Plastid Proteomics in Higher Plants: Current State and Future Goals

Plastids are plant cell organelles with many essential functions in plant metabolism. Among these are photosynthesis, amino acid and fatty acid biosynthesis, as well as the synthesis of several secondary metabolites. All plastids originate from undifferentiated proplastids, which are restricted to meristematic tissues and undifferentiated cells. Depending on the tissue, proplastids can develop into different plastid types (e.g. amyloplasts in storage tissue, chloroplasts in photosynthetic tissues, and chromoplasts in fruits and flowers). Other specialized plastid types include gerontoplasts, the plastids of senescent leaves that are important for resource allocation, oleoplasts, which are oil storage plastids in olive (Olea europaea), and etioplasts, the final stage of proplastid development in photosynthetic tissues in the dark (Wise, 2006). Finally, plastid types can possibly specialize to different degrees depending on cell type, developmental state, and (a)biotic conditions. An extreme case are the highly specialized C4 chloroplasts in bundle-sheath and mesophyll cells in the maize (Zea mays) leaf, with strong differences in proteome composition (Friso et al., 2010). With the ability to develop and differentiate, plastids add versatile biosynthetic capacity to the plant cell and are responsible for unique biosynthetic pathways that make plants unrivaled biochemical factories that are essential for life on earth. Thus, significant research efforts are underway that aim at understanding plastid biology in depth. A decade ago, the first plastid proteomics study was published and the potential of plastid proteomics was outlined (van Wijk, 2000). Since then, proteomics of plastids and plant (sub)proteomes has delivered on its promise. Here, we provide an update on the current status of plastid proteome research a decade after the first reports. Plant and plastid proteomics are now well established scientific disciplines with many laboratories contributing to their progress. Not surprisingly, a significant fraction of the plastid proteome is characterized today, and available information includes protein quantities, protein interactions, and posttranslational modifications (PTMs), as will be briefly highlighted in this update. However several of the challenges for plastid proteomics outlined 10 years ago still exist today, including the detection of low-abundance proteins (e.g. more than 10,000-fold lower than Rubisco) and capturing the dynamics of plastid proteomes. Much of the progress has been driven by technology development and improved genomics resources. The main difference between proteomic technologies today and 10 years ago is the much improved sensitivity (routinely at 1–50 fmol), the accelerated duty cycle (now tandem mass spectrometry [MS/MS] scans within a few hundred ms), the improved mass accuracy (down to a few ppm for peptides), and the increased resolution (up to 100,000) of the latest generation mass spectrometers. Furthermore coupling of nano-liquid chromatography (LC) with MS/MS is now routine, and split-free nano-LC systems now deliver low flow rates for nanospray ionization with excellent reproducibility. Also important are improved software tools for the reliable identification of peptides based on MS/MS spectra along with statistically sound estimates of false discovery rates in large data sets. With the maturation of proteomics work flows, quantitative information for plastid proteins became available. These new technologies, in combination with the availability of multiple sequenced plant genomes, now allow for answering more comprehensive and sophisticated questions as compared with a decade ago (Baginsky, 2009; Gstaiger and Aebersold, 2009; Schulze and Usadel, 2010; Walther and Mann, 2010). In this update, we review progress on plastid proteomics and lay out a series of challenges that can be addressed within the next few years. Table I provides an overview with Web-based plant proteomics resources that are relevant for plastid proteomics. We will center this update on the concept of a plastid protein atlas. Box 1 provides examples of the wide range of queries that a high-quality plastid protein atlas should ultimately be able to answer. For more exhaustive overviews on proteomics of plastids, we refer to two recent review articles and the references therein (Baginsky, 2009; Agrawal et al., 2010); space restrictions do not allow us to cite recent literature more extensively. Abbreviations for species are as follows: At, Arabidopsis; Ca, bell pepper; Cr, the green algae C. reinhardtii; Mt, Medicago truncatula; Nt, tobacco; Os, rice; St, potato; Zm, maize. Abbreviations for species are as follows: At, Arabidopsis; Ca, bell pepper; Cr, the green algae C. reinhardtii; Mt, Medicago truncatula; Nt, tobacco; Os, rice; St, potato; Zm, maize. Examples of the wide range of queries that a high-quality plastid protein atlas should ultimately be able to answer. Current answers for Arabidopsis proteins are provided with reference to the databases and resources listed in Table I. Information for maize and rice is available in a subset of the databases (Table I). This box also serves to better identify the lack of information and challenges for plastid proteome research and resource development for the immediate future. The concept of a protein atlas was established several years ago in particular for the human proteome (http://www.proteinatlas.org/). This concept involves the generation of protein inventories for each organ and subcellular localization tagged with additional protein information, such as splice variants, PTMs, protein-protein interactions, etc. Similar efforts are under way to collect all available information for plants to generate a plant proteome atlas that includes proteome information for the different plant organs and their organelles. Here, we concentrate on the plastid proteome atlas. In order to disseminate biologically useful information, such a plastid atlas should include the following: (1) protein accessions for each plastid type, including cellular specialization and subplastid localization; (2) information on peptide coverage of each identified protein and possibly different gene models; (3) steady-state protein abundance under a set of well-defined (a)biotic conditions as well as developmental states; (4) protein-protein interactions, protein-nucleotide assemblies, and oligomeric state; (5) reversible and irreversible PTMs; and (6) bioinformatics information such as subcellular localization predictions and network information. Plastids are among the best characterized cell organelles at the proteome level, and a quality chloroplast protein atlas is now emerging. However, the plastid protein atlas is far from complete, and strategies to improve proteome coverage and in-depth characterization must be developed and implemented. In the following paragraphs, we will review the status of each of the six components of the plastid protein atlas and outline strategies for improvement. The predicted size of the combined proteome of all plastid types ranges from 2,000 to 3,500 proteins in Arabidopsis (Arabidopsis thaliana), representing about 7% to 12% of all predicted protein-encoding genes. However, only about 1,200 proteins are currently recognized as being plastid localized (see the Plant Proteome Database [PPDB] at http://ppdb.tc.cornell.edu). Comparing this experimental plastid proteome data set with the predicted plastid proteome showed that, in particular, plastid proteins involved in signaling and plastid gene expression and RNA metabolism are strongly underrepresented. There are several reasons why a significant percentage of plastid proteins has not yet been recognized: (1) low abundance in chloroplasts (i.e. their detection is obscured by highly abundant photosynthetic proteins); (2) specific expression in a certain plastid type other than chloroplast; (3) only expressed under very specific conditions (developmental state, abiotic condition, or biotic challenge); or (4) too few ionizable tryptic peptides (e.g. transmembrane proteins with very short loops and tails or very small or basic proteins). Plastid proteome coverage can be improved by using better MS instrumentation with higher sensitivity, accuracy, and faster duty cycle, the use of alternative enzymes for protein digestion, more specific (e.g. affinity-based) fractionation of plastid proteomes, or increased efforts to analyze a more diverse set of plastid types, including heterotrophic plastids. However, as analytical sensitivity increases with these additional efforts, the challenge to distinguish between true positive and false positive plastid proteins increases as well. Based on the last decade of plastid proteome research, it is clear that objective filtering strategies for false positive identification and/or assignment to plastids are essential. The most practical solution involves repeated analysis of independent plastid preparations and the use of quantitative protein information for improved filtering of the identified proteins, based on two steps: (1) repeat observations in independent plastid preparations, because proteins that are observed at high frequency across these preparations are more likely to be bona fide plastid proteins; and (2) combined proteome information from unfractionated tissue and different purified organelles to recognize false positives that more highly accumulate in other subcellular locations, which requires quantitative information about relative protein abundance. Such relative quantification for these different sample types ideally should be done with the same experimental work flow, and a good example is available from the Localization of Organelle Proteins by Isotope Tagging (LOPIT) technique, which uses relative protein quantification along density gradients to assign proteins to organelles by association (Dunkley et al., 2006). The “frequency” filter is based on the assumption that or false positive are this first filter not false such as proteins, which can A small percentage of plastid proteins are also in the and proteins been for Arabidopsis far et al., of these with and are involved in plastid or gene expression (e.g. with the or also been of such requires independent information, from analysis using proteins and ideally also from analysis of of all available protein localization data from as well as proteomics is an important in the assignment of proteins to the For it to recognize abundant proteins identified in of proteomics as potential The information for Arabidopsis that is available about the localization of a certain protein (e.g. MS/MS and and of proteins with The information for Arabidopsis as well as maize and it with quantitative information on and plastid fraction in to this information and make a assignment for subcellular This using a (i.e. is is has been to in localization as by with independent experimental localization by and way to the plastid proteome is to analyze plastid types specialized for specific in their tissue or cell because in their protein However, this is for its and are small and it not develop storage Thus, is and proteome are better at the of the as by the analysis of plastids in et al., to this by using different plant species (e.g. bell or for the analysis of proplastids, and et al., 2010). However, this has not increased the of identified plastid proteins, in to the lack of information. are rice and because is available for these two and the coverage of the plastid proteome of maize is now to in because cell specialized for specific (Friso et al., 2010). this the identification of chloroplast and also identify many new of the involved in flow et al., et al., The of proteins within chloroplasts has been the of several proteome with the and proteins et al., by of the and proteomes, the specialized and proteins with the plastid (Baginsky, 2009; Agrawal et al., 2010). A recent study the Arabidopsis chloroplast proteome into proteins and and proteins et al., 2010). localization to each was based on the abundance of identified proteins in different purified Information about the protein composition of the chloroplast is available in and of space in this update, we refer the to the most recent and comprehensive review with literature et al., of the literature in this more than gene in the different only and in this the This is by different or by alternative has at the level, in particular new generation now allow for detection of of plant or more alternative The of these not been that in important plant including and and or can in proteins with different