1,720,983 research outputs found
Specification of the Peroxisome Targeting Signals Type 1 and Type 2 of Plant Peroxisomes by Bioinformatics Analyses
No full textImproved prediction of peroxisomal PTS1 proteins from genome sequences based on experimental subcellular targeting analyses as exemplified for protein kinases from Arabidopsis
No full textDue to current experimental limitations in peroxisome proteome research, the identification of low-abundance regulatory proteins such as protein kinases largely relies on computational protein prediction. To test and improve the identification of regulatory proteins by such a prediction-based approach, the Arabidopsis genome was screened for genes that encode protein kinases with predicted type 1 or type 2 peroxisome targeting signals (PTS1 or PTS2). Upon transient expression in onion epidermal cells, the predicted PTS1 domains of four of the seven protein kinases re-directed the reporter protein, enhanced yellow green fluorescent (EYFP), to peroxisomes and were thus verified as functional PTS1 domains. The full-length fusions, however, remained cytosolic, suggesting that PTS1 exposure is induced by specific signals. To investigate why peroxisome targeting of three other kinases was incorrectly predicted and ultimately to improve the prediction algorithms, selected amino acid residues located upstream of PTS1 tripeptides were mutated and the effect on subcellular targeting of the reporter protein was analysed. Acidic residues in close proximity to major PTS1 tripeptides were demonstrated to inhibit protein targeting to plant peroxisomes even in the case of the prototypical PTS1 tripeptide SKL >, whereas basic residues function as essential auxiliary targeting elements in front of weak PTS1 tripeptides such as SHL >. The functional characterization of these inhibitory and essential enhancer-targeting elements allows their consideration in predictive algorithms to improve the prediction accuracy of PTS1 proteins from genome sequences
Plant peroxisomes respire in the light: Some gaps of the photorespiratory C2 cycle have become filled—Others remain
No full textAbstractThe most prominent role of peroxisomes in photosynthetic plant tissues is their participation in photorespiration, a process also known as the oxidative C2 cycle or the oxidative photosynthetic carbon cycle. Photorespiration is an essential process in land plants, as evident from the conditionally lethal phenotype of mutants deficient in enzymes or transport proteins involved in this pathway. The oxidative C2 cycle is a salvage pathway for phosphoglycolate, the product of the oxygenase activity of ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO), to the Calvin cycle intermediate phosphoglycerate. The pathway is highly compartmentalized and involves reactions in chloroplasts, peroxisomes, and mitochondria. The H2O2-producing enzyme glycolate oxidase, catalase, and several aminotransferases of the photorespiratory cycle are located in peroxisomes, with catalase representing the major constituent of the peroxisomal matrix in photosynthetic tissues. Although photorespiration is of major importance for photosynthesis, the identification of the enzymes involved in this process has only recently been completed. Only little is known about the metabolite transporters for the exchange of photorespiratory intermediates between peroxisomes and the other organelles involved, and about the regulation of the photorespiratory pathway. This review highlights recent developments in understanding photorespiration and identifies remaining gaps in our knowledge of this important metabolic pathway
Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1)
No full textPlant peroxisomes are degraded by starvation-induced and constitutive autophagy in tobacco BY-2 suspension-cultured cells
No full textVery recently, autophagy has been recognized as an important degradation pathway for quality control of peroxisomes in Arabidopsis plants. To further characterize the role of autophagy in plant peroxisome degradation, we generated stable transgenic suspension-cultured cell lines of heterotrophic Nicotiana tabacum L. cv. Bright Yellow 2 expressing a peroxisome-targeted version of enhanced yellow fluorescent protein. Indeed, this cell line model system proved advantageous for detailed cytological analyses of autophagy stages and for quantification of cellular peroxisome pools under different culturing conditions and upon inhibitor applications. Complementary biochemical, cytological, and pharmacological analyses provided convincing evidence for peroxisome degradation by bulk autophagy during carbohydrate starvation. This degradation was slowed down by the inhibitor of autophagy, 3-methyladenine (3-MA), but the 3-MA effect ceased at advanced stages of starvation, indicating that another degradation mechanism for peroxisomes might have taken over. 3-MA also caused an increase particularly in peroxisomal proteins and cellular peroxisome numbers when applied under nutrient-rich conditions in the logarithmic growth phase, suggesting a high turnover rate for peroxisomes by basal autophagy under non-stress conditions. Together, our data demonstrate that a great fraction of the peroxisome pool is subject to extensive autophagy-mediated turnover under both nutrient starvation and optimal growth conditions. Our analyses of the cellular pool size of peroxisomes provide a new tool for quantitative investigations of the role of plant peroxisomes in reactive oxygen species metabolism
PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins
No full textPrediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e. novel PTS1 tripeptides and tripeptide residues. Here we describe the first plant-specific web server PredPlantPTS1 for the prediction of plant PTS1 proteins using the above-mentioned underlying models. The server allows the submission of protein sequences from diverse spermatophytes and also performs well for mosses and algae. The easy-to-use web interface provides detailed output in terms of (i) the peroxisomal targeting probability of the given sequence, (ii) information whether a particular non-canonical PTS1 tripeptide has already been experimentally verified, and (iii) the prediction scores for the single C-terminal 14 amino acid residues. The latter allows identification of predicted residues that inhibit peroxisome targeting and which can be optimized using site-directed mutagenesis to raise the peroxisome targeting efficiency. The prediction server will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants. PredPlantPTS1 is freely accessible at ppp.gobics.de
The proteome map of spinach leaf peroxisomes indicates partial compartmentalization of phylloquinone (vitamin K1) biosynthesis in plant peroxisomes
No full textLeaf peroxisomes are fragile, low-abundance plant cell organelles that are difficult to isolate from one of the few plant species whose nuclear genome has been sequenced. Leaf peroxisomes were enriched at high purity from spinach (Spinacia oleracea) and similar to 100 protein spots identified from 2-dimensional gels by a combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS) and de novo sequencing. In addition to the predominant enzymes involved in photorespiration and detoxification, several minor enzymes were detected, underscoring the high sensitivity of the protein identification. The tryptic peptides of three unknown proteins shared high sequence similarity with Arabidopsis proteins that carry putative peroxisomal targeting signals type 1 or 2 (PTS1/2). The apparent Arabidopsis orthologues are a short-chain alcohol dehydrogenase (SDRa/IBR1, At4g05530, SRL >) and two enoyl-CoA hydratases/isomerases (ECHIa, At4g16210, SKL >; NS/ECHId, At1g60550, RLx(5)HL). The peroxisomal localization of the three proteins was confirmed in vivo by tagging with enhanced yellow fluorescent protein (EYFP), and the targeting signals were identified. The single Arabidopsis isoform of naphthoate synthase (NS) is orthologous to MenB from cyanobacteria, which catalyses an essential reaction in phylloquinone biosynthesis, a pathway previously assumed to be entirely compartmentalized in plastids in higher plants. In an extension of a previous study, the present in vivo targeting data furthermore demonstrate that the enzyme upstream of NS, chloroplastic acyl-CoA activating enzyme isoform 14 (AAE14, SSL >), is dually targeted to both plastids and peroxisomes. This proteomic study, extended by in vivo subcellular localization analyses, indicates a novel function for plant peroxisomes in phylloquinone biosynthesis
Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis
No full textBackground High-accuracy prediction tools are essential in the post-genomic era to define organellar proteomes in their full complexity. We recently applied a discriminative machine learning approach to predict plant proteins carrying peroxisome targeting signals (PTS) type 1 from genome sequences. For Arabidopsis thaliana 392 gene models were predicted to be peroxisome-targeted. The predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. Results In this study, we experimentally validated the predictions in greater depth by focusing on the most challenging Arabidopsis proteins with unknown non-canonical PTS1 tripeptides and prediction scores close to the threshold. By in vivo subcellular targeting analysis, three novel PTS1 tripeptides (QRL>, SQM>, and SDL>) and two novel tripeptide residues (Q at position −3 and D at pos. -2) were identified. To understand why, among many Arabidopsis proteins carrying the same C-terminal tripeptides, these proteins were specifically predicted as peroxisomal, the residues upstream of the PTS1 tripeptide were computationally permuted and the changes in prediction scores were analyzed. The newly identified Arabidopsis proteins were found to contain four to five amino acid residues of high predicted targeting enhancing properties at position −4 to −12 in front of the non-canonical PTS1 tripeptide. The identity of the predicted targeting enhancing residues was unexpectedly diverse, comprising besides basic residues also proline, hydroxylated (Ser, Thr), hydrophobic (Ala, Val), and even acidic residues. Conclusions Our computational and experimental analyses demonstrate that the plant PTS1 tripeptide motif is more diverse than previously thought, including an increasing number of non-canonical sequences and allowed residues. Specific targeting enhancing elements can be predicted for particular sequences of interest and are far more diverse in amino acid composition and positioning than previously assumed. Machine learning methods become indispensable to predict which specific proteins, among numerous candidate proteins carrying the same non-canonical PTS1 tripeptide, contain sufficient enhancer elements in terms of number, positioning and total strength to cause peroxisome targeting
Immune-associated small GTPases of Arabidopsis thaliana: dissecting the activation mechanism and their roles in organelle biogenesis
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