The identification of metabolites was performed following a biologically-driven approach performing searches against the targeted in-house species-specific metabolic databases for Rhizoctonia and Stachybotrys. The libraries were constructed acquiring information from the literature and publicly available databases such as, KNApSAcK (http://kanaya.naist.jp/KNApSAcK/ ) and PubChem (http://pubchem.ncbi.nlm.nih.gov/ ). Identification of metabolites was based on mass accuracy (<2 ppm) and where available, on isotope and/or MS/MS fragmentation patterns (Supplementary Data Sets 1 –4 ) using data from the databases of METLIN (http://metlin.scripps.edu/index.php ) and mzCloud (https://www.mzcloud.org/ ) and the literature. In addition, the heuristic rules of Kind and Fiehn (2007 (link)), which are implemented in the MZmine 2 (Pluskal et al., 2010 (link)), were applied. These rules provide a valuable tool for reducing the number of candidate molecular formulae for a given ion. Detection of mass errors was confirmed by Xcalibur v.2.2 (Thermo Scientific).
Additionally, since the majority of the secondary metabolites have unique structures, the assignment of metabolites to the corresponding producing fungus during mycoparasitism was a feasible task at the applied mass resolution.
Additionally, since the majority of the secondary metabolites have unique structures, the assignment of metabolites to the corresponding producing fungus during mycoparasitism was a feasible task at the applied mass resolution.
Free full text: Click here
