Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

A Red Algal Bourbonane Sesquiterpene Synthase Defined by Microgram-Scale NMR-Coupled Crystalline Sponge X-ray Diffraction Analysis

Sesquiterpene scaffolds are the core backbones of many medicinally and industrially important natural products. A plethora of sesquiterpene synthases, widely present in bacteria, fungi, and plants, catalyze the formation of these intricate structures often with multiple stereocenters starting from linear farnesyl diphosphate substrates. Recent advances in next-generation sequencing and metabolomics technologies have greatly facilitated gene discovery for sesquiterpene synthases. However, a major bottleneck limits biochemical characterization of recombinant sesquiterpene synthases: the absolute structural elucidation of the derived sesquiterpene products. Here, we report the identification and biochemical characterization of LphTPS-A, a sesquiterpene synthase from the red macroalga Laurencia pacifica. Using the combination of transcriptomics, sesquiterpene synthase expression in yeast, and microgram-scale nuclear magnetic resonance-coupled crystalline sponge X-ray diffraction analysis, we resolved the absolute stereochemistry of prespatane, the major sesquiterpene product of LphTPS-A, and thereby functionally define LphTPS-A as the first bourbonane-producing sesquiterpene synthase and the first biochemically characterized sesquiterpene synthase from red algae. Our study showcases a workflow integrating multiomics approaches, synthetic biology, and the crystalline sponge method, which is generally applicable for uncovering new terpene chemistry and biochemistry from source-limited living organisms.

Expanding the chondroitin glycoproteome of Caenorhabditis elegans

Chondroitin sulfate proteoglycans (CSPGs) are important structural components of connective tissues in essentially all metazoan organisms. In vertebrates, CSPGs are involved also in more specialized processes such as neurogenesis and growth factor signaling. In invertebrates, however, knowledge of CSPGs core proteins and proteoglycan-related functions is relatively limited, even for Caenorhabditis elegans. This nematode produces large amounts of non-sulfated chondroitin in addition to low-sulfated chondroitin sulfate chains. So far, only nine core proteins (CPGs) have been identified, some of which have been shown to be involved in extracellular matrix formation. We recently introduced a protocol to characterize proteoglycan core proteins by identifying CS-glycopeptides with a combination of biochemical enrichment, enzymatic digestion, and nano-scale liquid chromatography MS/MS analysis. Here, we have used this protocol to map the chondroitin glycoproteome in C. elegans, resulting in the identification of 15 novel CPG proteins in addition to the nine previously established. Three of the newly identified CPGs displayed homology to vertebrate proteins. Bioinformatics analysis of the primary protein sequences revealed that the CPG proteins altogether contained 19 unique functional domains, including Kunitz and endostatin domains, suggesting direct involvement in protease inhibition and axonal migration, respectively. The analysis of the core protein domain organization revealed that all chondroitin attachment sites are located in unstructured regions. Our results suggest that CPGs display a much greater functional and structural heterogeneity than previously appreciated and indicate that specialized proteoglycan-mediated functions evolved early in metazoan evolution.

Microbiome and metabolome data integration provides insight into health and disease

For much of our history, the most basic information about the microbial world has evaded characterization. Next-generation sequencing has led to a rapid increase in understanding of the structure and function of host-associated microbial communities in diverse diseases ranging from obesity to autism. Through experimental systems such as gnotobiotic mice only colonized with known microbes, a causal relationship between microbial communities and disease phenotypes has been supported. Now, microbiome research must move beyond correlations and general demonstration of causality to develop mechanistic understandings of microbial influence, including through their metabolic activities. Similar to the microbiome field, advances in technologies for cataloguing small molecules have broadened our understanding of the metabolites that populate our bodies. Integration of microbial and metabolomics data paired with experimental validation has promise for identifying microbial influence on host physiology through production, modification, or degradation of bioactive metabolites. Realization of microbial metabolic activities that affect health is hampered by gaps in our understanding of (1) biological properties of microbes and metabolites, (2) which microbial enzymes/pathways produce which metabolites, and (3) the effects of metabolites on hosts. Capitalizing on known mechanistic relationships and filling gaps in our understanding has the potential to enable translational microbiome research across disease contexts.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Identification of rosmarinic acid and sulfated flavonoids as inhibitors of microfouling on the surface of eelgrass Zostera marina

A bioassay-guided approach was used to identify defense compounds that are present on the surface of Zostera marina and which inhibit settlement of microfoulers at natural concentrations. Moderately polar eelgrass surface extracts inhibited the settlement of seven marine bacteria and one yeast that originated from non-living substrata. In contrast, five other bacterial strains that had been directly isolated from eelgrass surfaces were all insensitive, which suggested a selective effect of surface metabolites on the microbial communities present on eelgrass. Bioassay-guided isolation of active compounds from the extracts in combination with UPLC-MS and 1H-NMR spectroscopy resulted in the identification of rosmarinic acid, luteolin-7-sulfate and diosmetin-7-sulfate or its isomer chrysoeriol-7-sulfate. All three compounds are nontoxic repellents, as they did not inhibit bacterial growth, but prevented bacterial settlement in a dose-dependent manner. Between 15.6 and 106.8 μg ml-1 of rosmarinic acid were present on the eelgrass surface, enough for half maximal settlement inhibition of bacteria.

