Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Heat stress-responsive transcriptome analysis in heat susceptible and tolerant wheat (Triticum aestivum L.) by using Wheat Genome Array

BMC Genomics volume 9, Article number: 432 (2008) Cite this article

Abstract

Background

Wheat is a major crop in the world, and the high temperature stress can reduce the yield of wheat by as much as 15%. The molecular changes in response to heat stress are poorly understood. Using GeneChip® Wheat Genome Array, we analyzed genome-wide gene expression profiles in the leaves of two wheat genotypes, namely, heat susceptible 'Chinese Spring' (CS) and heat tolerant 'TAM107' (TAM).

Results

A total of 6560 (~10.7%) probe sets displayed 2-fold or more changes in expression in at least one heat treatment (f alse d iscovery r ate, FDR, α = 0.001). Except for heat shock protein (HSP) and heat shock factor (HSF) genes, these putative heat responsive genes encode transcription factors and proteins involved in phytohormone biosynthesis/signaling, calcium and sugar signal pathways, RNA metabolism, ribosomal proteins, primary and secondary metabolisms, as well as proteins related to other stresses. A total of 313 probe sets were differentially expressed between the two genotypes, which could be responsible for the difference in heat tolerance of the two genotypes. Moreover, 1314 were differentially expressed between the heat treatments with and without pre-acclimation, and 4533 were differentially expressed between short and prolonged heat treatments.

Conclusion

The differences in heat tolerance in different wheat genotypes may be associated with multiple processes and mechanisms involving HSPs, transcription factors, and other stress related genes. Heat acclimation has little effects on gene expression under prolonged treatments but affects gene expression in wheat under short-term heat stress. The heat stress responsive genes identified in this study will facilitate our understanding of molecular basis for heat tolerance in different wheat genotypes and future improvement of heat tolerance in wheat and other cereals.

Background

High temperature is one of the limiting factors affecting crop production. Combining with drought stress, the elevated temperature often causes yield loss and reduces the quality of crops [1–3]. Indeed, the high temperatures during the post-heading stages affect yield [4, 5] and grain quality [6] of wheat, a major crop cultivated worldwide.

Plants can grow in the temperature above the optimal level that is known as basal thermotolerance. If plants are pretreated with a mild non-lethal temperature (heat acclimation) or if temperature increases gradually to a lethal level, they can survive under the lethal high temperature stress, which is known as acquired thermotolerance [7, 8]. However, little is known about the molecular changes affecting regulatory and biochemical pathways of heat stress responses in crops [9]. Thus, identifying novel genes and studying their expression patterns in response to heat stress will provide a molecular basis for improving heat tolerance in crops.

Microarray analysis of gene expression has been used to investigate transcriptome changes in response to heat stress as well as combined stresses in several plant species, such as Arabidopsis thaliana [10], Chinese cabbage [9], Festuca [11] and barley [12]. Gene expression changes in wheat seedlings exposed to heat stress were analyzed using Affymetrix Barley1 Genechip [13], and analysis of ESTs (expressed sequence tags) was used to screen for the heat stress responsive genes in wheat [14]. To study gene expression changes in wheat related to basal and acquired thermotolerance, we used two wheat genotypes with different tolerance to heat stress for gene expression studies. 'Chinese Spring' (CS) is susceptible to heat stress, whereas 'TAM107' (TAM) is tolerant to heat stress. GeneChip® Wheat Genome Array was applied to determine transcriptome changes in response to heat stress in these two genotypes. We identified a total of 6560 probe sets that were responsive to at least one heat treatment.

Results and discussion

Expressed probe sets in wheat leaves

The Affymetrix GeneChip® Wheat Genome Array contains 61,127 probe sets, representing 55,052 transcripts (Affymetrix, USA). In this study, two wheat genotypes were used for expression analysis under heat treatments. Based on the cell membrane stability test, genotypes with higher relative injury (RI) value are heat susceptible, whereas genotypes with low RI value are heat tolerant [15]. 'Chinese Spring' (CS) is heat susceptible with a RI of 80%, and TAM107 (TAM) is heat tolerant with a RI of 35%. Ten-day-old seedlings were used for different heat treatments (Figure 1), resulting in four heat treatments and one control for each genotype (for detail, see Methods). Seedling leaves were harvested and used for microarray analysis using three biological replications, and a total of 30 arrays were hybridized. The normalized signal intensity of each sample was used to evaluate the reproducibility between three replications, and the correlation coefficients between any two replications ranged from 0.956 to 0.994, indicating that microarray analysis was highly reproducible in this study (See Additional file 1).

Figure 1
figure 1

Heat treatments for comparison of acclimated and non-acclimated plants of the two genotypes.

Full size image

'Present' probe sets were detected by filtering with a fraction call 100% (for detail, see Methods). Those probe sets that were detected as 'Present' in all the three biological replications were defined as 'expressed' probe sets in this study. Based on these criteria, 32% and 30% of the 61,127 probe sets on the array were expressed in CS and TAM, respectively. The percentages of expressed probe sets were 26% for CS1h and 25% for TAM1h after short heat treatments, whereas the percentages increased in the long heat treatments (35% for CS24sh, 36% for CS24h, 34% for TAM24sh and 34% for TAM24h) (Table 1). The percentages in the short heat treatments with pre-heat acclimation were similar to those of the controls (Table 1). The data suggest that more genes were expressed when plants were treated under long heat stress (24 hours).

Table 1 Percentage of expressed probe sets and number of heat responsive probe sets in two wheat genotypes 'CS' and 'TAM107'.
Full size table

Analysis of h eat – r esponsive (HR) probe sets

Identification and validation of HR probe sets

The probe sets that were up- or down-regulated under heat stress in each treatment compared to the controls were referred to heat responsive (HR) probe sets. The fold changes should be equal or greater than 2 (f alse d iscovery r ate, α = 0.001). A total of 6560 probe sets were identified to be heat responsive in at least one stress treatment (See Additional file 2), which represented 10.7% of the total probe sets on the array. In Arabidopsis, 11% of the genes showed significant expression changes after a 1-hour heat treatment [16], whereas 2.6% were responsive to a 6-hour heat treatment [10]. The discrepancy of the number of HR genes in these studies might result from the different species studied, the developmental stages when heat stress was applied, temperature and duration of heat treatments, and/or criteria of selecting candidate HR genes.

In this experiment, 1 h had more candidate HR probe sets than 1 sh treatments, and the latter resulted in the least number of HR probe sets (2199 for CS and 2084 for TAM) (Table 1). Pre-heat acclimation prior to the one-hour heat treatment reduced the number of HR probe sets, which could be important to protect wheat seedlings against otherwise lethal exposure to high temperature. It may also suggest that many early heat responsive genes, such as those encoding heat shock proteins (HSPs), heat shock factors (HSFs), and other transcription factors, are turned on during heat acclimation (34°C for three hours). Notably, approximately 70% of HR probe sets in 24 sh and 24 h treatments were up-regulated in both genotypes, whereas only 58% and 50% in 1 sh and 1 h treatments were up-regulated in CS and TAM, respectively (Table 1). More HR probe sets were detected in the heat susceptible 'CS' than in the heat tolerant wheat 'TAM107' (Table 1), indicating that the response of heat susceptible wheat genotype to long heat stress is reflected in gene expression changes.

To validate the expression levels of candidate HR genes detected by microarray analysis, we analyzed expression patterns of ten randomly selected probe sets using quantitative RT-PCR (qRT-PCR). Depending on the probe sets analyzed, the correlations between qRT-PCR data and microarray signal intensity varied from 0.531* to 0.932**, indicating that the expression patterns detected by microarray analyses are in good agreement with that detected by qRT-PCR.

Hierarchical clustering of HR probe sets

To evaluate the relationship of the genome-wide expression profiles between different genotypes and different treatments, we analyzed linkage hierarchical clustering using 6560 HR probe sets in eight heat-treated samples (Figure 2). The HR genes in the long (24-h) heat treatment were clearly separated from those in the short (1-h) heat treatment. The genes detected under the same short-heat treatment between two genotypes were clustered together (CS1sh with TAM1sh and CS1h with TAM1h), whereas the genes between the two genotypes in the long-heat treatment (CS24sh with CS24h and TAM24sh with TAM24h) were separated, suggesting that gene expression changes under the short heat treatment are dependent on acclimation, and those under the long heat treatment are dependent on genotypes. Cluster analysis also indicated that some genes were up- or down-regulated in all heat treatments tested, while others displayed expression changes in response to different treatments (Figure 2).

Figure 2
figure 2

Average linkage hierarchical clustering analysis of the log 2 transformed changed ratio of the 6560 HR probe sets.

