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Publications - Stress and Develop Biology

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Publications

Ranf, S.; Gisch, N.; Schäffer, M.; Illig, T.; Westphal, L.; Knirel, Y. A.; Sánchez-Carballo, P. M.; Zähringer, U.; Hückelhoven, R.; Lee, J.; Scheel, D. A lectin S-domain receptor kinase mediates lipopolysaccharide sensing in Arabidopsis thaliana Nat Immunol 16, 426–433, (2015) DOI: 10.1038/ni.3124

The sensing of microbe-associated molecular patterns (MAMPs) triggers innate immunity in animals and plants. Lipopolysaccharide (LPS) from Gram-negative bacteria is a potent MAMP for mammals, with the lipid A moiety activating proinflammatory responses via Toll-like receptor 4 (TLR4). Here we found that the plant Arabidopsis thaliana specifically sensed LPS of Pseudomonas and Xanthomonas. We isolated LPS-insensitive mutants defective in the bulb-type lectin S-domain-1 receptor–like kinase LORE (SD1-29), which were hypersusceptible to infection with Pseudomonas syringae. Targeted chemical degradation of LPS from Pseudomonas species suggested that LORE detected mainly the lipid A moiety of LPS. LORE conferred sensitivity to LPS onto tobacco after transient expression, which demonstrated a key function in LPS sensing and indicated the possibility of engineering resistance to bacteria in crop species.
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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