A Comprehensive Analysis of the Soybean Genes and Proteins

A Comprehensive Analysis of the Soybean Genes and Proteins Expressed under Flooding Stress using Transcriptome and Proteome Techniques Setsuko Komatsu,*,† Ryo Yamamoto,† Yohei Nanjo,† Yoji Mikami,‡ Harunobu Yunokawa,‡ and Katsumi Sakata§ National Institute of Crop Science, Tsukuba 305-8518, Japan, MessengerScape Co., Ltd., Tokyo 151-0072, Japan, and Mitsubishi Space Software Co., Ltd., Tsukuba 305-0032, Japan Received May 24, 2009

The inducible genes and proteins were analyzed using transcriptome and proteome techniques to explore the mechanisms underlying soybean response to flooding stress. Soybean seedlings were germinated for 2 days and subjected to flooding for 12 h, and the total RNAs and proteins were extracted from the root and hypocotyl. High-coverage gene expression profiling analysis as transcriptome technique was performed. Ninety-seven out of the 29 388 peaks observed demonstrated a greater than 25-fold change following 12 h of flood-induced stress. Furthermore, 34 proteins out of 799 proteins were changed by 12 h stress. Genes associated with alcohol fermentation, ethylene biosynthesis, pathogen defense, and cell wall loosening were significantly up-regulated. Hemoglobin, acid phosphatase, and Kunitz trypsin protease inhibitor were altered at both transcriptional and translational levels. Reactive oxygen species scavengers and chaperons were changed only at the translational level. It is suggested that the early response of soybean under flooding might be important stress adaptation to ensure survival against not only hypoxia but also the direct damage of cell by water. Keywords: flooding stress • soybean • transcriptome • proteome

Introduction It is well established that water flooding exhibits a severe negative influence on the productivity of arable farmland, as the vast majority of crops are not selected to grow under such stressful conditions.1 Flooding leads to reduced gas exchange between the plant tissue and the atmosphere, as gases, including oxygen, may diffuse up to 10 000 times more slowly in water than in air.2 Although oxygen deprivation caused by flooding is likely to be one of the primary signals that triggers a survival response under these conditions, it has been suggested that it is not the only limiting factor for normal plant development.3 In addition, other soil chemical characteristics including variations in pH4 and redox potential5 are altered during flood periods and may effect plant growth and survival. To date, scant direct attention has been focused on the observation of changes that occur at the plant molecular level following flooding but prior to the onset of specific functional changes. At a protein level, low oxygen selectively induces the synthesis of anaerobic proteins, most of which are known to be enzymes involved in sugar metabolism, glycolysis, and fermentation pathways.6 The vast majority of these proteins * To whom correspondence should be addressed. Setsuko Komatsu, National Institute of Crop Science, 2-1-18 Kannondai, Tsukuba 305-8518, Japan. Fax: +81-29-838-8694. E-mail: [email protected]. † National Institute of Crop Science. ‡ MessengerScape Co., Ltd. § Mitsubishi Space Software Co., Ltd.

4766 Journal of Proteome Research 2009, 8, 4766–4778 Published on Web 08/07/2009

have been studied using the flood-intolerant Arabidopsis or flood-tolerant rice. Despite an increase in the knowledge of adaptive mechanisms and regulation at the molecular level, the understanding of the mechanisms underlying the specific plant responses to flooding is extremely limited. The observation that Arabidopsis switches on numerous genes associated with flooding stress7,8 suggests that the regulation of flood tolerance in plants is far more complex than originally anticipated. Investigation into the mechanisms underlying the response to flooding stress using model plants and other commercially available plants continues to accumulate, with the hope of one day developing flood-tolerant species. Soybean is an example of a flood-intolerant crop, whose growth and grain yield is significantly reduced by flooding.9 Flood-induced injury to soybean seeds prior to radicle protrusion, namely, during seed imbibition, is caused by physical disruption following rapid uptake of water and alleviated by using seeds with a high moisture content.10 The precise cause of flood-induced injury following radicle protrusion has not been fully elucidated. Shi et al.11 have reported that cytosolic ascorbate peroxidase 2 was involved in the flood-induced stress response of young soybean seedlings. These results arose using a proteomic technique; however, the number of proteins analyzed in the study was limited. Hashiguchi et al.12 subsequently conducted a proteomic analysis of soybean seedlings in response to a 1-day flooding period identified changes of 51 proteins associated 10.1021/pr900460x CCC: $40.75

 2009 American Chemical Society

Genes and Proteins Expressed during Soybean Flood-Induced Stress with energy generation, and in particular identified the upregulation of several glycolytic enzymes. Gene expression studies on Arabidopsis, rice and maize exposed to low oxygen levels revealed the up-regulation of genes coding for transcription factors,13 signal transduction components,14 nonsymbiotic hemoglobin,15 ethylene biosynthesis,16 nitrogen metabolism,17 and cell wall loosening.18 Given that flooding is one of the environmental constraints that may result in low oxygen-induced stress in crop plants, further studies are required at a transcriptional level to determine the precise effects of flooding on plant growth. High-coverage gene expression profiling analysis in rice has permitted the detection and analysis of all expressed genes.19,20 These reports demonstrate that high-coverage gene expression profiling analysis is an appropriate method of analysis and that it might be applied to high-coverage and quantitative gene expression analysis. In the current study, flooding-inducible genes were analyzed using high-coverage gene expression profiling analysis as the method of choice for transcriptome analysis to further understand the mechanisms underlying soybean plant response to floodinduced stress. Furthermore, the inducible flooding gene levels were compared to the proteins found to be induced by flooding using proteome techniques.

Materials and Methods Plant Growth and Treatment. Following sterilization with sodium hypochlorite solution, soybean seeds (Glycine max L.) cultivar Enrei were germinated on sand for 2 days and flooded with water in a growth chamber under white fluorescent light (600 µmol m-2 s-1, 12-h light period/day) at 25 °C and 70% relative humidity. To measure growth under these conditions, seedlings were allowed to grow until 6 days after germination. For gene and protein expression analysis, 2-day-old seedlings were flooded for 0, 12, 24, 36, and 48 h. Experiments were undertaken in triplicate, with 20 seeds observed for each growth analysis experiment, and with 12 seeds observed for each gene or protein expression analysis. RNA Isolation and High-Coverage Gene Expression Profiling Analysis. The root and hypocotyl of soybean seedlings were collected, rapidly frozen in liquid nitrogen and ground to a powder using a mortar and pestle. Total RNA was then extracted using the RNeasy plant mini kit (Qiagen, Chatsworth, CA) according to the manufacturer’s protocol, and digested with 1.5 U/mL DNase I at 37 °C for 10 min mRNA was then isolated from total RNA using Oligotex-dT30 (Takara Bio, Otsu, Japan). High-coverage gene expression profiling was performed according to a previous report.21 Briefly, mRNA was converted to single stranded cDNA using reverse transcriptase with 5′ biotinylated oligo-d(T) primer and the second strand synthesized. The double stranded cDNA was cut with MspI enzyme and ligated with MspI adapter using T4 DNA ligase. The ligand products bearing biotin at the 3′ terminus were collected using streptavidin-coated magnetic beads and washed twice with 1.0 mL of washing buffer containing 5 mM Tris-HCl (pH 7.5), 0.5 mM EDTA and 1.0 M NaCl. The cDNA fragments on the magnetic beads were then digested with MesI and the supernatants collected. Ligation was performed with MesI adapter using T4 DNA ligase in the presence of MesI in the reaction mixture. The resulting solution was used as a template for selective PCR and the PCR products analyzed using ABI PRISM 3100 (Applied Biosystems, Foster City, CA). Electropherograms of PCR products were analyzed with the MS-3000 Analyzer

