Expression and Function of Proteins during Development of the Basal Region in Rice Seedlings
2005; Elsevier BV; Volume: 4; Issue: 6 Linguagem: Inglês
10.1074/mcp.m400211-mcp200
ISSN1535-9484
AutoresNaoki Tanaka, Shigeyuki Mitsui, Hiroya Nobori, Koki Yanagi, Setsuko Komatsu,
Tópico(s)Polysaccharides and Plant Cell Walls
ResumoA differential display of proteins with a two-dimensional polyacrylamide gel electrophoresis approach was used to analyze protein expression changes during development of the basal region in rice seedlings (Oryza sativa L. cv. Nipponbare). The proteins were detected as 700 Coomassie Brilliant Blue-stained spots with pI values from around 3.5 to 9.0. A proteome reference map was established for the basal region of two-week-old seedlings. The basal region proteome map was used to analyze quantitative variations in the tissue during development from 2-, 4-, 6-, 8-, and 10-week-old seedlings. During development, 31 proteins were up-regulated, and 30 proteins were down-regulated compared with the 2-week-old basal region proteome map. The main functions of these proteins were primary metabolism and protein synthesis or maintenance. Calreticulin precursor, enolase, and voltage-dependent anion channel were identified among the up- and down-regulated proteins. The twin spots of calreticulin precursor and enolase with different pI values are possibly due to post-translational modifications such as phosphorylation. In addition, seven proteins showed developmental stage-specific expression. All of the developmentally regulated proteins of the basal region were clustered by the S-system, a differential equation that fit to time course of cluster and analyzed for cluster relationships. Proteins with unknown functions were tentatively assigned to functional groups based on cluster relationships. Basal region development proteome data will be valuable for resolving questions in functional genomics. In addition, cluster analysis of the basal region proteome during development will be useful for the assessment of functional proteins. A differential display of proteins with a two-dimensional polyacrylamide gel electrophoresis approach was used to analyze protein expression changes during development of the basal region in rice seedlings (Oryza sativa L. cv. Nipponbare). The proteins were detected as 700 Coomassie Brilliant Blue-stained spots with pI values from around 3.5 to 9.0. A proteome reference map was established for the basal region of two-week-old seedlings. The basal region proteome map was used to analyze quantitative variations in the tissue during development from 2-, 4-, 6-, 8-, and 10-week-old seedlings. During development, 31 proteins were up-regulated, and 30 proteins were down-regulated compared with the 2-week-old basal region proteome map. The main functions of these proteins were primary metabolism and protein synthesis or maintenance. Calreticulin precursor, enolase, and voltage-dependent anion channel were identified among the up- and down-regulated proteins. The twin spots of calreticulin precursor and enolase with different pI values are possibly due to post-translational modifications such as phosphorylation. In addition, seven proteins showed developmental stage-specific expression. All of the developmentally regulated proteins of the basal region were clustered by the S-system, a differential equation that fit to time course of cluster and analyzed for cluster relationships. Proteins with unknown functions were tentatively assigned to functional groups based on cluster relationships. Basal region development proteome data will be valuable for resolving questions in functional genomics. In addition, cluster analysis of the basal region proteome during development will be useful for the assessment of functional proteins. Rice (Oryza sativa L.) is an excellent model plant among the monocot crop species based on the fact that it has a relatively small genome of about 440 Mb (1Devos M.K. Gale D.M. Genome relationships: the grass model in current research.Plant Cell. 2000; 12: 637-646Google Scholar) and is suitable for efficient genetic analysis and transformation. Draft rice genome sequences of the major subtypes japonica (2Goff S.A. Ricke D. Lan T.