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Plant organelle proteomics Kathryn S Lilley1 and Paul Dupree2 It is important for cell biologists to know the subcellular localization of proteins to understand fully the functions of organelles and the compartmentation of plant metabolism. The accurate description of an organelle proteome requires the ability to identify genuine protein residents. Such accurate assignment is difficult in situations where a pure homogeneous preparation of the organelle cannot be achieved. Practical limitations in both organelle isolation and also analysis of low abundance proteins have resulted in limited datasets from high throughput proteomics approaches. Here, we discuss some examples of quantitative proteomic methods and their use to study plant organelle proteomes, with particular reference to methods designed to give unequivocal assignments to organelles. Addresses 1 Cambridge Centre for Proteomics, Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1QR, United Kingdom 2 Department of Biochemistry, University of Cambridge, Building O, Downing Site, Cambridge CB2 1QW, United Kingdom Corresponding author: Lilley, Kathryn S ([email protected]) and Dupree, Paul ([email protected]) Current Opinion in Plant Biology 2007, 10:594–599 This review comes from a themed issue on Cell Biology Edited by Ben Scheres and Volker Lipka Available online 2nd October 2007 1369-5266/$ – see front matter Crown Copyright # 2007 Published by Elsevier Ltd. All rights reserved. DOI 10.1016/j.pbi.2007.08.006 Introduction There is a plethora of information about cellular mechanisms in plants that we glean by studying mutants or changes in transcript levels, especially in Arabidopsis thaliana. The study of genes however is only a limited dimension for cell biologists. The study of the proteome is far more information rich as one gene does not necessarily give rise to a single protein isoform [1]. Additionally, proteomic studies allow the determination of post translation modifications such as phosphorylation [2], ubiquitination [3], acylation and proteolytic processing, as well as information about which proteins exist together in complexes. Here, we discuss another important dimension that is measurable only by studying the proteome, namely spatial resolution; where proteins reside within cells and the trafficking of proteins between compartments during normal cellular processes and after specific perturbation. Current Opinion in Plant Biology 2007, 10:594–599 Assigning a subcellular location to a protein is very desirable to biologists for two reasons. Firstly, it can help elucidate their role in the cell as proteins are spatially organised according to their function [4]. Secondly, it refines our knowledge of cellular processes by pinpointing certain activities to specific organelles [5]. Traditional methods to assign proteins to subcellular locations are mostly targeted to a single protein of interest, for example creating a GFP-tagged version or raising a specific antibody. The SUBA database, which allows easy access to collated information from the literature on subcellular localisation of Arabidopsis proteins, lists more than 1300 proteins studied with GFP fusions [6]. The data in SUBA indicates that the GFP approach has been particularly successful in studying nuclear proteins, with nearly 400 proteins localised. However, over a quarter of the GFP fusion proteins had cytosolic or unclear localisations, and many more have conflicting localisations reported [6]. Although there are several GFP-fusion screening programmes underway, they also have yet to report many localisations of organelle membrane proteins ([7,8] Protlocdb: http://bioinf.scri.ac.uk/cgi-bin/ProtLocDB/home). The relatively slow progress may reflect difficulties when expressing GFP-fusions to membrane proteins, where transient over expression, often in a foreign species and cell type, can lead to misleading results. In its simplest form, organelle proteomics involves isolating the organelle of interest and producing a catalogue of the proteins present in that organelle by some form of separation of proteins or their proteolytic fragments followed by identification utilizing mass spectrometry. In order for an organelle protein catalogue to be useful to biologists, it is essential to determine the specific localization of a protein with high confidence. If this is to be achieved by using a proteomics approach, the organelle preparation must be free from contamination from other organelle types. In plants however, as in every other eukaryotic system, some organelles such as the nucleus [9], mitochondria [10], and chloroplasts [11] are relatively easy to obtain in a pure form, whereas many endomembrane organelles are impossible to purify without considerable contamination from other organelles with similar densities [12–14]. For example, Golgi-fractions enriched over 100-fold from a cellular homogenate of Arabidopsis, still contain a minority of probable Golgi proteins (Authors’ unpublished data) (Figure 1a). Furthermore, proteins in the secretory and endocytic pathway may traffic through several organelles of the endomembrane system en route to their final destination. A final confounding factor is that some proteins within the endomembrane system cycle between compartments, for example endoplasmic www.sciencedirect.com Plant organelle proteomics Lilley and Dupree 595 Figure 1 Locations of proteins in multiple organelles of Arabidopsis thaliana. (a) Proteins identified in an organelle-enriched fraction may be derived from multiple compartments. Proteins in an approximately 100-fold Golgi-enriched sample from Arabidopsis callus homogenate were catalogued after identification by mass spectrometry. (i) A depiction of the number of proteins from this fraction with known or predicted localizations based on interrogation of the literature and proteins of no known location (80% of the total). (ii) The percentage of proteins with known or predicted locations within the ER, PM, vacuole, Golgi and mitochondria and plastids. (b) PCA plot of the LOPIT data of Dunkley et al. [28]. Proteins that co-fractionate in density gradients appear clustered together. The proteins predicted by multivariate statistical approaches to be localized in various organelles are highlighted. reticulum (ER) residents continuously escape to the Golgi and are retrieved in COPI vesicles [15], and some plasma membrane proteins are endocytosed and then return to the plasma membrane (PM). Furthermore, some proteins are dual targeted to mitochondria and plastids [16]. Approaches at organelle proteomics that attempt purification and analysis of a single organelle will therefore not reveal the broader picture, and could be misleading. These factors necessitate the measurement of the steady state distributions of proteins within the whole endomembrane system in order to obtain a realistic insight into the principal subcellular localization of individual endomembrane proteins. Novel proteins identified in many organelle proteomic studies cannot be confidently assigned to the organelle where the extent of contamination is not assessed www.sciencedirect.com by the use of an appropriate control, or unless the assigned localization is subsequently confirmed by microscopy. Methods to study the organelle proteome The repertoire of techniques utilized in proteomics studies continues to expand at a considerable rate. These techniques can be subdivided into those which are used ostensibly for cataloguing proteins within a given sample and those which allow comparative or quantitative analyses of proteomes. The ability of a technique to be applied in a quantitative manner is of particular importance in many proteomics studies as such technologies allow incorporation of control samples within an experimental design. In the context of organelle proteomics, the use of such a control allows comparison of the specificity of proteins Current Opinion in Plant Biology 2007, 10:594–599 596 Cell Biology in the sample, and is necessary for judging whether the presence indicates a specific enrichment in the sample or that the protein is a contaminating abundant protein. Technologies can be divided still further into those which are polyacrylamide gel-based and those which are non-gel based. Traditionally, two-dimensional polyacrylamide gel electrophoresis (2D PAGE) has been the protein separation technique most associated with proteomic studies [17] and remains one of the key methodologies in proteomics studies. A major drawback of using 2D gels is their incompatibility with hydrophobic membrane proteins which make up a mechanistically important subset of proteins and are likely to play crucial roles within organelles [18]. Non-2D gel based technologies therefore are more attractive methodologies to employ in a global study of the subcellular proteomes as they do not suffer the same bias towards analysis of the soluble proteome as 2D gels provided that the proteins are successfully solubilized before analysis [19]. Typically in non-2D gel based approaches, the proteome undergoes simplification before mass spectrometric analysis in order to maximize the amount of information about the protein content of the sample that can be achieved. These methods are often referred to as shotgun proteomics. One such approach, MudPIT (multidimensional protein identification technology [20]), involves a solution phase digestion of proteins to peptides and then multi-dimensional chromatographic separation of peptides before mass spectrometric analysis. These methods can be used in a quantitative manner by coupling them with either the use of differential stable isotopes, or label free technologies. In both cases the abundance of peptides is calculated from mass spectrometric measurements. Stable isotope labelling involves quantitation using differential incorporation of stable isotopes either in vivo or in vitro. There are several ways in which this can be achieved. One method involves the growth of cultures in the presence of a defined medium containing a heavy isotope, typically 15N ([21,22]. Samples grown in the presence of the natural isotope and the heavy isotope can be pooled, reduced to peptides, and the peptides separated by multidimensional liquid chromatography before application to mass spectrometry. The relative abundance of a peptide generated from a protein within cultures being compared is then calculated by measuring ion intensities of the ‘light’ and ‘heavy’ versions of the same peptide. A more widely applicable variation of this method is to label extracted protein with tags which can be produced Current Opinion in Plant Biology 2007, 10:594–599 in more than one isotopic form. One such method involves the incorporation of 16O or 18O during trypsinolysis. The most commonly used tagging system, however, involves the use of amine modifying labelling reagents for multiplexed relative and absolute protein quantitation (iTRAQ) reagents, which are a multiplexed set of four isotope tags which labelled peptides generated from extracted proteins by trypsin [23]. Since the iTRAQ tags are isobaric, differentially labelled versions of a peptide appear as a single precursor ion peak. When an iTRAQlabelled peptide is subjected to collision-induced dissociation in MS/MS mode, the iTRAQ tags release diagnostic, low-mass ions (reporter ions) that are used for quantitation. Label free quantitation is based entirely on peak intensity measurements of peptides detected by mass spectrometry [24,25] or on the number of ions per protein (spectral counts) detected in a mass spectrometric experiment [26]. Each of