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A B S T R A C T B O O K – A B S T R A C T S O F T A L K S 17 X X I V S P P S C O N G R E S S 2 0 1 1 Session 2. Systems biology and -omics IDENTIFICATION OF NOVEL PLANT PEROXISOMAL TARGETING SIGNALS BY A COMBINATION OF MACHINE LEARNING METHODS AND IN VIVO SUBCELLULAR TARGETING ANALYSES Thomas Lingner 1,2 , Amr R. Kataya 2 , Gerardo E. Antonicelli 2,3 , Aline Benichou 2 , Kjersti Nilssen 2 , Xiong-Yan Chen 2 , Tanja Siemsen 3 , Burkhard Morgenstern 1 , Peter Meinicke 1 , Sigrun Reumann 2,3 1 Department of Bioinformatics, Institute for Microbiology, Goettingen, Germany 2 Centre for Organelle Research, University of Stavanger, Stavanger, Norway 3 Department of Plant Biochemistry, Georg-August-University of Goettingen, Goettingen, Germany E-mail: sigrun.reumann@uis.no In the post-genomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisometargeted 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 2,500 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 genome, a total of 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.
Session 2. Systems biology and -omics A B S T R A C T B O O K – A B S T R A C T S O F T A L K S ARABIDOPSIS GLOBAL STRESS REGULON Pankaj Barah 1 , Simon Rasmussen 2 , Maria Cristina Suarez-Rodriguez 3 , Laurent Gautier 2 , John Mundy 3 , Henrik Bjørn Nielsen 2 , Atle M. Bones 1 1 Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway 2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark 3 Department of Biology, University of Copenhagen, Copenhagen, Denmark E-mail: pankaj.barah@bio.ntnu.no Co-expressed or co-regulated genes can indicate their involvement in similar biological processes, meaning that individual modules can be attributed to specific biological processes. Using this basic concept together with information about signal transduction and metabolic pathways, genes that share a similar expression profile across multiple spatial, temporal, environmental and genetic conditions could be considered as eukaryotic regulon. Plants have developed elaborate networks of defense mechanisms against different types of stresses, but the level of network crosstalk makes it challenging to correlate various types of responses to a particular stress. Meta-analysis of the Arabidopsis transcriptome offers the potential to identify regulons. As there are many standards on how to grow plants and to conduct transcription experiments, it is difficult to extract compatible information across data sets. To overcome this problem of incompatibility of independent microarray experiments, 23 different genotypes (10 ecotypes and 13 mutants) were subjected to a set of 5 individual stress treatments and 8 combinations of stress treatments under same experimental conditions. This was a part of ERA-NET Plant Genomics, MultiStress project (http://www.erapg.org/). Using this ERA-PG MultiStress global dataset, we have explored the organization of stress regulons in the model plant Arabidopsis. 18 X X I V S P P S C O N G R E S S 2 0 1 1
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Page 3 and 4: Foreword eword ord A B S T R A C T
Page 5 and 6: Program of the Congress gram of the
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Page 13 and 14: Plenary session 2 THE BIOLOGY OF AL
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Sponsors onsors ors A B S T R A C T
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