A label-free quantitative shotgun proteomics analysis of rice grain development
© Lee and Koh; licensee BioMed Central Ltd. 2011
Received: 23 March 2011
Accepted: 30 September 2011
Published: 30 September 2011
Although a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages.
Proteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and pI values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain.
This is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.
KeywordsMudPIT Rice Spectral Counts Shotgun proteomics
Rice is an important model plant because of its importance as a food crop, and because its genome is both known and relatively small in size. Rice is a major cereal crop for human consumption, and starch accumulation and physiochemical properties are important determinants of eating quality. Seed quality is also a critical biological concern. Genetic studies and transgenic analyses have revealed the mechanisms and genes involved in starch accumulation [1, 2]. Recently, the nature of allelic diversity in starch biosynthesis, which is related to eating quality, was analyzed via a transgenic approach . Monitoring mRNA expression patterns during seed development may elucidate the molecular mechanisms of seed development [4–6]. Xu et al. (2008) monitored proteome expression patterns during rice grain filling stages (from 6 days after flowering to 20 days after flowering). They reported a comprehensive rice proteome analysis to detect and identify 396 differentially expressed proteins. From expression analysis, they detected that the substantially up-regulated proteins were involved in starch synthesis and alcoholic fermentation, and down-regulated proteins were involved in central carbon metabolism and most of the other functional categories/subcategories such as cell growth/division, protein synthesis, proteolysis, and signal transduction. Their results suggest that a switch from the central carbon metabolism to alcoholic fermentation may be important for starch synthesis and accumulation in the developmental process .
With advances in mass spectrometry, multidimensional protein identification technology (MudPIT), a shotgun proteomic approach, was developed for large-scale, high-throughput protein identification . The benefits of MudPIT were first introduced in the context of plant sciences for the construction of rice leaf, root, and seed reference maps that included the most comprehensive proteome exploration available . MudPIT has also been applied to analyses of the common bean (Phaseolus vulgaris), a non-model plant . Although the mass spectrometry of MudPIT tends to be qualitative rather than quantitative, various methods for quantification in MudPIT have recently been developed [11–13]. In comparative analyses of protein expression, spectral count (SC), which assesses the total number of assigned MS/MS spectra for peptides from a given protein, is considered a label-free quantification method. Even though the estimated expression ratio for low-abundance peptides is more accurate when using the radiolabel quantification methods , SC is linearly correlated with protein abundance over a dynamic range of two orders of magnitude, and provides estimates of relative protein levels between samples comparable to estimates derived by radiolabel quantification [12, 15]. With proper normalization of SC, the relative concentrations of proteins can also be estimated .
After the comprehensive report of the rice grain proteome expression during grain filling stages , the rice grain proteome expression during entire developing stages, including grain filling, desiccation phase, and fully mature grain has not been studied yet. Here, we performed comparative shotgun proteomic analysis of rice grain development including grain filling and desiccation process. When constructing a proteome reference map for rice grain development, the approach of a shotgun proteomics analysis facilitates the detection of differentially expressed proteins during grain development and provides information regarding the relative concentrations of all identified proteins. We present construction of an in-depth proteome reference map, monitoring the expression patterns of the identified proteins, and to detect proteins that are expressed differentially during grain development.
Results and discussion
Morphological changes of rice grains during development
Both the fresh and dry weight increased drastically until 20 DAF and the dry weight was maximum in 30 DAF whereas the fresh weight was maximum in 20 DAF (Figure 1B), implying that starch accumulation lasted until sometime between 20 DAF and 30 DAF. This result was similar with the previous report by Kim et al (2011), where dry weight of Ilpumbyeo rice grains cultivated in the Korean natural field condition increased until ~25 DAF . Since grain development varies depending on the variety of rice studied and environmental condition, the developmental process documented for Ilpumbyeo in Korea is different from that described in a previous rice grain proteome study by Xu et al. (2008), in which their proteomic analysis focused on the grain filling stage until 20 DAF.
