A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics.

In complex biological systems, small molecules often mediate microbe-microbe and microbe-host interactions. Using a systematic approach, we identified 3,118 small-molecule biosynthetic gene clusters (BGCs) in genomes of human-associated bacteria and studied their representation in 752 metagenomic samples from the NIH Human Microbiome Project. Remarkably, we discovered that BGCs for a class of antibiotics in clinical trials, thiopeptides, are widely distributed in genomes and metagenomes of the human microbiota. We purified and solved the structure of a thiopeptide antibiotic, lactocillin, from a prominent member of the vaginal microbiota. We demonstrate that lactocillin has potent antibacterial activity against a range of Gram-positive vaginal pathogens, and we show that lactocillin and other thiopeptide BGCs are expressed in vivo by analyzing human metatranscriptomic sequencing data. Our findings illustrate the widespread distribution of small-molecule-encoding BGCs in the human microbiome, and they demonstrate the bacterial production of drug-like molecules in humans. PAPERCLIP:

Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters.

Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology.

Identification of the lipodepsipeptide selethramide encoded in a giant nonribosomal peptide synthetase from a Burkholderia bacterium.

Bacterial natural products have found many important industrial applications. Yet traditional discovery pipelines often prioritize individual natural product families despite the presence of multiple natural product biosynthetic gene clusters in each bacterial genome. Systematic characterization of talented strains is a means to expand the known natural product space. Here, we report genomics, epigenomics, and metabolomics studies of Burkholderia sp. FERM BP-3421, a soil isolate and known producer of antitumor spliceostatins. Its genome is composed of two chromosomes and two plasmids encoding at least 29 natural product families. Metabolomics studies showed that FERM BP-3421 also produces antifungal aminopyrrolnitrin and approved anticancer romidepsin. From the orphan metabolome features, we connected a lipopeptide of 1,928 Da to an 18-module nonribosomal peptide synthetase encoded as a single gene in chromosome 1. Isolation and structure elucidation led to the identification of selethramide which contains a repeating pattern of serine and leucine and is cyclized at the side chain oxygen of the one threonine residue at position 13. A (R)-3-hydroxybutyric acid moiety decorates the N-terminal serine. Initial attempts to obtain deletion mutants to probe the role of selethramide failed. After acquiring epigenome (methylome) data for FERM BP-3421, we employed a mimicry by methylation strategy that improved DNA transfer efficiency. Mutants defective in selethramide biosynthesis showed reduced surfactant activity and impaired swarming motility that could be chemically complemented with selethramide. This work unveils a lipopeptide that promotes surface motility, establishes improved DNA transfer efficiency, and sets the stage for continued natural product identification from a prolific strain.

High-throughput functional annotation of natural products by integrated activity profiling.

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform Functional Signature Ontology, and pairs these data with untargeted metabolomics analysis for de novo bioactive compound discovery. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using Kolmogorov-Smirnov (KS) tests to compare in-class to out-of-class similarity, we found that SNF retains the ability to identify significant in-class similarity across a diverse set of target classes, and could find target classes not detectable in either platform alone. This confirmed that integration of expression-based and image-based phenotypes can accurately report on MOA. Furthermore, we integrated untargeted metabolomics of complex natural product fractions with the SNF network to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites, including the discovery of the azoxy-containing biaryl compounds parkamycins A and B. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as a powerful approach for advancing natural products drug discovery.

Multivariate pattern analysis: a method and software to reveal, quantify, and visualize predictive association patterns in multicollinear data.

BACKGROUND: Strongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality. RESULTS: MVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org . MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors. CONCLUSIONS: Our method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny.

An isotopic labeling approach linking natural products with biosynthetic gene clusters.

Major advances in genome sequencing and large-scale biosynthetic gene cluster (BGC) analysis have prompted an age of natural product discovery driven by genome mining. Still, connecting molecules to their cognate BGCs is a substantial bottleneck for this approach. We have developed a mass-spectrometry-based parallel stable isotope labeling platform, termed IsoAnalyst, which assists in associating metabolite stable isotope labeling patterns with BGC structure prediction to connect natural products to their corresponding BGCs. Here we show that IsoAnalyst can quickly associate both known metabolites and unknown analytes with BGCs to elucidate the complex chemical phenotypes of these biosynthetic systems. We validate this approach for a range of compound classes, using both the type strain Saccharopolyspora erythraea and an environmentally isolated Micromonospora sp. We further demonstrate the utility of this tool with the discovery of lobosamide D, a new and structurally unique member of the family of lobosamide macrolactams.

The Natural Products Atlas 2.0: a database of microbially-derived natural products.

Within the natural products field there is an increasing emphasis on the study of compounds from microbial sources. This has been fuelled by interest in the central role that microorganisms play in mediating both interspecies interactions and host-microbe relationships. To support the study of natural products chemistry produced by microorganisms we released the Natural Products Atlas, a database of known microbial natural products structures, in 2019. This paper reports the release of a new version of the database which includes a full RESTful application programming interface (API), a new website framework, and an expanded database that includes 8128 new compounds, bringing the total to 32 552. In addition to these structural and content changes we have added full taxonomic descriptions for all microbial taxa and have added chemical ontology terms from both NP Classifier and ClassyFire. We have also performed manual curation to review all entries with incomplete configurational assignments and have integrated data from external resources, including CyanoMetDB. Finally, we have improved the user experience by updating the Overview dashboard and creating a dashboard for taxonomic origin. The database can be accessed via the new interactive website at https://www.npatlas.org.

