroid by a membership value. Then, 22 clusters were generated in a way that a gene was assigned to the cluster with which it had the highest membership value. A 0.2 membership cutoff was applied, which resulted in 1143 genes with clear patterns in these 22 clusters. Results were visualized by mapletree (http://rana.lbl.gov/EisenSoftware.htm) software.
Glass microarray data
A. thaliana (Col-0) plants were grown hydroponically in pots filled with isolite artificial soil (Sundine Enterprises, Arvada, CO), supplied with 0.5x Hoagland's nutrient solution with increased (4x) Fe amounts, at 24 °C (16/8 h light/dark; 
150 µmol photons m–2 s–1). Four-week-old plants before bolting were irrigated with 150 mM NaCl at midday, and remained in the presence of NaCl solution. Control plants were irrigated with nutrient solution. After treatment for 3 h and 24 h, respectively, plants, at least 10 per sample, were frozen in liquid N2. Two biological repeats, grown separately at different times, were used.
From these samples, total RNA was isolated (RNeasy; Qiagen, Carlsbad, CA). Glass microarray slides consisting of 70-mer oligonucleotide probes (http://ag.arizona.edu/microarray/) were used in hybridizations. RNA samples (70 µg each) for control and treatment conditions were reverse transcribed (SuperScript III; Invitrogen, Carlsbad, CA) and hybridization performed according to TIGR (http://atarrays.tigr.org/arabprotocols.shtml). For each time point in each biological repeat three hybridizations were carried out. To avoid bias in microarrays as a consequence of dye-related differences in labelling efficiency, dye labelling for each paired sample (stress/control) was swapped in one of three independent hybridizations. In total, 12 microarray hybridizations were carried out.
After hybridization, signal intensities for each array element were collected (GenePix 4000B; Axon Instruments, Union City, CA) and images analysed (GenePix Pro 4.0). Spots with intensities lower than local background or aberrant spot shape were flagged by the GenePix software, checked manually, and excluded. The resulting GPR files were analysed by TIGR-TM4 (http://www.tm4.org/) (Saeed et al., 2003). Total intensity normalization, Lowess (Locfit) normalization, standard deviation regulation, and intensity filtering were done for each slide with TIGR-MIDAS, version 2.18. Then, using ‘Multiple Experiment Viewer’ (MEV, a tool in TM4), version 3.0.3, a class t test (P=0.05, permutation=64) was applied to pick up the significantly regulated genes. Adjusted Bonferroni P-value correction was used at the same time to reduce FDR (false discovery rate). The t test output was then compared with salt stress microarray data from the AtGenExpress consortium projects.
Comparison of results with Affymetrix and glass microarray slides
The list of differentially regulated genes using 70-mer olionucleotide glass slides was compared with the list of genes identified by AtGenExpression as regulated. For this comparison, only the trend of regulation was considered. If the log2 ratio value was less than 0, the gene was considered repressed, otherwise induced.
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Results |
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Arabidopsis oligonucleotide-based microarrays
Based on results from previous studies (Kreps et al., 2002
; Seki et al., 2002; Taji et al., 2004), a shock treatment of 150 mM NaCl for 3 h and 24 h, respectively, was chosen. This concentration and times represent the maximum tolerable for a specific response without inducing pathological reactions. The gene expression levels were compared with those of untreated controls (see Materials and methods). Each experimental condition was represented by six slides from two biological repeats, including cy3/cy5 dye-swaps with a microarray platform that included 70-mer oligonucleotides, selected to reduce or abolish cross-hybridization, for approximately 26 000 genes. Normalization and statistical analysis (P <0.05) resulted in 2419 genes expressed differentially in the 3 h salt-stress experiment compared with the control, and 3930 genes at 24 h. These data were compared to those from AtGenExpress salt-stress experiments, which had been carried out using the Affymetrix ATH1 GeneChip platform. 2109 genes (out of 2419) were found in 3 h experiments in both types of slides, and 3415 genes (out of 3930) at the 24 h time point. While whole plants were used in these experiments, AtGenExpress experiments were done separately for roots and shoots. Considering this, only those genes regulated in the same direction in both roots and shoots were compared with our data. This resulted in 79% of the genes sharing the same trend in the 3 h data, and 84% in the 24 h data (Table 1). The numbers of genes regulated in the opposite direction in roots and shoots are also listed.