research articles

(Maze, Tokyo, Japan). The peaks of interest were fractionated using a standard stab gel (20 cm × 40 cm × 4 mm). The gel slices corresponding to the peaks of interest were excised and used for sequencing via the Big dye terminator method, or after cloning with the TA cloning kit (pGEM-T Easy, Promega, Madison, WI). To assign specific gene names, the gene sequences obtained were subjected to Blastn homology searches22 against the EST sequence database for soybean in the DNA Data Bank of Japan (http://www.ddbj.nig.ac.jp) and the genome sequence database for soybean (phytozome, http://www.phytozome.net/soybean. php). Matched cDNA sequences were then subjected to Blastx homology searches22 against the GenBank protein database and the Soybean UniGene Database. The identified genes were categorized using the criteria described by Bevan et al.23 for the analysis of a 1.9-Mb sequence of chromosome 4 from Arabidopsis. Protein Extraction and Two-Dimensional Polyacrylamide Gel Electrophoresis. Hypocotyl and root samples (200 mg) were homogenized in 400 µL of lysis buffer24 using a glass mortar and pestle on ice. The homogenates were then centrifuged twice at 15 000× g for 5 min each. The supernatants (70 µL, 400 µg protein) were separated by two-dimensional polyacrylamide gel electrophoresis (2-DE) in the first dimension using an isoelectric focusing (IEF) tube gel (24) for the low pI range (pI 3.5 to 8.0), or an immobilized pH gradient (IPG) tube gel (Daiichi Kagaku, Tokyo, Japan)25 for the high pI range (pI 6.0 to 10.0) and in the second dimension by SDS-PAGE. Electrophoresis using the IEF tube gel was carried out at 200 V for 30 min, followed by 400 V for 16 h and 600 V for 1 h. For IPG electrophoresis, samples were applied to the acidic side of gels. Electrophoresis using the IPG tube gel was carried out at 400 V for 1 h, followed by 1000 V for 16 h and 2000 V for 1 h. After IEF or IPG, SDS-PAGE was performed using a 15% polyacrylamide gel with a 5% stacking gel. The gels were stained with Coomassie brilliant blue (CBB), and image analysis was performed. Gel Image Analysis. 2-DE gels using IEF (pI 3.5 to 8.0) and IPG (pI 6.0 to 10.0) overlapped at a pI of approximately 5.6. The proteins located at the edge of each gel were carefully overlapped onto the corresponding proteins. 2-DE images were formed and evaluated automatically, and the amount of protein at each spot was estimated using Image Master 2D Elite software (version 3.1, GE Healthcare, Piscataway, NJ). The amount of protein at each spot was expressed as its volume, and was defined as the sum of the pixel intensity that formed the spot. To correct for variability due to CBB-staining and the quantitative variations in the intensity of the protein spots, spot volumes were normalized as a percentage of the total volume of all the spots present in a gel. The pI and molecular mass of each protein were determined using a 2-DE marker (Bio-Rad, Richmond, CA). N-Terminal Amino Acid Sequence Analyses. To analyze N-terminal amino acid sequences following separation using 2-DE, proteins were electroblotted onto a polyvinylidene difluoride (PVDF) membrane (Pall, Port Washington, NY) and detected by CBB staining. The stained proteins were excised from the PVDF membrane and directly subjected to Edman degradation on a gas-phase protein sequencer (Procise cLC, Applied Biosystems). The amino acid sequences were obtained and compared with those of known proteins in the Swiss-Prot, PIR, GenPept and PDBALL databases using the Web accessible search program FastA (http://www.dna.affrc.go.jp). Journal of Proteome Research • Vol. 8, No. 10, 2009 4767

research articles Protein Preparation for Mass Spectrometry. Protein spots were excised from CBB stained gels and destained with 50 mM NH4HCO3 for 1 h at 40 °C. Proteins were reduced with 10 mM DTT in 100 mM NH4HCO3 for 1 h at 60 °C and incubated with 40 mM iodoacetamide in 100 mM 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 with 0.1% trifluioroacetic acid in 50% acetonitrile three times. The procedure described above was performed with DigestPro (Intavis Bioanalytical Instruments AG, Cologne, Germany). The peptide solution obtained was dried and reconcentrated with 30 µL of 0.1% trifluioroacetic acid in 50% acetonitrile and desalted with NuTip C-18 pipet tips (Glygen, Columbia, MD). Desalted peptide solution was analyzed by matrix-assisted laser desorption ionization timeof-flight (MALDI TOF) MS (Voyager-DE RP, Applied Biosystems) or nanoliquid chromatography-tandem MS (Ultimate3000, Dionex, Sunnyvale, CA; LTQ Orbitrap, ThermoFisher Scientific, Waltham, MA). Analysis using Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry and Data Analysis for MS Spectra from Protein Spot. Calibration was external, and data were collected in the reflector mode. Data were searched on the Internet using an in-house licensed MASCOT search engine (version 2.2.18) software platform (Matrix Science, London, UK) against all entries in the GenBank (NCBI) database (release data 7 May 2008; 6 495 087 sequences) or the soybean genome database (version 4; 62 199 sequences), which was especially constructed for this research based on soybean genome preliminary sequences from the Department of Energy (DOE) Joint Genome Institute and Soybean Genome Sequencing Consortium. Soybean genome sequences were downloaded from the DOE database (http://www.phytozome.net, release data 24 January 2008) and then were converted into FASTA format. Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methionines was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. In the case of peptides matching among multiple members of a protein family, the protein presented was selected based on the highest score and the highest number of the matching peptides. For analysis, four criteria were used to assign a positive match with a known protein:1 the deviation between the experimental and theoretical peptide masses needed to be less than 50 ppm;2 at least six different predicted peptide masses needed to match the observed masses for an identification to be considered valid;3 the matching peptides needed to cover at least 30% of the known protein sequence; and4 individual ions had to score more than 72 identity or extensive homology (P < 0.05). Analysis using Nano-Liquid Chromatography-Tandem Mass Spectrometry and Data Analysis for MS/MS Spectra from Protein Spot. Nanospray LTQ XL Orbitrap MS (Thermo Fisher, San Jose, CA) was operated in data dependent acquisition mode with the Xcalibur software. Using Ultimate 3000 nanoLC (Dionex, Germering, Germany), peptides were loaded in 0.1% formic acid onto a 300 µm ID × 5 mm C18 PepMap trap column. Elution and separation of the peptides from the trap column on a 75 µm ID × 15 cm C18 PepMap100, 3 µm nanocolumn were done using 0.1% formic acid in acetonitrile at a flow rate of 200 nL/min. To spray sample in the MS, a PicoTip emitter (20 µm ID, 10 µm Tip ID, Woburn, MA, USA) 4768

Journal of Proteome Research • Vol. 8, No. 10, 2009

Komatsu et al. was used with a spray voltage of 1.8 kV. Full scan MS spectra were acquired in the Orbitrap on the 150-2000 m/z with a resolution of 15 000. The three most intense ions at a threshold above 1000 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 the peptides. Acquired MS/MS spectra were converted to single DTA files using BioWorks (version 3.3.1, Thermo Fisher Scientific). The following parameters were set for creation of the peak lists: 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. Data were searched using Mascot search engine against all entries in the NCBI database or the DOE database. Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methinine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. The mass tolerance of the precursor ion was set to 10 ppm and that of fragment ions was set to 1 Da. The instrument setting was specified as “ESI-Trap”. Protein hits were validated if the identification was with at least four top ranking peptides (P < 0.05). In the case of peptides matching to multiple members of a protein family, the presented protein was selected based on the highest score and the highest member of the matching peptides. Mathematical Analysis of Gene Interaction. Gene interactions were mathematically analyzed based on the time course of gene expression at the two stages. The first stage was defined as clustering. The time course was evaluated so that for each gene the time course was normalized and a logarithm was calculated. The time course of the logarithms were then clustered using the Unweighted Pair Group Method with Arithmetic mean (UPGMA). A median for the clustered genes was selected for each time point along the time course, and the medians comprised the time course of the cluster. The second stage involved the estimation of interactions among the clusters using Mathematical gene Interaction Network Optimization Software (MINOS).26 The MINOS employed the S-system differential equation27 and estimated an interaction using a set of coefficients that simulated the time course. For each cluster, the significant upstream clusters were investigated according to the modification of the method of Tanaka et al.27 They are the following process: (i) The S-system coefficients corresponding to an interaction for each set of upstream N (e2) clusters j1, j2 were estimated. (ii) An observation residual for the interaction was calculated from the observed and simulated expression time course of cluster i. (iii) Specificity of the fit was calculated for each candidate of the upstream cluster j from the observation residuals calculated from the models that contain the candidate cluster j. In the present study, the specificity of fit was defined so that the specificity increased as the residual decreased. (iv) The significance of the candidate cluster was evaluated using the z-score defined as z ) (f - m)/s, where f is the specificity of fit of the corresponding candidate cluster, m is the mean and s is the standard deviation of the specificity of fit among the candidate clusters.

Results and Discussion The Root Growth in Soybean Seedling Is Significantly Suppressed Following One Day of Flooding. In the current study, we first measured physiological changes in the early

Genes and Proteins Expressed during Soybean Flood-Induced Stress

research articles

seedlings utilized in this study were treated with flooding for 12 h and then subjected to transcriptome analysis.

