Integrative systems biology: an attempt to describe a simple weed

Genome-scale studies hold great promise for revealing novel plant biology. Because of the complexity of these techniques, numerous considerations need to be made before embarking on a study. Here we focus on the Arabidopsis model system because of the wealth of available genome-scale data. Many approaches are available that provide genome-scale information regarding the state of a given organism (e.g. genomics, epigenomics, transcriptomics, proteomics, metabolomics interactomics, ionomics, phenomics, etc.). Integration of all of these types of data will be necessary for a comprehensive description of Arabidopsis. In this review we propose that 'triangulation' among transcriptomics, proteomics and metabolomics is a meaningful approach for beginning this integrative analysis and uncovering a systems level perspective of Arabidopsis biology.

The protein expression landscape of the Arabidopsis root

Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein-protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.

Integrated sequence-structure motifs suffice to identify microRNA precursors

Background

Upwards of 1200 miRNA loci have hitherto been annotated in the human genome. The specific features defining a miRNA precursor and deciding its recognition and subsequent processing are not yet exhaustively described and miRNA loci can thus not be computationally identified with sufficient confidence.

Results

We rendered pre-miRNA and non-pre-miRNA hairpins as strings of integrated sequence-structure information, and used the software Teiresias to identify sequence-structure motifs (ss-motifs) of variable length in these data sets. Using only ss-motifs as features in a Support Vector Machine (SVM) algorithm for pre-miRNA identification achieved 99.2% specificity and 97.6% sensitivity on a human test data set, which is comparable to previously published algorithms employing combinations of sequence-structure and additional features. Further analysis of the ss-motif information contents revealed strongly significant deviations from those of the respective training sets, revealing important potential clues as to how the sequence and structural information of RNA hairpins are utilized by the miRNA processing apparatus.

Conclusion

Integrated sequence-structure motifs of variable length apparently capture nearly all information required to distinguish miRNA precursors from other stem-loop structures.

Known and novel post-transcriptional regulatory sequences are conserved across plant families

The sequence elements that mediate post-transcriptional gene regulation often reside in the 5' and 3' untranslated regions (UTRs) of mRNAs. Using six different families of dicotyledonous plants, we developed a comparative transcriptomics pipeline for the identification and annotation of deeply conserved regulatory sequences in the 5' and 3' UTRs. Our approach was robust to confounding effects of poor UTR alignability and rampant paralogy in plants. In the 3' UTR, motifs resembling PUMILIO-binding sites form a prominent group of conserved motifs. Additionally, Expansins, one of the few plant mRNA families known to be localized to specific subcellular sites, possess a core conserved RCCCGC motif. In the 5' UTR, one major subset of motifs consists of purine-rich repeats. A distinct and substantial fraction possesses upstream AUG start codons. Half of the AUG containing motifs reveal hidden protein-coding potential in the 5' UTR, while the other half point to a peptide-independent function related to translation. Among the former, we added four novel peptides to the small catalog of conserved-peptide uORFs. Among the latter, our case studies document patterns of uORF evolution that include gain and loss of uORFs, switches in uORF reading frame, and switches in uORF length and position. In summary, nearly three hundred post-transcriptional elements show evidence of purifying selection across the eudicot branch of flowering plants, indicating a regulatory function spanning at least 70 million years. Some of these sequences have experimental precedent, but many are novel and encourage further exploration.

Translation initiation factor AteIF(iso)4E is involved in selective mRNA translation in Arabidopsis thaliana seedlings

One of the most regulated steps of translation initiation is the recruitment of mRNA by the translation machinery. In eukaryotes, this step is mediated by the 5′end cap-binding factor eIF4E bound to the bridge protein eIF4G and forming the eIF4F complex. In plants, different isoforms of eIF4E and eIF4G form the antigenically distinct eIF4F and eIF(iso)4F complexes proposed to mediate selective translation. Using a microarray analysis of polyribosome- and non-polyribosome-purified mRNAs from 15 day-old Arabidopsis thaliana wild type [WT] and eIF(iso)4E knockout mutant [(iso)4E-1] seedlings we found 79 transcripts shifted from polyribosomes toward non-polyribosomes, and 47 mRNAs with the opposite behavior in the knockout mutant. The translationally decreased mRNAs were overrepresented in root-preferentially expressed genes and proteins from the endomembrane system, including several transporters such as the phosphate transporter PHOSPHATE1 (PHO1), Sucrose transporter 3 (SUC3), ABC transporter-like with ATPase activity (MRP11) and five electron transporters, as well as signal transduction-, protein modification- and transcription-related proteins. Under normal growth conditions, eIF(iso)4E expression under the constitutive promoter 35 S enhanced the polyribosomal recruitment of PHO1 supporting its translational preference for eIF(iso)4E. Furthermore, under phosphate deficiency, the PHO1 protein increased in the eIF(iso)4E overexpressing plants and decreased in the knockout mutant as compared to wild type. In addition, the knockout mutant had larger root, whereas the 35 S directed expression of eIF(iso)4E caused shorter root under normal growth conditions, but not under phosphate deficiency. These results indicate that selective translation mediated by eIF(iso)4E is relevant for Arabidopsis root development under normal growth conditions.

Positional correlation analysis improves reconstruction of full-length transcripts and alternative isoforms from noisy array signals or short reads

Motivation: A reconstruction of full-length transcripts observed by next-generation sequencer or tiling arrays is an essential technique to know all phenomena of transcriptomes. Several techniques of the reconstruction have been developed. However, problems of high-level noises and biases still remain and interrupt the reconstruction. A method is required that is robust against noise and bias and correctly reconstructs transcripts regardless of equipment used.