Full size image

Classification of HR probe sets based on expression patterns in different genotypes and heat treatments

To identify the co-regulated HR probe sets in different genotypes or heat treatments and genotype/treatment-specific HR probe sets, we classified the 6560 HR probe sets into 4 groups according to their expression patterns in different treatments and genotypes (Figure 3). They were: HR probe sets common in both CS and TAM (Group 1), CS-specific HR probe sets (Group 2), TAM-specific HR probe sets (Group 3), and other HR probe sets (Group 4). The HR probe sets in groups 1–3 were further divided into several subgroups based on different expression patterns (Figure 3, Additional file 3).

Figure 3
figure 3

Classification of expression patterns of 6560 HR probe sets in different genotypes and heat treatments. The columns represented different heat treatments in two genotypes, and number on the right side of each row represented the numbers of probe sets in each expression pattern. The row marked in gray indicated that the probe sets shown complicated expression patterns.

Full size image

Group 1 contained 2824 (~43% of total HR) probe sets. Group1-Pattern1 (G1-P1) subgroup was heat responsive in all heat treatments (Figure 4a), and 262 and 197 probe sets were up- and down-regulated, respectively. G1-P2 contained 238 probe sets that were responsive only in short heat treatments (1 sh and 1 h) in two genotypes (Figure 4b), and 165 and 72 probe sets were up- and down-regulated, respectively. G1-P3 (86 probe sets) was responsive only to short treatment with pre-acclimation (CS1sh and TAM1sh) (Figure 4c), indicating that they are acclimation-dependent HR genes. In this subgroup, 34 probe sets were up-regulated, and 52 were down-regulated. Among the 806 probe sets that were responsive only to the short treatment without pre-acclimation (G1-P4), 492 were up-regulated, and 314 were down-regulated (Figure 4d), indicating that they are responsive to the heat shock but independent of acclimation. G1-P5 contained 819 probe sets that were responsive to the long heat treatments in both genotypes (Figure 4e), and 705 (86%) probe sets in this subgroup were up-regulated.

Figure 4
figure 4

Expression patterns of probe sets in 5 subgroups of Group1. The horizontal axes indicated the 8 heat treated samples, and the vertical axes indicated the log2 transformed ratio. (a) G1-P1. (b) G1-P2. (c) G1-P3. (d) G1-P4. (e) G1-P5.

Full size image

A total of 1129 (~17% of the total) HR probe sets were responsive only in 'CS' but not in 'TAM', and therefore designated CS-specific HR probe sets (Figure 3). Among them, nine sub-groups with distinct expression patterns were identified. Expression profiles of the probe sets in subgroups G2-P3, G2-P4, G2-P5, G2-P6 and G2-P7 were shown in Additional file 4. G2-P3 contained 20 up-regulated and 45 down-regulated probe sets that were responsive only in CS1sh, indicating that they were CS-specific and acclimation-dependent genes. G2-P4 contained 163 probe sets that were responsive only to the direct heat treatment without pre-acclimation, and 55 and 108 probe sets were up and down regulated, respectively. A total of 382 probe sets in G2-P5 were responsive in both CS24sh and CS24h, indicating that they were regulated by prolonged heat treatments in 'CS', and 289 were up-regulated, 93 were down-regulated. G2-P6 contained 89 probe sets that were specifically responsive in CS24sh and majority (66) of them were up regulated, indicating that they were responsive to prolonged heat treatment only with pre-acclimation. Moreover, a total of 220 probe sets in G2-P7 were CS24h-specific HR probe sets, either up (158) or down (62) regulated by heat stress.

Group 3 containing 892 (~14% of the total) HR probe sets that were responsive only in 'TAM' but not in 'CS', and therefore designated TAM-specific HR probe sets (Figure 3). The TAM-specific HR probe sets are interesting because 'TAM' is highly tolerant to heat stress. In this group, 9 sub-groups with distinct expression patterns were identified. Subgroup G3-P4 contained 421 probe sets that were responsive only in TAM1h (See Additional file 5), and 179 were up regulated. G3-P5 contained 139 probe sets that were responsive in both TAM24sh and TAM24h, among which 100 were up-regulated (See Additional file 5).

Statistical analysis of HR probe sets in different genotypes and heat treatments

Furthermore, we compared the HR probe sets between different genotypes and heat treatments using three factors in the experimental design based on the nested F-test (for detail, see Methods), namely, genotypes (heat susceptible 'CS' and heat tolerant 'TAM'), heat treatments with or without heat acclimation (sh and h), and short or long treatments (1 hour and 24 hours).

HR probe sets between heat-susceptible 'CS' and heat-tolerant 'TAM': The HR probe sets overlapped substantially between the two genotypes within the same treatment, whereas genotype-specific HR probe sets were also identified (Figure 5). Using nested F-statistic, we found that 47 and 130 probe sets behaved differently between the two genotypes under different heat treatments if the p-value was set to 0.001 and 0.01, respectively. Totally, 313 (P < 0.05) HR probe sets behaved differently between the two genotypes in at least one of the four heat treatments (Table 2), and 18 were in all heat treatments (Table 3), among which 12 and 3 were CS- and TAM-specific, respectively. Among these 313 probe sets, 41 were up- or down regulated in both genotypes. These genes included four HSPs and two splicing coactivator subunit SRm300, which were induced much more in 'CS' than in 'TAM' (Table 3). Except that, they also included protein kinases, protein phosphatase and stress related genes, such as LEAs and peroxidase. For example, phosphatase (PP) 2C was induced for about 4 – fold in CS1h, but for more than 16 – fold in TAM1h, while another protein phosphatase was induced for more than 32 – fold in CS1h, but for 4 – fold in TAM1h. And in Arabidopsis, AtPP7 has been associated with thermotolerance [17]. Up to 159 probe sets were CS-specific responsive genes, including chlorophyll a/b-binding protein and two oxygen evolving enhancer proteins, and all of them were down regulated by heat stress, suggesting that photosynthesis was damaged more severely in the thermo-sensitive wheat than in the tolerant one. We also found that CS-responsive genes included some biotic stress responsive genes encoding beta-1,3-glucanase precursor, chitinase, PR-1 protein, and leucine-rich repeat domain. A few up-regulated genes encoding transcription factors, such as MYB, MADS, WRKY and also an AN1-like zinc finger, which was induced for more than 32 – fold in CS1h, may contribute to thermo-sensitive phenotype of CS. In contrast, the genes that were specifically responsive in TAM may be of great importance for the good performance of a highly thermo-tolerant genotype under heat stress. The 113 HR genes encoding putative ubiquitin protein, ubiquitin activating enzyme and ribosomal protein were responsive only to TAM1h. The former two types of proteins may help degrade toxic proteins during heat stress, while the latter were responsible for protein biosynthesis, which might help thermotolerant wheat maintain essential metabolism under heat stress. It was intriguing to note that some defense response genes were down-regulated in TAM, including pathogen-induced protein WIR1A, pathogenesis-related protein, stripe rust resistance protein, cell wall invertase, defensin, disease resistance protein, and beta-1,3-glucanase precursor. Actually, the down regulation of genes related to pathogenesis or disease in thermotolerant Arabidopsis under heat stress has been reported by other study [18].

Table 2 Number of differentially regulated HR probe sets between wheat genotypes 'CS' and 'TAM107' identified by nestF-statistic method.
Full size table
Table 3 Differentially regulated HR probe sets between all heat treatments in 'CS' and 'TAM107'
Full size table
Figure 5
figure 5

Overlap of the probe sets up- and down-regulated in 'CS' (left) and 'TAM107' (right) in response to heat stresses. (a) 1 sh, (b) 1 h, (c) 24 sh, (d) 24 h.

Full size image

HR probe sets between heat treatments with and without heat acclimation: Heat acclimation under non-lethal high temperature is important for plants to survive at temperature stress that may be lethal [7]. To elucidate the events involved in heat acclimation of wheat, we compared the HR probe sets between the heat-treated samples with and without pre-acclimation. A total of 1314 HR probe sets (See Additional file 6) were differentially regulated between 1 sh and 1 h heat treatments based on nest-F test (p < 0.001), and no probe sets were found to be significantly differentially regulated between 24 sh and 24 h treatments either in magnitude or in direction (Table 4), suggesting that heat acclimation had no effect on expression during the prolonged heat treatment. We focused on the 823 probe sets that were differentially regulated between 1 sh and 1 h both in CS and TAM, considering that they were real difference between treatment with and without pre-heat acclimation. They were further classified into the following functional categories: (1) transcription factors, including C2H2 zinc finger, MYB, HSF, bZIP, NAC, AP2, bHLH and WRKY; (2) RNA metabolism, including RNA polymerase, mRNA capping enzyme, double-stranded RNA binding motif, RNA helicase, RNA recognition motif, RNA processing factor, U3 snoRNP protein, U2 snRNP auxiliary factor, nuclear RNA binding protein and valyl-tRNA synthetase; (3) protein biosynthesis and degradation, including ubiquitin/ribosomal polyprotein, ubiquitin carboxyl-terminal hydrolase, ubiquitin conjugation factor, ribosomal protein and polyubiquitin; and (4) signal transduction, including calcium binding protein, PP2C, protein kinases. The results suggest that heat acclimation affects the expression of genes involved in transcriptional regulation as well as metabolic pathways.