Figure 1. Growth suppression in soybean seedling after flooding. Soybean seeds were germinated on sand for 2 days, and treated without or with flooding for 1, 2, 3, or 4 days. The photograph shows the physiological differences between treatments. The fresh weight of the hypocotyl and total roots, lengths of hypocotyl and main root, and number of total roots were measured after treatment without (white column) or with flooding (black column). Values are presented as mean ( SE from three physiologically independent experiments. For each experiment, 20 soybean plants were analyzed. A double asterisk indicates significance at P < 0.01.

stages of flood-induced stress. Soybean seeds were germinated on sand for 2 days and subjected to flooding for up to 4 days (Figure 1). The length of the lateral and adventitious roots and the overall growth of the plants was significantly suppressed following 1 day of flooding stress. This root growth suppression continued over the 4 days (Figure 1). Changes in the fresh weight of the hypocotyl and total roots, the length of the hypocotyl and main root, and the total number of roots were measured after 1, 2, 3, and 4 days of flooding stress. The fresh weight of the hypocotyl and total roots was decreased by 50% following the flooding stress when compared to the controls that had not been subjected to flooding. The length of hypocotyl was significantly inhibited by flooding; however, the length of the main root was inhibited by 25% following fV stress when compared with the controls. The number of total roots under flooding stress was only main root, and the taproot experienced the most flooding stress. Shi et al.11 reported that the total number of roots, the main root, the length of the lateral and adventitious roots, and the fresh weight of roots in soybean seedlings subjected to flooding using a sponge was significantly suppressed after 3 days of flooding stress when compared with controls. Wang and Jiang28 also reported that using the creeping bent-grass plant, a reduction in root growth was one of the most commonly identified parameters during periods of flooding. Combined with our results, these reports indicate that the growth of the soybean seedling root is significantly suppressed after 1 day of flooding. Given that the growth of the soybean seedling root is suppressed following only 1 day of flooding, the soybean

5831 Transcripts Are Altered Greater than Three-Fold under Flooding Stress. To investigate the mechanisms involved in the regulation of flooding stress in soybean seedlings, floodresponsive genes were identified via transcriptome analysis. In these experiments, soybeans were germinated for 2 days and treated with or without flooding for 0.5, 1.0, 1.5, or 2.0 days. RNA was then extracted from the root and hypocotyl, and was analyzed using high-coverage gene expression profiling analysis. Filtering of the peaks observed in high-coverage gene expression profiling analysis was performed based on the fold change of the peak intensity between control and flooding conditions. Out of 29 388 transcripts analyzed, we identified 5831 transcripts that demonstrated a greater than 3-fold change following 0.5, 1.0, 1.5, or 2.0 days of flooding. These genes were then divided into 65 clusters (Figure 2). Out of the 5,831 transcripts, 5002 (85.78%) were up-regulated under floodinduced stress for 0.5 to 2.0 days, and included c1, c38, c38, c43, c44, c53 and so on outlined in Figure 2. The transcripts of 5723 were found to express a 3- to 10-fold increase under flooding stress for 0.5 to 2.0 days and included c27, c32, c33, c52, c53 and so on outlined in Figure 2. Klok et al.29 performed expression profile analysis following a low-oxygen response in Arabidopsis root culture using a microarray chip containing 3500 cDNA probes, and found that 210 cDNA clones were differentially expressed over four time points within 0.5 h. Loreti et al.8 subsequently performed a genome-wide analysis to determine the effects of sucrose on gene expression in Arabidopsis seedlings under anozia. They used a gene chip containing more than 22 500 probe sets and found that 192 genes were induced and 556 genes were repressed by at least 3-fold at 6 h by anoxia. In soybean seedlings subjected to flooding conditions, we found 5831 out of 29 388 transcripts to be differentially expressed by more than 3-fold, suggesting that the soybean plant is extremely sensitive to low oxygen levels caused by flooding. In addition, we also identified 108 transcripts that demonstrated peaks with a 10fold change during flooding conditions for 2 days. Of these transcripts, 97 peaks were altered more than 25-fold at 12 h and were subsequently analyzed as potential flooding stressresponsible genes. Genes Related to Alcohol Fermentation, Ethylene Biosynthesis and Pathogen Defense Systems Are Significantly UpRegulated Following Flood-Induced Stress. To precisely define the individual genes, the sequences of 97 of the obtained mRNAs were subjected to Blastn homology searches (Altschul et al., 1990) against the EST sequence database for soybean in the DNA Data Bank of Japan and the genome sequence database for soybean. Matched cDNA sequences were then subjected to Blastx homology searches22 against the GenBank protein database and the Soybean UniGene Database (Table 1). We found that alcohol dehydrogenase, pathogen-inducible trypsin-inhibitor and ERF-like proteins were significantly upregulated by more than 10,000-fold after 12 h of flooding when compared to controls (Table 1). Furthermore, expansin-like B1, polygalacturonase inhibiting protein, aminotransferase, agglutinin-1 and stearol-ACP desaturase were also up-regulated Journal of Proteome Research • Vol. 8, No. 10, 2009 4769

research articles

Komatsu et al.

Figure 2. Summary of the significant clusters of genes altered following flooding. Soybeans were allowed to germinate for 2 days, and treated without or with flooding for 0.5, 1.0, 1.5, and 2.0 days. RNA was extracted from the root and hypocotyl, and high-coverage gene expression profiling analysis performed. Out of the 29 388 total transcripts, profiles demonstrating an alteration greater than 3-fold were observed for 5831 transcripts under flood conditions, and were divided into 65 clusters. Expression profile patterns are outlined on the longitudinal axis and are presented as the ratio of gene expression volume of flood-induced stress (F) against the control (C). The gene ratios were then transformed into the log scale. The horizontal axis indicates the time after flooding.

by more than 5,000-fold after 12 h of flooding when compared to controls (Table 1). Genes Related to Alcohol Fermentation, Ethylene Biosynthesis and Cell Wall Loosening Are Rapidly Up-Regulated following Flooding. Out of the 5,831 transcripts that were altered by more than 3-fold following flooding for 0.5, 1.0, 1.5, and 2.0 days, 97 genes exhibited a greater than 25-fold change at 12 h of flooding. These 97 genes were categorized using time course pattern analysis after flood-induced stress (Figure 3). Alcohol dehydrogenase, ERF-like protein and matrix metallo4770


proteinase MMP were rapidly up-regulated within 12 h of flooding (Table 2). Furthermore, in addition to these genes, ACC oxidase ACCO2 and pathogen-inducible trypsin-inhibitor were also up-regulated after 1.0 day of flooding (Table 2). Genes Related to Alcohol Fermentation, Ethylene Biosynthesis, Cell Wall Loosening and Pathogen Defense Are Involved in the Cellular Response to Flooding. Using highcoverage gene expression profiling analysis, we identified that genes related to alcohol fermentation, ethylene biosynthesis, cell wall loosening and pathogen defense were

research articles


Table 1. Differentially Expressed Genes in Root and Hypocotyl Of Soybean Seedlings under Flooding Conditions peak number

accession number

description

functional categorya

fold change (F/C)b

70 84 55 5 85 99 94 8 43 65 82 62 41 23 49 66 97 77 48 22 86 81 90 1 67 50 44 34 69 53 64 29 3 10 28 38 11 39 26 32 40 2 25 36 9 96 45 59 16 19 7 47 91 35 100 88 42 92 101 87 37 79 63 52 17

CAA80691 CAJ21172 AAT45389 BAD01556 AAC62469 CAO23976 AAT45389 NP_193436 CAI99392 ABA96039 Q39528 ABM45911 AAL27029 CAO23976 BAG50065 CAN76985 CAO47615 BAE71223 CAN63793 NP_192813 AAL27029 NP_188316 ABD33010 CAA42636 NP_180555 BAG50065 CAO48052 AAX84675 CAO67727 Q39528 CAO68094 BAD09434 AAG52992 AAL27029 AAL27029 AAA97887 AAP37978 AAR07598 AAL27029 CAO40667 AAR07598 NP_179899 AAL27029 NP_193436 AAT45389 AAT45389 ABQ63000 BAA21920 ABB90835 ABE80120 AAG13131 AAA97887 CAN80330 AAG00940 AAO72533 AAL11507 BAG50065 AAD03573 CAA80691 AAL66292 NP_196996 AAL27029 ACG27665 CAN82956 CAN78486

