Integrated Transcriptomics, Proteomics, and Metabolomics Analyses

Integrated Transcriptomics, Proteomics, and Metabolomics Analyses To Survey Ozone Responses in the Leaves of Rice Seedling Kyoungwon Cho,† Junko Shibato,‡ Ganesh Kumar Agrawal,§,¶ Young-Ho Jung,| Akihiro Kubo,† Nam-Soo Jwa,| Shigeru Tamogami,⊥ Kouji Satoh,∇ Shoshi Kikuchi,∇ Tetsuji Higashi,O Shinzo Kimura,# Hikaru Saji,† Yoshihide Tanaka,O Hitoshi Iwahashi,O Yoshinori Masuo,‡ and Randeep Rakwal*,‡,§ Environmental Biology Division, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan, Health Technology Research Center (HTRC), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba West, 16-1 Onogawa, Tsukuba 305-8569, Japan, Research Laboratory for Biotechnology and Biochemistry (RLABB), Kathmandu, Nepal, Department of Molecular Biology, College of Natural Science, Sejong University, Seoul 143-747, Korea, Laboratory of Growth Regulation Chemistry, Department of Biological Production, Akita Prefectural University, Akita 010-0195, Japan, Plant Genome Research Unit, National Institute of Agrobiological Sciences, Kannondai 2-1-2, Tsukuba 305-8602, Ibaraki, Japan, HTRC, AIST Kansai Center, 1-8-31 Midorigaoka, Ikeda 563-8577, Japan, and Hazard Assessment and Epidemiology Research Group, Japan National Institute of Occupational Safety and Health (Japan NIOSH), Kawasaki 214-8585, Japan Received February 15, 2008

Ozone (O3), a serious air pollutant, is known to significantly reduce photosynthesis, growth, and yield and to cause foliar injury and senescence. Here, integrated transcriptomics, proteomics, and metabolomics approaches were applied to investigate the molecular responses of O3 in the leaves of 2-week-old rice (cv. Nipponbare) seedlings exposed to 0.2 ppm O3 for a period of 24 h. On the basis of the morphological alteration of O3-exposed rice leaves, transcript profiling of rice genes was performed in leaves exposed for 1, 12, and 24 h using rice DNA microarray chip. A total of 1535 nonredundant genes showed altered expression of more than 5-fold over the control, representing 8 main functional categories. Genes involved in information storage and processing (10%) and cellular processing and signaling categories (24%) were highly represented within 1 h of O3 treatment; transcriptional factor and signal transduction, respectively, were the main subcategories. Genes categorized into information storage and processing (17, 23%), cellular processing and signaling (20, 16%) and metabolism (18, 19%) were mainly regulated at 12 and 24 h; their main subcategories were ribosomal protein, posttranslational modification, and signal transduction and secondary metabolites biosynthesis, respectively. Twodimensional gel electrophoresis-based proteomics analyses in combination with tandem mass spectrometer identified 23 differentially expressed protein spots (21 nonredundant proteins) in leaves exposed to O3 for 24 h compared to respective control. Identified proteins were found to be involved in cellular processing and signaling (32%), photosynthesis (19%), and defense (14%). Capillary electrophoresis-mass spectrometry-based metabolomic profiling revealed accumulation of amino acids, gamma-aminobutyric acid, and glutathione in O3 exposed leaves until 24 h over control. This systematic survey showed that O3 triggers a chain reaction of altered gene, protein and metabolite expressions involved in multiple cellular processes in rice. Keywords: ozone • oxidative stress • jasmonic acid • ethylene • gel-based approach • mass spectrometry • DNA microarray

1. Introduction Ozone (O3) is produced by the photochemical reactions of volatile organic compounds with nitrogen oxides in the tro* To whom correspondence should be addressed. Dr. Randeep Rakwal, HTRC, AIST, Tsukuba West, 16-1 Onogawa, Tsukuba 305-8569, Japan. E-mail, [email protected]; fax, +81-29-861-8508. † Environmental Biology Division, National Institute for Environmental Studies. ‡ Health Technology Research Center (HTRC), National Institute of Advanced Industrial Science and Technology (AIST). ¶ Present address: University of Missouri-Columbia, Biochemistry Department, 204 Life Sciences Center, Columbia, MO 65211, USA. § Research Laboratory for Biotechnology and Biochemistry (RLABB). | Department of Molecular Biology, Sejong University. ⊥ Department of Biological Production, Akita Prefectural University. ∇ Plant Genome Research Unit, National Institute of Agrobiological Sciences. O HTRC, AIST Kansai Center. # Hazard Assessment and Epidemiology Research Group, Japan National Institute of Occupational Safety and Health (Japan NIOSH).

2980 Journal of Proteome Research 2008, 7, 2980–2998 Published on Web 06/03/2008

posphere. Annually, used fossil fuels have further increased the ground-level of O3 causing serious biological and environmental problems. In plants, gaseous O3 enters a leaf through the stomata, reacts with water, spontaneously generates the reactive oxygen species (ROS) leading to multiple and complex oxidative injuries.1–6 Plants exposed to O3 over long period manifest early senescence (a chronic symptom) and reduction of photosynthesis, growth rate and crop yields. But a high-dose of O3 to plants even for short period causes visible lesions on leaves (an acute symptom) similar to hypersensitive cell death.2,6,7 O3-triggered molecular responses in plants have been investigated mainly in Arabidopsis, a dicot model (Arabidopsis thaliana) plant.2,6,7 However, considering the importance of rice as a model plant for socio-economic crop transcriptomes and proteomes,8,9 O3 responses in rice (Oryza sativa L.), a monocot genome model plant,10 remain largely unknown. 10.1021/pr800128q CCC: $40.75

 2008 American Chemical Society

research articles

Ozone Responses in the Leaves of Rice Seedling

Figure 1. Investigation for molecular responses of rice to O3 exposure. In this experiment, 2-week-old rice plants of cultivar Nipponbare (type japonica) were exposed to 0.2 ppm O3 for 24 h. Controls were exposed to filtered air. Morphological changes of O3-exposed leaves such as foliar injury were observed at 1, 12, and 24 h exposure. Changes in the expression of genes and proteins and the accumulation of metabolites were investigated in O3-exposed rice leaves using transcriptomics (DNA microarray chip), proteomics (2-DGE coupled with LC-MS/MS) and metabolomics (CE-MS). To understand O3 responses of rice, data derived from three “omics” approaches were used for a parallel comparative analysis.

Previously, initial proteomics studies have been conducted in plants such as rice,11 maize, and bean12 exposed to O3 using two-dimensional gel electrophoresis (2-DGE) coupled with mass spectrometry and N-terminal amino acid sequencing. Among these, the rice study has been more systematic with respect to protein identification and profiling at 24-72 h after O3 treatment.11 In that study, Agrawal and co-workers observed morphological changes of rice leaves exposed to O3 until 72 h and identified 37 differentially expressed proteins including photosynthetic proteins and defense/stress-related proteins.11 The down-regulation of the photosynthetic proteins [ribulose1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and RuBisCO activase] and up-regulation of defense-related proteins were seen.11 However, there has been no systematic investigation on the genes and metabolites of O3-exposed rice plants. To date, there are only few transcriptomics studies in Arabidopsis plants exposed to O3.13,14 In this study, we have performed a systematic analysis of molecular responses at gene, protein, and metabolite levels in rice seedling (2-week-old) leaves using parallel transcriptomics,15 proteomics,8,9,16–18 and metabolomics (capillary electrophoresis-mass spectrometry, CE-MS19) approaches. The strategy for these integrated “omics” approaches used here is outlined in Figure 1. This integrated analysis resulted in the identification of 1535 genes, 21 proteins, and 24 metabolites. Furthermore, based on the results, we have mapped some of the biochemical pathways with reverse transcriptase-polymerase chain reaction (RT-PCR) validation highlighting the importance of an integrated approach for understanding the cellular responses to stress. The results also provide a global perspective on the signaling and metabolic pathways involved in rice leaf response to O3.

2. Materials and Methods 2.1. Plant Material and Grinding of Rice Leaves in Liquid Nitrogen. Rice (cv. Nipponbare; O. sativa L. japonica-type) seeds were sterilized with 4-fold diluted sodium hypochlorite solution (WAKO, Tokyo, Japan), germinated and grown for 2 weeks under white fluorescent light (wavelength 390-500 nm, 150 µmol m-2 s-1, 12 h photoperiod) at 25 °C and 70% relative humidity as previously reported.20 Two-week-old rice plants were transferred to the O3 chamber and exposed continuously to 0.2 ppm O3 or to the filtered pollutant free air as a control for 24 h. The chambers were strictly controlled at 25 °C with relative humidity of 70%, wind velocity of 0.22 m/s, and light conditions of 350 µmol · m-2 · s-1 as previously described.11,12 Samples were colleted from the third and fourth leaves of 2-week-old rice plants at 0, 1, 12, and 24 h for DNA microarray and proteomics analysis and at 0, 6, 12, and 24 h for metabolites profiling. They were immediately frozen in liquid nitrogen and stored at -80 °C. Three biological replicates of 40 plants each per seedling pot were exposed to O3 and clean air, respectively. The leaves were pooled and ground thoroughly with a chilled mortar and pestle to a fine powder in liquid nitrogen and stored at -80 °C until extracted for total RNA, protein, and metabolites. 2.2. Total RNA Extraction and Transcriptomics Analysis. Total RNA was extracted from healthy (0 h), control (pure airexposed rice for 1, 12, and 24 h), and O3-stressed (O3-exposed rice for 1, 12, and 24 h) leaves using the QIAGEN RNeasy Plant Maxi Kit (QIAGEN, MD) according to the manufacturer’s instruction. The extracted RNA purity and yield were determined spectrophotometrically (NanoDrop, Wilmington, Delaware) and visually confirmed using formaldehyde-agarose gel electrophoresis. For microarray analysis, a rice 22K custom oligo DNA microarray chip (G4138A, Agilent Technologies) that contains Journal of Proteome Research • Vol. 7, No. 7, 2008 2981

