A label-free quantitative shotgun proteomics analysis of rice grain development

Lee and Koh Proteome Science 2011, 9:61 http://www.proteomesci.com/content/9/1/61 RESEARCH Open Access A label-free quantitative shotgun proteomics analysis of rice grain development Joohyun Lee1 and Hee-Jong Koh2* Abstract Background: 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. Results: 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. Conclusion: 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, TCAcycle, 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. Keywords: MudPIT, Rice, Spectral Counts, Shotgun proteomics Background 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 * Correspondence: 2 Department of Plant Science, Plant Genomics and Breeding Institute, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Korea Full list of author information is available at the end of the article 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 [3]. 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 © 2011 Lee and Koh; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lee and Koh Proteome Science 2011, 9:61 http://www.proteomesci.com/content/9/1/61 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 [7]. With advances in mass spectrometry, multidimensional protein identification technology (MudPIT), a shotgun proteomic approach, was developed for largescale, high-throughput protein identification [8]. 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 [9]. MudPIT has also been applied to analyses of the common bean (Phaseolus vulgaris), a non-model plant [10]. 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 lowabundance peptides is more accurate when using the radiolabel quantification methods [14], 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 [16]. After the comprehensive report of the rice grain proteome expression during grain filling stages [7], 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 pattern (...truncated)