or or protein subcellular localization and of the for is that two different proteins that in their are from a gene and MS data to these different gene can to identify the most relevant predicted protein In the Arabidopsis and and peptide identification data are on each gene of the most relevant However, a analysis of the of at the plant proteome has not been this is not the challenges with coverage (i.e. the percentage of amino acid for which peptides are that is to distinguish different gene In the case of quantitative to be on to protein For more peptides than solution is to only the information for the to collect and all peptides for all protein of a In this not most it is important to a The the higher protein across all for the specific and was difference in protein between the with the was (Friso et al., 2010). Other laboratories all for a gene and the information et al., has its and it is important that the be The range of protein in plant organs and within the plastid likely to 10 of by and the latest generation of tandem mass for analysis with proteins are identified within an abundance range of to of plastid protein abundance is important to the composition of protein of plastid and plastid such as or as well as understanding plastid metabolism and of In as in the relative protein abundance are also an important to proteins are plastid protein we must distinguish between (1) protein mass or protein within a sample and (2) relative protein of the same protein between different The case is to as protein expression or the of (a)biotic developmental or protein quantification to expression In this we will the first the case is briefly (see the Plastid Proteome for The two strategies that far been to protein abundance within the plastid are (1) analysis of and (2) quantification using using with as the first was in most (e.g. for the et al., or proteins in rice et al., in most other this was to with as the first was to a quantitative of chloroplast proteins and their oligomeric in the of Arabidopsis et al., 2006). In a Arabidopsis proteins using et al., also out for chloroplast and of and mesophyll cells of maize leaves et al., The of in the higher resolution of compared with to of higher mass proteins and proteins, proteins with extreme than or than are to For the of protein including proteins, or are the better and showed that quantification is very in the of proteins that can be because protein to be from other to protein the quantification is by the amino acid because current in particular to basic to or of proteins, depending on the amino acid quantification for the quantification of a much of proteins, in a higher However, highly abundant proteins (e.g. the most abundant proteins in a are because of the use of et al., proteins with low of MS/MS spectra can large In proteins are most multiple unique peptides are each in high of protein mass quantification by the or to protein requires by the of predicted tryptic peptides within the relevant mass the case of or by protein or mass and the still has a in quantitative in particular for the analysis of protein and because it provides an immediate overview of the The for protein abundance is to the sample with proteins or as et al., These peptides can be by in synthesis or by expression as a of peptides after of a significant and are to of proteins; these are currently not practical for the quantification of of proteins and far been only to analysis of plastid pathways et al., 2010). However, efforts are under way to to the of the and for the quantification of specific plastid pathways or plastid out their or signaling many plastid proteins or with other protein been out for by or by chromatography et al., 2010); these two provide an overview of the oligomeric of more than In particular, protein than are by functions in plastid gene including and The of plastid proteins with or RNA a network of gene The of several are also as which several of plastid and of and proteins, including proteins likely or et al., 2006). are by the at the and These are well characterized by a of including et al., and The abundant photosynthetic protein in the been a for biochemical research for several and are now well characterized a of proteins in these been identified and characterized by and for of been by protein MS protein-protein using or using plants that tagged are to better the plastid proteome This will to better in particular the of metabolism and plastid gene expression and to reliable protein to the plastid proteome atlas. proteins reversible and irreversible analysis of PTMs, using a mass was out for chloroplast and as well as and the of many et al., This analysis provides a for and the use of for improved assignment of in proteomics and in modifications from with a For plastid proteins, the most irreversible in is of the the In the case of most proteins, the is by amino which been identified in plastids. that after the of information is requires in it provides a reliable of the and information about the for peptides of chloroplast Thus, the in of plastid and et established for a small set of proteins from rice the in and that is good between the peptide and the predicted et identified a set of proteins in Arabidopsis chloroplasts and provided additional information for the also that the is from the for should be based on the now available and to the of cellular protein abundance likely to is not a good for in and its to cell metabolism. is well established that reversible and (e.g. the of different types of plastid are of plastid metabolism as well as plastid gene expression and 2010). proteomics identified by other proteomics under and conditions to identify proteins under in and 2010). These that many chloroplast functions are by or by currently Among these functions are and biosynthesis, and gene protein and and in the and of the of the cycle by (i.e. was established and 2010). the last few two and as well as a been and their functions by analysis of Arabidopsis and 2010). The reversible at the photosynthetic to as well as A of chloroplast proteins with and of the was to be an abundant in the of in the Information about the of was to that are useful for in et al., for plant proteome are in such as the for Arabidopsis The of cellular functions to cell organelles is an important that to be to and protein and to