Location, location, location: surveying the intracellular real estate through proteomics in plants

Understanding protein trafficking in plant cells through proteomics

The functions of approximately one-third of the proteins encoded by the Arabidopsis thaliana genome are completely unknown. Moreover, many annotations of the remainder of the genome supply tentative functions, at best. Knowing the ultimate localization of these proteins, as well as the pathways used for getting there, may provide clues as to their functions. The putative localization of most proteins currently relies on in silico-based bioinformatics approaches, which, unfortunately, often result in erroneous predictions. Emerging proteomics techniques coupled with other systems biology approaches now provide researchers with a plethora of methods for elucidating the final location of these proteins on a large scale, as well as the ability to dissect protein-sorting pathways in plants.

Quantitative proteomics of the tonoplast reveals a role for glycolytic enzymes in salt tolerance

To examine the role of the tonoplast in plant salt tolerance and identify proteins involved in the regulation of transporters for vacuolar Na(+) sequestration, we exploited a targeted quantitative proteomics approach. Two-dimensional differential in-gel electrophoresis analysis of free flow zonal electrophoresis separated tonoplast fractions from control, and salt-treated Mesembryanthemum crystallinum plants revealed the membrane association of glycolytic enzymes aldolase and enolase, along with subunits of the vacuolar H(+)-ATPase V-ATPase. Protein blot analysis confirmed coordinated salt regulation of these proteins, and chaotrope treatment indicated a strong tonoplast association. Reciprocal coimmunoprecipitation studies revealed that the glycolytic enzymes interacted with the V-ATPase subunit B VHA-B, and aldolase was shown to stimulate V-ATPase activity in vitro by increasing the affinity for ATP. To investigate a physiological role for this association, the Arabidopsis thaliana cytoplasmic enolase mutant, los2, was characterized. These plants were salt sensitive, and there was a specific reduction in enolase abundance in the tonoplast from salt-treated plants. Moreover, tonoplast isolated from mutant plants showed an impaired ability for aldolase stimulation of V-ATPase hydrolytic activity. The association of glycolytic proteins with the tonoplast may not only channel ATP to the V-ATPase, but also directly upregulate H(+)-pump activity.

The predicted subcellular localisation of the sugarcane proteome

Plant cells are highly organised, and many biological processes are associated with specialised subcellular structures. Subcellular localisation is a key feature of proteins, since it is related to biological function. The subcellular localisation of such proteins can be predicted, providing information that is particularly relevant to those proteins with unknown or putative function. We performed the first in silico genome-wide subcellular localisation analysis for the sugarcane transcriptome (with 11 882 predicted proteins) and found that most of the proteins were localised in four compartments: nucleus (44%), cytosol (19%), mitochondria (12%) and secretory destinations (11%). We also showed that ~19% of the proteins were localised in multiple compartments. Other results allowed identification of a potential set of sugarcane proteins that could show dual targeting by the use of N-truncated forms that started from the nearest downstream in-frame AUG codons. This study was a first step in increasing knowledge about the subcellular localisation of the sugarcane proteome.

Getting to grips with the plant metabolic network

Research into plant metabolism has a long history, and analytical approaches of ever-increasing breadth and sophistication have been brought to bear. We now have access to vast repositories of data concerning enzymology and regulatory features of enzymes, as well as large-scale datasets containing profiling information of transcripts, protein and metabolite levels. Nevertheless, despite this wealth of data, we remain some way off from being able to rationally engineer plant metabolism or even to predict metabolic responses. Within the past 18 months, rapid progress has been made, with several highly informative plant network interrogations being discussed in the literature. In the present review we will appraise the current state of the art regarding plant metabolic network analysis and attempt to outline what the necessary steps are in order to further our understanding of network regulation.

Mapping the proteome of poplar and application to the discovery of drought-stress responsive proteins

Poplar is the first forest tree genome to be decoded. As an initial step to the comprehensive analysis of poplar proteome, we described reference 2-D-maps for eight tissues/organs of the plant, and the functional characterization of some proteins. A total of 398 proteins were excised from the gels. About 91.2% were identified by nanospray LC-MS/MS, based on comparison with 260,000 Populus sp. ESTs. In comparison, reliable PMFs were obtained for only 51% of the spots by MALDI-TOF-MS, from which 43% (83 spots) positively matched gene models of the Populus trichocarpa genome sequence. Among these 83 spots, 58% matched with the same proteins as identified by LC-MS/MS, 21.7% with unknown function proteins and 19.3% with completely different functions. In the second phase, we studied the effect of drought stress on poplar root and leaf proteomes. The function of up- and down-regulated proteins is discussed with respect to the physiological response of the plants and compared with transcriptomic data. Some important clues regarding the way poplar copes with water deficit were revealed.

