Systematic analysis of Arabidopsis organelles and a protein localization database for facilitating fluorescent tagging of full-length Arabidopsis proteins

A New Method for Rapid Subcellular Localization and Gene Function Analysis in Cotton Based on Barley Stripe Mosaic Virus

The difficulty of genetic transformation has restricted research on functional genomics in cotton. Thus, a rapid and efficient method for gene overexpression that does not rely on genetic transformation is needed. Virus-based vectors offer a reasonable alternative for protein expression, as viruses can infect the host systemically to achieve expression and replication without transgene integration. Previously, a novel four-component barley stripe mosaic virus (BSMV) was reported to overexpress large fragments of target genes in plants over a long period of time, which greatly simplified the study of gene overexpression. However, whether this system can infect cotton and stably overexpress target genes has not yet been studied. In this study, we verified that this new BSMV system can infect cotton through seed imbibition and systemically overexpress large fragments of genes (up to 2340 bp) in cotton. The target gene that was fused with GFP was expressed at a high level in the roots, stems, and cotyledons of cotton seedlings, and stable fluorescence signals were detected in the cotton roots and leaves even after 4 weeks. Based on the BSMV overexpression system, the subcellular localization marker line of endogenous proteins localized in the nucleus, endoplasmic reticulum, plasma membrane, Golgi body, mitochondria, peroxisomes, tonoplast, and plastids were quickly established. The overexpression of a cotton Bile Acid Sodium Symporter GhBASS5 using the BSMV system indicated that GhBASS5 negatively regulated salt tolerance in cotton by transporting Na⁺ from underground to the shoots. Furthermore, multiple proteins were co-delivered, enabling co-localization and the study of protein–protein interactions through co-transformation. We also confirmed that the BSMV system can be used to conduct DNA-free gene editing in cotton by delivering split-SpCas9/sgRNA. Ultimately, the present work demonstrated that this BSMV system could be used as an efficient overexpression system for future cotton gene function research.

Virtual 2-D map of the fungal proteome

The molecular weight and isoelectric point (pI) of the proteins plays important role in the cell. Depending upon the shape, size, and charge, protein provides its functional role in different parts of the cell. Therefore, understanding to the knowledge of their molecular weight and charges is (pI) is very important. Therefore, we conducted a proteome-wide analysis of protein sequences of 689 fungal species (7.15 million protein sequences) and construct a virtual 2-D map of the fungal proteome. The analysis of the constructed map revealed the presence of a bimodal distribution of fungal proteomes. The molecular mass of individual fungal proteins ranged from 0.202 to 2546.166 kDa and the predicted isoelectric point (pI) ranged from 1.85 to 13.759 while average molecular weight of fungal proteome was 50.98 kDa. A non-ribosomal peptide synthase (RFU80400.1) found in Trichoderma arundinaceum was identified as the largest protein in the fungal kingdom. The collective fungal proteome is dominated by the presence of acidic rather than basic pI proteins and Leu is the most abundant amino acid while Cys is the least abundant amino acid. Aspergillus ustus encodes the highest percentage (76.62%) of acidic pI proteins while Nosema ceranae was found to encode the highest percentage (66.15%) of basic pI proteins. Selenocysteine and pyrrolysine amino acids were not found in any of the analysed fungal proteomes. Although the molecular weight and pI of the protein are of enormous important to understand their functional roles, the amino acid compositions of the fungal protein will enable us to understand the synonymous codon usage in the fungal kingdom. The small peptides identified during the study can provide additional biotechnological implication.

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins imply that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred.

