Skip to main content
NCBI home page
Plant Signaling & Behavior logoLink to Plant Signaling & Behavior
. 2013 Sep 11;8(11):e26300. doi: 10.4161/psb.26300

Comparative analysis of seed transcriptomes of ambient ozone-fumigated 2 different rice cultivars

Kyoungwon Cho 1,2, Junko Shibato 1,3,4, Akihiro Kubo 1, Yoshihisa Kohno 5, Kouji Satoh 6, Shoshi Kikuchi 6, Abhijit Sarkar 7, Ganesh Kumar Agrawal 7, Randeep Rakwal 3,7,8,*
PMCID: PMC4091349  PMID: 24025514

Abstract

High ozone (O3) concentrations not only damage plant life but also cause considerable losses in plant productivity. To screen for molecular factors usable as potential biomarkers to identify for O3-sensitive and -tolerant lines and design O3 tolerant crops, our project examines the effects of O3 on rice, using high-throughput omics approaches. In this study, we examined growth and yield parameters of 4 rice cultivars fumigated for a life-time with ambient air (mean O3: 31.4–32.7 ppb) or filtered air (mean O3: 6.6–8.3 ppb) in small open-top chambers (sOTCs) to select O3-sensitive (indica cv Takanari) and O3-tolerant (japonica cv Koshihikari) cultivars for analysis of seed transcriptomes using Agilent 4 × 44K rice oligo DNA chip. Total RNA from dry mature dehusked seeds of Takanari and Koshihikari cultivars was extracted using a modified protocol based on cethyltrimethylammonium bromide extraction buffer and phenol-chloroform-isoamylalcohol treatment, followed by DNA microarray analysis using the established dye-swap method. Direct comparison of Koshihikari and Takanari O3 transcriptomes in seeds of rice plants fumigated with ambient O3 in sOTCs successfully showed that genes encoding proteins involved in jasmonic acid, GABA biosynthesis, cell wall and membrane modification, starch mobilization, and secondary metabolite biosynthesis are differently regulated in sensitive cv Takanari and tolerant cv Koshihikari. MapMan analysis further mapped the molecular factors activated by O3, confirming Takanari is rightly classified as an O3 sensitive genotype.

Keywords: rice, ozone, whole genome DNA microarray, yield loss, MapMan analysis

Introduction

Annual increase in use of fossil fuels has caused the elevation of concentrations of carbon dioxide, a greenhouse gas with 26% contribution for radioactive forcing in the clear sky, resulting in increasing the earth’s average temperature, while ozone (O3), another greenhouse gas, is accounting for 8% of the total.1 Tropospheric O3, which is produced by photochemical reactions between volatile organic compounds and nitrogen oxides results in environmental pollution to living organisms.2 Climate models forecast that average ground-level O3 will reach phytotoxic range in the future with greater increase in its concentration in Asia, Africa, and USA.3-5 Currently, it has been reported that highly concentrated O3 (over 100 ppb) frequently occurs in Japan.6,7 According to a database of National Institute for Environmental Studies (NIES, Japan), maximum hourly oxidant concentrations exceeded 200 ppb at 18 monitoring sites in the Kanto region surrounding Tokyo in 2010.

In plants, the increase of ground-level O3 causes foliar symptoms accompanied with cell death,8 causing yield losses and decreased quality of the end product. The yield reduction in global socio-economic crops such as rice, wheat, maize, barley, soybean, bean, and potato has been increasingly observed corresponding to elevating O3 concentrations compared with the basal O3 level.3,9-13 The adverse effect of O3 on yield of the crops thus becomes a serious problem under a ground-level O3 “predicted to increase” in the future. Thus, to gain an insight into the adverse effects and a clue for early diagnosis and prevention of O3 symptoms, many research groups have been identifying O3-responsive genes, proteins, or metabolites in aspen,14,15 birch,16,17 pepper,18 Arabidopsis,19,20 bean,21 maize,21 and rice.22-26 These experiments utilize the power of “OMICS” providing clues to establish cellular response networks to O3 stress.

Considering that our previous studies were performed based on O3-induced visible injuries on the leaves in bean, maize, and rice, a global identification of genes tightly associated with O3-caused yield loss in crops remain largely unknown. The investigation of the “hidden” genes is essential to obtain clues for early diagnosis and prevention of O3 symptoms in the future crops. We used the rice, a main food of Japan and South East Asian countries, as our model system for investigating the dry mature seeds harvested from an ozone-sensitive cultivar (Oryza sativa L. indica cv Takanari) and a tolerant cultivar (O. sativa L. japonica cv Koshihikari), which were fumigated with ambient O3 (mean O3: 32.5–32.7 ppb) in small open-top chambers (sOTCs).26 Finally, the complex transcriptome data were interpreted by using a MapMan tool.27,28

Results

Physiological features of rice cultivars cultivated under ambient O3 fumigation

As one of the studies to minimize adverse effects of crops caused from the increased ground O3 level in South East Asia, we have investigated genes responsive to O3 stress and that might be associated with O3-triggered yield loss in rice seeds.

For this, japonica-type Koshihikari and Kirara397 cultivars and indica-type Takanari and Kasalath cultivars were planted in 6 sOTCs at Akagi Testing Center, Gunma, Japan (Fig. S1). Four rice cultivars (Kirara397 for 77 d, Koshihikari for 99 d, Kasalath for 106 d, and Takanari for 120 d) in CF-sOTCs were fumigated with filtered air (daily mean O3 concentration: from 6.6 to 8.3 ppb), but those in NF-sOTCs were exposed to ambient air (daily mean: from 31.4 to 32.7 ppb O3) for their whole lifetime. Considering atmospheric pollutant composition at Akagi Testing Center where levels of other air pollutants (data not shown) could be ignored excluding O3, the effect of ambient air in each cultivar means that of ambient O3.

To understand physiological features of the tested 4 rice cultivars and to evaluate effects of ambient O3 on the rice cultivars, physiological parameters of filtered air- or ambient O3-fumigated rice plants in each cultivar were examined as shown in Figure 1 and Figure S2. There were significant differences among the 4 rice cultivars (different genotypes) compared with yield components (Fig. 1A-C) or development components (Fig. 1A, Fig. S2A-D). The number of panicle (or culm) per plant was the most in Kirara397 and the fewest in Takanari and Kasalath (Fig. 1A). On the contrary, grain weight per panicle in a plant was the most in Takanari and the least in Kirara397 (Fig. 1B). Grain weight per plant was the most abundant in Koshihikari (Fig. 1C). Total height of plant, culm length, and flag leaf length were the longest in Kasalath and the shortest in Kirara397 (Fig. S2A-C). Biomass per plant was the richest in Koshihikari and the poorest in Kirara397 (Fig. S2D).

graphic file with name psb-8-e26300-g1.jpg

Open in a new tab

Figure 1. Yield components of 4 rice cultivars grown in sOTCs. The cultivars were grown to maturity under fumigation with ambient O3 or filtered air until harvested. Their yields components were measured as follows: (A) the average number of panicle (or culm) per plant, (B) the average dry weight of paddy grain per panicle, and (C) the average dry weight of paddy grain per plant. Significant differences between means of yield components of rice plants grown in CF- and NF-sOTCs (Treatment: *asterisk), among 4 rice cultivars (Genotype: large alphabet) in CF-sOTCs, and among ambient O3-fumigated 4 rice cultivars (Treated genotype: small alphabet) in NF-sOTCs, were tested using 1-way ANOVA (Scheffe Test) in an OriginPro7.5 program.

