Ecological niche-inspired genome mining leads to the discovery of crop-protecting nonribosomal lipopeptides featuring a transient amino acid building block.
Abstract
Investigating the ecological context of microbial predator-prey interactions enables the identification of microorganisms, which produce multiple secondary metabolites to evade predation or to kill the predator. In addition, genome mining combined with molecular biology methods can be used to identify further biosynthetic gene clusters that yield new antimicrobials to fight the antimicrobial crisis. In contrast, classical screening-based approaches have limitations since they do not aim to unlock the entire biosynthetic potential of a given organism. Here, we describe the genomics-based identification of keanumycins A-C. These nonribosomal peptides enable bacteria of the genus Pseudomonas to evade amoebal predation. While being amoebicidal at a nanomolar level, these compounds also exhibit a strong antimycotic activity in particular against the devastating plant pathogen Botrytis cinerea and they drastically inhibit the infection of Hydrangea macrophylla leaves using only supernatants of Pseudomonas cultures. The structures of the keanumycins were fully elucidated through a combination of nuclear magnetic resonance, tandem mass spectrometry, and degradation experiments revealing an unprecedented terminal imine motif in keanumycin C extending the family of nonribosomal amino acids by a highly reactive building block. In addition, chemical synthesis unveiled the absolute configuration of the unusual dihydroxylated fatty acid of keanumycin A, which has not yet been reported for this lipodepsipeptide class. Finally, a detailed genome-wide microarray analysis of Candida albicans exposed to keanumycin A shed light on the mode-of-action of this potential natural product lead, which will aid the development of new pharmaceutical and agrochemical antifungals.
Beteiligte Forschungseinheiten
Leibniz-HKI-Autor*innen
Themenfelder
Identifier
doi: 10.1021/jacs.2c11107
PMID: 36669196