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A comparison of data for genes represented on both platforms indicated a highly similar trend in gene regulation and significant superimposition in all functional categories (categories not shown). Considering the differences between the tools, technical differences, biological sampling and preparation, it seems reassuring to confirm that salt stress generated stable regulation patterns in Arabidopsis wild-type plants that can be replicated, independent of the platform used.
The Affymetrix data collection:
Advantages of Affymetrix transcript analysis slides are the inclusion of a standard probe set and well-defined hybridization protocols. With the generous contribution of the AtGenExpress projects, the public databases now include a variety of microarray experiments conducted after different treatments of the plants. The focus was on stress-relevant and hormone-specific AtGenExpress data to harness the high reproducibility of this hybridization platform in comparisons of different treatments. Raw average data were also used without statistical filtering as an acceptable strategy because general trends are the point of interest.
The overall pattern:
After extraction of all data from the AtGenExpress database, the analysis focused on 1496 genes, which represent the 1000 most highly up-regulated and 500 most strongly down-regulated salt-responsive genes in Arabidopsis Col-0 (see supplementary Table 1 at JXB online). Fuzzy k-means clustering (Gasch and Eisen, 2002) placed 1143 genes into 22 clusters (Fig. 1) (see supplementary Table 2 at JXB online). A total number of 353 genes was removed from further analysis based on their low membership values (see Materials and methods).
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Of the remaining 1143 genes 82% assembled into 10 major clusters, which distinguished responses under a selection of experimental conditions that included biotic interactions (viral, bacterial, and fungal), cold, osmotic, salinity, drought, oxidative, and wounding stress treatments, as well as different hormone treatments (clusters C0 through C9; Fig. 1). Approximately 18% of the genes were placed into the small clusters 10 through 21, which will not be discussed. Among the large groupings, clusters 0, 2, 4, 6, 8, and 9 include salt-stress up-regulated genes, and clusters 1, 3, 5, and 7 include the down-regulated genes. Interestingly, genes in cluster 0 and 8 were also up-regulated by elicitor treatments, genes in clusters 4 and 6 by ABA treatment, and cluster 9 united salt-responsive and methyl-jasmonate (MeJA)-induced genes. By contrast, the genes in cluster 2 were up-regulated only by salt-stress and only in roots. Notably, only a small portion of the genes was directly induced by more than one of the treatments by elicitors, ABA, and MeJA. A further distinction emerged in the timing of the response and in hormone-specific correlations that were different in space and time. The remaining clusters 1 and 3 included genes that were down-regulated in both abiotic and biotic stresses, while clusters 5 and 6 included genes down-regulated only by abiotic stresses. In the following sections, an analysis of the functionally annotated genes in each centroid will be presented. This provides a basis for dissecting the Arabidopsis salt-stress pathways, and also presents pointers that can guide future analyses into the function of currently unknown genes that appeared in each cluster.
C8: immediate responses:
Genes in cluster 8 (141 in total, C8; Fig. 1) showed immediate regulation changes and retained up-regulation in salt-stressed roots, but in shoots the changes were insignificant. Strong up-regulation of this group of genes was also observed following osmotic stress, cold stress, and a variety of biotic stress treatments. Transient induction was seen in drought-stressed roots and shoots, and in wounded shoots. Interestingly, genes in C8 were only minimally induced by exogenous ABA. One-third of the genes are functionally unknown, while the rest could be categorized. Ethylene appeared to be the dominant hormone here, suggested by the presence of At-ERFs 1, 5, 6, and 11; and the ACC synthase, ACS6. Various calcium-dependent signalling pathways seemed to be involved, exemplified by many calcium-binding proteins, calmodulins, calmodulin-binding proteins, including TCH3, calcineurin CBL1, and calcium-transporting ATPases. The transcription factors found in centroid C8 were mainly zinc finger and WRKY transcription factors such as ZAT10, ZAT12, WRKY 22, and WRKY 53. Finally, included were several disease-resistance protein genes, genes functioning in post-translational modification and protein degradation, and a few MAPKs (MPK 3, 5, and 11).
C6: early responses:
Cluster 6 included 76 genes (C6; Fig. 1) that were highly induced by salinity and osmotic stress treatments, early in roots and 1 h later in shoots, by cold after 6 h, and by drought early in roots. These genes were also early and strongly induced by ABA. Several genes in C6 have been established as key regulators in abiotic stress responses, such as RD29A and DREB2A (Yamaguchi-Shinozaki and Shinozaki, 1994; Liu et al., 1998). Also included were RD20 and KIN1. Not surprisingly, genes functioning in ABA synthesis and signal transduction appeared, including NCED3, ABF3, ABI1, ABI2, and other PP2Cs. A third large group included transcription factors, especially MYBs and NACs. Several have been studied for their involvement in abiotic stress responses, including ATAF1, ATHB12, NAP, AZF2, HSF2, and ATERF4. Finally, a few genes involved in cell wall biosynthesis and LEAs appeared in centroid C6. Overall, most genes are clearly involved in abiotic stresses, and have been characterized before, in the ABA-dependent or ABA-enhanced early response cascade of abiotic stress.
C4: delayed responses in roots:
The 89 genes in C4 (C4, Fig. 1) were strongly up-regulated by salt and osmotic stresses in roots after only a 3 h treatment, and also induced 3 h after ABA treatment, while in shoots up-regulation was observed earlier. Many of the C4-genes identified diverse metabolic pathways, including lipid, for example, LTP3 and LTP4, and carbohydrate metabolism, for example, a sucrose synthase isoform and APL3 and APL4 that are involved in starch biosynthesis.
C0: defence genes shared with biotic stress conditions:
Genes in cluster 0 (114 in total, C0; Fig. 1) were strongly induced in roots starting after 1 h of salt stress, but showed no significant change in shoots. Unambiguous induction could also be seen in osmotically stressed shoots, oxidatively stressed shoots, and in cold-treated roots. These genes were also greatly induced by various biotic stress treatments. Significantly, these genes showed only minor fluctuations following ABA, 1-aminocyclopropane-1-carboxylic acid (ACC), or MeJA treatment, and, hence, could not be identified as responsive to the typically invoked stress hormones. Enriched in this cluster were genes involved in redox homeostasis control and post-translational modification, including many GSTs, FAD-linked oxidoreductases, protein kinases and PP2Cs, and oxidoreductin AERO1. A significant number of genes were receptor-like protein kinases, suggesting the existence and involvement of dynamic intercellular signalling events. Defence genes abounded: cell wall proteins including AGP2 and AGP5, lignin synthesis genes including CCR2, P450 genes including PAD3 (phytoalexin biosynthesis), the calcium-transporting ATPase ACA12 and ABC transporters, and disease resistance proteins of various classes appeared in C0. Several WRKY transcription factors (At1g62300, At4g18170, At5g24110, At5g49520), and the ethylene biosynthesis gene ACS2 may be considered as defence-related as well.
C2: the salt- and root-specific response:
Cluster 2 included 171 genes (C2; Fig. 1; Table 2) that were only or most strongly up-regulated in salt-stressed roots. Some of these genes showed a moderate induction in osmotic or drought stresses, but no clear pattern could be seen, while ABA seemed to have no impact on their expression levels. Among the genes with functional annotations in C2, several categories emerged. Similar to C6, many ethylene synthesis and signalling genes were observed, including ERF1 and ACS8. More than 10 genes in C2 identified so-called disease resistance proteins (labelled as biotic stress responsive) and an equal number of receptor-like kinases. Also, genes with functions in post-translational modification and protein degradation were included. Surprisingly, nearly 20% of the genes in C2 were transcription factors. In addition to AP2 genes, that were otherwise almost exclusively found in C2, and a few Mybs and WRKYs, the group included a number of unknown, putative transcription factors, which should become important new targets in salt-stress studies. Finally, approximately 60 genes with unknown functions were C2-specific.
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C9: The cluster related to MeJA:
C9 comprises a small cluster with 49 genes (C9, Fig. 1) that showed strong induction only in salt-stressed roots, drought-stressed roots and shoots, wounded roots and shoots, and by MeJA. Most annotated members of this centroid are involved in the biosynthesis of various secondary metabolites. Among these, all major JA synthesis genes (AOS, AOC1, and OPR3), amidohydrolase ILL6 (for auxin homeostasis) and a 2-oxoglutarate-dependent dioxygenase (for ethylene synthesis), an anthocyanidin synthase, and two P450s were identified. The remaining genes included the well-known ATMYC2/JIN1 and two other bHLHs, and two annexins, ANNAT3 and ANNAT4.
The down-regulated genes: clusters C1 and C3:
Genes in clusters 1 and 3 (142 in total, C1, C3; Fig. 1) were down-regulated by salinity and osmotic stress treatment. Compared with roots, shoots showed higher (C3) or similar (C1) but slightly delayed repression that became obvious after the 3 h time point. These genes were also repressed in various biotic stress treatments, and by ABA treatment mainly at the same 3 h time point. Moderate down-regulation was observed in almost all other treatments with a slight bias towards a response in the shoots. An unusually large proportion, approximately 40%, of the genes in these two clusters is annotated as functionally unknown. Most of the remaining genes identified function in growth. Many belonged to transcription factor families such as bHLH, bZIP, and Myb. Also HAT1 and MYC1 were included here. The second group was made up of auxin-responsive genes including SAUR-AC1. A third group, finally, included cell wall modification genes and genes of related function, for instance the GDSL lipases, XTH9 and PEM3.
The down-regulated genes: clusters 5 and 7:
In contrast to the genes in clusters 1 and 3, clusters 5 and 7 (151 genes in total, C5, C7; Fig. 1) showed a root-specific pattern of down-regulation, initiated immediately after salt, osmotic, drought, and oxidative stress treatments. ABA moderately repressed their expression as well. However, biotic stress treatments have no effects on the expression of these genes. Unique to these two clusters were a group of peroxidases, metal transporters, and several aquaporins. Similar to C1 and C3 genes, a large number of genes were involved in cell wall modification, including several AGPs, FUT5, and PRP3; and the GDSL lipases and LTPs. A few AP2 transcription factors, bHLH and Mybs were also identified, together with genes involved in development. Finally, genes with a function in the biosynthesis of amino acids and secondary metabolites (terpene, glucosinolate, cytokinin, gibberellin) were also down-regulated.
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Discussion |
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Among the Arabidopsis transcript profiling platforms, the most complete set includes approximately 26 000 DNA elements for known and hypothetical coding regions. It is based on 70-mer oligonucleotides. In several constantly improving versions this array has become a reliable tool (http://www.ag.arizona.edu/microarray) in the hands of skilled experimenters. The Affymetrix GeneChip platform with a slightly lower complexity, approximately 22 000 genes, has become a standard because it represents a closed system, shows ease of use, and includes customized analysis software. This comparison of data for genes represented on both platforms indicated a highly similar trend in gene regulation, where approximately 80% of the transcripts behaved similarly when analysed by the two platforms. In essence, both platforms provide comparable results.
Clustering methods have been widely used to analyse large gene-expression datasets. The most commonly used methods included hierarchical clustering, k-means clustering, and SOM (self-organization map) (Eisen et al., 1998; Sherlock, 2000; Toronen et al., 1999). Here, fuzzy k-means clustering, in combination with principal component analysis (PCA) (Gasch and Eisen, 2002), was used to analyse the publicly available Affymetrix Arabidopsis gene-chip data on abiotic stress, biotic stress, and hormone treatments. Using this clustering method, the most informative expression patterns were captured as centroids. Instead of following the fuzzy k-means protocol where genes belong to multiple clusters, each gene was assigned to the cluster to which it had the highest membership value, because the focus was on the overall regulation pattern instead of the behaviour of individual genes, while discarding genes without significant membership to any cluster in order to reduce chance or false assignments. An important consideration in fuzzy k-means clustering is the selection of the cluster number k. By choosing a higher k, higher distinction is possible. For this study, increasing k from 30 to 120 generated a large number of clusters with very few genes, while the large clusters chosen here split into 2 or 3 smaller clusters (data not shown). Overall, this clustering method was found especially useful when dealing with large microarray data set with multiple time points.
After comparisons across both array platforms, analyses were focused on the Affymetrix data generated by the AtGenExpress consortium. The standardized protocol and data format, together with the strict experimental procedure employed by the consortium team, made it possible to integrate the whole dataset. Using the data without filtering genes with low expression was possible because the expression pattern over multiple treatments with multiple time points, for most of the genes, revealed trends of regulation at all time points that were consistent and without fluctuations within specific treatments (Fig. 1; see supplementary Table 1 at JXB online).