Furthermore, evaluating effects of ambient O3 on yield or development components in each rice cultivar, ambient O3-exposed Kirara397 showed no significant changes in growth and yield components (Fig. 1, Fig. S2). In ambient O3-fumigated Kasalath, significant reduction was observed in culm growth (p < 0.01) but not in total height, biomass, and yield components (Fig. 1, Fig. S2). Ambient O3-fumigated Takanari showed significant reductions in its total height (Fig. S2A), culm length (Fig. S2B), flag leaf length (Fig. S2C), grain weight per panicle (Fig. 1B), and grain weight per plant (Fig. 1C), indicating that Takanari is sensitive to O3 on growth and yield. In ambient O3-fumigated Koshihikari, significant reductions in panicle number per plant (Fig. 1A) and its total height (Fig. S2A) were observed but not in yield per plant (Fig. 1C) and other growth components (Fig. S2B-D). The reduction of panicle number in the ambient O3-fumigated Koshihikari was not accompanied with that of yield per plant, indicating the increase in yield per panicle. Figure 1B showed that yield per panicle is increased in Koshihikari contrary to the decrease in Takanari.

Investigation of seed O3 transcriptomes in O3-tolerant and -sensitive rice cultivars

From the evaluation of physiological changes in 4 rice cultivars derived from ambient O3 fumigation the effects of ambient O3 on yield components in Koshihikari and Takanari were differential, indicating that they have different O3 sensitivities (Fig. 1). We believe that their properties may be traced to seeds developed and matured under ambient O3. We next investigated these traces at the transcript level in the seeds using a rice DNA chip (4 × 44K custom oligo DNA microarray chip, Agilent). For this, high-quality total RNA was extracted from dehusked seeds using a modified CTAB extraction protocol including phenol-chloroform-isoamylalcohol treatment, acidic sodium acetate precipitation, and RNA clean-up steps.26 Using this total RNA, hybridizations were performed using a dye-swap method as described in Figure 2. The result showed that a total of 130 and 508 genes manifest significant (p < 0.01) differential response to ambient O3 by at least 2-fold in seeds of Koshihikari and Takanari, respectively (Fig. 2). The upregulated and downregulated genes in Koshihikari were 24 and 106, and in Takanari were 338 and 170, respectively. Twenty-three genes were found to overlap between seed O3 transcriptomes of Koshihikari and Takanari (listed in Table S1D). Seven genes were upregulated encoding glutelin type-B 4 precursor (LOC_Os02 g25860), α-amylase isozyme 3A precursor (LOC_Os09 g28400), sarcosine oxidase (LOC_Os09 g32290), wound-induced protein (win2) precursor (LOC_Os11 g37960), retrotransposon protein (LOC_Os05 g26260), and 2 uncharacterized proteins (LOC_Os05 g05290, LOC_Os04 g44500). Sixteen genes were downregulated encoding 2 early light-induced protein precursors (LOC_Os07 g08160, LOC_Os01 g14410), AP2 domain containing protein (LOC_Os02 g34260), DNA binding protein (LOC_Os02 g34270), gibberellin-regulated protein precursor (LOC_Os04 g39110), α-amylase isozyme 3D precursor (LOC_Os08 g36910), hydroxymethylglutaryl-CoA synthase (LOC_Os03 g02710), phytosulfokines 3 precursor (LOC_Os03 g47230), pyruvate decarboxylase isozyme 1 (LOC_Os05 g39310), glutathione S-transferase (LOC_Os03 g57200), basic endochitinase C precursor (LOC_Os05 g04690), phosphate-induced protein 1 (LOC_Os08 g37840), 2 transporters (LOC_Os03 g62270, LOC_Os10 g39980), transposon protein (LOC_Os03 g40070), and uncharacterized protein (AK068402). Collectively, 615 unique genes were identified to be differentially responsive to O3 in seeds of either Koshihikari or Takanari.

graphic file with name psb-8-e26300-g2.jpg

Open in a new tab

Figure 2. Procedure for O3 transcriptome profiling in seeds in ambient O3-exposed Takanari and Koshihikari rice plants. Seeds harvested from filtered air- and ambient O3-exposed rice plants were used as control and treatment samples in each rice cultivar, respectively. Total RNAs were extracted from dehusked seeds as described in Materials and Methods. Total cRNAs were synthesized with total RNAs extracted from seeds according to manufacturer’s instruction and microarray was duplicated using a dye-swap method. At forward experiment, the treated sample is in the red and the control in the green channel. At reverse experiment, the channels are flipped. The differential expression level of genes between control and treated samples of each slide image was processed by Agilent Feature Extraction software (ver. 9.5.3.1), which selected probes using a set by rank consistency filter for dye-normalization. The log ratio of dye-normalized Cy3- and Cy5-signals was calculated by using LOWESS (locally weighted linear regression). The significance (P) value was calculated based on the propagate error and universal error models. The list of genes, which log2 values of differential expression are over 1 (Up) or below –1 (Down) at significance threshold (p < 0.01), were generated and annotated using the GeneSpring (ver. GX 10, Agilent).

A total of non-redundant 615 (23 and 592) genes were sorted to 26 main BINs (157 subBINs, listed in Table S1E) using MapCave analysis as described in Materials and Methods, which further were simply classified into 16 groups based on their annotated functions (Fig. 3). Then, the frequency of genes in each class was calculated as a percentage. The result showed that the upregulated and downregulated genes in Koshihikari belong to 9 and 15 functional classes, but in Takanari 15 and 13 categories, respectively (Fig. 3). Some functional categories were abundantly represented in Koshihikari than Takanari including amino acid metabolism, secondary metabolism, hormone metabolism, and transport. In contrast, transcription factor (TF) and redox and antioxidant enzyme categories were abundant in Takanari. Genes of unknown function were highly represented and accounted for 38.46% and 43.59% in Koshihkari and Takanari, respectively, signifying the fact that the function of a large fraction of the rice genome remains to be identified. Together, results suggest that the seed O3 transcriptomes in Koshihikari and Takanari cultivar have a highly cultivar-specific response to ambient O3.

graphic file with name psb-8-e26300-g3.jpg

Open in a new tab

Figure 3. Functional categorization of seed O3-responsive genes in Koshihikari and Takanari. Non-redundant 615 genes which expression are changed over 2-fold in either seeds harvested from ambient O3-fumigated Koshihikari (K) or Takanari (T) were functionally categorized into MapMan BINs27,28 using a MapCave tool (http://mapman.gabipd.org) linked with Oryza sativa TIGR5 annotation databases. The frequency of genes in each BIN was represented as a percentage in a given group.

Different responses of metabolism-related genes in O3-fumigated Koshihikari and Takanari seeds

To understand different O3 responses marked in seeds of O3-fumigated Koshihikari and Takanari, expression levels of genes categorized into each subBINs were compared (Table S1E) and visualized as shown in Figures 3 and 4. In Koshihikari, genes encoding enolase in glycolysis, pyruvate decarboxylase in fermentation, β-1,3-glucan hydrolase for cell wall degradation, and 3 UDP-glucosyltransferases were downregulated; but TCA cycle-related genes could not be detected (Fig. 4A). In Takanari, genes encoding fructokinase in glycolysis, pyruvate decarboxylase in fermentation, and pyruvate dehydrogenase E3 and malic enzyme in TCA cycle were downregulated; but genes for cell wall degradation and UDP-glucosyltransferase were upregulated (Fig. 4B).

graphic file with name psb-8-e26300-g4A.jpg

Open in a new tab

Figure 4A. Overviews of metabolism pathways of ambient O3-responsive genes in seeds of an O3-totlerant cultivar, Koshihikari (A) and a sensitive cultivar, Takanari (B). Significant fold changes in non-redundant 615 genes were transformed to Log2-(fold) and mapped on various metabolic pathways using the MapMan tool. Small red, blue and dark gray squares within each metabolism pathway (large light gray boxes) indicate upregulation, downregulation and no detection of mapped genes in a given cultivar, respectively. Of the displayed pathways, major biochemical process such as starch-sucrose metabolism, glycolysis, the TCA cycle, and GABA shunt are indicated by arrows and the corresponding metabolites are indicated as circles. Solid red, blue, and black arrows indicate upregulation, downregulation, and no detection of relevant genes for each metabolic reaction in a given cultivar, respectively. Dotted arrows mean that genes related to the mapped reactions is not detected in both cultivars. Abbreviations: G-6-P: glucose-6-phosphate, F-6-P: fructose-6-phosphate, Glycerate-2-P: glycerate-2-phosphate, PEP: phosphoenolpyruvate, Gln: glutamine, Glu: glutamate, GABA: gamma-aminobutyric acid, Sar: Sarcosine (N-methylglycine), Gly: Glycine, Ser: Serine, O-Acetylser: O-Acetyl-l-serine, Cys: Cysteine, Homoser: Homoserine, Pi-homoser: Phosphohomoserine, Homocys: Homocysteine, Met: Methionine, Thr: Threonine, Ile: Isoleucine, Tyr: Tyrosine, Phe: Phenylalanine.

