Comparative Proteomic Analysis of Early-Stage ... - ACS Publications

Comparative Proteomic Analysis of Early-Stage Soybean Seedlings Responses to Flooding by Using Gel and Gel-Free Techniques Yohei Nanjo,† Ludovit Skultety,‡ Yahya Ashraf,§ and Setsuko Komatsu*,† National Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba 305-8518, Japan, Institute of Virology, Slovak Academy of Sciences, Bratislava 84505, Slovak Republic, and Pierre et Marie Curie University, Paris 75005, France Received February 28, 2010

Gel-based and gel-free proteomics techniques were used to investigate early responses to flooding stress in the roots and hypocotyls of soybean seedlings. Proteins from 2-day-old soybean seedlings flooded for 12 h were extracted and analyzed. Two mass-spectroscopy-based proteomics analyses, two-dimensional fluorescence difference gel electrophoresis, and nanoliquid chromatography identified 32 from 17 spots and 81 proteins, respectively, as responsive to flooding stress. On the basis of the number and function of proteins identified, glycolysis and fermentation enzymes and inducers of heat shock proteins were key elements in the early responses to flooding stress. Analysis of enzyme activities and carbohydrate contents in flooded seedlings showed that glucose degradation and sucrose accumulation accelerated during flooding due to activation of glycolysis and down-regulation of sucrose degrading enzymes. Additionally, the methylglyoxal pathway, which is detoxification system linked to glycolysis, was up-regulated. Furthermore, two-dimensional polyacrylamide gel electrophoresis-based phosphoproteomics analysis showed that proteins involved in protein folding and synthesis were dephosphorylated under flooding conditions. These results suggest that translational and posttranslational control during flooding possibly induces an imbalance in the expression of proteins involved in several metabolic pathways including carbohydrate metabolism that might cause flooding injury of soybean seedlings. Keywords: soybean • seedling • roots and hypocotyls • flooding • microarray

Introduction Flooding is one of the constraints on agricultural productivity.1 Flooding of plants decreases the oxygen concentration in their surroundings, which restricts ATP production via mitochondrial oxidative phosphorylation.2 To adapt to oxygen-poor conditions, plants generally activate anaerobic pathways, generate ATP through glycolysis, and regenerate NAD+ through ethanol fermentation as initial responses by selectively synthesizing flooding-inducible proteins involved in sucrose breakdown, glycolysis, and fermentation.3 Furthermore, plants exposed to long-term oxygen deficiency induce morphological adaptations, including the formation of aerenchyma, which is cortical air spaces that transport air from shoot to root, and the formation of adventitious roots, leaves, and elongated shoots through the induction of enzymes such as xyloglucan endotransglycosylase/hydrolase.3 Previous transcriptome studies on flooding or hypoxic stress using Arabidopsis,4-6 maize,7 rice,8 and gray poplar9 have * To whom correspondence should be addressed. Setsuko Komatsu, National Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba 305-8518, Japan. Tel: +81-29 838 8693. Fax: +81 29 838 8694. E-mail: [email protected]. † National Agriculture and Food Research Organization. ‡ Slovak Academy of Sciences. § Pierre et Marie Curie University. 10.1021/pr100179f

 2010 American Chemical Society

shown that hypoxic stress affects the amount of many gene transcripts. Among these plants, especially Arabidopsis and maize are more sensitive to flooding stress than the others. Earlier studies reported that the many accumulated mRNAs were not translated during oxygen deprivation and that mRNA translation is selective in maize.10 The regulation of mRNA translation is necessary to conserve energy because protein synthesis is an extremely energy-intensive process.11 Previous comparative analyses between flooding-tolerant and floodingintolerant genotypes have suggested that alteration of carbon and nitrogen metabolisms and morphological adaptation during flooding or hypoxia are key responses in flooding tolerance, and coordinating these responses due to alteration of numerous gene expression is responsible for the flooding tolerance.9,12,13 A common signaling event that occurs in response to abiotic and biotic stresses is protein phosphorylation. In maize roots under oxygen deprivation conditions, eukaryotic initiation factor 4 protein14 and sucrose synthase (SUS)15 are phosphorylated, and ribosomal proteins are dephosphorylated.16 Protein function is regulated by protein phosphorylation under oxygen deprivation conditions like flooding. The soybean is generally intolerant of flooding stress. In many regions of Japan, soybean seeds are sown in a paddy field during the summer-rainy season, and excess rainfall after sowing can often lead to soil flooding. Flooding after the sowing Journal of Proteome Research 2010, 9, 3989–4002 3989 Published on Web 06/14/2010

research articles causes severely decreased crop yields, and these lower yields may result from collapse of cotyledons due to rapid imbibition of water17 and from serious damage to the root system. Accordingly, it is important to understand flooding stress responses to improve crop yields. However, the flooding stress responses in soybean are not well characterized. Several studies of responses to flooding stress in soybean seedlings showed that flooding inhibits root elongation and hypocotyl pigmentation18 and affects the expression of some proteins involved in the processes of fermentation,19 scavenging reactive oxygen species,20 glycolysis, protein storage, and disease/defense.18 Furthermore, Komatsu et al.21 demonstrated that the responses to flooding stress are not exclusively the result of hypoxic stress by comparing responses to flooding with those to hypoxic stresses. Recently, the draft DNA sequence of the soybean genome was published, and the complete sequence is predicted to encode 66 153 protein-coding loci (http://www.phytozome.net/ soybean).22 In the present study, we use the genomic information to clarify the flooding-induced injury to soybeans; specifically, flooding stress-responsive proteins were identified in roots and hypocotyls of soybean seedlings at emergence using proteomics techniques facilitated by the soybean genome database. Our analysis identified proteins with expression and phosphorylation states that were significantly affected by flooding stress. Furthermore, analysis of carbohydrate contents and the activity of enzymes involved in carbohydrate metabolism showed that flooding stress induced dramatic changes in carbohydrate contents and the activity of enzymes involved in carbohydrate metabolism.

Experimental Procedures Plant Material. Soybean (Glycine max L. cultivar Enrei) seeds were sterilized with sodium hypochlorite solution and rinsed in water. They were sown in a plastic case (180 × 140 × 45 mm3) containing 400 mL of quartz sand wetted with 100 mL of water and grown at 25 °C and 70% humidity in a growth chamber (Sanyo, Tokyo, Japan) with a 16-h light (600 µmol m-2 s-1)/8-h dark cycle. Two-day-old seedlings were flooded with 700 mL of water for 12 to 48 h. The flooding condition was maintained at 2 cm of water above the quartz sand surface. For sampling, cotyledons of the soybean seedlings were eliminated. The remaining tissues which are referred as root and hypocotyl in this study were collected, immediately frozen in liquid nitrogen, and pooled at -80 °C before extraction. Protein Extraction. A portion (1 g) of frozen samples was ground to powder in liquid nitrogen with a mortar and pestle. The powder was transferred to 10% trichloro acetic acid and 0.07% 2-mercaptoethanol in acetone; the mixture was then vortexed. The suspension was sonicated for 5 min and then incubated at -20 °C for 45 min. After incubation, the suspension was centrifuged at 9000× g for 20 min at 4 °C. The supernatant was discarded and resulting pellet was washed with 0.07% 2-mercaptoethanol in acetone twice. The resulting pellet was dried using a Speed-Vac concentrator (Savant Instruments, Hicksville, NY). Dried samples were resuspended with 8 M urea, 2 M thiourea, 5% CHAPS, and 2 mM tributylphosphine by vortexing for 1 h at 25 °C. The resulting suspensions were centrifuged at 20 000× g for 20 min at 25 °C. Resulting supernatants were collected and used for proteomic analysis. Labeling Proteins with CyDye. Labeling of proteins for twodimensional fluorescence difference gel electrophoresis (2D3990