Visualization of Multicolored in vivo Organelle Markers for Co-Localization Studies in Oryza sativa

Eukaryotic cells consist of a complex network of thousands of proteins present in different organelles where organelle-specific cellular processes occur. Identification of the subcellular localization of a protein is important for understanding its potential biochemical functions. In the post-genomic era, localization of unknown proteins is achieved using multiple tools including a fluorescent-tagged protein approach. Several fluorescent-tagged protein organelle markers have been introduced into dicot plants, but its use is still limited in monocot plants. Here, we generated a set of multicolored organelle markers (fluorescent-tagged proteins) based on well-established targeting sequences. We used a series of pGWBs binary vectors to ameliorate localization and co-localization experiments using monocot plants. We constructed different fluorescent-tagged markers to visualize rice cell organelles, i.e., nucleus, plastids, mitochondria, peroxisomes, golgi body, endoplasmic reticulum, plasma membrane, and tonoplast, with four different fluorescent proteins (FPs) (G3GFP, mRFP, YFP, and CFP). Visualization of FP-tagged markers in their respective compartments has been reported for dicot and monocot plants. The comparative localization of the nucleus marker with a nucleus localizing sequence, and the similar, characteristic morphology of mCherry-tagged Arabidopsis organelle markers and our generated organelle markers in onion cells, provide further evidence for the correct subcellular localization of the Oryza sativa (rice) organelle marker. The set of eight different rice organelle markers with four different FPs provides a valuable resource for determining the sub-cellular localization of newly identified proteins, conducting co-localization assays, and generating stable transgenic localization in monocot plants.

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

There remain genes with no known function even in the most well studied, model species. One common way to hypothesize gene function is based on the assumption that genes with similar expression profiles tend to have similar functions. However, using datasets and biological pathway information from the model plant Arabidopsis thaliana as an example, we discovered that, although genes in the same pathways are functionally related, genes in only a subset of the pathways have highly similar expression patterns. In addition, our ability to hypothesize gene functions based on expression is significantly impacted by how the dataset is processed and combined as well as the methodology used to identify genes with similar expression. Therefore, multiple datasets and methods should be tested to maximize the functional information that we can get based on similarity in gene expression.

Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier

Predicting protein subcellular location is necessary for understanding cell function. Several machine learning methods have been developed for computational prediction of primary protein sequences because wet experiments are costly and time consuming. However, two problems still exist in state-of-the-art methods. First, several proteins appear in different subcellular structures simultaneously, whereas current methods only predict one protein sequence in one subcellular structure. Second, most software tools are trained with obsolete data and the latest new databases are missed. We proposed a novel multi-label classification algorithm to solve the first problem and integrated several latest databases to improve prediction performance. Experiments proved the effectiveness of the proposed method. The present study would facilitate research on cellular proteomics.

Systemic Immunity Requires SnRK2.8-Mediated Nuclear Import of NPR1 in Arabidopsis

In plants, necrotic lesions occur at the site of pathogen infection through the hypersensitive response, which is followed by induction of systemic acquired resistance (SAR) in distal tissues. Salicylic acid (SA) induces SAR by activating NONEXPRESSER OF PATHOGENESIS-RELATED GENES1 (NPR1) through an oligomer-to-monomer reaction. However, SA biosynthesis is elevated only slightly in distal tissues during SAR, implying that SA-mediated induction of SAR requires additional factors. Here, we demonstrated that SA-independent systemic signals induce a gene encoding SNF1-RELATED PROTEIN KINASE 2.8 (SnRK2.8), which phosphorylates NPR1 during SAR. The SnRK2.8-mediated phosphorylation of NPR1 is necessary for its nuclear import. Notably, although SnRK2.8 transcription and SnRK2.8 activation are independent of SA signaling, the SnRK2.8-mediated induction of SAR requires SA. Together with the SA-mediated monomerization of NPR1, these observations indicate that SA signals and SnRK2.8-mediated phosphorylation coordinately function to activate NPR1 via a dual-step process in developing systemic immunity in Arabidopsis thaliana.

The similarity between N-terminal targeting signals for protein import into different organelles and its evolutionary relevance

The proper distribution of proteins between the cytosol and various membrane-bound compartments is crucial for the functionality of eukaryotic cells. This requires the cooperation between protein transport machineries that translocate diverse proteins from the cytosol into these compartments and targeting signal(s) encoded within the primary sequence of these proteins that define their cellular destination. The mechanisms exerting protein translocation differ remarkably between the compartments, but the predominant targeting signals for mitochondria, chloroplasts and the ER share the N-terminal position, an α-helical structural element and the removal from the core protein by intraorganellar cleavage. Interestingly, similar properties have been described for the peroxisomal targeting signal type 2 mediating the import of a fraction of soluble peroxisomal proteins, whereas other peroxisomal matrix proteins encode the type 1 targeting signal residing at the extreme C-terminus. The structural similarity of N-terminal targeting signals poses a challenge to the specificity of protein transport, but allows the generation of ambiguous targeting signals that mediate dual targeting of proteins into different compartments. Dual targeting might represent an advantage for adaptation processes that involve a redistribution of proteins, because it circumvents the hierarchy of targeting signals. Thus, the co-existence of two equally functional import pathways into peroxisomes might reflect a balance between evolutionary constant and flexible transport routes.