graphic file with name psb-8-e26300-g4B.jpg

Open in a new tab

Figure 4B. Overviews of metabolism pathways of ambient O3-responsive genes in seeds of an O3-totlerant cultivar, Koshihikari (A) and a sensitive cultivar, Takanari (B). Significant fold changes in non-redundant 615 genes were transformed to Log2 (fold) and mapped on various metabolic pathways using the MapMan tool. Small red, blue and dark gray squares within each metabolism pathway (large light gray boxes) indicate upregulation, downregulation and no detection of mapped genes in a given cultivar, respectively. Of the displayed pathways, major biochemical process such as starch-sucrose metabolism, glycolysis, the TCA cycle, and GABA shunt are indicated by arrows and the corresponding metabolites are indicated as circles. Solid red, blue, and black arrows indicate upregulation, downregulation, and no detection of relevant genes for each metabolic reaction in a given cultivar, respectively. Dotted arrows mean that genes related to the mapped reactions is not detected in both cultivars. Abbreviations: G-6-P: glucose-6-phosphate, F-6-P: fructose-6-phosphate, Glycerate-2-P: glycerate-2-phosphate, PEP: phosphoenolpyruvate, Gln: glutamine, Glu: glutamate, GABA: gamma-aminobutyric acid, Sar: Sarcosine (N-methylglycine), Gly: Glycine, Ser: Serine, O-Acetylser: O-Acetyl-l-serine, Cys: Cysteine, Homoser: Homoserine, Pi-homoser: Phosphohomoserine, Homocys: Homocysteine, Met: Methionine, Thr: Threonine, Ile: Isoleucine, Tyr: Tyrosine, Phe: Phenylalanine.

Out of lipid metabolism-related genes, 1 for phospholipid synthesis was downregulated in Koshihikari (Fig. 4A). However, 2 genes encoding ω-6 fatty acid desaturase and triacylglycerol lipase and genes for phospholipid and glycolipid synthesis were upregulated in Takanari (Fig. 4B).

In Koshihikari, 1 gene for glycine synthesis was upregulated; 8 genes for synthesis of methionine and isoleucine, for degradation of lysine, asparagines and alliin, and for biosynthesis of terpenes and flavonoids were downregulated. The genes for GABA shunt were not detected. However, in Takanari, genes for synthesis of glycine, isoleucine, cystein and phenolic amino acids, for degradation of methionine and alliin, and for GABA shunt were upregulated; 1 gene for asparagine degradation was downregulated; but genes responsible for the biosynthesis of terpene and phenylpropanoid pathway metabolites were not detected.

Ozone-triggered regulatory events and response genes differ in the seeds of Koshihikari and Takanari cultivars

The annotated functions of those O3-responsive genes were mapped by a MapCave tool to obtain a view of the O3-controlled regulatory events in Koshihikari and Takanari seeds (Fig. 5). A glance of the mapped genes and their expressions on various regulatory events visualized major differences in the presence/absence of fundamental regulatory processes of hormonal and other signaling, TFs, proteolysis, biotic and abiotic stress, redox reaction, and development (Fig. 5). Importantly, the total number of genes involved in a particular functional process also differed dramatically. For example, genes involved in signaling, TFs, proteolysis and defense (biotic and abiotic stress) were 25, 48, 20, and 38 in Takanari, but only 6, 7, 7, and 9 in Koshihikari, respectively.

graphic file with name psb-8-e26300-g5.jpg

Open in a new tab

Figure 5. Molecular events and potential components for cellular response against O3 stress in seeds of an O3-tolerant cultivar, Koshihikari, and a sensitive cultivar, Takanari. Gene expression changes are depicted in MapMan format version 3.1.1 where each square presents a gene. Red and blue mean upregulation and downregulation in gene expression. The complete data set used for MapMan analysis is given in Table S1E.

All of 6 signaling-related genes in Koshihikari were downregulated. These encode the calcium-dependent protein kinase, receptor kinase, phosphate-induced protein, and 3 light-induced proteins (Fig. 5). Majority of downregulated genes of signaling in Takanari were 2 calcium-dependent protein kinases, phosphate-induced protein and 3 light-induced proteins, whereas of upregulated genes were calcium binding proteins (calmodulin, calmodulin binding protein) and G-protein-associated proteins.

Genes encoding TFs in Koshihikari were downregulated including 3 ethylene-responsive element binding proteins (AP2/EREBPs), 2 basic helix-loop-helix proteins (bHLHs), a C2C2(Zn) CO-like zinc finger protein, and a bZIP TF (Fig. 5). Majority of upregulated genes of TFs in Takanari were 4 CCAAT-box binding factors, 5 NACs, 4 bZIPs, 3 MYBs, 2 bHLHs, and 2 AP2/EREBPs, whereas of downregulated genes were 3 AP2/EREBPs, an auxin response factor, a heat-shock TF, and a bZIP TF.

Proteolysis-related genes in Koshihikari were downregulated encoding 4 components of ubiquitin complex, and 2 proteases (Fig. 5). Genes encoding 6 proteases were upregulated in Takanari and genes encoding 8 and 6 components for ubiquitin complex were upregulated and downregulated, respectively.

Moreover, genes involved in hormonal signaling and defense/stress response pathways were differentially regulated in Koshihikari and Takanari seeds. Three and 2 abscisic acid (ABA)-related genes were upregulated in Koshihikari and Takanari, respectively. One and 2 ethylene-related genes were downregulated in Koshihikari and Takanari, respectively. One jasmonic acid (JA)-related gene was downregulated in Koshihikari, whereas 2 and 1 JA-related genes were upregulated and downregulated in Takanari, respectively. Three auxin genes were upregulated only in Takanari but not in Koshihikari. In addition, 2 and 1 gibberellin-related genes were downregulated in Koshihikari and Takanari, respectively, but 2 upregulated only in Takanari. Similar to hormonal signaling, proteolysis, TFs, and secondary metabolism, the O3-responsive genes encoding 2 glutathione-S-transferases (GST) were downregulated in Koshihikari. However, various antioxidant-related genes were differently regulated in Takanari. Of upregulated genes there were 2 thioredoxins, 5 peroxidases, and 1 GST, whereas of downregulated genes were 3 ascorbate/glutathione-related proteins, 4 peroxidases and 1 GST.

Discussion

O3-caused yield and growth reductions in rice plants are determined by the combination of relevant components

To evaluate effects of ambient O3 on yield and development in rice, 4 rice cultivars (Koshihikari, Kirara397, Takanari, and Kasalath) were exposed to ambient O3 (daily mean: 31.4–32.7 ppb) in sOTCs for their lifetime (Fig. S1). We observed their yield components (the number of panicle (culm)/plant, grain weight/panicle, and grain weight/plant) and development (total height, culm length, flag leaf length, dry weight (biomass)/plant excluding panicle, and the number of culm (panicle)/plant) as shown in Figure 1 and Figure S2. Compared with development components among the 4 cultivars, Kirara397 was the most dwarf cultivar. Nevertheless, Kirara397 had the largest number of panicle (culm) per plant, indicating that its tillering capacity is predominant than other cultivars. In contrast, Kasalath was observed to be a giant cultivar but had the most recessive capacity for tillering. Biomass was the most abundant in Koshihikari, in which the tillering capacity and growth are dominant. The results indicate that each cultivar has its unique properties on the development components, and its biomass per plant is determined by the combination of the number of culm (panicle) per plant and total height of plant affected by culm length and flag leaf length.