Table 4 Differentially regulated HR probe sets between heat treatments with and without heat acclimation identified by nestF-statistic method.
Full size table

HR probe sets between short (1 hour) and prolonged (24 hours) heat treatment: We also compared the expression profiles between 1-hour and 24-hour heat treatments (See Additional file 7) to identify the genes involved in short and prolonged heat treatments. Nested F-statistic analysis showed that 4533 probe sets (p < 0.001) were differentially regulated between the two treatments (See Additional file 8), accounting for 69% of total HR probe sets. Among these probe sets, 1355 were between CS1sh and CS24sh, and 1278 were between TAM1sh and TAM1h. However, much more probe sets were differentially regulated between 1 h and 24 h, 3426 and 3405 were identified in CS and TAM, respectively. Considering the large number of probe sets, we focused on the 966 probe sets that were differentially regulated between all the four pair-wise comparisons. The genes encoding heat shock proteins and ribosomal proteins were among those genes. Moreover, these probe sets also included genes encoding putative zinc-finger protein, protein phosphatase and kinase, oxidative stress responsive genes (sulfur-rich/thionin-like protein, ferritin, GST, peroxidase), metabolism (GTP-binding protein, chlorophyll a/b-binding protein).

Major functional categories of the HR genes in wheat

A large number of HR genes do not match any genes with know functions probably because the wheat genomic information is very limited. Our further analysis was focused on the HR probe sets that have been assigned to annotated genes and ESTs by HarvEST [19], which account for ~75% of the HR probe sets.

Heat shock proteins (HSPs) and other chaperones were highly induced by heat stress

The induction of heat shock proteins when plants are exposed to elevated temperature has been well documented [20–22]. HSPs function as molecular chaperones in maintaining homeostasis of protein folding and are related to the acquisition of thermotolerance [23]. Coincident with this, 117 probe sets encoding various HSPs were up-regulated by the heat treatments in wheat, and the highest fold change was 11.8 (Log 2 transformed, Figure 6a). We also observed that expression of these HSPs altered more in 1-h heat treatment than in 24-h heat treatment. Seven HSFs were induced by heat stress (Figure 6b), and one (Ta.28772.1.S1_at) was induced for more than 128 – fold in 1 h. However, two probe sets representing HSFs were down-regulated after heat stress. This was inconsistent with studies in Arabidopsis, in which all the six heat responsive HSFs were up-regulated by the 1-hour heat treatment [16]. Peptidyl prolyl cis-trans isomerase is another chaperone family that has been reported to be induced by heat in wheat [24] and OsFKBP20 over-expression in yeast endowed capacity of high temperature tolerance in yeast cells [25]. In this analysis, 18 probe sets encoding peptidyl prolyl cis-trans isomerase were up-regulated by heat treatment, among which one (Ta.639.1.S1_at) was elevated for more than 26-fold in all heat treatments.

Figure 6
figure 6

Expression patterns of probe sets representing genes encoding heat shock proteins and heat shock factors. (a) Heat shock proteins. (b) Heat shock factors.

Full size image

Transcription Factors (TFs) responsive to heat stress

In addition to HSFs, DREB2A was reported to regulate heat tolerance [26] and act as a transcriptional activator of HsfA3 [27]. In our study, two probe sets encoding DREB2B and DREB6A were up-regulated by heat stress. Another three probe sets annotated as ethylene-responsive transcriptional coactivator (ERETC) gene, a member of MBF1 (M ultiprotein b ridging f actor), were up-regulated more than 8-fold in all heat treated samples. Previous study showed that AtMBF1c was strongly induced by both heat stress [12] and a combination of drought and heat stresses [10], and over-expression of the AtMBF1c enhanced the tolerance to both heat and osmotic stresses of Arabidopsis [28]. The strong induction of ERETC expression by heat stress in wheat provided another piece of evidences for its role in thermotolerance.

Many other transcription factor (TF) genes were also affected by heat stress in wheat, although their roles in heat tolerance are not clear. These TF genes included 162 probe sets for zinc-fingers genes, 53 for MYB genes, 11 for AP2 genes, 24 for bZIP genes, 8 for bHLH genes, 8 for NAC or NAM genes, 11 for WRKY genes, and genes for other factors, such as GRAS, MADS box and auxin response factor (ARF). The expression patterns of these HR TF genes were complex in various heat treatments (Table 5). Interestingly, the majority of HR TF genes were responsive in the heat shock treatment without pre-acclimation (1 h), and most of them were down-regulated (Table 5, Figure 7). The data were in agreement with the previous report in Arabidopsis [16]. AN1-like (Ta.5930.1.S1_s_at) and C3HC4-type RING fingers (TaAffx.20445.1.S1_at) genes were strongly up-regulated after 1 h treatment, and C3HC4-type finger (TaAffx.20445.1.S1_at) gene was up-regulated 16-fold in both genotypes. One NAC (Ta.16423.1.S1_at) gene was down-regulated 16-fold in 1 h, 24 sh and 24 h treatments in two genotypes with a less extent in 1 sh treatment. Two probe sets encoding putative WRKY factors were induced more in 1 h than in 1 sh in both genotypes. Intriguingly, one probe set (Ta.4725.1.S1_at) was repressed in TAM1sh but was induced in TAM1h, which may suggest different roles of this gene in acquired and basal thermotolerance. The function of WRKY genes in plant defense responses has been well characterized [29], and the expression of some members of this gene family was also affected by high temperature [30].

Table 5 Numbers of transcription factors up- and down-regulated in different heat treated samples.
Full size table
Figure 7
figure 7

Average linkage hierarchical clustering analysis for probe sets representing genes encoding transcription factors.

Full size image

Phytohormones and wheat heat response

The roles of phytohormones, including abscisic acid (ABA), brassinosteroids (BRs) and ethylene, in heat tolerance have been well documented [20, 31–33]. We found that the candidate genes involved in phytohormone metabolism or signaling, including ABA, auxin, ethylene and gibberellic acid (GA), were regulated by heat stress (See Additional file 9).

ABA-INSENSITIVE2, a component of ABA signaling pathway was up-regulated 7- and 28-fold, respectively, in CS1h and TAM1h. AREB2 and AREB3, also involved in ABA signaling, were down-regulated by the same heat treatments. Three putative homologs of Arabidopsis Arac7 and Arac10, the negative regulator of ABA-mediated signaling [34], were down-regulated by all heat treatments. Four probe sets encoding ABA-induced HVA22 were up-regulated by the heat treatments especially in the 1 h treatment. These data indicated that ABA signaling is involved in the heat shock response in wheat. Additional experiments are needed to determine their roles and downstream pathways in thermotolerance.

A link between ethylene production and heat susceptibility in wheat was reported recently [35]. In our study, we identified several HR genes in ethylene biosynthesis/signaling, including one for ACC synthase, seven for ACC oxidases, 12 for MAPKs and two for CTR1-like kinases. Six probe sets encoding ACC oxidases were down-regulated by heat stress, and one probe set for ACC synthase was also down-regulated by all the heat treatments, suggesting a negative response to ethylene level during heat stress. The candidate genes encoding MAPKs and a CTR1 kinase involved in the ethylene signal pathway were also repressed by 1-h or 24-h heat treatment. Candidate genes responsive to ethylene biosynthesis, such as ERETC just mentioned, chitinase and ER s (ethylene responsive genes), were also among the HR probe sets. A endochitinase-like protein gene was shown to be essential for heat, salt and drought tolerance [36].

The F-box-containing auxin receptors (TIR1) accelerate the 26S proteasome-dependent degradation of short-lived transcriptional repressors, AUX/IAA, which in turn allows ARFs to modulate gene expression by binding to the promoters of auxin regulated genes [37–39]. In this analysis, seven probe sets encoding AUX/IAA proteins were down-regulated by heat stress, whereas three putative HR genes encoding 26S proteasome regulatory subunits were up-regulated. Two genes encoding putative TIR1-like proteins, ARF3 and ARF10, were heat induced, and ARF10 (TaAffx.34454.1.S1_at) was induced for more than 8-fold in 1 sh and 1 h treatments in both genotypes. This implied that a positive response of auxin level to heat stress. Alternatively, high temperature stress may activate auxin signal pathway. Moreover, six probe sets encoding putative expansin were induced by the long heat treatment. The expression level of expansin gene AsEXP1 in different A. scabra genotypes was positively correlated with the level of heat tolerance in this grass species [40], which is reminiscent of the up-regulation of putative expansin genes in heat tolerant TAM compared to heat susceptible CS.