research articles 21 475 oligo nucleotides synthesized based on the sequence data of the rice full-length cDNA project (KOME: http:// red.dna.affrc.go.jp/cDNA/) was used as previously performed.15 Total RNA (400 ng) was labeled with Cy-3 or Cy-5 using an Agilent Low RNA Input Fluorescent Linear Amplification Kit. Fluorescently labeled targets of control (0 h) and O3-stressed samples (1, 12, and 24 h) were hybridized to the same microarray slide using a flip labeling (dye-swap or reverse labeling with Cy3 and Cy5 dyes) procedure as shown in Supplementary Figure 1. A flip labeling (dye-swap or reverse labeling with Cy3 and Cy5 dyes) procedure was followed in the second biological replication to nullify the dye bias associated with unequal incorporation of the two Cy dyes into cDNA.21–23 Hybridized microarray slides were scanned using a Gene Pix microarray scanner. Quantification of gene expression was done using the Genepix ver. 4.0 quantitative microarray analysis application program (Axon Instruments, Union City, CA). The ratio of intensity Cy3/Cy5 and Cy5/Cy3 was calculated and normalized [LOWESS (locally weighted linear regression)] with negative control spots using “Chip Cleanser” program.24 Genes with intensity ratio of over 5 compared with control and O3stressed samples were selected, annotated using NCBI database and homology alignment, and classified according to their functions. For the control experiment, using time-course samples from pure air-exposed rice plants under continuous light as mentioned above, we performed the hybridization using an Agilent microarray scanner G2565BA.25 For detection of significant differentially expressed genes between healthy leaves and each time point until 24 h in pure air, each slide was processed by Agilent Feature Extraction ver. 8.1.1.1. Dyebias tends to be signal intensity-dependent; therefore, this software selected a probe set by rank consistency filter for dyenormalization, the normalization was done by LOWESS, and it calculated log ratio of dye-normalized Cy3- and Cy5-signal, and final error of log ratio and significant value (P-value) based on propagate error model and universal error model. In this analysis, the threshold of significant differentially expressed genes determined p-value < 0.01. In addition, erroneous data generated due to artifacts were eliminated before data analysis. 2.3. Validation of Gene Expression Profiles by RT-PCR. Briefly, total RNA samples were DNase-treated with an RNasefree DNase (Stratagene, La Jolla, CA) prior to RT-PCR. Firststrand cDNA was synthesized in a 50 µL reaction mixture with a StrataScriptT RT-PCR Kit (Stratagene) according to the protocol provided by the manufacturer using 10 µg of total RNA isolated from control and O3-stressed rice leaves. The 50 µL reaction mixture (in 1× buffer recommended by the manufacturer of the polymerase) contained 200 mM dNTPs, 10 pmol of each primer set, and 0.5 U of Taq polymerase (TaKaRa Ex Taq Hot Start Version, TaKaRa Shuzo Co. Ltd., Shiga, Japan). Specific primers were designed from the 3′-UTR regions (forward and reverse primer sequences are provided in Supplementary Table 1) of each of the genes used in this study by comparison and alignment with all available related genes in NCBI and KOME (http://cdna01.dna.affrc.go.jp/cDNA/) databases. Thermal-cycling parameters were as follows: after an initial denaturation at 97 °C for 5 min, samples were subjected to a cycling regime of 25-30 cycles at 95 °C for 45 s, 55 °C for 45 s, and 72 °C for 1 min. At the end of the final cycle, an additional extension step was carried out for 10 min at 72 °C (TaKaRa PCR Thermal Cycle Dice, Model TP600, Tokyo, Japan). After completion of the PCR, the total reaction mixture was mixed with 2.0 µL of 10× loading buffer and vortexed, 10 µL 2982

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Cho et al. was loaded into wells of a 1.5% agarose (Agarose ME, Iwai Chemicals, Tokyo, Japan) gel, and electrophoreses was performed for ca. 30 min at 100 V in 1× TAE buffer, using a Mupidex electrophoresis system (ADVANCE, Tokyo, Japan). The gels were stained (20 µL of 50 mg/mL ethidium bromide in 100 mL of 1× TAE buffer) for 10 min, and the stained bands were visualized using an UV-transilluminator (ATTO, Tokyo, Japan). 2.4. Protein Extraction and 2-DGE. Total protein was extracted using two-step trichloroacetic acid/acetone protein extraction protocol (TCAAEB20) with some modifications. Proteins (from samples exposed to pure air (control) or O3 for 24 h) were precipitated with TCAAEB [acetone containing 10% (w/v) TCA, and 0.07% 2-mercaptoethanol (2-ME)] for 1 h at -20 °C, and centrifuged at 15 000 rpm for 15 min at 4 °C. The pellet was washed thrice with wash buffer (acetone containing 0.07% 2-ME, 2 mM EDTA, and 2 EDTA-free proteinase inhibitor cocktail tablets (Roche) in a final volume of 100 mL buffer) followed by removal of all the acetone by air drying the pellet at ambient room temperature (RT). Solubilization of the protein pellet was accomplished in lysis buffer (LB)-TT [7 M urea, 2 M thiourea, 4% (w/v) 3-[(3-cholamidopropyl) dimethylammonio]1-propanesulfonate, 18 mM Tris-HCl (pH 8.0), 14 mM Trizma base, two EDTA-free proteinase inhibitor cocktail tablets, 0.2% (v/v) Triton X-100 (R), and 50 mM dithiothreitol (DTT) to a final volume of 100 mL] for 30 min at RT with occasional vortexing and sonication, followed by centrifugation at 15 000 rpm for 15 min at 20 °C. Protein quantification was performed using a Coomassie Plus (PIERCE, Rockford, IL) protein assay kit. 2-DGE was carried out using precast IPG strip gels on an IPGphor unit (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) followed by the second dimension using ExcelGel XL sodium dodecyl sulfate-polyacrylamide gel (SDS-PAG) on a Multiphor II horizontal electrophoresis unit (GE Healthcare). The volume carrying 120 µg of total soluble protein was mixed with LB-TT containing 0.5% (v/v) pH 4-7 IPG buffer to bring to a final volume of 450 µL. A trace of BPB was added and the sample was centrifuged at 15 000 rpm for 15 min followed by pipetting into 24 cm strip holder tray placed into the IPGphor unit. IPG strips (pH 4-7; 24 cm, GE Healthcare) were carefully placed onto the protein samples avoiding air bubbles between the sample and the gel. The IPG strips were allowed to passively rehydrate with the protein samples for 1.5 h, followed by overlaying the IPG strips with cover fluid (mineral oil), and this was directly linked to a five-step active rehydration and focusing protocol as described (Supplementary Figure 2). The whole procedure was controlled at 20 °C, and a total of 76 908 Vh was used for the 24 cm strip. The strip gels were incubated in equilibration buffer (50 mM Tris-HCl (pH 8.8), 6 M urea, 30% (v/v) glycerol, and 2% (w/v) SDS) containing 2% (w/v) DTT for 10 min (twice) with gentle agitation, followed by incubation in the same equilibration buffer supplemented with 2.5% (w/ v) iodoacetamide for the same time periods as above at room temperature. The IPG strip was placed onto the precast gradient (12-14%) SDS-PAG and electrophoresis was carried out at 20 and 40 mA/gel for ca. 3.5 h. Three gel replications were performed for each sample. 2.5. Protein Visualization and Image Analysis. To visualize the protein spots, the two-dimensional polyacrylamide gels were stained with silver nitrate using the Plus One Silver Staining Kit (GE Healthcare). Protein patterns in the gels were recorded as digitalized (8 bit grayscale, resolution 300 dpi) images using a ProteomeScan2000 digital scanner (System Biotics KK, Yokohama,