-H. Presting G. Wang R. Dunn M. Glazebrook J. Sessions A. Oeller P. Varma H. Hadley D. Hutchison D. Martin C. Katagiri F. Lange B.M. Moughamer T. Xia Y. Budworth P. Zhong J. Miguel T. Paszkowski U. Zhang S. Colbert M. Sun W.-l. Chen L. Cooper B. Park S. Wood T.C. Mao L. Quail P. Wing R. Dean R. Yu Y. Zharkikh A. Shen R. Sahasrabudhe S. Thomas A. Cannings R. Gutin A. Pruss D. Reid J. Tavtigian S. Mitchell J. Eldregde G. Scholl T. Miller R.M. Bhatnagar S. Adey N. Rubano T. Tusneem N. Robinson R. Feldhaus J. Macalma T. Oliphant A. Briggs S. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica).Science. 2002; 296: 92-100Google Scholar) and indica (3Yu J. Hu S. Wang J. Wong G.K.-S. Li S. Liu B. Deng Y. Dai L. Zhou Y. Zhang X. Cao M. Liu J. Sun J. Tang J. Chen Y. Huang X. Lin W. Ye C. Tong W. Cong L. Geng J. Han Y. Li J. Liu Z. Li L. Liu J. Qi Q. Liu J. Li L. Li T. Wang X. Lu H. Wu T. Zhu M. Ni P. Han H. Dong W. Ren X. Feng X. Cui P. Li X. Wang H. Xu X. Zhai W. Xu Z. Zhang J. He S. Zhang J. Xu J. Zhang K. Zheng X. Dong J. Zeng W. Tao L. Ye J. Tan J. Ren X. Chen X. He J. Liu D. Tian W. Tian C. Xia H. Bao Q. Li G. Gao H. Cao T. Wang J. Zhao W. Li P. Chen W. Wang X. Zhang Y. Hu J. Wang J. Liu S. Yang J. Zhang G. Xiong Y. Li Z. Mao L. Zhou C. Zhu Z. Chen R. Hao B. Zheng W. Chen S. Guo W. Li G. Liu S. Tao M. Wang J. Zhu L. Yuan L. Yang H. A draft sequence of the rice genome (Oryza sativa L. ssp. indica).Science. 2002; 296: 79-92Google Scholar) have been published. The complete map-based genome sequences of chromosomes 1 (4Sasaki T. Matsumoto T. Yamamoto K. Sakata K. Baba T. Katayose Y. Wu J. Niimura Y. Cheng Z. Nagamura Y. Antonio B.A. Kanamori H. Hosokawa S. Masukawa M. Arikawa K. Chiden Y. Hayashi M. Okamoto M. Ando T. Aoki H. Arita K. Hamada M. Harada C. Hijishita S. Honda M. Ichikawa Y. Idonuma A. Iijima M. Ikeda M. Ikeno M. Ito S. Ito T. Ito Y. Ito Y. Iwabuchi A. Kamiya K. Karasawa W. Katagiri S. Kikuta A. Kobayashi N. Kono I. Machita K. Maehara T. Mizuno H. Mizubayashi T. Mukai Y. Nagasaki H. Nakashima M. Nakama Y. Nakamichi Y. Nakamura M. Namiki N. Negishi M. Ohta I. Ono N. Saji S. Sakai K. Shibata M. Shimokawa T. Shomura A. Song J. Takazaki Y. Terasawa K. Tsuji K. Waki K. Yamagata H. Yamane H. Yoshiki S. Yoshihara R. Yukawa K. Zhong H. Iwama H. Endo T. Ito H. Hahn J. Kim H.-L. Eun M.-Y. Yano M. Jiang J. Gojobori T. The genome sequence and structure of rice chromosome 1.Nature. 2002; 420: 312-316Google Scholar) and 4 (5Feng Q. Zhang Y. Hao P. Wang S. Fu G. Huang Y. Li Y. Zhu J. Liu Y. Hu X. Jia P. Zhang Y. Zhao Q. Ying K. Yu S. Tang Y. Weng Q. Zhang L. Lu Y. Mu J. Lu Y. Zhang L.S. Yu Z. Fan D. Liu X. Lu T. Li C. Wu Y. Sun T. Lei H. Li T. Hu H. Guan J. Wu M. Zhang R. Zhou B. Chen Z. Chen L. Jin Z. Wang R. Yin H. Cai Z. Ren S. Lv G. Gu W. Zhu G. Tu Y. Jia J. Zhang Y. Chen J. Kang H. Chen X. Shao C. Sun Y. Hu Q. Zhang X. Zhang W. Wang L. Ding C. Sheng H. Gu J. Chen S. Ni L. Zhu F. Chen W. Lan L. Lai Y. Cheng Z. Gu M. Jiang J. Li J. Hong G. Xue Y. Han B. Sequence and analysis of rice chromosome 4.Nature. 2002; 420: 316-320Google Scholar) of the Nipponbare cultivar have been reported and it is expected that the complete genome of japonica will be decoded and made available to the public (International Rice Genome Consortium, Japan). In the postgenomic era, proteomics is becoming increasingly important because proteins are directly related to function (6Pandy A. Mann M. Proteomics to study genes and genomes.Nature. 