Environmentally Friendly Procedure Based on Supercritical Fluid Chromatography and Tandem Mass Spectrometry Molecular Networking for the Discovery of Potent Antiviral Compounds from Euphorbia semiperfoliata

A supercritical fluid chromatography-based targeted purification procedure using tandem mass spectrometry and molecular networking was developed to analyze, annotate, and isolate secondary metabolites from complex plant extract mixture. This approach was applied for the targeted isolation of new antiviral diterpene esters from Euphorbia semiperfoliata whole plant extract. The analysis of bioactive fractions revealed that unknown diterpene esters, including jatrophane esters and phorbol esters, were present in the samples. The purification procedure using semipreparative supercritical fluid chromatography led to the isolation and identification of two new jatrophane esters (13 and 14) and one known (15) and three new 4-deoxyphorbol esters (16-18). The structure and absolute configuration of compound 16 were confirmed by X-ray crystallography. This compound was found to display antiviral activity against Chikungunya virus (EC₅₀ = 0.45 μM), while compound 15 proved to be a potent and selective inhibitor of HIV-1 replication in a recombinant virus assay (EC₅₀ = 13 nM). This study showed that a supercritical fluid chromatography-based protocol and molecular networking can facilitate and accelerate the discovery of bioactive small molecules by targeting molecules of interest, while minimizing the use of toxic solvents.

Cytotoxic Prenylated Stilbenes Isolated from Macaranga tanarius

With the aim of discovering new cytotoxic prenylated stilbenes of the schweinfurthin series, Macaranga tanarius was selected for detailed phytochemical investigation among 21 Macaranga species examined by using a molecular networking approach. From an ethanol extract of the fruits, seven new prenylated stilbenes, schweinfurthins K-Q (7-13), were isolated, along with vedelianin (1), schwenfurthins E-G (2-4), mappain (5), and methyl-mappain (6). The structures of the new compounds were established by spectroscopic data analysis. The relative configurations of compounds 8, 12, and 13 were determined based on ROESY NMR spectroscopic analysis. The cytotoxic activities of compounds 1-13 were evaluated against the human glioblastoma (U87) and lung (A549) cancer cell lines.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.

Three-Dimensional Microbiome and Metabolome Cartography of a Diseased Human Lung

Our understanding of the spatial variation in the chemical and microbial makeup of an entire human organ remains limited, in part due to the size and heterogeneity of human organs and the complexity of the associated metabolome and microbiome. To address this challenge, we developed a workflow to enable the cartography of metabolomic and microbiome data onto a three-dimensional (3D) organ reconstruction built off radiological images. This enabled the direct visualization of the microbial and chemical makeup of a human lung from a cystic fibrosis patient. We detected host-derived molecules, microbial metabolites, medications, and region-specific metabolism of medications and placed it in the context of microbial distributions in the lung. Our tool further created browsable maps of a 3D microbiome/metabolome reconstruction map on a radiological image of a human lung and forms an interactive resource for the scientific community.

Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?

The vast, diverse universe of organic pollutants is a formidable challenge for environmental sciences, engineering, and regulation. Nontarget screening (NTS) based on high resolution mass spectrometry (HRMS) has enormous potential to help characterize this universe, but is it ready to go for real world applications? In this Feature article we argue that development of mass spectrometers with increasingly high resolution and novel couplings to both liquid and gas chromatography, combined with the integration of high performance computing, have significantly widened our analytical window and have enabled increasingly sophisticated data processing strategies, indicating a bright future for NTS. NTS has great potential for treatment assessment and pollutant prioritization within regulatory applications, as highlighted here by the case of real-time pollutant monitoring on the River Rhine. We discuss challenges for the future, including the transition from research toward solution-centered and robust, harmonized applications.

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria

SUMMARY

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool.

AVAILABILITY AND IMPLEMENTATION

SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3).

CONTACT

chevrette@wisc.edu or marnix.medema@wur.nl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

How to boost marine fungal research: A first step towards a multidisciplinary approach by combining molecular fungal ecology and natural products chemistry

Marine fungi have attracted attention in recent years due to increased appreciation of their functional role in ecosystems and as important sources of new natural products. The concomitant development of various "omic" technologies has boosted fungal research in the fields of biodiversity, physiological ecology and natural product biosynthesis. Each of these research areas has its own research agenda, scientific language and quality standards, which have so far hindered an interdisciplinary exchange. Inter- and transdisciplinary interactions are, however, vital for: (i) a detailed understanding of the ecological role of marine fungi, (ii) unlocking their hidden potential for natural product discovery, and (iii) designing access routes for biotechnological production. In this review and opinion paper, we describe the two different "worlds" of marine fungal natural product chemists and marine fungal molecular ecologists. The individual scientific approaches and tools employed are summarised and explained, and enriched with a first common glossary. We propose a strategy to find a multidisciplinary approach towards a comprehensive view on marine fungi and their chemical potential.