Results: We propose a completely new statistical method that reconstructs full-length transcripts and can be applied on both next-generation sequencers and tiling arrays. The method called ARTADE2 analyzes ‘positional correlation’, meaning correlations of expression values for every combination on genomic positions of multiple transcriptional data. ARTADE2 then reconstructs full-length transcripts using a logistic model based on the positional correlation and the Markov model. ARTADE2 elucidated 17 591 full-length transcripts from 55 transcriptome datasets and showed notable performance compared with other recent prediction methods. Moreover, 1489 novel transcripts were discovered. We experimentally tested 16 novel transcripts, among which 14 were confirmed by reverse transcription–polymerase chain reaction and sequence mapping. The method also showed notable performance for reconstructing of mRNA observed by a next-generation sequencer. Moreover, the positional correlation and factor analysis embedded in ARTADE2 successfully detected regions at which alternative isoforms may exist, and thus are expected to be applied for discovering transcript biomarkers for a wide range of disciplines including preemptive medicine.

Availability:http://matome.base.riken.jp

Contact:toyoda@base.riken.jp

Supplementary information:Supplementary data are available at Bioinformatics online.

Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments. However, traditional antibody-based approaches are not optimal for analyzing large numbers of protein abundances and modifications across many samples. In this article, we will review recent advances in methodologies and philosophies behind several microarray-based, intermediate-level, 'protein-omic' methods, including a focus on reverse-phase lysate arrays and micro-western arrays, which have been helpful for bridging gaps between large- and small-scale protein analysis approaches and have provided insight into the roles that protein systems play in several biological processes.

Comparative large scale characterization of plant versus mammal proteins reveals similar and idiosyncratic N-α-acetylation features

N-terminal modifications play a major role in the fate of proteins in terms of activity, stability, or subcellular compartmentalization. Such modifications remain poorly described and badly characterized in proteomic studies, and only a few comparison studies among organisms have been made available so far. Recent advances in the field now allow the enrichment and selection of N-terminal peptides in the course of proteome-wide mass spectrometry analyses. These targeted approaches unravel as a result the extent and nature of the protein N-terminal modifications. Here, we aimed at studying such modifications in the model plant Arabidopsis thaliana to compare these results with those obtained from a human sample analyzed in parallel. We applied large scale analysis to compile robust conclusions on both data sets. Our data show strong convergence of the characterized modifications especially for protein N-terminal methionine excision, co-translational N-α-acetylation, or N-myristoylation between animal and plant kingdoms. Because of the convergence of both the substrates and the N-α-acetylation machinery, it was possible to identify the N-acetyltransferases involved in such modifications for a small number of model plants. Finally, a high proportion of nuclear-encoded chloroplast proteins feature post-translational N-α-acetylation of the mature protein after removal of the transit peptide. Unlike animals, plants feature in a dedicated pathway for post-translational acetylation of organelle-targeted proteins. The corresponding machinery is yet to be discovered.

Determining degradation and synthesis rates of arabidopsis proteins using the kinetics of progressive 15N labeling of two-dimensional gel-separated protein spots

The growth and development of plant tissues is associated with an ordered succession of cellular processes that are reflected in the appearance and disappearance of proteins. The control of the kinetics of protein turnover is central to how plants can rapidly and specifically alter protein abundance and thus molecular function in response to environmental or developmental cues. However, the processes of turnover are largely hidden during periods of apparent steady-state protein abundance, and even when proteins accumulate it is unclear whether enhanced synthesis or decreased degradation is responsible. We have used a (15)N labeling strategy with inorganic nitrogen sources coupled to a two-dimensional fluorescence difference gel electrophoresis and mass spectrometry analysis of two-dimensional IEF/SDS-PAGE gel spots to define the rate of protein synthesis (K(S)) and degradation (K(D)) of Arabidopsis cell culture proteins. Through analysis of MALDI-TOF/TOF mass spectra from 120 protein spots, we were able to quantify K(S) and K(D) for 84 proteins across six functional groups and observe over 65-fold variation in protein degradation rates. K(S) and K(D) correlate with functional roles of the proteins in the cell and the time in the cell culture cycle. This approach is based on progressive (15)N labeling that is innocuous for the plant cells and, because it can be used to target analysis of proteins through the use of specific gel spots, it has broad applicability.

Gel-based and gel-free quantitative proteomics approaches at a glance

Two-dimensional gel electrophoresis (2-DE) is widely applied and remains the method of choice in proteomics; however, pervasive 2-DE-related concerns undermine its prospects as a dominant separation technique in proteome research. Consequently, the state-of-the-art shotgun techniques are slowly taking over and utilising the rapid expansion and advancement of mass spectrometry (MS) to provide a new toolbox of gel-free quantitative techniques. When coupled to MS, the shotgun proteomic pipeline can fuel new routes in sensitive and high-throughput profiling of proteins, leading to a high accuracy in quantification. Although label-based approaches, either chemical or metabolic, gained popularity in quantitative proteomics because of the multiplexing capacity, these approaches are not without drawbacks. The burgeoning label-free methods are tag independent and suitable for all kinds of samples. The challenges in quantitative proteomics are more prominent in plants due to difficulties in protein extraction, some protein abundance in green tissue, and the absence of well-annotated and completed genome sequences. The goal of this perspective assay is to present the balance between the strengths and weaknesses of the available gel-based and -free methods and their application to plants. The latest trends in peptide fractionation amenable to MS analysis are as well discussed.

