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Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics–metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product–GCF linkages to direct future discovery. Using an integrated metabologenomics approach, the biosynthetic pathway for the pestalamides is revealed and over 200 high-confidence targets are identified for future studies.

Here, we provide evidence indicating that S. aureus secretes small linear peptides into the environment via a novel processing and secretion pathway. The discovery of a specialized pathway for the production of small linear peptides and the identification of these peptides leads to several important questions regarding their role in S. aureus biology, most interestingly, their potential to act as signaling molecules. The observations in this study provide a foundation for further in-depth studies into the biological activity of small linear peptides in S. aureus . Staphylococcus aureus can colonize the human host and cause a variety of superficial and invasive infections. The success of S. aureus as a pathogen derives from its ability to modulate its virulence through the release, sensing of and response to cyclic signaling peptides. Here we provide, for the first time, evidence that S. aureus processes and secretes small linear peptides through a specialized pathway that converts a lipoprotein leader into an extracellular peptide signal. We have identified and confirmed the machinery for each step and demonstrate that the putative membrane metalloprotease Eep and the EcsAB transporter are required to complete the processing and secretion of the peptides. In addition, we have identified several linear peptides, including the interspecies signaling molecule staph -cAM373, that are dependent on this processing and secretion pathway. These findings are particularly important because multiple Gram-positive bacteria rely on small linear peptides to control bacterial gene expression and virulence. IMPORTANCE Here, we provide evidence indicating that S. aureus secretes small linear peptides into the environment via a novel processing and secretion pathway. The discovery of a specialized pathway for the production of small linear peptides and the identification of these peptides leads to several important questions regarding their role in S. aureus biology, most interestingly, their potential to act as signaling molecules. The observations in this study provide a foundation for further in-depth studies into the biological activity of small linear peptides in S. aureus .

Fungi are prolific producers of natural products, compounds which have had a large societal impact as pharmaceuticals, mycotoxins, and agrochemicals. Despite the availability of over 1,000 fungal genomes and several decades of compound discovery efforts from fungi, the biosynthetic gene clusters (BGCs) encoded by these genomes and the associated chemical space have yet to be analyzed systematically. Here, we provide detailed annotation and analyses of fungal biosynthetic and chemical space to enable genome mining and discovery of fungal natural products. Using 1,037 genomes from species across the fungal kingdom (e.g., Ascomycota, Basidiomycota, and non-Dikarya taxa), 36,399 predicted BGCs were organized into a network of 12,067 gene cluster families (GCFs). Anchoring these GCFs with reference BGCs enabled automated annotation of 2,026 BGCs with predicted metabolite scaffolds. We performed parallel analyses of the chemical repertoire of fungi, organizing 15,213 fungal compounds into 2,945 molecular families (MFs). The taxonomic landscape of fungal GCFs is largely species specific, though select families such as the equisetin GCF are present across vast phylogenetic distances with parallel diversifications in the GCF and MF. We compare these fungal datasets with a set of 5,453 bacterial genomes and their BGCs and 9,382 bacterial compounds, revealing dramatic differences between bacterial and fungal biosynthetic logic and chemical space. These genomics and cheminformatics analyses reveal the large extent to which fungal and bacterial sources represent distinct compound reservoirs. With a >10-fold increase in the number of interpreted strains and annotated BGCs, this work better regularizes the biosynthetic potential of fungi for rational compound discovery.

