Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses

For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions and ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal plants are more likely to be used for treating similar indications. Next, we reconstructed the phytochemical space covered by medicinal plants to reveal that (i) taxonomically related medicinal plants cover a similar phytochemical space, and (ii) chemical similarity correlates with similar therapeutic usage. Lastly, we present several case scenarios illustrating how mining this information can be used for drug discovery applications, including: (i) investigating taxonomic hotspots around particular indications, (ii) exploring shared patterns of congeneric plants located in different geographic areas, but which have been used to treat the same indications, and (iii) showing the concordance between ethnobotanical patterns among non-taxonomically related plants and the presence of shared bioactive phytochemicals.

National Cancer Institute (NCI) Program for Natural Product Discovery: Exploring NCI-60 Screening Data of Natural Product Samples with Artificial Neural Networks

National Cancer Institute (NCI) Program for Natural Product Discovery is a new initiative aimed at creating new technologies for natural product-based drug discovery. Here, we present the development of a neural network-based bioinformatics platform for visualization and analysis of natural product high-throughput screening data using the NCI's 60 human tumor cell anticancer drug screen. We demonstrate how the tool enables visualization of similar patterns of response that can be parsed both chemically and taxonomically, grouping NCI-60 biological profiles in one easy-to-use bioinformatics interface.

Metabolomics and Genomics Enable the Discovery of a New Class of Nonribosomal Peptidic Metallophores from a Marine Micromonospora

Although microbial genomes harbor an abundance of biosynthetic gene clusters, there remain substantial technological gaps that impair the direct correlation of newly discovered gene clusters and their corresponding secondary metabolite products. As an example of one approach designed to minimize or bridge such gaps, we employed hierarchical clustering analysis and principal component analysis (hcapca, whose sole input is MS data) to prioritize 109 marine Micromonospora strains and ultimately identify novel strain WMMB482 as a candidate for in-depth "metabologenomics" analysis following its prioritization. Highlighting the power of current MS-based technologies, not only did hcapca enable the discovery of one new, nonribosomal peptide bearing an incredible diversity of unique functional groups, but metabolomics for WMMB482 unveiled 16 additional congeners via the application of Global Natural Product Social molecular networking (GNPS), herein named ecteinamines A-Q (1-17). The ecteinamines possess an unprecedented skeleton housing a host of uncommon functionalities including a menaquinone pathway-derived 2-naphthoate moiety, 4-methyloxazoline, the first example of a naturally occurring Ψ[CH2NH] "reduced amide", a methylsulfinyl moiety, and a d-cysteinyl residue that appears to derive from a unique noncanonical epimerase domain. Extensive in silico analysis of the ecteinamine (ect) biosynthetic gene cluster and stable isotope-feeding experiments helped illuminate the novel enzymology driving ecteinamine assembly as well the role of cluster collaborations or "duets" in producing such structurally complex agents. Finally, ecteinamines were found to bind nickel, cobalt, zinc, and copper, suggesting a possible biological role as broad-spectrum metallophores.

Genome Mining and Metabolomics Unveil Pseudonochelin: A Siderophore Containing 5-Aminosalicylate from a Marine-Derived Pseudonocardia sp. Bacterium

Pseudonochelin (1), a siderophore from a marine-derived Pseudonocardia sp. bacterium, was discovered using genome mining and metabolomics technologies. A 5-aminosalicylic acid (5-ASA) unit, not previously found in siderophore natural products, was identified in 1. Annotation of a putative psn biosynthetic gene cluster combined with bioinformatics and isotopic enrichment studies enabled us to propose the biosynthesis of 1. Moreover, 1 was found to display in vitro and in vivo antibacterial activity in an iron-dependent fashion.

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

A marine microbiome antifungal targets urgent-threat drug-resistant fungi

New antifungal drugs are urgently needed to address the emergence and transcontinental spread of fungal infectious diseases, such as pandrug-resistant Candida auris. Leveraging the microbiomes of marine animals and cutting-edge metabolomics and genomic tools, we identified encouraging lead antifungal molecules with in vivo efficacy. The most promising lead, turbinmicin, displays potent in vitro and mouse-model efficacy toward multiple-drug-resistant fungal pathogens, exhibits a wide safety index, and functions through a fungal-specific mode of action, targeting Sec14 of the vesicular trafficking pathway. The efficacy, safety, and mode of action distinct from other antifungal drugs make turbinmicin a highly promising antifungal drug lead to help address devastating global fungal pathogens such as C. auris.

hcapca: Automated Hierarchical Clustering and Principal Component Analysis of Large Metabolomic Datasets in R

Microbial natural product discovery programs face two main challenges today: rapidly prioritizing strains for discovering new molecules and avoiding the rediscovery of already known molecules. Typically, these problems have been tackled using biological assays to identify promising strains and techniques that model variance in a dataset such as PCA to highlight novel chemistry. While these tools have shown successful outcomes in the past, datasets are becoming much larger and require a new approach. Since PCA models are dependent on the members of the group being modeled, large datasets with many members make it difficult to accurately model the variance in the data. Our tool, hcapca, first groups strains based on the similarity of their chemical composition, and then applies PCA to the smaller sub-groups yielding more robust PCA models. This allows for scalable chemical comparisons among hundreds of strains with thousands of molecular features. As a proof of concept, we applied our open-source tool to a dataset with 1046 LCMS profiles of marine invertebrate associated bacteria and discovered three new analogs of an established anticancer agent from one promising strain.