The seed proteome web portal

The Seed Proteome Web Portal (SPWP; http://www.seed-proteome.com/) gives access to information both on quantitative seed proteomic data and on seed-related protocols. Firstly, the SPWP provides access to the 475 different Arabidopsis seed proteins annotated from two dimensional electrophoresis (2DE) maps. Quantitative data are available for each protein according to their accumulation profile during the germination process. These proteins can be retrieved either in list format or directly on scanned 2DE maps. These proteomic data reveal that 40% of seed proteins maintain a stable abundance over germination, up to radicle protrusion. During sensu stricto germination (24 h upon imbibition) about 50% of the proteins display quantitative variations, exhibiting an increased abundance (35%) or a decreasing abundance (15%). Moreover, during radicle protrusion (24-48 h upon imbibition), 41% proteins display quantitative variations with an increased (23%) or a decreasing abundance (18%). In addition, an analysis of the seed proteome revealed the importance of protein post-translational modifications as demonstrated by the poor correlation (r(2) = 0.29) between the theoretical (predicted from Arabidopsis genome) and the observed protein isoelectric points. Secondly, the SPWP is a relevant technical resource for protocols specifically dedicated to Arabidopsis seed proteome studies. Concerning 2D electrophoresis, the user can find efficient procedures for sample preparation, electrophoresis coupled with gel analysis, and protein identification by mass spectrometry, which we have routinely used during the last 12 years. Particular applications such as the detection of oxidized proteins or de novo synthesized proteins radiolabeled by [(35)S]-methionine are also given in great details. Future developments of this portal will include proteomic data from studies such as dormancy release and protein turnover through de novo protein synthesis analyses during germination.

Control of Arabidopsis root development

The Arabidopsis root has been the subject of intense research over the past decades. This research has led to significantly improved understanding of the molecular mechanisms underlying root development. Key insights into the specification of individual cell types, cell patterning, growth and differentiation, branching of the primary root, and responses of the root to the environment have been achieved. Transcription factors and plant hormones play key regulatory roles. Recently, mechanisms involving protein movement and the oscillation of gene expression have also been uncovered. Root gene regulatory networks controlling root development have been reconstructed from genome-wide profiling experiments, revealing novel molecular connections and models. Future refinement of these models will lead to a more complete description of the complex molecular interactions that give rise to a simple growing root.

The Rice Proteogenomics Database OryzaPG-DB: Development, Expansion, and New Features

Our recently developed rice proteogenomics database (OryzaPG-DB) is the first sustainable resource for rice shotgun-based proteogenomics, providing information on peptides identified in rice protein digested peptides measured by means of liquid chromatography-tandem mass spectrometry (LC-MS/MS), and mapping of the peptides to their genomic origins and the genomic novelty of each peptide. The sequences of the peptides, proteins, cDNAs and genes, and the gene annotations are available for download in FASTA and GFF3 formats, respectively. Further, an annotated visualization of the gene models, corresponding peptides, and genomic novelty is available for each gene, and MS/MS spectra are available for each peptide. In this article, we discuss the utilization of OryzaPG-DB and report on its development, recent content expansions, and newly added features in the current version (OryzaPG-DB v1.1).

Label-free protein quantitation using weighted spectral counting

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O (i)) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O (i)-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity

Plant metabolic engineering is commonly used in the production of functional foods and quality trait improvement. However, to date, computational model-based approaches have only been scarcely used in this important endeavor, in marked contrast to their prominent success in microbial metabolic engineering. In this study we present a computational pipeline for the reconstruction of fully compartmentalized tissue-specific models of Arabidopsis thaliana on a genome scale. This reconstruction involves automatic extraction of known biochemical reactions in Arabidopsis for both primary and secondary metabolism, automatic gap-filling, and the implementation of methods for determining subcellular localization and tissue assignment of enzymes. The reconstructed tissue models are amenable for constraint-based modeling analysis, and significantly extend upon previous model reconstructions. A set of computational validations (i.e., cross-validation tests, simulations of known metabolic functionalities) and experimental validations (comparison with experimental metabolomics datasets under various compartments and tissues) strongly testify to the predictive ability of the models. The utility of the derived models was demonstrated in the prediction of measured fluxes in metabolically engineered seed strains and the design of genetic manipulations that are expected to increase vitamin E content, a significant nutrient for human health. Overall, the reconstructed tissue models are expected to lay down the foundations for computational-based rational design of plant metabolic engineering. The reconstructed compartmentalized Arabidopsis tissue models are MIRIAM-compliant and are available upon request.

The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools

The Arabidopsis Information Resource (TAIR, http://arabidopsis.org) is a genome database for Arabidopsis thaliana, an important reference organism for many fundamental aspects of biology as well as basic and applied plant biology research. TAIR serves as a central access point for Arabidopsis data, annotates gene function and expression patterns using controlled vocabulary terms, and maintains and updates the A. thaliana genome assembly and annotation. TAIR also provides researchers with an extensive set of visualization and analysis tools. Recent developments include several new genome releases (TAIR8, TAIR9 and TAIR10) in which the A. thaliana assembly was updated, pseudogenes and transposon genes were re-annotated, and new data from proteomics and next generation transcriptome sequencing were incorporated into gene models and splice variants. Other highlights include progress on functional annotation of the genome and the release of several new tools including Textpresso for Arabidopsis which provides the capability to carry out full text searches on a large body of research literature.

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.

Meta-analysis of the expression profiles of the Arabidopsis ESCRT machinery

The Endosomal Sorting Complex Required for Transport (ESCRT) machinery is a set of multi-protein complexes that are well conserved among all eukaryotes and mediate a remarkable array of cellular processes including late endosome/multivesicular body (MVB) formation, retroviral particle release, and membrane abscission during cytokinesis. While the molecular mechanisms underlying ESCRT function have been relatively well characterized in yeasts and mammals, far less is known about ESCRT in plants. In this study, we utilized publicly-available microarray, massively parallel signature sequencing (MPSS) and proteome data sets in order to survey the expression profiles of many of the components of the Arabidopsis thaliana ESCRT machinery. Overall, the results indicate that ESCRT expression in Arabidopsis is highly dynamic across a wide range of organs, tissues and treatments, consistent with the complex interplay that likely exists between the spatial and temporal regulation of the ESCRT machinery and the diverse array of roles that ESCRT participates in during plant growth and development.