Increased genes under flooding condition alcohol dehydrogenase alcohol dehydrogenase pathogen-inducible trypsin-inhibitor ERF-like protein alcohol dehydrogenase Adh-1 unnamed protein product pathogen-inducible trypsin-inhibitor Expansin-like B1 polygalacturonase inhibiting protein aminotransferase, putative agglutinin-1 precursor stearoyl-ACP desaturase matrix metalloproteinase MMP2 unnamed protein product transcription factor AP2-EREBP hypothetical protein unnamed protein product hypothetical protein hypothetical protein HpcH/HpaI aldolase family protein matrix metalloproteinase MMP2 acyl-activating enzyme 7 fumarylacetoacetase auxin-responsive GH3 product ATPDR3/PDR3 ATPase transcription factor AP2-EREBP unnamed protein product ACC oxidase ACCO2 unnamed protein product agglutinin-1 precursor unnamed protein product 3-phosphoglycerate dehydrogenase Receptor-like protein kinase matrix metalloproteinase MMP2 matrix metalloproteinase MMP2 nonsymbiotic hemoglobin class 10 pathogenesis-related protein fiber protein Fb19 matrix metalloproteinase MMP2 unnamed protein product fiber protein Fb19 cytochrome P450, family 96 matrix metalloproteinase MMP2 Expansin like B1 pathogen-inducible trypsin-inhibitor pathogen-inducible trypsin-inhibitor RAP2-like protein ZPT2-11 VHS and GAT domain protein homeodomain-like Pyruvate decarboxylase nonsymbiotic hemoglobin hypothetical protein unknown Pyruvate decarboxylase Vacuolar H+-pyrophosphatase transcription factor AP2-EREBP putative senescence-associated rhodanese protein alcohol dehydrogenase serine acetyltransferase gibberellin-regulated family protein matrix metalloproteinase MMP2 hypothetical protein hypothetical protein hypothetical protein

Engy Engy DsDf Trcrpt Engy Unclr DsDf CelStr DsDf PriMtb DsDf PriMtb DsDf Unclr Trcrpt Unclr Unclr Unclr Unclr Unclr DsDf PriMtb PriMtb DsDf Trpotr Trcrpt Unclr ScdMtb Unclr DsDf Unclr Engy Sgnl DsDf DsDf PriMtb DsDf CelStr DsDf Unclr CelStr ScdMtb DsDf CelStr DsDf DsDf Trcrpt Trcrpt Sgnl Trcrpt Engy PriMtb Unclr Unclr Engy Trpotr Trcrpt PriMtb Engy PriMtb ScdMtb DsDf Unclr Unclr Unclr

17098 14462 12357 12008 11170 9904 8381 8019 7872 6950 6629 6517 4877 4695 4093 3940 3909 3500 3136 2803 2526 2182 2106 2036 1976 1964 1959 1648 1624 1300 1280 1187 1138 1113 1074 1044 960 930 809 800 776 633 492 488 485 472 460 440 356 157 153 95 92 85 71 66 64 61 51 50 49 48 47 40 40

Journal of Proteome Research • Vol. 8, No. 10, 2009 4771

research articles

Komatsu et al.

Table 1. Continued functional categorya

peak number

accession number

description

58 103 18 102 89 31 4 106 105 20 78 104 108 61 107 15 57

CAN73743 AAF86688 CAI99393 BAC41787 AAD03573 AAL27029 AAL27029 NP_180161 CAO42137 NP_566015 ABD28522 NP_187915 ABK96350 BAC11913 AAW78864 CAO62924 AAT45389

hypothetical protein MTD2 polygalacturonase inhibiting protein putative cyclin-dependent protein kinase putative senescence-associated rhodanese protein matrix metalloproteinase MMP2 matrix metalloproteinase MMP2 Cys3His zinc finger protein unnamed protein product calcium-binding EF hand family protein Cupin, RmlC-type multidrug resistance-associated protein 3 unknown glucose-6-phosphate isomerase respiratory burst oxidase unnamed protein product pathogen-inducible trypsin-inhibitor

Unclr Unclr DsDf Sgnl PriMtb DsDf DsDf Trcrpt Unclr Sgnl Unclr DsDf Unclr Engy Engy Unclr DsDf

30 83 54 33 98 74 72 21 24 27 95 80 6 76 93

CAA11075 BAF73620 ABV32544 NP_199107 CAD31838 NP_181179 ABC59088 AAA97907 ABD03937 ABD03937 AAS55083 ABB85236 ABX60409 CAK03587 ACA23207

Decreased genes under flooding condition acid phosphatase Malonyl-CoA: glucoside -malonyltransferase alpha-L-arabinofuranosidase protein Triacylglycerol lipase quinone oxidoreductase ATPase coupled to transmembrane movement of substrances cytochrome P450 monooxygenase cysteine proteinase inhibitor Vicianin hydrolase (glycosyl hydrolase) Vicianin hydrolase (glycosyl hydrolase) UDP-glucose glucosyltransferase glucosyltransferase lipoxygease L-4 PDR-like ABC-transporter Kunitz trypsin protease inhibitor

Sgnl ScdMtb CelStr Engy Engy Trpotr ScdMtb DsDf PriMtb PriMtb PriMtb PriMtb PriMtb Trpotr DsDf

fold change (F/C)b

40 40 39 39 37 36 34 32 32 32 32 31 30 29 28 26 25 -3396 -2789 -160 -861 -796 -718 -523 -485 -423 -365 -81 -50 -33 -31 -25

a PriMtb: Primary metabolism, Engy: Energy, CelGrw: Cell growth/division, Trcrpt: Transcription, ProSyn: Protein synthesis, ProDsSt: Protein destination/strage, Trpotr: Transporters, ICTrf: Intracellular traffie, CelStr: Cell structure, Sgnl: Signal transduction, DsDf: Disease/defense, ScdMtb: Secondary metabolism, Uncler: Unclear classification, Unclass: Unclassified. b “F/C”: Expression profile pattern are shown on the ratio of gene expression volume at 12 h of flooding stress against that of control.

involved in plant cellular response to flooding (Tables 1 and 2). Alcohol dehydrogenase is thought to be essential for anoxia survival, likely due to the observation that it recycles NAD+ required for continuing glycolysis in the absence of oxygen.30 Russell et al.31 reported that anoxic treatment of soybean seedlings induced the accumulation of alcohol dehydrogenase mRNA, the selective synthesis of alcohol dehydrogenase, and the accumulation of enzyme activity. During oxygen deprivation, pyruvate decarboxylase converts pyruvate to acetaldehyde, which is then metabolized by alcohol dehydrogenase to ethanol. In the current study, pyruvate decarboxylase was also up-regulated following stress caused by flooding. These results suggest that soybean plants exhibit a similar enzyme cascade as other plants under low oxygen conditions caused by flooding. Our study has demonstrated that the ethylene biosynthesis and signaling-related genes such as ACC oxidase, ERF-like protein and the transcription factor AP2-EREBP were upregulated after flooding of soybean seedlings. It has been reported that the submergence signal required for enhanced shoot elongation was gaseous ethylene.32 In rice, ethylene has not been detected in seedlings observed within 2 days of sowing. However, it is known to increase thereafter, with a greater increase in the tolerant genotypes initiating 3 days after sowing.33 The ethylene produced by the rice seedlings 4772


subjected to flood conditions is thought to promote elongation, and is one example of a survival mechanism that has aided submerged plants regain their contact with air. However, the soybeans subjected to flood conditions in the current study did not demonstrate any elongation (Figure 1), even though the ethylene biosynthesis and related signaling genes were significantly and rapidly up-regulated in these plants. In the plant Rumex palustris, ethylene has been shown to directly regulate the expression of expansin.34,35 Cell wall extensibility is also thought to be associated with cell-wallloosening proteins including expansions and xyloglucan endotransglysylase/hydrolases.37 In rice, submergence-induced elongation strongly correlates with an increase in mRNA encoding expansions,38 but the soybean exposed to flood condition in our study did not demonstrate any elongation (Figure 1), suggesting that the effect of flood stress on the rice and soybean is not the same. In this study, expansin-like B1 and matrix metalloproteinase MMP 2 were up-regulated within 1 day after flooding stress. Maidment et al.36 have previously reported that matrix metalloproteinase MMP in Arabidopsis played an important role in the events that lead to the remodeling or degradation of the plant extracellular matrix. Our study on soybean exposed flooding stress revealed the induc-

research articles


Table 2. Time Course Pattern of Soybean Genes Expressed by Flood-Induced Stress pattern

U0.5

U1.0

Figure 3. Time course pattern of genes regulated by flood conditions. Out of the 5831 transcripts demonstrating 3-fold changes under flood-induced stress conditions for 0.5, 1.0, 1.5, and 2.0 days, 97 genes were shown to demonstrate a 25-fold change after 0.5 days of flood-induced stress. The expression of these 97 genes was categorized using time course pattern analysis after flood-induced stress. “U” and “D” indicate upregulated and down-regulated genes following flooding, respectively. White and black circles outline the control and flood treatments, respectively.

tion of cell wall loosening by expansin-like B1 and matrix metalloproteinase MMP2. Pathogen-inducible trypsin-inhibitor-like protein and pathogenesis-related protein 10 were also found to be up-regulated under flood-induced stress. Oxidative stress has been shown to be enhanced by aluminum-induced pathogen-inducible protein 2, beta-glucanase and Bowman-Birk protease inhibitor.39 Although these proteins are termed pathogen-inducible proteins, there is little direct evidence that favors one over the others, indicating that these proteins may be stressrelated. The above results suggest that genes related to alcohol fermentation, ethylene biosynthesis and cell wall loosening are involved in the perception of oxygen deprivation in plants, and lead to an anaerobic response. The results for soybean seedlings obtained via high-coverage gene expression profiling analysis confirm most of the previously reported evidence that was

description

peak number

alcohol dehydrogenase ERF-like protein matrix metalloproteinase MMP2 receptor-like protein kinase vacuolar H+-pyrophosphatase serine acetyltransferase gibberellin-regulated family protein fumarylacetoacetate 3-phosphoglycerate dehydrogenase decarboxylase fiber protein nonsymbiotic hemoglobin functionally unknown

84 5 86, 79, 10 3 88 87 37 90 29 100 40 47 77, 44, 91, 52, 15, 103, 105, 78, 108, 8, 36 62 41, 31 34 9

expansin-like B1 stearoyl-ACP desaturase matrix metalloproteinase MMP2 ACC oxidase ACCO2 pathogen-inducible trypsin-inhibitor transcription factor AP2-EREBP alcohol dehydrogenase polygalacturonase inhibitor cyclin-dependent protein kinase glucose-6-phosphate isomerase nonsymbiotic hemoglobin fiber protein VHS and GAT domain protein respiratory burst oxidase functionally unknown U1.5 alcohol dehydrogenase acyl-activating enzyme 7 auxin-responsive GH3 product ATPDR3/PDR3 ATPase agglutinin-1 class 10 pathogenesis-related protein pathogen-inducible trypsin-inhibitor senescence-associated rhodances protein Cys3His zinc finger protein functionally unknown U pathogen-inducible trypsin-inhibitor 0.5-1.5 alcohol dehydrogenase aminotransferase registant-associated protein 3 functionally unknown U2.0 polygalacturonase inhibitor matrix metalloproteinase MMP2 cytochrome P450 RAP2-like protein ZPT2-11 homeodomain-like calcium-binding protein functionally unknown D-A glucocide malonyltransferase arabinofuranosidase protein triacylglycerol lipase cytochrome P450 monooxygenase UDP-glucose glucosyltransferase Kunitz trypsin protease inhibitor quinone oxidoreductase glucosyltransferase lipoxygease L-4 D-B acid phosphatase vicianin hydrolase ATPase coupled to transmembrane movement of substrances cysteine proteinase inhibitor PDR-like ABC-transporter

42, 49, 50 101 18 102 61 38 39 16 107 66, 48, 22, 32, 35 70 81 1 67 53, 82 11 96, 57 92, 89 106 97, 63 55, 94 85 65 104 23, 7 43 28, 26, 25, 4 2 45 59 19 20 99, 69, 64, 17, 58 83 54 33 72 95 93 98 80 6 30 24, 27 74 21 76


research articles

Komatsu et al.

Figure 4. Detection of flooding responsive proteins by 2-DE. Soybean seeds were germinated for 2 days and treated without (upper panel) or with flooding for 12 h (lower panel). Then, 400 µg of protein was extracted from the root and hypocotyl, and separated on 2-DE followed by CBB staining. IEF and IPG in the first dimension is presented from right to left and left to right, respectively. SDSPAGE was used for the second dimension and is presented from top to bottom. Merged images of IEF gels ranging from pI 3.5 to 6.0 and IPG gels ranging from pI 6.0 to 9.0 are shown. The isoelectric point (pI) and relative molecular weight of each protein was determined using a 2-DE marker (Bio-Rad). Protein spots were detected with ImageMaster 2D Elite software (GE Healthcare). Open circles show the spots with altered protein expression. Upward arrows show the spots with up-regulated protein expression, while downward arrows show down-regulated expression.

based on single-gene studies and biochemical analysis on lowoxygen using rice and Arabidopsis. In addition, we also suggest new explanations for the role of the novel genes that we identified. Reactive Oxygen Species Scavengers and Chaperons Are Altered at a Protein Level. To investigate the mechanisms by which proteins are regulated following flooding stress in soybean seedlings, flood-responsive proteins were identified using proteomic techniques. Hashiguchi et al.12 reported a proteomic analysis of soybean seedling in response to 1 day flooding and identified the up-regulation of several glycolytic enzymes. In this study, we analyzed the early change of the proteins at the same processing (12 h) as the transcriptome analysis (Table 1). Soybean seeds were germinated for 2 days and subjected to flooding for 12 h. Total protein was then extracted from the roots and hypocotyls of seedlings, separated by 2-DE, stained by CBB (Figure 4), and identified by protein sequencing and MS (Table 3). A total of 799 protein spots were detected, and the pI and molecular weight of these proteins ranged from 3.5 to 10.0 and 10.0 kDa to 100.0 kDa, respectively (Figure 4, upper panel). Among these protein spots, 34 proteins were altered by flooding. Specifically, 14 protein spots were upregulated, while 20 protein spots were down-regulated (Figure 4, lower panel). 4774


The proteins that were up-regulated included beta-conglycinin alpha prime subunit (spots 6 and 7), 1-Cys peroxiredoxin (spots 17 and 27), class-10 pathogenesis-related protein kinase 1 (spot20), protein phosphatase inhibitor 1 (spots 21 and 22), maturation polypeptide (spot 23), embryonic abundant protein (spots 24 and 25), hemoglobin alpha-II chain (spots 26 and 33), P24 pleosin isoform A (spot 28) and nucleoside diphosphate I (spot 30). The proteins that were down-regulated included calreticulin-1 (spot 1), protein disulfide isomerase (spot 2), 70 kDa heat shock protein (spot 3), putative ankyrin-repeat protein (spot 4), Kunitz trypsin protease inhibitor (spot 5), caffeoylCoA-O-methyltransferase (spot 8), lectin (spots 9 and 31), osmotin-like protein (spot 10), 10 Cys peroxiredoxin (spot 11), stem 28/31 kDa glycoprotein precursor (spots 29 and 12), acid phosphatase (spot 14), cp 10-like protein (spot 16) and ascorbate peroxidase (spot 32) (Table 3). Numerous reactive oxygen species scavengers were changed at a protein level. Reactive oxygen species including superoxide radicals, hydroxyl radicals and hydrogen peroxide are known to act as toxic products of normal cell metabolism and as regulatory molecules in stress perception and signal transduction in plants,40 as well as substances that prevent oxidative damage in light- or heat-stressed plants.41 However, ascorbate peroxidase has been down-regulated in soybean seedlings

research articles


Table 3. Differentially Expressed Proteins in Root and Hypocotyl of Soybean Seedlings under Flooding Conditions spot No.a

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Exp.b

Theor.c

MM

pI

MM

pI

44.6 61.9 65.7 49.7 22.4 30.9 26.1 29.4 29.8 29.2 35.1 33.6 25.4 29.7 24.4 29.7 26.8