Meta-mass shift chemical profiling of metabolomes from coral reefs

Untargeted metabolomics of environmental samples routinely detects thousands of small molecules, the vast majority of which cannot be identified. Meta-mass shift chemical (MeMSChem) profiling was developed to identify mass differences between related molecules using molecular networks. This approach illuminates metabolome-wide relationships between molecules and the putative chemical groups that differentiate them (e.g., H₂, CH₂, COCH₂). MeMSChem profiling was used to analyze a publicly available metabolomic dataset of coral, algal, and fungal mat holobionts (i.e., the host and its associated microbes and viruses) sampled from some of Earth's most remote and pristine coral reefs. Each type of holobiont had distinct mass shift profiles, even when the analysis was restricted to molecules found in all samples. This result suggests that holobionts modify the same molecules in different ways and offers insights into the generation of molecular diversity. Three genera of stony corals had distinct patterns of molecular relatedness despite their high degree of taxonomic relatedness. MeMSChem profiles also partially differentiated between individuals, suggesting that every coral reef holobiont is a potential source of novel chemical diversity.

Diversity and Antimicrobial Potential of Predatory Bacteria from the Peruvian Coastline

The microbiome of three different sites at the Peruvian Pacific coast was analyzed, revealing a lower bacterial biodiversity at Isla Foca than at Paracas and Manglares, with 89 bacterial genera identified, as compared to 195 and 173 genera, respectively. Only 47 of the bacterial genera identified were common to all three sites. In order to obtain promising strains for the putative production of novel antimicrobials, predatory bacteria were isolated from these sampling sites, using two different bait organisms. Even though the proportion of predatory bacteria was only around 0.5% in the here investigated environmental microbiomes, by this approach in total 138 bacterial strains were isolated as axenic culture. 25% of strains showed antibacterial activity, thereby nine revealed activity against clinically relevant methicillin resistant Staphylococcus aureus (MRSA) and three against enterohemorrhagic Escherichia coli (EHEC) strains. Phylogeny and physiological characteristics of the active strains were investigated. First insights into the chemical basis of the antibacterial activity indicated the biosynthetic production of the known compounds ariakemicin, kocurin, naphthyridinomycin, pumilacidins, resistomycin, and surfactin. However, most compounds remained elusive until now. Hence, the obtained results implicate that the microbiome present at the various habitats at the Peruvian coastline is a promising source for heterotrophic bacterial strains showing high potential for the biotechnological production of antibiotics.

Natural product diversity associated with the nematode symbionts Photorhabdus and Xenorhabdus

Xenorhabdus and Photorhabdus species dedicate a large amount of resources to the production of specialized metabolites derived from non-ribosomal peptide synthetase (NRPS) or polyketide synthase (PKS). Both bacteria undergo symbiosis with nematodes, which is followed by an insect pathogenic phase. So far, the molecular basis of this tripartite relationship and the exact roles that individual metabolites and metabolic pathways play have not been well understood. To close this gap, we have significantly expanded the database for comparative genomics studies in these bacteria. Clustering the genes encoded in the individual genomes into hierarchical orthologous groups reveals a high-resolution picture of functional evolution in this clade. It identifies groups of genes-many of which are involved in secondary metabolite production-that may account for the niche specificity of these bacteria. Photorhabdus and Xenorhabdus appear very similar at the DNA sequence level, which indicates their close evolutionary relationship. Yet, high-resolution mass spectrometry analyses reveal a huge chemical diversity in the two taxa. Molecular network reconstruction identified a large number of previously unidentified metabolite classes, including the xefoampeptides and tilivalline. Here, we apply genomic and metabolomic methods in a complementary manner to identify and elucidate additional classes of natural products. We also highlight the ability to rapidly and simultaneously identify potentially interesting bioactive products from NRPSs and PKSs, thereby augmenting the contribution of molecular biology techniques to the acceleration of natural product discovery.

Mass Spectrometry-Based Chemical Cartography of a Cardiac Parasitic Infection

Trypanosoma cruzi parasites are the causative agents of Chagas disease, a leading infectious form of heart failure whose pathogenesis is still not fully characterized. In this work, we applied untargeted liquid chromatography-tandem mass spectrometry to heart sections from T. cruzi-infected and uninfected mice. We combined molecular networking and three-dimensional modeling to generate chemical cartographical heart models. This approach revealed for the first time preferential parasite localization to the base of the heart and regiospecific distributions of nucleoside derivatives and eicosanoids, which we correlated to tissue-damaging immune responses. We further detected novel cardiac chemical signatures related to the severity and ultimate outcome of the infection. These signatures included differential representation of higher- vs lower-molecular-weight carnitine and phosphatidylcholine family members in specific cardiac regions of mice infected with lethal or nonlethal T. cruzi strains and doses. Overall, this work provides new insights into Chagas disease pathogenesis and presents an analytical chemistry approach that can be broadly applied to the study of host-microbe interactions.