Journal of Proteome Research • Vol. 9, No. 8, 2010

Nanjo et al. DIGE) was performed according to manufacturer’s protocol (GE Healthcare, Piscataway, NJ). The protein samples were adjusted to pH 8.0-9.0 with 2 M Tris-HCl (pH 9.0) using a pH-indicator strip (Merck, Darmstadt, Germany). pH-adjusted samples (50 µg) were labeled with CyDye DIGE Fluor minimal dyes (GE Healthcare) by mixing with 1 µL of Cy3 (untreated control) and Cy5 (flooding-treated) minimal dyes on ice in the dark. The labeling reactions proceeded for 30 min and were stopped by adding 1 µL of 10 mM lysine. The Cy3- and Cy5-labeled samples were combined and then subjected to two-dimensional polyacrylamide gel electrophoresis (2-DE). Two-Dimensional Polyacrylamide Gel Electrophoresis. The Protean IEF Cell system (BioRad, Hercules, CA) was used for isoelectric focusing (IEF). Protein samples in a final volume of 180 µL of lysis buffer containing 0.4% of Bio-Lyte pH 3/10 (BioRad) were directly loaded into a focusing tray. The immobilized pH gradient (IPG) strips (3-10NL, 11 cm, BioRad) were passively rehydrated for 2.5 h and then actively rehydrated for 14 h at 50 V. IEF was carried out with the Protean IEF Cell using following conditions: 250 V for 15 min with a linear ramp, 8000 V for 1 h with a linear ramp, and finally 8000 V at 35,000 V/h with a rapid ramp at 20 °C. After IEF, the strips were equilibrated with equilibration buffer I containing 6 M Urea, 2% SDS, 0.375 M Tris-HCl pH 8.8, 20% glycerol, and 130 mM dithiothreitol for 30 min followed with equilibration buffer II containing 6 M Urea, 2% SDS, 0.375 M Tris-HCl (pH 8.8), 20% glycerol, and 135 mM iodoacetamide for 30 min. Equilibrated strips were placed onto 15% SDS-polyacrylamide gels (160 × 140 × 1 mm3) and sealed with 1% low-melting temperature agarose. Electrophoresis in the second dimension was performed at a constant current of 30 mA. Gel images were acquired with a Typhoon 8610 scanner (GE Healthcare) using the optimal wavelength for each fluorochrome: Cy3 (532/580 nm) and Cy5 (633/670 nm). Staining of Phosphoproteins. To detect phosphoprotein, gels were stained with ProQ Diamond phosphoprotein gel stain (Molecular Probes, Eugene, OR) according to manufacturer’s protocol. The gels were fixed with a solution containing 50% methanol and 10% acetic acid for 30 min twice. The gels were then washed with water for 10 min three times. After washing, the gels were stained with ProQ Diamond stain for 60 min. The stained gels were destained with a solution containing 20% acetonitrile and 50 mM sodium acetate pH 4.0 for 30 min three times. Finally, the gels were washed with water for 5 min twice. Gel images were acquired with a Typhoon 8610 scanner (GE Healthcare) at 532/580 nm. Gel Image Analysis. 2D-DIGE images were evaluated automatically with PDQuest software (version 8.0, BioRad). The isoelectric point (pI) and molecular mass of each proteins were determined using 2-DE standard marker (Bio-Rad). The amount of protein in a spot was estimated using the PDQuest software with local regression model normalization. For comparative analysis, qualitative and quantitative analyses were performed using PDQuest software. The spots with more than a 1.5-fold change in the amount of protein between the control and flooding-treated samples with student’s t test (P < 0.05) were categorized as significantly changed but flooding conditions. Protein Preparation for Mass Spectrometry. Protein spots were excised from 2-DE gels that had been stained with Coomassie brilliant blue and destained with 50 mM NH4HCO3 for 1 h at 40 °C. Within the gel pieces, proteins were reduced with 10 mM dithiothreitol in 100 mM NH4HCO3 for 1 h at 60 °C and incubated with 40 mM iodoacetamide in 100 mM

Proteome Analysis of Soybean Seedling Under Flooding NH4HCO3 for 30 min. The gel pieces were minced and allowed to dry, then rehydrated in 100 mM NH4HCO3 with 1 pM trypsin (Sigma-Aldrich, St. Louis, MO) at 37 °C overnight. The tryptic peptides were extracted from the gel grains three times with 0.1% trifluoroacetic acid in 50% acetonitrile. The procedure described above was performed with a robot (DigestPro, Intavis Bioanalytical Instruments AG, Cologne, Germany). The peptide solution obtained was dried and reconcentrated with 30 µL of 0.1% trifluoroacetic acid in 50% acetonitrile and desalted with NuTip C-18 pipet tips (Glygen, Columbia, MD). The desalted peptide solution was analyzed nanoliquid chromatography followed by mass spectrometry (nanoLC-MS/MS). Analysis of Protein using nanoLC-MS/MS. A nanospray LTQ XL Orbitrap MS (ThermoFisher Science, San Jose, CA) was operated in data-dependent acquisition mode with the installed Xcalibur software installed. Using an Ultimate3000 nanoLC (Dionex, Germering, Germany), peptides in 0.1% formic acid were loaded onto a 300 µm ID × 5 mm C18 PepMap trap column. The peptides were eluted from the trap column and separated on a 75 µm ID × 15 cm C18 PepMap100, 3-µm nanocolumn with 0.1% formic acid in acetonitrile at a flow rate of 200 nL/min. Samples were sprayed into the MS using a PicoTip emitter (20 µm ID, 10 µm TipID, Woburn, MA) with a spray voltage of 1.8 kV. Fullscan mass spectra were acquired in the Orbitrap over 150-2000 m/z with a resolution of 15 000. The three most intense ions above the 1000 threshold were selected for collision-induced fragmentation in the linear ion trap at a normalized collision energy of 35% after accumulation to a target value of 1000. Dynamic exclusion was employed within 30 s to prevent repetitive selection of peptides. Acquired MS/MS spectra were converted to individual DTA files using BioWorks software (version 3.3.1, Thermo Fisher Science). The following parameters were set to create a list of peaks: parent ions in the mass range with no limitation, one grouping of MS/ MS scans, and threshold at 100. Precursor ion tolerance was 10.00 ppm. The resulting peptide sequence data were used to search the soybean peptide database obtained from the soybean genome database using the MASCOT search engine (Schmutz et al. 2010, phytozome version 4.0, http://www.phytozome.net/soybean). Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methionine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. The search parameters were peptide mass tolerance (10 ppm), fragment mass tolerance (1 Da), maximum missed cleavages,3 peptide charges (+1, +2, and +3), variable modification (methylation), and fixed modification (carbamidomethylation). The instrument setting was specified as “ESI-Trap”. For identification of protein spots, only the red bold peptides that appeared for the first time in the mascot report contributed to protein identification. The minimal requirement for accepting a protein as identified was at least six peptide sequence matches above the identity threshold in coincidence with at least 20% sequence coverage. The Mowse score of more than 21 peptides from the MS data were significant with P < 0.05. In the case of peptides matching multiple members of a protein family, the protein match was selected based on the highest score and the highest-ranking member of matching peptide. The amino acid sequences of identified proteins were subjected

research articles to homology search using BLASTP against the NCBI nonredundant sequence database to assign protein identities. Sample Preparation for nanoLC-MS/MS-Based Proteomics. A portion (100 µg) of protein samples extracted by the method described above were subjected to an additional chloroform methanol extraction to remove any detergent from the sample solutions. The samples were adjusted to a volume of 50 µL, 200 µL of methanol was added to the sample, and the resulting 250 µL solution was mixed. Subsequently, 50 µL of chloroform was mixed with the sample, and then 150 µL of water was added and mixed. The resulting mixture was centrifuged at 9000× g for 5 min to enhance phase separation. The upper aqueous phase was discarded, and 150 µL of methanol was added to the organic phase. The samples were centrifuged at 9000× g for 5 min and the resulting supernatants were discarded, and the pellets were dried. The dried pellets were dissolved 50 mM NH4HCO3, and the proteins were reduced with 30 mM dithiothreitol and were alkylated with 50 mM iodoacetamide. Proteins were digested using trypsin at a 1:50 enzyme/protein concentration at 37 °C for 16 h. The resulting peptide solutions were desalted with NuTip C-18 pipet tips and subjected to nanoLC-MS/MS. nanoLC-MS/MS-BasedProteomicsAnalysis.FornanoLC-MS/ MS-based proteomics, differential display of MS/MS spectra was carried out using Progenesis LC-MS software (version 2.5, Nonlinear Dynamics, Newcastle-upon-Tyne, UK). For this analysis, three experimental replications were carried out on each sample. Ion peaks yielding an m/z range of 500 to 1500 and a retention time of 10-120 min were used for each mass spectrum. Peptides using nanoLC-MS/MS were separated by the same method used for 2D-DIGE spot identification with an extended time gradient. The MS/MS data, including the information on retention times and peak intensities, were aligned with the software, and reproducible peaks were extracted. Among the detected reproducible peaks, peaks with a change in intensities greater than 1.5 fold (between control and treatment), and with changes at P < 0.05 (ANOVA) were selected as significantly changed peptide peaks. The MS/MS ion data from these peaks were subjected to MASCOT analysis to identify the corresponding proteins. The parameters for the MASCOT analysis were same as those used for the 2D-DIGE spot detection. The result of MASCOT search was imported into the Progenesis software to identify corresponding proteins. For identification of proteins in the analysis, the minimal requirement for accepting a protein as identified was at least 2 peptide sequence matches with the same regulation. The Mowse score of more than 23 peptides was significant (P < 0.05) from MS data. The amino acid sequences of identified proteins were subjected to homology search using BLASTP against the NCBI nonredundant sequence database to assign proteins identities. Assays of Enzyme Activities. Roots and hypocotyls (0.2-0.4 g) were ground with a mortar and pestle on ice with buffer containing 50 mM Hepes-NaOH (pH 7.5), 5 mM MgCl2, 1 mM EDTA, 2% (w/v) polyvinylpyrrolidone, 0.1% (w/v) Triton X-100, 1 mM dithiothreitol, and 1 mM phenylmethylsulfonyl fluoride. Homogenates were centrifuged at 15 000× g for 20 min at 4 °C. The supernatants were used for all enzyme assays. All enzyme assays, except for the alcohol dehydrogenase (ADH) assay, were carried out in 50-µL reaction mixtures at 37 °C, and the reactions were stopped by heat-inactivation at 105 °C for 5 min. Resulting carbohydrates were measured by coupled enzymatic assay methods. Journal of Proteome Research • Vol. 9, No. 8, 2010 3991