Ozone Responses in the Leaves of Rice Seedling Japan), and saved as TIFF file types. The scanned 2D gel images were sent to Ludesi Analysis Center (LUDESI AB, Ideon Science Park, Lund, Sweden; www.ludesi.com) for image analysis using Ludesi’s proprietary image analysis software, following a protocol with milestone quality evaluations of spot detection, spot segmentation, and spot matching, to ensure a high level of correctness (http://www.ludesi.com/analysis_center). The protein spots were automatically detected and the results were manually verified and edited where needed. The gels were matched using all-to-all spot matching, avoiding introduction of bias caused by the use of a reference gel. The matching was iteratively evaluated and parameters refined to optimize the matching quality. Integrated intensities were measured for each spot, background corrected, and then normalized. The normalization removes systematic gel intensity differences originating from variations in staining, scanning time, protein loading, and so forth by mathematically minimizing the median expression difference between matched spots. In the current experiment, only those spots that had >2.0-fold expression in the control versus O3 comparison with a p-value < 0.05 were selected for spot excision and further analysis, if mentioned otherwise. 2.6. In-Gel Digestion and Mass Spectrometric Analysis. Two stained protein spots were excised from the silver-stained 2-D gels using a gel picker (One Touch Spot Picker, P2D1.5 and 3.0; The GelCompany, San Francisco, CA) and transferred to sterilized eppendorf tubes (1.5 mL). The gel pieces were incubated until removal of the silver stain in 15 mM potassium ferricyanide and 50 mM sodium thiosulfate. The gel pieces were transferred to sterilized water and washed two times. The gel pieces were incubated in 0.2 M NH4HCO3 (pH 7.8) for 20 min. Gel pieces were shrunk by dehydration in acetonitrile, which was then removed, followed by washing with vortexing in the same volume of acetonitrile and 0.1 M NH4HCO3 (pH 7.8). After removing the solution, the gel pieces were dehydrated with vortexing by addition of acetonitrile, and swelled by rehydration in 0.1 M NH4HCO3 (pH 7.8). After repeating the above dehydration process twice, the gel pieces were completely dried in a vacuum centrifuge. The gel pieces were swollen in a digestion buffer containing 10 mg/mL trypsin (Promega, sequencing grade) in ice. After a 45 min incubation, the digestion buffer was removed and replaced with 20-30 µL of 50 mM NH4HCO3 (pH 7.8), and the gel pieces were incubated at 37 °C for 8-12 h. The supernatant was desalted through a C18 ZipTip (Millipore, Bedford, MA) according to the manufacturer and a 2-5 µL solution was injected for analysis with LC-MS/MS (Agilent, Palo Alto, CA). The nLC was performed with an Agilent 1100 Nano LC-1100 system combined with a microwell-plate sampler and thermostatted column compartment for preconcentration (LC Packings, Agilent). The samples were loaded on the column (Zorbax 300SB-C18, 150 mm × 75 µm, 3.5 µm) using a preconcentration step in a microprecolumn cartridge (Zorbax 300SB-C18, 5 mm × 300 µm, 5 µm). A total of 2-5 µL of the sample was loaded on the precolumn at a flow rate of 30 µL/min. After 5 min, the precolumn was connected with the separating column, and the gradient was started at 300 nL/min. The buffers used were 0.1% HCOOH in water (A) and 0.1% HCOOH in acetonitrile (B). A linear gradient from 15 to 45% B for 25 min was applied. A single run took 75 min, which included the regeneration step. An LC/MSD Trap XCT with a nanoelectrospary interface (Agilent) was used for MS. Ionization (2.0 kV ionization potential) was performed with

research articles a liquid junction and a noncoated capillary probe (New Objective, Cambridge). 2.7. Database Search and Protein Identification. Tandem MS spectra were searched against the National Center for Biological Information nonredundant (NCBInr) protein database, using the Agilent Spectrum Mill MS Proteomics Workbench (Spectrum Mill version; Agilent Technologies, Santa Clara, CA). This software includes a Data Extractor function that identifies good quality MS/MS spectrum for peptides by seeking CID fragment differences that correspond to known amino acids (sequence tag length >1) and thus functions as a filter to discard spectra that are unlikely to arise from peptides. MS/MS spectra were created with the Spectrum Mill Data Extractor program with the following setting: search parameters included unmodified and carbamidomethylation of cysteine. Scans with the same precursor (1.4 m/z were merged within a time frame of (15 s. Precursor ions needed to have a minimum signal-to-noise value of 25. Charges up to a maximum of 7 were assigned to the precursor ion, and the 12C peak was determined by the Data Extractor. The extracted MS/MS spectra were searched against the plants and O. sativa NCBInr databases for tryptic peptides with a mass tolerance of (2.5 Da for the precursor ions, a tolerance of (0.7 Da for the fragment ions, and 3 allowed maximum missed cleavages, in identity search mode. A Spectrum Mill autovalidation was preformed first in the protein details mode. Minimum scores, minimum scored peak intensity (SPI), forward minus reversed score threshold, and rank 1 minus rank 2 score threshold for peptides were dependent on the assigned precursor charge (see Supplementary Table 2). Then, autovalidation in the peptide mode was performed using a score threshold of 13 and SPI of 70% for 1+, 3+, and 4+ and 11 and 60% for 2+ precursor ions. Forward minus reversed score threshold and rank 1 minus rank 2 score threshold were set to 2. To eliminate redundancy, the Protein Summary Modes groups all proteins that have at least one common peptide, and only the highest scoring member of each protein group is shown and counted in the protein list. All MS spectra are listed in Supplementary Figure 3. 2.8. Metabolites Extraction and their Determination by CE-MS. The rice metabolites were extracted based on a previously published protocol developed for yeast metabolite extraction19 and the first rice metabolite report.26,27 Briefly, D,Lhomocysteine and O-acetyl-L-serine were obtained from MP Biomedicals (Irvine, CA), γ-L-glutamylcysteine and L-cysteinylglycine ammonium salt were obtained from Bachem AG (Bubendorf, Switzerland), O-acetyl-L-homoserine hydrochloride was obtained from Toronto Research Chemicals (North York, ON, Canada), and S-adenosyl-L-methionine chloride was from Sigma-Aldrich Japan (Tokyo, Japan). All other standard materials as their free forms were obtained from Wako Pure Chemical (Osaka, Japan) or Sigma-Aldrich Japan. As internal standards, L-methionine sulfone and piperazine-1,4-bis(2-ethanesulfonic acid), sesquisodium salt, monohydrate (PIPES-Na) were obtained from Sigma-Aldrich Japan and Dojin Chemicals (Kumamoto, Japan), respectively. Formic acid and methanol were of LC/MS grade, and 2-propanol was of HPLC grade. Water was purified with a Milli-Q (MQ) water purification system (Nihon Millipore, Tokyo, Japan). All other chemicals and reagents were of analytical grade and were obtained from commercial sources. The rice leaves were ground in liquid nitrogen and the leaf powder (ca. 100 mg) was extracted in 700 µL of chloroform and 500 µL of cold methanol followed by the Journal of Proteome Research • Vol. 7, No. 7, 2008 2983

research articles addition of 500 µL of cold MQ water containing a known amount of methionine sulfone and PIPES as internal standards. The resulting mixture was centrifuged at 13 000 rpm for 10 min. The supernatant was then filtered through 0.45 µm MILLEXHV pvdf syringe driven filter followed by an ultrafiltration membrane (Ultrafree-MC filter, 5000 NMWL, Millipore, Bedford, MA). The filtered solution was stored in the deep freeze at -80 °C until analyzed. CE-MS experiments were performed using a P/ACE MDQ system (Beckman Coulter, Tokyo, Japan). An ESI interface (Agilent Technologies Japan, Tokyo, Japan) was used to couple the CE instrument with a Bruker Esquire 3000 plus ion trap mass spectrometer (Bruker Daltonics, Yokohama, Japan). Sheath liquid consisting of 5 mM formic acid in 2-propanol/water mixture (50:50, v/v) was applied coaxially at a rate of 10 µL/ min using an Agilent1200 series isocratic pump (Agilent Technologies Japan, Tokyo, Japan). Analytical conditions are as follows: an untreated fused-silica capillary (50 µm i.d., 375 µm o.d. and 100 cm long, Polymicro, Phoenix, AZ) was used for CE separation. The CE capillary was conditioned for 20 min with 100 mM ammonia solution prior to initial use and was washed for 3 min with 2-propanol/50 mM ammonia solution (50:50, v/v), for 2 min with water and for 5 min with 1 M formic acid solution (pH 1.8) before each run. A small volume of sample was hydrodynamically injected into the capillary by applying 1 psi (6.9 kPa) pressure for 30 s. A constant voltage of 30 kV was applied across the capillary for the electrophoretic separation. For detection, the mass spectrometer was operated in the positive-ion ESI mode in the range 70-600 m/z ratio at a maximum accumulation time of 100 ms with a target of 30 000 ions. The target mass was set to 100 and 300 for tuning optimization.

3. Results and Discussion 3.1. Overview of Genes Regulated by O3 Stress. Previously, the alternations of visual symptoms and protein expression levels derived from O3-induced damage were observed.11 The results provided basic information involved in responses of rice to O3, but were not sufficient to understand the whole molecular responses. For this, we applied advanced systematic “omics” approaches such as transcriptomics, proteomics, and metabolomics (Figure 1). First, the morphological changes of rice leaves exposed to O3 for 24 h were observed. The result showed that visible injuries were initiated almost all over the leaf surface at 24 h exposure (Figure 2), and were highly aggravated until 72 h as reported previously.11 Furthermore, to investigate early and late molecular responses to O3 for inducing foliar lesions, we sampled the third and fourth leaves exposed to O3 for 1, 12, and 24 h and checked the transcript levels of approximately 22 000 rice genes (cDNA clones) using a DNA microarray chip (Supplementary Figure 1). After normalization, the genes whose transcript levels were altered more than 5-fold in O3-exposed leaves over the control were summarized and presented in Supplementary Tables 3-8. The result showed that in total 394, 983, and 615 genes are up-regulated in O3-exposed leaves for 1, 12, and 24 h, respectively, whereas 38, 244, and 71 genes are down-regulated at the same time periods (Figure 3). Finally, a total of 1535 nonredundant genes were grouped into 8 functional categories (Figure 4). Figure 4 showed that the 1 h exposure mainly regulates genes subcategorized into transcriptional factor in information storage and processing category and signal transduction in cellular processing and signaling category. In O32984

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Figure 2. O3 exposure initiates foliar injury in an exposure timedependent manner. Two-week-old rice plants were transferred to a O3 chamber and then exposed to 0.2 ppm O3 for 24 h. Morphological changes of their third leaves were observed at 1, 12, and 24 h exposure.

