Gapped spectral dictionaries and their applications for database searches of tandem mass spectra

Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. Our MS-Gapped-Dictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. MS-Gapped-Dictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches.

Comparative proteogenomic analysis of the Leptospira interrogans virulence-attenuated strain IPAV against the pathogenic strain 56601

The virulence-attenuated Leptospira interrogans serovar Lai strain IPAV was derived by prolonged laboratory passage from a highly virulent ancestral strain isolated in China. We studied the genetic variations of IPAV that render it avirulent via comparative analysis against the pathogenic L. interrogans serovar Lai strain 56601. The complete genome sequence of the IPAV strain was determined and used to compare with, and then rectify and reannotate the genome sequence of strain 56601. Aside from their highly similar genomic structure and gene order, a total of 33 insertions, 53 deletions and 301 single-nucleotide variations (SNVs) were detected throughout the genome of IPAV directly affecting 101 genes, either in their 5' upstream region or within their coding region. Among them, the majority of the 44 functional genes are involved in signal transduction, stress response, transmembrane transport and nitrogen metabolism. Comparative proteomic analysis based on quantitative liquid chromatography (LC)-MS/MS data revealed that among 1 627 selected pairs of orthologs, 174 genes in the IPAV strain were upregulated, with enrichment mainly in classes of energy production and lipid metabolism. In contrast, 228 genes in strain 56601 were upregulated, with the majority enriched in the categories of protein translation and DNA replication/repair. The combination of genomic and proteomic approaches illustrated that altered expression or mutations in critical genes, such as those encoding a Ser/Thr kinase, carbon-starvation protein CstA, glutamine synthetase, GTP-binding protein BipA, ribonucleotide-diphosphate reductase and phosphate transporter, and alterations in the translational profile of lipoproteins or outer membrane proteins are likely to account for the virulence attenuation in strain IPAV.

Comparative proteomics of seed maturation in oilseeds reveals differences in intermediary metabolism

Proteomics is increasingly being used to understand enzyme expression and regulatory mechanisms involved in the accumulation of storage reserves in crops with sequenced genomes. During the past six years, considerable progress has been made to characterize proteomes of both mature and developing seeds, particularly oilseeds - plants which accumulate principally oil and protein as storage reserves. This review summarizes the emerging proteomics data, with emphasis on seed filling in soy, rapeseed, castor and Arabidopsis as each of these oilseeds were analyzed using very similar proteomic strategies. These parallel studies provide a comprehensive view of source-sink relationships, specifically sucrose assimilation into organic acid intermediates for de novo amino acid and fatty acid synthesis. We map these biochemical processes for seed maturation and illustrate the differences and similarities among the four oilseeds. For example, while the four oilseeds appear capable of producing cytosolic phosphoenolpyruvate as the principal carbon intermediate, soybean and castor also express malic enzymes and malate dehydrogenase, together capable of producing malate that has been previously proposed to be the major intermediate for fatty acid synthesis in castor. We discuss these and other differences in the context of intermediary metabolism for developing oilseeds.

A partial set covering model for protein mixture identification using mass spectrometry data

Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.

A RESTORER OF FERTILITY-like PPR gene is required for 5'-end processing of the nad4 mRNA in mitochondria of Arabidopsis thaliana

Processing of 5'-ends is a frequently observed step during maturation of plant mitochondrial mRNAs. Up to now, very little is known about the biochemistry of this process and the proteins involved in the removal of 5' leader sequences. Based on natural genetic variation we have used linkage mapping and complementation studies to identify a nuclear gene required for the efficient generation of a 5'-end 228 nucleotides upstream of the mitochondrial nad4 gene encoding subunit 4 of the NADH dehydrogenase complex. This nuclear gene, At1g12700, that we designate RNA PROCESSING FACTOR 1 (RPF1), encodes a pentatricopeptide repeat (PPR) protein of the P-class containing canonical PPR-repeats. RPF1 belongs to a subgroup of PPR proteins, which includes the RESTORER OF FERTILITY (RF) gene products restoring cytoplasmic male sterility (CMS) in various plant species. CMS is a mitochondrially inherited trait caused by the expression of aberrant, chimeric genes, which has not been observed in the predominantly inbreeding species Arabidopsis thaliana. The here reported results are a further step towards the characterization of the plant mitochondrial RNA processing machinery and provide additional insights into the function of RF-like PPR proteins.

The chloroplast import receptor Toc90 partially restores the accumulation of Toc159 client proteins in the Arabidopsis thaliana ppi2 mutant

Successful import of hundreds of nucleus-encoded proteins is essential for chloroplast biogenesis. The import of cytosolic precursor proteins relies on the Toc- (translocon at the outer chloroplast membrane) and Tic- (translocon at the inner chloroplast membrane) complexes. In Arabidopsis thaliana, precursor recognition is mainly mediated by outer membrane receptors belonging to two gene families: Toc34/33 and Toc159/132/120/90. The role in import and precursor selectivity of these receptors has been intensively studied, but the function of Toc90 still remains unclear. Here, we report the ability of Toc90 to support the import of Toc159 client proteins. We show that the overexpression of Toc90 partially complements the albino knockout of Toc159 and restores photoautotrophic growth. Several lines of evidence including proteome profiling demonstrate the import and accumulation of proteins essential for chloroplast biogenesis and functionality.