research articles The measurements of SUS and invertase activities were performed essentially according to the method described by Morell and Copeland.23 SUS activity was assayed as sucrose degradation and measured by fructose accumulation. The SUS assay reaction mixture contained 20 mM Hepes-KOH (pH 7.6), 5 mM MgCl2, 2 mM UDP, and 200 mM sucrose. The reaction mixture without UDP was used as a negative control/blank. The invertase (alkaline) assay reaction mixture contained 20 mM Hepes-KOH (pH 7.6), 5 mM MgCl2, and 200 mM sucrose. The invertase (acid) assay reaction mixture contained 20 mM sodium acetate (pH 5.2), 5 mM MgCl2, and 200 mM sucrose. For both invertase assays, the amount of accumulated glucose was measured. The assays for fructokinase and hexokinase were performed essentially according to the method described by Copeland and Morell.24 The fructokinase assay reaction mixture contained 50 mM Tris-HCl (pH 8.0), 2 mM MgCl2, 1 mM ATP, and 0.4 mM fructose, and the accumulation of fructose 6-phosphate was measured. The assays for phosphoglucoisomerase (PGI) and phosphoglucomutase (PGM) were performed essentially according to the method described by Doehlert et al.25 The PGI assay reaction mixture contained 50 mM Hepes-KOH (pH 7.6), 5 mM MgCl2, and 5 mM fructose 6-phosphate, and the accumulation of glucose 6-phosphate was measured. The PGM assay reaction mixture contained 50 mM Hepes-KOH (pH 7.6), 5 mM MgCl2, 5 mM glucose 1-phosphate, and the accumulation of glucose 6-phosphate was measured. The assay for UDP-glucose pyrophosphorylase (UGP) was performed essentially according to the method described by Kerr et al.25 The assay reaction mixture contained 50 mM Hepes-NaOH (pH 7.6), 5 mM MgCl2, 5 mM UDP-glucose, 2 mM pyrophosphate, and the accumulation of glucose 1-phosphate was measured. The ADH assay was performed according to the method described by Kimmerer.26 The ADH assay reaction solution contained 50 mM Mes-NaOH, 5 mM MgCl2, 1 mM DTT, 0.1 mM NADH, and 4% (v/v) acetaldehyde, and NADH oxidation was monitored at 340 nm at 25 °C for 5 min. The protein was quantified with protein assay kit (BioRad) using a bovine serum albumin standard. Measurement of Carbohydrate Contents. Root and hypocotyl tissue samples (0.2-0.3 g) were ground in liquid nitrogen, suspended in 0.5 mL of 80% ethanol, and then boiled for 3 min. The suspensions were centrifuged at 15 000× g for 10 min. The resulting supernatants were collected, and the pellets were resuspended and boiled for 3 min, and then centrifuged. The extraction was performed again. The collected supernatants from three extractions for each sample were evaporated and resuspended with 1 mL of water. The solutions were used for carbohydrate measurements. Measurement of total soluble carbohydrate was performed by phenol-sulfuric acid method.27 Solution (300 µL) was mixed with same volume of 5% (w/v) of phenol. Then, 1.5 mL of sulfuric acid was added to the mixture. The absorbance at 490 nm of each reaction mixture was measured. Glucose was used as the standard for the measurement. Glucose, fructose, and sucrose were measured by coupled enzymatic assay methods that measure absorbance of NADH at 340 nm, as described by Guglielminetti et al.28 For glucose determination, samples were incubated with 100 mM Tris-HCl (pH 7.6), 3 mM MgCl2, 1 mM ATP, 0.6 mM NAD+, 1 U/mL hexokinase, and 1 U/mL glucose-6-phosphate dehydrogenase at 37 °C for 30 min. For fructose determination, samples were incubated with the solution used for glucose determination that 3992

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Nanjo et al.

Figure 1. 2D-DIGE analysis of roots and hypocotyls from 12-h flooded soybean seedlings. Extracted proteins from control and flooding treated samples were labeled with Cy3 (green) and Cy 5 (red) fluorescent dyes, respectively. (A) Representative gel image with spots numbered and indicated with arrows. Directions of arrows indicates whether the intensity of protein spots were increased or decreased by flooding treatment. (B) Relative intensities of significantly changed spots with more than 1.5-fold change in their expression. Data are means ( SD from three independent biological replicates. Asterisks indicate significant differences between untreated and flooding treatment (*P < 0.05, **P < 0.01).

also contained 2 U/mL PGI. For sucrose determination, samples were incubated with 20 mM sodium acetate (pH 4.6) and 900 U/mL invertase at 37 °C for 30 min, and then the resulting glucose and fructose were measured. The accuracy of each carbohydrate measurement was confirmed using known amounts of each carbohydrate.

Results Changes in Expression of Proteins Involved in Protein Destination/Storage Are Occurred in Roots and Hypocotyls of Soybean Seedling under Flooding Stress. Flooding stress caused significant suppression of the growth of soybean seedlings (Supplemental Figure 1, Supporting Information). To investigate early responses to flooding stress in roots and hypocotyls of soybean seedling, 2D-DIGE was used. Proteins were extracted from roots and hypocotyls soybean seedlings flooded for 12 h, and the protein samples were subjected to the 2D-DIGE analysis. The patterns of protein spots were compared between samples from the control and flooded seedlings. A total of 718 reproducible spots were detected in control samples, and 722 reproducible spots were detected in flooded samples. Of the shared spots in the two sample, 17 had more than a 1.5-fold change (P < 0.05) in amount between the two samples (Figure 1A, Supplemental Figure 2, Supporting Information). Eight changes represented increase in spot intensity in response to flooding, and nine changes represented

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Proteome Analysis of Soybean Seedling Under Flooding a

Table 1. Identification of Soybean Proteins Regulated by Flooding Stress using 2D-DIGE and MS experimental spot no.b

1

2 3 4 5 6

7

8 9

10 11 12 13 14 15 16 17

homologous protein

accession

UDP-glucose 6-dehydrogenase enolase methylenetetrahydrofolate reductase S-adenosyl-L-homocysteine hydrolase ferric leghemoglobin reductase-2 precursor enolase UDP-glucose pyrophosphorylase S-adenosylmethionine synthetase S-adenosylmethionine synthetase S-adenosylmethionine synthetase alcohol dehydrogenase-1F unknown aspartate aminotransferase alcohol dehydrogenase-1F aspartate aminotransferase unknown fructose-bisphosphate aldolase, cytoplasmic stem 31 kDa glycoprotein unknown lectin lactoylglutathione lyase stem 31 kDa glycoprotein unknown stem 31 kDa glycoprotein glyoxalase II 3 unknown stem 28 kDa glycoprotein N.D. glutathione peroxidase Kunitz trypsin protease inhibitor cyclophilin 40S ribosomal protein S13 eukaryotic translation initiation factor 5A2 N.D.

Q96558 ACN50180 XP_002310366 AAV31754 AAC26053 ACN50180 AAL33919 ACL14491 ACL14491 ACL14491 CAA80691 ACU18267 AAA33942 CAA80691 AAA33942 ACU18267 O65735 P10743 ACU23410 ABW72645 ACJ11750 P10743 ACU20843 P10743 NP_564636 ACU21409 P15490 AAQ03092 ACA23205 ACX37092 P62302 ACJ76773

Mr (kDa)

pI

2.3

53.8

5.6

4.9

53.7

5.7

-2.8 -2.8 -3.8 3.1

44.7 44.8 44.8 39.3

5.2 5.3 5.4 5.7

4.2

39.4

5.8

-3.2

32.7

6.9

-2.4

32.3

5.4

-3.2 q(+)d -2.2

32.2 31.6 30.7

5.5 5.5 8.7

-2.8 2.3

27.1 23.7

5.9 5.3

-2.7

20.6

5.1

2.3 3.3

20.9 15.8

5.4 5.0

FC

theoretical Mr (kDa)

pI

score

MP

cov (%)

blast score

functional categoryc

53.5 48.2 67.7 53.8 53.3 48.2 51.6 43.4 43.4 43.4 41.6 49.5 51.1 41.6 51.1 49.5 38.5 29.4 32.1 30.1 31.7 29.4 26.5 29.4 41.6 35.7 29.2

5.7 5.9 5.8 5.6 6.9 5.9 5.2 5.5 5.5 5.5 6.0 6.4 8.5 6.0 8.5 6.4 7.1 6.7 6.4 5.9 5.6 6.7 9.7 6.7 8.9 9.1 8.8

370 340 218 151 115 376 198 517 429 36 326 153 126 580 449 336 126 305 108 226 141 120 49 189 365 246 166

14 17 12 12 11 16 10 24 19 7 21 10 7 25 15 9 6 15 9 10 6 7 7 14 18 10 9

32 43 25 29 20 49 29 57 48 22 56 33 24 70 35 29 35 45 42 37 25 29 26 42 51 38 42

990 860 1084 941 1013 860 862 764 764 764 767 910 953 767 953 910 658 533 613 558 495 533 482 533 444 665 534

PriMtb Engy PriMtb ScdMtb Engy Engy PriMtb ScdMtb ScdMtb ScdMtb Engy Unclass PriMtb Engy PriMtb Unclass Energy ProDsSt Unclass ProDsSt DsDf ProDsSt Unclass ProDsSt DsDf Unclass ProDsSt

18.7 23.9 18.3 17.2 17.7

6.6 6.9 7.7 10.4 5.6

41 21 139 109 116

6 6 8 8 6

23 25 46 55 56

308 391 335 306 330

DsDf ProDsSt ProDsSt ProSyn ProSyn

a N.D.: unidentified protein spot, FC: fold change of protein intensity, Score: Mowse score of more than 21 from MS data, MP: number of matched peptides more than 6 from MS data, Cov: sequence coverage from MS data, Blast score: the protein score in BLAST search. b Spot numbers show the same as Figure 1. c PriMtb: Primary metabolism, Engy: Energy, CelGrw: Cell growth/division, Trcrpt: Transcription, ProSyn: Protein synthesis, ProDsSt: Protein destination/storage, Trpotr: Transporters, ICTrf: Intracellular traffie, CelStr: Cell structure, Sgnl: Signal transduction,DsDf: Disease/defense, ScdMtb: Secondary metabolism, Uncler: Unclear classification, Unclass: Unclassified. d q(+): increased spot with qualitative change.