exposed leaves for 12 and 24 h, transcriptional factor and ribosomal protein in information storage and processing category, post-translational modification and signal transduction in cellular process and signaling category, and metabolism related-related genes were the main subcategories. Additionally, compared to the categories of induced and repressed genes, cellular respiration- and defense-related genes were mainly induced at 12 and 24 h exposures, whereas photosynthesisrelated genes were repressed. The results suggest that damages from O3 are derived at 12 and 24 h exposure and their defense mechanisms are also activated with the regulation of transcription, translation, and signal transduction; the increase of cellular respiration; and the decrease of photosynthesis. 3.1.1. Transcriptional Factors. The categorization of genes regulated by O3 exposure showed that a number of genes grouped into the information storage and processing category are induced. Thus, the category was further divided into transcription factor and DNA repair, RNA processing and modification, and translation factor and ribosomal proteins subcategories, and then listed in Supplementary Table 3. The result showed that the O3 exposure mainly induces genes encoding transcription factors. In particular, considering the ratio of the number of transcription factors to a total of categorized genes at each time, the result suggests that at 1 h exposure, transcription factors mediate the early responses to O3. Therefore, based on the literature survey, transcription factors involved in responses to biotic and abiotic stresses have been shown in Table 1. WRKY proteins with conserved WRKY motif and zinc fingerlike domain function as transcriptional activators or repressors. Ross et al.28 reviewed the regulation and functions of 98 WRKY genes identified in japonica rice. Out of these genes, 14 WRKY genes differentially regulated in O3-stressed rice leaves are listed in Table 1. Compared to their transcript levels depending on the period of O3 exposure, those of OsWRKY1, 24, 26, 28, 42, 68, 69, and 71 were maximally enhanced at 1 h exposure, whereas those of OsWRKY11, 45, 55, 72, and 76 reached a maximum at 12 h and that of OsWRKY77 was strongly enhanced until 24 h. A previous study of the rice WRKY family showed that OsWRKY72 and 77 activate the ABA-inducible HVA22 promoter, whereas OsWRKY24 and 45 repress it.29 Moreover, OsWRKY71 induced by ABA, SA, JA, 1-aminocyclopropane-1-carboxylic acid (ACC), wounding and pathogen infection has been reported to repress the gibberellin (GA)

Ozone Responses in the Leaves of Rice Seedling

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Figure 3. Differentially expressed genes in rice leaves exposed to O3 for 1, 12, and 24 h. Totally, 625, 1705, and 1436 genes were induced and 323, 953, and 543 genes were repressed more than 2-fold in rice leaves exposed to 0.2 ppm O3 for 1, 12, and 24 h over control, respectively. Out of them, genes altered more than 5-fold over control represented that 394, 983, and 615 genes are induced and 38, 244, and 71 genes are repressed at 1, 12, and 24 h, respectively. On the whole, 1266 and 267 genes were induced and repressed, respectively, in O3-exposed rice leaves, and 2 genes overlapped.

signaling30 and to be involved in rice defense responses.31 On the basis of previous studies, the transcriptional enhancement of OsWRKY24 and 45 (negative regulators), and OsWRKY72 and 77 (positive regulators) in O3-stressed rice leaves demonstrate that ABA signaling is controlled by the competitive mechanism of positive regulators and negative regulators, the interaction with other factors, or unknown possible mechanisms. Additionally, within 1 h exposure, the enhanced transcription of OsWRKY71 may suggest the repression of GA signaling. On the basis of previously reported rice NAC family genes,32 we selected NAC genes differently expressed in O3-exposed rice leaves. The result showed that the transcript levels of ONAC009, ONAC014, ONAC022, ONAC048, ONAC052, and ONAC067 are increased at 1 h exposure, whereas those of ONAC066 and ONAC068 are maximized at 12 h. The subgroup ATAF of NAC family has been reported to be rapidly and transiently induced by wounding and to be involved in a crucial role in response to stresses.33,32 The result suggests that ONAC009 and ONAC048 classified into subgroup ATAF may be implicated in O3

responses at 1 h exposure. Although the functions of NAC proteins as yet remain unknown, the transcript levels of ONAC067 and ONAC068 genes classified to subgroup OsNAC3, which is believed to be involved in monocot-specific responses to stress,32 were respectively maximized at 1 and 12 h. The result suggests the importance of ONAC067 and ONAC068 in responses to O3 at 1 and 12 h exposures, respectively. Furthermore, 7 genes encoding MYB family proteins were regulated by O3 exposure. Out of them, the transcript level of MYBS3 gene, which is repressed under sugar supplement and encodes a repressor in the expression of sugar starvationinducible genes,34 was decreased in rice leaves exposed to O3 for 1 h. However, that of MYBS2 gene, which is induced under sugar supplement and encodes an activator in the expression of sugar starvation-inducible genes,34 was increased at 12 and 24 h exposure (Table 1). The results demonstrate that, until 24 h exposure of O3, sugar is sufficiently supplemented. In addition, the transcription of genes encoding OsMYB4 or OsMYB4-related protein were enhanced in O3-stressed rice Journal of Proteome Research • Vol. 7, No. 7, 2008 2985

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Figure 4. Functional categorization of induced and suppressed genes in O3-exposed rice leaves. The functional categorization was performed as described in section 2.2. The functional categories are indicated on the left-hand side in line with each bar of the histogram representing each named category.

leaves. OsMYB4, induced by cold stress and JA treatment has been reported to be involved in growth, to increase tolerance to cold and drought, and to integrate the accumulation of various components in responses to stresses.15,35,36 On the basis of the above-described results, the enhanced transcription of OsMYB4 or OsMYB4-related genes in O3-stressed rice may also cause accumulation of components such as glucose, fructose, sucrose, proline, glycine betaine and sinapoyl malate. In case of ERE family genes, OsERF#24 (OsDREB1A), OsERF#40 (OsDREB2A), OsERF#70 (OsEREBP1), OsERF#91 and OsERF#101 genes were regulated in O3-stressed rice leaves. The transcript level of OsERF#24 that plays crucial roles in cold-, salt-, and drought-stress-responsive gene expression37 was increased in rice leaves exposed to O3 for 12 and 24 h. The 1 h exposure decreased the transcript levels of two genes encoding OsERF#40, which is involved in dehydration and salt-stress and OsERF#91, which is linked to defense gene expression against pathogen infection.37 In the case of OsERF#70 and OsERF#101, their transcript levels were increased in rice leaves exposed to O3 for 12 h. The phylogenic analysis and the conserved motif search of rice and Arabidopsis ERE family proteins showed that OsERF#101 may act as a repressor of ABA signaling.38,39 Recently, OsERF#70 has been reported to be phosphorylated and activated by mitogen-activated protein kinases (OsBWMK1) induced by O3 exposure.39–41 In addition, three CONSTANS family genes involved in the regulation of photoperiod responses42 were repressed in O3-stressed rice leaves, though 2986

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their functions are unknown. The above-mentioned results showed that genes in each gene family are differently regulated depending on the O3 exposure time. This means that the understanding of signals derived from different exposure time is crucial to study responses to O3. Thus, in the next section, we checked signaling-related genes regulated by O3. 3.1.2. Signaling Pathways. Previously, a possible model of O3-dependent signaling pathways was proposed to be activated by alterations of ion conductance as Ca2+ influxes into the cytosol.43 Our microarray data showed that 1 h exposure to O3 enhances the transcription of cellular process and signalingrelated genes such as ion transporters, MAPK, Ca2+-dependent protein kinases (CPKs), other Ca2+-binding proteins, and receptor kinases (Figure 4 and Supplementary Table 4). The result based on the above proposed model suggests that the release of Ca2+ to cytosol by O3 exposure causes the disruption of redox-homeostasis and then activates CPKs. Thus, for understanding Ca2+-related signaling, CPK-related genes were summarized in Table 2. Homologous analysis based on their amino acid sequences was performed to predict their functions. Table 2 shows that the transcript levels of four OsCPKs (OsCPK13, 15, 20, and 24) responsive to multiple stresses such as cold, drought, salt, and pathogens44,45 and OsCPK4 nonresponsive to those stresses46 are increased at 1 h. The result demonstrates that Ca2+-related signals may play a role in responses to 1 h exposure.

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Ozone Responses in the Leaves of Rice Seedling Table 1. Transcript Profiling of Transcription Factors Regulated in O3-Exposed Rice Leaves fold annotation

WRKY

NAC

MYB

AP2

CONSTANS

gene accession

1h

12 h

24 h

protein name

AK058773 AK061266 AK066255 AK068337 AK101653 AK105509 AK106282 AK107199 AK108522 AK108555 AK108745 AK108860 AK110587 AK111606 AK063399 AK107746 AK073667 AK073848 AK073539 AK107090 AK068776 AK105493 AK066834 AK068565 AK071611 AK109011 AK111637 AK111798 AK112056 AK067313 AK073812 AK101501 AK103783 AK105599 AK058536 AK100097 AK109732

15.45 8.28 b b b 32.58 40.3 47.95 20.71 15.96 b 3.21 12.56 25.75 7.29 6.61 33.85 11.6 b 13.33 15.13 6.09 23.43 0.19 b 3.46 0.32 39.81 10.61 7.88 35.55 b b b 0.17 0.19 b

b b 17.69 11.58 6.33 19.61 9.91 5.51 23.15 7.35 6.3 24.15 6 7.27 b b b 26.3 5.36 b b b b b 5.15 4.77 0.06 22.03 3.54 6.05 8.95 5.16 12.9 0.19 0.08 0.1 0.05

b b 13.82 6.08 5.02 8.85 3.73 3.56 16.96 2.17 3.97 9.05 5.66 7.32 b b b 5.1 2.61 b b b b b 4.52 5.4 0.38 8.11 b b 2.61 3.71 b 0.17 0.29 0.31 0.14

OsWRKY71 OsWRKY68 OsWRKY45 OsWRKY76 OsWRKY55 OsWRKY1 OsWRKY28 OsWRKY24 OsWRKY77 OsWRKY26 OsWRKY11 OsWRKY72 OsWRKY42 OsWRKY69 ONAC009 ONAC048 ONAC067 ONAC068 ONAC066 ONAC022 ONAC052 ONAC014 Similar to MYBHv5 MYBS3 MYBS2 OsMYB4 Similar to GmMYB177 Similar to OsMYB4 Similar to ZmMYB39 OsERF#040 (OsDREB2A) OsERF#091 OsERF#101 OsERF#070 (OsEREBP1) OsERF#024 (OsDREB1A) OsD OsD OsH