3.2 4.6 4.7 4.1 4.1 4.3 4.5 5.2 5.5 5.6 5.8 6.0 5.7 5.5 5.3 5.1 5.1

48.6 37.8 23.9 72.5 70.5 28.1 30 29.4 23.5 29.0 25.6 26.6 -

4.5 4.5 5.2 5.5 5.1 5.5 5.9 6.7 6 6.3 5.5 6.8 -

11.8 61.5 61.5 55.1 54.9 55.2 54.2 28.8 25.1 29.6 14.1 29.8 34.2 52.6 50.2

5.1 6.2 6.5 6.2 6.4 6.7 7.0 6.8 7.8 7.7 6.2 5.5 5.7 5.9 5.9

47.8 24.5 23.6 29.2 16.5 -

6.0 6.4 8.9 8.8 6.3 -

homologous proteind

accession no.e

calreticulin-1 BAF36056.1 protein disulfide isomerase PX0084(EESSEKEFVL) 70 kDa heat shock protein P31082(EKVVGIDLGT) putative ankyrin-repeat protein AAQ96339.1 Kunitz trypsin protease inhibitor ACA23205.1 bata-conglycinin alpha prime subunit BAE02726.1 bata-conglycinin alpha subunit BAB56161.1 caffeoyl-CoA-O-methyltransferase ABF74683.1 lectin precursor CAH60173.1 osmotin-like protein Q41350 (LILTLVNN) 1-Cys peroxiredoxin Q6E2Z6 (PGLTIGDTIP) stem 31 kDa glycoprotein precursor P10743 Unknown ABK93121.1 acid phophatase CAA11075.1 Unknown ABK93363.1 cp 10-like protein AAM77651.1 1-Cys peroxiredoxin Q6E2Z6 (PGLTIGDTIP) N.D.d N.D.d class-10 pathogenesis- related protein 1 Q43560 (GVFTFEDETT) protein phosphatase inhibitor 1 Q9ERT9 (EHQLDQQKAG) protein phosphatase inhibitor 1 Q9ERT9 (EHQLDQQKAG) maturation polypeptide AAA33985.1 embryonic abundant protein S61428 (STTNKVSDYA) embryonic abundant protein S61428 (STTNKVSDYA) hemoglobin alpha-II chain P41330(TSVAHMD) 1-Cys peroxiredoxin Q6E2Z6 P24 pleosin isoform A(P89) P29530 stem 28 kDa glycoprotein precursor P15490 nucleoside diphosphate kinase I Q39839 Lectin DQ235094 (KDTVSFTFN) ascorbate peroxidase AB082932(GKSYPTVSAD) hemoglobin alpha-II chain P41330(TSVAHMD) N.D.d

scoref identity*

395 100* 100* 240 420 988 459 113 249 100* 100* 720 240 507 155 865 100*

100* 100* 100* 1689 100* 100* 100* 438 127 730 1067 100* 100* 100*

MP/Covg

FCh

UDi

12/29 5/16 5/35 17/19 9/15 8/36 9/36 22/65 5/26 11/49 5/20 20/71 -

DsDf ProDsSt ProDsSt Unclr DsDf ProDsSt ProDsSt DsDf DsDf DsDf DsDf ProDsSt Unclr ScdMtb Unclr Uncl DsDf

-

DsDf Sgnl Sgnl ProDsSt ProDsSt ProDsSt PriMtb DsDf PriMtb ProDsSt PiMtb DsDf DsDf PriMtb

D D D D D U U D D D D D D D D D U D D U U U U U U U U U D U D D U D

28/56 12/54 4/21 13/50 11/62 -

a The spot numbers are given in Figure 4. b Experimental (Exp.) molecular mass (MM) and pI were calculated from the gel in Figure 4. c Theoritical (Theor.) molecular mass (MM) and pI were added for MS data. d N.D. means the proteins whose amino acid sequence or MS/MS were not determined. Proteins were identified by protein sequencer and LC-MS/MS from three-day old soybean seedlings. e Accession numbers: Proteins were sequenced or identified by analysis of tryptic peptides following searching SWISS-Prot or Uniprot-Sprot databases for sequences and soybean genome database for MS/ MS data, respectively. Annotations were obtained by BLAST search using soybean protein against the NCBI nonredundant database. f Score: more than 20 was significant (P < 0.05) from MS data. Identity*: amino acid sequence identity from protein sequencer data. g MP: number of muched peptides more than 4 from MS data. Cov: coverage from MS data. h FC means Functional Category. PriMtb: Primary metabolism, Engy: Energy, CelGrw: Cell growth/ division, Trcrpt: Transcription, ProSyn: Protein synthesis, ProDsSt: Protein destination/strage, Trpotr: Transporters, ICTrf: Intracellular traffie, CelStr: Cell structure, Sgnl: Signal transduction, DsDf: Disease/defense, ScdMtb: Secondary metabolism, Uncler: Unclear classification, Unclass: Unclassified. i U and D mean up and downregulated proteins by flooding stress, respectively.

subjected to flood-induced stress (11). In the current study, ascorbate peroxidase (spot 31) and acidic 1-Cys peroxiredoxin (Pot 11, pI 5.8) were also down-regulated in early response to flooding, while basic 1-Cys peroxiredoxin (spot 27, pI 6.8) was up-regulated. Pulido et al.42 previously reported that the oxidation status of this protein analyzed in extracts of developing seeds and aleurone cells dissected from germinating seeds was affected by overoxidation. In our study, the basic form of 1-Cys peroxiredoxin (spot 27, 28.8 kDa) increased by flooding stress was significantly lower in molecular weight when compared to the acidic form (spot 11, 35.1 kDa), suggesting that the basic form may represent a breakdown product. These results indicate that ascorbate peroxidase and 1-Cys peroxiredoxin are exist during germinating stage and decreased under flooding stress. Chaperons including calreticulin-1 and protein disulfide isomerase 50 kDa were also down-regulated at a protein level following flooding stress. These proteins are known to be

involved in the normal folding and quality control of storage proteins such as molecular chaperones.43 The results of the current study suggest that the down-regulation of protein disulfide isomerase may induce misfolding of proteins, resulting in the down-regulation of glycoproteins such as stem 31 kDa/28 kDa glycoproteins, Kunitz-type trypsin inhibitor B and lectin. Disease/Defense-Related Proteins Are Up-Regulated at a Transcriptional Level, but Are Down-Regulated at a Translational Level Following Flooding. Ninety-seven genes (Figure 5A) and 34 proteins (Figure 5B) were found to demonstrate an altered expression after flooding (Tables 1 and 2). Disease/ defense-related proteins were up-regulated at a transcriptional level but were down-regulated at a translational level following flooding. Disease/defense-related genes included pathogeninducible trypsin-inhibitor and pathogenesis-related protein 10, while disease/defense-related proteins included reactive oxygen species scavengers. It is suggested that these differences are Journal of Proteome Research • Vol. 8, No. 10, 2009 4775

research articles

Komatsu et al. that hemoglobin 1 may play an essential role in the rescue of plants from flooding stress during their early stages of growth. Acid phosphatase and Kunitz trypsin protease inhibitor were commonly down-regulated by flooding stress at transcriptional and translational levels (Tables 1 and 2). Acid phosphatase was previously termed vegetative storage protein,46 and has been shown to be increased in ethylene-insensitive mutants.47 These results suggest that the ethylene signal pathway represses the induction of vegetative storage protein. The ethylene-insensitive mutants that constitutively express vegetative storage proteins are smaller than their wild-type controls exhibit stunted roots with long root hairs, accumulate anthocyanine, demonstrate constitutive expression of defense-related genes, and show an enhanced resistance to disease.48 The down-regulation of vegetative storage proteins and genes in soybean seedlings may prove useful in the definition of mechanisms underlying floodrelated stress.

Figure 5. Functional classification of genes and proteins demonstrating alterations following flooding. The presented chart shows the distribution of the 97 genes (A) and 34 proteins (B) indicated in Tables 1 and 2. Orange and blue bars present upregulation and down-regulation, respectively. The horizontal axis indicates the numbers of genes and proteins up- and downregulated under flood conditions.

dependent upon whether disease/defense-related proteins act via gene expression. Transcription factor alterations were only detected at an mRNA level in the current study, while protein destination/ storage proteins were altered at a protein level (Figure 5). Transcription factors could not be identified using the proteome technique, likely due to the observation that they are only expressed at relatively low levels and for brief periods of time. As a result, this difference was largely due to the technical limitations of proteomic techniques.44 The up-regulation of proteins within the same destination/storage category including maturation polypeptide, embryonic abundant protein and beta-conglycinin alpha-subunit were identified at a protein level, but not at an mRNA level. This is likely due to the observation that these proteins are only required for the initiation of germination and then disappear soon afterward. However, their presence continued in our study, as growth was delayed for several days by the flooding. Therefore, these proteins are unlikely to be affected or controlled by low oxygen stress. Hemoglobin and pathogenesis-related protein 10 is commonly up-regulated following flood-induced stress at both transcriptional and translational levels (Tables 1 and 2). The hemoglobin alpha-I chain, which is an oxygen binding protein, was also up-regulated following flood-induced stress. Overexpression of class 1 hemoglobin 1 is known to protect Arabidopsis thaliana plants from severe hypoxia,45 and it has been reported that constitutive expression of hemoglobin 1 results in a reduced number of root hairs and an increase in the number of laterals in the root system. These results suggest 4776


