Martinus de Kruijff

Virtually all microorganisms are in constant need for iron as an essential nutrient for growth. (1) However, the bioavailability of ferric iron (Fe(III)) is exceedingly limited due to its predominant occurrence in its highly insoluble form, ferric oxide. (2) To satisfy sufficient iron availability, microorganisms have evolved the capability to produce specialized natural products with metal ion chelating properties of low molecular weight (typically 500–1500 Da), also termed siderophores.

Natural product discovery driven by ecological insights and genome mining has emerged as a powerful strategy for identifying novel chemical scaffolds and biosynthetic chemistry driving their production. (1,2) Notably, members of the phylum Actinomycetota, particularly those involved in protective symbiosis have been recognized as prolific producers of natural products.

Abstract Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography–tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography–tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices.