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Publications - Stress and Develop Biology

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Publications

Siersleben, S.; Penselin, D.; Wenzel, C.; Albert, S.; Knogge, W. PFP1, a gene encoding an Epc-N domain-containing protein, is essential forpathogenicity of the barley pathogen Rhynchosporium commune. Eukaryot. Cell 13, 1026-1035, (2014) DOI: 10.1128/EC.00043-14

Scald caused by Rhynchosporium commune is an important foliar disease of barley. Insertion mutagenesis of R. commune generated a non-pathogenic fungal mutant, which carries the inserted plasmid in the upstream region of a gene named PFP1. The characteristic feature of the gene product is an Epc-N domain. This motif is also found in homologous proteins shown to be components of histone acetyltransferase (HAT) complexes of fungi and animals. Therefore, PFP1 is suggested to be the subunit of a HAT complex in R. commune with an essential role in the epigenetic control of fungal pathogenicity. Targeted PFP1 disruption also yielded non-pathogenic mutants, which showed wild-type like growth ex planta except for the occurrence of hyphal swellings. Complementation of the deletion mutants with the wild-type gene reestablished pathogenicity and suppressed the hyphal swellings. However, despite wild-type level PFP1 expression the complementation mutants did not reach wild-type level virulence. This indicates that the function of the protein complex and, thus, fungal virulence is influenced by a position-affected long-range control of PFP1 expression. 
Books and chapters

Hummel, J.; Strehmel, N.; Bölling, C.; Schmidt, S.; Walther D.; Kopka, J. Mass spectral search and analysis using the Golm metabolome. (Weckwerth, W.; Kahl, G.). 321-343, (2013) ISBN: 978-3-527-32777-5 DOI: 10.1002/9783527669882.ch18

The novel “omics” technologies of the postgenomic era generate large multiplexed phenotyping datasets, which can only inadequately be published in the traditional journal and supplemental formats. For this reason, public databases have been developed that utilize the efficient communication of knowledge through the World Wide Web. This trend also applies to the metabolomics field, which is, after genomics, transcriptomics, and proteomics, the fourth major systems-level phenotyping platform. Each different analytical technology used in metabolomics studies requires specific reference data for metabolite identification and optimal data formats for reporting the complex metabolite profiling data features. Therefore, we envision that every technology platform or even each high-throughput metabolomic laboratory will establish dedicated databases, which will communicate between each other and will be integrated by meta-databases and web services. The Golm Metabolome Database (GMD) (http://gmd.mpimp-golm.mpg.de/) is a metabolomic database, maintained by the Max Planck Institute of Molecular Plant Physiology, that was initiated around a nucleus of reference data from gas chromatography–mass spectrometry metabolite profiling data and is now developing toward a general mass spectrometry-based repository of reference metabolite profiles for essential plant tissues and typical variations of growth conditions. This chapter describes the mass spectral searches and analyses currently supported by the GMD. We specifically address the searches for the different chemical entities within GMD, namely the metabolites, reference substances, and the chemically derivatized analytes. We report the diverse options for mass spectral analyses and highlight the decision tree-supported prediction of chemical substructures, a feature of GMD that currently appears to be a unique among the many tools for the analysis of gas chromatography–electron ionization mass spectra.
Publications

Rasche, F.; Svatoš, A.; Maddula, R. K.; Böttcher, C.; Böcker, S. Computing Fragmentation Trees from Tandem Mass Spectrometry Data Anal Chem 83, 1243-1251, (2011) DOI: 10.1021/ac101825k

The structural elucidation of organic compounds in complex biofluids and tissues remains a significant analytical challenge. For mass spectrometry, the manual interpretation of collision-induced dissociation (CID) mass spectra is cumbersome and requires expert knowledge, as the fragmentation mechanisms of ions formed from small molecules are not completely understood. The automated identification of compounds is generally limited to searching in spectral libraries. Here, we present a method for interpreting the CID spectra of the organic compound’s protonated ions by computing fragmentation trees that establish not only the molecular formula of the compound and all fragment ions but also the dependencies between fragment ions. This is an important step toward the automated identification of unknowns from the CID spectra of compounds that are not in any database.
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