Impacts of the Human Gut Microbiome on Therapeutics

The human microbiome contains a vast source of genetic and biochemical variation, and its impacts on therapeutic responses are just beginning to be understood. This expanded understanding is especially important because the human microbiome differs far more among different people than does the human genome, and it is also dramatically easier to change. Here, we describe some of the major factors driving differences in the human microbiome among individuals and populations. We then describe some of the many ways in which gut microbes modify the action of specific chemotherapeutic agents, including nonsteroidal anti-inflammatory drugs and cardiac glycosides, and outline the potential of fecal microbiota transplant as a therapeutic. Intriguingly, microbes also alter how hosts respond to therapeutic agents through various pathways acting at distal sites. Finally, we discuss some of the computational and practical issues surrounding use of the microbiome to stratify individuals for drug response, and we envision a future where the microbiome will be modified to increase everyone's potential to benefit from therapy.

Omics-based natural product discovery and the lexicon of genome mining

Genome sequencing and the application of omic techniques are driving many important advances in the field of microbial natural products research. Despite these gains, there remain aspects of the natural product discovery pipeline where our knowledge remains poor. These include the extent to which biosynthetic gene clusters are transcriptionally active in native microbes, the temporal dynamics of transcription, translation, and natural product assembly, as well as the relationships between small molecule production and detection. Here we touch on a number of these concepts in the context of continuing efforts to unlock the natural product potential revealed in genome sequence data and discuss nomenclatural issues that warrant consideration as the field moves forward.

Microbes and associated soluble and volatile chemicals on periodically wet household surfaces

BACKGROUND

Microorganisms influence the chemical milieu of their environment, and chemical metabolites can affect ecological processes. In built environments, where people spend the majority of their time, very little is known about how surface-borne microorganisms influence the chemistry of the indoor spaces. Here, we applied multidisciplinary approaches to investigate aspects of chemical microbiology in a house.

METHODS

We characterized the microbial and chemical composition of two common and frequently wet surfaces in a residential setting: kitchen sink and bathroom shower. Microbial communities were studied using culture-dependent and independent techniques, including targeting RNA for amplicon sequencing. Volatile and soluble chemicals from paired samples were analyzed using state-of-the-art techniques to explore the links between the observed microbiota and chemical exudates.

RESULTS

Microbial analysis revealed a rich biological presence on the surfaces exposed in kitchen sinks and bathroom shower stalls. Microbial composition, matched for DNA and RNA targets, varied by surface type and sampling period. Bacteria were found to have an average of 25× more gene copies than fungi. Biomass estimates based on qPCR were well correlated with measured total volatile organic compound (VOC) emissions. Abundant VOCs included products associated with fatty acid production. Molecular networking revealed a diversity of surface-borne compounds that likely originate from microbes and from household products.

CONCLUSIONS

Microbes played a role in structuring the chemical profiles on and emitted from kitchen sinks and shower stalls. Microbial VOCs (mVOCs) were predominately associated with the processing of fatty acids. The mVOC composition may be more stable than that of microbial communities, which can show temporal and spatial variation in their responses to changing environmental conditions. The mVOC output from microbial metabolism on kitchen sinks and bathroom showers should be apparent through careful measurement, even against a broader background of VOCs in homes, some of which may originate from microbes in other locations within the home. A deeper understanding of the chemical interactions between microbes on household surfaces will require experimentation under relevant environmental conditions, with a finer temporal resolution, to build on the observational study results presented here.

Compliance with minimum information guidelines in public metabolomics repositories

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards.

MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification

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An update to the ab-initio network construction tool MetaNetter has been produced.

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The tool creates networks of masses from high resolution mass spectrometry data.

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The new plugin provides both chemical transformation and adduct mapping.

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Tables mapping adduct and transform counts across samples can be generated.

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Retention time windows are supported for both adduct and transform network generation.

Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them.

We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships.

We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset.

Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis.

The Face of a Molecule

Recent technological advances in mass spectrometry and NMR spectroscopy have enabled new approaches for the rapid and insightful profiling of natural product mixtures. MALDI-MS with the provision of biosynthetic heavy-isotope-labeled precursors can be a powerful method by which to interrogate a natural product metabolome and to gain insight into its unique constituents; this is illustrated herein by the detection, isolation, and characterization of cryptomaldamide. MS/MS-based Molecular Networks, facilitated by the Global Natural Products Social (GNPS) platform, is rapidly changing the way in which we dereplicate known natural products in mixtures, find new analogues in desired structure classes, and identify fundamentally new chemical entities. This method can be linked to genomic information to assist in genome-driven natural products discovery and is illustrated here with the characterization of the columbamides. Similarly, algorithmic interpretation of NMR data is facilitating the automatic identification or classification of new natural products. We developed such a tool named the Small Molecule Accurate Recognition Technology (SMART), which employs a convolutional neural network to classify HSQC spectra of organic molecules using pattern recognition principles. The discovery and rapid classification of several new peptides from a marine cyanobacterium as members of the viequeamide class provides an example of its utility in natural products research. These three illustrations represent different methods by which to look at the external features of a chemical substance and derive valuable insights into its identity or, as described herein, the "face of a molecule".