Defining the boundaries and characterizing the landscape of functional genome expression in vascular tissues of Populus using shotgun proteomics

Current state-of-the-art experimental and computational proteomic approaches were integrated to obtain a comprehensive protein profile of Populus vascular tissue. This featured: (1) a large sample set consisting of two genotypes grown under normal and tension stress conditions, (2) bioinformatics clustering to effectively handle gene duplication, and (3) an informatics approach to track and identify single amino acid polymorphisms (SAAPs). By applying a clustering algorithm to the Populus database, the number of protein entries decreased from 64,689 proteins to a total of 43,069 protein groups, thereby reducing 7505 identified proteins to a total of 4226 protein groups, in which 2016 were singletons. This reduction implies that ∼50% of the measured proteins shared extensive sequence homology. Using conservative search criteria, we were able to identify 1354 peptides containing a SAAP and 201 peptides that become tryptic due to a K or R substitution. These newly identified peptides correspond to 502 proteins, including 97 previously unidentified proteins. In total, the integration of deep proteome measurements on an extensive sample set with protein clustering and peptide sequence variants provided an exceptional level of proteome characterization for Populus, allowing us to spatially resolve the vascular tissue proteome.

Generic comparison of protein inference engines

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.

The phosphoglucan phosphatase like sex Four2 dephosphorylates starch at the C3-position in Arabidopsis

Starch contains phosphate covalently bound to the C6-position (70 to 80% of total bound phosphate) and the C3-position (20 to 30%) of the glucosyl residues of the amylopectin fraction. In plants, the transient phosphorylation of starch renders the granule surface more accessible to glucan hydrolyzing enzymes and is required for proper starch degradation. Phosphate also confers desired properties to starch-derived pastes for industrial applications. In Arabidopsis thaliana, the removal of phosphate by the glucan phosphatase Starch Excess4 (SEX4) is essential for starch breakdown. We identified a homolog of SEX4, LSF2 (Like Sex Four2), as a novel enzyme involved in starch metabolism in Arabidopsis chloroplasts. Unlike SEX4, LSF2 does not have a carbohydrate binding module. Nevertheless, it binds to starch and specifically hydrolyzes phosphate from the C3-position. As a consequence, lsf2 mutant starch has elevated levels of C3-bound phosphate. SEX4 can release phosphate from both the C6- and the C3-positions, resulting in partial functional overlap with LSF2. However, compared with sex4 single mutants, the lsf2 sex4 double mutants have a more severe starch-excess phenotype, impaired growth, and a further change in the proportion of C3- and C6-bound phosphate. These findings significantly advance our understanding of the metabolism of phosphate in starch and provide innovative options for tailoring novel starches with improved functionality for industry.

Plastid proteome assembly without Toc159: photosynthetic protein import and accumulation of N-acetylated plastid precursor proteins

Import of nuclear-encoded precursor proteins from the cytosol is an essential step in chloroplast biogenesis that is mediated by protein translocon complexes at the inner and outer envelope membrane (TOC). Toc159 is thought to be the main receptor for photosynthetic proteins, but lacking a large-scale systems approach, this hypothesis has only been tested for a handful of photosynthetic and nonphotosynthetic proteins. To assess Toc159 precursor specificity, we quantitatively analyzed the accumulation of plastid proteins in two mutant lines deficient in this receptor. Parallel genome-wide transcript profiling allowed us to discern the consequences of impaired protein import from systemic transcriptional responses that contribute to the loss of photosynthetic capacity. On this basis, we defined putative Toc159-independent and Toc159-dependent precursor proteins. Many photosynthetic proteins accumulate in Toc159-deficient plastids, and, surprisingly, several distinct metabolic pathways are negatively affected by Toc159 depletion. Lack of Toc159 furthermore affects several proteins that accumulate as unprocessed N-acetylated precursor proteins outside of plastids. Together, our data show an unexpected client protein promiscuity of Toc159 that requires a far more differentiated view of Toc159 receptor function and regulation of plastid protein import, in which cytosolic Met removal followed by N-terminal acetylation of precursors emerges as an additional regulatory step.

A proteogenomic analysis of Shigella flexneri using 2D LC-MALDI TOF/TOF

BACKGROUND

New strategies for high-throughput sequencing are constantly appearing, leading to a great increase in the number of completely sequenced genomes. Unfortunately, computational genome annotation is out of step with this progress. Thus, the accurate annotation of these genomes has become a bottleneck of knowledge acquisition.

RESULTS

We exploited a proteogenomic approach to improve conventional genome annotation by integrating proteomic data with genomic information. Using Shigella flexneri 2a as a model, we identified total 823 proteins, including 187 hypothetical proteins. Among them, three annotated ORFs were extended upstream through comprehensive analysis against an in-house N-terminal extension database. Two genes, which could not be translated to their full length because of stop codon 'mutations' induced by genome sequencing errors, were revised and annotated as fully functional genes. Above all, seven new ORFs were discovered, which were not predicted in S. flexneri 2a str.301 by any other annotation approaches. The transcripts of four novel ORFs were confirmed by RT-PCR assay. Additionally, most of these novel ORFs were overlapping genes, some even nested within the coding region of other known genes.