A maize database resource that captures tissue-specific and subcellular-localized gene expression, via fluorescent tags and confocal imaging (Maize Cell Genomics Database)

Maize is a global crop and a powerful system among grain crops for genetic and genomic studies. However, the development of novel biological tools and resources to aid in the functional identification of gene sequences is greatly needed. Towards this goal, we have developed a collection of maize marker lines for studying native gene expression in specific cell types and subcellular compartments using fluorescent proteins (FPs). To catalog FP expression, we have developed a public repository, the Maize Cell Genomics (MCG) Database, (http://maize.jcvi.org/cellgenomics), to organize a large data set of confocal images generated from the maize marker lines. To date, the collection represents major subcellular structures and also developmentally important progenitor cell populations. The resource is available to the research community, for example to study protein localization or interactions under various experimental conditions or mutant backgrounds. A subset of the marker lines can also be used to induce misexpression of target genes through a transactivation system. For future directions, the image repository can be expanded to accept new image submissions from the research community, and to perform customized large-scale computational image analysis. This community resource will provide a suite of new tools for gaining biological insights by following the dynamics of protein expression at the subcellular, cellular and tissue levels.

The Plant Organelles Database 3 (PODB3) update 2014: integrating electron micrographs and new options for plant organelle research

The Plant Organelles Database 2 (PODB2), which was first launched in 2006 as PODB, provides static image and movie data of plant organelles, protocols for plant organelle research and external links to relevant websites. PODB2 has facilitated plant organellar research and the understanding of plant organelle dynamics. To provide comprehensive information on plant organelles in more detail, PODB2 was updated to PODB3 (http://podb.nibb.ac.jp/Organellome/). PODB3 contains two additional components: the electron micrograph database and the perceptive organelles database. Through the electron micrograph database, users can examine the subcellular and/or suborganellar structures in various organs of wild-type and mutant plants. The perceptive organelles database provides information on organelle dynamics in response to external stimuli. In addition to the extra components, the user interface for access has been enhanced in PODB3. The data in PODB3 are directly submitted by plant researchers and can be freely downloaded for use in further analysis. PODB3 contains all the information included in PODB2, and the volume of data and protocols deposited in PODB3 continue to grow steadily. We welcome contributions of data from all plant researchers to enhance the utility and comprehensiveness of PODB3.

Using the SUBcellular database for Arabidopsis proteins to localize the Deg protease family

Sub-functionalization during the expansion of gene families in eukaryotes has occurred in part through specific subcellular localization of different family members. To better understand this process in plants, compiled records of large-scale proteomic and fluorescent protein localization datasets can be explored and bioinformatic predictions for protein localization can be used to predict the gaps in experimental data. This process can be followed by targeted experiments to test predictions. The SUBA3 database is a free web-service at http://suba.plantenergy.uwa.edu.au that helps users to explore reported experimental data and predictions concerning proteins encoded by gene families and to define the experiments required to locate these homologous sets of proteins. Here we show how SUBA3 can be used to explore the subcellular location of the Deg protease family of ATP-independent serine endopeptidases (Deg1-Deg16). Combined data integration and new experiments refined location information for Deg1 and Deg9, confirmed Deg2, Deg5, and Deg8 in plastids and Deg 15 in peroxisomes and provide substantial experimental evidence for mitochondrial localized Deg proteases. Two of these, Deg3 and Deg10, additionally localized to the plastid, revealing novel dual-targeted Deg proteases in the plastid and the mitochondrion. SUBA3 is continually updated to ensure that researchers can use the latest published data when planning the experimental steps remaining to localize gene family functions.

