|Title||Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides|
|Author(s)||Nguyen, Don D.; Melnik, Alexey V.; Koyama, Nobuhiro; Lu, Xiaowen; Schorn, Michelle; Fang, Jinshu; Aguinaldo, Kristen; Lincecum, Tommie L.; Ghequire, Maarten G.K.; Carrion, Victor J.; Cheng, Tina L.; Duggan, Brendan M.; Malone, Jacob G.; Mauchline, Tim H.; Sanchez, Laura M.; Marm Kilpatrick, A.; Raaijmakers, Jos M.; Mot, René De; Moore, Bradley S.; Medema, Marnix H.; Dorrestein, Pieter C.|
|Source||Nature Microbiology 2 (2016). - ISSN 2058-5276|
|Publication type||Refereed Article in a scientific journal|
Pseudomonads are cosmopolitan microorganisms able to produce a wide array of specialized metabolites. These molecules allow Pseudomonas to scavenge nutrients, sense population density and enhance or inhibit growth of competing microorganisms. However, these valuable metabolites are typically characterized one-molecule-one-microbe at a time, instead of being inventoried in large numbers. To index and map the diversity of molecules detected from these organisms, 260 strains of ecologically diverse origins were subjected to mass-spectrometry-based molecular networking. Molecular networking not only enables dereplication of molecules, but also sheds light on their structural relationships. Moreover, it accelerates the discovery of new molecules. Here, by indexing the Pseudomonas specialized metabolome, we report the molecular-networking-based discovery of four molecules and their evolutionary relationships: a poaeamide analogue and a molecular subfamily of cyclic lipopeptides, bananamides 1, 2 and 3. Analysis of their biosynthetic gene cluster shows that it constitutes a distinct evolutionary branch of the Pseudomonas cyclic lipopeptides. Through analysis of an additional 370 extracts of wheat-associated Pseudomonas, we demonstrate how the detailed knowledge from our reference index can be efficiently propagated to annotate complex metabolomic data from other studies, akin to the way in which newly generated genomic information can be compared to data from public databases.