Here, we provide evidence that Aspergillus terreus encodes a biosynthetic gene cluster containing a repurposed indoleamine 2,3-dioxygenase (IDO) dedicated to secondary metabolite synthesis. The discovery of this neofunctionalized IDO not only enabled discovery of a new compound with an unusual chemical scaffold but also provided insight into the numerous strategies fungi employ for diversifying and protecting themselves against secondary metabolites. The observations in this study set the stage for further in-depth studies into the function of duplicated IDOs present in fungal biosynthetic gene clusters and presents a strategy for accessing the biosynthetic potential of gene clusters containing duplicated primary metabolic genes. Advances in genome sequencing have revitalized natural product discovery efforts, revealing the untapped biosynthetic potential of fungi. While the volume of genomic data continues to expand, discovery efforts are slowed due to the time-consuming nature of experiments required to characterize new molecules. To direct efforts toward uncharacterized biosynthetic gene clusters most likely to encode novel chemical scaffolds, we took advantage of comparative metabolomics and heterologous gene expression using fungal artificial chromosomes (FACs). By linking mass spectral profiles with structural clues provided by FAC-encoded gene clusters, we targeted a compound originating from an unusual gene cluster containing an indoleamine 2,3-dioxygenase (IDO). With this approach, we isolate and characterize R and S forms of the new molecule terreazepine, which contains a novel chemical scaffold resulting from cyclization of the IDO-supplied kynurenine. The discovery of terreazepine illustrates that FAC-based approaches targeting unusual biosynthetic machinery provide a promising avenue forward for targeted discovery of novel scaffolds and their biosynthetic enzymes, and it also represents another example of a biosynthetic gene cluster “repurposing” a primary metabolic enzyme to diversify its secondary metabolite arsenal. IMPORTANCE Here, we provide evidence that Aspergillus terreus encodes a biosynthetic gene cluster containing a repurposed indoleamine 2,3-dioxygenase (IDO) dedicated to secondary metabolite synthesis. The discovery of this neofunctionalized IDO not only enabled discovery of a new compound with an unusual chemical scaffold but also provided insight into the numerous strategies fungi employ for diversifying and protecting themselves against secondary metabolites. The observations in this study set the stage for further in-depth studies into the function of duplicated IDOs present in fungal biosynthetic gene clusters and presents a strategy for accessing the biosynthetic potential of gene clusters containing duplicated primary metabolic genes.

IntroductionFungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways.ObjectivesTo prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences.MethodsThe 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics.ResultsWe isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated—Omics datasets.ConclusionsThis work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.

commonly known as goldenseal, is a botanical native to the southeastern United States that has been used for the treatment of infection. The activity of goldenseal is often attributed to the presence of alkaloids (cyclic, nitrogen-containing compounds) present within its roots. Chemical components of botanical supplements like goldenseal may face degradation if not stored properly. The purpose of the research was to analyze the stability of known and unknown metabolites of during exposure to different storage conditions using mass spectrometry. Three abundant metabolites of , berberine, canadine, and hydrastine, were chosen for targeted analysis, and the stability of unknown metabolites was evaluated using untargeted metabolomics. The analysis and evaluation of samples were performed utilizing LC-MS and Principal Component Analysis (PCA). The research project focused on identifying the chemical changes in the metabolite content of under different temperature conditions (40°C ± 5°C, 20°C ± 5°C , and 4°C ± 5°C), different light:dark (hr:hr) cycles (16:8, 12:12, and 0:24), and different sample conditions (powdered roots versus whole roots) over a six month period. The results of this 6-month study revealed that the storage conditions evaluated had no significant effects on the chemical composition of roots. Hence, as long as roots are stored within the storage conditions tested in the study, no significant changes in chemical compositions of metabolites are expected.