2000; 405: 837-846Google Scholar). Because proteins are involved in most processes of living cells, a detailed understanding of the proteome is critical to the study of cells and organisms at the molecular level. Furthermore nucleotide sequences provide limited information about the protein complement of the genome, in large measure due to post-transcriptional regulation, which results in a lack of correlation between transcript levels and protein abundance. With the availability of genome sequences, proteomics is playing an increasingly important role in genome annotation and has recently been applied toward the understanding of plant development, disease resistance, photosynthesis, and other aspects of the plant proteome (7Ressignol M. Analysis of the plant proteome.Curr. Opin. Biotechnol. 2001; 12: 131-134Google Scholar, 8van Wijk K.J. Challenges and prospects of plant proteomics.Plant Physiol. 2001; 126: 501-508Google Scholar). The last decade has seen considerable progress in developing the field of rice proteomics with several studies describing the separation and characterization of proteins from various tissues or subcellular components (9Rakwal R. Agrawal G.K. Rice proteomics: current status and future perspectives.Electrophoresis. 2003; 24: 3378-3389Google Scholar, 10Komatsu S. Tanaka N. Rice proteome analysis: a step toward functional analysis of the rice genome.Proteomics. 2005; 5: 938-949Google Scholar). Several studies have dealt with the construction of proteomes for complex samples from rice, such as leaf, embryo, endosperm, root, stem, shoot, anther, and callus (11Komatsu S. Kajiwara H. Hirano H. A rice protein library: a data-file of rice proteins separated by two-dimensional electrophoresis.Theor. Appl. Genet. 1993; 86: 935-942Google Scholar, 12Zhong B. Karibe H. Komatsu S. Ichimura H. Nagamura Y. Sasaki T. Hirano H. Screening of rice genes from a cDNA catalog based on the sequence data-file of proteins separated by two-dimensional electrophoresis.Breed. Sci. 1997; 47: 245-251Google Scholar, 13Komatsu S. Muhammad A. Rakwal R. Separation and characterization of proteins from green and etiolated shoots of rice (Oryza sativa L.): towards a rice proteome.Electrophoresis. 1999; 20: 630-636Google Scholar, 14Komatsu S. Rakwal R. Li Z. Separation and characterization of proteins of rice (Oryza sativa L.) suspension cultured cells.Plant Cell Tissue Organ Cult. 1999; 55: 183-192Google Scholar, 15Tsugita A. Kawakami T. Uchiyama Y. Kamo M. Miyatake N. Nozu Y. Separation and characterization of rice proteins.Electrophoresis. 1994; 15: 708-720Google Scholar, 16Shen S. Matsubae M. Takao T. Tanaka N. Komatsu S. A proteomic analysis of leaf sheath from rice.J. Biochem. 2002; 132: 613-620Google Scholar, 17Imin N. Kerim T. Weinman J.J. Rolfe B.G. Characterization of rice anther proteins expressed at the young microspore stage.Proteomics. 2001; 1: 1149-1161Google Scholar, 18Koller A. Washburn M.P. Lange B.M. Andon N.L. Deciu C. Haynes P.A. Hays L. Schieltz D. Ulaszek R. Wei J. Wolters D. Yates J.R. Proteomic survey of metabolic pathway in rice.Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 11969-11974Google Scholar, 19Tanaka N. Konishi H. Khan M.M.K. Komatsu S. Proteome analysis of rice tissues by two-dimensional electrophoresis: an approach to the investigation of gibberellin regulated proteins.Mol. Genet. Genomics. 