decrease (Figure 1B). Furthermore, the changes in the amount relative to the controls of these spots in seedlings treated with 24 h of flooding were also analyzed (Supplemental Figure 3, Supporting Information). The changes for 8 of spots changed by 12 h of flooding were stable between 12 and 24 h. In contrast, the fold-change relative to the controls of spots 1, 2, 3, 7, 8, and 15 significantly increased or decreased between 12 and 24 h of flooding. Among these, spots 1, 3, 8, and 15 increased or decreased depending on the duration of flooding. To identify proteins in these spots, the spots were excised from the gel and subjected to protein identification using nanoLC-MS/MS analysis and the soybean peptide database obtained from the soybean genome database. Matched protein sequences were subjected to homology searches using BLASTP against the NCBI nonredundant sequence database to assign proteins identities. Recently soybean protein and gene information available in public sequence databases, such as NCBI, has increased; however, numerous sequences are designated as “unknown” or “hypothetical.” Therefore, to determine more proteins affected by flooding, the information from the NCBI nonredundant sequence database rather than just the soybean database to assign protein identities was used. Thirty-two

proteins in 15 spots of the detected 17 spots were identified as the significant changed proteins under flooding stress (Table 1, Supplemental Table 1, Supporting Information), indicating that a single spot may contain more than one protein. The molecular weight and pI of most proteins identified from the same spot were not distinctly different; therefore, we could not exclude the possibility that a spot contained several proteins. For example, in this study MS/MS analysis suggested that spot 1 contained 5 proteins, and based on the molecular weight and pI of each protein in the spot was not distinctly different from one another. In addition, there is a possibility that posttranslational modification such as phosphorylation, glycosylation, proteolysis and proteolytic processing that can alter molecular weight and pI of proteins has occurred to these proteins. Accordingly, all proteins identified in these spots probably contribute to the observed changes, indicating that these proteins were affected from the early time of flooding. These identified proteins in the 2D-DIGE analysis were categorized according to function using the classification scheme of Bevan et al.29 (Figure 2A, Table 1). The affected proteins were categorized into energy, protein synthesis, protein destination/ storage, disease/defense, and secondary metabolism. Journal of Proteome Research • Vol. 9, No. 8, 2010 3993

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Figure 2. Number of differentially expressed proteins categorized into each functional category. (A) Number of identified proteins assigned to each functional category in the 2D-DIGE analysis. (B and C) Number of identified proteins assigned to each functional category in the nanoLC-MS/MS-based proteomics of 12 h (B) and 48 h (C) of flooding responses. Up (open bar) and down (filled bar) indicate increased and decreased proteins due to flooding, respectively.

Protein identification using 2-DE based proteomics is limited; for example, membrane proteins and extremely acidic or basic proteins are difficult to identify. 2D-DIGE result could not provide a clear picture of what metabolic changes occur under flooding conditions. For further analysis of proteins affected by flooding, nanoLC-MS/MS-based proteomics for 12 h of flooding response was also performed. Among the detected reproducible 18 128 peaks in the nanoLC-MS/MS analysis, 4898 of peptides with significant changes (Fold change >1.5, ANOVA-P < 0.05) in peptide amount were identified. Analysis of these peptides identified 81 proteins affected by flooding; 45 of which were increased in their peptide abundance and 36 of which were decreased due to flooding (Table 2 and Supplemental Table 2, Supporting Information). These proteins were also categorized according to function using the same scheme of 2D-DIGE analysis (Figure 2B, Table 2), distribution of the functional categories were quite similar to 2D-DIGE results. The functional classification showed that the nanoLC-MS/MS-based proteomics for 12 h of flooding identified many increased proteins involved in protein destination/storage (Figure 2B), including many heat shock proteins. In addition, increases of proteins involved in ATP production 3994

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Nanjo et al. such as glycolysis and fermentation were observed in both the nanoLC-MS/MS and 2D-DIGE analyses, including increases of ADH, fructose bisphosphate aldolase, phosphoglucomutase, and triosephosphate isomerase (Figure 2, Tables 1 and 2). However, decrease of phosphoglycerate kinase and pyruvate kinase was also identified in the nanoLC-MS/MS analyses. Furthermore, decrease of proteins in protein destination/ storage and secondary metabolism, decreases of stem 31 kDa glycoprotein and S-adenosylmethionine synthetase were apparent in both analyses, respectively (Tables 1 and 2). Taken together these results suggest that the expression of proteins involved in primary metabolism, energy production, protein destination/storage, disease/defense and secondary metabolism changed in roots and hypocotyls during the 12-h flooding treatment. On the other hand, nanoLC-MS/MS-based proteomics was performed for 48 h flooded samples (Supplemental Table 3, Supporting Information). Among the detected reproducible 23 932 peaks in the nanoLC-MS/MS analysis, 7112 of peptides with significant changes (Fold change >1.5, ANOVA-P < 0.05) in peptide amount were identified. Analysis of these peptides identified 241 proteins affected by flooding; 110 of which were up-regulated proteins and 131 of which were decreased due to flooding (Supplemental Table 3, Supporting Information). Functional classification showed that the nanoLC-MS/MSbased proteomics for 48 h of flooding identified more proteins categorized into primary metabolism, energy, desease/defense and secondary metabolism than 12 h result (Figure 2C). The analysis also showed that increases of many proteins involved in ATP production such as glycolysis and fermentation, indicating that these pathways respond from early time during flooding period. Flooding Induces Sucrose Accumulation in Root and Hypocotyl of Soybean Seedling. Glycolysis- and fermentationrelated proteins were increased in roots and hypocotyls of flooded soybean seedlings (Tables 1 and 2). In previous proteomic studies, ADH and fructose bisphosphate aldolase18,19,21 were also increased in response to flooding, indicating that glycolysis and fermentation pathways are regulated at the protein level in root and hypocotyl of flooded soybean seedling. The alteration of carbohydrate metabolism under low oxygen conditions is reported to be a crucial response for survival under these energy restricted conditions.9,12,13 It seems that alteration of carbohydrate metabolism is one of the important responses to flooding stress. However, it is unclear how flooding stress is related to the activity of enzymes involved in carbohydrate metabolism and carbohydrate contents in soybean seedlings. To elucidate whether the pathways for carbohydrate metabolism function adequately, we investigated carbohydrate metabolism in flooded seedlings by measuring relative enzyme activity and soluble carbohydrate contents (Figure 3). ADH activity was measured as an indicator of flooding stress, and its activity was markedly increased (Figure 3A), consistent with the increase observed in the proteomic analyses. The activities of both alkaline and acid invertase were increased in control plants relative to flooded seedling. The SUS, PGM and UGP activities were comparable between samples from the control and flooding treatments. PGI, fructokinase and hexokinase activities in flooded seedlings were significantly higher than those in control seedlings. Total soluble carbohydrate levels in root and hypocotyl was lower in control seedlings than in flooded seedlings (Figure 3B). The rate of decrease in

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Proteome Analysis of Soybean Seedling Under Flooding

Table 2. Identification of Soybean Proteins Regulated by 12 h of Flooding Stress using nanoLC-MS/MS Based Proteomicsa no

homologous protein

accession

FC

score

MP

ANOVA-P

blast score

functional categoryb

14 19 24 29 31 39 41 43 51 58 76 2 4 13 21 30 38 54 57 59 67 72 75 3 5 6 9 11 12 15 16 17 18 22 23 25 26 27 32 33 35 36 37 44 48 60 61 63 69 70 79 40 73 10 68 47 50 53 66 71 74 77 80 81 42 45

embryo-specific urease seed lipoxygenase phosphoglucomutase, cytoplasmic lipoxygenase L-3 seed lipoxygenase glycosylhydrolase 1 lipoxygenase-9 beta-glucosidase G4 rhamnose synthase UDP-glucose pyrophosphorylase L,L-diaminopimelate aminotransferase/transaminase alcohol dehydrogenase-1F alcohol dehydrogenase-1F fructose-bisphosphate aldolase, cytoplasmic isozyme fructose-bisphosphate aldolase, cytoplasmic isozyme triosephosphate isomerase, putative triosephosphate isomerase phosphoglycerate kinase, putative enolase, putative malate dehydrogenase mitochondrial ATPase beta subunit pyruvate kinase cytosolic phosphoglycerate kinase poly ADP-ribose polymerase 3 maturation protein pPM32 ubiquitin activating enzyme 2 proteasome subunit alpha type-3 calnexin homologue poly ADP-ribose polymerase 3 Kunitz trypsin protease inhibitor heat shock 70 kDa protein heat shock 70 kDa protein, mitochondrial 70 kDa heat shock cognate protein 1 heat shock protein 70 cysteine proteinase precursor heat shock protein 70 70 kDa heat shock cognate protein 1 70 kDa heat shock cognate protein 3 mitochondrial processing peptidase alpha subunit, putative Kunitz trypsin protease inhibitor heat shock protein 70-3 70 kDa heat shock cognate protein 2 heat shock protein 70 PsHSP71.2 protein disulfide isomerase-like protein heat shock protein, putative late-embryogenesis abundant protein 1 proteasome-like protein alpha subunit stem 31 kDa glycoprotein 26S proteasome subunit P45 protein disulfide isomerase-like protein PIP2,2 vacuolar H+-ATPase B subunit tubulin beta-2 chain alpha-1,4-glucan-protein synthase peroxidase2 DHAR class glutathione transferase DHAR2 peroxisomal betaine-aldehyde dehydrogenase caffeic acid 3-O-methyltransferase ascorbate oxidase allantoinase dihydroflavonol reductase ascorbate oxidase S-adenosylmethionine synthetase ripening related protein zinc finger (DHHC type) family protein