Moreover, compared with yield components among the 4 cultivars, Kirara397 showed the smallest grain weight per panicle but the largest number of panicle (culm) per plant. In contrast, Kasalath and Takanari showed the larger grain weight per panicle but the smaller number of panicle per plant. Yield per plant was the most abundant in Koshihikari. The number of panicle per plant depends on its tillering capacity, which is determined in the vegetative phase of plant growth. Grain weight per panicle is affected in its reproductive phase. The results indicate that each cultivar has its unique properties on yield components similar to those on the above development components, and its yield per plant is determined by the combination of the number of panicle (culm) per pant and grain weight per panicle.

Furthermore, comparison of each yield component between ambient O3-fumigated plants (treated plants) and filtered air-fumigated plants (controls) was performed in a given cultivar. There was no significant difference in grain weight per 1000 grains (data not shown), indicating that grain size in treated plants is similar to that in controls in each cultivar. In treated Koshihikari, the number of panicle was significantly reduced (p < 0.05), indicating that its tillering capacity decreased. The result may mean that Koshihikari is sensitive to O3 at vegetative phase than other cultivars and where the number of panicle was changed according to ambient O3 exposure. However, grain weight per panicle in the treated Koshihikari was increased (p < 0.05), but that in the treated Takanari was decreased (p < 0.05). The result indicates that Takanari and Koshihikari are also sensitive and tolerant to O3 at reproductive phase than other cultivars, respectively. Finally, yield per plant in the treated Takanari was significantly reduced (p < 0.01) but those in other cultivars were not changed, reconfirming that yield per plant is determined by the combination of the number of panicle (culm) per plant, and grain weight per panicle. Together, considering comprehensive effects of ambient O3 on yield components in each cultivar, an indica-type rice cultivar, Takanari, and a japonica-type rice cultivar, Koshihikari, have different O3-sensitivities than other cultivars. Thus, as a next step we examined their transcript-level characteristics in seeds harvested from O3-exposed Koshihikari or Takanari.

Seed quality in ambient O3-fumigated Takanari may be more impaired than that in Koshihikari

The functional mapping of seed transcriptomes to O3 in Koshihikari and Takanari showed that genes for starch degradation and glycolysis are impaired in both cultivars, indicating the possibility of carbohydrate content alternation in both the cultivars. Majority of the several identified genes for polysaccharide degradation were upregulated in Takanari but not changed in Koshihikari, suggesting that the alternation of carbohydrate content might be more severe in Takanari than in Koshihikari. Moreover, majority of the genes encoding proteolysis were downregulated in Koshihikari but upregulated in Takanari. Most of genes for amino acid synthesis were downregulated in Koshihkari but upregulated in Takanari except a glycine synthesis gene upregulated in both cultivars. Genes encoding components of GABA shunt such as glutamate dehydrogenase (LOC_Os04 g45970) and GABA transaminase (LOC_Os04 g52440) were upregulated in Takanari but not detected in Koshihikari. Considering that amino acid biosynthesis is derived from consumption of intermediates on TCA cycle and glycolysis, the increases in contents of amino acids and GABA in seeds of ambient O3-fumigated Takanari may impair quality of the seeds.

One and 5 genes were upregulated encoding storage protein glutelin precursor in Koshihikari and Takanari, respectively. Genes encoding OsbZIP21 (OsbZIP52, LOC_Os06 g45140) and OsbZIP23 (OsbZIP58, LOC_Os07 g08420) TFs upregulated in Takanari have been reported to be involved in seed development.29 OsbZIP23 (RISBZ1) activates endosperm-specific expression of storage protein genes.30 Its homolog, barley BLZ1, also regulates storage protein expression,31,32 indicating that the upregulation of OsbZIP21 and 23 may accumulate storage protein in seed of ambient O3-exposed Takanari. The results suggest that storage proteins seem to be abundant in seed of ambient O3-exposed Takanari than Koshihikari. Furthermore, our microarray data revealed that lipid synthesis-related genes are upregulated in Takanari but downregulated in Koshihikari, suggesting the increase in lipid contents in seeds of ambient O3-fumigated Takanari. Indeed, seed quality measurement at CRIEPI showed the increase in protein and free fatty acid contents but the decrease in amylose content in seeds harvested from ambient O3-fumigated Takanari in 2009 (data not shown). The results indicate that the quality of seeds in ambient O3-fumigated Takanari may be more impaired than in Koshihikari.

Genes involved in yield and biomass losses caused by O3 fumigation

The growth parameter data of O3-fumigated Takanari rice plants in sOTCs showed the significant decrease in total height, length of flag leaf, and the number of culms (Fig. S2A-C). However, no clear difference in biomass (Fig. S2D) indicated that culms or leaves in O3-fumigated Takanari rice plants might be thicker than those in filtered air-fumigated Takanari. The functional analysis of seed transcriptomes to O3 in Takanari revealed the upregulation of ONAC073 gene (LOC_Os01 g48130) and a novel NAC gene (LOC_Os02 g38130). The genes were homologous to ONAC003 subclass genes including ANAC010 and ANAC075, which are co-expressed with secondary cell wall biosynthesis genes33 and involved in secondary cell wall thickening of fibers.34

Figure 1 showed the decrease in the number of panicle per plant but the increase in yield per panicle in O3-fumigated Koshihikari. Our microarray data revealed the downregulation of a gene (LOC_Os12 g40590) encoding a homolog to AtbHLH73//EN98 in O3-fumigated Koshihikari seeds. AtbHLH73//EN98 in Arabidopsis plays an important role as a regulator of silique dehiscence. Early dehiscence leads to significant yield losses.35 The result indicates a possibility that the increase in yield per panicle may be involved in downregulation of LOC_Os12 g40590.

Furthermore, Kim et al. reported that methyl jasmonate reduces grain yield in rice.36 In both seeds of O3-fumigated Koshihikari and Takanari, 1 gene (LOC_Os06 g11210) encoding a homolog to 12-oxo-phytodienoic acid reductase 1 in Zea mays (ZmOPR1), which enhances the tolerance to osmotic and salt stress during seed germination,37 was downregulated. However, 2 genes were specifically upregulated in seeds of O3-fumigated Takanari encoding OsLOX L-2 (LOC_Os03 g52860) classified to 13-LOX participating in JA biosynthesis38 and a homolog (LOC_Os01 g27240) to AtOPR2 (71.14%), which is implicated in octadecanoid pathway biosynthesis and poorly involved in JA biosynthesis,39 suggesting the different regulation of octadecanoids in O3-fumigated Takanari and Koshihikari.

Analysis of seed transcriptomes against ambient O3 suggests that Takanari is more O3-sensitive than Koshihikari

The functional analysis of seed transcriptomes in ambient O3-exposed rice cultivars showed that 15 and 33 TFs were upregulated and downregulated in Takanari, respectively, but 7 TFs were downregulated in Koshihikari. Out of TFs, there are 6 genes encoding ethylene response element-binding proteins (AP2/EREBPs). Four AP2/EREBPs genes (LOC_Os02 g34260, LOC_Os02 g34270, LOC_Os03 g08490, LOC_Os11 g06770) were downregulated in either seeds of ambient O3-exposed Koshihikari or Takanari, but 2 (LOC_Os05 g39590, LOC_Os06 g09390) were upregulated in the Takanari. Except 1 (LOC_Os05 g39590), 5 genes are paralogues based on Rice Genomes Annotation Database (http://www.gramene.org/Oryza_sativa/Info/Index). Their translated sequences are similar to AtERF1 in Arabidopsis and TaERF1 in wheat. AtERF1 is upregulated by ethylene (ET) and JA and activates defense genes.40 TaERF1 is induced by drought, salinity, low temperature, exogenous ABA, ET and salicylic acid (SA) treatments, and pathogen infection, is phosphorylated in nucleus by TaMAPK1, activates stress-related genes, and then improves biotic and abiotic stress-tolerance.41 Moreover, 1 gene encoding OsDREB2D was upregulated in Takanari. This gene has 54.07% similarity to coffee ERF1, which activates defense genes during biotic or environmental stresses.42 Out of 6 defense-related AP2/EREBPs genes, the downregulation of 4 genes in either seeds of both the cultivars seems to indicate the recovery against ambient O3 stress, but the upregulation of 2 genes in Takanari seems to represent the existence of residual O3 stress.