2004; 270: 485-496Google Scholar), and subcellular components such as Golgi bodies, mitochondria, plasma membrane, vacuolar membrane, Golgi membrane, and chloroplasts have also been studied (20Mikami S. Hori H. Mitsui T. Separation of distinct compartments of rice Golgi complex by sucrose density gradient centrifugation.Plant Sci. 2001; 161: 665-675Google Scholar, 21Heazlewood J.L. Howell K.A. Whelan J. Millar A.H. Towards an analysis of the rice mitochondrial proteome.Plant Physiol. 2003; 132: 230-242Google Scholar, 22Tanaka N. Fujita M. Handa H. Murayama S. Uemura M. Kawamura Y. Mitsui T. Mikami S. Tozawa Y. Yoshinaga T. Komatsu S. Proteomics of the rice cell: systematic identification of the protein population in subcellular compartments.Mol. Genet. Genomics. 2004; 271: 566-576Google Scholar). These reports have focused on mapping the proteins and constructing a data base of expressed proteins of various tissues and subcellular components but have not addressed the changes in protein expression over time that are critical to understanding development in complex organisms. Differential proteome analyses, which systematically study changes in the proteome during growth and development and under diverse environmental stimuli, are the next challenge in proteomics. Studies of environmental stimuli have focused on searching for marker proteins in response to stimuli, such as treatment with the plant hormones jasmonic acid (23Rakwal R. Komatsu S. Role of jasmonate in the rice (Oryza sativa L.) self-defense mechanism using proteome analysis.Electrophoresis. 2000; 21: 2492-2500Google Scholar), brassinolide (24Konishi H. Komatsu S. A proteomics approach to investigating promotive effects brassinolide on lamina inclination and root growth in rice seedlings.Biol. Pharm. Bull. 2004; 26: 401-408Google Scholar), or gibberellin (19Tanaka N. Konishi H. Khan M.M.K. Komatsu S. Proteome analysis of rice tissues by two-dimensional electrophoresis: an approach to the investigation of gibberellin regulated proteins.Mol. Genet. Genomics. 2004; 270: 485-496Google Scholar, 25Shen S. Sharma A. Komatsu S. Characterization of proteins responsive to gibberellin in the leaf-sheath of rice (Oryza sativa L.) seedling using proteome analysis.Biol. Pharm. Bull. 2003; 26: 129-136Google Scholar); growth under salt (26Abbasi F.M. Komatsu S. A proteomic approach to analyze salt-responsive proteins in rice leaf sheath.Proteomics. 2004; 4: 2072-2081Google Scholar); drought (27Salekdeh G.H. Siopongco J. Wade L.J. Ghareyazie B. Bennett J. Proteomic analysis of rice leaves during drought stress and recovery.Proteomics. 2002; 2: 1131-1145Google Scholar); ozone stress (28Agrawal G.K. Rakwal R. Yonekura M. Kubo A. Saji H. Proteome analysis of differentially displayed proteins as a tool for investigating ozone stress in rice (Oryza sativa L.) seedlings.Proteomics. 2002; 2: 947-959Google Scholar); and infection with blast fungus (29Konishi H. Ishiguro K. Komatsu S. A proteomics approach towards understanding blast fungus infection of rice grown under different levels of nitrogen fertilization.Proteomics. 2001; 1: 1162-1171Google Scholar, 30Kim S.T. Cho K.S. Yu S. Kim S.G. Hong J.C. Han C.-d. Bae D.W. Nam M.H. Kang K.Y. Proteomic analysis of differentially expressed proteins induced by rice blast fungus and elicitor in suspension-cultured cells.Proteomics. 2003; 3: 2368-2378Google Scholar) or Rice yellow mottle virus (31Ventelon-Debout M. Delalande F. Brizard J.-P. Diemer H. Van Dorsselaer A. Brugidou C. Proteome analysis of cultivar-specific deregulation of Oryza sativa indica and O. sativa japonica cellular suspensions undergoing Rice yellow mottle virus infection.Proteomics. 