The role of gibberellic acid (GA) in plant thermotolerance is still debatable [41, 42] and GA was thought to play a role in antioxidant pathway [43, 44]. We found that the genes involved in GA biosynthesis, including GA3 and 2OG-Fe oxygenase, were responsive to heat stress (Additional file 9). RGA, the negative regulator of GA signaling, is a member of DELLA protein [45]. A putative wheat RGA was down-regulated by heat stress, and Ta.11162.1.S1_at encoding putative GA regulated protein GAST1 precursor was strongly (log2 > 5.1) induced in all eight heat treatments, suggesting a role in heat response in wheat. Indeed, some members of GASA family in Arabidopsis are associated with heat stress response [22], and over-expression of GASA enhanced the thermotolerance [46].

Calcium signaling

Calcium is a universal signaling molecule in both animals and plants, and the transient increase of Ca2+ level during heat stress was well documented in plants [47]. Heat shock triggers cytosolic Ca2+ bursts, which is transduced by Ca2+ binding proteins (CBP) such as calmodulin (CaM), CaM-related proteins, Ca2+-dependent protein kinases (CDPK), and calcineurin B-like protein (CBL), and then up-regulates the expression of HSPs [47, 48]. In this analysis, candidate genes encoding the components of calcium or calmodulin mediated signal pathway, including annexin, CBPs, CDPKs, voltage-gated calcium channel activity, Ca2+-binding protein EF hand, CBL and CIPK (CBL-interacting protein kinase), were also heat regulated, suggesting a role of Ca2+ mediated signal in wheat heat stress response. Indeed, some of these genes, such as annexin1 [49] and CDPK [13], are induced by heat stress.

Sugar signaling

The metabolomic analysis under high temperature stress indicated that sugar content increased very rapidly and maintained throughout heat treatments [50]. Parallel microarray analysis revealed that the promoters of a number of genes induced by heat shock contained sugar-responsive elements [50], suggesting that sugar signaling is important in the establishment and maintenance of acquired thermotolerance. In wheat, the genes in sugar-mediated signaling were regulated by heat stress. For example, four probe sets encoding the homolog of replication licensing factor MCM7 were induced in 1 h, and one (TaAffx.32695.1.S1_s_at) of them was induced more than 64-fold in CS1h and TAM1h. Five probe sets for glucose-6-phosphate/phosphate-translocator were also regulated by heat stress, and one of them was up-regulated 8-fold or more in CS1h and TAM1h. Eight probe sets for fructokinase, related to sugar sensing in plants [51], were also heat regulated, and five of them were up-regulated. We also found that 22 probe sets for sugar transporters were responsive to heat stress, and three of them were up-regulated.

RNA metabolism

Previous study demonstrated that alternative splicing of pre-mRNAs of Arabidopsis serine/arginine-rich proteins was regulated by heat stress [52]. A glycine rich-RNA binding protein from rice is responsive to high temperature stress, and its over-expression disrupts thermotolerance in wild type yeast cells [53]. Two genes encoding Arabidopsis DEAD-box RNA helicases, STRS1 and STRS2, are the negative regulators of heat tolerance [54]. Expression of 239 probe sets for the genes in RNA metabolism was affected by heat stress in wheat, and nearly half of them encode putative RNA binding proteins, including 12 probe sets for glycine-rich RNA binding proteins, 14 for ribonucleoproteins, and 40 for RNA recognition motif (RRM)-containing proteins. Some probe sets represented the genes related to RNA processing or modification, including RNA polymerase, mRNA capping enzyme, pre-mRNA splicing factor, RNA helicase, RNA methyltransferase, and U2 snRNP auxiliary factor. The genes encoding putative ASF/SF2-like pre-mRNA splicing factors and splicing coactivator subunit SRm300 were strongly induced by all heat treatments. Specifically, 43 HR probe sets were involved in tRNA synthesis, such as aspartyl-tRNA synthetase, trytophanyl-tRNA synthetase and phenylalanyl-tRNA synthetase. tRNAs are responsible for the transportation of amino acid during protein biosynthesis. We found that the expression levels of 41 probe sets for tRNAs were increased during 24-h heat treatments, suggesting an increased level of protein biosynthesis during the long-term heat stress. Significantly, several genes involved in the biogenesis of siRNA and miRNA, including Dicer homolog, AGO1, piwi, and dsRNA-binding proteins, were also regulated by heat stress, Several microRNA (miRNA) target genes, such as SPL2 (miR156) [55] and ARF16 (miR160) [56], were down-regulated, suggesting a role of miRNAs in heat response pathway in plants.

Ribosomal proteins

Ribosomal proteins play an important role in escaping protein synthesis from being inhibited by heat stress [57, 58]. We found that 264 probe sets for ribosomal protein were up-regulated under the long-term heat treatment (Figure 8). Notably, other components of the translation system, such as the genes encoding translation initiation factors and translation elongation factors, were also induced in the late time of heat treatments, indicating that protein synthesis is responsive to heat stress, especially in the long-term heat conditions.

Figure 8
figure 8

Expression pattern of probe sets representing genes encoding ribosomal proteins.

Full size image

Extensive remodeling of metabolism under heat stress

Metabolites can act as signaling/regulatory agents, compatible solutes, and antioxidants or in defense against pathogens [50]. Temperature is one of the most active environmental factors that affect all plant metabolic activities, including amino acid and carbohydrate metabolism [50]. Secondary metabolites is involved in resistance against heat shock [59]. We found that the genes functioning in primary or secondary metabolisms were affected by heat stress.

Photosynthesis is one of the most heat-sensitive processes. The damage of photosystem II (PSII) and Rubisco activity by high temperature is irreversible [11]. We found that 21 probe sets for chlorophyll a/b-binding protein were repressed by heat stress especially in the 24-hour heat treatments, which suggesting that long-term heat stress damaged the photosystems.

The gene shuffling technology has been applied to generate several Arabidopsis thaliana RCA1 (Rubisco Activase 1) variants, and the transgenic lines with the thermostable RCA1 variants exhibited higher photosynthetic rates, improved development patterns, higher biomass, and increased seed yields [60]. Under heat stress, we found that the RuBisCO activase B gene was induced more in TAM than in CS. The genes encoding PSI reaction center subunits were also repressed by heat stress, especially in CS24h, suggesting that a severe damage is caused by high temperature in the thermo-susceptible wheat. Other genes responsible for photosynthesis, such as oxygen evolving enhancer protein were also down-regulated by heat stress. In addition, plant respiration was also a heat sensitive physiological process. We found that 40 genes encoding electron transporters, including cytochrome b5, Glutaredoxin, disulfide-isomerase and rubredoxin, were affected by heat stress. Cytochrome P450s have been suggested to play roles in protecting organisms from oxidative damage, and the induction of P450 is related to different biotic and abiotic stresses [61]. In this study, 30 probe sets for P450 were heat responsive, and they may play a role in the detoxification during heat stress.

Several probe sets representing genes related to carbohydrate metabolism were regulated by heat stress in our experiment, including sugar and trehalose metabolism, such as sugar transporters, sucrose transporters, trehalose-6-phosphate phosphatase (TPP), trehalose-phosphate synthase (TPS), and cell wall invertase. Trehalose not only acts as a carbohydrate reserve but also plays an important role in plant abiotic stress tolerance [62]. TPP and TPS are known to be the key enzymes in the biosynthesis of trehalose [63–65]. Indeed, TPP is induced by heat stress, and TPP complements the default thermotolerance of TPS yeast mutant, a TPP homolog in other organisms [66].

Lipid metabolism is also heat regulated in this study. We found that 22 probe sets encoding lipid transfer proteins were regulated by long-term heat stress. In Arabidopsis, fad7fad8 double mutant was resistant to high temperature [67]. In our study, a wheat FAD3 was down-regulated in CS1sh and TAM1sh, indicating a role of pre-acclimation in heat stress probably through an increase in the fluidity of cell membrane under high temperature.

Genes related to other abiotic stresses are also regulated by heat stress

Heat stress can induce oxidative stress through the formation of free radicals [50] and reactive oxygen species (ROS). There is inter-linking between heat and oxidative stress responses [33]. Arabidopsis lacking thylakoid ascorbate peroxidase (tylapx) has enhanced basal thermotolerance [68]. We found that the HR genes include those encoding putative NADPH oxidase, mitochondrial alternative oxidase (AOX), germin-like oxalate oxidases and amine oxidases, which are related to ROS production [69]. The genes encoding enzymatic components of ROS-scavenging pathways, including ascorbate peroxidase, superoxide dismutase, glutathione-S-transferase (GST), glutathione peroxidase, ferritin and thioredoxin [69], were also affected by heat stress, suggesting that a link between these two stress signaling pathways. Two of four putative NADPH oxidase genes were induced by 24-h heat stress, but the other two were repressed by 1-h treatments, suggesting induction of oxidative stress by long-term heat treatments. Most of the genes involved in ROS-scavenging pathway, including AOXs, APX3, glutathione peroxidases, thioredoxin, ferritin and thioredoxin, showed higher expression levels during the short-term heat shock than the long-term heat treatments. The data suggest that plants can scavenge the ROS generated by heat shock, but the oxidative stress induced by long-term heat stress may impair their ability to generate these beneficial molecules.