Strategies for dereplication of natural compounds using high-resolution tandem mass spectrometry

Complete structural elucidation of natural products is commonly performed by nuclear magnetic resonance spectroscopy (NMR), but annotating compounds to most likely structures using high-resolution tandem mass spectrometry is a faster and feasible first step. The CASMI contest 2016 (Critical Assessment of Small Molecule Identification) provided spectra of eighteen compounds for the best manual structure identification in the natural products category. High resolution precursor and tandem mass spectra (MS/MS) were available to characterize the compounds. We used the Seven Golden Rules, Sirius2 and MS-FINDER software for determination of molecular formulas, and then we queried the formulas in different natural product databases including DNP, UNPD, ChemSpider and REAXYS to obtain molecular structures. We used different in-silico fragmentation tools including CFM-ID, CSI:FingerID and MS-FINDER to rank these compounds. Additional neutral losses and product ion peaks were manually investigated. This manual and time consuming approach allowed for the correct dereplication of thirteen of the eighteen natural products.

Upregulation and Identification of Antibiotic Activity of a Marine-Derived Streptomyces sp. via Co-Cultures with Human Pathogens

Marine natural product drug discovery has begun to play an important role in the treatment of disease, with several recently approved drugs. In addition, numerous microbial natural products have been discovered from members of the order Actinomycetales, particularly in the genus Streptomyces, due to their metabolic diversity for production of biologically active secondary metabolites. However, many secondary metabolites cannot be produced under laboratory conditions because growth conditions in flask culture differ from conditions in the natural environment. Various experimental conditions (e.g., mixed fermentation) have been attempted to increase yields of previously described metabolites, cause production of previously undetected metabolites, and increase antibiotic activity. Adult ascidians-also known as tunicates-are sessile marine invertebrates, making them vulnerable to predation and therefore are hypothesized to use host-associated bacteria that produce biologically active secondary metabolites for chemical defense. A marine-derived Streptomyces sp. strain PTY087I2 was isolated from a Panamanian tunicate and subsequently co-cultured with human pathogens including Bacillus subtilis, methicillin-sensitive Staphylococcus aureus (MSSA), methicillin-resistant Staphylococcus aureus (MRSA), and Pseudomonas aeruginosa, followed by extraction. Co-culture of Streptomyces sp. PTY087I2 with each of these human pathogens resulted in increased production of three antibiotics: granaticin, granatomycin D, and dihydrogranaticin B, as well as several analogues seen via molecular networking. In addition, co-cultures resulted in strongly enhanced biological activity against the Gram positive human pathogens used in these experiments. Expanded utilization of co-culture experiments to allow for competitive interactions may enhance metabolite production and further our understanding of these microbial interactions.

Nonribosomal peptide synthetase biosynthetic clusters of ESKAPE pathogens

Covering: up to 2017.Natural products are important secondary metabolites produced by bacterial and fungal species that play important roles in cellular growth and signaling, nutrient acquisition, intra- and interspecies communication, and virulence. A subset of natural products is produced by nonribosomal peptide synthetases (NRPSs), a family of large, modular enzymes that function in an assembly line fashion. Because of the pharmaceutical activity of many NRPS products, much effort has gone into the exploration of their biosynthetic pathways and the diverse products they make. Many interesting NRPS pathways have been identified and characterized from both terrestrial and marine bacterial sources. Recently, several NRPS pathways in human commensal bacterial species have been identified that produce molecules with antibiotic activity, suggesting another source of interesting NRPS pathways may be the commensal and pathogenic bacteria that live on the human body. The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) have been identified as a significant cause of human bacterial infections that are frequently multidrug resistant. The emerging resistance profile of these organisms has prompted calls from multiple international agencies to identify novel antibacterial targets and develop new approaches to treat infections from ESKAPE pathogens. Each of these species contains several NRPS biosynthetic gene clusters. While some have been well characterized and produce known natural products with important biological roles in microbial physiology, others have yet to be investigated. This review catalogs the NRPS pathways of ESKAPE pathogens. The exploration of novel NRPS products may lead to a better understanding of the chemical communication used by human pathogens and potentially to the discovery of novel therapeutic approaches.

NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity

The intrinsic value of biodiversity extends beyond species diversity, genetic heritage, ecosystem variability and ecological services, such as climate regulation, water quality, nutrient cycling and the provision of reproductive habitats it is also an inexhaustible source of molecules and products beneficial to human well-being. To uncover the chemistry of Brazilian natural products, the Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products Database (NuBBE_DB) was created as the first natural product library from Brazilian biodiversity. Since its launch in 2013, the NuBBE_DB has proven to be an important resource for new drug design and dereplication studies. Consequently, continuous efforts have been made to expand its contents and include a greater diversity of natural sources to establish it as a comprehensive compendium of available biogeochemical information about Brazilian biodiversity. The content in the NuBBE_DB is freely accessible online (https://nubbe.iq.unesp.br/portal/nubbedb.html) and provides validated multidisciplinary information, chemical descriptors, species sources, geographic locations, spectroscopic data (NMR) and pharmacological properties. Herein, we report the latest advancements concerning the interface, content and functionality of the NuBBE_DB. We also present a preliminary study on the current profile of the compounds present in Brazilian territory.

