Browsing by Author "Reumann, Sigrun"

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods 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.

Due 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.

Background 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.

Very 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.

The most prominent role of peroxisomes in photosynthetic plant tissues is their participation in photorespiration, a process also known as the oxidative C-2 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. (c) 2006 Elsevier B.V. All rights reserved.

Prediction 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.

We have established a protocol for the isolation of highly purified peroxisomes from mature Arabidopsis thaliana leaves and analyzed the proteome by complementary gel-based and gel-free approaches. Seventy-eight nonredundant proteins were identified, of which 42 novel proteins had previously not been associated with plant peroxisomes. Seventeen novel proteins carried predicted peroxisomal targeting signals (PTS) type 1 or type 2; 11 proteins contained PTS-related peptides. Peroxisome targeting was supported for many novel proteins by in silico analyses and confirmed for 11 representative fulllength fusion proteins by fluorescence microscopy. The targeting function of predicted and unpredicted signals was investigated and SSL>, SSI>, and ASL> were established as novel functional PTS1 peptides. In contrast with the generally accepted confinement of PTS2 peptides to the N-terminal domain, the bifunctional transthyretin-like protein was demonstrated to carry internally a functional PTS2. The novel enzymes include numerous enoyl-CoA hydratases, short-chain dehydrogenases, and several enzymes involved in NADP and glutathione metabolism. Seven proteins, including beta-glucosidases and myrosinases, support the currently emerging evidence for an important role of leaf peroxisomes in defense against pathogens and herbivores. The data provide new insights into the biology of plant peroxisomes and improve the prediction accuracy of peroxisome-targeted proteins from genome sequences.

PEROXIN11 ( PEX11) is a peroxisomal membrane protein in fungi and mammals and was proposed to play a major role in peroxisome proliferation. To begin understanding how peroxisomes proliferate in plants and how changes in peroxisome abundance affect plant development, we characterized the extended Arabidopsis thaliana PEX11 protein family, consisting of the three phylogenetically distinct subfamilies PEX11a, PEX11b, and PEX11c to PEX11e. All five Arabidopsis PEX11 proteins target to peroxisomes, as demonstrated for endogenous and cyan fluorescent protein fusion proteins by fluorescence microscopy and immunobiochemical analysis using highly purified leaf peroxisomes. PEX11a and PEX11c to PEX11e behave as integral proteins of the peroxisome membrane. Overexpression of At PEX11 genes in Arabidopsis induced peroxisome proliferation, whereas reduction in gene expression decreased peroxisome abundance. PEX11c and PEX11e, but not PEX11a, PEX11b, and PEX11d, complemented to significant degrees the growth phenotype of the Saccharomyces cerevisiae pex11 null mutant on oleic acid. Heterologous expression of PEX11e in the yeast mutant increased the number and reduced the size of the peroxisomes. We conclude that all five Arabidopsis PEX11 proteins promote peroxisome proliferation and that individual family members play specific roles in distinct peroxisomal subtypes and environmental conditions and possibly in different steps of peroxisome proliferation.

Leaf 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.