2004; 4: 216-225Google Scholar). Time course-dependent protein expression analyses would also be important because quantitative measures of reproducibility were not reported nor were rigorous quantitative analyses applied to group proteins into expression classes. Recently, using the de-etiolated (greening) of maize chloroplast as a model system, a general protocol that can be used to generate high quality, reproducible data sets for comparative plant proteomics was developed (32Lonhosky P.M. Zhang X. Honavar V.G. Dobbs D.L. Fu A. Rodermel S.R. A proteomic analysis of maize chloroplast biogenesis.Plant Physiol. 2004; 134: 560-574Google Scholar). In this report, hierarchical and nonhierarchical statistical methods were used to analyze the expression patterns of 526 high quality, unique protein spots on two-dimensional (2D) 1The abbreviations used are: 2D, two-dimensional; GA, gibberellin; CBB, Coomassie Brilliant Blue; VDAC, voltage-dependent anion channel; UPGMA, unweighted pair group method with arithmetic mean; EF, elongation factor; Os, O. sativa. 1The abbreviations used are: 2D, two-dimensional; GA, gibberellin; CBB, Coomassie Brilliant Blue; VDAC, voltage-dependent anion channel; UPGMA, unweighted pair group method with arithmetic mean; EF, elongation factor; Os, O. sativa. gels. The basal region of rice seedlings, including the crown, is a functionally important region where many critical metabolic and regulatory activities take place that eventually control the height and robustness of the plant. In a previous study, we analyzed the leaf sheath proteome, which includes proteins of the basal region (16Shen S. Matsubae M. Takao T. Tanaka N. Komatsu S. A proteomic analysis of leaf sheath from rice.J. Biochem. 2002; 132: 613-620Google Scholar, 19Tanaka N. Konishi H. Khan M.M.K. Komatsu S. Proteome analysis of rice tissues by two-dimensional electrophoresis: an approach to the investigation of gibberellin regulated proteins.Mol. Genet. Genomics. 2004; 270: 485-496Google Scholar). No clear function could be predicted for 20% of the proteins in the leaf sheath proteome, but a majority of proteins involved in central metabolic pathways and energy production were identified. Although the basal region is important for rice plant growth, there are as yet no quantitative measures of proteome data. In the present study, changes in basal region proteins at five time points after sowing were analyzed quantitatively. Clustering analysis of differentially accumulated proteins during development was also carried out to clarify relationships among the proteins. Rice (O. sativa L. cv. Nipponbare) seedlings were grown in plastic seedling pots under white fluorescent light (600 μm m−2 s−1; 12-h light period/day) at 25 °C and 70% relative humidity in a growth chamber (Sanyo, Osaka, Japan). Two grams of basal regions of fresh seedlings of each developmental stages were homogenized with 4 ml of a lysis buffer (33O'Farrell P.H. High resolution two-dimensional electrophoresis of proteins.J. Biol. Chem. 1975; 250: 4007-4021Google Scholar) containing 8 m urea, 2% Nonidet P-40, 0.8% Ampholine (pH 3.5–10 and pH 5–8, Amersham Biosciences), 5% 2-mercapthoethanol, and 5% polyvinyl pyrrolidone using a glass mortar and pestle on ice. Homogenates were centrifuged twice at 15,000 rpm in a RA-50 JS rotor (Kubota, Tokyo, Japan) for 5 min each. The supernatants of 40 μl (100 μg of protein) were subjected to 2D-PAGE. Three independent experiments of protein extraction from the rice tissues and of 2D-PAGE analysis were performed. Samples were separated by 2D-PAGE in the first dimension by IEF for low pI range (around pI 3.5–8.0) or IPG tube gels (Daiichi Kagaku, Tokyo, Japan) for high pI range (pI 6.0–10.0) and in the second dimension by SDS-PAGE. IEF tube gels (11-cm length and 0.3-cm diameter) were prepared by filling with IEF gel solution consisting of 8 m urea, 3.5% acrylamide, 2% Nonidet P-40, and 2% Ampholine (pH 3.5–10 and pH 5–8). Electrophoresis 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 IPG tube gels of 11 × 0.3 cm was carried out at 400 V for 1 h followed by 1,000 V for 16 h and 2,000 V for 1 h. After IEF or IPG, SDS-PAGE in the second dimension was performed using 15% polyacrylamide gels with 5% stacking gels. Gels were stained with Coomassie Brilliant Blue (CBB). 