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Publications - Stress and Develop Biology

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Books and chapters

Doell, S.; Arens, N.; Mock, H. Liquid Chromatography and Liquid Chromatography–Mass Spectrometry of Plants: Techniques and Applications (Meyers, R. A., ed.). (2019) ISBN: 9780470027318 DOI: 10.1002/9780470027318.a9912.pub2

Mass spectrometry coupled with LC (liquid chromatography) separation has developed into a technique routinely applied for targeted as well as for nontargeted analysis of complex biological samples, not only in plant biochemistry. Earlier on, LC‐MS (liquid chromatography–mass spectrometry) was mostly part of the efforts for identification of one or few unknown metabolites of interest as part of a phytochemical study. As a major strategy, unknown compounds had to be purified in sufficient quantities. The purified fractions were then subjected to LC‐MS/MS as part of the structural elucidation, mostly complemented by NMR (nuclear magnetic resonance) analysis. With the advance of mass spectrometry instrumentation, LC‐MS is now widely applied for analysis of crude plant extracts and large numbers (100s to 1000s) of samples. It has become an essential part of metabolomic studies (see Metabolomics), aiming at the comprehensive coverage of the metabolite profiles of cells, tissues, or organs. Owing to the huge chemical diversity of small molecules, conditions for the extraction will restrict the subfraction of the metabolome, which can be actually analyzed. The conditions for LC have to be adjusted to allow good separation of the particular metabolites from the respective extract. Major consideration will be the selection of an appropriate column and suitable eluents, the establishment of gradient profiles, temperature conditions, and so on.
Publications

Kumar, A.; Gopalswamy, M.; Wishart, C.; Henze, M.; Eschen-Lippold, L.; Donnelly, D.; Balbach, J. N-Terminal Phosphorylation of Parathyroid Hormone (PTH) Abolishes Its Receptor Activity ACS Chem Biol 9, 2465–2470, (2014) DOI: 10.1021/cb5004515

The parathyroid hormone (PTH) is an 84-residue peptide, which regulates the blood Ca2+ level via GPCR binding and subsequent activation of intracellular signaling cascades. PTH is posttranslationally phosphorylated in the parathyroid glands; however, the functional significance of this processes is not well characterized. In the present study, mass spectrometric analysis revealed three sites of phosphorylation, and NMR spectroscopy assigned Ser1, Ser3, and Ser17 as modified sites. These sites are located at the N-terminus of the hormone, which is important for receptor recognition and activation. NMR shows further that the three phosphate groups remotely disturb the α-helical propensity up to Ala36. An intracellular cAMP accumulation assay elucidated the biological significance of this phosphorylation because it ablated the PTH-mediated signaling. Our studies thus shed light on functional implications of phosphorylation at native PTH as an additional level of regulation 
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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