One gene encoding a bHLH protein (LOC_Os10 g39750) was downregulated in seeds of ambient O3-fumigated Koshihikari but 2 (LOC_Os01 g70310, LOC_Os11 g25560) upregulated in Takanari. The downregulated gene (LOC_Os10 g39750) in Koshihikari and the upregulated gene (LOC_Os01 g70310) in Takanari are paralogues. They have similarity with AtbHLH116/EN45/ICE1 in Arabidopsis, which is a regulator of cold-induced genes and cold tolerance in Arabidopsis.43 One upregulated gene bHLH (LOC_Os11 g25560) in Takanari is similar to AtbHLH62 in Arabidopsis, which is induced by low temperature.44 Moreover, our microarray data released that OsNAC8 (ONAC074, LOC_Os01 g15640) homologous (71%) to wheat TaNAC8 was upregulated in Takanari.45 TaNAC8 is expressed significantly in developing wheat seeds, by pathogen infection, JA treatment, and ET but not in stems and flowers, by SA and abscisic acid (ABA).46 Additionally, a gene (LOC_Os01 g74590) encoding a R2R3-type MYB was also upregulated in Takanari. The R2R3-type MYB is highly homologous to AtMYB44 and 77 in Arabidopsis, which are upregulated by ABA, ET, indole acetic acid (IAA), JA, or SA treatments and involved in seed dormancy and drought tolerance.47 Moreover, 2 genes encoding GST were downregulated in Koshihikari but various antioxidant-related genes were down- or upregulated in Takanari. Of upregulated genes there were 2 thioredoxins, 5 peroxidases, and 1 GST and of downregulated genes were 3 ascorbate/glutathione-related proteins, 4 peroxidases and 1 GST The above results seem to represent the existence of residual stress in seeds of ambient O3-fumigated Takanari.

Concluding Remarks

Comparing 2 rice cultivars differing in their sensitivity to O3, our study provided new insight into the O3-responsive molecular changes in the seeds of O3-tolerant Koshihikari with O3-sensitive Takanari rice cultivars. Moreover, a correlation between the sensitivity to O3 at the reproductive stage and changes in the expression of genes in seeds potentially responsible for low quality of seeds could be obtained. The study provides a data set of O3-responsive genes in the seed for the first time, which can be harnessed by researchers to utilize further in molecular genetic approaches to clarify the mechanism of O3 sensitivity or tolerance.

Materials and Methods

Plant materials and ambient O3 fumigation

The experiment used 4 rice cultivars: 2 japonica-type rice cultivars (O. sativa L. cv Koshihikari and Kirara397) and 2 indica-type cultivars (O. sativa L. cv Takanari and Kasalath). Twenty-four seeds of each cultivar were germinated in water for 3 d. Four germinated seedlings were planted in each of the 6 sOTCs – each with a dimension of 60 (L) × 60 (W) 120 cm (H) – at Akagi Testing Center of Central Research Institute of Electric Power Industry (Gunma, Japan) on 18 June, 2009 and grown to maturity there (Fig. S1). Three (CF-sOTC) of the 6 sOTCs were equipped with charcoal filters but the remaining 3 (NF-sOTC) were not. Twelve rice plants of each cultivar in 3 CF-sOTCs were fumigated with filtered air (daily mean 6.6–8.3 ppb O3) and those in 3 NF-sOTCs were fumigated with ambient O3 (daily mean 31.4–32.7 ppb) for their whole lifetime (Fig. S1).

Assessment of rice plants matured under ambient O3 fumigation

The rice plants planted in sOTCs were grown to maturity under fumigation with ambient O3 or filtered air, and harvested. To evaluate the O3 sensitivity of each cultivar, the morphological parameters of harvested plants were measured as follows: total height of plant (upper soil ~the uppermost point in each plant, Fig. S2A), culm length per plant (upper soil ~panicle neck, Fig. S2B), flag leaf length per plant (base of flag leaf ~its tip, Fig. S2C), dry weight of plant excluding panicles (Fig. S2D), the number of panicle (or culm) per plant (Fig. 1A), dry weight of paddy grain per panicle (Fig. 1B), and dry weight of paddy grain per plant (Fig. 1C). The harvested plant tissues excluding panicles were dried at 80°C in an oven for 6 d and weighed. The grains were separated from panicles and manually sorted into filled and unfilled grains. The filled grains were named as paddy grains herein, which were subsequently dried at room temperature (RT), weighed and stored at 4°C in sealed polybags for further use. To test the effects of ambient air (O3) on growth and yield components in each cultivar, significant differences: 1) between means of each parameter of rice plants grown in CF- and NF-sOTCs (between control and treatment), 2) among those of 4 rice cultivars in CF-sOTCs (among cultivars), and 3) among those of ambient O3-fumigated 4 rice cultivars in NF-sOTCs (among treated cultivars) were tested using 1-way ANOVA (Scheffe’s Test) using an OriginPro7.5 program.

Dehusking of dry mature rice seeds and extraction of high quality total RNA from seed of sensitive and tolerant cultivars

The seeds of O. sativa L. indica cv Takanari and japonica cv Koshihikari, selected as O3-sensitive and -tolerant cultivars, respectively, were randomly selected and dehusked. Three seeds per plant × 12 replicates, i.e., 36 seeds in total were used for preparing fine powders in liquid nitrogen.23,25,26 Briefly, the seeds were placed in a pre-chilled mortar and pestle containing liquid nitrogen, ground completely to a very fine powder with the chilled pestle in liquid nitrogen, and stored at –80°C till used for RNA extraction. For total RNA extraction, the stored sample powder (~100 mg) was transferred to a 2 ml sterile microfuge tube, followed by addition of 0.9 ml of CTAB buffer [a 10 ml volume of buffer contains 0.5 ml (50 mM) of 1 M stock Tris–HCl solution (pH 8.0), 1.0 ml (5 mM) of 500 mM EDTA (EDTA, pH 8.0), 0.2 g (2%, w/v) of CTAB, 1.68 ml (0.84 M) of 5 M NaCl, and 0.1 M β-mercaptoethanol, which is added just before use of the solution]. The contents were mixed by vortexing for 30 s and incubated for 5 min at RT. After an addition of 0.8 ml of phenol-chloroform-isoamylalcohol (PCIA; 25:24:1), the homogenate was mixed well (by gentle shaking) for 5 min at RT. After centrifugation at 15,000 × g for 5 min at 4°C, an aliquot (0.6 to 0.7 ml) of the upper phase was transferred to a 1.5-ml sterile microfuge tube, followed by addition of 1 volume of chloroform, and the mixture was centrifuged at 15,000 × g for 5 min at 4°C. The resulting supernatant was transferred to another 1.5 ml microfuge tube and 0.033 volume of 3 M sodium acetate, pH 5.5, and 1 volume of 2-isopropanol were added. The mixture was incubated for 15 min on ice, and then centrifuged at 15000 × g for 5 to 10 min at 4°C to collect the RNA. The supernatant was completely removed and the pellet was dissolved in 0.1 ml of RNase-free water (SDW; double sterilized distilled water) followed by using the RNeasy mini protocol for RNA cleanup exactly as described by the manufacturer (QIAGEN, Gaithersburg, MD USA). To verify the quality of this RNA, the yield and purity were determined spectrophotometrically (NanoDrop, Wilmington, DE USA) and visually confirmed using formaldehyde-agarose gel electrophoresis. Prior to the microarray experiment, to check the quality of synthesized cDNA, reverse transcription-polymerase chain reaction (RT-PCR) was performed as described previously on the β-actin (AK100267) gene using primer pairs: RJSR43 forward, 5′ CTCCTAGCAG CATGAAGATC AA 3′, and RJSR44 reverse 5′ ATGATAACAG ATAGGCCGGT TG 3′.25,26