AAO85884 P24095 Q9SM60 ABX60408 P24095 ABY48758 ABS32275 ABW76289 ACJ11756 AAL33919 NP_567934 CAA80691 CAA80691 O65735 O65735 XP_002529248 ABA86966 XP_002513353 XP_002512011 AAD56659 AAD03392 ABE80121 AAF85975 Q9SWB4 AF166485 BAD00984 O24362 Q39817 Q9SWB4 ACA23205 P26413 Q01899 AAS57912 ACJ11741 CAB17076 ACJ11741 AAS57912 AAS57914 XP_002515947 ACA23207 AAR17080 AAS57914 ACJ11741 AAA82975 BAD24715 XP_002518865 ACJ31819 ABB16990 P10743 ABD32889 BAD34455 AAX86046 ABO61030 P12460 O04300 CAA62228 ADB11344 BAG09377 ACC63885 BAH28261 AAR29343 ABY81885 BAH28261 ABY25855 AAD50376 NP_177101

1.9 1.7 1.7 1.6 1.6 1.6 1.5 1.5 -1.8 -2 -2.7 3.1 2.5 1.9 1.7 1.6 1.6 -1.9 -2 -2 -2.2 -2.3 -2.6 2.8 2.5 2.4 1.9 1.9 1.9 1.9 1.8 1.8 1.8 1.7 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.6 1.6 1.5 -1.7 -2 -2 -2.1 -2.3 -2.3 -3.6 1.5 -2.3 1.9 -2.2 -1.6 -1.7 -1.9 -2.2 -2.3 -2.4 -3.3 -3.9 -6.9 1.5 1.5

80 268 131 210 143 28 187 44 160 65 104 155 316 165 116 54 356 129 89 106 240 28 156 96 95 39 77 58 68 115 241 183 53 359 72 267 430 414 91 96 142 102 126 52 116 142 78 49 165 163 99 71 97 82 51 145 92 248 258 71 102 53 114 121 50 30

2 5 6 4 5 2 4 2 3 2 3 3 7 4 3 2 5 4 2 2 5 3 4 3 2 2 2 3 2 2 5 4 2 9 2 7 10 9 2 3 3 2 3 2 2 3 2 2 2 2 2 3 3 2 2 3 2 5 5 2 3 2 2 2 2 2

0.0039 0.0054 0.0064 0.0008 0.0102 0.0236 0.0035 0.0348 0.0006 0.0214 0.0138 0.0003 0.00005 0.0028 0.0077 0.0001 0.001 0.0085 0.022 0.0037 0.0254 0.0339 0.0002 0.0087 0.0248 0.0132 0.0168 0.0173 0.0007 0.0106 0.0066 0.0026 0.0125 0.0047 0.0209 0.0084 0.004 0.006 0.0181 0.0075 0.0027 0.0027 0.0049 0.0036 0.0076 0.0033 0.0126 0.0086 0.0025 0.0016 0.0228 0.0136 0.006 0.0049 0.0026 0.0091 0.008 0.0064 0.0004 0.0121 0.0156 0.0088 0.0084 0.0005 0.0124 0.0294

1642 869 1112 1367 1403 508 1783 889 1265 863 744 734 767 658 659 522 508 806 822 680 962 1002 749 1687 319 1815 442 1019 1372 391 1309 1296 888 1260 790 1271 1279 1275 764 427 1200 751 1273 836 689 1291 617 451 533 874 1000 575 980 931 687 574 354 1028 421 733 919 604 989 806 312 759

PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb PriMtb Engy Engy Engy Engy Engy Engy Engy Engy Engy Engy Engy Engy ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt ProDsSt Trpotr Trpotr CelStr CelStr DsDf DsDf DsDf ScdMtb ScdMtb ScdMtb ScdMtb ScdMtb ScdMtb Uncler Uncler

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Table 2 Continued no

1 7 8 20 28 34 46 49 52 55 56 62 64 65 78

homologous protein

unknown unknown unknown unknown unknown unknown unknown unknown hypothetical protein unknown unknown unknown unknown unknown unknown

accession

FC

score

MP

ANOVA-P

blast score

functional categoryb

ACU24607 ACU23410 ACR38614 ACU17802 ACU13903 ACU20843 ACU19529 ACU21478 XP_002276172 ACJ84413 ACU13486 ACU15826 ACU13726 ACU23564 ACU19529

6.9 2.1 2 1.7 1.6 1.6 -1.6 -1.7 -1.8 -1.9 -2 -2.1 -2.1 -2.1 -3.5

76 159 74 102 42 201 33 110 57 68 72 137 68 125 38

2 4 2 2 2 5 2 5 2 2 2 2 2 2 2

0.0002 0.0059 0.0256 0.0008 0.0157 0.0022 0.0381 0.0113 0.0133 0.0103 0.0004 0.0188 0.0121 0.0112 0.0236

332 613 145 431 315 482 520 848 925 577 221 406 511 927 324

Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass Unclass

a FC: fold change, Score: Mowse score of more than 23 from MS data, MP: number of matched peptides of more than 2 from MS data, Blast score: the protein score in BLAST search. b PriMtb: Primary metabolism, Engy: Energy, CelGrw: Cell growth/division, Trcrpt: Transcription, ProSyn: Protein synthesis, ProDsSt: Protein destination/storage, Trpotr: Transporters, ICTrf: Intracellular traffie, CelStr: Cell structure, Sgnl: Signal transduction, DsDf: Disease/ defense, ScdMtb: Secondary metabolism, Uncler: Unclear classification, Unclass: Unclassified.

carbohydrate contents seemed repressed after 12 h of flooding relative to the rate seen in control seedlings. The difference in the concentration of total soluble carbohydrates between flooded and control seedlings was 20.3, 19.5, and 14.5 µmol Glc equivalent g-1 FW at 24, 36, and 48 h, respectively. The amount of glucose in flooded plants at 48 h was significantly lower than that in control plants; the difference in glucose concentration between control and flooded plants was 6.1 µmol Glc equivalent g-1 FW. The fructose concentration in flooded plants was similar to that in control plants throughout the experimental period. In sharp contrast after 12 h, sucrose concentrations were much higher in flooded plants than in control plants; the difference in sucrose concentration between control and flooded plants was 13.0, 18.6, and 19.7 µmol Glc equivalent g-1 FW at 24, 36, and 48 h, respectively. These results indicate that higher levels of total soluble carbohydrate in flooded seedlings probably resulted from the sucrose accumulation caused by a decrease in invertase activity and in the activities of carbohydrate-requiring metabolic pathways. Combined the results of proteomics and enzyme assays in terms of carbohydrate metabolism were summarized in Figure 4. SUS was not identified in proteomics and the activity was not changed significantly, indicating that SUS was not affected by flooding. A decrease of invertase was detected in both analyses. UGP was identified as increased and decreased in proteomics analyses, however the enzyme activities were not changed significantly. PGM was not identified as significantly changed protein in proteomic analysis and the activity was not significantly changed. ADH, HK, FK, and PGI were significantly increased in both analyses. GA3PDH was identified as increased and decreased in the proteomics; however, most of these were identified as increased. PFK, FBPA, TPI, ENO, and PDC were identified as increased in proteomics. PGK was identified as decreased in proteomics. PK was identified as increased and decreased in proteomics. On the other hand, in proteomic analysis, we identified enzymes in methylglyoxal pathway such as glyoxalase I (lactoylglutathione lyase, GLOI) and glyoxalase 3996

Journal of Proteome Research • Vol. 9, No. 8, 2010

II (GLOII). Although an increase of GLOII was detected, the regulation of GLOI was detected as increased and decreased. Dephosphorylation of Proteins Involved in Protein Synthesis is One of the Early Responses to Flooding Stress. To analyze regulation of protein function by de/phosphorylation under flooding conditions, 2-DE with ProQ Diamond staining was performed. The analysis of protein phosphorylation in control and flooded seedlings revealed a difference in phosphorylation states of 16 protein spots (Figure 5A); 3 protein spots were phosphorylated in flooded seedlings and dephosphorylated in controls, while 13 were dephosphorylated spots in flooded seedling and phosphorylated in controls. These data suggested that proteins in most of the identified spots had been dephosphorylated in response to flooding stress (Figure 5B). The intensity of spots 8, 11, 13, and 15 was higher than other detected spots. Proteins in spot 8 were highly phosphorylated, and those in spots 11, 13, and 15 were highly dephosphorylated in flooded seedlings. The identification of proteins within the spots with an altered phosphorylation state due to flooding also showed that 36 proteins were identified in 15 spots of the detected 16 spots, indicating that each spot contained several proteins (Table 3, Supplemental Tables 4 and 5, and Supplemental Figure 4, Supporting Information). It was difficult to distinguish any of the proteins in an individual spot because the ProQ Diamond staining depends on the degree of phosphorylation, indicating that the phosphorylation state of each identified protein could be regulated by flooding stress. These affected proteins were found to be related to primary metabolism, energy production, protein synthesis, protein destination/ storage, cell structure, disease/defense, and secondary metabolism. High amounts of protein destination/storage-related proteins were identified (Figure 4C). In addition, protein spots that were highly phosphorylated in flooded seedlings included stem 31 kDa glycoprotein, 17.7 kDa class I small heat shock protein (spot 14), eukaryotic translation initiation factor 5A2 (spot 15) and cyclophilin (spot 15), as well as unknown proteins (spots 8, 14, and 15). On the other hand, a comparison of differentially expressed proteins detected using 2D-DIGE analysis and differentially phosphorylated proteins showed that UDP-glucose pyrophosphorylase (spot 6), ADH (spot 7), stem

research articles

Proteome Analysis of Soybean Seedling Under Flooding

2D-DIGE gel and a dephosphorylated spot in ProQ Diamond stained gel. The molecular weight and pI of the spots in the analyses were different. Analysis of the pI of these spots indicates that the dephosphorylation of these proteins did not influence the detection of proteins in the 2D-DIGE analysis. The eukaryotic translation initiation factor 5A2 was detected as an increased protein in the expression analysis. The eukaryotic translation initiation factor 5A2 was clearly dephosphorylated by flooding, suggesting that dephosphorylation of the protein caused a pI shift from acidic to basic. The dephosphorylation and associated pI shift resulted in detection of the spot as a differentially expressed spot in the 2D-DIGE analysis. To confirm phosphorylation of peptides in the detected proteins, MASCOT search was used. The analysis could identify only two phosphorylated peptides in malate dehydorgenase in spot 4. These phosphorylated peptides contained phosphorylated serines; one of two was detected with neutral loss of phosphate group, the other was detected without the neutral loss (data not shown). Failure of identification of phosphorylated peptides in other proteins was thought to be attributed to low amount of phosphorylated peptides in the proteins and low ionization efficiency of phosphorylated peptide in the ESI-MS system.