Constructing a large-scale rice grain proteome reference data set
Enriched GO terms of biological processes in the constructed rice grain proteome
Number in the identified rice grain proteome
Number in rice genome
Adjusted p-value *
cellular amino acid and derivative metabolic process
carbohydrate metabolic process
generation of precursor metabolites and energy
establishment of localization
regulation of biological quality
lipid metabolic process
cellular protein metabolic process
secondary metabolic process
response to biotic stimulus
Based on the annotation of Rice Genome Pseudomolecules Release V6.1, 12 proteins were annotated as hypothetical proteins. Under the hypothesis that these proteins play specific roles in grains, their mRNA expression patterns throughout the life cycle of the rice plant were searched using the Rice Expression Profile Database (RiceXPro: http://ricexpro.dna.affrc.go.jp/index.html). Only one gene, LOC_Os06g44190.1 reported expression that was higher during endosperm and embryo development, even though low level expression was detected in other tissues or organs. We suggest that this hypothetical protein may play a grain specific role in development. However, it is difficult to predict the roles of proteins using expression patterns, and therefore the specific role of this protein should be explored in future studies, such as knock out studies of the gene by the RNAi technique.
Differentially expressed proteins during rice grain development
For the comparison analysis, the relative abundances of the identified proteins were obtained with the method of spectral count, a label-free method (refer to the material and method section). All 4,172 proteins were not reproducibly identified in all experiments including developmental stages and replicates due to analytical incompleteness in shotgun proteomic analysis, in which any single analytical run may only identify a fraction of the relevant peptides in a highly complex mixture of peptides . Thus, for comparative analysis, we distinguished qualitatively expressed proteins from proteins that were not qualitatively expressed, but were identified only at certain time points due to analytical incompleteness. We included only proteins that were identified for all three biological replicates with at least two SCs for each replicate in the comparative analysis. After applying this criterion, 1,784 proteins were subjected to comparative analysis. The SCs for these 1,784 proteins were globally normalized (NSpC), followed by ANOVA test with logarithmically transformed NSpC (the natural log (Ln) of NSpC). The average coefficient of determination (R2) between NSpCs for the biological replicates was ~0.75, suggesting linear correlation. Among the statistically significant proteins detected by the ANOVA test, proteins with expression levels that changed less than two-fold were discarded. Following these strict criteria, we detected a total of 484 proteins that are differentially expressed during rice grain development (Additional file 2).
Hierarchical clustering analysis
Composite expression profile of functional categories during rice grain development
With a label-free shotgun proteomic approach, we were able to identify a rice grain proteome at large scale where physiochemical properties of the identified proteins were unbiased and to monitor protein expression patterns during rice grain development. The comparison analysis of protein expressions, clustering analysis, and GO category enrichment analysis revealed proteome changes in the grain filling stage, desiccation phase, and the fully mature grains. Interestingly, we detected that proteins involved in metabolic process were enriched during the entire developmental stages, suggesting that even in the desiccation phase, changes occurred in molecular levels. Especially, we detected increase of chaperone proteins in the late development stages including desiccation phase and fully mature grains, hypothesizing that the role of chaperone proteins accumulated during the desiccation phase may be associated with conserving proteins associated with germination from desiccation stress. With the advantage that a mass spectrometry based high-throughput proteomic analysis can provide the relative quantities of all of the identified proteins, we were able to draw composite expression profiles which can represent global expression trends for proteins involved in specific processes. Composite expression profiles revealed that proteins required in germination such as glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain.
Materials and methods
A Korean commercial variety, Ilpumbyeo (Japonica rice), was grown in 15 × 30 cm rows at the Seoul National University Experimental Field. Rice grains were harvested at 10 days after flowering (DAF), 20 DAF, and 30 DAF and then freeze-dried. Air dried fully mature grains which were desiccated during the desiccation phase were harvested at 45 DAF.
Proteins were extracted from brown rice powders with extraction buffer (100 mM Tris-HCl pH 8.5, 5 mM DTT, 1 mM EDTA, 2% (m/v) dodecyl-β-maltoside, and 1% (v/v) Plant Proteinase Inhibition Cocktail; Sigma, St. Louis, MO, USA). The suspension was incubated at room temperature for 30 minutes followed by centrifugation at 14,000 g for 15 minutes. The supernatant was retained and filtered through 5 μm membrane filters, and then through 0.45 μm membrane filters (Millipore, Billerica, MA, USA). Extracted proteins were precipitated overnight with 20% (v/v) trichloroacetic acid (TCA), washed three times with cold acetone, and re-solubilized in 8 M Urea/Tris-HCl pH 8.5. Protein concentration was assayed by the 2D-Protein Quant Kit (GE Healthcare, Piscataway, NJ, USA).