Analysis of phagosomal proteomes: from latex-bead to bacterial phagosomes

Phagosomal proteome characterization has contributed significantly to the understanding of host-pathogen interaction and the mechanism of infectious diseases caused by intracellular bacteria. The latex bead-containing phagosome has been widely used as a model system to study phagosomal proteomes at a global level. In contrast, the study of bacteria-containing phagosomes at a similar level has just begun. A number of intracellular microbial species are studied for their proteomes during the invasion of a host, providing insight into their metabolic adaptation in host cells and interaction with host-cell antimicrobial environments. In this review, we attempt to summarize the most recent advancements in the proteomic study of microbial phagosomes, especially those originating from mouse or human cells. We also briefly describe the proteomics of latex bead-containing phagosomes because they are often used as model phagosomes for study. We provide descriptions on major biological and technological components in phagosomal proteome studies. We also discuss the role of phagosomal proteome study in the broader horizon of systems biology and the technological challenges in phagosomal proteome characterization.

iTRAQ protein profile analysis of Arabidopsis roots reveals new aspects critical for iron homeostasis

Iron (Fe) deficiency is a major constraint for plant growth and affects the quality of edible plant parts. To investigate the mechanisms underlying Fe homeostasis in plants, Fe deficiency-induced changes in the protein profile of Arabidopsis (Arabidopsis thaliana) roots were comprehensively analyzed using iTRAQ (Isobaric Tag for Relative and Absolute Quantification) differential liquid chromatography-tandem mass spectrometry on a LTQ-Orbitrap with high-energy collision dissociation. A total of 4,454 proteins were identified with a false discovery rate of less than 1.1%, and 2,882 were reliably quantified. A subset of 101 proteins was differentially expressed upon Fe deficiency. The changes in protein profiles upon Fe deficiency show low congruency with previously reported alterations in transcript levels, indicating posttranscriptional changes, and provide complementary information on Fe deficiency-induced processes. The abundance of proteins involved in the synthesis/regeneration of S-adenosylmethionine, the phenylpropanoid pathway, the response to oxidative stress, and respiration was highly increased by Fe deficiency. Using Fe-responsive proteins as bait, genome-wide fishing for partners with predictable or confirmed interologs revealed that RNA processing and ribonucleoprotein complex assembly may represent critical processes that contribute to the regulation of root responses to Fe deficiency, possibly by biasing translation efficiency.

A reciprocal 15N-labeling proteomic analysis of expanding Arabidopsis leaves subjected to osmotic stress indicates importance of mitochondria in preserving plastid functions

Plants respond to environmental stress by dynamically reprogramming their growth. Whereas stress onset is accompanied by rapid growth inhibition leading to smaller organs, growth will recover and adapt once the stress conditions become stable and do no threaten plant survival. Here, adaptation of growing Arabidopsis thaliana leaves to mild and prolonged osmotic stress was investigated by means of a complete metabolic labeling strategy with the (15)N-stable isotope as a complement to a previously published transcript and metabolite profiling. Global analysis of protein changes revealed that plastidial ATPase, Calvin cycle, and photorespiration were down-regulated, but mitochondrial ATP synthesis was up-regulated, indicating the importance of mitochondria in preserving plastid functions during water stress. Although transcript and protein data correlated well with the stable and prolonged character of the applied stress, numerous proteins were clearly regulated at the post-transcriptional level that could, at least partly, be related to changes in protein synthesis and degradation. In conclusion, proteomics using the (15)N labeling helped understand the mechanisms underlying growth adaptation to osmotic stress and allowed the identification of candidate genes to improve plant growth under limited water.

pep2pro: a new tool for comprehensive proteome data analysis to reveal information about organ-specific proteomes in Arabidopsis thaliana

pep2pro is a comprehensive proteome analysis database specifically suitable for flexible proteome data analysis. The pep2pro database schema offers solutions to the various challenges of developing a proteome data analysis database and because data integrated in pep2pro are in relational format, it enables flexible and detailed data analysis. The information provided here will facilitate building proteome data analysis databases for other organisms or applications. The capacity of the pep2pro database for the integration and analysis of large proteome datasets was demonstrated by creating the pep2pro dataset, which is an organ-specific characterisation of the Arabidopsis thaliana proteome containing 14 522 identified proteins based on 2.6 million peptide spectrum assignments. This dataset provides evidence of protein expression and reveals organ-specific processes. The high coverage and density of the dataset are essential for protein quantification by normalised spectral counting and allowed us to extract information that is usually not accessible in low-coverage datasets. With this quantitative protein information we analysed organ- and organelle-specific sub-proteomes. In addition we matched spectra to regions in the genome that were not predicted to have protein coding capacity and provide PCR validation for selected revised gene models. Furthermore, we analysed the peptide features that distinguish detected from non-detected peptides and found substantial disagreement between predicted and detected proteotypic peptides, suggesting that large-scale proteomics data are essential for efficient selection of proteotypic peptides in targeted proteomics surveys. The pep2pro dataset is available as a resource for plant systems biology at www.pep2pro.ethz.ch.

Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control

Combining translating ribosome affinity purification with RNA-seq for cell-specific profiling of translating RNAs in developing flowers.

Cell type comparisons of cell type-specific hormone responses, promoter motifs, coexpressed cognate binding factor candidates, and splicing isoforms.

