Mining for a new class of fungal natural products: the evolution, diversity, and distribution of isocyanide synthase biosynthetic gene clusters

Crystal Clear: Decoding Isocyanide Intermolecular Interactions through Crystallography

The isocyanide group is the chameleon among the functional groups in organic chemistry. Unlike other multiatom functional groups, where the electrophilic and nucleophilic moieties are typically separated, isocyanides combine both functionalities in the terminal carbon. This unique feature can be rationalized using the frontier orbital concept and has significant implications for its intermolecular interactions and the reactivity of the functional group. In this study, we perform a Cambridge Crystallographic Database-supported analysis of isocyanide intramolecular interactions to investigate the intramolecular interactions of isocyanides in the solid state, excluding isocyanide-metal complexes. We discuss examples of different interaction classes, including the isocyanide as a hydrogen bond acceptor (RNC···HX), halogen bonding (RNC···X), and interactions involving the isocyanide and carbon atoms (RNC···C). The latter interaction serves as an intriguing illustration of a Bürgi-Dunitz trajectory and represents a crucial experimental detail in the well-known multicomponent reactions such as the Ugi- and Passerini-type mechanisms. Understanding the spectrum of intramolecular interactions that isocyanides can undergo holds significant implications in fields such as medicinal chemistry, materials science, and asymmetric catalysis.

A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography-mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments' ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.