CONCLUSIONS

Our findings demonstrate that current Shigella genome annotation methods are not perfect and need to be improved. Apart from the validation of predicted genes at the protein level, the additional features of proteogenomic tools include revision of annotation errors and discovery of novel ORFs. The complementary dataset could provide more targets for those interested in Shigella to perform functional studies.

Evidence for the involvement of the Arabidopsis SEC24A in male transmission

Eukaryotic cells use COPII-coated carriers for endoplasmic reticulum (ER)-to-Golgi protein transport. Selective cargo capture into ER-derived carriers is largely driven by the SEC24 component of the COPII coat. The Arabidopsis genome encodes three AtSEC24 genes with overlapping expression profiles but it is yet to be established whether the AtSEC24 proteins have overlapping roles in plant growth and development. Taking advantage of Arabidopsis thaliana as a model plant system for studying gene function in vivo, through reciprocal crosses, pollen characterization, and complementation tests, evidence is provided for a role for AtSEC24A in the male gametophyte. It is established that an AtSEC24A loss-of-function mutation is tolerated in the female gametophyte but that it causes defects in pollen leading to failure of male transmission of the AtSEC24A mutation. These data provide a characterization of plant SEC24 family in planta showing incompletely overlapping functions of the AtSEC24 isoforms. The results also attribute a novel role to SEC24 proteins in a multicellular model system, specifically in male fertility.

Plant organelle proteomics: collaborating for optimal cell function

Organelle proteomics describes the study of proteins present in organelle at a particular instance during the whole period of their life cycle in a cell. Organelles are specialized membrane bound structures within a cell that function by interacting with cytosolic and luminal soluble proteins making the protein composition of each organelle dynamic. Depending on organism, the total number of organelles within a cell varies, indicating their evolution with respect to protein number and function. For example, one of the striking differences between plant and animal cells is the plastids in plants. Organelles have their own proteins, and few organelles like mitochondria and chloroplast have their own genome to synthesize proteins for specific function and also require nuclear-encoded proteins. Enormous work has been performed on animal organelle proteomics. However, plant organelle proteomics has seen limited work mainly due to: (i) inter-plant and inter-tissue complexity, (ii) difficulties in isolation of subcellular compartments, and (iii) their enrichment and purity. Despite these concerns, the field of organelle proteomics is growing in plants, such as Arabidopsis, rice and maize. The available data are beginning to help better understand organelles and their distinct and/or overlapping functions in different plant tissues, organs or cell types, and more importantly, how protein components of organelles behave during development and with surrounding environments. Studies on organelles have provided a few good reviews, but none of them are comprehensive. Here, we present a comprehensive review on plant organelle proteomics starting from the significance of organelle in cells, to organelle isolation, to protein identification and to biology and beyond. To put together such a systematic, in-depth review and to translate acquired knowledge in a proper and adequate form, we join minds to provide discussion and viewpoints on the collaborative nature of organelles in cell, their proper function and evolution.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

Leptospira interrogans encodes an ROK family glucokinase involved in a cryptic glucose utilization pathway

Although Leptospira interrogans is unable to utilize glucose as its carbon/energy source, the LA_1437 gene of L. interrogans serovar Lai potentially encodes a group III glucokinase (GLK). The L. interrogans GLK (LiGLK) heterogeneously expressed in Escherichia coli was purified and proved to be a homodimeric enzyme with its specific activity of 12.3 ± 0.6 U/mg x protein determined under an improved assay condition (pH 9.0, 50 ° C), 7.5-fold higher than that assayed under the previously used condition (pH 7.3, 25 ° C). The improved sensitivity allowed us to detect this enzymatic activity of (5.0 ± 0.6) × 10(-3) U/mg x protein in the crude extract of L. interrogans serovar Lai cultured in standard Ellinghausen-McCullough-Johnson-Harris medium. The k(cat) and K(m) values for d-glucose and ATP were similar to those of other group III GLKs, although the K(m) value for ATP was slightly higher. Site-directed mutagenesis analysis targeting the conserved amino acid residues in the potential ATP-binding motif hinted that a proper array of Gly residues in the motif might be important for maintaining the conformation that was essential for its function. Gene expression profiling and quantitative proteomic data mining provided preliminary evidence for the absence of efficient systems involved in glucose transport and glycolysis that might account for the failure of glucose utilization in L. interrogans.

Jasmonate controls polypeptide patterning in undamaged tissue in wounded Arabidopsis leaves

Wounding initiates a strong and largely jasmonate-dependent remodelling of the transcriptome in the leaf blades of Arabidopsis (Arabidopsis thaliana). How much control do jasmonates exert on wound-induced protein repatterning in leaves? Replicated shotgun proteomic analyses of 2.5-mm-wide leaf strips adjacent to wounds revealed 106 differentially regulated proteins. Many of these gene products have not emerged as being wound regulated in transcriptomic studies. From experiments using the jasmonic acid (JA)-deficient allene oxide synthase mutant we estimated that approximately 95% of wound-stimulated changes in protein levels were deregulated in the absence of JA. The levels of two tonoplast proteins already implicated in defense response regulation, TWO-PORE CHANNEL1 and the calcium-V-ATPase ACA4 increased on wounding, but their transcripts were not wound inducible. The data suggest new roles for jasmonate in controlling the levels of calcium-regulated pumps and transporters, proteins involved in targeted proteolysis, a putative bacterial virulence factor target, a light-dependent catalyst, and a key redox-controlled enzyme in glutathione synthesis. Extending the latter observation we found that wounding increased the proportion of oxidized glutathione in leaves, but only in plants able to synthesize JA. The oxidizing conditions generated through JA signaling near wounds help to define the cellular environment in which proteome remodelling occurs.