the quantitative methods listed above have differing and, in many cases, complementary strengths and weaknesses. A full description of these is outside the scope of this manuscript, but is explored in many publications ([18] and others). High throughput organelle proteomics methodologies Recently several high throughput methods have emerged involving quantitative strategies, which have overcome the need to produce a pure organelle for analysis. Each of these methods relies on quantitative proteomics to characterize the distribution pattern of organelles amongst partially enriched fractions generated by various separation technologies and have the potential to discriminate between genuine organelle residents and contaminants without preparation of pure organelles [27,28,29,30]. The methods developed by Gilchrist et al. and Foster et al. used samples generated from rat liver and both use label-free quantitation. The method of Dunkley et al., named localization of organelle proteins by isotope tagging (LOPIT), used callus tissue derived from Arabidopsis roots [27,28]. LOPIT has resulted in the first large scale data set enabling the simultaneous assignment of proteins to multiple subcellular locations with a high degree of confidence. The general principle of LOPIT relies on analysis of the distribution of organelle proteins within fractions from self-generating iodixanol density gradients. Organelle distributions are first visualized by Western blotting with antibodies specific to known marker proteins. Four fractions, enriched with different organelles, are initially selected for comparison. Additional overlapping comparisons can then be carried out to cover a wider area of the gradient. Unlike the label-free approach taken by Gilchrist and Foster and their co-workers, protein distributions are www.sciencedirect.com Edited by Foxit Reader Copyright(C) by Foxit Software Company,2005-2007 organelle proteomics Lilley and Dupree 597 For EvaluationPlant Only. then determined by measuring their relative abundance using iTRAQ reagents and tandem mass spectrometry (MS/MS). Multivariate statistical techniques such as principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) are employed to cluster proteins according to the similarities in their gradient distributions and thus to assign proteins to organelles. Figure 1b shows a PCA plot generated using the LOPIT approach to analyse protein localization within Arabidopsis callus in the authors’ laboratories. The LOPIT data set of Dunkley et al., provides localization information for 527 proteins in total, 182 ER, 92 PM proteins, 89 Golgi proteins, 24 tonoplast proteins, and 140 mitochondria/plastid proteins which co-cluster in this particular analysis (Figure 1b) [28]. The majority of these assignments presented proteins for which there was no previous localization data. The predictions of LOPIT were subsequently validated by microscopy. In 16 of the 18 cases that gave a clear result, GFP fusions were targeted to the LOPIT-predicted organelle. We have recently compared this LOPIT data set with all the data on localization by GFP-tagging in SUBA (as of May 2007). Of the 1349 GFP-tagged proteins in the SUBA database, 50 were assigned to specific organelles in both the GFP experiments and the LOPIT dataset. The localizations were consistent in 60% of the cases. Some of the discrepancy may be because of incorrect predictions from the proteomic datasets. However, the possibility of incorrect localization of GFP fusions must also be considered. In most cases where discrepancies have been investigated, the transiently expressed GFP-fusion has not reflected the steady state localization of the endogenous protein (Authors’ unpublished observations). The plant organelle proteome Many researchers have taken an approach where a single organelle is the focus of the study. In the cases where highly efficient organelles preparation has been possible robust catalogues have been achieved, but in all cases the robustness of this dataset can only be assessed by inclusion of appropriate control samples into the experimental schema. Pendle and co-workers, for example, carried out analysis of the Arabidopsis nucleolus proteome [31]. A highly efficient nucleolus purification process allowed over 200 proteins to be identified. However, there was no control sample for the purification, and thus the authors attempted confirmation of localization of 72 proteins with microscopy of GFP fusions, of which 87% showed some nucleolar labelling. Furthermore, since two thirds of the proteins had a direct counterpart in the human nucleolar proteome, this is a dataset with high confidence assignments, although given the weak nucleolar labelling of some of the proteins, the steady state proportion of some of the proteins in the nucleolus might be low. www.sciencedirect.com Mitochondria and plastids can be obtained relatively purely, and there are several excellent databases of proteomic identified proteins from these organelles [32,33]. There are promising purification approaches to isolating plastids from specialized cell types which may allow studies of plastid subtype proteomes [34]. Plastid subproteomes have also been studied, including stromal, envelope, thylakoid proteins and the plastoglobules: lipoprotein particles present in chloroplasts. Ytterberg et al. used stable isotopic labelling of peptides with formaldehyde to investigate the change in protein composition of highly enriched plastoglobule preparations between different light stress regimes [35]. These preparations were apparently free of contaminating membranes from other organelles, although some of the proteins have been found in proteomic studies of thylakoids, stroma and plastid envelopes. It is unclear whether plastoglobules are cross contaminants of those suborganelle