ABSTRACT
Screening microbial secondary metabolites is an established method to identify novel biologically active molecules. Preparation of biological screening samples from microbial fermentation extracts requires growth conditions that promote synthesis of secondary metabolites and extraction procedures that capture the secondary metabolites produced. High-performance liquid chromatography (HPLC) analysis of fermentation extracts can be used to estimate the number of secondary metabolites produced by microorganisms under various growth conditions but is slow. In this study we report on a rapid (approximately 1 min per assay) surrogate measure of secondary metabolite production based on a metabolite productivity index computed from the electrospray mass spectra of samples injected directly into a spectrometer. This surrogate measure of productivity was shown to correlate with an HPLC measure of productivity with a coefficient of 0.78 for a test set of extracts from 43 actinomycetes. This rapid measure of secondary metabolite productivity may be used to identify improved cultivation and extraction conditions by analyzing and ranking large sets of extracts. The same methods may also be used to survey large collections of extracts to identify subsets of highly productive organisms for biological screening or additional study.
Subject(s)
Actinomycetales/metabolism , Spectrometry, Mass, Electrospray Ionization/methods , Actinomycetales/growth & development , Algorithms , Chromatography, High Pressure Liquid/methods , Culture Media/chemistry , Fermentation , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This paper describes a method for quantitatively differentiating crude natural extracts using high-performance liquid chromatography-electrospray mass spectrometry (HPLC-ESI-MS). The method involves performing an HPLC-MS analysis using standard reversed-phase C18 gradient separation on the crude extract. The HPLC system used in this study was a dual-column system designed to optimize throughput. Using image analysis techniques, the data are reduced to a list containing the m/z value and retention time of each ion. The ion lists are then compared in a pairwise fashion to compute a sample similarity index between two samples. The similarity index is based on the number of ions common to both and is scaled from 0 to 1. Extract controls were analyzed throughout a run of 88 unknown fungal extracts. The controls provided information about column and spectrometer stability and overall sensitivity. Pairwise comparison of all control samples indicates that the similarity index is high (0.8) for replicate samples. Comparison between the unknown extract samples produces a distribution of similarities ranging from replicates (0.8) to very dissimilar (0.1). This information can be used to judge the chemical diversity of natural extract samples, which is one approach to determining the quality of libraries being used for drug discovery via high-throughput screening.