The results, for the 1500 most strongly salt-regulated genes, revealed an unexpectedly complex interaction network between Arabidopsis stress-signalling pathways. Of 680 salt-induced genes, fewer than 25% (171, C2) were strictly salt-specific. Strikingly, most of the remaining genes were also induced by at least two different biotic stress treatments (C0, C6, C8, C9) and, in addition, shared common regulation with other abiotic stresses. Based on this co-induction pattern, the salt-induced signalling pathways in Arabidopsis may be divided into four categories (Fig. 2). One cluster includes salt-responsive genes that are also induced by elicitors (C0 and C8). Then, salt and ABA treatment (C4 and C6), and salt and MeJA exposure (C9) form distinct groupings of genes. Only cluster C2 contains genes that specifically respond to the ionic component of salt stress.
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The structure of the clusters, the types of genes within each cluster, and their appearance early or late in the time-courses identify functions that unite as well as distinguish different stresses. In cluster 8, for example, calcium signalling-related genes (e.g. TCH3) and ethylene-related ATERFs may represent early sensing and signalling components (Sistrunk et al., 1994
; Fujimoto et al., 2000), as is the case for gene CBL1, which has been shown to mediate stress signalling without affecting ABA-related pathways (Cheong et al., 2003). Furthermore, cluster 6 includes ABA biosynthesis and signalling pathways, with high probability representing the general signal transduction chain related to osmotic adjustments. ABA has been recognized as a key regulator in abiotic stress responses (Gazzarrini and McCourt, 2001; Zhu, 2003; Sharp et al., 2004). A MPSS (massively parallel signature sequencing) study identified the ABA up-regulated genes in Arabidopsis (Hoth et al., 2002). Not surprising, the majority of the overlapping genes between the MPSS results and this analysis fell into clusters 2 and 4, the only two clusters that included ABA-responsive genes. Cluster 9 salt-induced genes are also highly induced by MeJA. Apart from the significant involvement of MeJA in biotic stresses, this hormone has also been reported to play a role during potassium starvation, which would make it an additional specific mediator of abiotic stress responses (Armengaud et al., 2004).
Of the 171 genes placed into cluster C2 most were induced only in roots, and they were specifically induced only by salt stress. This set of transcripts had not been observed before; it may constitute the ionic stress component of the Arabidopsis transcriptome. The reasons for this exclusivity might be that leaves, compared with roots, have a larger sodium storage capacity, or it may be a consequence of the relative higher concentration of sodium ions in the roots, as it has been reported in the wild type (Volkov et al., 2004), while sos1 mutants deposit more sodium into the shoot system (Shi et al., 2002).
The SOS system, which has been established as an important defence mechanism potentially leading to salt tolerance (Zhu, 2003), is not represented among the strongly responding genes, because the SOS pathway seems to operate mainly at the protein modification and not the transcript level. However, among the early induced, ionic stress-specific genes in clusters C2 are most likely the components that, upstream of SOS, lead to the initiation and engagement of the SOS pathway. For example, 11 protein kinases of unknown function in this cluster represent a category that could make them candidates of early sensing or signalling.
In summary, it was demonstrated that large-scale microarray data can be used to recognize the cross-talk between different signalling pathways, providing information that will be useful in elucidating unknown signalling networks. Comparisons across different high-throughput transcript profiling platforms are possible and indicate the relative maturity of the procedures, in particular, of the statistical analyses and data representation tools. The general salt-stress signalling and response pattern, the multiple input elements, and a reliable, across-platform, identification of the many functionally unknown components, revealed by the analysis can provide guidance for forward genetic analysis of salt stress.
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Acknowledgements |
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We acknowledge members of the AtGenExpress consortium for help (Thomas Altmann, Pascal von Koskull-Döring, Jörg Kudla, Lutz Nover, and Detlef Weigel) and the people in their laboratories for generating the Affymetrix expression profiles. We apologize for not citing the large number of manuscripts and functional studies related to the genes discussed here; their inclusion would have exceeded the allotted space. The work has been funded by NSF (DBI-0223905) and UIUC institutional grants.
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