A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry

Anopheles gambiae is a major mosquito vector responsible for malaria transmission, whose genome sequence was reported in 2002. Genome annotation is a continuing effort, and many of the approximately 13,000 genes listed in VectorBase for Anopheles gambiae are predictions that have still not been validated by any other method. To identify protein-coding genes of An. gambiae based on its genomic sequence, we carried out a deep proteomic analysis using high-resolution Fourier transform mass spectrometry for both precursor and fragment ions. Based on peptide evidence, we were able to support or correct more than 6000 gene annotations including 80 novel gene structures and about 500 translational start sites. An additional validation by RT-PCR and cDNA sequencing was successfully performed for 105 selected genes. Our proteogenomic analysis led to the identification of 2682 genome search-specific peptides. Numerous cases of encoded proteins were documented in regions annotated as intergenic, introns, or untranslated regions. Using a database created to contain potential splice sites, we also identified 35 novel splice junctions. This is a first report to annotate the An. gambiae genome using high-accuracy mass spectrometry data as a complementary technology for genome annotation.

Green systems biology - From single genomes, proteomes and metabolomes to ecosystems research and biotechnology

Plants have shaped our human life form from the outset. With the emerging recognition of world population feeding, global climate change and limited energy resources with fossil fuels, the relevance of plant biology and biotechnology is becoming dramatically important. One key issue is to improve plant productivity and abiotic/biotic stress resistance in agriculture due to restricted land area and increasing environmental pressures. Another aspect is the development of CO(2)-neutral plant resources for fiber/biomass and biofuels: a transition from first generation plants like sugar cane, maize and other important nutritional crops to second and third generation energy crops such as Miscanthus and trees for lignocellulose and algae for biomass and feed, hydrogen and lipid production. At the same time we have to conserve and protect natural diversity and species richness as a foundation of our life on earth. Here, biodiversity banks are discussed as a foundation of current and future plant breeding research. Consequently, it can be anticipated that plant biology and ecology will have more indispensable future roles in all socio-economic aspects of our life than ever before. We therefore need an in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex natural as well as anthropogenic ecosystems. Latest developments in biological and bioanalytical research will lead into a paradigm shift towards trying to understand organisms at a systems level and in their ecosystemic context: (i) shotgun and next-generation genome sequencing, gene reconstruction and annotation, (ii) genome-scale molecular analysis using OMICS technologies and (iii) computer-assisted analysis, modeling and interpretation of biological data. Systems biology combines these molecular data, genetic evolution, environmental cues and species interaction with the understanding, modeling and prediction of active biochemical networks up to whole species populations. This process relies on the development of new technologies for the analysis of molecular data, especially genomics, metabolomics and proteomics data. The ambitious aim of these non-targeted 'omic' technologies is to extend our understanding beyond the analysis of separated parts of the system, in contrast to traditional reductionistic hypothesis-driven approaches. The consequent integration of genotyping, pheno/morphotyping and the analysis of the molecular phenotype using metabolomics, proteomics and transcriptomics will reveal a novel understanding of plant metabolism and its interaction with the environment. The analysis of single model systems - plants, fungi, animals and bacteria - will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches. In this review the foundations of green systems biology are described and applications in ecosystems research are presented. Knowledge exchange of ecosystems research and green biotechnology merging into green systems biology is anticipated based on the principles of natural variation, biodiversity and the genotype-phenotype environment relationship as the fundamental drivers of ecology and evolution.

Comparative phosphoproteome profiling reveals a function of the STN8 kinase in fine-tuning of cyclic electron flow (CEF)

Important aspects of photosynthetic electron transport efficiency in chloroplasts are controlled by protein phosphorylation. Two thylakoid-associated kinases, STN7 and STN8, have distinct roles in short- and long-term photosynthetic acclimation to changes in light quality and quantity. Although some substrates of STN7 and STN8 are known, the complexity of this regulatory kinase system implies that currently unknown substrates connect photosynthetic performance with the regulation of metabolic and regulatory functions. We performed an unbiased phosphoproteome-wide screen with Arabidopsis WT and stn8 mutant plants to identify unique STN8 targets. The phosphorylation status of STN7 was not affected in stn8, indicating that kinases other than STN8 phosphorylate STN7 under standard growth conditions. Among several putative STN8 substrates, PGRL1-A is of particular importance because of its possible role in the modulation of cyclic electron transfer. The STN8 phosphorylation site on PGRL1-A is absent in both monocotyledonous plants and algae. In dicots, spectroscopic measurements with Arabidopsis WT, stn7, stn8, and stn7/stn8 double-mutant plants indicate a STN8-mediated slowing down of the transition from cyclic to linear electron flow at the onset of illumination. This finding suggests a possible link between protein phosphorylation by STN8 and fine-tuning of cyclic electron flow during this critical step of photosynthesis, when the carbon assimilation is not commensurate to the electron flow capacity of the chloroplast.

iTRAQ-based analysis of changes in the cassava root proteome reveals pathways associated with post-harvest physiological deterioration

The short storage life of harvested cassava roots is an important constraint that limits the full potential of cassava as a commercial food crop in developing countries. We investigated the molecular changes during physiological deterioration of cassava root after harvesting using isobaric tags for relative and absolute quantification (iTRAQ) of proteins in soluble and non-soluble fractions prepared during a 96 h post-harvest time course. Combining bioinformatic approaches to reduce information redundancy for unsequenced or partially sequenced plant species, we established a comprehensive proteome map of the cassava root and identified quantitatively regulated proteins. Up-regulation of several key proteins confirmed that physiological deterioration of cassava root after harvesting is an active process, with 67 and 170 proteins, respectively, being up-regulated early and later after harvesting. This included regulated proteins that had not previously been associated with physiological deterioration after harvesting, such as linamarase, glutamic acid-rich protein, hydroxycinnamoyl transferase, glycine-rich RNA binding protein, β-1,3-glucanase, pectin methylesterase, maturase K, dehydroascorbate reductase, allene oxide cyclase, and proteins involved in signal pathways. To confirm the regulation of these proteins, activity assays were performed for selected enzymes. Together, our results show that physiological deterioration after harvesting is a highly regulated complex process involving proteins that are potential candidates for biotechnology approaches to reduce such deterioration.