The spatial organization of metabolism within the plant cell

Identifying the correct subcellular locations for all enzymes and metabolites in plant metabolic networks is a major challenge, but is critically important for the success of the new generation of large-scale metabolic models that are driving a network-level appreciation of metabolic behavior. Even though the subcellular compartmentation of many central metabolic processes is thought to be well understood, recent gene-by-gene studies have revealed several unexpected enzyme localizations. Metabolite transport between subcellular compartments is crucial because it fundamentally affects the metabolic network structure. Although new metabolite transporters are being steadily identified, modeling work suggests that we have barely scratched the surface of the catalog of intracellular metabolite transporter proteins. In addition to compartmentation among organelles, it is increasingly apparent that microcompartment formation via the interactions of enzyme groups with intracellular membranes, the cytoskeleton, or other proteins is an important regulatory mechanism. In particular, this mechanism can promote metabolite channeling within the metabolic microcompartment, which can help control reaction specificity as well as dictate flux routes through the network. This has clear relevance for both synthetic biology in general and the engineering of plant metabolic networks in particular.

Phylogenomic study of lipid genes involved in microalgal biofuel production-candidate gene mining and metabolic pathway analyses

Optimizing microalgal biofuel production using metabolic engineering tools requires an in-depth understanding of the structure-function relationship of genes involved in lipid biosynthetic pathway. In the present study, genome-wide identification and characterization of 398 putative genes involved in lipid biosynthesis in Arabidopsis thaliana Chlamydomonas reinhardtii, Volvox carteri, Ostreococcus lucimarinus, Ostreococcus tauri and Cyanidioschyzon merolae was undertaken on the basis of their conserved motif/domain organization and phylogenetic profile. The results indicated that the core lipid metabolic pathways in all the species are carried out by a comparable number of orthologous proteins. Although the fundamental gene organizations were observed to be invariantly conserved between microalgae and Arabidopsis genome, with increased order of genome complexity there seems to be an association with more number of genes involved in triacylglycerol (TAG) biosynthesis and catabolism. Further, phylogenomic analysis of the genes provided insights into the molecular evolution of lipid biosynthetic pathway in microalgae and confirm the close evolutionary proximity between the Streptophyte and Chlorophyte lineages. Together, these studies will improve our understanding of the global lipid metabolic pathway and contribute to the engineering of regulatory networks of algal strains for higher accumulation of oil.

A set of GFP organelle marker lines for intracellular localization studies in Medicago truncatula

Genomics advances in the model legume, Medicago truncatula, have led to an increase in the number of identified genes encoding proteins with unknown biological function. Determining the intracellular location of uncharacterized proteins often aids in the elucidation of biological function. To expedite such localization studies, we have generated a set of intracellular organelle green fluorescence protein (GFP) marker lines in M. truncatula. In addition to fluorescent detection, this set of organelle marker lines can also be used in immunohistochemical and cellular fractionation detection assays. Moreover, this set of marker lines is compatible with both transient and stable expression systems. Thus, this marker set should prove to be a useful resource for the M. truncatula research community.

Statistical organelle dissection of Arabidopsis guard cells using image database LIPS

To comprehensively grasp cell biological events in plant stomatal movement, we have captured microscopic images of guard cells with various organelles markers. The 28,530 serial optical sections of 930 pairs of Arabidopsis guard cells have been released as a new image database, named Live Images of Plant Stomata (LIPS). We visualized the average organellar distributions in guard cells using probabilistic mapping and image clustering techniques. The results indicated that actin microfilaments and endoplasmic reticulum (ER) are mainly localized to the dorsal side and connection regions of guard cells. Subtractive images of open and closed stomata showed distribution changes in intracellular structures, including the ER, during stomatal movement. Time-lapse imaging showed that similar ER distribution changes occurred during stomatal opening induced by light irradiation or femtosecond laser shots on neighboring epidermal cells, indicating that our image analysis approach has identified a novel ER relocation in stomatal opening.

