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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 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.

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.

Coupling the use of artificial chromosomes with metabolomics enables the high-throughput linkage of fungal natural products with their biosynthetic gene clusters. This method was used here to identify a novel polyketide–nonribosomal peptide scaffold. The genomes of filamentous fungi contain up to 90 biosynthetic gene clusters (BGCs) encoding diverse secondary metabolites—an enormous reservoir of untapped chemical potential. However, the recalcitrant genetics, cryptic expression, and unculturability of these fungi prevent scientists from systematically exploiting these gene clusters and harvesting their products. As heterologous expression of fungal BGCs is largely limited to the expression of single or partial clusters, we established a scalable process for the expression of large numbers of full-length gene clusters, called FAC-MS. Using fungal artificial chromosomes (FACs) and metabolomic scoring (MS), we screened 56 secondary metabolite BGCs from diverse fungal species for expression in Aspergillus nidulans . We discovered 15 new metabolites and assigned them with confidence to their BGCs. Using the FAC-MS platform, we extensively characterized a new macrolactone, valactamide A, and its hybrid nonribosomal peptide synthetase–polyketide synthase (NRPS–PKS). The ability to regularize access to fungal secondary metabolites at an unprecedented scale stands to revitalize drug discovery platforms with renewable sources of natural products.

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.

Natural products from fungi have had an immeasurable societal impact as both friends and foes to human health. This includes numerous therapeutics that have had widespread clinical success, mycotoxins that are implicated in fungal pathogenicity, and various agrochemicals. Recent genome sequencing efforts have revealed that fungal genomes contain the capacity to produce an enormous wealth of natural products, representing a significant resource for future compound discovery. Given this vast wealth of new natural products, high-throughput approaches are needed for systematically accessing the compounds encoded by fungal genomes. Traditional natural products discovery approaches are decidedly low-throughput and operate in an information-poor fashion that leads to rediscovery of known compounds and diminishing returns. Alternative approaches that take full advantage of recent advances in “omics” technologies such as genomics and metabolomics are needed for tackling the vast number of fungal compounds and biosynthetic pathways that awaits discovery. In order to meet this challenge, integrative approaches were developed that combine large-scale metabolomics and genomics for joint discovery of new compounds and their biosynthetic pathways. A heterologous expression approach was developed and implemented that expresses entire fungal biosynthetic gene clusters, enabling compound identification by mass spectrometry-based metabolomics. This platform was used to study two unusual biosynthetic pathways with peculiar metabolite scaffolds: terreazepine, a kynurenine-derived benzazepine metabolite, and acu-dioxomorpholine, a diketomorpholine metabolite formed by a non-canonical peptide synthetase. Large-scale genome mining for new natural products requires informatics tools for cataloguing, visualizing, and navigating this chemical space. A web resource based on 1000 fungal genomes was created to meet this need. With this resource in hand, a large-scale analysis of biosynthetic pathways in the fungal kingdom was performed, revealing the relationship between secondary metabolism and phylogeny. This work provides a roadmap and tool for systematically accessing new natural products from fungal genomes.

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.

Most patients who die of cancer have disseminated disease that has become resistant to multiple therapeutic modalities. Ample evidence suggests that the expression of ATP-binding cassette (ABC) transporters, especially the multidrug resistance protein 1 (MDR1, also known as P-glycoprotein or P-gp), which is encoded by ABC subfamily B member 1 (ABCB1), can confer resistance to cytotoxic and targeted chemotherapy. However, the development of MDR1 as a therapeutic target has been unsuccessful. At the time of its discovery, appropriate tools for the characterization and clinical development of MDR1 as a therapeutic target were lacking. Thirty years after the initial cloning and characterization of MDR1 and the implication of two additional ABC transporters, the multidrug resistance-associated protein 1 (MRP1; encoded by ABCC1)), and ABCG2, in multidrug resistance, interest in investigating these transporters as therapeutic targets has waned. However, with the emergence of new data and advanced techniques, we propose to re-evaluate whether these transporters play a clinical role in multidrug resistance. With this Opinion article, we present recent evidence indicating that it is time to revisit the investigation into the role of ABC transporters in efficient drug delivery in various cancer types and at the blood–brain barrier.

Capillary endothelial cells of the human blood–brain barrier (BBB) express high levels of P-glycoprotein (P-gp, encoded by ABCB1 ) and ABCG2 (encoded by ABCG2 ). However, little information is available regarding ATP-binding cassette transporters expressed at the zebrafish BBB, which has emerged as a potential model system. We report the characterization and tissue localization of two genes that are similar to ABCB1, zebrafish abcb4 and abcb5. When stably expressed in HEK293 cells, both Abcb4 and Abcb5 conferred resistance to P-gp substrates; however, Abcb5 poorly transported doxorubicin and mitoxantrone compared to zebrafish Abcb4. Additionally, Abcb5 did not transport the fluorescent P-gp probes BODIPY-ethylenediamine or LDS 751, while they were transported by Abcb4. High-throughput screening of 90 human P-gp substrates confirmed that Abcb4 has an overlapping substrate specificity profile with P-gp. In the brain vasculature, RNAscope probes for abcb4 colocalized with staining by the P-gp antibody C219, while abcb5 was not detected. The abcb4 probe also colocalized with claudin-5 in brain endothelial cells. Abcb4 and Abcb5 had different tissue localizations in multiple zebrafish tissues, potentially indicating different functions. The data suggest that zebrafish Abcb4 functionally phenocopies P-gp and that the zebrafish may serve as a model to study the role of P-gp at the BBB.

Background The β‐secretase‐1 inhibitors (BACEi), including verubecestat, were extensively studied in prodromal to moderate AD and demonstrated early cognitive decline (negative effect) at doses achieving >50% inhibition of amyloid production. Questions remain as to whether BACEi may still have utility, if used earlier in disease and at lower levels of inhibition. A mechanistic model of the progression of Alzheimer’s disease was used to predict effects of alternative BACEi therapeutic approaches on disease progression. Method The mechanistic model could holistically describe the available clinical data including observational trials and interventional trials with BACE inhibitors, anti‐amyloid mAbs, and anti‐tau antisense oligonucleotides. Varying inhibition levels (15‐ 56%) were simulated to predict amyloid PET, CSF Aβ42, CSF and plasma p‐tau, tau PET response. Delay‐times to BRAAK3‐4 region tau PET SUVr of 1.44 (MK‐6240; or 1.2 FTP) were calculated from simulations of intervention relative to untreated. Simulations of Down’s Syndrome populations (represented by increased production rate of amyloid) tested the impact on the dose and timing of the intervention needed to achieve benefits. Result Sporadic AD simulations predicted that NFT progression could be delayed by decade(s) if started very early in disease (amyloid PET of 20‐50 centiloids) with moderate (33‐56%) levels of inhibition maintained for remainder of life. It’s unknown whether these inhibition levels have less cognitive decline than seen previously. Benefits were substantially reduced for later inventions (>60 centiloids), lower levels of inhibition or shorter durations of treatment (<10 years). Predicted biomarker timecourses suggested that separation from placebo within 4‐5 years would be demonstrable for CSF Aβ42 and amyloid PET, but p‐tau and tau PET alterations could take longer, especially for interventions starting at very low plaque loads. Limitations include the limited data from tau‐targeted approaches and on inhibition‐dependencies of the negative cognition effect, which create uncertainties in the predictions. Conclusion Mechanistic model‐based simulations suggest that moderate level BACEi interventions very early in the process of amyloid plaque accumulations could lead to substantial delays in progression. The time to differentiation from untreated marker responses was long for tau‐based markers, suggesting that demonstration of effects during a typical trial timeline may be challenging.