The effects of drought and heat stress on cereals are interlinked [70]. We found that some HR probe sets were assigned to be responsive to drought or desiccation. The genes encoding putative osmoprotectants, such as mannitol, trehalose and late embryogenesis abundant (LEA) proteins, were affected by high temperature, suggesting a common mechanism for heat, drought and other osmotic stresses. Moreover, some major intrinsic protein (MIP), tonoplast intrinsic protein (TIP) and ion transporters may also play an important role because some HR probe sets for these proteins were affected by heat stress.

Conclusion

Using Affymetrix Wheat Genome Array, we found that ~11% (6560) of total probe sets were responsive to short and prolonged heat stress treatments in wheat. First, the two wheat genotypes with contrasting heat stress responses displayed different gene expression changes in short and prolonged heat treatments. Second, heat acclimation had minor effects on gene expression in the prolonged heat treatment but had measurable effects on gene expression in the short-term heat stress. Third, different genes and pathways were up- or down-regulated under the short and prolonged heat treatments, and more genes in metabolism and protein synthesis were affected by the prolonged heat treatment than the heat shock. Fourth, the heat responsive genes identified in this study belong to a large number of important factors and biological pathways, including HSPs, transcription factors, phytohormone biosynthesis/signaling, calcium and sugar signal pathways, RNA metabolism, ribosomal proteins, primary and secondary metabolisms, and biotic and abiotic stresses. We have identified several sets of common genes and factors as well as novel genes responsive to heat stress, which may help elucidate molecular bases for thermotolerance and heat tolerance in wheat and other plant species.

Methods

Plant materials and heat treatments

Two wheat genotypes, heat susceptible 'Chinese Spring' (CS) and heat tolerant 'TAM107' were used in this study. Seeds were surface-sterilized in 1% sodium hypochlorite for 15 min, rinsed in distilled water, and soaked in dark overnight at room temperature. The germinated seeds were transferred into the pots (25 seedlings per pot) containing vermiculite. A total of 75 seedlings in three pots were used for each treatment, and three independent biological replications were employed. Prior to heat treatments, the seedlings were grown for 10 days in a growth chamber with 22°C/18°C (day/night), 12 h/12 h (light/dark), and 60% humidity. All heat treatments were carried out at the beginning of the light cycle to avoid circadian rhythm variation [71]. The seedlings that were subjected to 40°C (heat stress) for 1 hour with and without heat acclimation (34°C, 3 hours) designated 1 sh and 1 h, respectively, and the seedlings that were subjected to 40°C for 24 hours with and without heat acclimation (34°C, 3 hours) designated 24 sh and 24 h, respectively (Figure 1). At the end of heat treatments, the leaves of the seedlings from the three pots were pooled together as one biological replicate and frozen immediately in the liquid nitrogen, and stored at -80°C for further use. A total of 30 samples including controls were prepared from three independent biological replications.

Microarray analysis

Total RNA was extracted using TRIzol reagent (Invitrogen) following the manufacturer's recommendations. Briefly, mRNA was enriched from 80~90 μg total RNA using the RNeasy Plant Mini Kit (QIAGEN) according to the instructions and was subsequently reverse-transcribed to double stranded cDNA using the GeneChip® Two-Cycle cDNA Synthesis Kit. The biotin-labeled cRNA was made using the GeneChip® IVT Labelling Kit (Affymetrix, CA, USA). Twenty micrograms of cRNA samples were fragmented and hybridized for 16 hours at 45°C to the Affymetrix Wheat Genome Array (Santa Clara, CA, USA). After washing using the Genechip® Fluidics Station 450, the arrays were scanned using the Genechip® 3000 Scanner that is located in the Bioinformatics Center at China Agricultural University. The microarray data for this paper have been deposited in the public repository of ArrayExpress [72] (ArrayExpress accession Number: E-MEXP-1523).

Data analysis

The chip images were scanned and extracted using default settings and the CEL files were produced with the Affymetrix GeneChip Operating Software (GCOS 1.2). The resulting CEL files were imported into the Bioconductor (version 2.0) [73] in R 2.5.1 statistical environment. Subsequent background adjustment, normalization of the raw data and estimation of probe sets signal intensities were done using GC-RMA method [74]. In order to assess the reproducibility of the experiment, the normalized signal intensities from 3 replications of each sample were used to calculate correlation coefficients.

To identify HR probe sets, the probe sets prefixed with "AFFX" and "RPTR" were removed and filtered by fraction call 100% suggested by McClintick and Edenberg [75]. A total of 26,669 probe sets that were 'present' in at least one sample were selected for analysis. Fold changes and p-values of probe sets were calculated using limma [76] nested F-test, and the p-values for multiple testing were corrected using the false discovery rate (FDR) [77]. As a result, 6560 were identified as HR probe sets (cut-off: adjusted p-value = 0.001, log2FoldChange = 1).

The identification of HR probe sets that were significantly different in expression between two of the three factors (genotypes, heat treatments with and without acclimation, 1-hour and 24-hour heat treatments) was performed using the same method as used in the identification of HR probe sets. The fold change was 2 or larger, and the FDR adjusted p-values were 0.05, 0.001, and 0.001, respectively.

Annotations of the wheat probe sets were obtained from HarvEST: Affymetrix Wheat 1 Array 1.12 [19]. Cluster software [78] was used to perform hierarchical clustering analysis using Euclidean Distance and average linkage method to create probe sets and sample trees. JAVA Treeview [79] was used to view cluster image.

Quantitative Real-Time PCR (QRT-PCR) Analysis

A portion of the seedling leaves that were used for microarray analysis was used for qRT-PCR analysis to validate the expression patterns. Total RNA was extracted using TRIzol reagent (Invitrogen). Two microgram total RNA was used to synthesize first-strand cDNA using oligo (dT)18 primer with M-MLV reverse transcriptase (Promega, USA) according to the manufacturer's instructions. The product of reverse transcription was tested by amplifying a wheat actin gene fragment (330-bp), which was used as the endogenous control. PCR primers were designed using DNAMAN software, and the primer pairs used to amplify probe set-specific products were listed in Additional file 10. QRT-PCR was performed using the cDNA samples in a 10-μL mixture containing 1 × LightCycler-FastStart DNA master SYBR Green I. PCR was performed as follows: initial denaturation for 10 min at 95°C, followed by 40 cycles of 30 s at 95°C, 45 s at 55 to 60°C, 10 s at 72°C with a final extension at 72°C for 5 min. The threshold cycles (Ct) of each test target were averaged for triplicate reactions, and the values were normalized according to the Ct of the control products (Ta-actin). Each PCR product was evaluated in at least two independent experiments.

References

  1. Chen J, Burke JJ, Velten J, Xin Z: FtsH11 protease plays a critical role in Arabidopsis thermotolerance. Plant J. 2006, 48 (1): 73-84. 10.1111/j.1365-313X.2006.02855.x.

    Article  CAS  Google Scholar 

  2. Mullarkey M, Jones P: Isolation and analysis of thermotolerant mutants of wheat. J Exp Bot. 2000, 51 (342): 139-146. 10.1093/jexbot/51.342.139.

    Article  CAS  Google Scholar 

  3. Maestri E, Klueva N, Perrotta C, Gulli M, Nguyen HT, Marmiroli N: Molecular genetics of heat tolerance and heat shock proteins in cereals. Plant Mol Biol. 2002, 48 (5–6): 667-681. 10.1023/A:1014826730024.

    Article  CAS  Google Scholar 

  4. Wardlaw IF, Willenbrink J: Carbohydrate storage and mobilisation by the culm of wheat between heading and grain maturity: the relation to sucrose synthase and sucrose-phosphate synthase. Functional Plant Biol. 1994, 21: 255-271. 10.1071/PP9940255.

    CAS  Google Scholar 

  5. Wiegand CL, Cuellar JA: Duration of Grain Filling and Kernel Weight of Wheat as Affected by Temparature. Crop Sci. 1981, 21 (1): 95-101.

    Article  Google Scholar 

  6. Blumenthal C, Bekes F, Gras PW, Barlow EWR, Wrigley CW: Identification of wheat genotypes tolerant to the effects of heat stress on grain quality. Cereal chemistry. 1995, 72: 539-544.