HGPD: Human Gene and Protein Database, 2012 update

The Human Gene and Protein Database (HGPD; http://www.HGPD.jp/) is a unique database that stores information on a set of human Gateway entry clones in addition to protein expression and protein synthesis data. The HGPD was launched in November 2008, and 33,275 human Gateway entry clones have been constructed from the open reading frames (ORFs) of full-length cDNA, thus representing the largest collection in the world. Recently, research objectives have focused on the development of new medicines and the establishment of novel diagnostic methods and medical treatments. And, studies using proteins and protein information, which are closely related to gene function, have been undertaken. For this update, we constructed an additional 9974 human Gateway entry clones, giving a total of 43,249. This set of human Gateway entry clones was named the Human Proteome Expression Resource, known as the 'HuPEX'. In addition, we also classified the clones into 10 groups according to protein function. Moreover, in vivo cellular localization data of proteins for 32,651 human Gateway entry clones were included for retrieval from the HGPD. In 'Information Overview', which presents the search results, the ORF region of each cDNA is now displayed allowing the Gateway entry clones to be searched more easily.

The Plant Organelles Database 2 (PODB2): an updated resource containing movie data of plant organelle dynamics

The Plant Organelles Database (PODB) was launched in 2006 and provides imaging data of plant organelles, protocols for plant organelle research and external links to relevant websites. To provide comprehensive information on plant organelle dynamics and accommodate movie files that contain time-lapse images and 3D structure rotations, PODB was updated to the next version, PODB2 (http://podb.nibb.ac.jp/Organellome). PODB2 contains movie data submitted directly by plant researchers and can be freely downloaded. Through this organelle movie database, users can examine the dynamics of organelles of interest, including their movement, division, subcellular positioning and behavior, in response to external stimuli. In addition, the user interface for access and submission has been enhanced. PODB2 contains all of the information included in PODB, and the volume of data and protocols deposited in the PODB2 continues to grow steadily. Moreover, a new website, Plant Organelles World (http://podb.nibb.ac.jp/Organellome/PODBworld/en/index.html), which is based on PODB2, was recently launched as an educational tool to engage members of the non-scientific community such as students and school teachers. Plant Organelles World is written in layman's terms, and technical terms were avoided where possible. We would appreciate contributions of data from all plant researchers to enhance the usefulness of PODB2 and Plant Organelles World.

Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.

Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis

A complete map of the Arabidopsis (Arabidopsis thaliana) proteome is clearly a major goal for the plant research community in terms of determining the function and regulation of each encoded protein. Developing genome-wide prediction tools such as for localizing gene products at the subcellular level will substantially advance Arabidopsis gene annotation. To this end, we performed a comprehensive study in Arabidopsis and created an integrative support vector machine-based localization predictor called AtSubP (for Arabidopsis subcellular localization predictor) that is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. When used to predict seven subcellular compartments through a 5-fold cross-validation test, our hybrid-based best classifier achieved an overall sensitivity of 91% with high-confidence precision and Matthews correlation coefficient values of 90.9% and 0.89, respectively. Benchmarking AtSubP on two independent data sets, one from Swiss-Prot and another containing green fluorescent protein- and mass spectrometry-determined proteins, showed a significant improvement in the prediction accuracy of species-specific AtSubP over some widely used "general" tools such as TargetP, LOCtree, PA-SUB, MultiLoc, WoLF PSORT, Plant-PLoc, and our newly created All-Plant method. Cross-comparison of AtSubP on six nontrained eukaryotic organisms (rice [Oryza sativa], soybean [Glycine max], human [Homo sapiens], yeast [Saccharomyces cerevisiae], fruit fly [Drosophila melanogaster], and worm [Caenorhabditis elegans]) revealed inferior predictions. AtSubP significantly outperformed all the prediction tools being currently used for Arabidopsis proteome annotation and, therefore, may serve as a better complement for the plant research community. A supplemental Web site that hosts all the training/testing data sets and whole proteome predictions is available at http://bioinfo3.noble.org/AtSubP/.

