Understanding Plant Sub-cellular (Organellar) Metabolome
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Published: Wed, 04 Apr 2018
Dissection of organismal metabolomes into smaller subunits of life holds the potential to unravel the minuscule details of operative metabolic pathways and metabolic compartmentation at the sub-cellular level. Although metabolomes have been characterized at tissue, cellular, and cell-population types, little efforts have been put forth in sub-cellular metabolomes. In the post-genomic era, significant advances have been made in predicting plant protein and transcriptomic localization to subcellular organelles through computational approaches. For obvious challenges such as, difficulty in pure preparations of organelles, shared metabolites among them, and associated complicated regulations in them delimits the growth in this area. We summarize the recent efforts and progresses made in directions of understanding the plant sub-cellular (organellar) metabolomes.
Keywords: organelle, plastid, mitochondria, vacuole, proteomics, nonaqueous fractionation,
The metabolic compartmentation adds a complex dimension to subcellular metabolomes
Systems biology approaches, including bioinformatics, genomics, transcriptomics, proteomics, and metabolomics have begun to contribute to our growing knowledge of cellular signaling and metabolism. However, the extensive and unique metabolic compartmentation is characteristic of eukaryotic cells, such as plant cells, thus rendering the analysis of compartmented metabolic networks complicated by virtue of separation and parallelization of pathways and intracellular transport (Wahrheit et al., 2011). Consequently, the study of plant cellular metabolomic networks becomes even more challenging (Toubian et al., 2013). Although the single cell and single-cell type metabolomics studies (Misra et al., 2014) bring in homogeneity in preparations to reflect on cellular (micro-metabolome) as the basic unit of life, the subcellular (nano-metabolome) pose a great deal of challenges for their investigation. Major plant subcellular structures include but are not limited to apoplast, cell plate, cell wall, endoplasmic reticulum and related structures, endosome, Golgi apparatus, microfilament, microtubule, mitochondrion, oil bodies, nucleus, peroxisome, plasma membrane, plastid and related structures, and vacuole. Metabolic pathways are highly segregated in different subcellular organelles (Browsher and Tobin, 2011). Undoubtedly, the compartmentalization of plant metabolites, add another complex dimension to principal regulatory aspects in plants, apart from the temporal dimensions. In addition, the diffusion of metabolites, the role of active transport by membrane-based transporters, and limitations in labeling and visualization of metabolites in cells render the localization even more difficult. Moreover, the genetic variation within these organelles have a widespread effect on the stochastic variation in primary metabolism with discrete impacts that differed from the organelle effect on the average metabolome (Joseph et al., 2015). As such, pathways of communication between various organelles of a plant cell are quite complex and interdependent, for example the rampant signaling between organelles such as chloroplasts and nuclei (Jung and Chory, 2009). Thus efforts to understand their individual metabolites would aid in understanding of these complex regulatory exchanges, in addition to what is established at the levels of transcripts and proteins.
Omics-based approaches in identifying subcellular functionalities are powerful resources
There have been considerable efforts to catalog the information content in organelles starting from imaging based approaches to omics-based systems biology perspectives. For instance, the aim of the plant organelles database (http://podb.nibb.ac.jp/Organellome) is to promote the understanding of organelle dynamics such as organelle function, biogenesis, differentiation, movement and interactions with other organelles (Mano et al. 2013). Although, genomics-based efforts are much more prevalent. Such as a unique database of RNA-editing sites found in plant organelle genes with the results mapped onto amino acid sequences and 3D structures (Yura et al. 2009) are available. In addition, to catalog fluorescent protein expression, public repositories such as the Maize Cell Genomics (MCG) database, (http://maize.jcvi.org/cellgenomics) have bene developed that represents major subcellular structures and also developmentally important progenitor cell populations (Krishnakumar et al., 2014). Another noteworthy approach was the use of subcellular organelle expression microarray to study the organic acid changes in post-harvest Citrus fruit (Sun et al., 2013) and organelle membrane proteome during germination and tube growth of lily pollen (Pertl et al., 2009). In addition, proteomics efforts have revealed secretome, extracellular matrix, cell wall (14), vacuoles, plastids, and peroxisomes-specific changes in plants are catalogued (Liley and Dupree, 2007; Dai and Chen, 2012). Similarly, proteomics-based approaches for characterization of seed proteomes have been reviewed recently (Repetto and Gallardo, 2012). Rapid subcellularfractionationin combination with targeted proteomics allowed for measuring subcellularproteinconcentrations in attomole per 1000cells of Chlamydomonas reinhardtii (Weinkeeop et al., 2010). The importance of the spatial resolution of plant cellular metabolomes have been realized (Sumner et al., 2011). However, such efforts and databases are missing for plant subcellular metabolomes.