Widespread post-transcriptional regulation at both the intron splicing and translational stages.

A new class of noncoding RNAs associated with polysomes.

What constitutes a differentiated cell type? How much do cell types differ in their transcription of genes? The development and functions of tissues rely on constant interactions among distinct and nonequivalent cell types. Answering these questions will require quantitative information on transcriptomes, proteomes, protein–protein interactions, protein–nucleic acid interactions, and metabolomes at cellular resolution. The systems approaches emerging in biology promise to explain properties of biological systems based on genome-wide measurements of expression, interaction, regulation, and metabolism. To facilitate a systems approach, it is essential first to capture such components in a global manner, ideally at cellular resolution.

Recently, microarray analysis of transcriptomes has been extended to a cellular level of resolution by using laser microdissection or fluorescence-activated sorting (for review, see Nelson et al, 2008). These methods have been limited by stresses associated with cellular separation and isolation procedures, and biases associated with mandatory RNA amplification steps. A newly developed method, translating ribosome affinity purification (TRAP; Zanetti et al, 2005; Heiman et al, 2008; Mustroph et al, 2009), circumvents these problems by epitopetagging a ribosomal protein in specific cellular domains to selectively purify polysomes. We combined TRAP with deep sequencing, which we term TRAP-seq, to provide cell-level spatiotemporal maps for Arabidopsis early floral development at single-base resolution.

Flower development in Arabidopsis has been studied extensively and is one of the best understood aspects of plant development (for review, see Krizek and Fletcher, 2005). Genetic analysis of homeotic mutants established the ABC model, in which three classes of regulatory genes, A, B and C, work in a combinatorial manner to confer organ identities of four whorls (Coen and Meyerowitz, 1991). Each class of regulatory gene is expressed in a specific and evolutionarily conserved domain, and the action of the class A, B and C genes is necessary for specification of organ identity (Figure 1A).

Using TRAP-seq, we purified cell-specific translating mRNA populations, which we and others call the translatome, from the A, B and C domains of early developing flowers, in which floral patterning and the specification of floral organs is established. To achieve temporal specificity, we used a floral induction system to facilitate collection of early stage flowers (Wellmer et al, 2006). The combination of TRAP-seq with domain-specific promoters and this floral induction system enabled fine spatiotemporal isolation of translating mRNA in specific cellular domains, and at specific developmental stages.

Multiple lines of evidence confirmed the specificity of this approach, including detecting the expression in expected domains but not in other domains for well-studied flower marker genes and known physiological functions (Figures 1B–D and 2A–C). Furthermore, we provide numerous examples from flower development in which a spatiotemporal map of rigorously comparable cell-specific translatomes makes possible new views of the properties of cell domains not evident in data obtained from whole organs or tissues, including patterns of transcription and cis-regulation, new physiological differences among cell domains and between flower stages, putative hormone-active centers, and splicing events specific for flower domains (Figure 2A–D). Such findings may provide new targets for reverse genetics studies and may aid in the formulation and validation of interaction and pathway networks.

Beside cellular heterogeneity, the transcriptome is regulated at several steps through the life of mRNA molecules, which are not directly available through traditional transcriptome profiling of total mRNA abundance. By comparing the translatome and transcriptome, we integratively profiled two key posttranscriptional control points, intron splicing and translation state. From our translatome-wide profiling, we (i) confirmed that both posttranscriptional regulation control points were used by a large portion of the transcriptome; (ii) identified a number of cis-acting features within the coding or noncoding sequences that correlate with splicing or translation state; and (iii) revealed correlation between each regulation mechanism and gene function. Our transcriptome-wide surveys have highlighted target genes transcripts of which are probably under extensive posttranscriptional regulation during flower development.

Finally, we reported the finding of a large number of polysome-associated ncRNAs. About one-third of all annotated ncRNA in the Arabidopsis genome were observed co-purified with polysomes. Coding capacity analysis confirmed that most of them are real ncRNA without conserved ORFs. The group of polysome-associated ncRNA reported in this study is a potential new addition to the expanding riboregulator catalog; they could have roles in translational regulation during early flower development.

Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post-transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type-specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell-specific transcripts and uncovered many new ones, from which we inferred cell type-specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady-state overall transcripts, we found evidence for widespread post-transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development.

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data

Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.

Proteome databases and other online resources for chloroplast research in Arabidopsis

Proteomics aimed at addressing sub cellular fractions, such as chloroplasts, are a complex challenge. In the past few years, several studies in different laboratories have identified and, more recently, quantified, thousands of proteins within whole chloroplasts or chloroplast fractions. A considerable number of these studies are available for querying, using online resources, such as databases containing the proteins identified, encoding genes, acquired spectra, and phosphopeptides. The main purpose of this review is to identity and highlight useful features of these online resourses, mainly focused in proteomics databases related to chloroplast research in Arabidopsis thaliana. Several web sites were consulted. Among them, 11 were selected and discussed herein. The databases were classified into Plastid Databases, General Organelle Proteome Databases, and General Arabidopsis Proteome Databases. Special care was taken to present information regarding protein identification, protein quantification, and data integration. A selected list of online resources is presented in two tables. The databases analyzed are a useful source of information for researchers in the plastid organelle and plant proteomics fields.