Illuminating plant biology: using fluorescent proteins for high-throughput analysis of protein localization and function in plants

First discovered in jellyfish, fluorescent proteins (FPs) have been successfully optimized for use as effective biomarkers within living plant cells. When exposed to light, FPs fused to a protein or regulatory element will fluoresce, and non-invasively mark expression and protein localization, which allows for the in vivo monitoring of diverse cellular processes. In this review, we discuss how FP technology has evolved from small-scale analysis of individual genes to more high-throughput techniques for global expression and functional profiling in plants.

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

Diversity in subcellular targeting of the PP2A B'eta subfamily members

Protein phosphatase 2A (PP2A) is a serine/threonine-specific phosphatase comprising a catalytic subunit (C), a scaffolding subunit (A), and a regulatory subunit (B). The B subunits are believed to be responsible for substrate specificity and localization of the PP2A complex. In plants, three families of B subunits exist, i.e. B (B55), B', and B''. Here, we report differential subcellular targeting within the Arabidopsis B'eta subfamily, which consists of the close homologs B'eta, B'theta, B'gamma and B'zeta. Phenotypes of corresponding knockouts were observed, and particularly revealed delayed flowering for the B'eta knockout. The B' subunits were linked to fluorescent tags and transiently expressed in various tissues of onion, tobacco and Arabidopsis. B'eta and B'gamma targeted the cytosol and nucleus. B'zeta localized to the cytoplasm and partly co-localized with mitochondrial markers when the N-terminus was free. Provided its C-terminus was free, the B'theta subunit targeted peroxisomes. The importance of the C-terminal end for peroxisomal targeting was further confirmed by truncation of the C-terminus. The results revealed that the closely related B' subunits are targeting different organelles in plants, and exemplify the usage of the peptide serine-serine-leucine as a PTS1 peroxisomal signaling peptide.

Whole-organism screening: plants

The small plant Arabidopsis thaliana has been an indispensable tool for plant biologists working in fields that utilize cell biology, molecular biology, and genetics; these topics are almost universal in plant biology studies, ranging from genomics to ecology. In this chapter, we present a start-to-finish approach to high-throughput imaging of Arabidopsis that caters to two different audiences: those who are working with plants for the first time, and plant scientists looking to the apply high-throughput imaging to existing projects.

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.

Overexpression of the Arabidopsis 10-kilodalton acyl-coenzyme A-binding protein ACBP6 enhances freezing tolerance

Small 10-kD acyl-coenzyme A-binding proteins (ACBPs) are highly conserved proteins that are prevalent in eukaryotes. In Arabidopsis (Arabidopsis thaliana), other than the 10-kD ACBP homolog (designated Arabidopsis ACBP6), there are five larger forms of ACBPs ranging from 37.5 to 73.1 kD. In this study, the cytosolic subcellular localization of Arabidopsis ACBP6 was confirmed by analyses of transgenic Arabidopsis expressing autofluorescence-tagged ACBP6 and western-blot analysis of subcellular fractions using ACBP6-specific antibodies. The expression of Arabidopsis ACBP6 was noticeably induced at 48 h after 4 degrees C treatment by northern-blot analysis and western-blot analysis. Furthermore, an acbp6 T-DNA insertional mutant that lacked ACBP6 mRNA and protein displayed increased sensitivity to freezing temperature (-8 degrees C), while ACBP6-overexpressing transgenic Arabidopsis plants were conferred enhanced freezing tolerance. Northern-blot analysis indicated that ACBP6-associated freezing tolerance was not dependent on the induction of cold-regulated COLD-RESPONSIVE gene expression. Instead, ACBP6 overexpressors showed increased expression of mRNA encoding phospholipase Ddelta. Lipid profiling analyses of rosettes from cold-acclimated, freezing-treated (-8 degrees C) transgenic Arabidopsis plants overexpressing ACBP6 showed a decline in phosphatidylcholine (-36% and -46%) and an elevation of phosphatidic acid (73% and 67%) in comparison with wild-type plants. From our comparison, the gain in freezing tolerance in ACBP6 overexpressors that was accompanied by decreases in phosphatidylcholine and an accumulation of phosphatidic acid is consistent with previous findings on phospholipase Ddelta-overexpressing transgenic Arabidopsis. In vitro filter-binding assays indicating that histidine-tagged ACBP6 binds phosphatidylcholine, but not phosphatidic acid or lysophosphatidylcholine, further imply a role for ACBP6 in phospholipid metabolism in Arabidopsis, including the possibility of ACBP6 in the cytosolic trafficking of phosphatidylcholine.