Advances in qualitative and quantitative plant membrane proteomics

The membrane proteome consists of integral and membrane-associated proteins that are involved in various physiological and biochemical functions critical for cellular function. It is also dynamic in nature, where many proteins are only expressed during certain developmental stages or in response to environmental stress. These proteins can undergo post-translational modifications in response to these different conditions, allowing them to transiently associate with the membrane or other membrane proteins. Along with their increased size, hydrophobicity, and the additional organelle and cellular features of plant cells relative to mammalian systems, the characterization of the plant membrane proteome presents unique challenges for effective qualitative and quantitative analysis using mass spectrometry (MS) analysis. Here, we present the latest advancements developed for the isolation and fractionation of plant organelles and their membrane components amenable to MS analysis. Separations of membrane proteins from these enriched preparations that have proven effective are discussed for both gel- and liquid chromatography-based MS analysis. In this context, quantitative membrane proteomic analyses using both isotope-coded and label-free approaches are presented and reveal the potential to establish a wider-biological interpretation of the function of plant membrane proteins that will ultimately lead to a more comprehensive understanding of plant physiology and their response mechanisms.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

The GreenCut2 resource, a phylogenomically derived inventory of proteins specific to the plant lineage

The plastid is a defining structure of photosynthetic eukaryotes and houses many plant-specific processes, including the light reactions, carbon fixation, pigment synthesis, and other primary metabolic processes. Identifying proteins associated with catalytic, structural, and regulatory functions that are unique to plastid-containing organisms is necessary to fully define the scope of plant biochemistry. Here, we performed phylogenomics on 20 genomes to compile a new inventory of 597 nucleus-encoded proteins conserved in plants and green algae but not in non-photosynthetic organisms. 286 of these proteins are of known function, whereas 311 are not characterized. This inventory was validated as applicable and relevant to diverse photosynthetic eukaryotes using an additional eight genomes from distantly related plants (including Micromonas, Selaginella, and soybean). Manual curation of the known proteins in the inventory established its importance to plastid biochemistry. To predict functions for the 52% of proteins of unknown function, we used sequence motifs, subcellular localization, co-expression analysis, and RNA abundance data. We demonstrate that 18% of the proteins in the inventory have functions outside the plastid and/or beyond green tissues. Although 32% of proteins in the inventory have homologs in all cyanobacteria, unexpectedly, 30% are eukaryote-specific. Finally, 8% of the proteins of unknown function share no similarity to any characterized protein and are plant lineage-specific. We present this annotated inventory of 597 proteins as a resource for functional analyses of plant-specific biochemistry.

Synchronization of cytoplasmic and transferred mitochondrial ribosomal protein gene expression in land plants is linked to Telo-box motif enrichment

Background

Chloroplasts and mitochondria evolved from the endosymbionts of once free-living eubacteria, and they transferred most of their genes to the host nuclear genome during evolution. The mechanisms used by plants to coordinate the expression of such transferred genes, as well as other genes in the host nuclear genome, are still poorly understood.

Results

In this paper, we use nuclear-encoded chloroplast (cpRPGs), as well as mitochondrial (mtRPGs) and cytoplasmic (euRPGs) ribosomal protein genes to study the coordination of gene expression between organelles and the host. Results show that the mtRPGs, but not the cpRPGs, exhibit strongly synchronized expression with euRPGs in all investigated land plants and that this phenomenon is linked to the presence of a telo-box DNA motif in the promoter regions of mtRPGs and euRPGs. This motif is also enriched in the promoter regions of genes involved in DNA replication. Sequence analysis further indicates that mtRPGs, in contrast to cpRPGs, acquired telo-box from the host nuclear genome.

Conclusions

Based on our results, we propose a model of plant nuclear genome evolution where coordination of activities in mitochondria and chloroplast and other cellular functions, including cell cycle, might have served as a strong selection pressure for the differential acquisition of telo-box between mtRPGs and cpRPGs. This research also highlights the significance of physiological needs in shaping transcriptional regulatory evolution.

Unpredictability of metabolism--the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype-phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science--the quantitative measurement of metabolism in conjunction with metabolic modelling--is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype-phenotype relationship.

Many needles in a haystack: cell-type specific abiotic stress responses

Plants react to abiotic stress with a number of physiological, biochemical, and developmental alterations. These responses include changes in signaling components, gene transcription, non-coding RNAs, proteins, and metabolites that occur in a cell-type and tissue-specific manner. Recent advances in cell-type specifically isolating protoplasts and nuclei from plants, extracting mRNA from targeted cells, and whole-genome transcriptional profiling have enabled scientists to gain insight into how cells and tissues respond transcriptionally to abiotic stress. Continued technological advances in profiling the proteomes, metabolomes, and other biological components of specific cells will continue to broaden our understanding of plant stress responses.

Reconstructing regulatory network transitions

Cellular responses often involve a transition of cells from one state to another. A transition from a stem cell to a differentiated cell state, for example, might occur in response to gene expression changes induced by a transcription factor, or to signaling cascades triggered by a hormone or pathogen. Regulatory networks are thought to control such cellular transitions. Thus, many researchers are interested in reconstructing regulatory networks, not only with the aim of gaining a deeper understanding of cellular transitions, but also of using networks to predict and potentially manipulate cellular transitions and outcomes. In this review, we highlight approaches to the reconstruction of regulatory networks underlying cellular transitions, with special attention to transcriptional regulatory networks. We describe recent regulatory network reconstructions in a variety of organisms, and discuss the success they share in identifying new regulatory components, shared relationships and phenotypic outcomes.