NP-MRD: the Natural Products Magnetic Resonance Database.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the 'gold standard' for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

MultiplexMS: A Mass Spectrometry-Based Multiplexing Strategy for Ultra-High-Throughput Analysis of Complex Mixtures.

High-throughput chemical analysis of natural product mixtures lags behind developments in genome sequencing technologies and laboratory automation, leading to a disconnect between library-scale chemical and biological profiling that limits new molecule discovery. Here, we report a new orthogonal sample multiplexing strategy that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. Profiled pooled samples undergo subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using in silico experiments and demonstrated a high assignment precision (>97%) for large, pooled samples (r = 30), particularly for infrequently occurring metabolites of relevance in drug discovery applications. Requiring only 5% of the previously required MS acquisition time, this approach was repeated in a recent biological activity profiling study on 925 natural product extracts, leading to the rediscovery of all previously reported bioactive metabolites. This new method is compatible with MS data from any instrument vendor and is supported by an open-source software package: https://github.com/liningtonlab/MultiplexMS.

Tolypocaibols: Antibacterial Lipopeptaibols from a Tolypocladium sp. Endophyte of the Marine Macroalga Spongomorpha arcta.

Two new lipopeptaibols, tolypocaibols A (1) and B (2), and the mixed NRPS-polyketide-shikimate natural product maximiscin [(P/M)-3)] were isolated from a Tolypocladium sp. fungal endophyte of the marine alga Spongomorpha arcta. Analysis of NMR and mass spectrometry data revealed the amino acid sequences of the lipopeptaibols, which both comprise 11 residues with a valinol C-terminus and a decanoyl acyl chain at the N-terminus. The configuration of the amino acids was determined by Marfey's analysis. Tolypocaibols A (1) and B (2) showed moderate, selective inhibition against Gram-positive and acid-fast bacterial strains, while maximiscin [(P/M)-3)] showed moderate, broad-spectrum antibiotic activity.

Interaction Metabolomics to Discover Synergists in Natural Product Mixtures.

Mass spectrometry metabolomics has become increasingly popular as an integral aspect of studies to identify active compounds from natural product mixtures. Classical metabolomics data analysis approaches do not consider the possibility that interactions (such as synergy) could occur between mixture components. With this study, we developed "interaction metabolomics" to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms (CITs), which are calculated as the product of the intensities of each pair of features (detected ions) in the data matrix. Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high-resolution mass spectrometry. When the data set was processed without CITs (classical metabolomics), statistical analysis yielded a pattern of false positives. However, interaction metabolomics correctly identified berberine and piperine as the compounds responsible for the synergistic activity. To further validate the interaction metabolomics approach, we prepared mixtures from extracts of goldenseal (Hydrastis canadensis) and habañero pepper (Capsicum chinense) and correctly correlated synergistic activity of these mixtures to the combined action of berberine and several capsaicinoids. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for applications in natural products research and other research areas that require comprehensive mixture analysis.

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data.

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases either are not designed for natural products data or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data.

Modulation of in Vitro SARS-CoV-2 Infection by Stephania tetrandra and Its Alkaloid Constituents.

Botanical natural products have been widely consumed for their purported usefulness against COVID-19. Here, six botanical species from multiple sources and 173 isolated natural product compounds were screened for blockade of wild-type (WT) SARS-CoV-2 infection in human 293T epithelial cells overexpressing ACE-2 and TMPRSS2 protease (293TAT). Antiviral activity was demonstrated by an extract from Stephania tetrandra. Extract fractionation, liquid chromatography-mass spectrometry (LC-MS), antiviral assays, and computational analyses revealed that the alkaloid fraction and purified alkaloids tetrandrine, fangchinoline, and cepharanthine inhibited WT SARS-CoV-2 infection. The alkaloids and alkaloid fraction also inhibited the delta variant of concern but not WT SARS-CoV-2 in VeroAT cells. Membrane permeability assays demonstrate that the alkaloids are biologically available, although fangchinoline showed lower permeability than tetrandrine. At high concentrations, the extract, alkaloid fractions, and pure alkaloids induced phospholipidosis in 293TAT cells and less so in VeroAT cells. Gene expression profiling during virus infection suggested that alkaloid fraction and tetrandrine displayed similar effects on cellular gene expression and pathways, while fangchinoline showed distinct effects on cells. Our study demonstrates a multifaceted approach to systematically investigate the diverse activities conferred by complex botanical mixtures, their cell-context specificity, and their pleiotropic effects on biological systems.

Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures.