UDP-Glucose Flavonoid Glycosyltransferase and Cystein Protease Inhibitor Negatively Interact with Numerous Flooding-Responsible Genes. To identify the genes that were regulated under flood-induced stress but whose functions were unknown, the interaction between genes were analyzed (Table 1). As the time course data consisted of five points (0, 0.5, 1.0, 1.5, and 2.0 days) and demonstrated very few constants with the 108 regulated genes, the gene time course data were clustered for the estimation of interactions (Figure 6). Using this analysis, genes that were regulated by other genes or genes that regulate additional genes were classified into the same cluster. When genes were classified into the same cluster, they were assumed to demonstrate the same regulation as other members of the cluster. The 108 peaks that showed a greater than 10-fold change following flooding for 0.5, 1.0, 1.5, or 2.0 days were divided into 45 clusters. Out of these genes, 97 peaks were altered by more than 25-fold at 12 h following flooding stress. The 108 genes were analyzed using the UPGMA clustering method. Interactions between these clusters were estimated using the S-system (Figure 6). The UDP-glucose flavonoid glycosyltransferase (Figure 6, G73) and cysteine proteinase inhibitor (Figure 6, G21) were interacted negatively with many of the flooding-responsible genes. Isoflavonoids represent a diverse group of biologically active natural products that accumulate in soybean seeds during development. The vast majority of isoflavonoids accumulate in the form of their glyco- and malonyl-conjugates in the seeds. In the current study, isoflavonoid specific glycosyltransferase and malonyltransferase were down-regulated in response to flooding stress. Isoflavonoids are known to play an important signal molecule role in the induction of nod genes during the symbiosis between legumes and rhizobia,49 and also serve as precursors for the production of phytoalexins during plant microbe interactions.50 Solomon et al.51 have previously reported that programmed cell death, that was activated during oxidative stress and pathogen attack in plants, was regulated by activity between the cysteine proteases and the cysteine proteinase inhibitor. Oxidative stress and programmed cell death have been shown to accompany many of the osmotic stresses.52-54 However, because the oxidative stress is not caused in the soybean under flooding stress, programmed cell death might be inhibited by down-regulation of cysteine proteinase inhibitor.


research articles

Figure 6. Interaction between genes regulated by flooding stress. The figure presents the network of genes for which associated genes are connected. The 108 peaks demonstrating a greater than 10-fold change following flood for 0.5, 1.0, 1.5, and 2.0 days were divided into 45 clusters. Out of these clusters, 97 peaks were found to be altered by more than 25-fold at 12 h after flooding. Red arrows present the induced interactions and the blue T-bars demonstrate the suppressed interactions. A thick line denotes an interaction with a z-score ranked in the top 1% and a fine line the top 2%, respectively.

Concluding Remarks The technique in which genes are differentially displayed using high-coverage gene expression profiling analysis introduces a powerful tool to the study of complex patterns of gene expression following flooding. In addition, cluster interaction analyses based on the S-system may prove valuable for the identification of precise gene function. This approach may also be applied to the definition of interactions between genes, and may even lead to identification of the roles for genes involved in the response of soybean plants to flood. These results conclude several pathways in the response of soybean to flooding stress: (i) Genes associated with alcohol fermentation, ethylene biosynthesis, cell wall loosening process and pathogen defense were up-regulated. (ii) Hemoglobin, acid phosphatase and Kunitz trypsin protease inhibitor were altered at both a transcriptional and translational level. (iii) Reactive oxygen species scavengers and chaperons were changed only at the translational level. It is suggested that the early response of soybean under flooding might be important stress adaptation to ensure survival against not only hypoxia but also the direct damage of cell by water. Abbreviations: 2-DE, two-dimensional polyacrylamide gel electrophoresis; IEF, isoelectric focusing; IPG, immobilized pH

gradient; CBB, Coomassie brilliant blue; PVDF, polyvinylidene difluoride; UPGMA, unweighted pair group method with arithmetic mean; MINOS, Mathematical gene Interaction Network Optimization Software.

Acknowledgment. We thank Dr. Hashiguchi, Dr. T. Hoshino and Mr. T. Sugimoto for their support of this work. We also thank Dr. T. Nakamura and Dr. S. Shimamura for their helpful discussions. This work was supported by the grants from National Agriculture and Food Research Organization, Japan. References (1) Setter, T. L.; Waters, I. Review of prospect for germplasm improvement for water-logging tolerance in wheat, barley and oats. Plant Soil 2003, 253, 1–34. (2) Armstrong, W. Aeration in higher plants. Adv. Bot. Res. 1979, 7, 225–232. (3) Jackson, M. B.; Colmer, T. D. Response and adaptation by plants to flooding stress. Ann. Bot. (London) 2005, 96, 501–505. (4) Probert, M. E.; Keating, B. A. What soil constraints should be included in crop and forest model. Agric. Ecosyst. Environ. 2000, 82, 273–281. (5) Pezeshki, S. R. Wetland plant responses to soil flooding. Environ. Exp. Bot. 2001, 46, 299–312. (6) Huang, S.; Greenway, H.; Colmer, T. D.; Millar, A. H. Protein synthesis by rice coleoptiles during prolonged anoxia: implications


research articles (7) (8) (9) (10)

(11)

(12) (13) (14) (15) (16)

(17) (18) (19)

(20) (21)

(22) (23) (24) (25) (26) (27) (28) (29)

(30)

(31)

4778

for glycolysis, growth and energy utilization. Ann. Bot. (London) 2005, 96, 661–668. Gonzali, S.; Loreti, E.; Novi, G.; Poggi, A.; Alpi, A.; Perata, P. The use of microarrays to study the anaerobic response in Arabidopsis. Ann. Bot. (London) 2005, 96, 661–668. Loreti, E.; Poggi, A.; Novi, G.; Alpi, A.; Perata, P. Genome-wide analysis of gene expression in Arabidopsis seedlings under anoxia. Plant Physiol. 2005, 137, 1130–1138. Githiri, S. M.; Watanabe, S.; Harada, K.; Takahashi, R. QTL analysis of flooding tolerance in soybean at an early vegetative growth stage. Plant Breed. 2006, 125, 613–618. Nakayama, N.; Hashimoto, S.; Shimada, S.; Takahashi, M.; Kim, Y.; Oya, T.; Arihara, J. The effect of flooding stress at the germination stage on the growth of soybean in relation to initial seed moisture content (Japanese). Jpn. J. Crop Sci. 2004, 74, 325–329. Shi, F.; Yamamoto, R.; Shimamura, S.; Hiraga, S.; Nakayama, N.; Nakamura, T.; Yukawa, K.; Hachinohe, M.; Matsumoto, H.; Komatsu, S. Cytosolic ascorbate peroxidase 2 (cAPX 2) is involved in the soybean response to flooding. Phytochemistry 2008, 69, 1295–1303. Hashiguchi, A.; Sakata, K.; Komatsu, S. Proteome analysis of earlystage soybean seedling under flooding stress. J. Proteome Res. 2009, 8, 2058–2069. Liu, F.; Vantoai, T.; Moy, L.; Bock, G.; Linford, L. D.; Quackenbush, J. Global transcription profiling reveals novel insights into hypoxic response in Arabidopsis. Plant Physiol. 2005, 137, 1115–1129. Baxter-Burrell, A.; Yang, Z.; Springer, P. S.; Bailey-Serres, J. RopGAP4-dependent Rop GTPase rheostat control of Arabidopsis oxygen deprivation tolerance. Science 2002, 296, 2026–2028. Dordas, C.; Hasinoff, B. B.; Rivoal, J.; Hill, R. D. Class-1 hemoglobins, nitrate and NO levels in anoxic maize cell-suspension cultures. Planta 2004, 219, 66–72. Vriezen, W. H.; Hulzink, R.; Mariani, C.; Voesenek, L. A. 1-aminocyclopropane-1-carboxylate oxidase activity limits ethylene biosynthesis in Rumex palustris during submergence. Plant Physiol. 1999, 121, 189–196. Mattana, M.; Coraggio, I.; Bertani, A.; Reggiani, R. Expression of the enzymes of nitrate reduction during the anaerobic germination of rice. Plant Physiol. 1994, 106, 1605–1608. Saab, I. N.; Sachs, M. M. A flooding-induced xyloglucan endotransglycosylase homolog in maize is responsive to ethylene and associated with aerenchyma. Plant Physiol. 1996, 112, 385–391. Suzuki, K.; Hattori, A.; Tanaka, S.; Masumura, T.; Abe, M.; Kitamura, S. High-coverage profiling analysis of genes expressed during rice seed development, using an improves amplified fragment length polymorphism technique. Funct. Integr. Genomics 2005, 5, 117–127. Komatsu, S.; Takasaki, H. Gibberellin-regulated gene in the basal region of rice leaf sheath encodes basic helix-loop-helix transcription factor. Amino Acids 2009, 37, 231–238. Fukumura, R.; Takahashi, H.; Saito, T.; Tsutsumi, Y.; Fujimori, A.; Sato, S.; Tatsumi, K.; Araki, R.; Abe, M. A sensitive transcriptome analysis method that can detect unknown transcripts. Nucleic Acids Res. 2003, 31, e94. Altschul, S. F.; Gish, W.; Miller, W.; Myers, E. W.; et al. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. Bevan, M.; Bancroft, I.; Bent, E.; Love, K.; Goodman, H.; et al. Analysis of 1.9 Mb of contiguous sequence from chromosome 4 of Arabidopsis thaliana. Nature 1998, 391, 485–488. O’Farrell, P. H. High resolution two-dimensional electrophoresis of protein. J. Biol. Chem. 1975, 250, 4007–4021. Hirano, H.; Kawasaki, H.; Sassa, H. Two-dimensional gel electrophoresis using immobilized pH gradient tube gels. Electrophoresis 2000, 21, 440–445. Mitsui, S.; Nobori, H.; Takada, T.; Kishida, K.; Miura, Y.; Ikawa, H. Development of a new gene-network estimation system. Genome Informatics 2003, 14, 384–385. Tanana, N.; Mitsui, S.; Nobori, M.; Yanagi, K.; Komatsu, S. Expression and function of proteins during development of the basal region in rice seedlings. Mol. Cell. Proteomics 2005, 6, 796–808. Wang, K.; Jiang, Y. Antioxidant responses of creeping bent-grass roots to water-logging. Crop Sci. 2007, 47, 232–238. Klok, E. J.; Wilson, I. W.; Wilson, D.; Chaman, S. C. C.; Ewing, R. M.; Spmerville, S. C.; Peacock, W. J.; Dolferus, R.; Dennis, E. S. Expression profile analysis of low-oxygen response in Arabidopsis root cultures. Plant Cell 2002, 14, 2481–2494. Jacobs, M.; Dolferus, R.; van den Bossche, D. Isolation and biochemical analysis of ethyl methanesulfonate-induced alcohol dehydrogenase null mutants of Arabidopsis thaliana (L.) Heynth. Biochem. Genet. 1988, 26, 105–122. Russell, D. A.; Wong, D.M.-L.; Sachs, M. M. The anaerobic response of soybean. Plant Physiol. 1990, 92, 401–407.