    CAS  Google Scholar 

  7. Hong SW, Lee U, Vierling E: Arabidopsis hot mutants define multiple functions required for acclimation to high temperatures. Plant Physiol. 2003, 132 (2): 757-767. 10.1104/pp.102.017145.

    Article  CAS  PubMed Central  Google Scholar 

  8. Larkindale J, Hall JD, Knight MR, Vierling E: Heat stress phenotypes of Arabidopsis mutants implicate multiple signaling pathways in the acquisition of thermotolerance. Plant Physiol. 2005, 138 (2): 882-897. 10.1104/pp.105.062257.

    Article  CAS  PubMed Central  Google Scholar 

  9. Yang KA, Lim CJ, Hong JK, Park CY, Cheong YH, Chung WS, Lee KO, Lee SY, Cho MJ, Lim CO: Identification of cell wall genes modified by a permissive high temperature in Chinese cabbage. Plant Science. 2006, 171 (1): 175-182. 10.1016/j.plantsci.2006.03.013.

    Article  CAS  Google Scholar 

  10. Rizhsky L, Liang H, Shuman J, Shulaev V, Davletova S, Mittler R: When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress. Plant Physiol. 2004, 134 (4): 1683-1696. 10.1104/pp.103.033431.

    Article  CAS  PubMed Central  Google Scholar 

  11. Zhang Y, Mian MA, Chekhovskiy K, So S, Kupfer D, Lai H, Roe BA: Differential gene expression in Festuca under heat stress conditions. J Exp Bot. 2005, 56 (413): 897-907. 10.1093/jxb/eri082.

    Article  CAS  Google Scholar 

  12. Oshino T, Abiko M, Saito R, Ichiishi E, Endo M, Kawagishi-Kobayashi M, Higashitani A: Premature progression of anther early developmental programs accompanied by comprehensive alterations in transcription during high-temperature injury in barley plants. Mol Genet Genomics. 2007, 278 (1): 31-42. 10.1007/s00438-007-0229-x.

    Article  CAS  Google Scholar 

  13. Peng DH, Peng HR, Ni ZF, Nie XL, Yao YY, Qin DD, He KX, Sun QX: Heat stress-responsive transcriptome analysis of wheat by using GeneChip Barley1 Genome Array. Progress in Natural Science. 2006, 16: 1379-1387. 10.1080/10020070612330037.

    Article  Google Scholar 

  14. Mochida K, Kawaura K, Shimosaka E, Kawakami N, Shin IT, Kohara Y, Yamazaki Y, Ogihara Y: Tissue expression map of a large number of expressed sequence tags and its application to in silico screening of stress response genes in common wheat. Mol Genet Genomics. 2006, 276 (3): 304-312. 10.1007/s00438-006-0120-1.

    Article  CAS  Google Scholar 

  15. Sun QX, Quick JS: Chromosomal locations of genes for heat tolerance in tetraploid wheat. Cer Res Commun. 1991, 19: 431-437.

    Google Scholar 

  16. Busch W, Wunderlich M, Schoffl F: Identification of novel heat shock factor-dependent genes and biochemical pathways in Arabidopsis thaliana. Plant J. 2005, 41 (1): 1-14. 10.1111/j.1365-313X.2004.02272.x.

    Article  CAS  Google Scholar 

  17. Liu H-T, Li G-L, Chang HUI, Sun D-Y, Zhou R-G, Li B: Calmodulin-binding protein phosphatase PP7 is involved in thermotolerance in Arabidopsis. Plant, Cell & Environment. 2007, 30 (2): 156-164. 10.1111/j.1365-3040.2006.01613.x.

    Article  CAS  Google Scholar 

  18. Larkindale J, Vierling E: Core genome responses involved in acclimation to high temperature. Plant Physiol. 2008, 146 (2): 748-761. 10.1104/pp.107.112060.

    Article  CAS  PubMed Central  Google Scholar 

  19. [http://harvest.ucr.edu/]

  20. Kotak S, Larkindale J, Lee U, von Koskull-Doring P, Vierling E, Scharf KD: Complexity of the heat stress response in plants. Curr Opin Plant Biol. 2007, 10 (3): 310-316. 10.1016/j.pbi.2007.04.011.

    Article  CAS  Google Scholar 

  21. Lee JH, Hubel A, Schoffl F: Derepression of the activity of genetically engineered heat shock factor causes constitutive synthesis of heat shock proteins and increased thermotolerance in transgenic Arabidopsis. Plant J. 1995, 8 (4): 603-612. 10.1046/j.1365-313X.1995.8040603.x.

    Article  CAS  Google Scholar 

  22. Ogawa D, Yamaguchi K, Nishiuchi T: High-level overexpression of the Arabidopsis HsfA2 gene confers not only increased themotolerance but also salt/osmotic stress tolerance and enhanced callus growth. J Exp Bot. 2007, 58 (12): 3373-3383. 10.1093/jxb/erm184.

    Article  CAS  Google Scholar 

  23. Wang W, Vinocur B, Shoseyov O, Altman A: Role of plant heat-shock proteins and molecular chaperones in the abiotic stress response. Trends Plant Sci. 2004, 9 (5): 244-252. 10.1016/j.tplants.2004.03.006.

    Article  CAS  Google Scholar 

  24. Kurek I, Aviezer K, Erel N, Herman E, Breiman A: The Wheat Peptidyl Prolyl cis-trans-Isomerase FKBP77 Is Heat Induced and Developmentally Regulated. Plant Physiol. 1999, 119 (2): 693-704. 10.1104/pp.119.2.693.

    Article  CAS  PubMed Central  Google Scholar 

  25. Nigam N, Singh A, Sahi C, Chandramouli A, Grover A: SUMO-conjugating enzyme (Sce) and FK506-binding protein (FKBP) encoding rice (Oryza sativa L.) genes: genome-wide analysis, expression studies and evidence for their involvement in abiotic stress response. Mol Genet Genomics. 2008, 279 (4): 371-383. 10.1007/s00438-008-0318-5.

    Article  CAS  Google Scholar 

  26. Qin F, Kakimoto M, Sakuma Y, Maruyama K, Osakabe Y, Tran LS, Shinozaki K, Yamaguchi-Shinozaki K: Regulation and functional analysis of ZmDREB2A in response to drought and heat stresses in Zea mays L. Plant J. 2007, 50 (1): 54-69. 10.1111/j.1365-313X.2007.03034.x.

    Article  CAS  Google Scholar 

  27. Schramm F, Larkindale J, Kiehlmann E, Ganguli A, Englich G, Vierling E, von Koskull-Doring P: A cascade of transcription factor DREB2A and heat stress transcription factor HsfA3 regulates the heat stress response of Arabidopsis. Plant J. 2008, 53 (2): 264-274. 10.1111/j.1365-313X.2007.03334.x.

    Article  CAS  Google Scholar 

  28. Suzuki N, Rizhsky L, Liang H, Shuman J, Shulaev V, Mittler R: Enhanced tolerance to environmental stress in transgenic plants expressing the transcriptional coactivator multiprotein bridging factor 1c. Plant Physiol. 2005, 139 (3): 1313-1322. 10.1104/pp.105.070110.

    Article  CAS  PubMed Central  Google Scholar 

  29. Liu X, Bai X, Wang X, Chu C: OsWRKY71, a rice transcription factor, is involved in rice defense response. J Plant Physiol. 2007, 164 (8): 969-979. 10.1016/j.jplph.2006.07.006.

    Article  CAS  Google Scholar 

  30. Qiu YP, Jing SJ, Fu J, Li L, Yu DQ: Cloning and analysis of expression profile of 13 WRKY genes in rice. Chinese Science Bulletin. 2004, 49: 2159-2168. 10.1360/982004-183.

    CAS  Google Scholar 

  31. Robertson AJ, Ishikawa M, Gusta LV, MacKenzie SL: Abscisic acid-induced heat tolerance in Bromus inermis Leyss cell-suspension cultures. Heat-stable, abscisic acid-responsive polypeptides in combination with sucrose confer enhanced thermostability. Plant Physiol. 1994, 105 (1): 181-190. 10.1104/pp.105.1.181.

    Article  CAS  PubMed Central  Google Scholar 

  32. Dhaubhadel S, Browning KS, Gallie DR, Krishna P: Brassinosteroid functions to protect the translational machinery and heat-shock protein synthesis following thermal stress. Plant J. 2002, 29 (6): 681-691. 10.1046/j.1365-313X.2002.01257.x.

    Article  CAS  Google Scholar 

  33. Larkindale J, Knight MR: Protection against heat stress-induced oxidative damage in Arabidopsis involves calcium, abscisic acid, ethylene, and salicylic acid. Plant Physiol. 2002, 128 (2): 682-695. 10.1104/pp.128.2.682.