Discussion

Figure 3. Carbohydrate metabolic enzyme activities and soluble carbohydrate contents in roots and hypocotyls of soybean seedlings during flooding treatment. Two-day-old seedlings were flooded with water. The roots and hypocotyls of seedlings were cut and withdrawn at each indicated time point. (A) Relative enzyme activities in protein extracts from the tissues collected at each time point were measured. (B) Total soluble carbohydrates, sucrose, glucose, and fructose in the ethanol soluble fractions from the tissues collected at each time point were measured. Total soluble carbohydrates were measured using the phenol-sulfuric acid method. Sucrose, glucose, and fructose were measured using coupled enzymatic assay methods measuring the increase in NADH. White and black circles indicate untreated and flooding treatment, respectively. Data are means ( SD from three independent biological replicates. Asterisks indicate significant differences between untreated and flooding treatment (*P < 0.05, **P < 0.01).

31 kDa glycoprotein (spot 11), and eukaryotic translation initiation factor 5A2 (spot 15) were repeatedly detected in both analyses, suggesting that the effects on the phosphorylation state of these protein may affect their identification in the differential expression analysis due to pI shift through protein phosphorylation. UDP-glucose pyrophosphorylase was detected in an increased spot in the intensity, and the pI of this spot was 5.7 in the 2D-DIGE analysis. In the ProQ Diamond stained gel, spot 6 was detected as dephosphorylated, and the pI of the spot was 5.4, indicating that dephosphorylation of the protein caused a pI shift from acidic to basic, reflecting its detection as a differentially expressed spot in 2D-DIGE analysis. The stem 31 kDa glycoprotein was detected in a decreased spot in the

Combination of 2D-DIGE and NanoLC-MS/MS Analyses is Valuable for Understanding Physiological Mechanisms of Stress Responses. To investigate early responses to flooding stress in roots and hypocotyls of soybean seedling, 2D-DIGE and nanoLC-MS/MS analyses were performed for 12 h of flooded soybean seedlings. In total, 113 proteins were identified as early responsive proteins by two techniques, indicating that a combination of the two methods provides a more complete picture of the physiological mechanisms of stress responses because the nanoLC-MS/MS analysis could compensate the limitation in 2-DE based proteomics described above. In fact, the nanoLC-MS/MS analysis could identify membrane proteins. Alternatively, the overall functional classification of the two methods showed quite similar functional groups identified except for cell structure and transporters (Figure 2), suggesting that both methods could survey the early responses to flooding of soybean seedlings adequately. Previously we have reported proteomic studies on responses to flooding which analyzed proteomic responses after 1 or 2 day of flooding using 2-DE based method.18,20,21 In this study, we investigated more early responses to flooding responses with 2D-DIGE and nanoLC-MS/MS analyses. In comparison with these studies, we identified more proteins and could demonstrate clear picture in responses to flooding in this study, suggesting that the combination of 2D-DIGE and nanoLC-MS/ MS analyses is more valuable for understanding physiological mechanisms of flooding responses. The identified proteins were detected in one of the methods or both methods. The proteins detected by both methods with same regulations are more reliable than by one of the methods. In our analyses, several proteins were identified in the both methods. Among these proteins, proteins categorized into energy metabolism, especially glycolysis related proteins were identified in both methods. Therefore, we focused on carbohydrate metabolism in this study. In contrast, UGP was detected in the both methods; however, the regulations were detected as opposite. In this case, post-translational modification possibly influences the proteomic detection. Although the Journal of Proteome Research • Vol. 9, No. 8, 2010 3997

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Figure 4. Schematic overview of flooding responsive carbohydrate metabolic pathways. Abbreviations of protein names and metabolites are indicated as follows: ADH, alcohol dehydrogenase; ALDH, aldehyde dehydrogenase; ENO, enolase; FBPA, fructose bisphosphate aldolase; FK, fructokinase; GA3PDH, glyceraldehyde 3-phosphate dehydrogenase; GLOI, glyoxalase I; GLOII, glyoxalase II; HK, hexokinase; INV, invertase; LDH, lactate dehydrogenase; MGS, methylglyoxal synthase; PDC, pyruvate decarboxylase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGI, phosphoglucoisomerase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; SUS, sucrose synthase; UGP, UDP-glucose pyrophosphorylase. The colors of each protein names indicate their regulations, green color indicates the regulation was not changed or unclear, blue color indicates down-regulated, red color indicates up-regulated. The arrows after the protein names indicate regulations of proteins identified in this study.

protein was detected as phosphorylated protein, the enzyme activity was not significantly changed (Figure 3), indicating that the proteins may not be affected the function by flooding. Thus, sometimes verification is needed in this kind of approach. On the other hand, our proteomic analyses could not identify low abundance protein such as proteins involved in transcription, such as transcription factors. To clarify the mechanisms of regulation of responses to stress condition, identifying differentially expressed transcription factors is important. Recent analysis of responses of flooding or hypoxic stress revealed that numerous transcription factors could be affected in transcriptional level,4-9 suggesting that these transcription factors should be identified as differentially expressed proteins. Recent nuclear proteome analysis with nuclear separation could identify many of transcription factors,30,31 suggesting that identification of low abundant proteins such as transcription factors needs separation of cellular compartments. Flooding Affects Expressions of Proteins Involved in Glycolysis and Fementation in Early Time. The early responses to flooding included changes in the expression of proteins involved in primary metabolism, energy production, protein destination/storage, and secondary metabolism. In previous proteomic studies, increases of glycolysis- and fermentationrelated enzymes, ADH and fructose bisphosphate aldolase,18,19,21 were identified in flooded soybean seedlings. In this study, we confirmed the changes in expression of these proteins and identified changes in the expression of other glycolysis-related proteins in soybean seedlings flooded for 12 h, suggesting that modulation of glycolysis is a key element of the early response to flooding stress. Several proteins involved in primary metabolism of sugars and polysaccharides (UDP-glucose dehydrogenase, UGP, β-glu3998

Journal of Proteome Research • Vol. 9, No. 8, 2010

cosidase G4 and rhamnose synthase), of amino acids (aspartate aminotransferase), and of lipids (lipoxygenase) were identified as early responsive proteins. UDP-glucose dehydrogenase catalyzes conversion of UDP-glucose to UDP-glucuronate, and it is potentially involved in hemicellulose synthesis.32 It was identified as increased protein. UGP functions in carbohydrate metabolism, and is primarily involved in the sucrose biosynthesis pathway. UGP was identified in proteomis analyses, however, considering its enzyme activity, it may not be affected bv flooding. β-Glucosidase G4 possibly hydrolyzes flavonol and anthocyanidin 3-O-glucosides, is induced by the wound signal methyl jasmonate, and is possibly involved in biosynthesis of isoflavonoid phytoalexin medicarpin in Medicago truncatula.33 β-Glucosidase G4 was increased in flooded soybean seedlings. Moreover, lipoxygenase, which was increased in flooded soybean seedlings, may be involved in jasmonate biosynthesis via fatty acid hydroperoxidation.34 Dihydroflavonol reductase, which is involved in isoflavonoid synthesis, was decreased in flooded soybean seedling. Furthermore, proteomic analysis of 48 h flooded soybean seedlings showed that decreases proteins involved in the pathway such as phenylalanine ammonia-lyase, dihydroflavonol reductase, and 6′-deoxychalcone synthase. Taken together these results suggest that down-regulation of the phenolics synthesis pathway is occurred by flooding. Rhamnose synthase was decreased by flooding. Rhamnose is a plant cell wall component. Decrease of rhamnose synthase together with the observed inhibition of growth in flooded seedlings suggests that cell wall synthesis may be downregulated in response to flooding which may reduce energy consumption through catabolism of sugars as an adaption to the energy-restricted condition associated with flooding. It has been reported that aspartate aminotransferase is induced by

Proteome Analysis of Soybean Seedling Under Flooding

Figure 5. Analysis of phosphorylation states of proteins in roots and hypocotyls from 12-h flooded soybean seedlings. (A) Representative images of 2D-gel stained with ProQ Diamond. Proteins (400 µg) were separated by 2-DE and stained with ProQ Diamond phosphoprotein gel stain. Left and right gel images indicate control and flooded samples. The direction of the arrow indicates whether the protein spot was up- or down-regulated by flooding treatment. (B) Relative intensities of significantly changed spots that had a more than 1.5-fold change in their expression. Data are means ( SD from three independent biological replicates. Asterisks indicate significant differences between untreated and flooding treatment (*P < 0.05, **P < 0.01). (C) Number of identified proteins assigned to each functional category.

