Unser 10. Leibniz Plant Biochemistry Symposium am 7. und 8. Mai war ein großer Erfolg. Thematisch ging es in diesem Jahr um neue Methoden und Forschungsansätze der Naturstoffchemie. Die exzellenten Vorträge über Wirkstoffe…
Omanische Heilpflanze im Fokus der Phytochemie IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…
Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Differentiation of cancer cells entails the reversion of phenotype from malignant to the original. The conversion to cell type characteristic for another tissue is named transdifferentiation. Differentiation/transdifferentiation of malignant cells in high grade tumor mass could serve as a nonaggressive approach that potentially limits tumor progression and augments chemosensitivity. While this therapeutic strategy is already being used for treatment of hematological cancers, its feasibility for solid malignancies is still debated. We will presently discuss the natural compounds that show these properties, with focus on anthraquinones from Aloe vera, Senna, Rheum sp. and hop derived prenylflavonoids.
Publikation
Frainay, C.; Schymanski, E. L.; Neumann, S.; Merlet, B.; Salek, R. M.; Jourdan, F.; Yanes, O.;Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered AreasMetabolites851(2018)DOI: 10.3390/metabo8030051
The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.