Klitgaard, Andreas7; Iversen, Anita1; Andersen, Mikael Rørdam1; Larsen, Thomas Ostenfeld1; Frisvad, Jens Christian1; Nielsen, Kristian Fog1
1 Department of Systems Biology, Technical University of Denmark2 Metabolic Signaling and Regulation, Department of Systems Biology, Technical University of Denmark3 Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark4 Network Engineering of Eukaryotic Cell Factories, Department of Systems Biology, Technical University of Denmark5 Natural Product Chemistry, Department of Systems Biology, Technical University of Denmark6 Metabolomics Platform, Department of Systems Biology, Technical University of Denmark7 Office for HR, Administration, Technical University of Denmark
In natural-product drug discovery, finding new compounds is the main task, and thus fast dereplication of known compounds is essential. This is usually performed by manual liquid chromatography-ultraviolet (LC-UV) or visible light-mass spectroscopy (Vis-MS) interpretation of detected peaks, often assisted by automated identification of previously identified compounds. We used a 15 min high-performance liquid chromatography–diode array detection (UHPLC–DAD)–high-resolution MS method (electrospray ionization (ESI)+ or ESI−), followed by 10–60 s of automated data analysis for up to 3000 relevant elemental compositions. By overlaying automatically generated extracted-ion chromatograms from detected compounds on the base peak chromatogram, all major potentially novel peaks could be visualized. Peaks corresponding to compounds available as reference standards, previously identified compounds, and major contaminants from solvents, media, filters etc. were labeled to differentiate these from compounds only identified by elemental composition. This enabled fast manual evaluation of both known peaks and potential novel-compound peaks, by manual verification of: the adduct pattern, UV–Vis, retention time compared with log D, co-identified biosynthetic related compounds, and elution order. System performance, including adduct patterns, in-source fragmentation, and ion-cooler bias, was investigated on reference standards, and the overall method was used on extracts of Aspergillus carbonarius and Penicillium melanoconidium, revealing new nitrogen-containing biomarkers for both species.
Analytical and Bioanalytical Chemistry, 2014, Vol 406, Issue 7, p. 1933-1943