Recently, the need for understanding the challenges in cellular compartmentalization for successful plant metabolic engineering was identified (Heining et al., 2013). The enrichment of other omics-based subcellular localization tools would allow understanding of the metabolic pathways operative in them for tinkering them for commercial success. Some widely used computational approaches for proteome level assignment of localization include, Some widely used prediction programs are: TargetP, http://www.cbs.dtu.dk/services/TargetP/, Predotar,http://www.inra.fr/predotar/, iPSORT, http://hc.ims.u-tokyo.ac.jp/iPSORT/, and SubLoc, http://www.bioinfo. tsinghua.edu.cn/SubLoc/, etc. For example, LocDB is a manually curated database with experimental annotations for the subcellular localizations of proteins inA. thaliana (Rastogi and Rost, 2011). Recently, the Peroxisome database (http://www.peroxisomeDB.org) was released which serves as a huge resource for cross-lineage comparison of functional genomic and metabolomic information on organisms such as fungi, yeasts, plants, human and lower eukaryotes, with an ensemble of 139 peroxisomal protein families and ~2706 putative peroxisomal protein homologs (Schlüter et al., 2010). On the other hand, databases such as SUBA (Heazlewood et al.,2007) are excellent inventories of subcellular compartmentation supported by experimental evidence mainly drawn from organellar proteome studies, which enable the integration of experimentation and prediction (Tanz et al., 2012). In the AraGEM genome-scale model ofArabidopsismetabolism the vast majority of reactions are assigned to the cytosol (1265 reactions in the cytosol, with 60, 159, and 98 reactions assigned to mitochondria, plastid, and peroxisome, respectively) (de Oliveira Dal’Molin et al.,2010). However, there are no available collage of information on subcellular metabolomes of plants to our knowledge, and hence this effort.
Plant subcellular metabolome studies revisited: non-aqueous fractionation (NAF) methods
There has bene several successful attempts at obtaining the qualitative and quantitative snap shots of sub-cellular metabolomes in plants. These efforts relied on fractionation of the or isolation of pure organelles followed by characterization of the metabolomes by gas chromatography mass-spectrometry (GC-MS), liquid chromatography- mass spectrometry (LC-MS) among other approaches. Cell fractionation and immunohistochemical studies in the last 40 years have revealed the extensive compartmentation of plant metabolism from protein-based information (Lunn, 2007). Majority of the classical studies in compartmentation of plant metabolism focused on plastids, mitochondria, and vacuole and reflected on their structural and functional heterogeneity operative primary metabolic (photosynthesis, respiratory etc.) pathways (Lunn, 2007, Bowsher and Tobin, 2011). Plastids are involved in carbon and nitrogen metabolism, in particular nitrate and ammonium assimilation, the Calvin cycle, oxidative pentose-phosphate pathway, glycolysis, and terpenoid biosynthesis, and these have been reviewed from a metabolic perspective (Tobin and Bowsher, 2005). Thus plastidial proteomics have interested researchers for a long time (van Wijk and Baginsky, 2011). Analysis of the chloroplast proteome confirmed indicated biosynthesis of fatty acids, lipids, amino acids, nucleotides, hormones, alkaloids, and isoprenoids, Calvin cycle enzymes and proteins belonging to the light-harvesting apparatus and photosynthetic electron transport chain (van Wijk, 2004). Protoplast fractionation in combination with enzymatic determination of metabolites has been widely used to quantify a subset of metabolites like adenylates, phosphorylated sugars and Calvin cycle intermediates in different compartments(Kueger et al., 2012). The metabolomes of highly purified barley vacuoles isolated from mesophyll cell protoplasts by silicon oil centrifugation revealed the presence of 59 primary metabolites and ~200 secondary metabolites by GC-MS and FT-MS (Fourier transform-mass spectrometry) such as amino acids, organic acids, sugars, sugar alcohols, shikimate pathway intermediates, vitamins, phenylpropanoids, and flavonoids, of which 12 were found exclusively in the vacuole (Tohge et al., 2011). Similarly, a single vacuole of single cell of the alga Chara australis revealed the localization and dynamics of 125 known metabolites(Oikawa et al., 2011). In plants, vacuoles are known for detoxification of xenobiotics (Coleman et al., 1997). In addition, the analysis of subcellular metabolite levels of potato tubers (Solanum tuberosum) indicated that either the cytosol or apoplast leads to a decrease in total sucrose content and to an increase in glucose and hexoses accumulate in the vacuole independently of their site of production (Farre et al., 2008). Furthermore, in the medicinal plant Catharanthus roseus, LC-MS analysis of the phenols from isolated leaf vacuoles detected the presence of three caffeoylquinic acids and four flavonoids(Ferreres et al., 2010). Another example of the use of vibrational (Raman) spectroscopy in metabolomics was exemplified in the localization of β-carotene by its 1150 and 1515 cm−1 Raman bands with subcellular resolution (~550 nm per pixel) in the cells of alga Euglena gracilis. Complementary single-cell MS data were also recorded which indicated the colocalization of β-carotene and the plastids containing internal antennae of photosystem II (Urban et al., 2011).