DNA microarray analysis

A rice 4 × 44K custom (eARRAY, AMAdid-017845) oligo DNA microarray chip (G2514F: Agilent Technologies, Palo Alto, CA, USA) was used for genome-wide gene profiling as described previously.26 The flip labeling (dye-swap or reverse labeling with Cy3 and Cy5 dyes) procedure was used to nullify the dye bias associated with unequal incorporation of the 2 Cy dyes into cRNA.48-50 The dye-swap approach, which is well established in our laboratories and research,23,25,26,51-55 provides a more stringent selection condition for profiling differentially expressed genes rather than simply doing only 2 or 3 replicates, which overlook the dye bias.

Total RNA (800 ng) was labeled with either Cy3 or Cy5 dye using an Agilent Low RNA Input Fluorescent Linear Amplification Kit. Hybridization and wash processes were performed according to the manufacturer’s instructions. Hybridized microarray chips were scanned using the Agilent Microarray Scanner G2565BA. To detect differentially expressed significant genes between control and treated samples, each slide image was processed by Agilent Feature Extraction software (version 9.5.3.1). Normalization of Cy3 and Cy5 signals was performed by LOWESS (locally weighted linear regression), which calculates the log ratio of dye-normalized Cy3- and Cy5-signals. The significance (P) value is based on the propagate error and universal error models. In this analysis, the threshold of significantly expressed differential genes was set to < 0.01 (for the confidence that the feature was not differentially expressed). Lists of differentially expressed gene [upregulated (≥ 2.0 fold) and downregulated (≤ 0.5 fold) genes] were generated and annotated using the Agilent GeneSpring version GX 10.

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE49963 (http://www.ncbi.nlm.nih.gov/geo/info/linking.html).

Functional classification

Microarray in each cultivar was duplicated using the dye-swap method as mentioned above (Fig. 2). O3-responsive genes (≥ 2.0-fold or ≤ 0.5-fold) in either mature dry seeds harvested from ambient O3-fumigated 2 rice cultivars (O3-sensitive Takanari and tolerant Koshihikari) were selected (Table S1A and B). The total number of non-redundant 615 genes ware listed in Table S1C, their fold values were transformed to Log2(fold), and then their means were calculated. The non-redundant 615 genes were also classified into MapMan BINs (Table S1E) and their annotated functions were visualized using a MapMan program (version 3.1.1, Max Plant Institute of Molecular Plant Physiology, Germany),27,28 based on a newly constructed rice mapping file for all the genes on Agilent 4 × 44K rice DNA chip. The mapping file was established by automated searches using systematic names (as locus identifiers) of all the genes on the DNA chip released from the GeneSpring program (version GX 10, Agilent) and a MapCave tool (http://mapman.gabipd.org), which is linked with 6 different databases, such as Arabidopsis thaliana TAIR8, Arabidopsis thaliana TAIR9, Hordeum vulgare, Oryza sativa TIGR5, SwissProt/PPAP, and Vitis vinifera Gene Index R5. Furthermore, the functionally classified non-redundant 615 genes were divided into 4 groups: upregulated genes or downregulated genes in Koshihikari (KUp or KDown), and those in Takanari (TUp or TDown) as shown in Figure 2. The frequency of genes in each BIN was calculated as a percentage in a given group (Fig. 3).

Supplementary Material

Additional material
psb-8-e26300-s01.pdf (635.1KB, pdf)

Acknowledgments

We thank Dr. Isamu Nouchi (NIAES) for Koshihikari and Kirara397 seeds, Dr. Ikuo Ando (NICS) for Takanari seeds, and Dr. Masanori Tamaoki (NIES) for Kasalath seeds. This work was supported by the Environment Research and Technology Development Fund (A-0806) of the Ministry of Environment, Japan. Authors appreciate the facility used for growing rice under the direction of Dr. Yoshihisa Kohno at Akagi Testing Center, Central Research Institute of Electric Power Industry (Gunma, Japan), and the workers who maintained the plants. KC was an Eco-Frontier Fellow (09-Ba086–02). GKA appreciates Japan Society for the Promotion of Science (JSPS; ID Number S-10182) for research at NIAS. RR acknowledges the great support of Prof. Seiji Shioda and Dr. Tetsuo Ogawa (Department of Anatomy I, Showa University School of Medicine) and Prof. Yoshihiro Shiraiwa (Provost, Life and Environmental Sciences, University of Tsukuba) and Prof. Koji Nomura (Organization for Educational Initiatives, University of Tsukuba) in promoting interdisciplinary research and unselfish encouragement.

10.4161/psb.26300

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Supplementary Materials

Supplementary materials may be found here: www.landesbioscience.com/journals/PSB/article/26300