MS-Derived Isotopic Fine Structure Reveals Forazoline A as a Thioketone-Containing Marine-Derived Natural Product

Forazoline A is a structurally complex PKS-NRPS hybrid produced by marine-derived Actinomadura sp. During the course of studies highlighting the application of IFS analysis as a powerful tool for natural products analysis, we were alerted to an earlier misinterpretation with respect to forazoline A structure elucidation. In particular, IFS reveals that forazoline A contains a thioketone moiety rarely seen in secondary metabolites and, thus, constitutes an even more intriguing structure than originally thought.

Phallusialides A-E, Pyrrole-Derived Alkaloids Discovered from a Marine-Derived Micromonospora sp. Bacterium Using MS-Based Metabolomics Approaches

Integrating MS-based metabolomics approaches, LC-MS-PCA and molecular networking enabled the targeted isolation of five new pyrrole-derived alkaloids, phallusialides A-E (1-5), from a marine-derived Micromonospora sp. bacterium. The structures of 1-5 were elucidated by analysis of their HRMS, MS/MS, and NMR spectroscopic data. The absolute configuration of phallusialide A (1) was determined on the basis of comparisons of experimental and theoretically calculated ECD spectra. Compounds 1 and 2 exhibited antibacterial activity against methicillin resistant S. aureus (MRSA) and E. coli, with MIC values of 32 and 64 μg/mL, respectively, whereas 3-5 showed no antibacterial activity even at 256 μg/mL, yielding important SAR insights for this class of compounds.

Nanogel-DFO conjugates as a model to investigate pharmacokinetics, biodistribution, and iron chelation in vivo

Deferoxamine (DFO) to treat iron overload (IO) has been limited by toxicity issues and short circulation times and it would be desirable to prolong circulation to improve non-transferrin bound iron (NTBI) chelation. In addition, DFO is currently unable to efficiently target the large pool of iron in the liver and spleen. Nanogel-Deferoxamine conjugates (NG-DFO) can prove useful as a model to investigate the pharmacokinetic (PK) properties and biodistribution (BD) behavior of iron-chelating macromolecules and their overall effect on serum ferritin levels. NG-DFO reduced the cytotoxicity of DFO and significantly reduced cellular ferritin levels in IO macrophages in vitro. PK/BD studies in normal rats revealed that NG-DFO displayed prolonged circulation and preferential accumulation into the liver and spleen. IO mice treated with NG1-DFO presented significantly lower levels of serum ferritin compared to DFO. Total renal and fecal elimination data point to the need to balance prolonged circulation with controlled degradation to accelerate clearance of iron-chelating macromolecules.

Natural Product Discovery Using Planes of Principal Component Analysis in R (PoPCAR)

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming that common actinobacterial metabolites are likely known, focus can be shifted away from dereplication and towards discovery. LC-MS-based principal component analysis (PCA) provides a perfect tool to distinguish unique vs. common metabolites, but the variability inherent within natural products leads to datasets that do not fit ideal standards. To simplify the analysis of PCA models, we developed a script that identifies only those masses or molecules that are unique to each strain within a group, thereby greatly reducing the number of data points to be inspected manually. Since the script is written in R, it facilitates integration with other metabolomics workflows and supports automated mass matching to databases such as Antibase.

Brackish habitat dictates cultivable Actinobacterial diversity from marine sponges

Bacterial communities associated with marine invertebrates such as sponges and ascidians have demonstrated potential as sources of bio-medically relevant small molecules. Metagenomic analysis has shown that many of these invertebrates harbor populations of Actinobacteria, many of which are cultivable. While some populations within invertebrates are transmitted vertically, others are obtained from the environment. We hypothesized that cultivable diversity from sponges living in brackish mangrove habitats have associations with Actinobacterial populations that differ from those found in clear tropical waters. In this study, we analyzed the cultivable Actinobacterial populations from sponges found in these two distinct habitats with the aim of understanding the secondary metabolite potential. Importantly, we wanted to broadly evaluate the potential differences among these groups to guide future Actinobacterial collection strategies for the purposes of drug discovery.