Table 2. Transcript Profiling of MAP Kinases Regulated in O3-Exposed Rice Leaves fold annotation

CPK

MAPKKK

MAPKK MAPK

gene accession

1h

12 h

24 h

protein name

AK060738 AK066495 AK068315 AK070346 AK102308 AK058518 AK071585 AK105196 AK105946 AK059461 AK111598 AK067339 AK111579

5.45 6.07 5.09 7.28 7.11 b 18.28 30.46 18.22 8.02 b 49.38 b

b b b 12.5 8.91 0.12 b 5.65 b b 7.11 10.55 7.05

b b b 5.95 b 0.2 b b b b b b b

OsCPK4 OsCPK13 OsCPK20 OsCPK15 OsCPK24 NPK1-related protein kinase NPK1-related protein kinase NPK1-related protein kinase NPK1-related protein kinase OsMEK1 OsMAPKK2 OsMPK6 (OsMSRMK2) OsMPK16

Additionally, we observed the regulation of MAPK cascaderelated genes in O3-exposed leaves such as 4 MAPK kinase kinases (MAPKKKs; NPK1-related protein kinases), 2 MAPK kinases (MAPKKs; OsMEK and OsMAPKK2), and 2 MAPKs (OsMPK6 and OsMPK16). In our previous studies, we characterized various rice MAPKs involved in the multiple environmental stresses, signal molecules, and pathogens40,47–49 and reviewed MAPK cascades in rice briefly.50 Therein, the pos-

sibility of OsMPK6 activation by OsMEK1 was proposed.50 On the basis of the responses of MAPK cascades proposed by Zhang and Klessig51 and the alternation of the transcript levels of MAPK cascade-related genes according to O3 exposure time, Table 2 suggests the activation of NPK1-OsMEK1-OsMPK6 cascade at 1 h exposure and that of NPK1-OsMAPKK2-OsMPK6/OsMPK16 cascade at 12 h, although their experimental evidence are poor. Journal of Proteome Research • Vol. 7, No. 7, 2008 2987

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Figure 5. O3 exposure induces genes in pentose phosphate pathway. On the basis of the genes functionally categorized from transcriptome (Supplementary Table 5), putative participants in pentose phosphate pathway were sorted. The pathway was diagramed based on KEGG pathway database linked to enzyme database BRENDA (http://www.brenda-enzymes.info/). The AK numbers represent the gene array numbers and correspond to the cDNA clones identified in the rice full-length cDNA cloning project (http:// cdna01.dna.affrc.go.jp/cDNA/). RT-PCR for validating different expression of sorted genes was performed as described in section 2.3.

3.1.3. Antioxidant System. Previously, another signaling pathway to O3 was proposed to be derived from changes in the total cellular redox balance of low molecular antioxidants such as ascorbic acid, glutathione, or the total cellular NAD(P)H/NAD(P).43,52 Therein, it was suggested that when the level of ROS produced from O3 exceeds the capacity of antioxidants, the redox balance is altered and then relevant responses to O3 are turned on.52 Figure 4 showed that the transcript level of antioxidant system-related genes is increased in O3-exposed rice leaves. Thus, to investigate antioxidants involved in signaling responses to O3, we sorted antioxidant genes induced at 1 h exposure from genes functionally categorized in Supplementary Table 5 such as glutathione Stransferase (GST), glutathione peroxidase, and glutathione reductase. Two proteins except GST are well-known to be involved in the pentose phosphate pathway to protect the cell against ROS (Figure 5). Moreover, the transcript levels of participants in the pentose phosphate pathway such as glucose6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase was observed to be enhanced at 1 h (Supplementary Table 5). The results suggest that the pentose phosphate pathway and GSTs are involved in protecting the cell against oxidative damage derived from 1 h exposure of O3. In rice leaves exposed to O3 for 12 and 24 h, the transcript levels of GST, glutathione synthetase, peroxidase, catalase, monodehydroascobate reductase, and redoxin-related proteins were increased as summarized in Supplementary Table 5. The result indicates that the antioxidant proteins may play crucial roles in reducing oxidative damages derived from 12 and 24 h exposure. Interestingly, the transcription of several genes repressed by JA-treatment was shown to be induced at 12 and 2988

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24 h exposure.15 For example, the transcript levels of GST (AK059760, AK063773, and AK103316) and L-ascorbate peroxidase (APX, AK061841) were shown to be increased at 12 and 24 h exposure but to be decreased in JA-treated rice root. However, cationic peroxidase (AK102307) was induced in both O3-exposed rice leaves and JA-treated rice root. The results seem to be caused by a cross-talk between JA-mediated signaling and its antagonistic pathway. Thus, we investigated the transcript levels of JA and other signal molecules biosynthesis-related genes, and discussed possible signals induced in O3-exposed rice leaves in the following section. 3.1.4. Regulation of JA and ET Biosynthesis-Related Genes. In determining sensitivity of plant cultivars to O3, plant hormones such as JA, ET, salicylic acid (SA), and ABA have been reported to play important roles.52 Therein, it was described that ABA for regulation of stomatal closure for controlling O3 influx into intracellular space, SA for initiation of the O3 lesion, ET for lesion propagation, and JA for counteraction of lesion spread are needed in Arabidopsis.52 Thus, to investigate the involvement of JA, SA, and ET in rice leaves damaged from O3 exposure, we tried to sort their biosynthesis-related genes among the differentially expressed genes by O3 (Supplementary Table 6). Results revealed the presence of changed genes encoding for components in JA and ET biosynthesis (Figures 6 and 8) but not for SA. JA biosynthesis is initiated by the conversion of linolenic acid into 13(S)-hydroperoxy linolenic acid by lipoxygenase (LOX) reaction and than terminated by the subsequent reactions of allene oxide synthase (AOS), allene oxide cyclase (AOC), OPDA reductase (OPR), and β-oxidation three times. Finally, JA is converted to methyl-JA (MeJA) by the methyltransferase reac-

Ozone Responses in the Leaves of Rice Seedling

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Figure 6. Participants in JA biosynthesis are induced in O3-exposed leaves. Candidates for JA biosynthesis were selected from functionally categorized transcriptome (Supplementary Table 6). The pathway diagram and the RT-PCR were performed as described in the legend to Figure 5.

tion.53 Figure 6 shows that the transcription of LOX, AOS, OPR, and methyltransferase is enhanced at 1 h exposure, and in the case of LOX and AOS genes, their transcription is activated at 12 and 24 h exposure. Validation by RT-PCR demonstrated that isoforms of the participants of JA biosynthesis are differently regulated depending on exposure time as described in the microarray data. However, on the whole, each part of JA biosynthesis seemed to be constitutively transcripted regardless of O3 exposure time. Furthermore, to investigate the relevance of JA signaling in responses induced in O3-exposed rice leaves, we checked how many genes of JA-regulated genes are involved in O3 response based on our previous DNA microarray analysis.15 The result showed that most of JA-regulated genes (over 70%) are nonresponsive to O3 exposure; 13, 24, and 23% of JAinduced genes are enhanced; and 8, 16, and 13% of JArepressed genes are suppressed at 1, 12, and 24 h exposure, respectively (Figure 7). Interestingly, 6, 12, and 12% of JArepressed genes and 2.4, 4.4, and 3.7% of JA-induced genes were

antagonistically regulated at 1, 12, and 24 h, respectively. RTPCR data also showed that the transcription of PR1 (AK107926, a JA-repressed gene) is enhanced and that of OsRelA2 (AK058438, a JA-induced gene) is suppressed at 12 and 24 h exposure (Supplementary Figure 4). Additionally, the quantification of endogenous JA in O3-exposed rice leaves showed that JA is not accumulated until 24 h exposure (data not shown). The results lead us to propose that, until 24 h exposure, other signaling pathways, such as Ca2+, the disruption of redox homeostasis, ROS, and so forth, are partially overlapped with JA signaling and are involved in the O3 response. Figure 7 revealed the existence of an antagonistic signal pathway to JA signaling. ET signaling was reported to function independent of JA, to inhibit JA-dependent responses,54–56 and to induce synergistic responses by correlation with JA.57–59 The report suggested possibility of ET as an antagonistic signal in rice leaf response to O3. The categorized transcript profiling also showed that the transcript levels of participants in ET Journal of Proteome Research • Vol. 7, No. 7, 2008 2989

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Figure 7. O3 exposure partially regulates JA-induced and repressed genes. Our previous report showed the induction of 454 and the repression of 252 genes in JA-treated rice seedling.15 Out of JA-induced (A) and repressed (B) genes, the relative number of O3regulated genes was investigated based on transcriptome altered more than 2-fold in leaves exposed to O3 for 1, 12, and 24 h than in control. Red color represents O3-induced genes, blue is O3-repressed genes, and yellow means nonresponsive genes to O3.