Isolation of intact, functional mitochondria from the model plant Arabidopsis thaliana

The ability to isolate intact, functional mitochondria from plant tissues is a key technique in the study of the genome, proteome, and metabolic function of the plant mitochondrion. Traditionally, mitochondrial plant researchers have turned to specific plant systems and organs (such as potato tubers and pea shoots) from which mitochondria are readily isolated in large quantities. However, increasingly, research is focused on a small number of model species, and there is a need to adapt existing protocols to allow the isolation of mitochondria from these model species. Arguably, the most important of these is Arabidopsis thaliana, for which a formidable array of genetic resources is available. However, because of its relatively small size and the absence of large heterotrophic organs, Arabidopsis is a challenging plant from which to isolate mitochondria. Here, we present two methods for isolating mitochondria from Arabidopsis, either from heterotrophic cell suspension cultures or from hydroponic seedling cultures. We also present details of commonly used assays to assess the physical and functional integrity of the isolated organelles.

SUBA: the Arabidopsis Subcellular Database

Knowledge of protein localisation contributes towards our understanding of protein function and of biological inter-relationships. A variety of experimental methods are currently being used to produce localisation data that need to be made accessible in an integrated manner. Chimeric fluorescent fusion proteins have been used to define subcellular localisations with at least 1100 related experiments completed in Arabidopsis. More recently, many studies have employed mass spectrometry to undertake proteomic surveys of subcellular components in Arabidopsis yielding localisation information for approximately 2600 proteins. Further protein localisation information may be obtained from other literature references to analysis of locations (AmiGO: approximately 900 proteins), location information from Swiss-Prot annotations (approximately 2000 proteins); and location inferred from gene descriptions (approximately 2700 proteins). Additionally, an increasing volume of available software provides location prediction information for proteins based on amino acid sequence. We have undertaken to bring these various data sources together to build SUBA, a SUBcellular location database for Arabidopsis proteins. The localisation data in SUBA encompasses 10 distinct subcellular locations, >6743 non-redundant proteins and represents the proteins encoded in the transcripts responsible for 51% of Arabidopsis expressed sequence tags. The SUBA database provides a powerful means by which to assess protein subcellular localisation in Arabidopsis (http://www.suba.bcs.uwa.edu.au).

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria

BACKGROUND

Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation.

RESULTS

In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods.

CONCLUSION

We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.

Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis

Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.

Digging deeper into the plant cell wall proteome

The proteome of the plant cell wall/apoplast is less well characterized than those of other subcellular compartments. This largely reflects the many technical challenges involved in extracting and identifying extracellular proteins, many of which resist isolation and identification, and in capturing a population that is both comprehensive and relatively uncontaminated with intracellular proteins. However, a range of disruptive techniques, involving tissue homogenization and subsequent sequential extraction and non-disruptive approaches has been developed. These approaches have been complemented more recently by other genome-scale screens, such as secretion traps that reveal the genes encoding proteins with N-terminal signal peptides that are targeted to the secretory pathway, many of which are subsequently localized in the wall. While the size and complexity of the wall proteome is still unresolved, the combination of experimental tools and computational prediction is rapidly expanding the catalog of known wall-localized proteins, suggesting the unexpected extracellular localization of other polypeptides and providing the basis for further exploration of plant wall structure and function.

Plant membrane proteomics

Plant membrane proteins are involved in many different functions according to their location in the cell. For instance, the chloroplast has two membrane systems, thylakoids and envelope, with specialized membrane proteins for photosynthesis and metabolite and ion transporters, respectively. Although recent advances in sample preparation and analytical techniques have been achieved for the study of membrane proteins, the characterization of these proteins, especially the hydrophobic ones, is still challenging. The present review highlights recent advances in methodologies for identification of plant membrane proteins from purified subcellular structures. The interest of combining several complementary extraction procedures to take into account specific features of membrane proteins is discussed in the light of recent proteomics data, notably for chloroplast envelope, mitochondrial membranes and plasma membrane from Arabidopsis. These examples also illustrate how, on one hand, proteomics can feed bioinformatics for a better definition of prediction tools and, on the other hand, although prediction tools are not 100% reliable, they can give valuable information for biological investigations. In particular, membrane proteomics brings new insights over plant membrane systems, on both the membrane compartment where proteins are working and their putative cellular function.