References

  • 1.Kiehl JT, Trenberth KE. Earth’s annual global mean energy budget. Bull Am Meteorol Soc. 1997;78:197–208. doi: 10.1175/1520-0477(1997)078&#x0003c;0197:EAGMEB&#x0003e;2.0.CO;2. [DOI] [Google Scholar]
  • 2.Varotsos C, Cartalis C, Vlamakis A, Tzanis C, Keramitsoglou I. The long term coupling between column ozone and tropopause properties. J Clim. 2004;17:3843–54. doi: 10.1175/1520-0442(2004)017&#x0003c;3843:TLCBCO&#x0003e;2.0.CO;2. [DOI] [Google Scholar]
  • 3.Wang X, Mauzerall DL. Characterizing distribution of tropospheric ozone and its impacts on grain production in China, Japan and South Korea: 1990 and 2000. Atmos Environ. 2004;38:4383–402. doi: 10.1016/j.atmosenv.2004.03.067. [DOI] [Google Scholar]
  • 4.Dentener F, Stevenson D, Ellingsen K, Van Noije T, Schultz M, Amann M, Atherton C, Bell N, Bergmann D, Bey I, et al. The global atmospheric environment for the next generation. Environ Sci Technol. 2006;40:3586–94. doi: 10.1021/es0523845. [DOI] [PubMed] [Google Scholar]
  • 5.Emberson LD, Buker P, Ashmore MR, Mills G, Jackson LS, Agrawal M, et al. A comparison of North American and Asian exposure-response data for ozone effects on crop yields. Atmos Environ. 2009;43:1945–53. doi: 10.1016/j.atmosenv.2009.01.005. [DOI] [Google Scholar]
  • 6.Yoshikado H. One possible factor causing recent trend of photochemical oxidants. J Jpn Soc Atmos Environ. 2004;39:188–99. [Google Scholar]
  • 7.Yamaguchi M, Inada H, Satoh R, Hoshino D, Nagasawa A, Negishi Y, et al. Effects of ozone on the growth, yield and leaf gas exchange rates of two Japanese cultivars of rice (Oryza sativa L.). J. Agric Meteorol. 2008;64:131–41. doi: 10.2480/agrmet.64.3.8. [DOI] [Google Scholar]
  • 8.Omasa K, Saji H, Youssefian S, Kondo N. Air pollution and plant biotechnology: Prospects for phytomonitoring and phytoremediation, Springer-Verlag, Tokyo, 2002. [Google Scholar]
  • 9.Heagle AS. Ozone and crop yield. Annu Rev Phytopathol. 1989;27:397–423. doi: 10.1146/annurev.py.27.090189.002145. [DOI] [Google Scholar]
  • 10.Kobayashi K, Okada M. Effects of ozone on the light use of rice (Oryza sativa L.) plants. Agric Ecosyst Environ. 1995;53:1–12. doi: 10.1016/0167-8809(94)00560-2. [DOI] [Google Scholar]
  • 11.Morgan PB, Mies TA, Bollero GA, Nelson RL, Long SP. Season-long elevation of ozone concentration to projected 2050 levels under fully open-air conditions substantially decreases the growth and production of soybean. New Phytol. 2006;170:333–43. doi: 10.1111/j.1469-8137.2006.01679.x. [DOI] [PubMed] [Google Scholar]
  • 12.Feng ZZ, Kobayashi K, Ainsworth EA. Impact of elevated ozone concentration on growth, physiology and yield of wheat (Triticum aestivum L.): A meta-analysis. Glob Change Biol. 2008;14:2696–708. [Google Scholar]
  • 13.Feng ZZ, Kobayashi K. Assessing the impacts of current and future concentrations of surface ozone on crop yield with meta-analysis. Atmos Environ. 2009;43:1510–9. doi: 10.1016/j.atmosenv.2008.11.033. [DOI] [Google Scholar]
  • 14.Gupta P, Duplessis S, White H, Karnosky DF, Martin F, Podila GK. Gene expression patterns of trembling aspen trees following long-term exposure to interacting elevated CO2 and tropospheric O3. New Phytol. 2005;167:129–41. doi: 10.1111/j.1469-8137.2005.01422.x. [DOI] [PubMed] [Google Scholar]
  • 15.Bohler S, Bagard M, Oufir M, Planchon S, Hoffmann L, Jolivet Y, Hausman JF, Dizengremel P, Renaut J. A DIGE analysis of developing poplar leaves subjected to ozone reveals major changes in carbon metabolism. Proteomics. 2007;7:1584–99. doi: 10.1002/pmic.200600822. [DOI] [PubMed] [Google Scholar]
  • 16.Kontunen-Soppela S, Ossipov V, Ossipova S, Oksanen E. Shift in birch leaf metabolome and carbon allocation during long-term open-field ozone exposure. Glob Change Biol. 2007;13:1053–67. doi: 10.1111/j.1365-2486.2007.01332.x. [DOI] [Google Scholar]
  • 17.Ossipov V, Ossipova S, Bykov V, Oksanen E, Koricheva J, Haukioja E. Application of metabolomics to genotype and phenotype discrimination of birch trees grown in a long-term open-field experiment. Metabolomics. 2008;4:39–51. doi: 10.1007/s11306-007-0097-8. [DOI] [Google Scholar]
  • 18.Lee S, Yun SC. The ozone stress transcriptome of pepper (Capsicum annuum L.) Mol Cells. 2006;21:197–205. [PubMed] [Google Scholar]
  • 19.Tamaoki M, Nakajima N, Kubo A, Aono M, Matsuyama T, Saji H. Transcriptome analysis of O3-exposed Arabidopsis reveals that multiple signal pathways act mutually antagonistically to induce gene expression. Plant Mol Biol. 2003;53:443–56. doi: 10.1023/B:PLAN.0000019064.55734.52. [DOI] [PubMed] [Google Scholar]
  • 20.Mahalingam R, Jambunathan N, Gunjan SK, Faustin E, Weng H, Ayoubi P. Analysis of oxidative signalling induced by ozone in Arabidopsis thaliana. Plant Cell Environ. 2006;29:1357–71. doi: 10.1111/j.1365-3040.2006.01516.x. [DOI] [PubMed] [Google Scholar]
  • 21.Torres NL, Cho K, Shibato J, Hirano M, Kubo A, Masuo Y, Iwahashi H, Jwa NS, Agrawal GK, Rakwal R. Gel-based proteomics reveals potential novel protein markers of ozone stress in leaves of cultivated bean and maize species of Panama. Electrophoresis. 2007;28:4369–81. doi: 10.1002/elps.200700219. [DOI] [PubMed] [Google Scholar]
  • 22.Agrawal GK, Rakwal R, Yonekura M, Kubo A, Saji H. Proteome analysis of differentially displayed proteins as a tool for investigating ozone stress in rice (Oryza sativa L.) seedlings. Proteomics. 2002;2:947–59. doi: 10.1002/1615-9861(200208)2:8&#x0003c;947::AID-PROT947&#x0003e;3.0.CO;2-J. [DOI] [PubMed] [Google Scholar]
  • 23.Cho K, Shibato J, Agrawal GK, Jung YH, Kubo A, Jwa NS, Tamogami S, Satoh K, Kikuchi S, Higashi T, et al. Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling. J Proteome Res. 2008;7:2980–98. doi: 10.1021/pr800128q. [DOI] [PubMed] [Google Scholar]
  • 24.Cho K, Tiwari S, Agrawal SB, Torres NL, Agrawal M, Sarkar A, Shibato J, Agrawal GK, Kubo A, Rakwal R. Tropospheric ozone and plants: absorption, responses, and consequences. Rev Environ Contam Toxicol. 2011;212:61–111. doi: 10.1007/978-1-4419-8453-1_3. [RECT] [DOI] [PubMed] [Google Scholar]
  • 25.Cho K, Kubo A, Shibato J, Agrawal GK, Saji H, Rakwal R. Global identification of potential gene biomarkers associated with ozone-induced foliar injury in rice seedling leaves by correlating their symptom severity with transcriptome profiling. Int J Life Sci. 2012;6:1–13. doi: 10.3126/ijls.v6i1.6059. [DOI] [Google Scholar]
  • 26.Cho K, Shibato J, Kubo A, Kohno Y, Satoh K, Kikuchi S, Agrawal GK, Sarkar A, Rakwal R. Genome-wide mapping of the ozone-responsive transcriptomes in rice panicle and seed tissues reveals novel insight into their regulatory events. Biotechnol Lett. 2013;35:647–56. doi: 10.1007/s10529-012-1118-x. [DOI] [PubMed] [Google Scholar]
  • 27.Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37:914–39. doi: 10.1111/j.1365-313X.2004.02016.x. [DOI] [PubMed] [Google Scholar]
  • 28.Usadel B, Poree F, Nagel A, Lohse M, Czedik-Eysenberg A, Stitt M. A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize. Plant Cell Environ. 2009;32:1211–29. doi: 10.1111/j.1365-3040.2009.01978.x. [DOI] [PubMed] [Google Scholar]
