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    Multimodal Molecular Imaging and Identification of Bacterial Toxins Causing Mushroom Soft Rot and Cavity Disease
    (Weinheim : Wiley-VCH, 2021) Dose, Benjamin; Thongkongkaew, Tawatchai; Zopf, David; Kim, Hak Joong; Bratovanov, Evgeni V.; García-Altares, María; Scherlach, Kirstin; Kumpfmüller, Jana; Ross, Claudia; Hermenau, Ron; Niehs, Sarah; Silge, Anja; Hniopek, Julian; Schmitt, Michael; Popp, Jürgen; Hertweck, Christian
    Soft rot disease of edible mushrooms leads to rapid degeneration of fungal tissue and thus severely affects farming productivity worldwide. The bacterial mushroom pathogen Burkholderia gladioli pv. agaricicola has been identified as the cause. Yet, little is known about the molecular basis of the infection, the spatial distribution and the biological role of antifungal agents and toxins involved in this infectious disease. We combine genome mining, metabolic profiling, MALDI-Imaging and UV Raman spectroscopy, to detect, identify and visualize a complex of chemical mediators and toxins produced by the pathogen during the infection process, including toxoflavin, caryoynencin, and sinapigladioside. Furthermore, targeted gene knockouts and in vitro assays link antifungal agents to prevalent symptoms of soft rot, mushroom browning, and impaired mycelium growth. Comparisons of related pathogenic, mutualistic and environmental Burkholderia spp. indicate that the arsenal of antifungal agents may have paved the way for ancestral bacteria to colonize niches where frequent, antagonistic interactions with fungi occur. Our findings not only demonstrate the power of label-free, in vivo detection of polyyne virulence factors by Raman imaging, but may also inspire new approaches to disease control. © 2021 The Authors. ChemBioChem published by Wiley-VCH GmbH
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    Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry
    (Oxford : Oxford University Press, 2015) Oetjen, Janina; Veselkov, Kirill; Watrous, Jeramie; McKenzie, James S.; Becker, Michael; Hauberg-Lotte, Lena; Kobarg, Jan Hendrik; Strittmatter, Nicole; Mróz, Anna K.; Hoffmann, Franziska; Trede, Dennis; Palmer, Andrew; Schiffler, Stefan; Steinhorst, Klaus; Aichler, Michaela; Goldin, Robert; Guntinas-Lichius, Orlando; von Eggeling, Ferdinand; Thiele, Herbert; Maedler, Kathrin; Walch, Axel; Maass, Peter; Dorrestein, Pieter C.; Takats, Zoltan; Alexandrov, Theodore
    Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.