Sixty Challenges – A 2030 Perspective on Natural Products and Medicines Security

Natural products matter, for they are essential contributors to societal well-being and global health. Flavors, fragrances, essential oils, traditional medicines and phytopharmaceuticals, and prescription and over-the-counter products all utilize constituent materials from natural sources. However, these vast natural resources of Earth are disappearing, and climate change and market expansion by a dramatically increasing and ageing population will continue to strain plant sourcing in the decades ahead. It was with these and other considerations that the term “ecopharmacognosy” was developed as both a philosophy and a practice, and from which the necessity for a “medicines security” strategy evolved.

Extending previous presentations, a series of sixty challenges for 2030 to the status quo promotes the discussion of a different vision for the natural product sciences as applied to traditional medicines. Among the topics presented are areas for global collaborative initiatives in science and in data management, the impact of climate change on medicinal plant accessibility, the sustainability and quality of the natural products that patients receive, and the integration of new technologies, particularly the genomics of secondary metabolite biosynthesis, hand-held detection systems and artificial intelligence, and the implications of increased life expectancy on future health care needs. The presentation closes with two examples of newer approaches in drug discovery.

A scalable platform to identify fungal secondary metabolites and their gene clusters

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.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Isolation of Premyrsinane, Myrsinane, and Tigliane Diterpenoids from Euphorbia pithyusa Using a Chikungunya Virus Cell-Based Assay and Analogue Annotation by Molecular Networking

Six new premyrsinol esters (1-6) and one new myrsinol ester (8) were isolated from an aerial parts extract of Euphorbia pithyusa, together with a known premyrsinol (7) and two known dideoxyphorbol esters (9 and 10), following a bioactivity-guided purification procedure using a chikungunya virus (CHIKV) cell-based assay. The structures of the new diterpene esters (1-6 and 8) were elucidated by MS and NMR spectroscopic data interpretation. Compounds 1-10 were evaluated against CHIKV replication, and results showed that the 4β-dideoxyphorbol ester 10 was the most active compound, with an EC₅₀ value of 4.0 ± 0.3 μM and a selectivity index of 10.6. To gain more insight into the structural diversity of diterpenoids produced by E. pithyusa, the initial extract and chromatographic fractions were analyzed by LC-MS/MS. The generated data were annotated using a molecular networking procedure and revealed that dozens of unknown premyrsinane, myrsinane, and tigliane analogues were present.

Metabolic Fingerprints from the Human Oral Microbiome Reveal a Vast Knowledge Gap of Secreted Small Peptidic Molecules

Metabolomics is the ultimate tool for studies of microbial functions under any specific set of environmental conditions (D. S. Wishart, Nat Rev Drug Discov 45:473–484, 2016, https://doi.org/10.1038/nrd.2016.32). This is a great advance over studying genes alone, which only inform about metabolic potential. Approximately 25,000 compounds have been chemically characterized thus far; however, the richness of metabolites such as SMs has been estimated to be as high as 1 × 10³⁰ in the biosphere (K. Garber, Nat Biotechnol 33:228–231, 2015, https://doi.org/10.1038/nbt.3161). Our classical, one-at-a-time activity-guided approach to compound identification continues to find the same known compounds and is also incredibly tedious, which represents a major bottleneck for global SM identification. These challenges have prompted new developments of databases and analysis tools that provide putative classifications of SMs by mass spectral alignments to already characterized tandem mass spectrometry spectra and databases containing structural information (e.g., PubChem and AntiMarin). In this study, we assessed secreted peptidic SMs (PSMs) from 27 oral bacterial isolates and a complex oral in vitro biofilm community of >100 species by using the Global Natural Products Social molecular Networking and the DEREPLICATOR infrastructures, which are methodologies that allow automated and putative annotation of PSMs. These approaches enabled the identification of an untapped resource of PSMs from oral bacteria showing species-unique patterns of secretion with putative matches to known bioactive compounds.

Recent research indicates that the human microbiota play key roles in maintaining health by providing essential nutrients, providing immune education, and preventing pathogen expansion. Processes underlying the transition from a healthy human microbiome to a disease-associated microbiome are poorly understood, partially because of the potential influences from a wide diversity of bacterium-derived compounds that are illy defined. Here, we present the analysis of peptidic small molecules (SMs) secreted from bacteria and viewed from a temporal perspective. Through comparative analysis of mass spectral profiles from a collection of cultured oral isolates and an established in vitro multispecies oral community, we found that the production of SMs both delineates a temporal expression pattern and allows discrimination between bacterial isolates at the species level. Importantly, the majority of the identified molecules were of unknown identity, and only ~2.2% could be annotated and classified. The catalogue of bacterially produced SMs we obtained in this study reveals an undiscovered molecular world for which compound isolation and ecosystem testing will facilitate a better understanding of their roles in human health and disease.

