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The field of proteomics has experienced numerous milestones over the course of the past 35–40 years. As an introductory chapter to this larger review text on plant proteomics, this article provides a cursory historical perspective on protein separation and identification techniques widely used in plant biochemistry laboratories today. In the past 10 years alone, advancements in techniques such as two-dimensional gel electrophoresis, mass spectrometry, and mass spectral data mining have made previously intractable proteomics problems almost routine by today’s standards. In analyzing these various proteomics approaches I also discuss and project their utility for the next generation of proteomics research.

Two-dimensional gel electrophoresis (2-DE) in combination with mass spectrometry (MS) is a principal tool for plant proteomic studies. Global protein identification together with analysis of the intrinsic patterns and relationships of protein populations provide new insights into the interrelated biochemical processes of specific proteomes. High-resolution 2-D gels in which proteins are clearly resolved facilitate MS identification and pattern analysis. Of the many factors that affect 2-D gel quality, optimization of protein extraction, solubilization, and detection methods ensure high resolution 2-D gels and maximal proteome coverage. Plant proteomic studies most often investigate total protein populations. Protein extraction protocols that utilize TCA or phenol are widely used because protein degradation and non-protein components that interfere with protein separation during electrophoresis are minimized. The high dynamic range of protein abundance in eukaryotic cells makes analysis of low abundance proteins problematic, but a wide array of separation techniques is available for isolating specific protein populations. Protein solubilization requires a buffer that denatures and reduces proteins as well as preventing their aggregation and precipitation. 2-DE gel patterns can differ substantially depending on the combination of chaotropes, reducing agents, and surfactants in the chosen buffer. Protein detection methods must be compatible with MS analysis and have the sensitivity and dynamic range required for pattern analysis. The sensitivity of Coomassie brilliant blue can rival that of silver stains, but the quantitative linear dynamic range for both these stains is only one order of magnitude. Alternatively, fluorescent stains are more sensitive, with a dynamic range covering three to four orders of magnitude. Obtaining high resolution 2-D gels of plant proteins can be challenging, but the technique, when optimized, is invaluable for profiling and comparing plant proteomes.

Recent advances in techniques and instrumentation now allow many researchers to acquire large proteomics data sets. Such data sets require more rigorous analysis than was required when single proteins were identified from purified or partially purified samples. This chapter discusses the types of software programs used to analyze these data sets. A detailed examination of the open software program X!-Tandem is used to illustrate how such programs are used to generate reliable protein identifications from mass spectra.

Reversible modification of proteins by phosphorylation is crucial in regulating signal transduction in plants and other organisms. Research on protein phosphorylation has greatly benefitted from recent developments in mass spectrometry (MS)-based technology. In combination with this technology, different, highly specific phosphopeptide purification methods have been explored to determine hundreds to thousands of phosphorylation sites. Using accurate mass spectrometers, researchers are now able to quantitatively determine phosphopeptide concentrations from different samples on a large scale. Contrasting with studies on yeast and animal systems, phosphoproteomic research on plants has concentrated mainly on the identification of novel phosphorylation sites. Here, we describe recent MS-based approaches that will enable the elucidation of dynamic changes in plant phosphoproteomes induced by environmental signals.

Proteomic approaches, such as protein microarray technology, play an important role in the study of complex biological systems. Their application in plant science has been strongly supported by the completion of genome sequence projects in the model plants and rice. This chapter focuses on the identification of substrate phosphorylation by protein kinases using protein microarrays. Protein microarrays are widely used to profile antibodies and sera for their specificity, and/or to screen entire proteomes for new protein interactions. Here, we emphasise the feasibility of using protein microarrays in the discovery of potential protein kinase substrates in plants. Our group has identified potential substrates in two different plant species: first barley and later . A signal quantification and threshold-based selection method was introduced during optimisation of protein microarray technology for substrate identification in order to better evaluate the microarray data, and to provide a preliminary list of candidate substrates for further investigation.

Proteomic techniques were used to identify plant extracellular matrix (ECM) proteins. Among the many classical cell wall proteins with known biochemical activity that were identified, new proteins predicted from their open reading frame in the database were localised to the ECM. Putative protein kinases and ATP-binding proteins were also identified in these cell wallenriched protein fractions. Bio-informatic analysis of the primary sequences of the putative kinases and ATP-binding proteins confirmed their ECM localisation, prompting a search for extracellular phosphoproteins. After identification of phosphorylated proteins in the plant ECM, subsequent experiments confirmed the existence of extracellular ATP and led to the discovery that extracellular ATP is a repressor of cell death in plants. Furthermore, external ATP has emerged as a control point of pathogen elicitor-induced programmed cell death. The studies described here constitute an example of how proteomics can be a powerful tool to drive discovery of novel signalling and/or metabolic pathways through integration of systematic protein identification, bio- informatics, and downstream hypothesis testing.