Integration of deep transcriptome and proteome analyses reveals the components of alkaloid metabolism in opium poppy cell cultures

BACKGROUND

Papaver somniferum (opium poppy) is the source for several pharmaceutical benzylisoquinoline alkaloids including morphine, the codeine and sanguinarine. In response to treatment with a fungal elicitor, the biosynthesis and accumulation of sanguinarine is induced along with other plant defense responses in opium poppy cell cultures. The transcriptional induction of alkaloid metabolism in cultured cells provides an opportunity to identify components of this process via the integration of deep transcriptome and proteome databases generated using next-generation technologies.

RESULTS

A cDNA library was prepared for opium poppy cell cultures treated with a fungal elicitor for 10 h. Using 454 GS-FLX Titanium pyrosequencing, 427,369 expressed sequence tags (ESTs) with an average length of 462 bp were generated. Assembly of these sequences yielded 93,723 unigenes, of which 23,753 were assigned Gene Ontology annotations. Transcripts encoding all known sanguinarine biosynthetic enzymes were identified in the EST database, 5 of which were represented among the 50 most abundant transcripts. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of total protein extracts from cell cultures treated with a fungal elicitor for 50 h facilitated the identification of 1,004 proteins. Proteins were fractionated by one-dimensional SDS-PAGE and digested with trypsin prior to LC-MS/MS analysis. Query of an opium poppy-specific EST database substantially enhanced peptide identification. Eight out of 10 known sanguinarine biosynthetic enzymes and many relevant primary metabolic enzymes were represented in the peptide database.

CONCLUSIONS

The integration of deep transcriptome and proteome analyses provides an effective platform to catalogue the components of secondary metabolism, and to identify genes encoding uncharacterized enzymes. The establishment of corresponding transcript and protein databases generated by next-generation technologies in a system with a well-defined metabolite profile facilitates an improved linkage between genes, enzymes, and pathway components. The proteome database represents the most relevant alkaloid-producing enzymes, compared with the much deeper and more complete transcriptome library. The transcript database contained full-length mRNAs encoding most alkaloid biosynthetic enzymes, which is a key requirement for the functional characterization of novel gene candidates.

TESTLoc: protein subcellular localization prediction from EST data

Background

The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.

Results

We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%).

Conclusions

TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html

Global protein interactome exploration through mining genome-scale data in Arabidopsis thaliana

Background

Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant.

Results

We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage.

Conclusions

Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.

Mass spectrometry at the interface of proteomics and genomics

With the onset of modern DNA sequencing technologies, genomics is experiencing a revolution in terms of quantity and quality of sequencing data. Rapidly growing numbers of sequenced genomes and metagenomes present a tremendous challenge for bioinformatics tools that predict protein-coding regions. Experimental evidence of expressed genomic regions, both at the RNA and protein level, is becoming invaluable for genome annotation and training of gene prediction algorithms. Evidence of gene expression at the protein level using mass spectrometry-based proteomics is increasingly used in refinement of raw genome sequencing data. In a typical "proteogenomics" experiment, the whole proteome of an organism is extracted, digested into peptides and measured by a mass spectrometer. The peptide fragmentation spectra are identified by searching against a six-frame translation of the raw genomic assembly, thus enabling the identification of hitherto unpredicted protein-coding genomic regions. Application of mass spectrometry to genome annotation presents a range of challenges to the standard workflows in proteomics, especially in terms of proteome coverage and database search strategies. Here we provide an overview of the field and argue that the latest mass spectrometry technologies that enable high mass accuracy at high acquisition rates will prove to be especially well suited for proteogenomics applications.

A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans

Efficient experimental strategies are needed to validate computationally predicted microRNA (miRNA) target genes. Here we present a large-scale targeted proteomics approach to validate predicted miRNA targets in Caenorhabditis elegans. Using selected reaction monitoring (SRM), we quantified 161 proteins of interest in extracts from wild-type and let-7 mutant worms. We demonstrate by independent experimental downstream analyses such as genetic interaction, as well as polysomal profiling and luciferase assays, that validation by targeted proteomics substantially enriched for biologically relevant let-7 interactors. For example, we found that the zinc finger protein ZTF-7 was a bona fide let-7 miRNA target. We also validated predicted miR-58 targets, demonstrating that this approach is adaptable to other miRNAs. We propose that targeted mass spectrometry can be applied generally to validate candidate lists generated by computational methods or in large-scale experiments, and that the described strategy should be readily adaptable to other organisms.

Evaluation of protein extraction methods for Vitis vinifera leaf and root proteome analysis by two-dimensional electrophoresis

An efficient protein extraction method is crucial to ensure successful separation by two-dimensional electrophoresis (2-DE) for recalcitrant plant species, in particular for grapevine (Vitis vinifera L.). Trichloroacetic acid-acetone (TCA-acetone) and phenol extraction methods were evaluated for proteome analysis of leaves and roots from the Tunisian cultivar 'Razegui'. The phenol-based protocol proved to give a higher protein yield, a greater spot resolution, and a minimal streaking on 2-DE gels for both leaf and root tissues compared with the TCA-based protocol. Furthermore, the highest numbers of detected proteins on 2-DE gels were observed using the phenol extraction from leaves and roots as compared with TCA-acetone extraction.