Compounds from natural sources, as well as those inspired by them, represent the majority of small molecule drugs on the market today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals and other natural products, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Additionally, natural product mixtures are incredibly complex, and the standard reductionist approaches often ignore the presence of combination effects such as synergy and antagonism. Bioinformatics tools can be used to integrate biological and chemical datasets, and statistical analyses of these datasets are broadly termed “biochemometrics.” Biochemometric approaches enable researchers to predict active constituents early in the fractionation process and to tailor isolation efforts toward the most biologically relevant compounds. Throughout the course of this project, bioinformatics approaches were used to (1) discover biologically active constituents from the botanical medicines, (2) develop and improve data filtering, data transformation, and model simplification parameters to optimize biochemometrics models, and (3) produce a new approach capable of predicting mixture constituents that contribute to synergy, additivity, and antagonism in complex mixtures.The first goal was achieved by applying bioassay-guided fractionation, biocheomometric selectivity ratio analysis, and molecular networking to comprehensively evaluate the antimicrobial activity of the botanical Angelica keiskei Koidzumi against Staphylococcus aureus. This approach enabled the identification of putative active constituents early in the fractionation process, and provided structural information for these compounds. A subset of chalcone analogs were prioritized for isolation, yielding antimicrobial compounds 4-hydroxyderricin, xanthoangelol, and xanthoangelol K. This approach successfully identified a low abundance compound (xanthoangelol K) that has not been previously reported to possess antimicrobial activity.Two studies were undertaken to achieve the second goal. First ,we demonstrated the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites were prepared from the A. keiskei and analyzed in triplicate using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the datasets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. This filtering process identified 128 ions that did not show consistent peak area from injection to injection that likely originated from the UPLC-MS system. When interferents were removed, replicates clustered in all datasets, highlighting the importance of technical replication in mass spectrometry-based studies and providing tool for evaluating the effectiveness of data filtering prior to statistical analysis.As a follow up study, the impact of data acquisition and data processing parameters on selectivity ratio models were assessed using an inactive botanical mixture spiked with known antimicrobial compounds. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, as well as an additional antimicrobial compound, randainal, which was masked by the presence of antagonists in the mixture. This study revealed that data processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. This study emphasized the importance of data processing for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.Often, analytical tools aimed to assess biological mixtures ascribe the activity to a few known components. Although researchers recognize this as an oversimplification, research methodologies to address this problem have not been developed. To overcome this and to achieve the third goal of this project, a new approach called Simplify was developed that can both identify mixture components that contribute to biological activity and characterize the nature of their interactions prior to isolation. As a test case, this approach was applied to the botanical Salvia miltiorrhiza and successfully utilized to identify both additive and synergistic compounds. These findings illustrate the efficacy of this approach for understanding how natural product mixtures work in concert and are expected to serve as a launching point for the comprehensive evaluation of mixtures in future studies.

Abstract Fungi are prolific producers of natural products, compounds which have had a large societal impact as pharmaceuticals, mycotoxins, and agrochemicals. Despite the availability of over 1000 fungal genomes and several decades of compound discovery efforts from fungi, the biosynthetic gene clusters (BGCs) encoded by these genomes and the associated chemical space have yet to be analyzed systematically. Here we provide detailed annotation and analyses of fungal biosynthetic and chemical space to enable genome mining and discovery of fungal natural products. Using 1037 genomes from species across the fungal kingdom (e.g., Ascomycota, Basidiomycota, and non-Dikarya taxa), 36,399 predicted BGCs were organized into a network of 12,067 gene cluster families (GCFs). Anchoring these GCFs with reference BGCs enabled automated annotation of 2,026 BGCs with predicted metabolite scaffolds. We performed parallel analyses of the chemical repertoire of Fungi, organizing 15,213 fungal compounds into 2,945 molecular families (MFs). The taxonomic landscape of fungal GCFs is largely species-specific, though select families such as the equisetin GCF are present across vast phylogenetic distances with parallel diversifications in the GCF and MF. We compare these fungal datasets with a set of 5,453 bacterial genomes and their BGCs and 9,382 bacterial compounds, revealing dramatic differences between bacterial and fungal biosynthetic logic and chemical space. These genomics and cheminformatics analyses reveal the large extent to which fungal and bacterial sources represent distinct compound reservoirs. With a >10-fold increase in the number of interpreted strains and annotated BGCs, this work better regularizes the biosynthetic potential of fungi for rational compound discovery. Significance Statement Fungi represent an underexploited resource for new compounds with applications in the pharmaceutical and agriscience industries. Despite the availability of >1000 fungal genomes, our knowledge of the biosynthetic space encoded by these genomes is limited and ad hoc. We present results from systematically organizing the biosynthetic content of 1037 fungal genomes, providing a resource for data-driven genome mining and large-scale comparison of the genetic and molecular repertoires produced in fungi and compare to those present in bacteria. Competing Interest Statement The authors have declared no competing interest.