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

Global Analysis of Natural Products Biosynthetic Diversity Encoded in Fungal Genomes

Fungal secondary metabolites (SMs) represent an invaluable source of therapeutic drugs. Genomics-based approaches to SM discovery have revealed a vast and largely untapped biosynthetic potential within fungal genomes. Here, we used the publicly available fungal genome sequences from the NCBI public database, as well as tools such as antiSMASH, BIG-SLiCE, etc., to analyze a total of 11,598 fungal genomes, identifying 293,926 biosynthetic gene clusters (BGCs), which were subsequently categorized into 26,825 gene cluster families (GCFs). It was discovered that only a tiny fraction, less than 1%, of these GCFs could be mapped to known natural products (NPs). Some GCFs that only contain a single BGC internally are crucial for the biodiversity of fungal biosynthesis. Evident patterns emerged from our analysis, revealing popular taxa as prominent sources of both actual and potential biosynthetic diversity. Our study also suggests that the genus rank distribution of GCF is generally consistent with NP diversity. It is noteworthy that genera Xylaria, Hypoxylon, Colletotrichum, Diaporthe, Nemania, and Calonectria appear to possess a higher potential for SM synthesis. In addition, 7213 BGCs match possible known compound structures, and homologous gene clusters of well-known drugs can be located in different genera, facilitating the development of derivatives that share structural similarity to these drugs and may potentially possess similar biological activity. Our study demonstrated the various types of fungi with mining potential, assisting researchers in prioritizing their research efforts and avoiding duplicate mining of known resources to further explore fungal NP producers.

Comparative genomics of Metarhizium brunneum strains V275 and ARSEF 4556: unraveling intraspecies diversity

Entomopathogenic fungi belonging to the Order Hypocreales are renowned for their ability to infect and kill insect hosts, while their endophytic mode of life and the beneficial rhizosphere effects on plant hosts have only been recently recognized. Understanding the molecular mechanisms underlying their different lifestyles could optimize their potential as both biocontrol and biofertilizer agents, as well as the wider appreciation of niche plasticity in fungal ecology. This study describes the comprehensive whole genome sequencing and analysis of one of the most effective entomopathogenic and endophytic EPF strains, Metarhizium brunneum V275 (commercially known as Lalguard Met52), achieved through Nanopore and Illumina reads. Comparative genomics for exploring intraspecies variability and analyses of key gene sets were conducted with a second effective EPF strain, M. brunneum ARSEF 4556. The search for strain- or species-specific genes was extended to M. brunneum strain ARSEF 3297 and other species of genus Metarhizium, to identify molecular mechanisms and putative key genome adaptations associated with mode of life differences. Genome size differed significantly, with M. brunneum V275 having the largest genome amongst M. brunneum strains sequenced to date. Genome analyses revealed an abundance of plant-degrading enzymes, plant colonization-associated genes, and intriguing intraspecies variations regarding their predicted secondary metabolic compounds and the number and localization of Transposable Elements. The potential significance of the differences found between closely related endophytic and entomopathogenic fungi, regarding plant growth-promoting and entomopathogenic abilities, are discussed, enhancing our understanding of their diverse functionalities and putative applications in agriculture and ecology.

Characterization of a High-Affinity Copper Transporter in the White-Nose Syndrome Causing Fungal Pathogen Pseudogymnoascus destructans

Copper is an essential micronutrient and the ability to scavenge tightly bound or trace levels of copper ions at the host-pathogen interface is vital for fungal proliferation in animal hosts. Recent studies suggest that trace metal ion acquisition is critical for the establishment and propagation of Pseudogymnoascus destructans, the fungal pathogen responsible for white-nose syndrome (WNS), on their bat host. However, little is known about these metal acquisition pathways in P. destructans. In this study, we report the characterization of the P. destructans high-affinity copper transporter VC83_00191 (PdCTR1a), which is implicated as a virulence factor associated with the WNS disease state. Using Saccharomyces cerevisiae as a recombinant expression host, we find that PdCTR1a localizes to the cell surface plasma membrane and can efficiently traffic Cu-ions into the yeast cytoplasm. Complementary studies in the native P. destructans fungus provide evidence that PdCTR1a transcripts and protein levels are dictated by Cu-bioavailability in the growth media. Our study demonstrates that PdCTR1a is a functional high-affinity copper transporter and is relevant to Cu-homeostasis pathways in P. destructans.

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.