    Article  CAS  PubMed Central  Google Scholar 

  34. Brady SM, Sarkar SF, Bonetta D, McCourt P: The ABSCISIC ACID INSENSITIVE 3 (ABI3) gene is modulated by farnesylation and is involved in auxin signaling and lateral root development in Arabidopsis. The Plant Journal. 2003, 34 (1): 67-75. 10.1046/j.1365-313X.2003.01707.x.

    Article  CAS  Google Scholar 

  35. Hays DB, Do JH, Mason RE, Morgan G, Finlayson SA: Heat stress induced ethylene production in developing wheat grains induces kernel abortion and increased maturation in a susceptible cultivar. Plant Science. 2007, 172 (6): 1113-1123. 10.1016/j.plantsci.2007.03.004.

    Article  CAS  Google Scholar 

  36. Kwon Y, Kim SH, Jung MS, Kim MS, Oh JE, Ju HW, Kim KI, Vierling E, Lee H, Hong SW: Arabidopsis hot2 encodes an endochitinase-like protein that is essential for tolerance to heat, salt and drought stresses. Plant J. 2007, 49 (2): 184-193. 10.1111/j.1365-313X.2006.02950.x.

    Article  CAS  Google Scholar 

  37. Kepinski S, Leyser O: The Arabidopsis F-box protein TIR1 is an auxin receptor. Nature. 2005, 435 (7041): 446-451. 10.1038/nature03542.

    Article  CAS  Google Scholar 

  38. Dharmasiri N, Estelle M: Auxin signaling and regulated protein degradation. Trends Plant Sci. 2004, 9 (6): 302-308. 10.1016/j.tplants.2004.04.003.

    Article  CAS  Google Scholar 

  39. Gray WM, del Pozo JC, Walker L, Hobbie L, Risseeuw E, Banks T, Crosby WL, Yang M, Ma H, Estelle M: Identification of an SCF ubiquitin-ligase complex required for auxin response in Arabidopsis thaliana. Genes Dev. 1999, 13 (13): 1678-1691. 10.1101/gad.13.13.1678.

    Article  CAS  PubMed Central  Google Scholar 

  40. Xu J, Tian J, Belanger FC, Huang B: Identification and characterization of an expansin gene AsEXP1 associated with heat tolerance in C3 Agrostis grass species. J Exp Bot. 2007, 58 (13): 3789-3796. 10.1093/jxb/erm229.

    Article  CAS  Google Scholar 

  41. Vettakkorumakankav NN, Falk D, Saxena P, Fletcher RA: A Crucial Role for Gibberellins in Stress Protection of Plants. Plant Cell Physiol. 1999, 40 (5): 542-548.

    Article  CAS  Google Scholar 

  42. Chen Y-M, Kamisaka S, Masuda Y: Enhancing effects of heat shock and gibberellic acid on the thermotolerance in etiolated Vigna radiata. I. Physiological aspects on thermotolerance. Physiologia Plantarum. 1986, 66 (4): 595-601. 10.1111/j.1399-3054.1986.tb05585.x.

    Article  CAS  Google Scholar 

  43. Wigoda N, Ben-Nissan G, Granot D, Schwartz A, Weiss D: The gibberellin-induced, cysteine-rich protein GIP2 from Petunia hybrida exhibits in planta antioxidant activity. Plant J. 2006, 48 (5): 796-805. 10.1111/j.1365-313X.2006.02917.x.

    Article  CAS  Google Scholar 

  44. Sarkar S, Perras MR, Falk DE, Zhang R, Pharis RP, Austin FR: Relationship between gibberellins, height, and stress tolerance in barley (Hordeum vulgare L.) seedlings. Plant Growth Regulation. 2004, 42: 125-135. 10.1023/B:GROW.0000017492.56792.64.

    Article  CAS  Google Scholar 

  45. Peng J, Carol P, Richards DE, King KE, Cowling RJ, Murphy GP, Harberd NP: The Arabidopsis GAI gene defines a signaling pathway that negatively regulates gibberellin responses. Genes Dev. 1997, 11 (23): 3194-3205. 10.1101/gad.11.23.3194.

    Article  CAS  PubMed Central  Google Scholar 

  46. Ko CB, Woo YM, Lee DJ, Lee MC, Kim CS: Enhanced tolerance to heat stress in transgenic plants expressing the GASA4 gene. Plant Physiol Biochem. 2007, 45 (9): 722-728. 10.1016/j.plaphy.2007.07.010.

    Article  CAS  Google Scholar 

  47. Liu HT, Li B, Shang ZL, Li XZ, Mu RL, Sun DY, Zhou RG: Calmodulin is involved in heat shock signal transduction in wheat. Plant Physiol. 2003, 132 (3): 1186-1195. 10.1104/pp.102.018564.

    Article  CAS  PubMed Central  Google Scholar 

  48. Takatsuji H, Mori M, Benfey PN, Ren L, Chua NH: Characterization of a zinc finger DNA-binding protein expressed specifically in Petunia petals and seedlings. EMBO J. 1992, 11 (1): 241-249.

    CAS  PubMed Central  Google Scholar 

  49. Rhee HJ, Kim G-Y, Huh JW, Kim S-W, Na DS: Annexin I is a stress protein induced by heat, oxidative stress and a sulfhydryl-reactive agent. European Journal of Biochemistry. 2000, 267 (11): 3220-3225. 10.1046/j.1432-1327.2000.01345.x.

    Article  CAS  Google Scholar 

  50. Kaplan F, Kopka J, Haskell DW, Zhao W, Schiller KC, Gatzke N, Sung DY, Guy CL: Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol. 2004, 136 (4): 4159-4168. 10.1104/pp.104.052142.

    Article  CAS  PubMed Central  Google Scholar 

  51. Rolland F, Moore B, Sheen J: Sugar sensing and signaling in plants. Plant Cell. 2002, 14 (Suppl): S185-205.

    CAS  PubMed Central  Google Scholar 

  52. Palusa SG, Ali GS, Reddy AS: Alternative splicing of pre-mRNAs of Arabidopsis serine/arginine-rich proteins: regulation by hormones and stresses. Plant J. 2007, 49 (6): 1091-1107.

    Article  CAS  Google Scholar 

  53. Sahi C, Agarwal M, Singh A, Grover A: Molecular characterization of a novel isoform of rice (Oryza sativa L.) glycine rich-RNA binding protein and evidence for its involvement in high temperature stress response. Plant Science. 2007, 173 (2): 144-155. 10.1016/j.plantsci.2007.04.010.

    Article  CAS  Google Scholar 

  54. Kant P, Kant S, Gordon M, Shaked R, Barak S: STRESS RESPONSE SUPPRESSOR1 and STRESS RESPONSE SUPPRESSOR2, two DEAD-box RNA helicases that attenuate Arabidopsis responses to multiple abiotic stresses. Plant Physiol. 2007, 145 (3): 814-830. 10.1104/pp.107.099895.

    Article  CAS  PubMed Central  Google Scholar 

  55. Schwarz S, Grande AV, Bujdoso N, Saedler H, Huijser P: The microRNA regulated SBP-box genes SPL9 and SPL15 control shoot maturation in Arabidopsis. Plant Mol Biol. 2008, 67: 1-2. 10.1007/s11103-008-9310-z.

    Article  Google Scholar 

  56. Wang JW, Wang LJ, Mao YB, Cai WJ, Xue HW, Chen XY: Control of root cap formation by MicroRNA-targeted auxin response factors in Arabidopsis. Plant Cell. 2005, 17 (8): 2204-2216. 10.1105/tpc.105.033076.

    Article  CAS  PubMed Central  Google Scholar 

  57. Kim YK, Jang SK: Continuous heat shock enhances translational initiation directed by internal ribosomal entry site. Biochem Biophys Res Commun. 2002, 297 (2): 224-231. 10.1016/S0006-291X(02)02154-X.

    Article  CAS  Google Scholar 

  58. Duncan RF, Hershey JW: Protein synthesis and protein phosphorylation during heat stress, recovery, and adaptation. J Cell Biol. 1989, 109 (4 Pt 1): 1467-1481. 10.1083/jcb.109.4.1467.

    Article  CAS  Google Scholar 

  59. Zu YG, Tang ZH, Yu JH, Liu SG, Wang W, Guo XR: Different responses of camptothecin and 10-hydroxycamptothecin to heat shock in Camptotheca acuminate seedlings. Acta Botanica Sinica. 2003, 45: 809-814.

    CAS  Google Scholar 

  60. Kurek I, Chang TK, Bertain SM, Madrigal A, Liu L, Lassner MW, Zhu G: Enhanced Thermostability of Arabidopsis Rubisco activase improves photosynthesis and growth rates under moderate heat stress. Plant Cell. 2007, 19 (10): 3230-3241. 10.1105/tpc.107.054171.