Comprehensive proteomics

Extensive proteome discovery projects using a variety of mass spectrometric techniques have identified proteins matching to 50-70% of the predicted gene models of various species. Comprehensive proteome coverage is desirable for the unbiased comparison of protein quantities between different biological states and for the meaningful comparison of data from multiple samples. Here we discuss the feasibility of this goal in the light of recent technological developments.

Arabidopsis methyltransferase fingerprints by affinity-based protein profiling

Precise annotation of time and spatial distribution of enzymes involved in plant secondary metabolism by gel electrophoresis are usually difficult due to their low abundance. Therefore, effective methods to enrich these enzymes are required to correlate available transcript and metabolite data with the actual presence of active enzymes in wild-type and mutant plants or to monitor variations of these enzymes under various types of biotic and abiotic stress conditions. S-Adenosyl-L-methionine-dependent O-methyltransferases play important roles in the modification of natural products such as phenylpropanoids or alkaloids. In plants they occur as small superfamilies with defined roles for each of its members in different organs and tissues. We explored the use of S-adenosyl-L-homocysteine as a selectivity function in affinity-based protein profiling supported by capture compound mass spectrometry. Due to their high affinity to this ligand it was possible to identify developmental changes of flower-specific patterns of plant natural product O-methyltransferases and corroborate the absence of individual O-methyltransferases in the corresponding Arabidopsis knockout lines. Developmental changes in the OMT pattern were correlated with transcript data obtained by qPCR.

Investigating the validity of current network analysis on static conglomerate networks by protein network stratification

Background

A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems.

Results

In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues.

Conclusions

We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.

The tricks plants use to reach appropriate light

The perception of ambient light signals that produce a relevant response to ensure exposure to appropriate levels of light energy is vital for plants. In response to this, intricate molecular mechanisms to mediate light signaling have evolved in plants. Among the responses induced by light, seedling extension is a determining event for plant survival in darkness, especially in the initial stage of plant growth. Here we review previous studies and recent progress towards an understanding of light signaling that regulates seedling elongation. We focus on the three regions of the sunlight spectrum that primarily control seedling elongation, namely red/far-red light, blue/UV-A light and UV-B light, and summarize the four signaling pathways that correspond to the three effective spectra.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Advances in protein turnover analysis at the global level and biological insights

The concept of a dynamic state of body constituents, a precursor of the modern term of proteome dynamics, was conceived over a century ago. But, not until recently can we examine the dynamics of individual "constituents" for example, proteins at a truly global level. The path of advancement in our understanding of protein turnover at the global level is marked by the introduction of some key technological innovations. These methods include the isotopic tracer technique in the 1930s, the two-dimensional gel electrophoresis technique in the 1970s, the sector mass spectrometer that could analyze isotopomers of peptides in the early 1990s, the 2D gel/MALDI-TOF proteomics technology in the late 1990s, the booming liquid chromatography/mass spectrometry proteomics technology in this decade, and the recently emerging protein-tagging approaches that offer single-cell resolution for protein turnover measurements. The long-standing inquiry raised in the 1950s about the existence of a dynamic state in different organisms at different physiological conditions can now be answered with an individual "constituent" resolution on a truly global scale. Now it appears that protein degradation is not necessarily an end to the protein function. Rather, it can be the start of a new function because protein degradation clears the way for the action of other proteins. Protein turnover participates in a multi-layer complex regulatory network and shares equal importance with gene transcription and protein translation. The advances in technologies for protein turnover analysis and the improved understanding of the biological role of protein turnover will likely help to solve some long-standing biomedical problems such as the tuberculosis disease that at the present day still affects one-third of the world population.

Model organisms--A historical perspective

Much of our knowledge on heredity, development, physiology and the underlying cellular and molecular processes is derived from the studies of model, or reference, organisms. Despite the great variety of life, a common base of shared principles could be extracted by studying a few life forms, selected based on their amenability to experimental studies. Very briefly, the origins of a few model organisms are described, including E. coli, yeast, C. elegans, Drosophila, Xenopus, zebrafish, mouse, maize and Arabidopsis. These model organisms were chosen because of their importance and wide use, which made them systems of choice for genome-wide studies. Many of their genomes were between the first to be fully sequenced, opening unprecedented opportunities for large-scale transcriptomics and proteomics studies.

Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions

Natural genetic diversity provides a powerful resource to investigate how networks respond to multiple simultaneous changes. In this work, we profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass. Most enzyme activities change in a highly coordinated manner, especially those in the Calvin-Benson cycle. Metabolites show coordinated changes in defined sectors of metabolism. Little connectivity was observed between maximum enzyme activities and metabolites, even after applying multivariate analysis methods. Measurements of posttranscriptional regulation will be required to relate these two functional levels. Individual enzyme activities correlate only weakly with biomass. However, when they are used to estimate protein abundances, and the latter are summed and expressed as a fraction of total protein, a significant positive correlation to biomass is observed. The correlation is additive to that obtained between starch and biomass. Thus, biomass is predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use.