Figure 8. Participants in ET biosynthesis are induced in O3-exposed leaves. Candidates for ET biosynthesis were selected from functionally categorized transcriptome (Supplementary Table 6). The pathway diagram and the RT-PCR were performed as described in the legend to Figure 5.

biosynthesis are enhanced in O3-exposed rice leaves. Particularly, Figure 8 shows that the transcription of aminocyclopropane-1-carboxylic acid (ACC) synthase is enhanced at 1, 12, and 24 h. Considering that ET biosynthesis is more strictly regulated at the level of ACC produced by ACC synthase than ACC oxidase,60 the initiation of ET biosynthesis may be induced 2990

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from 1 h exposure. However, most of the ET biosynthesis pathway genes were enhanced at 12 and 24 h. Therefore, to know when ET is accumulated, we checked ET levels in a timedependent manner. The result showed that ET level gradually increased 2.5-, 5.5-, 7-, and 7.5-fold at 3, 6, 12, and 24 h exposure over the pure air control, respectively. Moreover,

Ozone Responses in the Leaves of Rice Seedling Supplementary Figure 5A showed that the transcript levels of photophosphorylation-related genes are repressed at 12 h exposure similar to the reported repression of the same genes in ET-treated Arabidopsis plants.61 Additionally, our microarray data showed that the transcription of glycolysis-, citric acid- and oxidative respiration-, the shikimate pathway- and lignin biosynthesis-related genes is enhanced at 12 or 24 h exposure (Supplementary Figures 5 and 6). However, we need a further detailed investigation into what signaling pathways are involved therein. 3.2. Differentially Expressed Protein Profiles in Leaves at 24 h after O3-Exposure. Previously, Agrawal and co-workers working with small (100 × 120 mm) Coomassie brilliant blue (CBB)-stained 2-D gels reported that several protein spots are differentially expressed in rice leaves exposed to O3 for 24-72 h, and most of the identified proteins were at 48 and 72 h exposure.11 At that time, their (spot) analysis by N-terminal amino acid sequences was limited due to technical constraints and lack of mass spectrometer-based protein identification. However, the advancement in identifying sliver nitrate-stained proteins spots by MS (Jwa, N. S., personal communication) again provided us a challenge to investigate proteins involved in responses of rice plant to O3. Thus, to get more information involved in O3 response and to supplement the interpretation of O3 effect based on transcriptomics, total proteins extracted from O3-exposed rice leaves were loaded on large-format 2-D gradient gel (pH 4-7 IPG was used in the first dimension) as previously described9 followed by staining with silver nitrate. Results revealed 28 spots to be differentially expressed in O3exposed rice leaves (Figure 9). Further, a total of 32 spots including 4 reference spots (in yellow, Figure 9) were analyzed using nESI-LC-MS/MS analysis. Twenty-seven spots including 4 reference spots were identified and summarized in Table 3. The result showed that there are 21 nonredundant proteins. Their functional categorization indicated that cellular processing and signaling-, defense-, and photosynthesis-related proteins represent 65% of total nonredundant proteins (Figure 10). On the basis of their functions, their responses to O3 have been discussed below. In rice leaves exposed to O3 for 24 h as previously reported, ATP-dependent Clp protease (spot 3) having a high homology (94%) with ClpC was induced. It was reported that ClpC regulates chlorophyll β synthesis by controlling the level of chlorophyllide R oxygenase and its inactivation causes growth retardation, leaf chlorosis, lower photosynthetic activity, and a specific reduction in photosystem content.62,63 Moreover, the induction of chloroplast cell division protease ftsH homologue (spot 4) was also observed. Its amino acid sequence has 90% similarity to that of chloroplast FtsH protein in tobacco. In tobacco mosaic virus-infected tobacco leaves, the decrease of its level was shown to result in a loss of function of the chloroplasts and a hypersensitive reaction.64 Additionally, HSP 90 (spot 6), which is similar (93%) to barley HSP 90 induced by pathogen and heat shock and involved in processing of secreted proteins,65 and chloroplast HSP 70 (spot 7), which has 91% similarity to pea stromal HSP 70 involved in chloroplast protein maturation under stressed conditions,66 were found to be induced. On the basis of the functions of their homologous proteins, the aforementioned proteins may be induced to reduce oxidative damages derived from O3, such as disruption of photosynthetic apparatus. We also identified photosynthesis-related proteins such as RuBisCO activase small isoform (spot 11), RuBisCO large

research articles subunit (LSU: spots 20, 25, and 26), and small subunit (SSU: spots 27-29), of which spots 11, 20, and 29 were significantly increased in O3-exposed rice leaves excluding spots 25-28 as reference markers. In the case of RuBisCO activase small isoform (spot 11) which assists in the assembly of RuBisCO, its induction may also result in the decrease of misfolded RuBisCO by O3 damage. Moreover, considering molecular weights of RuBisCO LSU and SSU, spots 20 and 29 seemed to be fragmented or prematured. Indeed, the reduction of chloroplast 33 kDa ribonucleoprotein (spot 15), similar to tobacco chloroplast 31 kDa ribonucleoprotein involved in splicing and/ or processing of chloroplast RNAs,67 and the increases of oligopeptidase B (spot 23) and proteasome subunit alpha type 1 (spot 17), similar to 20S proteasome alpha subunit participating in the ATP-dependent degradation of proteins conjugated with ubiquitin,68 may lead to protein misfolding and fragmentation. The results demonstrate damages to the photosynthetic apparatus from O3. Additionally, we also observed the induction of defenserelated proteins. One is the probable chloroplast L-APX (spot 8) which plays a crucial role in hydrogen peroxide removal.69 Another is glutathione peroxidase (spot 21) which is similar (91%) to mitochondrial phospholipid hydroperoxide glutathione peroxidase 6 (AtGPX6) and is involved in protecting the cell and enzymes from oxidative damage by reducing ROS.70 Finally, putative basic secretory protein (spot 19) thought to be part of the plant’s defense mechanism and PR10s (spots 30-32) which is described as an important member of PR proteins involved in plant defense responses11 were strongly induced. We observed the induction of three spots identified as aconitate hydratase (spot 2) which has 95% similarity to pumpkin cytosolic aconitase that catalyzes citrate into isocitrate in the glyoxylate cycle,72 fumarylacetoacete hydrolase (spot 9) which catalyzes 4-fumarylacetoacetate into acetoacetate and fumarate as the last step of phenylalanine degradation pathway,73 and Ca2+-binding domain-conserved protein (spot 13). Furthermore, the decrease of four spots identified as glycine dehydrogenase P protein (spot 1) which is one component of glycine decarboxylase multienzyme complex involved in the interconversion of glycine-serine as a part of photorespiratory pathway, thiamine biosynthetic protein (spot 16) that participates in the biosynthesis of the thiazole precursor for thiamine, and the unknown function germ-like protein 5 (spots 12 and 14) was found in O3-exposed rice leaves. Furthermore, to investigate the correlation between proteomics and transcriptomics data derived from 24 h of O3 exposure, we checked the transcript levels of genes corresponding to the identified proteins. The result showed that 12 protein spots up- or down-regulated at 24 h exposure are derived from increase or decrease at their transcript levels at 12 h, respectively. However, in case of fumarylacetoacete hydrolase (spot 9) and RuBisCO LSU (spots 20) and SSU (spot 29), microarray data and 2-DGE showed their down-regulation at transcript levels and up-regulation at protein levels, respectively. Additionally, changes at transcript levels of 8 protein spots could not be observed at 12 and 24 h exposure. The phenomena may result from post-transcriptional modification caused by O3 exposure. 3.3. Time-Course Dependent Metabolite Profiling Supplements Proposals Derived from Transcriptomics Analysis. Transcript profiling in O3-stressed rice leaves and their functional categorization showed that JA and ET biosynthesisJournal of Proteome Research • Vol. 7, No. 7, 2008 2991

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Figure 9. Differentially expressed protein profiles in O3-exposed leaves by 2-DGE. The proteins were extracted by TCA/acetone protocol as described in section 2.4. and the pellet was solubilized in LB-TT. Total soluble protein (ca. 120 µg) was loaded on 2-DGE. Proteins were visualized by silver staining. The pI and molecular masses of these protein spots varied between 4-7, and 250-10 kDa. Scaned 2-D gel images were analyzed using Ludesi’s proprietary image analysis software. Protein spots (blue and red arrows) altered more than 2.0-fold in O3-exposed leaves (p-value < 0.05) and reference markers (yellow arrows) were selected for further analysis. The spots marked with red arrows indicate infinitely increased protein spots over control.

related genes are regulated. We confirmed the gradual increase of ET level in O3-exposed rice leaves until 24 h, but could not detect any significant changes in endogenous JA levels. The result is very similar to that in O3-tolerant Arabidopsis cultivar (Col-0). However, in O3-sensitive Arabidopsis cultivars, it was reported that O3 leads to the increase of ET levels, which subsequently spreads oxidative cell death, followed by increase in the JA level and finally prevents the propagation of cell death.52,74 Therein, it was proposed that JA accumulation is 2992

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regulated by the rate of substrate supply. In other words, membrane disruption such as wounding or cell death is thought to be needed for JA accumulation. Therefore, although the transcript levels of genes for JA biosynthesis and their RT-PCR data showed that each component of JA biosynthesis is constitutively transcripted, no clear change in JA level in O3-exposed rice leaves can be attributed to insufficient cell death under the present experimental condition as shown in Figure 2.

2.18

5.2

2.03

4.32

2.06

2.17

2.63

1.73 down 2.53

1.82 down 2.06 down

3.99 down

3.24

2.83

3.15

3.03

2

3

4

6

7

8

9

11

12

14

16

17

19

20

21

15

13

4.10 down 2.25

1

2

5

2

2

6

5

3

5

2

8

3

2

3

10

2

2

2

4

22.96

72.53

33.04

26.44

106.94

72.04

47.53

92.61

34.67

113.9

45.13

22.49

37.59

143.05

39.53

18.1

22.7

63.26

10

9

9

12

20

19

15

19

5

20

6

5

4

13

4

3

2

4

25838/9.42 115447759

59032/9.01 115487804

24699/5.50 125532460

29755/5.35 125537976

37225/5.44 115472485

31673/6.06 115460220

21861/6.01 115476760

30344/4.77 115477851

21861/6.01 115476760

52085/5.59 62733297

50893/5.93 125538476

51187/5.36 115446663

73550/5.12 125593660

95425/4.89 125598513

72743/5.51 125598556

113631/6.65 125590233

98083/5.67 75225211

111728/6.50 125571778

MS/MS spot fold distinct search % AA number change peptides score coverage MW (Da)/pI NCBI number

DGGLLQLTAIDGR/GQAGGLTFFAPSEER

GSGFVAVEIPFTPR/NPNRPIASFIFSGPTGVGK

AAGFDLNVIVADAK/IIGVSVDSSGKPALR/ GNINIEELR/IAILNANYMAK FYSLPALNDPR/ILLESAIR

matched peptide sequence

NFMTLPNIK/MGINPIMMSAGELESGNAGEPAK/ MCCLFINDLDAGAGR/FYWAPTR/ TDNVPDEDIVK/WVSDTGVENIGK/ EGPPEFEQPK/LMEYGYMLVK VTFLDDAQVK/K

ILSADEPVLR/NCGFIFR/QEPEPLPYLAEK

FDAELSHGANAGLINALK/FDNSYFK

-

AK105018 (99) 12 h

-

-

AK102478 (100) 12/24 h

-

-

AK059070 (100) 12/24 h

AK111968 (100) 12 h

homologous gene (positives)

Metabolism (Amino acid metabolism) Photosynthesis

Cellular Process and Signaling (Post-translational modification and turnover) Antioxidant System

Cellular Respiration (Citric aicd cycle) Cellular Process and Signaling (Post-translational modification and turnover) Cellular Process and Signaling (Post-translational modification and turnover) Cellular Process and Signaling (Post-translational modification and turnover)

Photosynthesis

functional category

AK065284 (100) 1/12/24 h Defense-Related Protein Putative C2 NGDLKPYAVLWVDDGAK/VDLDNADNPNWDDK/ AK064971 (100) 1/24 h Cellular Process and domain-containing protein LTLPLPPSSR/LPLR/DVLDDAGVGAR Signaling (Signal transduction) Germin-like protein 5 AAVTPAFVGQFPGVNGLGISAAR/ AK065284 (100) 1/12/24 h Defense-Related VTFLDDAQVK/K Protein 33 kDa ribonucleoprotein, LNSLDVMGR/KPAPPPPPGTILER/ Information storage chloroplast, putative LYVSNLPWK/SAGYGFVSFGTK/ and processing EEAEAALTELDGK Thiamine biosynthetic enzyme LFNAVAVEDLIVK/VVVSSCGHDGPFGATGVK/ Metabolism RLQDIGMIDAVPGMR/ALDMNTAEDEIVR/ (Carbohydrate MGPTFGAMMISGQK metabolism) Putative Proteasome subunit VADHAGVALAGLTADGR/SECINHAFVYDAPLPVSR AK059589 (100) 12/24 h Cellular Process and alpha type 1 Signaling (Post-translational modification and turnover) Putative basic secretory RPVDAVVLAVR/TVQQLWQDYK AK100816 (100) 12/24 h Cellular Process and protein Signaling (Intracellular trafficking and secretion) Ribulose bisphosphate LTYYTPEYETK/DTDILAAFR/ AK105600 (98) 12 h Photosynthesis carboxylase large chain ALR/LEDLR/IPPTYSK/ precursor TFQGPPHGIQVER Putative glutathione VDVNGDNTAPIYK/YAPTTSPLSMEK AK058456 (100) 24 h Antioxidant System peroxidase

Germin-like protein 5

Probable L-ascorbate peroxidase 8, chloroplast precursor Putative fumarylacetoacetate hydrolase RuBisCO activase small isoform precursor

ELISNASDALDK/IR/FLALTDK/ EVLGEGDTAK/ETTTEWELLNDVK/ ALLFVPPK/GLVDSDTLPLNVSR/ LGIIEDATNR/LASLDEYISR/ DIFYITGSSK/ALDTESVDSVK/ VLEINPR Chloroplast heat shock protein QAVVNPENTFFSVK/IAGLEVLR/ 70 IPAVQDLVK

Heat shock protein 90

Cell division protease ftsH homologue, chloroplast precursor

Glycine dehydrogenase P protein Aconitate hydratase, cytoplasmic ATP-dependent Clp protease ATP-binding subunit clpA homologue CD4B, chloroplast precursor

protein name

Table 3. Identification of the differentially expressed protein spots on 2-D gel by MS

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Journal of Proteome Research • Vol. 7, No. 7, 2008 2993

2994

5.22

NA

NA

NA

NA

infinity

infinity

infinity

infinity

23

25

26

Journal of Proteome Research • Vol. 7, No. 7, 2008

27

28

29

30

31

32

4

2

6

2

11

10

17

20

1

spot fold distinct number change peptides

Table 3. Continued

64.07

26.84

89.39

23.76

158.84

153.25

266.67

338.27

8.2

25

13

46

12

53

53

35

40

1

125556840

16981/4.96 115489016

17207/4.88 9230757

16900/4.88 115489014

19720/9.04 125536346

19720/9.04 125536346

19720/9.04 125536346

53701/6.33 109156602

53701/6.33 109156602

84220/

MS/MS search % AA score coverage MW (Da)/pI NCBI number

PBZ1

PR-10b

Ribulose bisphosphate carboxylase small chain PR-10

Ribulose bisphosphate carboxylase small chain

Ribulose bisphosphate carboxylase small chain

Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit

Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit

Putative oligopeptidase B

protein name

VCLDVHSLPK/LNPAADAGSVYK/ IVVCDSATHVLK/VEVLEVK

VFSDAPAMPK/LNPAVDDGGSFK/ DNAAHIIK/SEVLDVPAGSK/ INVEYELEDGGSLSPEK/EK/ MIEDYLVAHPTEYA VCLDVHSLPK/LNPAADAGSVYK

LTYYTPEYETK/DTDILAAFR/ TFQGPPHGIQVER/ACYECLR/GGLDFTK/ DDENVNSQPFMR/DR/FVFCAEAIYK/ GHYLNATAGTCEEMIKR/ ELGVPIVMHDYLTGGFTANTSLAHYCR/ DNGLLLHIHR/MSGGDHI HAGTVVGK/ FMTLGFVDLLR/DDFIEK/DR/ VALEACVQAR/WSPELAAACEIWK/ AIK/FEFEPVDK/LDS LTYYTPEYETK/DTDILAAFR/LEDLR/ IPPYTSK/TFQGPPHGIQVER/ACYECLR/ GGLDFTK/DDENVNSQPFMR/ FVFCAEAIYK/GHYLNATAGTCEEMIKR/ DNGLLLHIHR/MSGGDHIHAGTVVGK/ EMTLGFVDLLR/VALEACVQAR/ WSPELAAACEIWK/AIK/FEFEPVDK/LDS KFETLSYLPPLTVEDLLK/SK/ WVPCLEFSK/VGFVYR/YWTMWK/ LPMFGCTDATQVLK/KAYPDAFVR/ IIGFDNVR/QVQLISFIAYKPPGCEESGGN KFETLSYLPPLTVEDLLK/SK/WVPCLEFSK/ VGFVYR/YWTMWK/LPMFGCTDATQVLK/ KAYPDAFVR/IIGFDNVR/ QVQLISFIAYKPPGCEESGGN LPMFGCTDATQVLK/IIGFDNVR

TLTGK/EYVSPNGK

matched peptide sequence

Photosynthesis

Photosynthesis

Photosynthesis

Cellular Process and Signaling (Post-translational modification and turnover) Photosynthesis

functional category

AK099157 (100) 12/24 h

AK099157 (98) 12/24 h

AK061606 (100) 12 h

Defense-Related Protein Defense-Related Protein

Defense-Related Protein

AK068266 (99) 1/12/24 h Photosynthesis

AK068266 (99)

AK068266 (99)

AK105600 (96)

AK105600 (96)

-

homologous gene (positives)

research articles Cho et al.

research articles

Ozone Responses in the Leaves of Rice Seedling

Figure 10. Twenty-one nonredundant proteins identified in O3-exposed leaves belonged to 7 functional categories. A total of 28 protein spots were excised from 2-D gels, excluding 4 reference markers, and analyzed by LC-MS/MS. Twenty-three proteins were identified and 21 were nonredundant proteins. The pie chart shows the distribution of 21 nonredundant proteins identified in O3-exposed leaves into their functional classes in percentage. Table 4. Metabolite Profiling in O3-Exposed Rice Leavesa period of filtered pure air exposure 0h metabolites (µg/gFW)

mean

6h SE

Glycine 3.24 0.45 4(gamma)-Aminobutyric acid 20.24 0.93 Serine 108.25 5.77 Proline 38.23 0.82 Valine 46.20 0.72 Homoserine 1.80 0* Threonine 47.55 0.23 Iso-leucine 19.58 0.78 Leucine 26.77 1.22 Asparagine 291.14 0.38 Aspartic acid 201.23 10.91 Lysine 16.54 1.21 Glutamine 175.45 13.91 Glutamic acid 770.44 21.37 Methionine 5.30 0* Histidine 10.43 0.95 O-Acetyl-L-homoserine Phenylalanine 25.62 1.13 Arginine 17.03 1.22 Tyrosine 15.35 0.69 Tryptophan Adenosine 104.29 5.18 Glutathione, reduced 2.22 0* Glutathione, oxidized 17.41 1.25 a

12 h

period of O3 exposure

24 h

6h

12 h

24 h

mean

SE

mean

SE

mean

SE

mean

SE

mean

SE

mean

SE

3.43 33.09 58.49 31.84 28.79 1.13 72.79 10.59 16.83 117.94 203.84 12.09 137.67 806.85 6.44 6.97 16.13 33.72 12.15 110.24 3.05 16.45

0.01 0.61 1.40 0.74 0.64 0* 2.23 0.41 3.76 2.97 4.08 0.78 5.10 9.88 0.29 1.25 0.39 1.65 1.27 1.33 0.10 1.83