A total of 500 μg of protein was reduced with Tris(2-carboxyethyl)phosphine hydrochloride (TCEP) by adjusting the protein sample solution to 5 mM TCEP, followed by a 30-minute incubation at room temperature. The reduced sample was carbamidomethylated by adjusting iodoacetamide to 10 mM, followed by a 30-minute incubation at room temperature in the dark. The protein solution was diluted from 8 M urea to 2 M urea with 100 mM Tris-HCl pH 8.5, and then the CaCl2 was adjusted to 2 mM. A total of 5 μg of trypsin was added, and the solution was incubated overnight at 37°C. Protein digestion was terminated by adding formic acid to 5%.
Home-made biphasic columns were prepared with 365 μm o.d. × 100 μm i.d. fused-silica capillaries (Polymicro Technologies, Phoenix, AZ, USA). The tip of each capillary was pulled to 5 μm by a P-2000 laser puller (Sutter Instrument Co., Novato, CA, USA). Each capillary was then packed using a pressure cell under 600 psi of helium with 9 cm of 5 μm reverse phase C18 resin (Phenomenex, Torrance, CA, USA), followed by 4 cm of 5 μm strong cation exchange resin (Phenomenex). Separate desalting columns were prepared with 3 cm of a 365 μm o.d. × 250 μm i.d. fused-silica capillary packed with 5 μm reverse phase C18 resin. Digested peptide samples were loaded onto the desalting column using the same pressure cell that was to be desalted, and then the desalting column was attached to the biphasic column. The sample loaded column was then placed in a home-made ion source, which was connected in-line to a Nanospace SI-2 HPLC (Shiseido, Tokyo, Japan), having a liquid junction with a T-split for the application of electrospray voltage and obtaining the nano scale mobile phase flow rate. Peptides were eluted in a 12-step process by increasing the concentrations of salt solution of 250 mM ammonium formate, followed by an increasing gradient of organic mobile phase at each step, as previously described . The peptide eluent was directly electrosprayed into an LXQ ion trap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Tandem mass spectra were obtained using Xcalibur 2.0. A parent-ion scan was performed over the range 400-1600 m/z. Automated peak recognition, dynamic exclusion, and MS/MS-ion scanning of the top five most intense parent ions were performed.
Identification of the rice grain proteome
Each peptide from the MS/MS spectra was searched by MASCOT against the TIGR Rice Pseudomolecule protein database Release V6.1 (http://rice.plantbiology.msu.edu/annotation_pseudo_current.shtml), as well as general contaminant lists. The search parameters were as follows: tryptic digests with one possible missed cleavage, carbamidomethylation set as a fixed amino acid modification, oxidation considered a variable amino acid modification, averaged mass values, peptide mass tolerance of +/- 1.4 Da, and fragment mass tolerance of +/- 0.8 Da. The MASCOT generic file was used, and the analytical instrument was ESI-TRAP. MASCOT output was processed using PANORAMICS, a probability-based program that determines the false-positive rate of identification .
Comparative analysis of relative protein abundances
where the total number of MS/MS spectra matching peptides from protein k (SpC) is divided by the protein's length (L), then divided by SpC/L for all N proteins in the experiment.
GO annotations of the rice proteins were retrieved the from TIGR Rice Pseudomolecule protein database Release V6.1. The GO enrichment analysis was performed in agriGO (http://bioinfo.cau.edu.cn/agriGO/) with default parameters using the rice whole genome as the background/reference.
The hierarchical clustering analysis were conducted with the Cluster 3.0 software using centered correlation and the average linkage procedure and the tree was visualized with the Java Treeview 1.1.1. Composite expression profile analysis was performed by summing averages of NSpC for all proteins of a given functional category at each of the four developmental stages .
This work was supported by a grant from the Next-Generation BioGreen 21 Program (Plant Molecular Breeding Center No. PJ008125), Rural Development Administration, Republic of Korea.