Proteogenomics to discover the full coding content of genomes: a computational perspective

Proteogenomics has emerged as a field at the junction of genomics and proteomics. It is a loose collection of technologies that allow the search of tandem mass spectra against genomic databases to identify and characterize protein-coding genes. Proteogenomic peptides provide invaluable information for gene annotation, which is difficult or impossible to ascertain using standard annotation methods. Examples include confirmation of translation, reading-frame determination, identification of gene and exon boundaries, evidence for post-translational processing, identification of splice-forms including alternative splicing, and also, prediction of completely novel genes. For proteogenomics to deliver on its promise, however, it must overcome a number of technological hurdles, including speed and accuracy of peptide identification, construction and search of specialized databases, correction of sampling bias, and others. This article reviews the state of the art of the field, focusing on the current successes, and the role of computation in overcoming these challenges. We describe how technological and algorithmic advances have already enabled large-scale proteogenomic studies in many model organisms, including arabidopsis, yeast, fly, and human. We also provide a preview of the field going forward, describing early efforts in tackling the problems of complex gene structures, searching against genomes of related species, and immunoglobulin gene reconstruction.

Functional analysis of proteins and protein species using shotgun proteomics and linear mathematics

Covalent post-translational modification of proteins is the primary modulator of protein function in the cell. It greatly expands the functional potential of the proteome compared to the genome. In the past few years shotgun proteomics-based research, where the proteome is digested into peptides prior to mass spectrometric analysis has been prolific in this area. It has determined the kinetics of tens of thousands of sites of covalent modification on an equally large number of proteins under various biological conditions and uncovered a transiently active regulatory network that extends into diverse branches of cellular physiology. In this review, we discuss this work in light of the concept of protein speciation, which emphasizes the entire post-translationally modified molecule and its interactions and not just the modification site as the functional entity. Sometimes, particularly when considering complex multisite modification, all of the modified molecular species involved in the investigated condition, the protein species must be completely resolved for full understanding. We present a mathematical technique that delivers a good approximation for shotgun proteomics data.

ABA-regulated G protein signaling in Arabidopsis guard cells: a proteomic perspective

Signaling cascades mediated by heterotrimeric G proteins are ubiquitous and important signal transduction mechanisms in both metazoans and plants. In the model plant Arabidopsis thaliana, the sole canonical G protein alpha subunit, GPA1, has been implicated in multiple signaling events, including guard cell movement regulated by the plant stress hormone abscisic acid (ABA). However, only a handful of proteins have been demonstrated to be involved in GPA1 signaling to date. Here, we compared the proteome composition of guard cells from wild type Col vs gpa1-4 null mutants with and without ABA treatment using iTRAQ technology to identify guard cell proteins whose abundance was affected by ABA and/or GPA1. After imposition of strict selection criteria, the abundance of two proteins in Col and six proteins in gpa1-4 was found to be affected by ABA in guard cells, and 18 guard cell proteins were quantitatively affected by the mutation of GPA1. On the basis of known functions of the differentially expressed proteins, our data suggest that GPA1 inhibits guard cell photosynthesis and promotes the availability of reactive oxygen species (ROS) in guard cells. These results exemplify how iTRAQ can be used to quantitatively study single cell signaling pathways in Arabidopsis.

The proteome complement of Nicotiana tabacum Bright-Yellow-2 culture cells

The Nicotiana tabacum Bright-Yellow-2 (BY2) cell line is one of most commonly used plant suspension cell lines and offers interesting properties, such as fast growth, amenability to genetic transformation, and synchronization of cell division. To build a proteome reference map of BY2 cell proteins, we isolated the soluble proteins from N. tabacum BY2 cells at the end of the exponential growth phase and analyzed them by 2-DE and MALDI TOF-TOF. Of the 1422 spots isolated, 795 were identified with a significant score, corresponding to 532 distinct proteins.

Shotgun proteomics data from multiple organisms reveals remarkable quantitative conservation of the eukaryotic core proteome

Genome-wide, absolute quantification of expressed proteins is not yet within reach for most eukaryotes. However, large numbers of MS-based protein identifications have been deposited in databases, together with information on the observation frequencies of each peptide spectrum ("spectral counts"). We have conducted a meta-analysis using several million peptide observations from five model eukaryotes, establishing a consistent, semi-quantitative analysis pipeline. By inferring and comparing protein abundances across orthologs, we observe: (i) the accuracy of spectral counting predictions increases with sampling depth and can rival that of direct biochemical measurements, (ii) the quantitative makeup of the consistently observed core proteome in eukaryotes is remarkably stable, with abundance correlations exceeding R(S)=0.7 at an evolutionary distance greater than 1000 million years, and (iii) some groups of proteins are more constrained than others. We argue that our observations reveal stabilizing selection: central parts of the eukaryotic proteome appear to be expressed at well-balanced, near-optimal abundance levels. This is consistent with our further observations that essential proteins show lower abundance variations than non-essential proteins, and that gene families that tend to undergo gene duplications are less well constrained than families that keep a single-copy status.