IMPORTANCE Metabolomics is the ultimate tool for studies of microbial functions under any specific set of environmental conditions (D. S. Wishart, Nat Rev Drug Discov 45:473–484, 2016, https://doi.org/10.1038/nrd.2016.32). This is a great advance over studying genes alone, which only inform about metabolic potential. Approximately 25,000 compounds have been chemically characterized thus far; however, the richness of metabolites such as SMs has been estimated to be as high as 1 × 10³⁰ in the biosphere (K. Garber, Nat Biotechnol 33:228–231, 2015, https://doi.org/10.1038/nbt.3161). Our classical, one-at-a-time activity-guided approach to compound identification continues to find the same known compounds and is also incredibly tedious, which represents a major bottleneck for global SM identification. These challenges have prompted new developments of databases and analysis tools that provide putative classifications of SMs by mass spectral alignments to already characterized tandem mass spectrometry spectra and databases containing structural information (e.g., PubChem and AntiMarin). In this study, we assessed secreted peptidic SMs (PSMs) from 27 oral bacterial isolates and a complex oral in vitro biofilm community of >100 species by using the Global Natural Products Social molecular Networking and the DEREPLICATOR infrastructures, which are methodologies that allow automated and putative annotation of PSMs. These approaches enabled the identification of an untapped resource of PSMs from oral bacteria showing species-unique patterns of secretion with putative matches to known bioactive compounds.

Author Video: An author video summary of this article is available.

Imaging mass spectrometry and MS/MS molecular networking reveals chemical interactions among cuticular bacteria and pathogenic fungi associated with fungus-growing ants

The fungus-growing ant-microbe symbiosis is an ideal system to study chemistry-based microbial interactions due to the wealth of microbial interactions described, and the lack of information on the molecules involved therein. In this study, we employed a combination of MALDI imaging mass spectrometry (MALDI-IMS) and MS/MS molecular networking to study chemistry-based microbial interactions in this system. MALDI IMS was used to visualize the distribution of antimicrobials at the inhibition zone between bacteria associated to the ant Acromyrmex echinatior and the fungal pathogen Escovopsis sp. MS/MS molecular networking was used for the dereplication of compounds found at the inhibition zones. We identified the antibiotics actinomycins D, X2 and X_0β, produced by the bacterium Streptomyces CBR38; and the macrolides elaiophylin, efomycin A and efomycin G, produced by the bacterium Streptomyces CBR53.These metabolites were found at the inhibition zones using MALDI IMS and were identified using MS/MS molecular networking. Additionally, three shearinines D, F, and J produced by the fungal pathogen Escovopsis TZ49 were detected. This is the first report of elaiophylins, actinomycin X_0β and shearinines in the fungus-growing ant symbiotic system. These results suggest a secondary prophylactic use of these antibiotics by A. echinatior because of their permanent production by the bacteria.

Molecular inter-kingdom interactions of endophytes isolated from Lychnophora ericoides

The importance of microbial natural products has been widely demonstrated in the search for new antibiotics. However, the functional role of microbial metabolites in nature remains to be deciphered. Several natural products are known to mediate microbial interactions through metabolic exchange. One approach to investigate metabolic exchange in the laboratory is through microbial interactions. Here, we describe the chemical study of selected endophytes isolated from the Brazilian medicinal plant Lychnophora ericoides by pairwise inter-kingdom interactions in order to correlate the impact of co-cultivation to their metabolic profiles. Combining mass spectrometry tools and NMR analyses, a total of 29 compounds were identified. These compounds are members of polyene macrocycles, pyrroloindole alkaloids, angucyclines, and leupeptins chemical families. Two of the identified compounds correspond to a new fungal metabolite (29) and a new actinobacterial angucycline-derivative (23). Our results revealed a substantial arsenal of small molecules induced by microbial interactions, as we begin to unravel the complexity of microbial interactions associated with endophytic systems.

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.

MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability

Molecular networking is becoming more and more popular into the metabolomic community to organize tandem mass spectrometry (MS²) data. Even though this approach allows the treatment and comparison of large data sets, several drawbacks related to the MS-Cluster tool routinely used on the Global Natural Product Social Molecular Networking platform (GNPS) limit its potential. MS-Cluster cannot distinguish between chromatography well-resolved isomers as retention times are not taken into account. Annotation with predicted chemical formulas is also not implemented and semiquantification is only based on the number of MS² scans. We propose to introduce a data-preprocessing workflow including the preliminary data treatment by MZmine 2 followed by a homemade Python script freely available to the community that clears the major previously mentioned GNPS drawbacks. The efficiency of this workflow is exemplified with the analysis of six fractions of increasing polarities obtained from a sequential supercritical CO₂ extraction of Stillingia lineata leaves.