- James MG, Denyer K, Myers AM: Starch synthesis in the cereal endosperm. Curr Opin Plant Biol 2003, 6: 215–222. 10.1016/S1369-5266(03)00042-6PubMedView ArticleGoogle Scholar
- Tetlow IJ: Understanding storage starch biosynthesis in plants: a means to quality improvement. Can J Bot 2006, 84: 1167–1185. 10.1139/b06-089View ArticleGoogle Scholar
- Tian Z, Qian Q, Liu Q, Yan M, Liu X, Yan C, Liu G, Gao Z, Tang S, Zeng D, et al.: Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proc Natl Acad Sci USA 2009, 106: 21760–21765. 10.1073/pnas.0912396106PubMed CentralPubMedView ArticleGoogle Scholar
- Girke T, Todd J, Ruuska S, White J, Benning C, Ohlrogge J: Microarray analysis of developing Arabidopsis seeds. Plant Physiol 2000, 124: 1570–1581. 10.1104/pp.124.4.1570PubMed CentralPubMedView ArticleGoogle Scholar
- Lai J, Dey N, Kim CS, Bharti AK, Rudd S, Mayer KF, Larkins BA, Becraft P, Messing J: Characterization of the maize endosperm transcriptome and its comparison to the rice genome. Genome Res 2004, 14: 1932–1937. 10.1101/gr.2780504PubMed CentralPubMedView ArticleGoogle Scholar
- Ruuska SA, Girke T, Benning C, Ohlrogge JB: Contrapuntal networks of gene expression during Arabidopsis seed filling. Plant Cell 2002, 14: 1191–1206. 10.1105/tpc.000877PubMed CentralPubMedView ArticleGoogle Scholar
- Xu SB, Li T, Deng ZY, Chong K, Xue Y, Wang T: Dynamic proteomic analysis reveals a switch between central carbon metabolism and alcoholic fermentation in rice filling grains. Plant Physiol 2008, 148: 908–925. 10.1104/pp.108.125633PubMed CentralPubMedView ArticleGoogle Scholar
- Wolters DA, Washburn MP, Yates JR: An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem 2001, 73: 5683–5690. 10.1021/ac010617ePubMedView ArticleGoogle Scholar
- Koller A, Washburn MP, Lange BM, Andon NL, Deciu C, Haynes PA, Hays L, Schieltz D, Ulaszek R, Wei J, et al.: Proteomic survey of metabolic pathways in rice. Proc Natl Acad Sci USA 2002, 99: 11969–11974. 10.1073/pnas.172183199PubMed CentralPubMedView ArticleGoogle Scholar
- Lee J, Feng J, Campbell KB, Scheffler BE, Garrett WM, Thibivilliers S, Stacey G, Naiman DQ, Tucker ML, Pastor-Corrales MA, Cooper B: Quantitative proteomic analysis of bean plants infected by a virulent and avirulent obligate rust fungus. Mol Cell Proteomics 2009, 8: 19–31. 10.1074/mcp.M800156-MCP200PubMedView ArticleGoogle Scholar
- Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999, 17: 994–999. 10.1038/13690PubMedView ArticleGoogle Scholar
- Liu H, Sadygov RG, Yates JR: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 2004, 76: 4193–4201. 10.1021/ac0498563PubMedView ArticleGoogle Scholar
- Washburn MP, Ulaszek R, Deciu C, Schieltz DM, Yates JR: Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Anal Chem 2002, 74: 1650–1657. 10.1021/ac015704lPubMedView ArticleGoogle Scholar
- Usaite R, Wohlschlegel J, Venable JD, Park SK, Nielsen J, Olsson L, Yates Iii JR: Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression saccharomyces cerevisiae strains: the comparison of two quantitative methods. J Proteome Res 2008, 7: 266–275. 10.1021/pr700580mPubMed CentralPubMedView ArticleGoogle Scholar
- Dong MQ, Venable JD, Au N, Xu T, Park SK, Cociorva D, Johnson JR, Dillin A, Yates JR: Quantitative mass spectrometry identifies insulin signaling targets in C. elegans. Science 2007, 317: 660–663. 10.1126/science.1139952PubMedView ArticleGoogle Scholar
- Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP: Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res 2006, 5: 2339–2347. 10.1021/pr060161nPubMedView ArticleGoogle Scholar