Detection of alternative splice variants at the proteome level in Aspergillus flavus

Identification of proteins from proteolytic peptides or intact proteins plays an essential role in proteomics. Researchers use search engines to match the acquired peptide sequences to the target proteins. However, search engines depend on protein databases to provide candidates for consideration. Alternative splicing (AS), the mechanism where the exon of pre-mRNAs can be spliced and rearranged to generate distinct mRNA and therefore protein variants, enable higher eukaryotic organisms, with only a limited number of genes, to have the requisite complexity and diversity at the proteome level. Multiple alternative isoforms from one gene often share common segments of sequences. However, many protein databases only include a limited number of isoforms to keep minimal redundancy. As a result, the database search might not identify a target protein even with high quality tandem MS data and accurate intact precursor ion mass. We computationally predicted an exhaustive list of putative isoforms of Aspergillus flavus proteins from 20 371 expressed sequence tags to investigate whether an alternative splicing protein database can assign a greater proportion of mass spectrometry data. The newly constructed AS database provided 9807 new alternatively spliced variants in addition to 12 832 previously annotated proteins. The searches of the existing tandem MS spectra data set using the AS database identified 29 new proteins encoded by 26 genes. Nine fungal genes appeared to have multiple protein isoforms. In addition to the discovery of splice variants, AS database also showed potential to improve genome annotation. In summary, the introduction of an alternative splicing database helps identify more proteins and unveils more information about a proteome.

Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes

Background

MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20–22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.

Principal Findings

We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis ‘orphan’ hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the “ancient” (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for “new” rapidly-evolving MIRNA genes.

Conclusions

Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.

Tools for high-spatial and temporal-resolution analysis of environmental responses in plants

Understanding how plants cope with environmental change requires a spatiotemporal perspective. In this review, we highlight recent work which has led to the development and use of novel tools for the high spatial and temporal-resolution analysis of the plant-environment interaction. FACS-based transcriptome and immunoprecipitation-based translatome data sets have provided an important foundation for the analysis of the transcriptional and translational control of environmental responses in each tissue layer of the plant. Complementary approaches, based on a proteomic toolkit, have provided insight into the biological response of Arabidopsis to NaCl and the relationship between transcript and protein levels. The development and adaptation of biosensors and ion-specific dyes provides the capacity to visualize changes in the transport and accumulation of metabolites and small molecules such as sugars, Na(+) and Ca(2+) at the cellular level. Finally, live-imaging approaches coupled with automated image-analysis algorithms are revealing new levels of dynamism and plasticity in the response to light and gravity. Together, these tools will provide a more comprehensive understanding of environmental responses in plants, which will aide in the development of new crop varieties for sustainable agriculture.

Enzyme activity profiles during fruit development in tomato cultivars and Solanum pennellii

Enzymes interact to generate metabolic networks. The activities of more than 22 enzymes from central metabolism were profiled during the development of fruit of the modern tomato cultivar Solanum lycopersicum 'M82' and its wild relative Solanum pennellii (LA0716). In S. pennellii, the mature fruit remains green and contains lower sugar and higher organic acid levels. These genotypes are the parents of a widely used near introgression line population. Enzymes were also profiled in a second cultivar, S. lycopersicum 'Moneymaker', for which data sets for the developmental changes of metabolites and transcripts are available. Whereas most enzyme activities declined during fruit development in the modern S. lycopersicum cultivars, they remained high or even increased in S. pennellii, especially enzymes required for organic acid synthesis. The enzyme profiles were sufficiently characteristic to allow stages of development and cultivars and the wild species to be distinguished by principal component analysis and clustering. Many enzymes showed coordinated changes during fruit development of a given genotype. Comparison of the correlation matrices revealed a large overlap between the two modern cultivars and considerable overlap with S. pennellii, indicating that despite the very different development responses, some basic modules are retained. Comparison of enzyme activity, metabolite profiles, and transcript profiles in S. lycopersicum 'Moneymaker' revealed remarkably little connectivity between the developmental changes of transcripts and enzymes and even less between enzymes and metabolites. We discuss the concept that the metabolite profile is an emergent property that is generated by complex network interactions.

Distinct pathways mediate the sorting of tail-anchored proteins to the plastid outer envelope

BACKGROUND

Tail-anchored (TA) proteins are a distinct class of membrane proteins that are sorted post-translationally to various organelles and function in a number of important cellular processes, including redox reactions, vesicular trafficking and protein translocation. While the molecular targeting signals and pathways responsible for sorting TA proteins to their correct intracellular destinations in yeasts and mammals have begun to be characterized, relatively little is known about TA protein biogenesis in plant cells, especially for those sorted to the plastid outer envelope.

METHODOLOGY/PRINCIPAL FINDINGS

Here we investigated the biogenesis of three plastid TA proteins, including the 33-kDa and 34-kDa GTPases of the translocon at the outer envelope of chloroplasts (Toc33 and Toc34) and a novel 9-kDa protein of unknown function that we define here as an outer envelope TA protein (OEP9). Using a combination of in vivo and in vitro assays we show that OEP9 utilizes a different sorting pathway than that used by Toc33 and Toc34. For instance, while all three TA proteins interact with the cytosolic OEP chaperone/receptor, AKR2A, the plastid targeting information within OEP9 is distinct from that within Toc33 and Toc34. Toc33 and Toc34 also appear to differ from OEP9 in that their insertion is dependent on themselves and the unique lipid composition of the plastid outer envelope. By contrast, the insertion of OEP9 into the plastid outer envelope occurs in a proteinaceous-dependent, but Toc33/34-independent manner and membrane lipids appear to serve primarily to facilitate normal thermodynamic integration of this TA protein.

CONCLUSIONS/SIGNIFICANCE

Collectively, the results provide evidence in support of at least two sorting pathways for plastid TA outer envelope proteins and shed light on not only the complex diversity of pathways involved in the targeting and insertion of proteins into plastids, but also the molecular mechanisms that underlie the delivery of TA proteins to their proper intracellular locations in general.