2D polyacrylamide gels using IEF (around pI 3.5–8.0) and IPG (pI 6.0–10.0) for first dimension were overlapped around pI 5.6. The proteins in the edges of each gel were placed carefully to overlap the corresponding proteins. 2D-PAGE images were formed and evaluated automatically, and the amount of protein in each spot was estimated with Image-Master 2D Elite software (Version 2.0; Amersham Biosciences). The amount of a protein spot was expressed as its volume, defined as the sum of the intensities of the pixels that form the spot. To correct for variability due to CBB staining and to reflect quantitative variations in the intensity of 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 2D-PAGE marker (Bio-Rad). Following separation by 2D-PAGE, gel pieces containing protein spots were excised, and the protein was electroeluted using an electrophoretic concentrator (Nihon-Eido, Tokyo, Japan) at 2 watts of constant power for 2 h. After electroelution, the protein solution was dialyzed against deionized water for 2 days and lyophilized. Proteins were redissolved in 20 μl of SDS sample buffer (0.5 m Tris-HCl (pH 6.8), 10% glycerol, 2.5% SDS, 5% 2-mercaptoethanol) and loaded onto an SDS-polyacrylamide gel. The sample was overlaid with 20 μl of a solution containing 10 μl Staphylococcus aureus V8 protease (0.1 μg μl−1; Pierce) and 10 μl of SDS sample buffer. Electrophoresis was performed until the sample and protease were stacked in the stacking gel, interrupted for 30 min to allow the protein to digest (34Cleveland D.W. Fisher S.G. Kirschner M.W. Laemmli U.K. Peptide mapping by limited proteolysis in sodium dodecyl sulphate and analysis by gel electrophoresis.J. Biol. Chem. 1977; 252: 1102-1106Google Scholar). After electrophoresis, gels were stained with CBB. Proteins were sequenced by electroblotting onto a PVDF membrane (Pall, Port Washington, NY) and detected by CBB staining. Stained proteins were excised from the PVDF membrane and directly subjected to Edman degradation on a gas-phase protein sequencer (Procise 494, Applied Biosystems, Foster City, CA). CBB-stained proteins were excised from gels, washed with 25% methanol, 7% acetic acid for 12 h at room temperature, and destained with 50 mm NH4HCO3 in 50% methanol for 1 h at 40 °C. After drying under vacuum, gel spots were incubated in 50 μl of a reduction solution (10 mm EDTA, 10 mm DTT, and 100 mm NH4HCO3) at 60 °C for 1 h. The gel spots were dried under vacuum and incubated in 50 μl of an alkylation solution (10 mm EDTA, 10 mm iodoacetamide, 100 mm NH4HCO3) at room temperature for 30 min in the dark. After washing with water, the gel spots were minced, dried under vacuum, and digested in 50 μl of 10 mm Tris-HCl (pH 8.0) containing 1 pm trypsin (Sigma) at 37 °C for 10 h. Acetonitrile (100 μl) containing 0.1% trifluoroacetic acid was added to each gel piece and sonicated. Purification of the generated peptides was achieved using Zip-Tips (Millipore, Bedford, MA). The purified peptides (2 μl) were added directly to a 10 mg ml−1 α-cyano-4-hydroxycinnamic acid, 0.3% trifluoroacetic acid, 50% acetonitrile matrix and air-dried onto a plate for analysis using MALDI-TOF MS (Voyager-DE RP, Applied Biosystems). Matching of empirical peptide mass values with theoretical digests and sequence information obtained from the data base was performed using Mascot Version 2.0 software (Matrix Science Ltd., London, UK). For MALDI-TOF analysis, four