Secondary Metabolites of Marine Microbes: From Natural Products Chemistry to Chemical Ecology

Much of natural product chemistry concerns a group of compounds known as secondary metabolites. These low-molecular-weight metabolites often have potent physiological activities. Digitalis, morphine and quinine are plant secondary metabolites, whereas penicillin, cephalosporin, ergotrate and the statins are equally well known fungal secondary metabolites. Although chemically diverse, all secondary metabolites are produced by a few common biosynthetic pathways, often in conjunction with morphological development. Recent advances in molecular biology, bioinformatics and comparative genomics have revealed that the genes encoding specific fungal secondary metabolites are clustered and often located near telomeres. In this review, we address some important questions, including which evolutionary pressures led to gene clustering, why closely related species produce different profiles of secondary metabolites, and whether fungal genomics will accelerate the discovery of new pharmacologically active natural products.

METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN's high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool's capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/ z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN's MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.