hypoxia. de Sousa and Sodek35 reported that in roots of soybean plants subjected to hypoxia, aspartate aminotransferase and alanine aminotransferase enzyme activity increased and alanine and γ-aminoburyric acid accumulated, and they speculated that increased aspartate aminotransferase activity under hypoxic conditions contributes to alanine accumulation via glutamate when coupled to alanine aminotransferase. The group of destination/storage proteins affected by flooding included proteolysis-, protein folding-, and storage-related proteins. For example, the expression of many heat shock 70 kDa proteins and some proteolysis proteins, such as proteosome subunits, changed in flooded soybean seedlings relative to controls. The heat shock 70 kDa proteins function as a molecular chaperone in a variety of cellular processessincluding

research articles the prevention of protein aggregation, facilitating the translocation of nascent chains across membranes, mediating the assembly or disassembly of multimeric protein complexes, and targeting proteins to lysosomes or proteasomes for degradation. These proteins help the cells adapt to unfavorable environmental conditions, such as heat, cold, drought, toxins, and other stresses.36 These proteins may be involved in the response to flooding stress through regulation and monitoring of protein folding and degradation. It has been reported that exogenous sucrose greatly enhances anoxia tolerance of Arabidopsis seedlings, and the enhanced tolerance is due to the induction of heat shock protein gene expression by the exogenous sucrose.6 In soybean seedlings, sucrose accumulation was observed in the root and hypocotyls, suggesting that the accumulation of sucrose might enhance the expression of the heat shock proteins. Moreover, the induction of heat shock proteins in soybean seedlings might contribute to the adaption to flooding. Several secondary metabolism proteinssS-adenosylmethionine synthetase, caffeic acid 3-O-methyltransferase, and dihydroflavonol reductaseswere decreased in the flooded soybean seedlings. Previously, protein-protein interaction analysis using mathematical modeling system has predicted that Sadenosylmethionine synthetase is one of several proteins that plays central roles in the flooding stress response in soybean.18 Caffeic acid 3-O-methyltransferase is involved in lignin biosynthesis derived from the phenylpropanoid pathway, and the enzyme catalyzes a methylation reaction using S-adenosylmethionine.37 Dihydroflavonol reductase is involved in isoflavonoid synthesis, which is also derived from the phenylpropanoid pathway, suggesting that down-regulation metabolism linked to the phenylpropanoid pathway occurs during flooding. Consistent with this hypothesis, the pigmentation in soybean hypocotyls was inhibited by flooding,18 and the pigments, such as anthocyanin, are derived from the phenlypropanoid pathway. Eukaryotic Translation Initiation Factor 5A Is Regulated by Dephosphorylation under Flooding Stress. Eukaryotic translation initiation factor 5A is a highly conserved 18 kDa protein present in all eukaryotic cells, and it is involved in a broad spectrum of cellular functions, including cell proliferation and cell cycle control.38 It promotes the formation of the first peptide bond during the initial stage of protein synthesis. In our phosphoprotein analysis, eukaryotic translation initiation factor 5A2 was dephosphorylated in flooded seedlings. It has been reported that phosphorylation of eukaryotic initiation factor 4 proteins14 and dephosphorylation of ribosomal proteins16 occurred in maize roots under oxygen deprivation condition. Recently, it has been reported the eukaryotic translation initiation factor 5A can be phosphorylated by protein kinase CK2, and this phosphorylation may regulate localization of the protein to the nucleus.39 It is likely that the dephosphorylation of eukaryotic translation initiation factor 5A in flooded seedling regulates the translation of mRNAs by affecting the localization of eukaryotic translation initiation factor 5A. Inhibition of Up-Regulation of Sucrose Synthase May Cause Accumulation of Sucrose in Roots and Hypocotyls of Flooded Soybean Seedlings. Investigation of responses to flooding of root and hypocotyl of soybean seedling in this study demonstrated that flooding causes alteration of carbohydrate metabolism (Figure 3). Proteomic analysis and enzyme activity assay revealed that up-regulation of several glycolysis, fermentation and related enzymes are occurred from early time of Journal of Proteome Research • Vol. 9, No. 8, 2010 3999

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Table 3. Identification of Soybean Phosphoproteins Regulated by Flooding Stress using 2-DE and MS experimental spot no.b

1 2

homologous protein

embryo-specific urease heat shock protein 70 subtilase family protein 70 kDa heat shock cognate protein 3 unknown 2-isopropylmalate synthase 1 malate dehydrogenase peroxisomal betaine-aldehyde dehydrogenase alpha-tubulin serine hydroxymethyltransferase 2 unknown GDP dissociation inhibitor beta-amylase nucleosome assembly protein 1 UDP-glucose pyrophosphorylase alcohol dehydrogenase-1F 60S ribosomal protein L4/L1 (RPL4A) unknown gamma-glutamyl hydrolase eukaryotic translation initiation factor 3 subunit, putative peroxidase2 unknown unknown signal peptidase I, putative unknown unknown 5′-methylthioadenosine nucleosidase 26S proteasome non-ATPase regulatory subunit stem 31 kDa glycoprotein N.D. proteasome subunit alpha type-6 unknown 17.7 kDa class I small heat shock protein eukaryotic translation initiation factor 5A2 cyclophilin unknown unknown

3 4

5

6 7 8 9

10

11 12 13 14 15

16

accession

FC

AAO85884 -1.8 ACJ11741 q(-)d NP_566473 AAS57914 q(+)d ACU18337 ABA26446 -2.2 AAB08874 BAG09377 ABO47739 -5.6 ACM45952 ACU22671 CAA06731 BAA09462 AAA88792 AAL33919 -2.2 CAA80691 -3.2 NP_187574 ACU19453 3.7 P93164 -7.5 XP_002519577 CAA62228 ACU18947 ACU23239 XP_002514291 ACU14857 ACU23239 ACI22358 AAM64349 P10743 O48551 ACU13586 ABS72445 ACJ76773 ACX37092 ACU14498 ACU16259

Mr (kDa)

pI

76.4 68.1

5.7 5.4

67.6

4.4

58.8

5.4

53.3

4.4

50.7 41.0

5.4 5.7

39.1 34.5

6.0 4.7

-5.4

33.9

4.4

-3.6 -4.3 9.0 -3.0

31.7 30.5 26.0 21.5

5.7 4.6 5.0 4.5

q(-)d

20.9

5.1

-12.3

18.2

6.2

theoretical Mr (kDa)

pI

91.0 71.9 82.5 71.8 50.6 68.2 65.3 55.4 50.3 55.7 43.7 49.9 56.4 41.7 51.6 41.6 44.7 38.6 38.3 25.8

5.7 5.1 6.2 5.1 4.4 6.4 5.8 5.2 5.0 7.2 4.9 5.4 5.3 4.4 5.2 6.0 10.5 6.0 6.7 4.8

35.9 40.8 39.0 32.5 24.4 39.0 28.7 34.6 29.4 27.5 17.4 17.9 17.7 18.3 22.8 21.8

score MP

cov blast functional (%) score categoryc

227 351 186 174 90 162 113 31 154 137 65 58 32 27 240 47 39 59 279 227

16 20 12 16 10 12 19 9 13 10 8 16 8 7 10 8 11 9 9 8

22 32 21 24 34 34 32 24 45 23 21 40 24 22 30 27 28 33 32 42

1642 1272 1031 1275 814 980 1045 1028 879 961 513 875 1035 566 862 767 694 707 671 271

PriMtb ProDsSt ProDsSt ProDsSt Unclass ScdMtb Engy DsDf CelStr PriMtb Unclass ProDsSt PriMtb DNA PriMtb Engy ProSyn Unclass ProDsSt ProSyn

9.2 214 5.2 200 5.1 162 5.9 42 4.5 382 5.1 140 4.7 134 6.2 83 6.7 1068

10 9 7 6 18 9 7 12 25

38 35 22 24 70 39 32 36 70

574 776 672 354 451 672 460 633 533

DsDf Unclass Unclass ProDsSt Unclass Unclass PriMtb ProDsSt ProDsSt

6 6 8 6 9 6 17

28 41 67 50 69 41 57

508 315 243 330 335 411 314

ProDsSt Unclass ProDsSt ProSyn ProDsSt Unclass Unclass

5.6 10.4 6.9 5.6 7.7 8.7 6.3

71 266 244 60 38 22 84

a

N.D.: unidentified protein spot, FC: fold change of protein phosphorylation, Score: Mowse score of more than 21 from MS data, MP: number of matched peptides more than 6 from MS data, Cov: sequence coverage from MS data, Blast score: the protein score in BLAST search. Accession and score in parentheses indicate the homologous protein accession and the protein score in BLAST search, respectively. b Spot numbers shown the same as in Figure 5. c PriMtb: Primary metabolism, Engy: Energy, CelGrw: Cell growth/division, Trcrpt: Transcription, ProSyn: Protein synthesis, ProDsSt: Protein destination/storage, Trpotr: Transporters, ICTrf: Intracellular traffie, CelStr: Cell structure, Sgnl: Signal transduction,DsDf: Disease/defense, ScdMtb: Secondary metabolism, Uncler: Unclear classification, Unclass: Unclassified. d q(+) or (-): increased spot with qualitative change.