Komatsu et al. (32) Bailey-Serres, J.; Voesnek, L. A. C. J. Flooding stress: Acclimations and genetic diversity. Annu. Rev. Plant Biol. 2008, 59, 313–339. (33) Ismail, A. M.; Ella, E. S.; Vergara, G. A.; Mackkill, D. J. Mechanism associated with tolerance to flooding during germination and early seedling growth in rice (Oryza sativa). Ann. Bot. (London) 2009, 103, 197–209. (34) Vreeburg, R. A. M.; Benschop, J. J.; Peeters, A. J. M.; Colmer, T. D.; Ammerlaan, A. H. M.; et al. Ethylene regulates fast apoplastic acidification and expansin A transcription during submergenceinduced petiole elongation in Rumex palustris. Plant J. 2005, 43, 597–610. (35) Vriczen, W. H.; Dc Graaf, B.; Mariani, C.; Vocsnek, L. A. C. J. Submergence induces expansin gene expression in floodingtolerant Rumex palustris and not in flooding-intolerant R. acetosa. Planta 2000, 210, 956–963. (36) Maidment, J. M.; Moore, D.; Murphy, G. P.; Murphy, G.; Clark, I. M. Matrix metalloproteinase homologues from Arabidopsis thaliana. Expression and activity. J. Biol. Chem. 1999, 274, 34706–34710. (37) Darley, C. P.; Forrester, A. M.; McQueen-Mason, S. J. The molecular basis of plant cell wall extension. Plant Mol. Biol. 2001, 47, 179–195. (38) Lee, Y.; Kende, H. Expression of R-expansins is correlated with intermodal elongatoion in deepwater rice. Plant Physiol. 2001, 127, 645–654. (39) Richards, K. D.; Schott, E. J.; Sharma, Y. K.; Davis, K. R.; Gardner, R. C. Aluminum induced oxidative stress genes in Arabidopsis thaliana. Plant Physiol. 1998, 116, 409–418. (40) Navrot, N.; Collin, V.; Gualberto, J.; Gelhaye, E.; Hirasawa, M.; Rey, P.; Knaff, D. B.; Issakidis, E.; Jacquot, J. P.; Rouhier, N. Plant glutathione peroxidase are functional peroxiredixins distributed in several subcellular compartments and regulated during biotic and abiotic stresses. Plant Physiol. 2006, 142, 1364–1379. (41) Sato, Y.; Murakami, T.; Funatsuki, H.; Matsuba, S.; Saruyama, H.; Tanida, M. Heat shock-mediated APX gene expression and protection against chilling injury in rice seedlings. J. Exp. Bot. 2001, 52, 145–151. (42) Pulido, P.; Cazalis, R.; Cejudo, F. J. An antioxidant redox system in the nucleus of wheat seed cells suffering oxidative stress. Plant J. 2009, 57, 132–145. (43) Kamauchi, S.; Wadahama, K.; Iwasaki, K.; Nakamoto, Y.; Nishizawa, K.; Ishimoto, M.; Kawada, T.; Urada, R. Molecular cloning and characterization of two soybean protein disulfide isomerases as molecular chaperones for seed storage proteins. FEBS J. 2008, 275, 2644–2658. (44) Komatsu, S.; Yano, H. Update and challenges on proteomics in rice. Proteomics 2006, 6, 4057–4068. (45) Hunt, P. W.; Klok, E. J.; Trevaskis, B.; Watts, R. A.; Ellis, M. H.; Peacock, W. J.; Dennis, E. S. Increased level of hemoglobin 1 enhances survival of hypoxic stress and promotes early growth in Arabidopsis thaliana. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 17197–17202. (46) Gamboa, A.; Paez-Valencia, J.; Acevedo, G. F.; Vazquez-Moreno, L.; Alvarez-Buylla, R. E. Floral transcription factor AGAMOUS interacts in vitro with a leucine-rich repeat and an acid phosphatase protein complex. Biochem. Biophys. Res. Commun. 2001, 288, 1018–1026. (47) Norman-Setterblad, C.; Vidal, S.; Palva, E. T. Interacting signal pathway control defense gene expression in Arabidopsis in response to cell wall-degrading enzymes from Erwinia carotovora. Mol. Plant-Microbe Interact. 2000, 4, 430–438. (48) Ellis, C.; Turner, J. G. The Arabidopsis mutant cev1 has constitutively active jasmonate and ethylene signal pathways and enhanced resistance to pathogens. Plant Cell 2001, 13, 1025–1033. (49) Ferguson, J. A.; Mathesius, U. Signaling interactions during nodule development. J. Plant Growth Reg. 2003, 22, 47–72. (50) Dixon, R. A. Phytoestrogens. Annu. Rev. Plant Biol. 2004, 55, 225–261. (51) Solomon, M.; Belenghi, B.; Delledonne, M.; Menachem, E.; Levine, A. The involvement of cysteine proteases and protease inhibitor genes in the regulation of programmed cell death in plants. Plant Cell 1999, 11, 431–44. (52) Prassad, T. K.; Anderson, M. D.; Martin, B. A.; Stewant, C. R. Evidence for chilling-induced oxidative stress in maize seedlings and a regulatory role for hydrogen peroxide. Plant Cell 1994, 6, 65–74. (53) Hernander, J. A.; Corpas, F. J.; Gomez, M.; Dei Rio, L. A.; Sevilla, D. F. Salt-induced oxidative stress mediated by activated oxygen species in pea leaf mitochondria. Physiol. Plant 1993, 89, 103–110. (54) Smirnoff, N. Tansley Review No. 52: The role of active oxygen in the response of plants to water deficit and desiccation. New Phytol. 1993, 125, 27–58.

PR900460X

A Comprehensive Analysis of the Soybean Genes and Proteins

Recommend Documents