preparations, or whether proteins are shared in localization. In studies that focus on organelles which are impossible to isolate to such a degree of purity, more stringent controls have to be taken to avoid mis-assignment of proteins to subcellular locations. The tonoplast has been subject of many plant subcellular localization studies, because of the importance of the tonoplast in plant cell metabolism and the enigmatic identity of many of the membrane transport proteins. Several vacuole proteomic studies have been attempted and yielded large datasets. A particularly successful recent example was the identification of HvSUT2 and AtSUT4 as tonoplast sucrose transporters after proteomic analysis of barley tonoplasts [36]. The localization was supported by expression of GFP fusion proteins. However, in general vacuolar purification is difficult, and there is often some contamination within the datasets from other organelles, possibly because the vacuole has an autophagic function and therefore proteins from elsewhere within the cell will co-purify in the vacuolar lumen [37,38]. Of the proteins found in the vacuolar fraction of the dataset of Jaquinod et al, there is an overlap of 142 proteins with the LOPIT set. Only 22 proteins however are in agreement with Dunkley et al.’s vacuolar list, the remainder includes 90 which were classified by LOPIT not vacuolar in location. Attempts to further purify the organelle, for example using free flow zonal electrophoresis [39], may assist, but will not obviate the need for controls for the specific enrichment of the majority of the protein in the vacuoles. Several papers have characterized the PM proteome using a range of quantitative techniques. The group of Sussman used trypsin-catalysed 18O labelling to address the issue of quantitative enrichment of proteins in dextran-PEG preparation of PM from Arabidopsis [40]. On the basis of low quantitative enrichment, the authors were able to disregard the majority of the proteins in the sample, and Current Opinion in Plant Biology 2007, 10:594–599 598 Cell Biology identified 70 proteins as enriched more than the bona fide PM protein, the proton pumping ATPase. There were 29 of these 70 proteins in common with the LOPIT dataset, of which 23 were predicted PM by LOPIT, indicating a very high level of consistency between these quantitative methods. Lanquar and co-workers showed that a change in composition of PM protein samples on cadmium stress could be detected using 15N stable isotopic labelling of proteins in culture, followed by PM preparation and MS [41]. More recently, Benschop et al (2007) used similar stable isotopic labelling of Arabidopsis tissue culture cells, and identified over 1000 proteins in their PM samples [21]. However, their aim was to use the quantitative analysis coupled with phosphoproteomics to identify proteins in PM preparations that were differentially phosphorylated in response to flagellin peptide or xylanase treatment. Of the proteins in common with the LOPIT dataset, nearly 40% were predicted PM by Dunkley and co-workers, suggesting a good enrichment of PM in the analysis. The lipid raft hypothesis proposes that certain proteins are trafficked to the PM in membrane domains of specialized lipid and protein composition. This clustering may also be important during endocytosis. These specialized membrane domains have been studied by preparation of detergent resistant membrane (DRM) domains. In Arabidopsis, these have been prepared from a mixture of organelles, and shown to be largely derived from the PM [42]. DRMs have also been isolated from PM preparations from tobacco [43] and Medicago truncatula [44], and the authors suggest they contain proteins involved in signalling processes. To understand precisely which plasma membrane proteins use these membrane domains, it will be interesting to see the specificity of enrichment of the proteins in the DRMs by application of quantitative proteomics. Conclusions Undoubtedly, by allowing protein quantity in samples to be compared, quantitative approaches are improving the quality of organelle proteome datasets. The alternatives of stable isotopic labelling in culture or after sample preparation, as well as label-free techniques, will all contribute flexibility of the experimental design. Ideally, quantitative techniques for organelle dynamics need to be able to compare both enrichment of organelles with a control and also change between control and treatment in the same analysis. One of the advantages of LOPIT over alternative quantitative techniques is that is provides information on the steady state localization of proteins in cells. Proteins found in multiple locations in the cell will appear at an intermediate position in the analysis plots. Therefore, LOPIT appears to be suited to addressing questions where the localization of proteins changes in different conditions, for example, in stress, drug treatment, or in comparison of mutants. Current Opinion in Plant Biology 2007, 10:594–599 References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1. Peck SC: Update on proteomics in arabidopsis. Where do we go from here? Plant Physiol 2005, 138:591-599. 2. Nuhse TS, Stensballe A, Jensen ON, Peck SC: Phosphoproteomics of the arabidopsis plasma membrane and a new phosphorylation site database. Plant Cell 2004, 16:2394-2405. 3. Kwon SJ, Choi EY, Choi YJ, Ahn JH, Park OK: Proteomics studies of post-translational modifications in plants. J Exp Bot 2006, 57:1547-1551. 4. Dreger M: Subcellular proteomics. Mass Spectrom Rev 2003, 22:27-56. 5. Lunn JE: Compartmentation in plant metabolism. J Exp Bot 2007, 58:35-47. 6. Heazlewood JL, Verboom RE, Tonti-Filippini J, Small I, Millar AH: SUBA: The Arabidopsis subcellular database. Nucleic Acids Res 2007, 35:D213-D218. 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