Non-aqueous fractionation (NAF) is the most widely used method for studying metabolite pool sizes at a subcellular level in plants(Kueger et al., 2012), where NAF method is based on the enrichment of compartments within a continuous non-aqueous density gradient instead of purifying individual intact organelles. This method is associated with true metabolomics studies allowing the subcellular localization of a large number of metabolites to be analyzed in parallel (Farre et al., 2001, Krueger et al., 2011). Assessment of metabolome compartmentation of soybean leaves using non-aqueous fractionation by GC-MS of about 100 compounds indicated a greater number of compounds identified in vacuole when compared to cytosol or stroma (Benkeblia et al., 2007). Furthermore, the NAF method allowed the identification and quantification of the subcellular distributions of metabolites in developing potato (Solanum tuberosumL. cv Desiree) tubers which revealed that ~60% of most sugars, sugar alcohols, organic acids, and amino acids were found in the vacuole, the substrates for starch biosynthesis, hexose phosphates, and ATP were found in the plastid, while pyrophosphate was located almost exclusively in the cytosol (Farré et al., 2011). Similarly, in A. thaliana leaves, using NAF methods about 1,000 proteins and 70 metabolites, including 22 phosphorylated intermediates were separated into plastidial, cytosolic, and vacuolar metabolites and proteins which indicated that cytosolic, mitochondrial, and peroxisomal proteins clustered together. Metabolites from the Calvin–Benson cycle, photorespiration, starch and sucrose synthesis, glycolysis, and the tricarboxylic acid cycle grouped with their associated proteins of the respective compartment, indicating NAF as a powerful tool for the study of the organellar, and in some cases sub-organellar, distribution of proteins and their association with metabolites.
Unfortunately, organelles extracted from whole tissue homogenates are generally originated from a range of cell types (Bowsher and Tobin, 2001), but from specific organs such as leaves. However, the single largest study depicting the compartmentalized A. thaliana metabolome (Krueger et al., 2011), revealed the subcellular distribution of 1,117 polar and 2,804 lipophilic mass spectrometric features associated to known and unknown compounds. In conjunction with GC-MS and LC-MS-based metabolite profiling, 81.5% of the metabolic data could be associated to one of three subcellular compartments: the cytosol (including mitochondria), vacuole, or plastids. Nonetheless, the authors conceded that localizations of several known metabolites and structurally undetermined compounds (unknowns) were difficult to unambiguously explain on the basis of three compartments due to either unresolved compartments, or the interconnections of subcellular metabolic networks.
Advances in mass spectrometry based lipidomics have enabled the simultaneous identification and quantification of lipid species from complex structures at the tissue, cellular and organelle resolution levels (Horn and Chapman, 2012). The authors showed that at the nano scale, ‘direct organelle MS’ (DOMS) holds immense potential to profile lipids at the organelle level by extracting lipids from organelles in isolation, or from intact cells, within a capillary tip, followed by their identification and quantification using direct-infusion nanospray MS. Furthermore, it was underscored that fluorescent protein technology can be used to image subcellular dynamics of plant cell organelles at a spatial and temporal resolution, and to manipulate the distribution of fluorescent markers to identify the genes responsible for the inner activities of plant cells by means of light microscopy alongside genomics (Sparkes and Brandizzi, 2012).
Conclusion and future prospects
Although used in most instances, NAF is static, invasive, has no cellular resolution, and is sensitive to artifacts. (Looger et al., 2005), validation of NAF technique is understood to hold the key for successful implementation (Klie et al., 2011). Spectroscopic methods such as nuclear magnetic resonance (NMR) imaging and positron emission tomography (PET) provide dynamic data, but poor spatial resolution. Thus, genetically encoded fluorescence resonance energy transfer(FRET) sensors (i.e., green florescence protein (GFP)-based, enzyme based etc.) have been proposed for visualizing metabolites with subcellular resolution (Looger et al., 2005). Flux-balance modeling of plant metabolic networks provides an important complement to13C-based metabolic flux analysis. Recently, several flux-balance models of plant metabolism have been published including genome-scale models ofA. thaliana metabolism (Sweetlove and Ratcliffe, 2011). Approaches for flux balance analysis have been reviewed elsewhere (Lee et al., 2011; Lakshmanan et al., 2012). To achieve greater insights into metabolic fluxes across subcellular metabolomes several flux analyses tools are available, such as FiatFlux (Zamboni et al., 2005), OpenFLUX (Quek et al., 2009) that are based on 13C-based analysis, OptFlux (Rocha et al., 2010), FluxAnalyzer (Klamt et al., 2013), YANA (Schwarz et al., 2005). Model SEED, FAME, and MetaFlux have included several routines to facilitate the reconstruction of genome-scale metabolic models (Lakshmanan et al., 2012). NAF methods for obtaining subcellular fractions allows direct quenching of metabolism by snap-freezing in liquid nitrogen, thus, the combination of NAF with metabolic flux analysis using13C labeled CO2is a very attractive approach for the future (Keuger et al., 2012). On the other hand, MALDI associated secondary ion mass spectrometry (SIMS) imaging, on research-grade MALDI-MS instruments, MSI is possible with a spatial resolution of <1 μm at ambient pressures, and with very high mass accuracy and mass resolution which corresponds to subcellular resolution for many kinds of cells (Zenobi, 2013). These approaches certainly hold immense promises in pushing the field forward to decipher compartmentalized plant metabolomes.
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