criteria were used to assign a positive match with a known protein. (i) The deviation between the experimental and theoretical peptide masses should be less than 50 ppm. (ii) At least four different predicted peptide masses needed to match the observed masses for an identification to be considered valid. (iii) The coverage of protein sequences by the matching peptides must reach a minimum of 10%. (iv) The score that was obtained from the analysis with Mascot software indicates the probability of a true positive identification and must be at least 50. Clustering protein time course data for the estimation of interactions were analyzed using the clustering method unweighted pair group method with arithmetic mean (UPGMA). Clustering analysis was performed by two steps as follows. The first step was clustering. Time course-specified clustering analysis was generated in the following way. The time course was normalized with the initial value and then evaluated by the natural logarithm of that normalized value. Next the clustering process in two phases was performed. The first phase was a normal clustering using time course, and the second phase was using fluctuation of time course where fluctuation was evaluated at each time point as the difference between the current and its previous time course. Because the intersection in these two phases of clustering was considered, the cluster was always contained in the same group regardless of the clustering process (using time course or its fluctuation) if the group contained the cluster. The second step was estimating interaction between clusters. The clusters that interacted were estimated by the representative time course that was calculated at each time point using the medium value. The medium value was calculated using the representative time course for each cluster evaluated in the first step. Mathematical gene interaction network optimization software (Minos) was developed to estimate cluster interaction. Minos utilized the S-system differential equation and estimated cluster interaction by a set of differential equation coefficients that simulates the time course. Fig. 1 shows the formula of the S-system. In the S-system differential equation, αi and βi were positive values. Minos integrated the S-system differential equation and picked out the set of αi, gij, βi, and hij that seemed to fit to the time course of clusters. gij represented the relation between expression of the jth cluster and the expression generation rate of the ith cluster. As for hij, if hij was negative or positive, the formula was increased or decreased, respectively. To estimate cluster interaction, gij and hij for simulating the time course were calculated. Building a reference proteome map for the basal region in rice seedlings is a prerequisite for analyzing protein expression during development of the tissue and is constructed by separating the proteins according to their intrinsic charge (pI) and molecular weight. The 2D map of proteins was reproducibly observed in several independent experiments with the high and low pI ranges overlapping at around pI 5.6 (Fig. 2A). The 2D map of the basal region consisted of about 700 proteins identified using Image-Master 2D Elite software (Fig. 2B). The basal region contained a high abundance of proteins in the acidic pI range from around pI 4.5 to 6.0 (Fig. 2B). Using only the IEF (for pI range around pI 3.5–8.0) gel for first dimension separation, 352 protein spots were detected by 2D-PAGE and CBB staining (16Shen S. Matsubae M. Takao T. Tanaka N. Komatsu S. A proteomic analysis of leaf sheath from rice.J. Biochem. 