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Publications - Stress and Develop Biology

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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.

Wirsing, L.; Naumann, K.; Vogt, T. Arabidopsis methyltransferase fingerprints by affinity-based protein profiling Anal Biochem 408, 220-225, (2011) DOI: 10.1016/j.ab.2010.09.029

Precise annotation of time and spatial distribution of enzymes involved in plant secondary metabolism by gel electrophoresis are usually difficult due to their low abundance. Therefore, effective methods to enrich these enzymes are required to correlate available transcript and metabolite data with the actual presence of active enzymes in wild-type and mutant plants or to monitor variations of these enzymes under various types of biotic and abiotic stress conditions. S-Adenosyl-L-methionine-dependent O-methyltransferases play important roles in the modification of natural products such as phenylpropanoids or alkaloids. In plants they occur as small superfamilies with defined roles for each of its members in different organs and tissues. We explored the use of S-adenosyl-L-homocysteine as a selectivity function in affinity-based protein profiling supported by capture compound mass spectrometry. Due to their high affinity to this ligand it was possible to identify developmental changes of flower-specific patterns of plant natural product O-methyltransferases and corroborate the absence of individual O-methyltransferases in the corresponding Arabidopsis knockout lines. Developmental changes in the OMT pattern were correlated with transcript data obtained by qPCR.

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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