flooding period (Figure 4). Measurement of carbohydrates showed that acceleration of catabolism of glucose and sucrose accumulation. The acceleration of catabolism of glucose may be attributed to acceleration of consumption through upregulation of gycolysis and down-regulation of invertase by flooding. The up-regulation of glycolysis is consistent with previous studies using several plants.3 On the other hand, although the down-regulation of invertase should influence the fructose content, the fructose content in flooded sample was not much different from control. Furthermore, as an increase of FK was identified in this study, it should also influence the fructose content. The enzyme activity in the cells might be regulated by substrate inhibition or some inhibition. The sucrose accumulation was probably occurred by down-regulation of invertase and translocation sucrose from cotyledon. Furthermore, another sucrose degrading enzyme, SUS was not affected, and the downstream, UGP and PGM were also not affected by flooding. Under low oxygen or flooding conditions, 4000

Journal of Proteome Research • Vol. 9, No. 8, 2010

expression of the SUS protein is generally enhanced by rapid induction of the SUS mRNAs. This enhancement was observed in rice,28 wheat,40 potato,41 and Arabidopsis,42 and the sucrose degradation through SUS-catalyzed reaction is energetically favored when compared with those through invertase-catalyzed reaction under low oxygen conditions.3 However, in soybean seedlings, up-regulation of SUS was not observed in terms of both the abundance and activity. Our observations are similar to those seen in roots of submerged maize seedlings, despite induction of the SUS mRNA observed after the submergence treatment.43 Microarray analysis using polysomal mRNA from Arabidopsis under hypoxia stress showed that selective mRNA translation under hypoxia is regulated by the association of mRNAs with polyribosomes, and the suppression of translation was reversed rapidly after reaeration.11 It is likely that the dephosphorylation of eukaryotic translation initiation factor 5A2 by flooding, as described above, is involved in the regulation of selective mRNA translation, and this selective translation

research articles

Proteome Analysis of Soybean Seedling Under Flooding might inhibit the up-regulation of the SUS protein under flooding conditions. The accumulation of sucrose may be caused by a combination of factors, including decreased invertase activity, the inhibition of the induction of SUS activity, and decreased activity of carbohydrate-consuming metabolic pathway such as cell wall metabolism. The fact that flooded soybean seedlings could not extend their roots and hypocotyls is consistent with this suggestion.18 Carbohydrate accumulation has been observed in roots of hypoxia tolerant plants;9,12 however these plants accumulated stores of glucose, fructose, and sucrose. Therefore, the carbohydrate accumulation observed in flooded soybean seedling is distinct from the carbohydrate accumulation in flooding-tolerant plants, suggesting that an imbalance in accumulated carbohydrates in flooded soybean seedling might result in flooding-induced injury. We found that flooding affects the enzymes in the methylglyoxal pathway. The pathway is one of the glutathionemediated detoxification systems in plants.44 The methylglyoxal is formed from the metabolism of triose phosphates, acetone, and threonine and is highly toxic, reacting with both DNA and protein. The pathway can convert methylglyoxal to lactate with two enzymes. The enzymes, GLOI and GLOII, were identified in this study (Figure 4). Up-regulation of glycolysis is possibly accompanied by production of methylglyoxal in flooded soybean seedlings; the pathway might be up-regulated to detoxify. In the present study, we demonstrated that proteins involved in glycolysis, fermentation, and protein destination/storage were up-regulated in flooded soybean seedlings, whereas proteins involved in secondary metabolism were downregulated. In addition, we found that proteins involved in protein folding and synthesis might be regulated by modulating the phosphorylation states under flooding conditions. Furthermore, analysis of enzyme activities related to carbohydrate consumption and carbohydrate accumulation revealed that sucrose accumulation and inhibition of SUS induction occurred in roots and hypocotyls of flooded soybean seedlings. These results suggest that the translational or post-translational control through phosphorylation/dephosphorylation of proteins involved in protein folding and synthesis during flooding induces an imbalance in the expression of proteins involved in several metabolic pathways, including carbohydrate metabolism, which may cause flooding-induced injury of soybean seedlings. Abbreviations: 2D-DIGE, two-dimensional fluorescence difference gel electrophoresis; 2-DE, Two-dimensional polyacrylamide gel electrophoresis; LC, liquid chromatography; MS, mass spectrometry; IEF, isoelectric focusing; pI, isoelectric point; IPG, immobilized pH gradient; ADH, alcohol dehydrogenase; ALDH, aldehyde dehydrogenase; ENO, enolase; FBPA, fructose bisphosphate aldolase; FK, fructokinase; GA3PDH, glyceraldehyde 3-phosphate dehydrogenase; GLOI, glyoxalase I; GLOII, glyoxalase II; HK, hexokinase; INV, invertase; LDH, lactate dehydrogenase; MGS, methylglyoxal synthase; PDC, pyruvate decarboxylase; PFK, phosphofructokinase; PGAM, phosphoglycerate mutase; PGI, phosphoglucoisomerase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; SUS, sucrose synthase; UGP, UDP-glucose pyrophosphorylase.

Acknowledgment. We thank Mr. S. Sagara and Mr. T. Hattori of Scrum Inc. for their technical help. We also thank Dr. N. Ahsan, Mr. M. Z. Nouri, Dr. T. Nakamura, Dr. K. Nishizawa, Dr. S. Shimamura, and Dr. R. Yamamoto for

their helpful discussions. This work was supported by a Grant-in-Aid for Scientific Research (B) (1980015) from the Japan Society for the Promotion of Science.

Supporting Information Available: Supplemental figures and tables. Figure S1. Effect of flooding on seedling growth of soybean. Seedling growth was measured at indicated time point. (A) Appearances of control and flooding treated soybean seedlings. Parenthetical times indicate duration of flooding treatment. Bars indicate 1 cm. (B) Length of root and hypocotyls of soybean seedlings. C) Fresh weight of soybean seedling. Each value represents the average ( SD of 30 seedlings. Figure S2. 2D-DIGE analysis of roots and hypocotyls from 12 and 24 h flooded soybean seedlings. Proteins extracted from control and flooded (12 or 24 h) seedlings were labeled with Cy3 and Cy 5 fluorescent dyes, respectively. The figure shows the relative intensities of significantly changed spots, those with more than 1.5-fold change in their expression and significance of P < 0.05. Figure S3. Magnified spots detected in 2D-DIGE analysis and identified protein name. Spot numbers and the identified protein names are corresponding to Figure 1 and Table 1. Figure S4. Representative 2-DE images used for phosphoproteomics analysis. After ProQ Diamond staining, the gels were stained by CBB. Left and right gel images indicate control and flooded samples. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Jackson, M. B.; Colmer, T. D. Response and adaptation by plants to flooding stress. Ann. Bot. 2005, 96, 501–505. (2) Gibbs, J.; Greenway, H. Mechanisms of anoxia tolerance in plants. I. Growth, survival and anaerobic catabolism. Funct. Plant Biol. 2003, 30, 1–47. (3) Bailey-Serres, J.; Voesenek, L. A. C. J. Flooding stress: acclimations and genetic diversity. Annu. Rev. Plant Biol. 2008, 59, 313–339. (4) Klok, E. J.; Wilson, I. W.; Wilson, D.; Chapman, S. C.; Ewing, R. M.; Somerville, S. C.; Peacock, W. J.; Dolferus, R.; Dennis, E. S. Expression profile analysis of the low-oxygen response in Arabidopsis root cultures. Plant Cell 2002, 14, 2481–2494. (5) Liu, F.; Vantoai, T.; Moy, L. P.; Bock, G.; Linford, L. D.; Quackenbush, J. Global transcription profiling reveals comprehensive insight into hypoxic response in Arabidopsis. Plant Physiol. 2005, 137, 1115–1129. (6) Loreti, E.; Poggi, A.; Novi, G.; Alpi, A.; Perata, P. A genome-wide analysis of the effects of sucrose on gene expression in Arabidopsis seedlings under anoxia. Plant Physiol. 2005, 137, 1130–1138. (7) Zhang, Z. X.; Zou, X. L.; Tang, W. H.; Zheng, Y. L. Revelation on early response and molecular mechanism of submergence tolerance in maize roots by microarray and suppression subtractive hybridization. Environ. Exp. Bot. 2006, 58, 53–63. (8) Lasanthi-Kudahettige, R.; Magneschi, L.; Loreti, E.; Gonzali, S.; Licausi, F.; Novi, G.; Baretta, O.; Vitulli, F.; Alpi, A.; Perata, P. Transcript profiling of the anoxic rice coleoptiles. Plant Physiol. 2007, 144, 218–231. (9) Kreuzwieser, J.; Katharine, J. H.; Howell, A.; Carroll, A.; Rennenberg, H.; Millar, A. H.; Whelan, J. Differential response of gray poplar leaves and roots underpins stress adaptation during hypoxia. Plant Physiol. 2009, 149, 461–473. (10) Fennoy, S. L.; Bailey-Serres, J. Post-transcriptional regulation of gene expression in oxygen-deprived roots of maize. Plant J. 1995, 7, 287–295. (11) Branco-Price, C.; Kaiser, K. A.; Jang, C. J. H.; Larive, C. K.; BaileySerres, J. Selective mRNA translation coordinates energetic and metabolic adjustments to cellular oxygen deprivation and reoxygenation in Arabidopsis thaliana. Plant J. 2008, 56, 743–755. (12) Huang, B.; Johnson, J. W. Root respiration and carbohydrate status of two wheat genotypes in response to hypoxia. Ann. Bot. 1995, 75, 427–432. (13) Jackson, M. B.; Ram, P. C. Physiological and molecular basis of susceptibility and tolerance of rice plants to complete submergence. Ann. Bot. 2003, 91, 227–241.

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