ABA-dependent and -independent G-protein signaling in Arabidopsis roots revealed through an iTRAQ proteomics approach

Heterotrimeric G-proteins are important signal transducers in all eukaryotes. The plant hormone abscisic acid (ABA) has emerged as a key regulator of G-protein-mediated signaling pathways in plants. ABA-regulation of G-protein signaling involves both conventional and novel mechanisms. We have utilized the null mutant of the Arabidopsis G-protein α subunit gpa1 to evaluate to what extent ABA-dependent changes in the proteome are regulated by G-proteins. We used Arabidopsis root tissue as both ABA and G-proteins, individually and in combination, affect root growth and development. We identified 720 proteins, of which 42 showed GPA1-dependent and 74 showed ABA-dependent abundance changes. A majority of ABA-regulated proteins were also GPA1-dependent. Our data provide insight into how tissue specificity might be achieved in ABA-regulated G-protein signaling. A number of proteins related to ER body formation and intracellular trafficking were altered in gpa1 mutant, suggesting a novel role for GPA1 in these pathways. A potential link between ABA metabolism and ABA signaling was also revealed. The comparison of protein abundance changes in the absence of ABA offers clues to the role of GPA1 in ABA-independent signaling pathways, for example, regulation of cell division. These findings substantially contribute to our knowledge of G-protein signaling mechanisms in plants.

Assessing the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana using proteomics data

BACKGROUND

Large-scale analyses of genomics and transcriptomics data have revealed that alternative splicing (AS) substantially increases the complexity of the transcriptome in higher eukaryotes. However, the extent to which this complexity is reflected at the level of the proteome remains unclear. On the basis of a lack of conservation of AS between species, we previously concluded that AS does not frequently serve as a mechanism that enables the production of multiple functional proteins from a single gene. Following this conclusion, we hypothesized that the extent to which AS events contribute to the proteome diversity in Arabidopsis thaliana would be lower than expected on the basis of transcriptomics data. Here, we test this hypothesis by analyzing two large-scale proteomics datasets from Arabidopsis thaliana.

RESULTS

A total of only 60 AS events could be confirmed using the proteomics data. However, for about 60% of the loci that, based on transcriptomics data, were predicted to produce multiple protein isoforms through AS, no isoform-specific peptides were found. We therefore performed in silico AS detection experiments to assess how well AS events were represented in the experimental datasets. The results of these in silico experiments indicated that the low number of confirmed AS events was the consequence of a limited sampling depth rather than in vivo under-representation of AS events in these datasets.

CONCLUSION

Although the impact of AS on the functional properties of the proteome remains to be uncovered, the results of this study indicate that AS-induced diversity at the transcriptome level is also expressed at the proteome level.

Comparative proteomic profiles of the soybean (Glycine max) root apex and differentiated root zone

The root apical meristem (RAM) is responsible for the growth of the plant root system. Because of the importance of root architecture in the performance of crop plants, we established a proteome reference map of the soybean root apex and compared this with the proteome of the differentiated root zone. The root apex samples contained the apical 1 mm of the root, comprising the RAM, quiescent center and root cap. We identified 342 protein spots from 550 excised proteins (∼62%) of root apex samples by MALDI-TOF MS/MS analysis. All these proteins were also present in the differentiated root, but differed in abundance. Functional classification showed that the most numerous protein categories represented in the root were those of stress response, glycolysis, redox homeostasis and protein processing. Using DIGE, we identified 73 differentially accumulated proteins between root apex and differentiated root. Proteins overrepresented in the root apex belonged primarily to the pathways for protein synthesis and processing, cell redox homeostasis and flavonoid biosynthesis. Proteins underrepresented in the root apex were those of glycolysis, tricarboxylic acid metabolism and stress response. Our results highlight the importance of stress and defense response, redox control and flavonoid metabolism in the root apex.

OryzaPG-DB: rice proteome database based on shotgun proteogenomics

BACKGROUND

Proteogenomics aims to utilize experimental proteome information for refinement of genome annotation. Since mass spectrometry-based shotgun proteomics approaches provide large-scale peptide sequencing data with high throughput, a data repository for shotgun proteogenomics would represent a valuable source of gene expression evidence at the translational level for genome re-annotation.

DESCRIPTION

Here, we present OryzaPG-DB, a rice proteome database based on shotgun proteogenomics, which incorporates the genomic features of experimental shotgun proteomics data. This version of the database was created from the results of 27 nanoLC-MS/MS runs on a hybrid ion trap-orbitrap mass spectrometer, which offers high accuracy for analyzing tryptic digests from undifferentiated cultured rice cells. Peptides were identified by searching the product ion spectra against the protein, cDNA, transcript and genome databases from Michigan State University, and were mapped to the rice genome. Approximately 3200 genes were covered by these peptides and 40 of them contained novel genomic features. Users can search, download or navigate the database per chromosome, gene, protein, cDNA or transcript and download the updated annotations in standard GFF3 format, with visualization in PNG format. In addition, the database scheme of OryzaPG was designed to be generic and can be reused to host similar proteogenomic information for other species. OryzaPG is the first proteogenomics-based database of the rice proteome, providing peptide-based expression profiles, together with the corresponding genomic origin, including the annotation of novelty for each peptide.

CONCLUSIONS

The OryzaPG database was constructed and is freely available at http://oryzapg.iab.keio.ac.jp/.