Systems analysis of seed filling in Arabidopsis: using general linear modeling to assess concordance of transcript and protein expression

Previous systems analyses in plants have focused on a single developmental stage or time point, although it is often important to additionally consider time-index changes. During seed development a cascade of events occurs within a relatively brief time scale. We have collected protein and transcript expression data from five sequential stages of Arabidopsis (Arabidopsis thaliana) seed development encompassing the period of reserve polymer accumulation. Protein expression profiling employed two-dimensional gel electrophoresis coupled with tandem mass spectrometry, while transcript profiling used oligonucleotide microarrays. Analyses in biological triplicate yielded robust expression information for 523 proteins and 22,746 genes across the five developmental stages, and established 319 protein/transcript pairs for subsequent pattern analysis. General linear modeling was used to evaluate the protein/transcript expression patterns. Overall, application of this statistical assessment technique showed concurrence for a slight majority (56%) of expression pairs. Many specific examples of discordant protein/transcript expression patterns were detected, suggesting that this approach will be useful in revealing examples of posttranscriptional regulation.

Proteogenomics of Pristionchus pacificus reveals distinct proteome structure of nematode models

Pristionchus pacificus is a nematode model organism whose genome has recently been sequenced. To refine the genome annotation we performed transcriptome and proteome analysis and gathered comprehensive experimental information on gene expression. Transcriptome analysis on a 454 Life Sciences (Roche) FLX platform generated >700,000 expressed sequence tags (ESTs) from two normalized EST libraries, whereas proteome analysis on an LTQ-Orbitrap mass spectrometer detected >27,000 nonredundant peptide sequences from more than 4000 proteins at sub-parts-per-million (ppm) mass accuracy and a false discovery rate of <1%. Retraining of the SNAP gene prediction algorithm using the gene expression data led to a decrease in the number of previously predicted protein-coding genes from 29,000 to 24,000 and refinement of numerous gene models. The P. pacificus proteome contains a high proportion of small proteins with no known homologs in other species ("pioneer" proteins). Some of these proteins appear to be products of highly homologous genes, pointing to their common origin. We show that >50% of all pioneer genes are transcribed under standard culture conditions and that pioneer proteins significantly contribute to a unimodal distribution of predicted protein sizes in P. pacificus, which has an unusually low median size of 240 amino acids (26.8 kDa). In contrast, the predicted proteome of Caenorhabditis elegans follows a distinct bimodal protein size distribution, with significant functional differences between small and large protein populations. Combined, these results provide the first catalog of the expressed genome of P. pacificus, refinement of its genome annotation, and the first comparison of related nematode models at the proteome level.

The PPH1 phosphatase is specifically involved in LHCII dephosphorylation and state transitions in Arabidopsis

The ability of plants to adapt to changing light conditions depends on a protein kinase network in the chloroplast that leads to the reversible phosphorylation of key proteins in the photosynthetic membrane. Phosphorylation regulates, in a process called state transition, a profound reorganization of the electron transfer chain and remodeling of the thylakoid membranes. Phosphorylation governs the association of the mobile part of the light-harvesting antenna LHCII with either photosystem I or photosystem II. Recent work has identified the redox-regulated protein kinase STN7 as a major actor in state transitions, but the nature of the corresponding phosphatases remained unknown. Here we identify a phosphatase of Arabidopsis thaliana, called PPH1, which is specifically required for the dephosphorylation of light-harvesting complex II (LHCII). We show that this single phosphatase is largely responsible for the dephosphorylation of Lhcb1 and Lhcb2 but not of the photosystem II core proteins. PPH1, which belongs to the family of monomeric PP2C type phosphatases, is a chloroplast protein and is mainly associated with the stroma lamellae of the thylakoid membranes. We demonstrate that loss of PPH1 leads to an increase in the antenna size of photosystem I and to a strong impairment of state transitions. Thus phosphorylation and dephosphorylation of LHCII appear to be specifically mediated by the kinase/phosphatase pair STN7 and PPH1. These two proteins emerge as key players in the adaptation of the photosynthetic apparatus to changes in light quality and quantity.

Arabidopsis and primary photosynthetic metabolism - more than the icing on the cake

Historically speaking, Arabidopsis was not the plant of choice for investigating photosynthesis, with physiologists and biochemists favouring other species such as Chlorella, spinach and pea. However, its inherent advantages for forward genetics rapidly led to its adoption for photosynthesis research. In the last ten years, the availability of the Arabidopsis genome sequence - still the gold-standard for plant genomes - and the rapid expansion of genetic and genomic resources have further increased its importance. Research in Arabidopsis has not only provided comprehensive information about the enzymes and other proteins involved in photosynthesis, but has also allowed transcriptional responses, protein levels and compartmentation to be analysed at a global level for the first time. Emerging technical and theoretical advances offer another leap forward in our understanding of post-translational regulation and the control of metabolism. To illustrate the impact of Arabidopsis, we provide a historical review of research in primary photosynthetic metabolism, highlighting the role of Arabidopsis in elucidation of the pathway of photorespiration and the regulation of RubisCO, as well as elucidation of the pathways of starch turnover and studies of the significance of starch for plant growth.