- Kim J, Sho J, Lee C, Yang WYY, Yang W, Kim Y, Lee B: Relationship between grain filling duration and leaf senescence of temperate rice under high temperature. Field Crop Research 2011, 122: 207–213. 10.1016/j.fcr.2011.03.014View ArticleGoogle Scholar
- le Coutre J, Whitelegge JP, Gross A, Turk E, Wright EM, Kaback HR, Faull KF: Proteomics on full-length membrane proteins using mass spectrometry. Biochemistry 2000, 39: 4237–4242. 10.1021/bi000150mPubMedView ArticleGoogle Scholar
- Du Z, Zhou X, Ling Y, Zhang Z, Su Z: agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 2010, 38: W64–70. 10.1093/nar/gkq310PubMed CentralPubMedView ArticleGoogle Scholar
- Sorefan K, Booker J, Haurogne K, Goussot M, Bainbridge K, Foo E, Chatfield S, Ward S, Beveridge C, Rameau C, Leyser O: MAX4 and RMS1 are orthologous dioxygenase-like genes that regulate shoot branching in Arabidopsis and pea. Genes Dev 2003, 17: 1469–1474. 10.1101/gad.256603PubMed CentralPubMedView ArticleGoogle Scholar
- Wilkins MR, Appel RD, Van Eyk JE, Chung MC, Gorg A, Hecker M, Huber LA, Langen H, Link AJ, Paik YK, et al.: Guidelines for the next 10 years of proteomics. Proteomics 2006, 6: 4–8.PubMedView ArticleGoogle Scholar
- Bevan M, Bancroft I, Bent E, Love K, Goodman H, Dean C, Bergkamp R, Dirkse W, Van Staveren M, Stiekema W, et al.: Analysis of 1.9 Mb of contiguous sequence from chromosome 4 of Arabidopsis thaliana. Nature 1998, 391: 485–488. 10.1038/35140PubMedView ArticleGoogle Scholar
- Soupene E, Dinh NP, Siliakus M, Kuypers FA: Activity of the acyl-CoA synthetase ACSL6 isoforms: role of the fatty acid Gate-domains. BMC Biochem 2010, 11: 18. 10.1186/1471-2091-11-18PubMed CentralPubMedView ArticleGoogle Scholar
- Han B, Hughes DW, Galau GA, Bewley JD, Kermode AR: Changes in late-embryogenesis-abundant (LEA) messenger RNAs and dehydrins during maturation and premature drying of Ricinus communis L. seeds. Planta 1997, 201: 27–35. 10.1007/BF01258677PubMedView ArticleGoogle Scholar
- Kamauchi S, Wadahama H, Iwasaki K, Nakamoto Y, Nishizawa K, Ishimoto M, Kawada T, Urade R: Molecular cloning and characterization of two soybean protein disulfide isomerases as molecular chaperones for seed storage proteins. FEBS J 2008, 275: 2644–2658. 10.1111/j.1742-4658.2008.06412.xPubMedView ArticleGoogle Scholar
- Sharma SK, Christen P, Goloubinoff P: Disaggregating chaperones: an unfolding story. Curr Protein Pept Sci 2009, 10: 432–446. 10.2174/138920309789351930PubMedView ArticleGoogle Scholar
- Florens L, Washburn MP: Proteomic analysis by multidimensional protein identification technology. Methods Mol Biol 2006, 328: 159–175.PubMedGoogle Scholar
- Feng J, Naiman DQ, Cooper B: Probability model for assessing proteins assembled from peptide sequences inferred from tandem mass spectrometry data. Anal Chem 2007, 79: 3901–3911. 10.1021/ac070202ePubMedView ArticleGoogle Scholar
- Chiang DY, Brown PO, Eisen MB: Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles. Bioinformatics 2001,17(Suppl 1):S49–55. 10.1093/bioinformatics/17.suppl_1.S49PubMedView ArticleGoogle Scholar
- Mechin V, Thevenot C, Le Guilloux M, Prioul JL, Damerval C: Developmental analysis of maize endosperm proteome suggests a pivotal role for pyruvate orthophosphate dikinase. Plant Physiol 2007, 143: 1203–1219. 10.1104/pp.106.092148PubMed CentralPubMedView ArticleGoogle Scholar
- Benjamini Y, Yekutieli D: The control of false discovery rate under dependency. Ann Stat 2001, 29: 1165–1188. 10.1214/aos/1013699998View ArticleGoogle Scholar
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