  • 29.Nijhawan A, Jain M, Tyagi AK, Khurana JP. Genomic survey and gene expression analysis of the basic leucine zipper transcription factor family in rice. Plant Physiol. 2008;146:333–50. doi: 10.1104/pp.107.112821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Onodera Y, Suzuki A, Wu CY, Washida H, Takaiwa F. A rice functional transcriptional activator, RISBZ1, responsible for endosperm-specific expression of storage protein genes through GCN4 motif. J Biol Chem. 2001;276:14139–52. doi: 10.1074/jbc.M007405200. [DOI] [PubMed] [Google Scholar]
  • 31.Vicente-Carbajosa J, Oñate L, Lara P, Diaz I, Carbonero P. Barley BLZ1: a bZIP transcriptional activator that interacts with endosperm-specific gene promoters. Plant J. 1998;13:629–40. doi: 10.1111/j.1365-313X.1998.00068.x. [DOI] [PubMed] [Google Scholar]
  • 32.Oñate L, Vicente-Carbajosa J, Lara P, Díaz I, Carbonero P. Barley BLZ2, a seed-specific bZIP protein that interacts with BLZ1 in vivo and activates transcription from the GCN4-like motif of B-hordein promoters in barley endosperm. J Biol Chem. 1999;274:9175–82. doi: 10.1074/jbc.274.14.9175. [DOI] [PubMed] [Google Scholar]
  • 33.Shen H, Yin Y, Chen F, Xu Y, Dixon RA. A bioinformatic analysis of NAC Genes for plant cell wall development in relation to lignocellulosic bioenergy production. Bioenerg Res. 2009;2:217–32. doi: 10.1007/s12155-009-9047-9. [DOI] [Google Scholar]
  • 34.Zhong R, Lee C, Zhou J, McCarthy RL, Ye ZH. A battery of transcription factors involved in the regulation of secondary cell wall biosynthesis in Arabidopsis. Plant Cell. 2008;20:2763–82. doi: 10.1105/tpc.108.061325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Rajani S, Sundaresan V. The Arabidopsis myc/bHLH gene ALCATRAZ enables cell separation in fruit dehiscence. Curr Biol. 2001;11:1914–22. doi: 10.1016/S0960-9822(01)00593-0. [DOI] [PubMed] [Google Scholar]
  • 36.Kim EH, Kim YS, Park SH, Koo YJ, Choi YD, Chung YY, Lee IJ, Kim JK. Methyl jasmonate reduces grain yield by mediating stress signals to alter spikelet development in rice. Plant Physiol. 2009;149:1751–60. doi: 10.1104/pp.108.134684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Gu D, Liu X, Wang M, Zheng J, Hou W, Wang G, Wang J. Overexpression of ZmOPR1 in Arabidopsis enhanced the tolerance to osmotic and salt stress during seed germination. Plant Sci. 2008;174:124–30. doi: 10.1016/j.plantsci.2007.09.010. [DOI] [Google Scholar]
  • 38.Ohta H, Shirano Y, Tanaka K, Morita Y, Shibata D. cDNA cloning of rice lipoxygenase L-2 and characterization using an active enzyme expressed from the cDNA in Escherichia coli. Eur J Biochem. 1992;206:331–6. doi: 10.1111/j.1432-1033.1992.tb16931.x. [DOI] [PubMed] [Google Scholar]
  • 39.Beynon ER, Symons ZC, Jackson RG, Lorenz A, Rylott EL, Bruce NC. The role of oxophytodienoate reductases in the detoxification of the explosive 2,4,6-trinitrotoluene by Arabidopsis. Plant Physiol. 2009;151:253–61. doi: 10.1104/pp.109.141598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Lorenzo O, Piqueras R, Sánchez-Serrano JJ, Solano R. ETHYLENE RESPONSE FACTOR1 integrates signals from ethylene and jasmonate pathways in plant defense. Plant Cell. 2003;15:165–78. doi: 10.1105/tpc.007468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Xu ZS, Xia LQ, Chen M, Cheng XG, Zhang RY, Li LC, Zhao YX, Lu Y, Ni ZY, Liu L, et al. Isolation and molecular characterization of the Triticum aestivum L. ethylene-responsive factor 1 (TaERF1) that increases multiple stress tolerance. Plant Mol Biol. 2007;65:719–32. doi: 10.1007/s11103-007-9237-9. [DOI] [PubMed] [Google Scholar]
  • 42.Bustamante-Porras J, Noirot M, Campa C, Hamon S, de Kochko A. Isolation and characterization of a Coffea canephora ERF-like cDNA. Afr J Biotechnol. 2004;4:157–9. [Google Scholar]
  • 43.Chinnusamy V, Ohta M, Kanrar S, Lee BH, Hong X, Agarwal M, Zhu JK. ICE1: a regulator of cold-induced transcriptome and freezing tolerance in Arabidopsis. Genes Dev. 2003;17:1043–54. doi: 10.1101/gad.1077503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Hannah MA, Heyer AG, Hincha DK. A global survey of gene regulation during cold acclimation in Arabidopsis thaliana. PLoS Genet. 2005;1:e26. doi: 10.1371/journal.pgen.0010026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Ooka H, Satoh K, Doi K, Nagata T, Otomo Y, Murakami K, Matsubara K, Osato N, Kawai J, Carninci P, et al. Comprehensive analysis of NAC family genes in Oryza sativa and Arabidopsis thaliana. DNA Res. 2003;10:239–47. doi: 10.1093/dnares/10.6.239. [DOI] [PubMed] [Google Scholar]
  • 46.Xia N, Zhan G, Sun Y, Lin Z, Xu L, Chen X, et al. TaNAC8, a novel NAC transcription factor gene in wheat, responds to stripe rust pathogen infection and abiotic stresses. Physiol Mol Plant Pathol. 2010;74:394–402. doi: 10.1016/j.pmpp.2010.06.005. [DOI] [Google Scholar]
  • 47.Jung C, Seo JS, Han SW, Koo YJ, Kim CH, Song SI, Nahm BH, Choi YD, Cheong JJ. Overexpression of AtMYB44 enhances stomatal closure to confer abiotic stress tolerance in transgenic Arabidopsis. Plant Physiol. 2008;146:623–35. doi: 10.1104/pp.107.110981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Rosenzweig BA, Pine PS, Domon OE, Morris SM, Chen JJ, Sistare FD. Dye bias correction in dual-labeled cDNA microarray gene expression measurements. Environ Health Perspect. 2004;112:480–7. doi: 10.1289/ehp.6694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Altman N. Replication, variation and normalisation in microarray experiments. Appl Bioinformatics. 2005;4:33–44. doi: 10.2165/00822942-200504010-00004. [DOI] [PubMed] [Google Scholar]
  • 50.Martin-Magniette ML, Aubert J, Cabannes E, Daudin JJ. Evaluation of the gene-specific dye bias in cDNA microarray experiments. Bioinformatics. 2005;21:1995–2000. doi: 10.1093/bioinformatics/bti302. [DOI] [PubMed] [Google Scholar]
  • 51.Hirano M, Rakwal R, Shibato J, Sawa H, Nagashima K, Ogawa Y, Yoshida Y, Iwahashi H, Niki E, Masuo Y. Proteomics- and transcriptomics-based screening of differentially expressed proteins and genes in brain of Wig rat: a model for attention deficit hyperactivity disorder (ADHD) research. J Proteome Res. 2008;7:2471–89. doi: 10.1021/pr800025t. [DOI] [PubMed] [Google Scholar]
  • 52.Tano K, Mizuno R, Okada T, Rakwal R, Shibato J, Masuo Y, Ijiri K, Akimitsu N. MALAT-1 enhances cell motility of lung adenocarcinoma cells by influencing the expression of motility-related genes. FEBS Lett. 2010;584:4575–80. doi: 10.1016/j.febslet.2010.10.008. [DOI] [PubMed] [Google Scholar]
  • 53.Ogawa T, Rakwal R, Shibato J, Sawa C, Saito T, Murayama A, Kuwagata M, Kageyama H, Yagi M, Satoh K, et al. Seeking gene candidates responsible for developmental origins of health and disease. Congenit Anom (Kyoto) 2011;51:110–25. doi: 10.1111/j.1741-4520.2011.00315.x. [DOI] [PubMed] [Google Scholar]
  • 54.Hori M, Nakamachi T, Rakwal R, Shibato J, Nakamura K, Wada Y, Tsuchikawa D, Yoshikawa A, Tamaki K, Shioda S. Unraveling the ischemic brain transcriptome in a permanent middle cerebral artery occlusion mouse model by DNA microarray analysis. Dis Model Mech. 2012;5:270–83. doi: 10.1242/dmm.008276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Waditee-Sirisattha R, Shibato J, Rakwal R, Sirisattha S, Hattori A, Nakano T, Takabe T, Tsujimoto M. The Arabidopsis aminopeptidase LAP2 regulates plant growth, leaf longevity and stress response. New Phytol. 2011;191:958–69. doi: 10.1111/j.1469-8137.2011.03758.x. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional material
psb-8-e26300-s01.pdf (635.1KB, pdf)

Articles from Plant Signaling & Behavior are provided here courtesy of Taylor & Francis

RESOURCES