The comprehensive chemical characterization of biological samples remains a central challenge in the field of natural products. Conventional workflows using liquid chromatography (LC)-coupled high-resolution tandem mass spectrometry (MS/MS or MS2) allow the detection of relevant small molecules while providing diagnostic fragment ions for their structural assignment. Still, many natural product extracts are of a molecular complexity that challenges the resolving power of modern LC-MS2 pipelines. In this study, we examined the effect of integrating ion mobility spectrometry (IMS) to our LC-MS2 platform for the characterization of natural product mixtures. IMS provides an additional axis of separation in the gas phase as well as experimental collision cross-sectional (CCS) values. We analyzed a mixture of 20 commercial standards at 2 concentration ranges, either solubilized in solvent or spiked into an actinobacterial extract. Data were acquired in positive ion mode using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) MS2 fragmentation approaches and assessed for both chemical coverage and spectral quality. IMS-DIA identified the largest number of standards in the spiked extract at the lower concentration of standards (17), followed by IMS-DDA (10), DDA (8), and DIA (6). In addition, we examined how these data sets performed in the Global Natural Products Social Molecular Networking (GNPS) platform. Overall, integrating IMS increased both metabolite detection and the quality of MS2 spectra, particularly for samples analyzed in DIA mode.

Dereplication of Fungal Metabolites by NMR-Based Compound Networking Using MADByTE.

Strategies for natural product dereplication are continually evolving, essentially in lock step with advances in MS and NMR techniques. MADByTE is a new platform designed to identify common structural features between samples in complex extract libraries using two-dimensional NMR spectra. This study evaluated the performance of MADByTE for compound dereplication by examining two classes of fungal metabolites, the resorcylic acid lactones (RALs) and spirobisnaphthalenes. First, a pure compound database was created using the HSQC and TOCSY data from 19 RALs and 10 spirobisnaphthalenes. Second, this database was used to assess the accuracy of compound class clustering through the generation of a spin system feature network. Seven fungal extracts were dereplicated using this approach, leading to the correct prediction of members of both families from the extract set. Finally, NMR-guided isolation led to the discovery of three new palmarumycins (20-22). Together these results demonstrate that MADByTE is effective for the detection of specific compound classes in complex mixtures and that this detection is possible for both known and new natural products.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function.

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

Microbial natural product databases: moving forward in the multi-omics era.

Covering: 2010-2020The digital revolution is driving significant changes in how people store, distribute, and use information. With the advent of new technologies around linked data, machine learning and large-scale network inference, the natural products research field is beginning to embrace real-time sharing and large-scale analysis of digitized experimental data. Databases play a key role in this, as they allow systematic annotation and storage of data for both basic and advanced applications. The quality of the content, structure, and accessibility of these databases all contribute to their usefulness for the scientific community in practice. This review covers the development of databases relevant for microbial natural product discovery during the past decade (2010-2020), including repositories of chemical structures/properties, metabolomics, and genomic data (biosynthetic gene clusters). It provides an overview of the most important databases and their functionalities, highlights some early meta-analyses using such databases, and discusses basic principles to enable widespread interoperability between databases. Furthermore, it points out conceptual and practical challenges in the curation and usage of natural products databases. Finally, the review closes with a discussion of key action points required for the field moving forward, not only for database developers but for any scientist active in the field.

Solid-phase synthesis of coralmycin A/epi-coralmycin A and desmethoxycoralmycin A.

The total synthesis of the natural product coralmycin A/epi-coralmycin A, as well as a desmethoxy analogue is described. Synthesis was achieved via a divergent, bidirectional solid-phase strategy, including a key on-resin O-acylation, O to N acyl shift, and O-alkylation protocol to incorporate the unusual 4-amino-2-hydroxy-3-isopropoxybenzoic acid motifs. The synthetic natural product was generated as a 1 : 1 mixture of epimers at the central ß-methoxyasparagine residue and exhibited potent antibacterial activity against a panel of ten Gram-negative and seven Gram-positive organisms. The desmethoxy analogue possessed significantly more potent antimicrobial activity against this panel with minimal inhibitory concentrations (MICs) as low as 50 nM.

Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures.

The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.

Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability.

Despite the value of mass spectrometry in modern natural products discovery workflows, it remains very difficult to compare data sets between laboratories. In this study we compared mass spectrometry data for the same sample set from two different laboratories (quadrupole time-of-flight and quadrupole-Orbitrap) and evaluated the similarity between these two data sets in terms of both mass spectrometry features and their ability to describe the chemical composition of the sample set. Somewhat surprisingly, the two data sets, collected with appropriate controls and replication, had very low feature overlap (25.7% of Laboratory A features overlapping 21.8% of Laboratory B features). Our data clearly demonstrate that differences in fragmentation, charge state, and adduct formation in the ionization source are a major underlying cause for these differences. Consistent with other recent literature, these findings challenge the conventional wisdom that electrospray ionization mass spectrometry (ESI-MS) yields a simple one-to-one correspondence between analytes in solution and features in the data set. Importantly, despite low overlap in feature lists, principal component analysis (PCA) generated qualitatively similar PCA plots. Overall, our findings demonstrate that comparing untargeted metabolomics data between laboratories is challenging, but that data sets with low feature overlap can yield the same qualitative description of a sample set using PCA.