Coupling Targeted and Untargeted Mass Spectrometry for Metabolome-Microbiome-Wide Association Studies of Human Fecal Samples

Increasing appreciation of the gut microbiome's role in health motivates understanding the molecular composition of human feces. To analyze such complex samples, we developed a platform coupling targeted and untargeted metabolomics. The approach is facilitated through split flow from one UPLC, joint timing triggered by contact closure relays, and a script to retrieve the data. It is designed to detect specific metabolites of interest with high sensitivity, allows for correction of targeted information, enables better quantitation thus providing an advanced analytical tool for exploratory studies. Procrustes analysis revealed that untargeted approach provides a better correlation to microbiome data, associating specific metabolites with microbes that produce or process them. With the subset of over one hundred human fecal samples from the American Gut project, the implementation of the described coupled workflow revealed that targeted analysis using combination of single transition per compound with retention time misidentifies 30% of the targeted data and could lead to incorrect interpretations. At the same time, the targeted analysis extends detection limits and dynamic range, depending on the compounds, by orders of magnitude. A software application has been developed as a part of the workflow to allows for quantitative assessments based on calibration curves. Using this approach, we detect expected microbially modified molecules such as secondary bile acids and unexpected microbial molecules including Pseudomonas-associated quinolones and rhamnolipids in feces, setting the stage for metabolome-microbiome-wide association studies (MMWAS).

Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics

In untargeted metabolomics approaches, the inability to structurally annotate relevant features and map them to biochemical pathways is hampering the full exploitation of many metabolomics experiments. Furthermore, variable metabolic content across samples result in sparse feature matrices that are statistically hard to handle. Here, we introduce MS2LDA+ that tackles both above-mentioned problems. Previously, we presented MS2LDA, which extracts biochemically relevant molecular substructures (“Mass2Motifs”) from a collection of fragmentation spectra as sets of co-occurring molecular fragments and neutral losses, thereby recognizing building blocks of metabolomics. Here, we extend MS2LDA to handle multiple metabolomics experiments in one analysis, resulting in MS2LDA+. By linking Mass2Motifs across samples, we expose the variability in prevalence of structurally related metabolite families. We validate the differential prevalence of substructures between two distinct samples groups and apply it to fecal samples. Subsequently, within one sample group of urines, we rank the Mass2Motifs based on their variance to assess whether xenobiotic-derived substructures are among the most-variant Mass2Motifs. Indeed, we could ascribe 22 out of the 30 most-variant Mass2Motifs to xenobiotic-derived substructures including paracetamol/acetaminophen mercapturate and dimethylpyrogallol. In total, we structurally characterized 101 Mass2Motifs with biochemically or chemically relevant substructures. Finally, we combined the discovered metabolite families with full scan feature intensity information to obtain insight into core metabolites present in most samples and rare metabolites present in small subsets now linked through their common substructures. We conclude that by biochemical grouping of metabolites across samples MS2LDA+ aids in structural annotation of metabolites and guides prioritization of analysis by using Mass2Motif prevalence.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Dudawalamides A-D, Antiparasitic Cyclic Depsipeptides from the Marine Cyanobacterium Moorea producens

A family of 2,2-dimethyl-3-hydroxy-7-octynoic acid (Dhoya)-containing cyclic depsipeptides, named dudawalamides A-D (1-4), was isolated from a Papua New Guinean field collection of the cyanobacterium Moorea producens using bioassay-guided and spectroscopic approaches. The planar structures of dudawalamides A-D were determined by a combination of 1D and 2D NMR experiments and MS analysis, whereas the absolute configurations were determined by X-ray crystallography, modified Marfey's analysis, chiral-phase GCMS, and chiral-phase HPLC. Dudawalamides A-D possess a broad spectrum of antiparasitic activity with minimal mammalian cell cytotoxicity. Comparative analysis of the Dhoya-containing class of lipopeptides reveals intriguing structure-activity relationship features of these NRPS-PKS-derived metabolites and their derivatives.

Large Diversity and Original Structures of Acyl-Homoserine Lactones in Strain MOLA 401, a Marine Rhodobacteraceae Bacterium

Quorum sensing (QS) is a density-dependent mechanism allowing bacteria to synchronize their physiological activities, mediated by a wide range of signaling molecules including N-acyl-homoserine lactones (AHLs). Production of AHL has been identified in various marine strains of Proteobacteria. However, the chemical diversity of these molecules still needs to be further explored. In this study, we examined the diversity of AHLs produced by strain MOLA 401, a marine Alphaproteobacterium that belongs to the ubiquitous Rhodobacteraceae family. We combined an original biosensors-based guided screening of extract microfractions with liquid chromatography coupled to mass spectrometry (MS), High Resolution MS/MS and Nuclear Magnetic Resonance. This approach revealed the unsuspected capacity of a single Rhodobacteraceae strain to synthesize 20 different compounds, which are most likely AHLs. Also, some of these AHLs possessed original features that have never been previously observed, including long (up to 19 carbons) and poly-hydroxylated acyl side chains, revealing new molecular adaptations of QS to planktonic life and a larger molecular diversity than expected of molecules involved in cell–cell signaling within a single strain.