Following the development of genome sequence analysis in cereals such as rice, wheat, barley and maize, attention has focused on proteome analysis. This consists of the large scale separation and identification of proteins, determination of their functions and functional networks, and construction of appropriate databases. Many novel techniques for proteome analysis, such as two-dimensional electrophoresis (2-DE), nano-liquid chromatography and mass spectrometry (MS), have developed rapidly. Such techniques have made possible the efficient separation and identification of cereal proteins, and high-throughput analysis of functions and functional networks of these proteins. This chapter describes the current status of protein expression profiling (2-DE profiling, shotgun profiling, quantitative profiling), analyses of the subcellular localization of proteins, protein post-translational modifications (PTMs; e.g., phosphorylation and glycosylation) and protein-protein interaction in cereals. In addition, the development of software for proteome analysis and the construction of databases for the enormous amount of information obtained from cereal proteome analysis are outlined.

Proteomics, the systematic identification of proteins, has become an important asset for the study of cellular processes in a systems biology context. During the last few years significant technological improvements have been reported for high-throughput proteomics, both at the level of data analysis software and mass spectrometry hardware. With the maturation of proteomics technology, scientists now aim at proteome-wide protein identification to complement data from genome-wide transcriptional profiling and metabolomics experiments. A complete map of the proteome is expected to provide important information on genome activities and gene structures. Peptides identified in proteomics experiments are extremely valuable because they manifest the expression of a gene and thus complement the annotation of open reading frames and confirm or correct gene structure prediction. Furthermore, knowledge of repeatedly identified peptides in large-scale proteomics experiments allows peptide arrays with a selected set of proteotypic peptides for absolute protein quantification to be designed. Last but not least, knowledge of protein abundance, posttranslational modification and localisation is the key to a better understanding of the molecular mechanisms of cell functioning and pathway compartmentalisation. In this chapter, we will briefly highlight the current status of Arabidopsis proteomics and discuss existing limitations and anticipated new developments in plant proteomics.

Legumes are unique in their ability to fix atmospheric nitrogen through symbiotic relationships with rhizobia resulting in high protein content in the plants and the portioning of nitrogen in the soil. As a result legumes have become a worldwide staple in both human and animal nutrition. Unfortunately, commercial legumes such as soybean and alfalfa have large complex genomes that make the direct molecular and genetic study of these species more challenging. As a result, has been adopted as a model species for studying legume biology. These studies now include a large number of detailed proteome analyses, which are reviewed in this chapter. Topics reviewed include proteomic approaches, systematic identification of tissue-specific proteomes, rhizobia interactions, nodulation, arbuscule mycorrhizal interactions, seed development, and embryogenesis. The impact of biotic and abiotic environmental stresses on the proteome are also reviewed, including responses to pathogenic interactions, sewage treatment, desiccation tolerance, methyl jasmonate elicitation, and yeast elicitation.

Despite limited genome resources, recent proteomics research on select oilseed crops has shown that global analyses of proteins is currently possible. This review summarizes recent proteomic research efforts on seed development in oilseed crops, most notably soybean and oilseed rape. In-depth, systematic analyses of protein expression during the reserve deposition phase of seed development in soybean and oilseed rape have recently provided the first proteomic perspective of carbon assimilation into storage reserves for any oilseed crop (Hajduch et al. 2005, 2006). This is perhaps the first use of plant proteomics data to predict metabolic pathways with high confidence. In this review we present an updated comparison of intermediary metabolic pathways operating during pod filling in both soybean and oilseed rape. This review also discusses recent systematic analysis of phosphorylated proteins expressed during seed filling in oilseed rape using Pro-Q Diamond phosphoprotein stain in conjunction with high resolution two-dimensional gel electrophoresis. Identification of 103 of these proteins by tandem mass spectrometry revealed approximately 80 novel phosphoproteins, of which 45% were involved in metabolism or energy production. These data suggest that more metabolic enzymes could be regulated by protein phosphorylation than previously thought. Recent large-scale identification of soybean and rapeseed proteins and phosphoproteins by mass spectrometry demonstrates that proteomics research on oilseed crops is an exciting new area of research with tremendous possibilities.