    Article  CAS  PubMed Central  Google Scholar 

  61. Guo P, Bai G, Carver B, Li R, Bernardo A, Baum M: Transcriptional analysis between two wheat near-isogenic lines contrasting in aluminum tolerance under aluminum stress. Mol Genet Genomics. 2007, 277 (1): 1-12. 10.1007/s00438-006-0169-x.

    Article  CAS  Google Scholar 

  62. Strom AR, Kaasen I: Trehalose metabolism in Escherichia coli: stress protection and stress regulation of gene expression. Mol Microbiol. 1993, 8 (2): 205-210. 10.1111/j.1365-2958.1993.tb01564.x.

    Article  CAS  Google Scholar 

  63. Elbein AD, Pan YT, Pastuszak I, Carroll D: New insights on trehalose: a multifunctional molecule. Glycobiology. 2003, 13 (4): 17R-27R. 10.1093/glycob/cwg047.

    Article  CAS  Google Scholar 

  64. Crowe JH, Crowe LM, Chapman D: Preservation of Membranes in Anhydrobiotic Organisms: The Role of Trehalose. Science. 1984, 223 (4637): 701-703. 10.1126/science.223.4637.701.

    Article  CAS  Google Scholar 

  65. Garg AK, Kim JK, Owens TG, Ranwala AP, Choi YD, Kochian LV, Wu RJ: Trehalose accumulation in rice plants confers high tolerance levels to different abiotic stresses. Proc Natl Acad Sci USA. 2002, 99 (25): 15898-15903. 10.1073/pnas.252637799.

    Article  CAS  PubMed Central  Google Scholar 

  66. Wang YJ, Hao YJ, Zhang ZG, Chen T, Zhang JS, Chen SY: Isolation of trehalose-6-phosphate phosphatase gene from tobacco and its functional analysis in yeast cells. J Plant Physiol. 2005, 162 (2): 215-223. 10.1016/j.jplph.2004.06.014.

    Article  CAS  Google Scholar 

  67. Murakami Y, Tsuyama M, Kobayashi Y, Kodama H, Iba K: Trienoic fatty acids and plant tolerance of high temperature. Science. 2000, 287 (5452): 476-479. 10.1126/science.287.5452.476.

    Article  CAS  Google Scholar 

  68. Miller G, Suzuki N, Rizhsky L, Hegie A, Koussevitzky S, Mittler R: Double mutants deficient in cytosolic and thylakoid ascorbate peroxidase reveal a complex mode of interaction between reactive oxygen species, plant development, and response to abiotic stresses. Plant Physiol. 2007, 144 (4): 1777-1785. 10.1104/pp.107.101436.

    Article  CAS  PubMed Central  Google Scholar 

  69. Mittler R, Vanderauwera S, Gollery M, Van Breusegem F: Reactive oxygen gene network of plants. Trends Plant Sci. 2004, 9 (10): 490-498. 10.1016/j.tplants.2004.08.009.

    Article  CAS  Google Scholar 

  70. Barnabas B, Jager K, Feher A: The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 2008, 31 (1): 11-38.

    CAS  Google Scholar 

  71. Lee BH, Henderson DA, Zhu JK: The Arabidopsis cold-responsive transcriptome and its regulation by ICE1. Plant Cell. 2005, 17 (11): 3155-3175. 10.1105/tpc.105.035568.

    Article  CAS  PubMed Central  Google Scholar 

  72. ArrayExpress. [http://www.ebi.ac.uk/arrayexpress]

  73. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.

    Article  PubMed Central  Google Scholar 

  74. Wu ZJ, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F: A model-based background adjustment for oligonucleotide expression arrays. Journal of the American Statistical Association. 2004, 99: 909-917. 10.1198/016214504000000683.

    Article  Google Scholar 

  75. McClintick JN, Edenberg HJ: Effects of filtering by Present call on analysis of microarray experiments. BMC Bioinformatics. 2006, 7: 49-10.1186/1471-2105-7-49.

    Article  PubMed Central  Google Scholar 

  76. Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: Article3-

    Google Scholar 

  77. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995, 57: 289-300.

    Google Scholar 

  78. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.

    Article  CAS  PubMed Central  Google Scholar 

  79. Page RD: TreeView: an application to display phylogenetic trees on personal computers. Comput Appl Biosci. 1996, 12 (4): 357-358.

    CAS  Google Scholar 

Download references

Acknowledgements

We thank Mr. Jidong Feng and Mrs. Hong Yan in the Functional Genomics Center at China Agricultural University for their assistance in microarray experiments. The work was supported by 863 Project of China (2007A10Z100) and National Natural Science Foundation of China (30771343)

Author information

Author notes

    Authors and Affiliations

    1. Department of Plant Genetics & Breeding and State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, 100193, PR China

      Dandan Qin, Haiyan Wu, Huiru Peng, Yingyin Yao, Zhongfu Ni, Zhenxing Li, Chunlei Zhou & Qixin Sun

    2. Key Laboratory of Crop Heterosis and Utilization (MOE), Key Laboratory of Crop Genomics and Genetic Improvement (MOA) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193, PR China

      Dandan Qin, Haiyan Wu, Huiru Peng, Yingyin Yao, Zhongfu Ni, Zhenxing Li, Chunlei Zhou & Qixin Sun

    Authors
    1. Dandan Qin
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Haiyan Wu
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Huiru Peng
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Yingyin Yao
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Zhongfu Ni
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Zhenxing Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Chunlei Zhou
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Qixin Sun
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Qixin Sun.

    Additional information

    Authors' contributions

    DDQ performed the microarray experiments and drafted the manuscript, HYW performed computational analysis of the array data, HRP and CLZ grew wheat plants and performed heat stress experiments, ZXL performed qRT-PCR assays, ZFN and YYY analyzed the data, and QXS designed the experiments and revised the manuscript. All authors have read and approved the final manuscript.

    Dandan Qin, Haiyan Wu, Huiru Peng contributed equally to this work.

    Electronic supplementary material

    Additional file 1:Correlation coefficient of any two of the three replications of the 10 samples.(DOC 54 KB)

    12864_2007_1625_MOESM2_ESM.xls

    Additional file 2:Log2 values of the 6560 HR probe sets in different heat treatment as compared to the untreated control samples.(XLS 2 MB)

    12864_2007_1625_MOESM3_ESM.xls

    Additional file 3:Classification of HR probe sets based on expression patterns in different genotypes and heat treatments.(XLS 2 MB)

    12864_2007_1625_MOESM4_ESM.jpeg

    Additional file 4: Expression patterns of the probe sets in Group 2. (a) G2-P3, (b)G2-P4, (c) G2-P5, (d) G2-p6, (e) G2-P7. (JPEG 5 MB)

    Additional file 5: Expression patterns of probe sets in Group 3. (a) G3-P4, (b) G3-P5. (JPEG 3 MB)

    12864_2007_1625_MOESM6_ESM.jpeg

    Additional file 6: Overlap of the probe sets regulated in response to heat treatments with and without pre-acclimation. (a) CS1sh (left) and CS1h (right), (b) CS24sh and CS24h, (c) TAM1sh and TAM1h, (d) TAM24sh and TAM24h. (JPEG 1013 KB)

    12864_2007_1625_MOESM7_ESM.jpeg

    Additional file 7: Overlap of the probe sets regulated in response to 1-h and 24-h heat treatments. (a) CS1sh and CS24sh, (b) CS1h and CS24h, (c) TAM1sh and TAM1h, (d) TAM1h and TAM24h. (JPEG 1 MB)

    12864_2007_1625_MOESM8_ESM.doc

    Additional file 8: Differentially regulated HR probe sets between 1 hour and 24 hours heat treatments identified by nested F-statistic method. 'U' represents up-regulation, 'D' represents down-regulation, 'NC' represents no change (FDR p < 0.001). (DOC 30 KB)

    12864_2007_1625_MOESM9_ESM.doc

    Additional file 9:Expression patterns of HR probe sets representing genes in hormone biosynthesis and signaling pathway.(DOC 138 KB)

    Additional file 10:Primer pairs for qRT-PCR.(DOC 30 KB)

    Authors’ original submitted files for images

    Rights and permissions

    Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Reprints and permissions

    About this article

    Cite this article

    Qin, D., Wu, H., Peng, H. et al. Heat stress-responsive transcriptome analysis in heat susceptible and tolerant wheat (Triticum aestivum L.) by using Wheat Genome Array. BMC Genomics 9, 432 (2008). https://doi.org/10.1186/1471-2164-9-432

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1186/1471-2164-9-432

    Keywords

    BMC Genomics

    ISSN: 1471-2164

    Contact us