make predictions at the systems Thus, of the protein composition of cell organelles for all plant cell organelles or (Baginsky, 2009; Agrawal et al., 2010). plant and systems analysis with subcellular organelles from proteome identification and inventories will efforts, and higher network should be In order to make a to protein localization data should low false positive rates (e.g. assignment of protein subcellular localization in and databases is better than of proteins, it is not to a for subcellular localization assignment based on experimental Thus, the should be a plastid proteome atlas with high sensitivity and a very low false positive In to the experimental proteome subcellular localization is a of information for plastid proteins, The generation of software to subcellular protein localization for other as well as has been in progress for well a in particular by the of protein inventories for different subcellular These inventories provide essential and sets. the of peptides for to the is and of plastid localization is much and still A several using a was to improve sensitivity and for plastid and proteins et al., In the last to several new localization (e.g. published for on each the it is not clear that their has a better true positive discovery for plastid proteins (i.e. a higher at a lower false positive discovery (i.e. a better than the most is still the most for plastid as well as plant localization that not only localization also the and There is still the true positive of which was to between experimental data sets. plastid proteome from the and true positive rates in the range of with the other much lower rates on their plastid protein set et al., true positive rates are observed proteins not in plastid preparations, also for protein identification (see of low-abundance Arabidopsis proteins of lower than large involved in RNA true positive rates as proteins et al., 2010). However, proteins in the plastid or with the should be from such analysis because do not a The main of is the high false positive likely to an for plastid The current sensitivity and accuracy of are not and the much of established subcellular for Arabidopsis to a also maize and should be useful to improve the of plastid localization In it is likely that a subset of plastid proteins information. For it has been for a few plastid proteins that are to the plastid the and that the of these proteins a by a et al., However, for peptides of established plastid proteins in Arabidopsis that very few proteins this et al., is that is yet for protein across the that for the true positive the recent of an be relevant et al., Finally, it be to develop and localization software for specific plant or For such as and maize different protein information as compared with such as and of established rice plastid proteins as well as rice for Arabidopsis chloroplast proteins showed that of or is in the et al., et al., With information about the of their specific to metabolism and signaling is also to for the and C4 plants Arabidopsis and as well as the green algae that into and allow of the specific of cell organelles to metabolism et al., 2010). protein-protein also from about the of proteins in the same This information false discovery rates in data for the of predicted has been for the of plant and for in particular the of in the and 2010). in species that, using information, it is to in from to provide information about with localization information, in Thus, protein inventories of cell organelles are important in not available and reliable protein subcellular localization will be and in the analysis of and proteins as the to the localization for each protein is the plastid protein atlas is not complete, it provide a of information and a for Table I the available proteomics resources with to plastid and Box 1 provides a of example questions that can be addressed with the available that the subcellular localization of many proteins is it is to analyze the and quantitative of of specific organelles these organelles. For quantitative proteome analysis of chloroplasts from and different chloroplast was done using quantification of Arabidopsis chloroplasts et al., The of quantitative on the chloroplast proteome analysis of than analysis of are that (1) with strong can be because of chloroplast from such can be very or and (2) more are for chloroplast with in their with in the because of chloroplasts from such leaves in of a subset of chloroplast not representing the chloroplast such subcellular proteome information for maize to the of the of cellular and metabolism maize development and C4 cellular et al., 2010). The current generation of mass sensitivity and to and a high of chloroplast proteins in such a can be for of that et al., in particular can be to in such With a plastid protein atlas for Arabidopsis and maize at it can be that of chloroplast proteomes, their PTMs, and under different conditions and in different or developmental will provide into plastid published plastid proteomics of Arabidopsis provide of the identified proteins using provided The Arabidopsis Information For other plant this is more because is sequenced or significant available or because databases are such as the for that of accessions (e.g. and of this can the of such data by other However, of the mass spectra with to such as the Database will allow other laboratories to make use of these for Arabidopsis and other new species such as maize and it is important that the mass data be for to improve improve or allow for analysis by other several (e.g. and and now the of mass data to such as is for data or data sets. more of experimental conditions and are outlined in the Information a and by several We strongly following these and the of mass data (e.g. into or other of chloroplasts and other plastid types has provided protein inventories as well as information about PTMs, protein and protein and MS technologies into plastid proteome information now allow analysis of chloroplast including chloroplast and For the reasons we a high-quality plastid proteome atlas a in the for biologically systems biology with efforts for other organelles (e.g. and this will to a better understanding of plant and development and to the potential of plant systems We the of laboratories for and on the we to all work not be because of space