The plant organelles database (PODB): a collection of visualized plant organelles and protocols for plant organelle research

The plant organelles database (PODB; http://podb.nibb.ac.jp/Organellome) was built to promote a comprehensive understanding of organelle dynamics, including organelle function, biogenesis, differentiation, movement and interactions with other organelles. This database consists of three individual parts, the organellome database, the functional analysis database and external links to other databases and homepages. The organellome database provides images of various plant organelles that were visualized with fluorescent and nonfluorescent probes in various tissues of several plant species at different developmental stages. The functional analysis database is a collection of protocols for plant organelle research. External links give access primarily to other databases and Web pages with information on transcriptomes and proteomes. All the data and protocols in the organellome database and the functional analysis database are populated by direct submission of experimentally determined data from plant researchers and can be freely downloaded. Our database promotes the exchange of information between plant organelle researchers for the comprehensive study of the organelle dynamics that support integrated functions in higher plants. We would also appreciate contributions of data and protocols from all plant researchers to maximize the usefulness of the database.

Plant organelle proteomics

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.

The illuminated plant cell

The past decade has provided biologists with a palette of genetically encoded, multicolored fluorescent proteins. The living plant cell turned into a 'coloring book' and today, nearly every text-book organelle has been highlighted in scintillating fluorescent colors. This review provides a concise listing of the earliest representative fluorescent-protein probes used to highlight various targets within the plant cell, and introduces the idea of using the numerous multicolor, subcellular probes for the development of an early intracellular response profile of plants.

A multicolored set of in vivo organelle markers for co-localization studies in Arabidopsis and other plants

Genome sequencing has resulted in the identification of a large number of uncharacterized genes with unknown functions. It is widely recognized that determination of the intracellular localization of the encoded proteins may aid in identifying their functions. To facilitate these localization experiments, we have generated a series of fluorescent organelle markers based on well-established targeting sequences that can be used for co-localization studies. In particular, this organelle marker set contains indicators for the endoplasmic reticulum, the Golgi apparatus, the tonoplast, peroxisomes, mitochondria, plastids and the plasma membrane. All markers were generated with four different fluorescent proteins (FP) (green, cyan, yellow or red FPs) in two different binary plasmids for kanamycin or glufosinate selection, respectively, to allow for flexible combinations. The labeled organelles displayed characteristic morphologies consistent with previous descriptions that could be used for their positive identification. Determination of the intracellular distribution of three previously uncharacterized proteins demonstrated the usefulness of the markers in testing predicted subcellular localizations. This organelle marker set should be a valuable resource for the plant community for such co-localization studies. In addition, the Arabidopsis organelle marker lines can also be employed in plant cell biology teaching labs to demonstrate the distribution and dynamics of these organelles.

PSITE vectors for stable integration or transient expression of autofluorescent protein fusions in plants: probing Nicotiana benthamiana-virus interactions

Plant functional proteomics research is increasingly dependent upon vectors that facilitate high-throughput gene cloning and expression of fusions to autofluorescent proteins. Here, we describe the pSITE family of plasmids, a new set of Agrobacterium binary vectors, suitable for the stable integration or transient expression of various autofluorescent protein fusions in plant cells. The pSITE vectors permit single-step Gateway-mediated recombination cloning for construction of binary vectors that can be used directly in transient expression studies or for the selection of transgenic plants on media containing kanamycin. These vectors can be used to express native proteins or fusions to monmeric red fluorescent protein or the enhanced green fluorescent protein and its cyan and yellow-shifted spectral variants. We have validated the vectors for use in transient expression assays and for the generation of transgenic plants. Additionally, we have generated markers for fluorescent highlighting of actin filaments, chromatin, endoplasmic reticulum, and nucleoli. Finally, we show that pSITE vectors can be used for targeted gene expression in virus-infected cells, which should facilitate high-throughput characterization of protein dynamics in host-virus interactions.

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).