2.11 28.40 107.74 21.71 22.70 90.45 10.67 10.73 108.18 172.96 10.47 132.66 890.36 8.73 4.17 15.64 24.18 15.78 94.66 2.41 15.19

0* 0.40 6.90 0.56 2.41 3.71 2.03 2.22 2.95 1.32 0.47 0.62 1.34 1.56 0.19 3.06 2.58 0.12 5.90 0.81 0.38

4.25 34.62 139.43 37.19 47.11 3.97 103.86 13.79 23.72 238.85 247.45 14.59 740.53 1080.58 10.56 2.91 20.66 63.33 15.23 107.73 3.28 25.35

1.36 1.69 0.64 1.20 0.71 0.56 6.09 2.59 1.06 18.03 2.86 1.22 30.70 59.29 2.04 2.91 0.18 1.15 0.70 4.20 0.03 2.20

2.80 58.67 86.79 45.19 66.46 100.15 47.27 55.75 117.18 115.57 24.93 93.05 611.87 7.62 19.81 153.17 25.48 29.03 39.72 128.10 2.45 43.43

0.71 4.95 1.75 1.18 2.51 5.13 2.39 2.65 4.95 4.57 1.06 3.01 25.91 0.28 0.16 2.07 1.10 1.50 1.91 7.95 0.05 0.39

3.84 88.45 119.63 66.27 183.20 100.43 179.58 214.15 92.87 209.46 55.01 259.19 955.43 11.94 58.12 22.58 177.02 40.24 83.51 78.29 123.48 1.97 71.16

0.71 3.09 0.20 2.19 1.04 2.09 3.34 12.91 0.68 7.48 3.76 6.44 44.27 0.75 3.83 0.70 9.94 1.88 3.34 0.88 1.55 0* 0.29

6.60 209.82 321.60 107.78 360.85 5.22 126.51 298.35 337.14 568.82 431.79 81.78 1987.26 1955.10 18.05 124.21 36.55 183.37 180.20 91.28 110.04 223.11 111.18

0.24 4.45 2.36 3.56 9.41 0.12 4.38 1.28 8.57 6.11 5.25 7.69 45.84 10.55 0.81 0.66 6.32 1.63 5.77 2.92 5.35 11.57 12.94

Sign (-) means that their peaks could not be detected and asterisk (*) represents that out of two replicates they could be detected only one time.

Moreover, our microarray data showed that O3 exposure for 12 or 24 h enhances the transcription of components in glycolysis, the citric acid cycle, and the shikimate pathway (Supplementary Figures 5 and 6). Intermediates of glycolysis and the citric acid cycle such as 3-phosphoglycerate, erythrose 4-phosphate, phosphoenolpyruvate, pyruvate, oxaloacetate and R-ketolglutarate are used as precursors for amino acid biosynthesis. The shikimate pathway leads to the biosynthesis of tryptophan, tyrosine and phenylalanine from erythrose 4-phosphate and phosphoenolpyruvate. Thus, to confirm the activation of glycolysis, citric acid cycle, and skimate pathway

according to exposure time, we measured the concentration of amino acids, oxidized/reduced glutathione, and gammaaminobutyric acid (GABA; Table 4). The result showed that most amino acids are increased in rice leaves exposed to O3 for 12 and 24 h. Particularly, the accumulation of phenylalanine, tyrosine, and tryptophan and the up-regulation of their relevant genes at 12 and 24 h exposure suggest the biosynthesis of secondary metabolites derived from phenolic amino acids (Supplementary Figure 6). Indeed, Figure 11 shows that the transcription of naringenin-related genes is enhanced at 12 and 24 h exposure, and sakuranetin, a main rice secondary meJournal of Proteome Research • Vol. 7, No. 7, 2008 2995

research articles

Cho et al.

Figure 11. Components in secondary metabolite metabolism are induced in O3-exposed leaves. Candidates for naringenin (a secondary metabolite precursor) metabolism were selected from functionally categorized transcriptome (Supplementary Table 6). Sakuranetin was extracted from rice leaves (50 mg) with 80% methanol (total volume of 5 mL) by boiling for 5 min. Three microliters of the crude extract was injected onto a high-performance liquid chromatography (HPLC) separation system and analyzed by a LC-tandem mass spectrometry (LC-MS/MS) technique exactly as described previously.76 The pathway diagram and the RT-PCR were performed as described in the legend to Figure 5.

tabolite,75 is accumulated at 24 h. Furthermore, the levels of total glutathione and its precursors such as glycine and glutamic acid were accumulated to high levels in O3-exposed rice leaves compared with those in control. Microarray data also showed that the transcription of glutathione-related genes is enhanced by O3 exposure. The results imply that glutathione may play crucial roles in reducing oxidative damages derived from O3. A precursor for ET biosynthesis (methionine) was also shown to be increased in O3-stressed rice leaves along with the corresponding genes (Figure 8). Additionally, GABA synthesized from glutamate by glutamate decarboxylase reaction was dramatically accumulated according to O3 exposure time. The transcript level of the gene encoding glutamate decarboxylase also was shown to be strongly induced by O3 exposure (Figure 12). The result suggests that, although the role and function of GABA is still unknown in plant, GABA could play an important role in O3 response. Moreover, Figure 12 reveals that glutamate produced from R-ketoglutarate by glutamate dehydrogenase is used as an intermediate for GABA, glutamine, proline, arginine, and glutathione biosynthesis. The transcript and metabolite profiling showed the enhanced transcription of glutamate dehydrogenase, glutamate decarboxylase, and genes in citric acid cycle, and the accumulation of glutamate, glutamine, proline, arginine ,and glutathione in O3-exposed rice leaves, respectively (Figure 12, and Supplementary Figure 5). The results demonstrate the 2996

Journal of Proteome Research • Vol. 7, No. 7, 2008

exciting possibility of glutamate, GABA, and glutamate dehydrogenase as biomarkers for index of damages derived from O3 in rice.

4. Conclusions and Future Perspectives Currently, the increase in ground-level of O3 has caused serious problems in the growth and yield of crops. Thus, to understand systematical responses of rice, a main and critical food crop for Asia, to O3, we tried to integrate outputs that resulted from morphological, transcriptomics, proteomics, and metabolomics approaches. Functional categorization of genes profiled from transcriptomics revealed that O3 exposure induces the transcriptional alteration of transcription factors, MAPK cascades, and participants in JA, ET, shikimate, tryptophan, and lignin biosynthesis, glycolysis, citric acid cycle, oxidative respiration, and photosynthesis. Furthermore, protein and metabolite profiling supplemented and verified the molecular responses of rice to O3 derived from transcriptomics. For example, in O3-exposed rice leaves, the enhanced transcription of components in citric acid cycle and shikimate pathway and the accumulation of phenolic amino acids and sakuranetin demonstrated how secondary metabolites may be regulated. The transcriptional enhancement of glutamate dehydrogenase and glutamate decarboxylase, and the accumulation of glutamate, GABA, glutamine, proline, arginine, and glutathione showed the importance of glutamate and glutamate

research articles

Ozone Responses in the Leaves of Rice Seedling

carried out with the support of “On-Site Cooperative Agriculture Research Project (No. 20070301080003), RDA, Republic of Korea (N.-S.J.). We greatly appreciate the COG analysis of rice genes by Drs. Yeon-Ki Kim and Baek Hie Nahm from the Division of Bioscience and Bioinformatics, Myongji University, GreenGene BioTech, Inc., Kyonggido 449-728, Korea.

Supporting Information Available: Figures of microarray protocol, second-dimension Multiphor II, all MS spectra, RT-PCR data, and pathways. Tables of primer used for RT-PCR; MS database search parameters; and differentially regulated genes categorized into information storage and processing; cellular process and signaling; antioxidant system, cellular respiration and photosynthesis; metabolism; poorly characterized protein; and unknown. This material is available free of charge via the Internet at http://pubs.acs.org. References

Figure 12. Participants in GABA biosynthesis are induced in O3exposed leaves. Candidates for GABA biosynthesis were selected from functionally categorized transcriptome (Supplementary Table 6). The RT-PCR was performed as described in the legend to Figure 5.

dehydrogenase. The comparative analysis between JA-regulated transcriptome and O3-regulated transcriptome showed the partial participation of JA downstream of the signaling pathway and the existence of its antagonistic pathway. In further study based on the relevance between JA signal and ET signal in Arabidopsis, ET-regulated transcriptome will suggest a crucial cue to reveal the antagonistic pathway of JA signal and the roles of ET in O3 response. Considering the importance of transcription factors and signaling pathways in defense mechanism against environmental stresses, studies on pentose phosphate pathway, several transcription factors (WRKY, NAC, MYB, ERE), and MAPK cascades (NPK1-OsMEK1-OsMPK6 and NPK1OsMAPKK2-OsMPK6/OsMPK16) will be actively pursued. Particularly, to investigate MAPK cascades proposed from transcriptome, the profiling of phosphorylated proteins by the removal of abundant proteins (RuBisCO) will improve our understanding of O3 response in rice. Data Availability. All microarray data from this work is available in the NCBI Gene Expression Omnibus (GEO, (www. ncbi.nlm.nih.gov/geo) database under the series entry GSE11157 (ozone and control) and GSE11158 (jasmonic acid).

Acknowledgment. Authors appreciate the institutional support from NIES and AIST. This work was partly supported by a Grant-in-Aid from the Ministry of the Environment in Japan to H.I. The study was supported by the Korea Research Foundation Grant funded by the Korea Government (KRF-2006-352-F00009) to K.C. The study was funded in part by a grant from the Plant Signaling Network Research Center, Korea Science and Engineering Foundation. The study was also

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PR800128Q