2002; 132: 613-620Google Scholar), whereas using the IEF and IPG (for high pI range pI 6.0–10.0) gels, 431 proteins were detected (19Tanaka N. Konishi H. Khan M.M.K. Komatsu S. Proteome analysis of rice tissues by two-dimensional electrophoresis: an approach to the investigation of gibberellin regulated proteins.Mol. Genet. Genomics. 2004; 270: 485-496Google Scholar, 25Shen S. Sharma A. Komatsu S. Characterization of proteins responsive to gibberellin in the leaf-sheath of rice (Oryza sativa L.) seedling using proteome analysis.Biol. Pharm. Bull. 2003; 26: 129-136Google Scholar). The IEF and IPG gels with gradient SDS-PAGE can resolve proteins over a wide range (from around pI 3.5 to 10). The present study showed 700 protein spots in 2D-PAGE using IEF and IPG gels in the first dimension. To identify proteins that are regulated during development, 2D maps of tissue from seedlings grown for 2, 4, 6, 8, and 10 weeks after sowing were constructed (Fig. 2 and data not shown). Observations of triplicate 2D gels of the ∼700 proteins detected in the basal region showed that 31 were increased and 30 were decreased relative to expression in 2-week-old seedlings during the observed 8 weeks of development with 10% increasing or decreasing as determine by 2D Elite software, respectively (Fig. 3). These regulated proteins were identified by MALDI-TOF MS and/or protein sequencing (Tables I and II). The N-terminal sequences of about two-thirds of the proteins could not be sequenced, likely due to a blocking group at the N terminus. The percentage of N-terminally blocked proteins encountered in the basal region was 64%. Of the proteins found to increase in expression during development, 65% (20 of 31) and 63% (19 of 31) of proteins showing decreased expression were N-terminally blocked. This result was consistent with a previous study that found that of 134 rice proteins, 79 (59%) were found to have blocked N termini (15Tsugita A. Kawakami T. Uchiyama Y. Kamo M. Miyatake N. Nozu Y. Separation and characterization of rice proteins.Electrophoresis. 1994; 15: 708-720Google Scholar).Table IIncreased proteins during development of basal region in rice seedlingsNo.aThe numbers refer to the spot numbers as given in Fig. 1.MMbMolecular mass (MM) and pI are from the gel in Fig. 1.pIbMolecular mass (MM) and pI are from the gel in Fig. 1.IdentificationcMethods of protein identification: MS and Edman degradation (Ed).Amino acid sequencesdN-terminal (N-) and internal (I-) amino acid sequences as determined by Edman degradation.Homologous proteinPercentageeThe values indicate the sequence coverage for MS and the homology for the identity protein sequences for Edman degradation.Accession no.fAccession numbers in NCBI data base.kDa%Metabolism 11960.84.9MS(N-blocked)Fatty acid α-oxidase98AF229813 12160.55.1MS(N-blocked)α-Amylase49X6419 25350.45.0Ed(N-blocked) I-IHTFNQSQYUTP-glucose-1-phosphate uridyltransferase100Q9SDX3 38040.86.2MS(N-blocked)Hydrogenase nickel incorporation protein51P26409 57228.35.3MS(N-blocked)Nitrate reductase20P36858Energy 19354.15.1MS(N-blocked)Enolase114AY335488 41638.45.4Ed(N-blocked) I-KNITXLTRLDMalate dehydrogenase90Q9SML8 43337.44.5Ed(N-blocked) I-VAFLTQGDAN I-FMFYRNPSADFructokinase100P37829 46535.25.2MS(N-blocked)NADH-ubiquinone oxidoreductase78AF124786 50232.57.2EdN-VGPGLYPEIGVoltage-dependent anion channel (OsVDAC1)100Y18104 54730.24.5Ed(N-blocked) I-AEPTKNF1-ATPase δ-subunit100D88375Protein synthesis 10562.84.8MS(N-blocked)Elongation factor Tu89AF303468 14358.04.6EdN-SAKEIAFDFKBP-type peptidyl-prolyl cis-trans isomerase100Q8E7Q0 39339.54.5MS(N-blocked)Translation initiation factor 5A46AF094773 49133.24.3EdN-AVTFTDLHTA I-SGKSSVLLDVElongation factor 1β′100P29545 49632.84.4EdN-AVTFTDLHTA I-SGKSSVLLDVElongation factor 1β′100P29545 51831.54.0EdN-TAAEIIKK30 S ribosomal prote
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